THE FIRST JOINT
WCRP-WWRP SYMPOSIUM ON
DATA ASSIMILATION AND REANALYSIS
Book of Abstracts

Joint WCRP-WWRP Symposium on
Data Assimilation and Reanalysis
in collaboration with the ECMWF Annual Seminar 2021
13 - 17 September 2021 (virtual)organized by

Monday, 13 Sep 2021 at 11UTC - room RED

DA applications
Impact of data assimilation on fog forecasting: A case study of WiFEX campaign
Parde, Avinash(1,2); Govardhan, Gaurav(2,3); Ghude, Sachin(2); Dhangar, Narendra(2)(1)

Savitribai Phule Pune University [India], (2)Indian Institute of Tropical Meteorology, (3)National Centre for Medium Range Weather Forecasting

Fog is a low-cloud and high-impact weather phenomenon that evolves at various Spatio-temporal scales. However, its severe impact over the
IGP (Indo Gangetic Plain) region has been noticed from the last decade in terms of railroad and airline transportation. Therefore, accurate fog
prediction on local to regional scales is crucial for mitigation of adverse impact on human life in the northern part of India. Nevertheless, thefaithful reproduction of fog in the numerical model is a still threshold problem. One of the model-based case study from Winter Fog EXperiment(WiFEX) campaign has shown the warm bias in the initial condition and onset/dissipation error in real-time fog forecast. Using temperatureand humidity profiles from the Radiosonde and Microwave Radiometer, this study investigates the data assimilation impact on fog forecastingat the Indira Gandhi International Airport (IGIA). Here, the Gridpoint Statistical Interpolation (GSI) Three-Dimensional Variational (3D-Var) based
Weather Research and Forecasting (WRF) setup is utilized to improve the initial condition and minimize fog onset and dissipation error in themodel. In addition, this study is further extended to explore the horizontal as well as vertical fog structure and their evolution.

Data assimilation for landscape-scale wildland fire behavior
Rochoux, Mélanie C.(1); Costes, Aurélien(1,2); Paugam, Ronan(3); Trouvé, Arnaud(4)(1)

CECI, Université de Toulouse, CNRS, CERFACS, (2)CNRM, Université de Toulouse, Météo-France, CNRS, (3)Department of Chemical Engineering,
Centre for Technological Risk Studies, Universitat Politecnica de Catalunya, (4)Department of Fire Protection Engineering, University of Maryland
The recent occurrence of extreme wildfire events highlights the urgent need of designing accurate modeling systems to provide insights into
the wildland fire dynamics and its driving factors at the scale of an event. Predicting landscape-scale wildfire behavior remains a challenge dueto the strong atmosphere/fire interactions and to the uncertainties associated with environmental factors (e.g. biomass fuel properties andmoisture content). In this context, data assimilation offers a promising mathematical framework to take advantage of the recent advances ininfrared remote sensing technology to improve model predictions of the landscape-scale wildfire behavior [1]. We will discuss how to reconstruct a complete time history of a given wildland fire event using joint state-parameter estimation combined with infrared sensor observationsof active burning areas [2]. A front shape similarity measure derived from image processing and based on the Chan-Vese contour fitting functional was introduced to handle position and topological errors in the propagating fire front [3]. We will also discuss the challenges to extend
the data assimilation approach from controlled burns to actual wildfires, with the ultimate objective to produce reanalysis of extreme wildfire
events. One cornerstone lies in its extension to a coupled atmosphere-fire model to have a better representation of the fire-induced flows near
the wildland fire. [1] Rochoux et al. (2022) Book Chapter in Inversion and Data Assimilation, SCIENCES, in press. [2] Zhang et al. (2019) Fire Saf.
J., https://doi.org/10.1016/j.firesaf.2019.03.009 [3] Rochoux et al. (2018) ESAIM Proc. Surv. https://doi.org/10.1051/proc/201863258

Data Assimilation on the Sub-Kilometer Scale for the Urban Environment
Valmassoi, Arianna(1,2); Keller, Jan D.(2,3)(1)

University of Bonn, (2)Hans-Ertel-Center for Weather Research, Monitoring and Diagnostic group, (3)Deutscher Wetterdienst

The ongoing urbanization makes urban areas increasingly crucial for NWP especially in relation to extreme events or climate monitoring in thecontext of regional reanalysis. In this regard, the increase in computational power allows for ever-higher horizontal resolutions in NWP models
towards the kilometer scale which is important to tackle the representation of urban environments. This study investigates high-resolution
DA below the kilometer-scale for urban areas in order to assess the potential for future regional reanalyses at the German Meteorological
Service (DWD). Therefore, we employ the Icosahedral Nonhydrostatic (ICON) Model in the Limited Area Mode (ICON-LAM), together with DWD's
LETKF. We perform experiments at horizontal resolutions of 2.1km (operational) and 500m. We use a modified ICON-LAM surface module
compared to the operational mode by employing the Corine land-use dataset (100-meter resolution) instead of the default Globcover 2009(300 meters) and three urban land use categories instead of one to better capture surface heterogeneity. With respect to DA, we investigate
the impact of assimilating various observing systems such as conventional upper air and surface observations as well as satellite-based land
surface temperature (LST) data. We aim to assess their value for enhancing the representation of near-surface temperatures, especially in urban
areas. A focus is therefore given to the tuning of relevant parameters such as the localization length to be able to better represent small-scalespatial features such as the UHI. In this regard, the LST measurements can provide valuable information on the spatial structure of surface
temperatures and thus help to correct the UHI representation in ICON-LAM.

Monday, 13 Sep 2021 at 11UTC - room GREEN

Reanalysis evaluation I
Overview of the SPARC Reanalysis Intercomparison Project (S-RIP) during 2013-2021
Fujiwara, Masatomo(1); Manney, Gloria(2,3); Gray, Lesley(4,5); Wright, Jonathon(6); Anstey, James(7); Birner, Thomas(8,9); Davis,
Sean(10); Gerber, Edwin(11); Harvey, V. Lynn(12); Hegglin, Michaela(13); Homeyer, Cameron(14); Knox, John(15); Krüger, Kirstin(16);
Lambert, Alyn(17); Long, Craig(18); Martineau, Patrick(19); Monge-Sanz, Beatriz(5); Santee, Michelle(17); Tegtmeier, Susann(20);
Chabrillat, Simon(21); Compo, Gilbert P.(22); Dragani, Rossana(23); Ebisuzaki, Wesley(24); Harada, Yayoi(25); Kobayashi, Chiaki(25);
Onogi, Kazutoshi(25); Wargan, Krzysztof(26); Whitaker, Jeffrey S.(27)(1)

Hokkaido University, (2)NorthWest Research Associates, (3)New Mexico Institute of Mining and Technology, (4)NERC National Centre for Atmospheric Science, (5)University of Oxford, (6)Tsinghua University, Beĳing, (7)Canadian Centre for Climate Modelling and Analysis, Environment andClimate Change Canada, University of Victoria, (8)Deutsches Zentrum für Luft- und Raumfahrt Oberpfaffenhofen, (9)Ludwig-Maximilians-Universität München, (10)Chemical Sciences Laboratory, National Oceanic and Atmospheric Administration, (11)New York University, (12)University ofColorado Boulder, (13)University of Reading, (14)University of Oklahoma, (15)University of Georgia, (16)University of Oslo, (17)Jet Propulsion Laboratory, California Institute of Technology, (18)National Oceanic and Atmospheric Administration, (19)Japan Agency for Marine-Earth Science andTechnology, (20)University of Saskatchewan, (21)Royal Belgian Institute for Space Aeronomy, (22)Cooperative Institute for Research in Environmental Sciences, (23)European Centre for Medium-Range Weather Forecasts, (24)National Oceanic and Atmospheric Administration, (25)Japan Meteo-

rological Agency, (26)National Aeronautics and Space Administration, USA; and Science Systems and Applications, Inc., (27)National Oceanic andAtmospheric Administration
The Stratosphere-troposphere Processes And their Role in Climate (SPARC) project is one of the four core projects of the World Climate Research Programme (WCRP). Researchers interested in SPARC use global atmospheric reanalysis products to understand a wide range of processes and variability in the atmosphere, to validate chemistry climate models, and to investigate and identify climate change. The SPARC
Reanalysis Intercomparison Project (S-RIP) was initiated in 2011 and officially started in 2013 to conduct a coordinated intercomparison of all
major global atmospheric reanalysis data sets. The S-RIP has been aiming at writing up an assessment report in the SPARC report series (to be
published by September 2021) (1) on overall quality of temperature, winds, ozone, and water vapor data, (2) on more process- and regionoriented evaluation of the Brewer–Dobson circulation, extratropical stratosphere-troposphere coupling, extratropical upper troposphere andlower stratosphere, the tropical tropopause layer, the quasi-biennial oscillation, polar processes, and the upper stratosphere and lower mesosphere, and (3) with a coordinated description of the reanalysis systems. We also have an inter-journal special issue on "The SPARC Reanalysis
Intercomparison Project (S-RIP)" in Atmospheric Chemistry and Physics (ACP) and Earth System Science Data (ESSD). In the presentation, we will
discuss key findings and recommendations as well as the evaluation of this first phase of the S-RIP activity.

The 20th century global warming signature on the ocean at global and basin scales as depicted from historicalreanalyses
Storto, Andrea(1); Balmaseda, Magdalena Alonso(2); De, Boisseson Eric(2); Giese, Benjamin(3); Masina, Simona(4); Yang, Chunxue(1)(1)

ISMAR, (2)ECMWF, (3)Texas A&M University, (4)Centro Euro-Mediterraneo per i Cambiamenti Climatici [Bologna]

More than 90% of excess energy trapped by greenhouse gases is accumulated in the oceans. Ocean heat content (OHC) and its changes are
therefore fundamental metrics to monitor climate change. However, due to sparse observation sampling before the 1950s, accurate observation-based estimations only exist for the second half of the 20 th century. A 16-member ensemble of historical ocean reanalyses is used forthe first time to compile a unique estimate of 20 th century oceanic warming rates. The reanalyses combine dynamical ocean general circulation
models with historical observations and observation-based atmospheric forcing. Ocean heat content tendencies (OHCT) from the multi-reanalysis ensemble agree well with independent estimates of ocean heat content, and show a coherent evolution with records of Earth's energy
imbalance from atmospheric reanalyses and atmospheric CO 2 concentration at a range of timescales. OHCT from reanalyses proves to be amore effective climate change proxy than observed surface warming tendencies that contain high-frequency variability not related to the climate change signature, or historical coupled model simulations, which neglect interannual fluctuations. The ensemble mean estimate of the
century-long ocean warming rate is 0.26 ± 0.08 W m -2 . The warming rate of 0.84 ± 0.21 W m -2 estimated from 1993 onwards is unprecedented. The global decadal warming rate is persistently positive from about 1925 onwards, except for two neutral periods. The Indian Ocean
exhibits the highest relative contribution to centennial heat accumulation, while the Atlantic Ocean plays an increasingly prominent role, especially during the 1995-2004 decade. These findings are in agreement with previous studies.

Representation of the past weather prior to the International Geophysical Year (1957-1958) in JRA-3Q
Kosaka, Yuki(1); Kobayashi, Shinya(1); Chiba, Jotaro(1); Tokuhiro, Takayuki(1); Harada, Yayoi(2); Kobayashi, Chiaki(2); Naoe,Hiroaki(2)(1)

Office of Earth System Modeling / Numerical Prediction Division / Japan Meteorological Agency, (2)Meteorological Research Institute / Japan
Meteorological Agency
The Japan Meteorological Agency (JMA) is currently producing the third reanalysis, called the Japanese Reanalysis for Three Quarters of a Century (JRA-3Q). JRA-3Q is based on the TL479 version of JMA's operational NWP system as of December 2018, which was extensively improved
since the Japanese 55-year Reanalysis (JRA-55). The JRA-3Q reanalysis provides improved products using rescued historical observation data
as well as reprocessed satellite data in the data assimilation system. JRA-3Q covers the period starting in the late 1940's. The reanalysis isdivided into three streams, stream A (a period from the 1990's onward), stream B (a period from the 1960's to 1980's) and stream C (a period
from the late 1940's to 1950's). The stream C corresponds to the period for which JMA has never produced reanalysis products. This period isimportant because several meteorological disasters happened in Japan at that time. For example, Typhoon Kathleen in September 1947
brought severe flood damage to eastern Japan and since then criteria of flood disaster prevention in Japan has been based on the recordbreaking daily precipitation due to that typhoon. However, it is difficult to produce a high-quality reanalysis for this period because there wasno network of regular radiosonde observations on a global basis. In this presentation, we show the past weather reconstructed in JRA-3Q
during stream C and then examine the accuracy of the reanalysis compared to other reanalyses (e.g., 20CR and CERA-20C).

Monday, 13 Sep 2021 at 11UTC - room BLUE

Ground-based remote sensing I
Ground-based atmospheric boundary layer profiling and data assimilation experiments within the EU COST
Action PROBE
Löhnert, Ulrich(1); Haeffelin, Martial(2); Cimini, Domenico(3); Nemuc, Anca(4); Kotthaus, Simone(2); Diemoz, Henri(5); Martinet,
Pauline(6); O'connor, Ewan(7); Hirsikko, Anne(7); Knist, Christine(8); Walden, Chris(9); Acquistapace, Claudia(1); Jurcakova, Klara(10);Stachlewska, Iwona(11); Batchvarova, Ekaterina(12)(1)

University of Cologne, (2)IPSL, (3)CNR-IMAA, (4)INOE, (5)ARPA, (6)CNRM Meteo France, (7)FMI, (8)DWD, (9)UK Research and Innovation and NCAS,
CAS-IT, (11)University of Warsaw, (12)CAWRI-BAS(10)

The atmospheric boundary layer (ABL) remains the most relevant under-sampled part of the atmosphere. Surface sensor networks, radiosonde
soundings as well as satellite observations do not provide sufficient information on the high temporal variability and strong vertical gradients
occurring in the ABL. This observational gap currently hampers our ability to improve weather forecasts, air quality prediction, and climate
model parameterizations. Measurement systems which have the potential to close this gap are enhanced commercial aircraft observations,operational UAS (Uncrewed Aircraft Systems) as well as ground-based remote sensing. On the one hand, already existing state-of-the-art
ground-based remote sensing instruments with the ability to provide ABL profiles (such as those of temperature, humidity, wind, aerosol,
cloud) are currently deployed at numerous sites in Europe, but the harmonization of data and procedures is still under development, limiting
their effective use and societal benefits. On the other hand, the use of automated UAS for profiling the ABL is currently experiencing significant
advancements. This presentation describes PROBE (2019-2023), a European cooperation initiative funded by COST (www.cost.eu) aiming at
filling the ABL observational gap, discussing the challenges, objectives, and the implementation plan. Preliminary results are presented includ-ing new technology, quality control and assurance measures, studies on NWP data assimilation, regional impact, and the role of the network
configuration.

Towards operational assimilation of surface based microwave radiometer and Raman lidar data at MeteoSwiss
Merker, Claire(1); Crezee, Bas(1); Leuenberger, Daniel(1); Haefele, Alexander(2); Vural, Jasmin(3); Hervo, Maxime(2); Martucci,
Giovanni(2); Arpagaus, Marco(1)(1)

MeteoSwiss, (2)MeteoSwiss, (3)Deutscher Wetterdienst

The current atmospheric observing systems fail to provide observations in the planetary boundary layer (PBL) with satisfactory spatial andtemporal resolutions despite their potential positive impact on numerical weather prediction. This is particularly critical for humidity, which
exhibits a very high variability in space and time, and for the vertical profile of temperature, which determines the atmospheric stability. Hence,
the analyzed thermodynamic structure of the PBL can be prone to errors, leading to poor forecasts for relevant phenomena, such as severe
storms or winter fog and low stratus. One approach to improve the model's representation of the PBL is to include novel, ground-based remotesensing profiler observations in the data assimilation system to improve the forecast initial conditions. This also improves the quality of downstream applications relying on a good representation of the PBL. In this contribution, we present our efforts to include observations frommicrowave radiometers and a Raman lidar into the 1km mesh-size ensemble data assimilation system KENDA-1. Brightness temperatures fromthe microwave radiometers are assimilated using the RTTOV-gb forward operator. The Raman lidar data is assimilated using a forward operator
for water vapor mixing ratio and temperature. We show the evaluation of extensive observation-minus-background statistics and results fromdata assimilation cycling experiments during summer-time convective situations.

Doppler radial wind assimilation in the GFS with an observing system simulation experiment
Lippi, Donald(1,2); Carley, Jacob(2); Kleist, Daryl(2)(1)

I.M. Systems Group, (2)NOAA National Centers for Environmental Prediction

An observing system simulation experiment using the GFS was performed to test the impacts of assimilating Doppler radial wind observationsin an idealized framework. The goal of this study is to lay the groundwork for assimilating radial wind observations within the GFS by (1) firstassess the impact of assimilating radial winds from a network that is accessible to the US today (i.e., the NEXRAD network), (2) explore ascenario where all worldwide radial winds are available for assimilation, and (3) a hypothetical scenario where the worldwide network was
designed with highly uniform spatial coverage. The final experiment is intended to provide an upper limit on the impact, useful for assessing
the relative values of the real networks. In each of these experiments, only radial wind observations are assimilated to simplify and isolate theimpact of such observations. The NEXRAD network alone improves the forecast skill over the US but is not able to constrain the forecast in
up/downstream data void regions. The realistic worldwide radar network shows improved results over both continental as well as oceanic data
void regions and demonstrates quantitatively comparable performance to the purely hypothetical network. This work suggests that the assimilation of global Doppler radial wind data could be beneficial to the GFS, and likely other global NWP applications; however, such potential canonly be realized if Doppler radial wind data are accessible.

Monday, 13 Sep 2021 at 12UTC - room RED

Coupled DA I
Development of Coupled Data Assimilation for the Bureau of Meteorology's Operational Climate Forecast System
ACCESS–S
Yin, Yonghong(1); Alves, Oscar(1); Sakov, Pavel(1); Hudson, Debra(1); Gray-Weale, Angus(1); Zhao, Mei(1); Zhou, Xiaobing(1)(1)

Research Program, Science & Innovation Group, Bureau of Meteorology

The Bureau of Meteorology's climate forecast system for weekly to seasonal and longer-range climate outlooks is called the Australian Community Climate Earth-System Simulator – Seasonal (ACCESS–S). The first version ACCESS-S1 operational in August 2018 is based on the UK Met
Office GloSea5-GC2 seasonal prediction system, but has enhancements to the ensemble generation strategy to make it appropriate for multiweek forecasting. The ocean and sea ice are initialised using the FOAM (Forecast Ocean Assimilation Model) analyses as per GloSea5. A weaklycoupled data assimilation system for the new version ACCESS-S2 has been developed and implementing in operations. First guess fields are
generated from 1-day coupled model integration but cross error covariances between different components are not considered in the data
assimilation. Sea surface temperature is strongly relaxed to the daily observations. Ocean in-situ temperature and salinity profiles are assimilated using the ensemble optimal interpolation (EnOI) method with seasonally varying background error covariance estimated from historicalintra-seasonal anomalies. The atmospheric component is nudged towards a pre-exiting atmospheric analysis and land surface responds tothe atmospheric forcing. Results from experiments and their validation for multi-year reanalysis with both assimilated and independent observations will be presented. Comparisons are made with both the previous ACCESS-S1 and a non-assimilating control system. Assessments
reveal considerable improvement in the ACCESS-S2 to the subsurface ocean current fields. Further experiment with sea level assimilation thatshow further improvements to the surface currents will also be presented.

The impact of incorporating flow-dependent oceanic background-error covariance information into air-sea
coupled data assimilation on the evolution of a tropical cyclone
Leung, Tsz Yan(1,2); Lawless, Amos S.(3,1,2); Nichols, Nancy K.(3,1,2); Lea, Daniel J.(4); Martin, Matthew J.(4)(1)

National Centre for Earth Observation, UK, (2)Department of Mathematics and Statistics, University of Reading, UK, (3)Department of Meteorology, University of Reading, UK, (4)Met Office, UK
Tropical cyclones tend to result in distinctive spatial and temporal characteristics in the upper ocean, which means that traditional, parametrisation-based background-error covariances in oceanic data assimilation (DA) may not be suitable. Using the case study of Cyclone Titli, which
affected the Bay of Bengal in 2018, we explore hybrid methods that combine the traditional covariance modelling strategy with flow-dependent
estimates of the ocean's error covariance structures based on a short-range ensemble forecast. This hybrid approach is investigated in the UK
Met Office's state-of-the-art system. Single-observation experiments in the ocean reveal that the hybrid approach is capable of producing
more anisotropic and vertically less uniform analysis increments. When the hybrid oceanic covariances are included in cycled, weakly coupledatmosphere-ocean DA, the resulting temperature differences beneath the ocean mixed layer are brought to the surface as the cyclone passes
through the region, due to vertical mixing induced by the strong surface winds. Through air-sea coupling, such differences in the sea-surface
temperature can then change the analysed structure of atmospheric fields associated with the cyclone.

Weakly and strongly coupled data assimilation with the coupled ocean-atmosphere model AWI-CM
Tang, Qi(1,2); Mu, Longjiang(3,1); Goessling, Helge(1); Semmler, Tido(1); Nerger, Lars(1)(1)

Alfred-Wegener-Institut, Helmholtz-Zentrum für Polar- und Meeresforschung, (2)Institute of Geographic Sciences and Natural Resources Research, (3)Qingdao National Laboratory for Marine Science and Technology
We compare the results of strongly coupled data assimilation and weakly coupled data assimilation by analyzing the assimilation effect on theprediction of the ocean as well as the atmosphere variables. The AWI climate model (AWI-CM), which couples the ocean model FESOM and theatmospheric model ECHAM, is coupled with the parallel data assimilation framework (PDAF, http://pdaf.awi.de). The satellite sea surface temperature is assimilated. For the weakly coupled data assimilation, only the ocean variables are directly updated by the assimilation while theatmospheric variables are influenced through the model. For the strongly coupled data assimilation, both the ocean and the atmosphericvariables are directly updated by the assimilation algorithm. The results are evaluated by comparing the estimated ocean variables with thedependent/independent observational data, and the estimated atmospheric variables with the ERA-interim data. In the ocean, both the WCDA
and the SCDA improve the prediction of the temperature and SCDA and WCDA give the same RMS error of SST. In the atmosphere, WCDA givesslightly better results for the 2m temperature and 10m wind velocity than the SCDA. In the free atmosphere, SCDA yields smaller errors for thetemperature, wind velocity and specific humidity than the WCDA in the Arctic region, while in the tropical region, the error are larger in general.

Incorporating flow dependent ocean information into weakly coupled atmosphere-ocean 4D-Var data
assimilation: experiments with an idealised system
Smith, Polly(1); Lawless, Amos(1,2); Nichols, Nancy(1,2)(1)

School of Mathematical, Physical and Computational Sciences, University of Reading, (2)National Centre for Earth Observation

Strongly coupled data assimilation (DA) is scientifically and technically challenging; as a first step operational centres are implementing weakly
coupled DA systems in which the model-observation misfits for the separate atmosphere and ocean domains are measured against the coupledmodel forecast state, but the analysis is computed independently for each model component. One of the challenges with this approach isrepresentation of the flow dependent interaction between the atmosphere and ocean at the air-sea interface within the DA scheme. This isespecially important in the prediction of phenomena where strong air-sea interaction is important. We explore this problem in an idealised
single-column coupled atmosphere-ocean system. In particular, we consider methods for incorporating flow dependent forecast error covariance information derived from ocean ensembles into variational weakly coupled DA. A key aspect of this is to investigate different methods forgenerating the ocean ensemble, with the goal of producing a limited sized ensemble that best represents the uncertainty in the ocean fields.
We examine how the structure of the ocean ensemble forecast error correlation matrices change when different ensemble generation methods
are used, and how the inclusion of this information alters the coupled analysis compared to using static forecast error covariances.

Monday, 13 Sep 2021 at 14UTC - room RED

DA platforms
PDAF - features and recent developments
Nerger, Lars(1)(1)

Alfred Wegener Institute

PDAF, the Parallel Data Assimilation Framework (http://pdaf.awi.de), is an open-source framework for ensemble data assimilation (DA). PDAF
is designed to be particularly easy to use and a DA system can be quickly build, while PDAF ensures the computational efficiency. PDAF's
ensemble-component provides online-coupled DA functionality, thus data transfers in memory and by using the MPI parallelization standard,by inserting 3 function calls into the model code. These additions convert a numerical model into a data-assimilative model, which can be runlike the original model, but with additional options. Alternatively, one can use separate programs to compute the forecasts and the DA analysis
update. PDAF further provides DA methods (solvers), in particular ensemble Kalman filters and particle filters. Tools for diagnostics, ensemble
generation, and for generating synthetic observations for OSSEs or twin experiments, provide additional functionality for DA. PDAF is used forresearch purposes, teaching, but also operationally. In the operational context, PDAF is e.g. used at the CMEMS marine forecasting center for
the Baltic Sea and in the Chinese Global Ocean Forecasting System (CGOFS). A recent addition to PDAF is OMI, the Observation Module Infrastructure, a library extension for observation handling. OMI is inspired by object-oriented programming, but for ease of use, it is not codedusing classes. Recent developments further include support for strongly-coupled DA across components of Earth system models, model bindings for NEMO, SCHISM, and the climate model AWI-CM and ensemble-variational solvers. This presentation discusses the PDAF's features andrecent infrastructure developments in PDAF.

JCSDA's vision of a community data assimilation for research and operations
Auligne, Tom(1,2)(1)

Joint Center for Satellite Data Assimilation, (2)University Corporation for Atmospheric Research

The Joint Center for Satellite Data Assimilation (JCSDA) is spearheading a revolutionary approach to developing next-generation data assimilation capabilities in support of scientific research and operations. Our major objectives are to reduce duplication of effort without imposing asingle approach, to improve our agility to use observations, and to enable innovative solutions for coupled analysis of the Earth system. This
presentation provides JCSDA's overarching vision, which is supported by the following three pillars: 1. Collaborative Infrastructure: we arecommitted to developing, maintaining, and distributing open-source software via modern working practices such as agile/SCRUM methodology, automated and multi-tier testing, continuous integration and continuous delivery. Additional focus is devoted to training and facilitating
rapid community contributions. 2. Generic software components: leveraging the separation of concerns and generic programming, the data
assimilation combines generic components (e.g., forward operators, error covariance modeling, data assimilation algorithms), which get instantiated for each specific model, set of observations and configuration. This approach allows the use of a common data assimilation framework for a variety of applications. 3. Joint testbed applications: we are now deploying end-to-end systems to continuously test all components
in several pseudo-operational environments and inform on future design updates. A shared ecosystem to manage research experiments will
ensure that results are transparent and easily reproduceable throughout the community, thus maximizing the benefits of "jointness".

A CESM+DART Atmospheric Reanalysis for Forcing Ocean, Land, and Other Surface Models.
Raeder, Kevin(1); Anderson, Jeffrey(1); Hoar, Timothy(1); El, Gharamti Mohamad(1); Johnson, Benjamin(1); Collins, Nancy(1);Steward, Jeff(2); Coady, Mick(1)(1)

National Center for Atmospheric Research [Boulder], (2)Spire Global Inc.

Ensemble reanalyses of the ocean, sea ice and land are crucially dependent on both the mean and variability of atmospheric forcing. An ensemble atmospheric reanalysis using the Community Earth System Model with the Data Assimilation Research Testbed generated a uniquedataset of ensembles of surface fluxes and other variables required by non-atmosphere component models. These data can force reanalyses
and hindcasts with CESM component models; CLM5 (land), CICE5 (sea ice), POP2 and MOM6 (ocean), MOSART (atmospheric chemistry), and
CISM (land ice). They can also force non-CESM Earth system models. This presentation describes the generation and quality of the reanalysis
and how to access and use the dataset for reanalyses with other models. This reanalysis used a 1 degree CAM6-FV configuration of CESM to
assimilate millions of observations per day for 2011-2019. Data saved include 80-member ensembles of: 1) High-frequency atmosphericforcing for non-atmospheric models of CESM, 2) Weekly CAM6, CLM and CICE restart files, 3) 6 hourly prior estimates of the assimilated observations, 4) 6 hourly land model plant growth variables, 5) 6 hourly ensemble mean atmospheric analyses. The reanalysis' ensemble spread
and agreement with observations vary with atmospheric field, location, and time. These can be evaluated in great detail using 3), above. This
120 Tb dataset has a combination of a large ensemble, high frequency, and multiyear time span providing opportunities for statistical analysis
and use as a training dataset for machine learning. It is freely available from the Research Data Archive at NCAR.

Monday, 13 Sep 2021 at 15UTC - room RED

Coupled DA II
Skillful Coupled Atmosphere-Ocean Data Assimilation - on a Laptop
Hakim, Gregory(1); Snyder, Chris(2); Newman, Matthew(3); Penny, Steven(3)(1)

University of Washington, (2)NCAR, (3)NOAA ESRL

A wide range of problems in weather and climate science depend upon dynamically consistent coupled states. For example, in climate science,
direct ocean observations become increasingly sparse back in time prior to the 21st century, increasing the importance of effectively using
atmospheric observations to inform on the state of the ocean. For weather, the S2S forecast problem depends upon consistent initialization of
the coupled state, and operational assimilation systems typically employ weakly coupled assimilation (coupled model and uncoupled assimilation). Perhaps the main challenges to progress involve the computational burden of both the forecast and analysis steps, and the technicaldifficulty of developing and testing assimilation techniques in large, complex coupled modeling systems Here we use a low-dimensional linear
emulator, trained on source datasets. This emulator has demonstrable skill on S2S timescales and allows fully coupled data assimilation using
the unapproximated Kalman filter. Specifically, we use a linear inverse model (LIM), trained on data from the Climate Forecast System Reanalysis(CFSR), and observations also drawn from that dataset. We conduct a hierarchy of assimilation experiments, including uncoupled, weakly coupled, and strongly coupled cases. Results show a reduction in analysis error for the weakly and strongly coupled cases over the uncoupled case.
Improvement is particularly large for midlatitude sea-surface temperature, where strongly coupled data assimilation reduces errors by over

10% compared to the weakly coupled case. The LIM framework facilitates decomposition of the dynamics into coupled and uncoupled subspaces, which are used to diagnose the sources of improvement.

Skin temperature analysis used for the assimilation of clear-sky radiances
Massart, Sebastien(1)(1)

European Centre for Medium-Range Weather Forecasts

We recently proposed a new approach to the skin temperature analysis used for the assimilation of clear-sky radiances, as part of ECMWF
atmospheric 4D-Var system. For that purpose, we added hourly skin temperature fields in the 4D-Var control vector. Some of these fields areassociated with the infrared (IR) instruments and others with the microwave (MW) instruments. We will first present the impact of the new
approach on the ECMWF Integrated Forecasting System forecast skill. This new approach is the first stepping stone toward a seamless coupling
between atmosphere and surface (land and ocean). We will show for example how the IR skin temperature fields could be used to derive a sea
surface temperature that could be used to force the ocean model. We will also show that, with this approach, we change the surface emissivity
used for the assimilation of MW radiances. This emissivity change could be used to provide information on the emissivity models.

Modernising the Land Data Assimilation and Land Model Uncertainty Estimation in NOAA's Global NWP Systems
Draper, Clara(1); Gichamo, Tseganeh(2); Barlage, Michael(3); Dong, Jiarui(3); Fox, Andrew(4); Kleist, Daryl(3); Pegion, Philip(2);Whitaker, Jeffrey(2)(1)

NOAA, ESRL, PSL, (2)NOAA, ESRL, PSL, (3)NOAA National Centers for Environmental Prediction, (4)JCSDA

In contrast to other national NWP centers, the soil moisture and temperature in NOAA's global NWP models is not directly constrained withobservations, and our operational snow analysis is also outdated. This presentation then provides an overview of research efforts to modernise
the land data assimilation used in our global atmospheric systems, together with relevant experimental and scientific results. For snow, wehave developed an Optimal Interpolation (OI) snow depth analysis to assimilate station snow depth and remotely sensed snow cover observations, based on the schemes used elsewhere. The OI corrects our current analysis' bias against adding snow, while improving the snow depth
and low-level atmospheric temperatures over land. It is now being implemented in NOAA's global prediction systems within the JEDI software
framework. For the soil states, a coupled land/atmosphere Ensemble Kalman Filter (EnKF) has been developed to constrain the soil temperatureand soil moisture with observations of 2m temperature and humidity. The EnKF is in early testing, and results from initial experiments will bepresented. Additionally, since NWP ensembles are consistently under-dispersed at and near the land surface, prior to developing the EnKF we
introduced a scheme to account for land model uncertainty in the generation of our NWP ensembles. This scheme perturbs key parameters
controlling the land/atmosphere fluxes,thus generating ensemble-based land/atmosphere error covariance estimates that represent uncertainty in these fluxes, while also showing agreement with independent estimates of the model uncertainty. This scheme is intended to be
introduced operationally together with the land/atmosphere EnKF described above.

Improved Understanding of Land-Atmosphere Interactions Using Profiling and Surface Flux Observations
Turner, Dave(1); Wakefield, Ryann(2); Wagner, Tim(3); Heus, Thĳs(4); Rosenberger, Tessa(4); Meyers, Tilden(5)(1)

NOAA Global Systems Laboratory, (2)University of Oklahoma, (3)University of Wisconsin, (4)Cleveland State University, (5)NOAA Air Resources
Laboratory

Land-atmosphere interactions play a critical role in both the atmospheric water and energy cycles. Mixing diagrams provide insight into theevolution of the heat and moisture budget within the convective boundary layer (CBL). Mixing diagrams enable us to quantify the contributions
from surface fluxes, advection, radiative heating, encroachment, and entrainment to the evolution of the CBL moisture and energy budgets asa function of the time-of-day. We demonstrate that observations from the ARM Southern Great Plains (SGP) site provide all of the inputs needed
for the mixing diagram framework. In particular, the high temporal resolution profiles of temperature and humidity retrieved from the Atmospheric Emitted Radiance Interferometer (AERI) are a critical component in this analysis, as they provide a unique opportunity to quantify encroachment, advection, and mixed-layer quantities from observations. We demonstrate that the mixed-layer mean values of temperature andhumidity are optimal for understanding the evolution of the CBL; past work using 2 m above ground in-situ observations in mixing diagrams
results in biases. We also demonstrate that encroachment is a critical term for understanding the CBL evolution; past work had convolved thisterm with entrainment. We use this framework to evaluate both large-eddy simulation (LES) and the High-Resolution Rapid Refresh (HRRR)
models using this approach.

Monday, 13 Sep 2021 at 15UTC - room GREEN

Reanalysis evaluation II
Overview of MERRA-2 for Applications, Decision-making, and Climate Assessment
Bosilovich, Michael(1); Collow, Allison(2); Stackhouse, Paul(3); Thomas, Natalie(2); Patel, Kanan(4); Amos, Helen(5); Prados, Ana(6);Schollaert, Uz Stephanie(1)(1)

NASA GSFC, (2)NASA GSFC USRA, (3)NASA LaRC, (4)University of Texas, (5)NASA GSFC SSAI, (6)NASA GSFC and University of Maryland Baltimore
CountyThe Modern Era Retrospective-analysis for Research and Applications (MERRA) began development in the early 2000s with intention to provide
informed regional climate assessment for decision support. Retrospective-analyses include an abundance of observations so that synopticscale weather is faithfully depicted. However, key features in high time resolution (1 hourly), long duration (starting in the late 70s early 80s)
and timely near real time data production are particularly useful for decision-making. In addition, attention to useful output diagnostics canalso increase the usefulness of the reanalyses (e.g. boundary layer winds for wind energy production). Here, we review some recent efforts that
employ reanalyses output in applied uses, including heatwaves, drought, extreme rainfall, renewable energy, sustainable building systems andhealth (air quality). We also consider lessons learned and potential directions in the future.

Mechanisms Associated with Daytime and Nighttime Heat Waves over the Contiguous United States
Thomas, Natalie(1,2); Bosilovich, Michael(1); Collow, Allison(1,2); Koster, Randal(1); Schubert, Siegfried(1,3); Dezfuli, Amin(1,3);Mahanama, Sarith(1,3)(1)

NASA GSFC GMAO, (2)USRA, (3)Science Systems and Applications, Inc. [Lanham]

Heat waves are extreme climate events that have the potential to cause immense stress on human health, agriculture and energy systems.
Previous studies have found heat wave frequency to have increased over recent decades in many regions. There is no single accepted definition

of a heat wave, but it is generally defined as temperature exceeding a threshold for a sustained amount of time. However, multiple different
temperature variables can be used – daily mean, maximum and minimum temperature are potentially relevant, as are variables which accountfor humidity, such as apparent temperature, equivalent temperature and heat index. In this study, heat waves are examined over the UnitedStates using the NASA Modern-Era Retrospective Analysis for Research and Applications 2 (MERRA-2). We focus explicitly on the difference
between heatwaves manifest during daytime versus nighttime hours. Both can be detrimental for human health, so it is important to understand the conditions leading to their onset. A daytime (nighttime) heat wave is defined as average daytime (nighttime) temperature exceedingits calendar day 90th percentile for at least 3 days, where daytime and nighttime hours are differentiated locally using hourly downward shortwave radiation at the top of the atmosphere. We characterize the large-scale circulation and identify local and remote processes associatedwith these two heatwave types over different regions of the United States.

Homogenetity of time series and trends from ERA5
Kozubek, Michal(1); Podolska, Katerina(1); Krizan, Peter(1)(1)

Institute of Atmospheric Physics [Prague]

The stratosphere and its dynamics are a very important part of atmospheric circulation. We need to analyze its climatology, as well as longterm trends. A long-term trend study needs homogenous datasets without significant artificial discontinuities. The analysis is based on thenewest released reanalyses, European Center for Medium-Range Weather Forecast Reanalysis (ERA5). The aim of this study is to detect discontinuities and homogeneity in the temperature and wind time series from the above reanalysis with the various homogeneity tests for pressurelayers above 500 hPa up to 1 hPa and show a comparison of temperature trends from the studied reanalyses and GPS radio occultation (GPSRO). We search for individual grid points where these discontinuities occur, and also for the years when they occur (geographical and temporal
distribution). As expected, the study confirms better results for the Northern Hemisphere due to the denser data coverage. A high number ofgrid points with jumps on the Southern Hemisphere is found, especially at higher pressure levels (from 50 hPa). The spatial and vertical distribution of discontinuities is also presented. The vertical distribution reveals the reduction of the number of jumps around 10 hPa for ERA5
reanalysis. We also study temperature trends from reanalyses and GPS RO and our analysis shows that the agreement between the reanalyses
and observations are very good for the period 2006–2018. We will add also comparison with AEOLUS measurements.

Tropical wave analyses: variability, trends and uncertainties in ERA-Interim, JRA-55, MERRA and ERA5 reanalysesŽagar, Nedjeljka(1); Zaplotnik, Žiga(2); Karami, Khalil(2)(1)

Universität Hamburg, (2)Faculty of Mathematics and Physics, University of Ljubljana

We discuss uncertainties in tropical wave circulation using ERA-Interim, JRA-55, MERRA and ERA5 reanalyses. The equatorial Rossby and inertia-gravity waves are filtered using the three-dimensional linear wave theory. The results quantify amplitudes and trends in the equatorial wave
activity across scales. We show that the four reanalyses agree regarding recently increased subseasonal variability in the large-scale Kelvinwaves, mixed Rossby-gravity waves and westward-propagating inertio-gravity waves with the lowest meridional mode. The amplitude and signof trends at smaller zonal scales with greater meridional modes differ between the ERA-Interim and JRA-55 datasets on one hand, and the
ERA5 and MERRA data on the other. Increased variability in the ERA-Interim and JRA-55 accounts for positive trends in their global subseasonalvariability.

Monday, 13 Sep 2021 at 15UTC - room BLUE

Hybrid DA
4D-hybrid formulation of 4DEnVar for global data assimilation at Météo-France
Berre, Loïk(1); Arbogast, Etienne(2)(1)

Météo-France, (2)Météo-France

There is growing interest for using ensemble information in the formulation of data assimilation, which can be combined with advantages ofvariational approaches. The 4DEnVar formulation is a variational method based on the use of 4D ensemble non linear trajectories, at relatively
high resolution, in order to represent 4D background error covariances, which are localized spatially. A usual approach in 4DEnVar is also to
employ an hybridation with error covariances which are spatially averaged, for representing long-distance correlations. Moreover, these spatially averaged covariances are usually static within the assimilation window. This can be seen as a 3D-hybrid approach, partly relying on 3D
static error covariances, which are employed repeatedly for the different time slots of the 4D data assimilation window. A new (4D) formulationof the hybridation is proposed at Météo-France for 4DEnVar, in order to combine respective advantages of 4DEnVar and of more conventionalvariational approaches. Its properties will be discussed both in the perfect model framework and also in the case where model errors are taken
into account. Preconditioning aspects will be considered too. A preliminary implementation of this 4D-hybrid formulation of 4DEnVar has been
made possible thanks to the flexibility offered by the OOPS (Object-Oriented Prediction System) software, and experimental results with the
Météo-France global ARPEGE system will be presented.

Ensemble-variational assimilation with constrained non-stationary spatial convolutions
Tsyrulnikov, Michael(1); Sotskiy, Arseniy(1)(1)

Hydrometcenter of Russia

We propose a new approach to hybrid ensemble-variational assimilation based on direct estimation of a square root, W, of the prior covariancematrix. The W matrix is a spatial convolution filter constrained to model slowly varying in space covariances. It is estimated online by (1)applying a spatial multi-scale bandpass filter to ensemble perturbations, (2) estimating a local spectrum from the ensemble band variances,and (3) converting the local spectra to the rows of W. The estimation of the local spectra is regularized with climatological statistics and canbe done in a scale-dependent manner. The estimation scheme can be efficiently parallelized. The estimated online W matrix is used in avariational analysis scheme. The scheme is fast because a square root of the prior covariance matrix is known to be an efficient preconditioner.
The scheme does not require localization. The technique was tested in static simulation experiments with synthetic data on circular and spherical spatial domains. With a reasonable degree of spatial non-stationarity, the new analysis outperforms the classical covariance-localized
analysis for ensemble sizes up to 100. With the ensemble size of 10, the RMSE of the new analysis is 3-4 times closer to the RMSE of the best
possible analysis (which has access to the true spatially non-stationary covariance matrix) than the RMSE of the covariance-localized analysis.
The performance of the new technique in cycled data assimilation experiments on a 1D domain will be presented at the symposium.

Integrated Hybrid Data Assimilation for an Ensemble Kalman Filter
Lei, Lili(1); Wang, Zhongrui(1); Tan, Zhe-Min(1)(1)

Nanjing University

Hybrid ensemble-variational assimilation methods have been widely applied for numerical weather predictions. The commonly used hybrid
assimilation methods compute the hybrid analysis increment using a variational framework and update the ensemble perturbations by an EnKF.
To avoid the inconsistence resulted from separated hybrid assimilation and EnKF systems, two integrated hybrid ensemble Kalman filters(EnKFs) that update both the ensemble mean and ensemble perturbations by a hybrid background error covariance in the framework of EnKF
are proposed here. The integrated hybrid EnKFs approximate the static background error covariance by use of climatological perturbations
through augmentation or additive approaches. The integrated hybrid EnKFs are investigated in the Lorenz (2005) models II and III with different
magnitudes of model error. Results show that the static background error covariance can be sufficiently estimated by climatological perturbations withan order of hundreds. The integrated hybrid EnKFs are superior to the traditional hybrid assimilation methods, which demonstrates
the benefit to update ensemble perturbations by the hybrid background error covariance. Sensitivity results reveal that the advantages of theintegrated hybrid EnKFs over traditional hybrid assimilation methods maintained with varying ensemble sizes, inflation values and localization
length scales.

Tests of hybrid EnKF-Variational Data Assimilation capabilities using JEDI with NOAA's Next Generation Regional
High Resolution NWP System
Lei, Ting(1,2); Liu, Shun(3,4); Carley, Jacob(4); Martin, Cory(5,4); Holdaway, Daniel(6); Ménétrier, Benjamin(7,6); Rogers, Eric(4); Zhang,
Xiaoyan(3,8); Blake, Benjamin(3,4); Hu, Ming(9); Kleist, Daryl(4)(1)

IM Systems Group, Inc., (2)Environmental Modeling Center, MNOAA National Centers for Environmental Prediction, (3)IM Systems Group, Inc,
Environmental Modeling Center, NOAA National Centers for Environmental Prediction, (5)RedLine Performance Methodology, (6)Joint Center for
Satellite Data Assimilation, (7)Institut de recherche en informatique de Toulouse, (8)NOAA National Centers for Environmental Prediction, (9)Global
System Laboratory(4)

The JEDI (Joint Effort for Data assimilation Integration) system is the next generation data assimilation framework planned to underpin the NCEP
production suite and is developed following the principles of object-oriented design. At NCEP's Environmental Modeling Center, the hybrid
EnKF and Variational Applications of JEDI on regional domains has been tested and evaluated using various configurations in comparison withthe current operational data assimilation framework (Gridpoint Statistical Interpolation; GSI) to prepare for its future role in NOAA's Next Generation Regional High Resolution NWP System - the Rapid Refresh Forecast System. The evaluation of JEDI in comparison to GSI will focus on
the performance and analysis characteristics of the hybrid EnVar algorithms of both systems and provide helpful insight toward the ongoing
JEDIagile development process.

Tuesday, 14 Sep 2021 at 11UTC - room RED

Operational DA I

National Analysis System
Krysta, Monika(1); Rennie, Susan(1); Steinle, Peter(1); Lee, Jin(1); Le, Tan(1); Lu, Wenming(1); Dharssi, Imtiaz(1); Bermous, Ilia(1); Smith,Andy(1); Cooper, Shaun(1); Tingwell, Chris(1); Smith, Fiona(1); Franklin, Charmaine(1)(1)

Australian Bureau of Meteorology [Melbourne]

National Analysis System, NAS, is essentially a response to internal customers' requirements to provide a rapid and frequent update on thecurrent state of the atmosphere in order to increase situational awareness, particularly in an advent of extreme meteorological conditions. For
this purpose, Australian Bureau of Meteorology is developing a high-resolution limited area cycling suite, for a domain covering entire Australia
at 2.2 km of horizontal grid spacing and 80 vertical levels. This suite is to a large extent based on existing implementations of an hourly cycling
4D-Var suite for limited area applications. This latter concept constitutes a backbone of the new system, while essential additions consist
in supplementary 3D-Var analyses performed using low-latency observations. The 3D-Var processing comes in two steps, a standard stepwhich adjusts large scales and an additional step for which errors of conventional observations are tuned. This second step limits smoothing
and provides 3D-Var overfitting objective analyses with a closer match to those observations which are considered reliable and representative,
rather than achieving a balanced analysis created for the purpose of initialising a weather forecast. In this presentation we propose to illustrate
the new system and its applications. We will start with a brief description of NAS and its components. The observations taking part in various
analyses will be listed as well as their numbers and timeliness. In the context of case studies three types of analysis increments shall be illustrated. The added value of the overfitting 3D-Var will be discussed alongside modifications to the observation error. Cross-validation verification results will be presented.

New Variational Data Assimilation System for Regional Model at JMA
Ikuta, Yasutaka(1)(1)

Meteorological Research Institute [Tsukuba]

The four-dimensional variational method needs to keep up with the updates of the forecast models used as strong constraints. In JMA, withthe development of non-hydrostatic model “ASUCA”, we have developed the variational data assimilation system based on ASUCA (ASUCA-Var).
In the ASUCA-Var, the control variables include the underground temperature and the soil moisture in addition to atmospheric variables, andthe climatological background error covariances are classified by sea/land and local time. The cost function is designed to be in fully quadratic
form, is minimized by three times the line search as inner loop with the basic field update twice in between the inner loops. For advanced
observation processing within the variational system, variational quality control and variational bias correction have also been introduced. In
addition, we developed an optimization method for the model's topography based on the variational method. Since the various validation
indicators showed that the forecast accuracy of the NWP system using ASUCA-Var has been significantly improved compared to the former
system, ASUCA-Var has been operated as the Meso-scale Analysis since March 2020. Currently, we have been developing the introduction of
hybrid 4D-Var with ensemble of data assimilation and the extension of control variables to hydrometeors. In this presentation, the overview
of ASUCA-Var, the impact of the observations using the degree of freedom signal and the verification results of the ASUCA-Var's performance
will be shown.

Building a JEDI- and FV3-based Rapid Refresh Forecast System (RRFS) upon Decade of Development andImplementation of the High Resolution Rapid Refresh (HRRR)
Hu, Ming(1); Kenyon, Jaymes(2,1); Holt, Christina(2,1); Trahan, Samuel(2,1); Li, Ruifang(2,1); Ge, Guoqing(2,1); Beck, Jeff(3,1); Weygandt,
Stephen(1); Curtis, Alexander(1); Ladwig, Terra(1); Rogers, Eric(4); Carley, Jacob(4); Lei, Ting(4); Liu, Shun(4); Zhang, Xiaoyan(4)(1)

Global System Laboratory, (2)Cooperative Institute for Research in Environmental Sciences, (3)Cooperative Institute for Research in the Atmosphere, (4)NOAA National Centers for Environmental Prediction
The Rapid Refresh Forecast System (RRFS) is NOAA's next generation rapidly-updating regional/storm scale operational NWP system underdevelopment collaboratively by many partners. It will replace current regional and storm scale operational NWP systems, including RAP/HRRR,
HREF, and NAM. In the past ten years, NOAA has worked with the research community to develop HRRR for operational storm forecasts. Many
data assimilation and physical schemes used in HRRR are still valuable for RRFS development. The RRFS uses a limited area model version of
the FV3 (FV3LAM), but retains the majority of the HRRR physics, hourly cycling, and data assimilation capabilities. The data analysis engine istransitioning from the Gridpoint Statistical Interpolation (GSI) to the Joint Effort for Data assimilation Integration (JEDI). We are currently evaluating various data assimilation capabilities using the GSI coupled with the FV3LAM inside the UFS Short-Range Weather Application. These
include 3D hybrid EnVar, non-variational cloud analysis, soil adjustment, and the use of radar reflectivity via a derived temperature tendency.
We are also conducting tests of these analysis capabilities using JEDI coupled with the FV3LAM, benchmarking them against similar GSIFV3LAM capabilities. To effectively couple the RRFS with the FV3-based Global Forecast System (GFS), we are running a single large 3-km RRFS
domain covering North America and adjacent oceanic regions to assess the need for a 13-km resolution intermediate RAP-like domain between
GFS and 3-km CONUS domain as was done with RAP/HRRR. In this talk, we will summarize the latest RRFS retrospective and real-time tests
results with comparisons against the HRRR and other systems.

Tuesday, 14 Sep 2021 at 11UTC - room GREEN

Reanalysis evaluation III
Brewer-Dobson circulation represented in JRA-3Q
Kobayashi, Chiaki(1); Iwasaki, Toshiki(2); Kobayashi, Shinya(3); Kosaka, Yuki(3); Chiba, Jotaro(3); Tokuhiro, Takayuki(3); Harada,
Yayoi(1); Naoe, Hiroaki(1)(1)

Meteorological Research Institute, Japan Meteorological Agency, (2)Graduate School of Science, Tohoku University, (3)Office of Earth System
Modeling, Numerical Prediction Division, Japan Meteorological Agency
The Brewer-Dobson circulation (BD circulation) represented in the reanalysis data is known to have differences in structure and strength,
although seasonality and asymmetry in the northern and southern hemispheres are common among the reanalyses (Iwasaki et al., 2009). The
BD circulation, represented in JRA-55, shows similar characteristics to that in ERA-Interim climatologically, but it has been pointed out that thelong-term trend of the circulation strength, indexed by the upward mass flux near the tropopause, shows an increasing trend and is opposite
to that of ERA-Interim (Abalos et al. 2015, Kobayashi and Iwasaki, 2016). The Japan Meteorological Agency (JMA) has been conducting the third
Japanese global atmospheric reanalysis for Three Quarters of a Century (JRA-3Q). The characteristics of the mean meridional circulation represented in JRA-3Q in recent 30 years, especially the BD circulation, were compared with those in other reanalyses such as JRA-55 and ERA5

(5.1). The results show that the BD circulation represented in JRA-3Q is weaker than that of JRA-55 throughout the year, and the associatedtropospheric and stratospheric mass exchange rates are smaller throughout the year. The exchange rates in JRA-3Q are less than ERA5 (5.1)
in DJF season and similar in JJA season, indicating a difference in the representation of the Northern Hemisphere cell of BD circulation.

Comparison of land surface data assimilation results driven by MERRA-2 and ERA5 meteorological forcings
Heyvaert, Zdenko(1); De, Lannoy Gabrielle(1); Gruber, Alexander(1); Dorigo, Wouter(2); Scherrer, Samuel(2); Bechtold, Michel(1);Kumar, Sujay(3)(1)

Catholic University of Leuven, (2)Technische Universität Wien, (3)NASA Goddard Space Flight CenterIn this study, the sensitivity of a land surface data assimilation system to the choice of meteorological forcing is explored. We assimilate microwave-based satellite retrievals of surface soil moisture into the Noah-MP land surface model using NASA's Land Information System (LIS).
Two forcing data sets are tested: the Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA-2) from NASA, andthe European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis Version 5 (ERA5). We explore the impact of this choice of meteorological forcing on the performance of the data assimilation system. More specifically, surface and root-zone soil moisture estimates ofboth data assimilation runs are compared with each other and evaluated against in situ observations as well as existing root-zone soil moistureproducts, such as the SMAP Level 4 Surface and Root-zone soil moisture product. Our data assimilation approach uses a one-dimensional
Ensemble Kalman Filter (EnKF) and allows to obtain consistent surface and root-zone soil moisture estimates over the European continent forthe period 2002-2020. Satellite data are acquired from the combined active-passive ESA CCI Soil Moisture product, which harmonizes andmerges soil moisture retrievals from multiple satellites. Acknowledgments: this work is part of the ESA CCN1 CCI+ Soil Moisture Scientific
Evolution project and the FWO-FWF CONSOLIDATION project.

Data assimilation impact studies with the AROME-WMED reanalysis during HyMeX SOP1
Fourrie, Nadia(1); Caumont, Olivier(2); Brousseau, Pierre(2)(1)

Centre National de Recherches Météorologiques, (2)Centre National de Recherches Météorologiques

The Hydrological cycle in the Mediterranean Experiment (HyMeX) aimed to study the heavy precipitation that regularly affects the Mediterranean area. A reanalysis with a convective-scale model AROME-WMED (Application of Research to Operations at MEsoscale western Mediterranean) was performed, which assimilated most of the available data for a 2-month period corresponding to the first special observation periodof the field campaign in September-November 2012 (Fourrié et al., 2019). Among them, observations related to the low-level humidity flow
were assimilated : a dense reprocessed network of high-quality Global Navigation Satellite System (GNSS) zenithal total delay (ZTD) observations, reprocessed data from wind profilers and Spanish radar data. The aim of this study is to assess the impact of the assimilation of theseobservation types on the analyses and the forecasts through several observing system experiments by removing one single data set from theobservation data set assimilated in the reanalysis. Ground-based GNSS ZTD data set provides the largest impact on the analyses and theforecasts, as it represents an evenly spread and frequent data set providing information at each analysis time over the AROME-WMED domain.
The impact of the reprocessing of GNSS ZTD data also improves the forecast quality, but significantly. The assimilation of the Spanish radar
data improves the 3 h precipitation forecast quality as well as the short-term (30 h) precipitation forecasts, but this impact remains locatedover Spain. In addition, marginal impact from wind profilers was observed on wind background quality.

Tuesday, 14 Sep 2021 at 11UTC - room BLUE

Theoretical Developments in DA
An optimal linear transformation for data assimilation
Snyder, Chris(1); Hakim, Greg(2)(1)

National Center for Atmospheric Research, (2)University of Washington

Estimation of extremely large covariance matrices and their use computationally to update prior state estimates (whether through minimization
or solution of linear systems) are key difficulties in data assimilation for geophysical systems. It is often helpful to apply linear transformationsof the state or observations, for example to facilitate the implementation of multivariate spatial covariances or improve the conditioning of thenumerical problem. In particular, recent literature on multiscale ensemble Kalman filters can be cast as a search for transformations to coordinates in which localization of sample covariances is especially straightforward and effective. This leads to two questions: What linear transformation yields the simplest form of the update equations for the Kalman filter or best linear unbiased estimator? And what linear transformation is optimal to precede localization in ensemble Kalman filters? Answers to these questions appear to be the same. The resulting transformation diagonalizes the update, in the sense that all covariances become the identity and each coordinate in the observation space depends
on a single, unique coordinate in the state space. The number and signal-to-noise ratio of the observations in the transformed space thencharacterize the update, including measures of information content for the observations, the condition number of the minimization problem,
and the effects of sampling error from ensemble techniques.

A Numerical Approximation Method for Fast Computations of Matrix-Vector Products with Spatially CorrelatedObservation Error Statistics
Hu, Guannan(1); Dance, Sarah L.(1)(1)

University of Reading

Recent studies have shown that observations such as geostationary satellite observations and Doppler radar radial winds can exhibit strong
spatial error correlations. The estimated error correlation lengthscales are found to be significantly longer than the observation-thinning distances used by many operational centres. Therefore, there is a need to include the correlated error statistics in the data assimilation procedures. However, this can potentially increase the computation costs for the matrix-vector multiplications arising in the solution of the variational minimization problem. The calculation of these matrix-vector products is perfectly parallel when observations are distributed acrossdifferent processing elements (PEs) according to their geophysical locations and if their errors are assumed to be uncorrelated. However, if
correlated error statistics are used, the PEs cannot complete their own tasks without excessive communications between each other. Previous
studies have proposed many methods or strategies that can be used to reduce the computational cost. These include allocating observations
with correlated errors into one PE and using the Cholesky decomposition, assimilating spatial difference observations, using truncated eigendecomposition to approximate the error covariance matrix, using data compression technique to reduce the number of observations and using
diffusion operators to model correlated errors. In this presentation, we will present a new numerical approximation method developed basedon the fast multipole method. This method works with the domain decomposition of the observations and can compute the matrix-vector
products with reduced communication costs.

Randomised preconditioning in variational data assimilation
Dauzickaite, Ieva(1); Lawless, Amos(1,2); Scott, Jennifer(1,3); Van, Leeuwen Peter Jan(4,1)(1)

School Of Mathematical, Physical and Computational Sciences, (2)National Centre for Earth Observation, (3)Scientific Computing Department,
STFC Rutherford Appleton Laboratory, (4)Department of Atmospheric Sciences, Colorado State University [Fort Collins]
In variational data assimilation, the analysis can be obtained by minimising a series of linear least-squares cost functions. Each minimisation(called an inner loop) can be performed by using the conjugate gradient (CG) method, which needs preconditioning for fast convergence. In
previous work, limited memory preconditioners (LMPs) have been constructed using approximations of the eigenvalues and eigenvectors ofthe Hessian of the cost function from the previous inner loop. If the Hessian changes significantly between consecutive inner loops, the LMP
may perform poorly. To circumvent this, we propose using randomised methods for low rank eigenvalue decomposition to construct LMPs
using information from the current inner loop. These methods can be easily parallelised and the cost can be comparable to just a few CG
iterations if there are enough computational resources available. We compare three randomised methods when minimising the quadratic costfunction arising in incremental weak constraint 4D-Var. Numerical experiments in idealized systems show that the resulting randomised LMPs
perform better than the existing deterministic LMPs: a randomised LMP gives faster minimisation than a deterministic LMP, even when theformer is constructed with fewer eigenvectors than the latter. Using these methods may allow more efficient and robust implementations ofvariational data assimilation methods.

Tuesday, 14 Sep 2021 at 12UTC - room RED

Operational DA II
NCEP Operational Global Data Assimilation System (GDAS): Recent Upgrades and Future Plans
Kleist, Daryl(1); Treadon, Russ(1); Thomas, Catherine(1); Liu, Haixia(1,2); Liu, Emily(1); Mahajan, Rahul(1)(1)

Environmental Modeling Center, NOAA National Centers for Environmental Prediction, (2)IMSGThe Global Forecast System (GFS) and GDAS were recently updated to version 16. This included a doubling of the number of vertical layers in
the model and raising the model top to ~80km. The data assimilation system continues to utilize hybrid 4DEnVar within the Gridpoint Statistical
Interpolation (GSI). Additional changes included replacing the Ensemble Serial Filter with the Local Ensemble Transform Kalman Filter (LETKF)
for the ensemble perturbation update, 4D incremental analysis update (IAU), improvements to the inline sea surface temperature analysis, andsemi-coupled land analysis as forced by observed precipitation. Additional changes included calibration to account for the new model layer
specification, changes to stratospheric humidity increments, satellite channel selection to include higher peaking channels from ATMS and
AMSU-A, bias correction, the use of inter-channel correlated observation errors for IASI and CrIS, and incorporation of additional clear-sky
radiance data from geostationary satellites. Work is underway on future upgrades to include improved use of observations such as the utilization of commercial GPS radio occultation, additional atmospheric motion vectors, new sensors for constraining sea surface temperature, antenna corrected radiances, and updates to the use of scatterometer winds. This presentation will provide details regarding the upgrades included in version 16 as well as preliminary results from the upcoming changes related to improved use of observations. Progress and plans onthe transition from GSI to the Joint Effort for Data assimilation Integration will be discussed. Finally, plans for coupled assimilation as part of
the Unified Forecast System and GFSv17 will be presented.

The Global-to-Regional Data Assimilation System for the ICON Model
Potthast, Roland(1,2)(1)

University of Reading, (2)German Weather Service

The Icosahedral Non-Hydrostatic (ICON) Model and its Ensemble Data Assimilation Systems EnVAR+LETKF are developed by an international
consortium with Deutscher Wetterdienst (DWD), the Max-Planck-Institute for Meteorology (MPI), the German Climate Computing Center(DKRZ), the Karlsruhe Institute of Technology (KIT), the COSMO Consortium (Germany, Russia, Switzerland, Italy, Romania, Poland, Greece andIsrael) and the Center for Climate Systems Modeling (C2SM). Globally, an ensemble data assimilation system based on an ensemble-variational
system (EnVAR) in combination with a Localized Ensemble Transform Kalman Filter (LETKF) is used for the 13 km global deterministic ICONand the 40 km ensemble prediction system (ICON-EPS) with 40 members. The models are run with a two-way nested 6.5 km resolution area
ICON-EU over Europe, where the ICON-EU-EPS has 20 km resolution. The analysis is carried out every 3 hours. Operationally, the full LETKF
ensemble analysis is coupled to the high-resolution EnVAR analysis by relaxation of the ensemble mean to the deterministic analysis. The
convective-scale ICON-D2-EPS is run on 2 km resolution with 40 members. The operational system employs the LETKF with an additional
deterministic analysis for the deterministic run, where the Kalman matrix of the LETKF is employed. Regional analysis is carried out every hour.
Rapid Update Cycle analyses and forecasts hourly are under preparation within the SINFONY project in combination with Nowcasting. For ICOND2 we also develop an EnVAR, which can use any of the available ICON-EPS (global, mesoscale and convective-scale) as input for its covarianceestimator. The system is portable and can be employed for regional data assimilation anywhere on the globe.

Advancing Data Assimilation in Global NWP and Climate: the ECMWF Perspective
Bonavita, Massimo(1)
(1)

ECMWF

Data assimilation (DA) is traditionally used to combine observations and models in a statistically optimal fashion in order to provide the best
estimates of the initial state of the Earth system, given the assumed uncertainties of their inputs. This is a crucial activity in alloperational NWP
and Climate Centre, as the accuracy and reliability of the analysis pdf is one of the main drivers of forecast skill from hour to seasonal predictions. Standard DA systems need to evolve to respond to the opportunities and challenges presented by emerging trends in e.g. the everincreasing availability, density and temporal frequency of new observations; the increasing importance of nonlinearities in both observations
and model; the rapidly increasing resolution of the prognostic models; the demands of a seamless and physically consistent initialisation acrossthe Earth system components. In the first part of the talk we review the recent evolution of the ECMWF DA system and its planned future
developments aimed at tackling the methodological challenges connected to these trends in observations and model. More recently, there has
been a growing realisation that limitations and imperfections of the current generation of prognostic models are arguably the biggest hurdle
in order to overcome historical predictability barriers in both NWP and Climate prediction. This has led to renewed efforts to extend data
assimilation methodologies and employ new hybrid DA-Machine Learning techniques to enhance, improve and correct forecast models. In thesecond part of the talk we describe the ECMWF experience and plans in this area and discuss some of the opportunities and challenges aheadin light of the ambitious goals set out in the ECMWF 2021-2030 Strategy.

Status and Plans of Data Assimilation at KIAPS
Kwon, In-Hyuk(1); Clayton, Adam(1); Kang, Jeon-Ho(1); Lee, Sihye(1)(1)

Korea Institute of Atmospheric Prediction Systems

The renewed Korea Institute of Atmospheric Prediction Systems (KIAPS) aims at developing a unified framework for seamless prediction fromvery short range (~6 hours) to extended medium range (~30 days), including coupling to various earth system components, such as the land
surface, oceans, and sea ice. The first phase of the KIAPS project delivered the global atmosphere-only NWP system that was made operationalat the Korea Meteorological Administration (KMA) in April 2020. The NWP model - named the Korean Integrated Model (KIM) - is a non-hydrostatic model based on a cubed-sphere grid, utilizing the spectral element method within the dynamics. The global data assimilation (DA)system is based on a hybrid-4DEnVar system for the deterministic analysis, and an LETKF for ensemble perturbation updates, and is alreadygiving good performance. Many types of observations, including conventional data, GPS-RO, AMSR2, AMSU-A, MHS, ATMS, MWHS2, IASI, CrIS,
and clear sky radiances from GK-2A, are quality controlled by the KIM Package for Observation Processing (KPOP). Trials to test assimilationof ALADIN wind data and Ground GNSS observations are also due to begin shortly. As well as continuing to strengthen the existing atmospheric
DA system, we are planning new developments which include convection-permitting scale DA for areas of interest, coupled atmosphere-ocean
DA, machine learning techniques for observation quality control, and an extended observation processing system for newly available observations.

Tuesday, 14 Sep 2021 at 14UTC - room RED

Observation Impact I
Aircraft data and Covid-19: impact and mitigation measures at ECMWF
Ingleby, Bruce(1)
(1)

ECMWF

Meteorological reports from aircraft are an important source of data for global weather forecasting. Their largest impact is in the upper troposphere at short-range as determined from observing system experiments. Due to the pandemic the number of aircraft reports reduced byabout 75% between mid-March and mid-April 2020, recovering somewhat to almost 50% of normal by July 2020. However it is difficult to see
any evidence of degradation in forecast skill - partly because forecast skill varies on daily, monthly and anual time-scales. New satellite data
became available during 2020 and ECMWF started using European Mode-S aircraft winds to mitigate the loss of standard aircraft reports. So,
despite the downturn in aviation we are now using more aircraft reports over Europe than before the pandemic.

Estimation of the benefits of remote-sensing profilers for sustainable energy applications
Nomokonova, Tatiana(1); Löhnert, Ulrich(1); Necker, Tobias(2); Griewank, Philipp(2); Weissmann, Martin(2)(1)

Institute of Geophysics and Meteorology, (2)Institute of Meteorology and Geophysics [Vienna]

Over the last years, climate monitoring and operational weather forecasts have become an important topic for renewable energy management.
One of the ways for a substantial improvement of numerical weather prediction (NWP) is the assimilation of new observational data. Dataassimilation (DA) combines observations with short-term weather forecasts to achieve an optimal estimate of the atmospheric state required
for NWP. One of the sources of information potentially valuable for DA are ground-based remote-sensing instruments. Our study focuses on
the potential impact of ground-based remote sensors for energy applications. The potential impact is analyzed using ensemble sensitivity
analysis which allows us to investigate how the assimilation of hypothetical ground-based profilers can reduce the forecast variance. We analyze relative changes in the variance associated with the assimilation of synthetic observations from a wind Doppler lidar. The variance reduction is investigated for domain-averaged sensitivities of 80 m wind (typical hub-height of wind turbines). For our analysis, we apply the first
convective-scale 1000-member ensemble simulation over Germany. The simulation uses a full-physics non-hydrostatic regional model, consists of 16 ensemble forecasts and covers a high impact weather period in May/June 2016. Our study focuses on the urban and highly populated Rhein-Ruhr area and surrounding regions that feature wind parks.

EFSO at different geographical locations verified with observing system experiments
Yamazaki, Akira(1); Miyoshi, Takemasa(2); Inoue, Jun(3); Enomoto, Takeshi(4); Komori, Nobumasa(5)(1)(4)

Japan Agency for Marine-Earth Science and Technology, (2)RIKEN Center for Computational Science, (3)National Institute of Polar Research,
Kyoto University, (5)JAMSTEC

An ensemble-based forecast sensitivity to observations (EFSO) diagnosis has been implemented in an AGCM–LETKF data assimilation system
to estimate the impacts of specific observations from the quasi-operational global observing system on weekly short-range forecasts. It was
examined whether EFSO reasonably approximates the impacts of a subset of observations from specific geographical locations for 6-h forecasts, and how long the 6-h observation impacts can be retained during the 7-day forecast period. The reference for these forecasts was
obtained from 12 data-denial experiments in each of which a subset of three radiosonde observations launched from a geographical location
was excluded. The 12 locations were selected from three latitudinal bands comprising (i) four Arctic regions, (ii) four midlatitude regions in the
Northern Hemisphere, and (iii) four tropical regions during the Northern Hemisphere winter of 2015/16. The estimated winter-averaged EFSOderived observation impacts well corresponded to the 6-h observation impacts obtained by the data denials and EFSO could reasonably estimate the observation impacts by the data denials on short-range (from 6 h to 2 day) forecasts. Furthermore, during the medium-range (4–7day) forecasts, it was found that the Arctic observations tend to seed the broadest impacts and their short-range observation impacts could
be projected to beneficial impacts in Arctic and midlatitude North American areas. The midlatitude area was located just downstream of dynamical propagation from the Arctic toward the midlatitudes. Results obtained by repeated Arctic data- denial experiments were found to begenerally common to those from the non-repeated experiments.

Tuesday, 14 Sep 2021 at 15UTC - room RED

Operational DA III

The JEDI-GEOS application: NASA's development pathway for coupled Earth system data assimilation
Gelaro, Ron(1); Holdaway, Dan(2,3); Todling, Ricardo(3)(1)

NASA Goddard Space Flight Center, (2)University Corporation for Atmospheric Research, (3)NASA Goddard Space Flight Center

NASA's Global Modeling and Assimilation Office is developing a unified coupled data assimilation system based on the GEOS model and theJCSDA's Joint Effort for Data assimilation Integration (JEDI) infrastructure, to achieve its objectives in weather analysis and prediction, reanalysis,composition forecasting, and S2S prediction. The unified system does not mean a single configuration for all GEOS applications, but rather aconfigurable GEOS system within the JEDI framework. The focusing vehicle for building this capability is the recently initiated JEDI-GEOS application – a continuously evolving, continuously functional testbed encompassing the configuration files, workflows, and data access for instantiating end-to-end tests of new developments and establishing trusted baselines. An important objective of JEDI-GEOS is to produce the first
JEDI-based candidates for replacing current-generation GEOS production systems. This talk summarizes the plans and progress in developing
the JEDI-GEOS application. Work is progressing on three separate but convergent subprojects: (i) development of the GEOS-configured unified
forward operator (UFO), to produce the JEDI-based observing system configurations for GEOS systems; (ii) development of a prototype weakly
coupled atmosphere-ocean data assimilation system based on the coupled GEOS-MOM6 model currently operating within JCSDA's Sea-Ice
Ocean and Coupled Assimilation (SOCA) framework; and (iii) development of the GEOS-JEDI applications workflow environment based on
JCSDA's Experiments and Workflows Orchestration Kit (EWOK) and Research Repository for Data and Diagnostics (R2D2) into which (i) and (ii),and their extensions, will be incorporated.

Data Assimilation for NOAA's Next Generation Regional High Resolution NWP System
Carley, Jacob(1); Rogers, Eric(1); Lei, Ting(2); Zhang, Xiaoyan(2); Liu, Shun(2); Martin, Cory(3); Kleist, Daryl(1); Alexander, Curtis(4);Ladwig, Terra(4); Hu, Ming(4); Dowell, David(4)(1)

NOAA Environmental Modeling Center, (2)IM Systems Group at NOAA/EMC, (3)Redline at NOAA/EMC, (4)NOAA Global Systems Laboratory

The Rapid Refresh Forecast System (RRFS) is NOAA's next generation rapidly-updating regional/storm scale operational Numerical Weather
Prediction (NWP) system. Development is a highly collaborative process involving many partners. The RRFS is targeted to replace many current
regional and storm scale operational NWP systems, including the Rapid Refresh (RAP), High Resolution Rapid refresh (HRRR), High Resolution
Ensemble Forecast System (HREF), and North American Mesoscale Forecast System (NAM). The RRFS is based upon a hybrid EnVar framework
having rapid analysis updates of at least an hourly cadence, ensemble forecasts initialized every hour, and covers North America on a 3 km
grid - over 9 million grid cells in the horizontal dimension. This presentation will cover a broad, programmatic description of the RRFS data
assimilation system, current progress, and scientific as well as computational design considerations.

Initial Evaluation of JEDI Unified Forward Operator For Use in NCEP's Global Data Assimilation System
Martin, Cory(1); Dougherty, Kevin(2); Kleist, Daryl(3); Liu, Emily(3); Liu, Shun(2); Mahajan, Rahul(3); Treadon, Russ(3); Auligne,
Thomas(4); Diniz, Fabio(4); Han, Wei(4); Shao, Hui(4); Thompson, Greg(4); Tremolet, Yannick(4); Vandenberghe, Francois(4)(1)

RedLine @ NOAA NWS NCEP EMC, (2)IMSG @ NOAA NWS NCEP EMC, (3)NOAA National Centers for Environmental Prediction, (4)Joint Center for
Satellite Data Assimilation

The Joint Effort for Data assimilation Integration (JEDI) project aims to create a modern, advanced, and configurable data assimilation system
to be used by numerous operational forecast centers and researchers alike. Led by the Joint Center for Satellite Data Assimilation (JCSDA),
groups from NOAA, NASA, the US Navy and Air Force, and the UK MetOffice all contribute to development and testing of this system. One
component of JEDI, the Unified Forward Operator (UFO) is a collection of model-agnostic, generic forward operators and quality control procedures that can be used with a variety of observation types to compute the first-guess departures needed for assimilation. Currently, theNational Centers for Environmental Prediction (NCEP) uses the Gridpoint Statistical Interpolation (GSI) software in the operational Global Data
Assimilation System (GDAS). Here we present a comprehensive evaluation of the UFO in comparison to the operational GSI system for the same
set of observations and model backgrounds. This evaluation includes comparing first-guess departures and final observation counts after
quality control produced using UFO to that from GSI for a variety of observation types (conventional and satellite) over a period of one month.
Overall results and statistics will be shown as well as a deeper investigation into specific cases/scenarios where the two systems differ. The
results presented here represent the first milestone in the process of NCEP's acceptance of JEDI and its path towards a transition to operations.

Progress towards a global hourly-updating data assimilation system
Slivinski, Laura(1,2); Lippi, Donald(3,4); Whitaker, Jeffrey(2); Ge, Guoqing(1,5); Carley, Jacob(4); Alexander, Curtis(5); Compo, Gilbert(1,2)(1)

Cooperative Institute for Research in Environmental Sciences, (2)NOAA Physical Sciences Laboratory, (3)IM Systems Group, Inc, (4)NOAA Environmental Modeling Center, (5)NOAA Global Systems Laboratory
Currently, the US global forecast system (GFS) uses a 6-hour data assimilation window within a hybrid 4DEnVar framework, providing analyses
from which to initialize forecasts every 6 hours. This “long” window provides less accurate information on rapidly-evolving systems such as
hurricanes and continental convective storms. In order to improve forecasts of these fast systems, more frequent analysis updates, and possibly shorter assimilation windows, will be necessary. To this end, we are testing different approaches to updating the GFS every hour. A
challenge for hourly updating is data latency: many observations are not available for assimilation until 1-2 hours after the valid observation
time. One method to overcome this challenge is the “catch-up cycle”, in which an hourly cycling system is re-initialized 2-4 times per day andcatches back up to real-time through a succession of rapid-update cycles. This procedure allows late-arriving observations, that would haveotherwise been missed, to be included into the hourly system. Another possible solution is to implement overlapping assimilation windows, inwhich the system is updated every hour with observations that have arrived within the last hour, but that are valid in a longer window (3-6
hours), removing the need for a separate 6-hourly global system. Preliminary results suggest that the overlapping windows technique can
improve upon short-term fit-to-observations in the GFS, relative to the current operational method. Comparisons with catch-up cycles areunderway.

Tuesday, 14 Sep 2021 at 15UTC - room GREEN

Reanalysis evaluation IV
Daily extreme temperatures in ERA5-LAND versus in-situ measurements in Poland 1991-2020
Marosz, Michał(1); Kitowski, Michał(1); Szwed, Małgorzata(1); Kusek, Klaudia(1)(1)

Institute of Meteorology and Water Management

Monitoring and analysing extreme events in high spatial and temporal resolution is one of the most critical tasks facing national hydrometeorological services in the context of present climate change. It is also crucial in contemporary mitigation measures and necessary adaptation
actions awaiting in the future. Reanalyses may serve as a tool that allows the monitoring and analysis of meteorological variables' long-termfeatures, thus allowing a proper estimation of probabilistic characteristics of extreme events. The research aimed to assess the concordanceof ERA5-LAND reanalysis data (air temperature) with the resolution of 0.1x0.1 degree (~9km) with in-situ measurements acquired from the
network of synoptic stations in Poland (56 stations). The temporal scope of analysis comprises the last normal period 1991-2020. Analysed variables comprised daily maximum and minimum temperatures. Those originated, where possible, from 1h temporal resolution datasetsfor both ERA5-LAND and in-situ measurements. The analysis covered the biases and the comparison of statistical distributions and characteristics of analysed variables. Additionally, GEV distribution characteristics at selected stations and the resulting differences of return levels forselected return periods were compared. The results show reasonably good concordance of ERA5-LAND reanalysis data with in-situ measurements, making it a suitable tool in long-term climate characteristics analysis.

Evaluation of a high-resolution dust regional reanalysis using in-situ and remote sensing observations
Mytilinaios, Michail(1); Mona, Lucia(1); Trippetta, Serena(1); Barnaba, Francesca(2); Basart, Sara(3); Di, Tomaso Enza(3); Jorba,
Oriol(3); Pérez, García-Pando Carlos(4,3); Proestakis, Emmanouil(5); Marinou, Eleni(6,5); Gkikas, Antonis(5); Amiridis, Vassilis(5);Formenti, Paola(7); Cuesta, Juan(7); Di, Biagio Claudia(7); Laurent, Benoît(7); Marticorena, Beatrice(7)(1)

National Research Council of Italy, (2)National Research Council of Italy, (3)Barcelona Supercomputing Center, (4)Catalan Institution for Researchand Advanced Studies, (5)National Observatory of Athens, (6)German Aerospace Center, (7)Laboratoire Interuniversitaire des Systèmes Atmosphériques
An advanced dust regional reanalysis has been recently released in the framework of the ERA4CS DustClim project at the high spatial resolutionof 0.1°. The reanalysis has been produced by ingesting an innovative dust optical depth data set, derived from the MODIS Deep Blue products,
in the dust module of the MONARCH atmospheric model. The reanalysis products cover a wide range of dust-related properties, both opticaland physical, such as dust optical depth (DOD), dust PM10 mass concentration and dust extinction profiles. One of the DustClim's main tasks
is to provide a thorough evaluation of the reanalysis performance through the synergy of various measurement techniques. A wide variety of
dust-filtered products, retrieved from independent data sets of in-situ and remote sensing aerosol observations was compared to collocatedreanalysis fields. In particular, we have considered the following reference data sets: LIVAS and EARLINET dust extinction profiles; AEROIASI setof dust–related products; AERONET, MISR and MODIS DOD; INDAAF and EIONET–derived dust PM10 concentrations. Here we present the resultsof our extensive validation of the dust reanalysis in terms of common metrics that are used to quantify the mean departure between modeled
and observed quantities. By assessing its quality, we provide a useful reference to potential users of this novel reanalysis data set which issuitable for studies as well as services of air quality and climate. Acknowledgment Project DustClim is part of ERA4CS, an ERA-NET initiatedby JPI Climate, and funded by FORMAS (SE), DLR (DE), BMWFW (AT), IFD (DK), MINECO (ES), ANR (FR) with co-funding by the European Union(Grant 690462).

Comparison of a Multidecadal Walker Circulation in European reanalyses
Kosovelj, Katarina(1); Žnidaršič, Zala(2); Žagar, Nedjeljka(3)(1)

University of Ljubljana, Faculty of mathematics and physics, (2)Slovenian Environment Agency, (3)Meteorological Institute, Center for Earth
System Research and Sustainability, Universität Hamburg

We investigate tropical multidecadal variability using ERA-20C, ERA5, and ERA Interim reanalyses. One of the most prominent features oftropical wave circulation is the Kelvin wave that what can be used as a proxy of the Walker circulation over the Pacific. This is demonstrated bycomparing a new Walker circulation index, based on the three-dimensional Kelvin wave energy integral, with the classical Walker circulation
indices defined in terms of surface pressure differences and upper-troposphere velocity potential. We show that the new index compares wellin the three reanalysis datasets during the satellite era. Multidecadal variability of the Walker circulation in 20th century in ERA-20C is subsequently discussed using the new index.

Operating in risky sand and dust storm environments in Northern Africa, the Middle East and Europe: a portfolioof climate services
Basart, Sara(1); Votsis, Athanasios(2,3); Rautio, Tuukka(3); Barnaba, Francesca(4); Lindfors, Anders(3); Di, Tomaso Enza(1); Mona,
Lucia(5); Mytilinaios, Michalis(5); Formenti, Paola(6); Werner, Ernest(7); Pérez, García-Pando Carlos(8,1)(1)

Barcelona Supercomputing Center, (2)University of Twente, (3)Finnish Meteorological Institute, (4)Istituto di Scienze dellÁtmosfera e del Clima[Roma], (5)Consiglio Nazionale delle Ricerche [Potenza], (6)Laboratoire Interuniversitaire des Systèmes Atmosphériques, (7)Agencia Estatal deMeteorología, (8)Institució Catalana de Recerca i Estudis Avançats
Over the last few years, there has been an increasing need for Sand and Dust Storms (SDS) accurate information and predictions. SDS information is fundamental to support early warning systems, and preparedness and mitigation plans in addition to growing interest from diverse
stakeholders in the aviation sector (including airlines, airports, engine manufacturers) and solar energy (plant operators and market consultants). The ongoing ERA4CS "Dust Storms Assessment for the development of user-oriented Climate services in Northern Africa, the Middle
East and Europe" (DustClim) project is enhancing our knowledge on the way SDS affect society by producing and delivering an advanced dust
regional reanalysis. The resulting 10-year (2007-2016) dust reanalysis covers Northern Africa, the Middle East and Europe at a horizontal
resolution of around 10km. This data set has been obtained by combining satellite remote sensing observations based on MODIS Deep Blue
dust-filtered retrievals with the MONARCH chemical weather system. Here, we will present our approach to developing purpose-specific products that help air quality, aviation and solar energy production industry in understanding and reducing SDS-related risks for long-term managing purposes. This process includes the identification of SDS-related ʻobjective threats', the dialogue with the identified final users (thatincludes industry partners and public stakeholders) and the portfolio of proposed services.

Tuesday, 14 Sep 2021 at 15UTC - room BLUE

New approaches to DA I
A General Ensemble Filtering Framework Using Quantiles
Anderson, Jeffrey(1)(1)

National Center for Atmospheric Research [Boulder]

Ensemble Kalman filters are commonly used for data assimilation for numerical weather prediction and climate applications. These methods
fit a normal distribution to the prior ensemble estimate and use Bayes' rule with a normal likelihood to generate a sample from a normal

posterior. A novel efficient algorithm that allows use of arbitrary continuous priors and likelihoods is presented. The key innovation is to selectposterior ensemble members with the same quantiles with respect to the continuous posterior distribution as the prior ensemble had withrespect to the prior continuous distribution. This is a generalization of previously documented square root ensemble Kalman filters for normal
distributions. It also generalizes non-parametric ensemble filters such as the rank histogram filter. Examples using continuous priors that are
gamma, inverse gamma, beta and a sum of normal kernels are presented. These examples are useful for physical variables that are bounded
on one or both sides such as the concentration of a pollutant, relative humidity, or fractional sea ice coverage. They are also useful for variables
that have multimodal or other non-normal distributions. The method can be extended to directly estimate the marginal distributions of statevariables, mostly eliminating the linear constraints imposed by traditional ensemble methods that use a Kalman gain (regression).

A new multiscale data assimilation method: Multiscale Local Gain Form Ensemble Transform Kalman Filter(MLGETKF)
Wang, Xuguang(1); Chipilski, Hristo(2); Bishop, Craig(3); Satterfield, Elizabeth(4); Baker, Nancy(4); Whitaker, Jeffrey(5)(1)

School of Meteorology, University of Oklahoma, (2)School of Meteorology, University of Oklahoma, (3)University of Melbourne, (4)Naval ResearchLab, (5)NOAA Earth System Research Lab
A new multiscale, ensemble-based data assimilation (DA) method, MLGETKF (Multiscale Local Gain Form Ensemble Transform Kalman Filter),is introduced. MLGETKF allows simultaneous update of multiple scales for both the mean and ensemble perturbations through assimilating all
observations at once. MLGETKF performs DA in independent local volumes, which lends the algorithm a high degree of computational scalability. The multiscale analysis is enabled through the rapid creation of many pseudo ensemble perturbations via a multiscale ensemble modulation procedure. The Kalman gain therefore intrinsically includes multi-scale model space localization. Experiments with a statistical modelshow that the full background covariance estimated by MLGETKF more accurately resembles the shape of the true covariance than a scaleunaware localization. The mean analysis from the best-performing MLGETKF is statistically significantly more accurate than the best performing scale unaware LGETKF. MLGETKF is further examined in a cycling DA context with a Surface Quasi-Geostrophic model. The root-meansquare potential temperature analysis error of the best performing MLGETKF is 17.2% lower than that of the best-performing LGETKF. MLGETKFreduces analysis errors measured in kinetic energy spectra space by 30-80% relative to LGETKF with the largest improvement at large scales.
MLGETKF forecasts are more accurate than LGETKF, gaining 12-hour ~ 1-day of predictability.

Operational Data Assimilation using the Ensemble Kalman Filter with a Modified Cholesky decomposition
Ortega, Randy Steven Consuegra(1); Niño, Ruiz Elías David(1)(1)

Universidad del Norte, Barranquilla

Numerical Weather Prediction (NWP) is of high relevance nowadays as it allows us to model the behave of the weather given physical models
to produce short-term forecasts. But even the best models have uncertainty associate. To improve the quality of such, we can employ Data
Assimilation techniques that allow us to manage that uncertainty, given the dynamics of the system. Since these models are computationallyexpensive, we require the use of High-performance computing to produce those analyses, in a reasonable amount of time (computationally
speaking, as the model's parameters is in the range of O(10**8)). Given those models and meteorological information obtained by the National
Oceanic and Atmospheric Administration (NOAA), we can give a better estimate using the Ensemble Kalman Filter (EnKF). We believe we cantake advantage of those observations provided by the NOAA in their Reanalysis II dataset and making use of the atmospheric general circulation

model SPEEDY with the Ensemble Kalman Filter formulation based on a Modified Cholesky decomposition (EnKF - MC) for inverse covariancematrix estimation. We interpolate the data from the NOAA dataset to the SPEEDY grid resolution to perform the forecasts. Results show how
the use of the EnKF-MC reduces the impact of spurious correlations during the assimilation cycle.

Adaptive Tuning of Innovation Weight Parameters: Formulation and Results with NAVDAS-AR/NAVGEM
Daescu, Dacian(1); Langland, Rolf(2)(1)

Portland State University [Portland], (2)Naval Research Laboratory

This work investigates new applications of the observation sensitivity tools for improving the performance of the Naval Research Laboratory
Atmospheric Variational Data Assimilation System-Accelerated Representer (NAVDAS-AR) and the U.S. Navy's Global Environmental Model(NAVGEM). Adaptive tuning of innovation-weight parameters is used for adjusting a suboptimal analysis increment and alleviate issues relatedwith radiance bias correction. Our work relies on a validation state produced by ECMWF to evaluate the observation sensitivity of an initialcondition (analysis) error aspect, here measured in a dry total energy norm, and avoids the increased uncertainty associated with a nonlinear
model forecast. A training stage is set for April 1-14 of 2018 when information derived from adjoint-based observation sensitivity and innovations is used to adaptively tune the weight parameters. Validation is performed for April 15 – May 31 of 2018 when the performance of thetuned NAVDAS-AR/NAVGEM system is investigated in terms of the reduction in the analysis errors and the 24-hour forecast errors. The validation results show that in average, as compared with the control run and verified against the ECMWF analyses, the tuning procedure provides
10%-20% reduction of the analysis errors and 2%-5% reduction of the 24-hour forecast errors. A comparative assessment of the observation
impact on reducing the initial-condition and forecast errors is provided. The analysis of the time series of the errors in the temperature fields
shows a significantly improved performance in the first half of the validation period, indicating that systematic updates of the innovation
weights are necessary to maintain the performance of the tuned system.

Wednesday, 15 Sep 2021 at 11UTC - room RED

DA for atmospheric composition I
A novel regional reanalysis of dust aerosols
Di, Tomaso Enza(1); Escribano, Jeronimo(1); Basart, Sara(1); Ginoux, Paul(2); Macchia, Francesca(1); Barnaba, Francesca(3);Benincasa, Francesco(1); Bretonnière, Pierre-Antoine(1); Buñuel, Arnau(1); Castrillo, Miguel(1); Formenti, Paola(4); Gonçalves,
Maria(1); Jorba, Oriol(1); Klose, Martina(5); Mona, Lucia(6); Montané, Gilbert(1); Mytilinaios, Michail(6); Obiso, Vincenzo(7); Olid,
Miriam(1); Schutgens, Nick(8); Votsis, Athanasios(9,10); Werner, Ernest(11); Pérez, García-Pando Carlos(1,12)(1)

Barcelona Supercomputing Center, (2)NOAA Geophysical Fluid Dynamics Laboratory, (3)CNR Institute of Atmospheric Sciences and Climate,
Laboratoire Imagerie et Systèmes dÁcquisition, (5)Karlsruhe Institute of Technology, (Institut für Meteorologie und Klimaforschung)Troposphärenforschung,(6)
Consiglio Nazionale delle Ricerche-Istituto di Metodologie per l'Analisi Ambientale, (7)NASA Goddard Institute for Space Studies, (8)Departmentof Earth Science, Vrĳe Universiteit Amsterdam, (9)University of Twente, Department of Governance and Technology for Sustainability, (10)Finnish
Meteorological Institute, (11)Agencia Estatal de Meteorología, (12)Institució Catalana de Recerca i Estudis Avançats(4)

We present a regional reanalysis of dust aerosols which has been recently released by the Barcelona Supercomputing Center for a 10-year
period and at the high spatial resolution of 0.1°. The reanalysis has been obtained by ingesting an innovative dust optical depth data set,
derived from the MODIS Deep Blue products, in the dust module of the MONARCH atmospheric model by means of a LETKF with a four-dimensional extension. We outline the different modelling, observational and assimilation aspects related with the production of the reanalysis,
whose unprecedented high resolution has required the use of advanced computing and workflow strategies, which are also described. By
providing an accurate and complete reconstruction of dust for a recent decade, this novel reanalysis yields useful information to support
operational early warning systems, as well as the development and refinement of environmental monitoring and mitigation strategies, in linewith the mission of the WMO Sand and Dust Storm Warning Advisory and Assessment System. Acknowledgment The authors acknowledge cofunding from the H2020 ERA-net ERA4CS (GA 690462) as part of the project DustClim; HPC access from PRACE (eDUST/eFRAGMENT1/eFRAGMENT2) and RES (AECT-2020-3-0013/AECT-2019-3-0001/AECT-2020-1-0007). Carlos Pérez García-Pando acknowledges support from the
ERC (GA 773051) and the AXA Research Fund.

Radiance intercalibration of INSAT-3D ozone channel with MSG-SEVIRI and successive improvements in ozone
optimal/ML retrieval and validations
Rawat, Prajjwal(1); Naja, Manish(2); Thapliyal, Pradeep(3); Gangwar, Rishi(3)(1)

Aryabhatta Research Institute of Observational Sciences, (2)Aryabhatta Research Institute of Observational Sciences, (3)Space Applications Center
Among various trace gases, ozone has a very unique vertical distribution and plays different roles at different altitudes. The useful stratosphereozone absorbs harmful UV radiations, while tropospheric ozone, a powerful greenhouse gas adversely affects living beings and vegetations. In
Asian countries, the increasing anthropogenic emissions, higher solar radiation, and water vapor further intensifying ozone photochemistry.
Thus the continuous monitoring of the ozone is necessary. Nowadays ozone monitoring via satellite-based remote sensing has gained wideimportance. The Indian geostationary satellite INSAT-3D/3DR is accomplishing this need for India. Apart from the meteorological sounding, it
is also incorporated with a 9.6 um strong ozone absorption channel, for ozone observations. We have assessed the INSAT-3D retrieved totalozone with respect to our ozonesonde observations. The total ozone column from INSAT-3D showed a maximum difference upto 10% with theozone-derived total ozone column that also varies in seasons. To mitigate these deviations or errors we have checked the radiance biases in
INSAT-3D/3DR observations by inter-calibration with MSG-SEVIRI for collocated pixels, and observed biases in INSAT by more than 4K. Further, in the new retrieval algorithm based on a feed-forward neural network and the optimal estimation (1D Variational assimilation) method,
we have inculcated these biases and observed very significant improvements in the INSAT-3D/3DR ozone retrievals.

Development of an Ensemble-Variational Data Assimilation System for Global Aerosol Forecasting at NOAA
Huang, Bo(1,2); Pagowski, Mariusz(1,2); Martin, Cory(3); Trahan, Samuel(1,2); Holdaway, Dan(4,5); Tangborn, Andrew(6); Kleist, Daryl(7);Kondragunta, Shobha(8)(1)

Cooperative Institute for Research in Environmental Sciences, CU Boulder, (2)NOAA Earth System Research Laboratory/GSL, (3)RedLine Performance Solutions at NCEP/EMC, (4)Joint Center for Satellite Data Assimilation, (5)NASA Global Modeling and Assimilation Office, (6)I. M. Systems
Group Inc., (7)NOAA National Centers for Environmental Prediction, (8)NOAA National Environmental Satellite, Data, and Information Service

A hybrid ensemble-variational (EnVar) aerosol data assimilation (DA) system is being developed within the Joint Effort for Data assimilation
Integration (JEDI) to improve global aerosol forecasting in the NOAA's operational Global Ensemble Forecast System - Aerosols (GEFS-Aerosols)
model. The GEFS-Aerosols model adopts the Finite-Volume Cubed-Sphere (FV3) dynamical core and the aerosol parameterization is based on
the Goddard Chemistry Aerosol Radiance and Transport (GOCART) model. In the variational solver, the ensemble background covariances
updated by the Local Ensemble Transform Kalman Filter (LETKF) are blended with static background covariances. Aerosol optical depth (AOD)
retrievals at 550 nm derived from the Visible/Infrared Imager Radiometer Suite (VIIRS) instruments are assimilated. AOD forward operator is
calculated using NASA lookup tables. Stochastically-perturbed emissions are developed and implemented in the GEFS-Aerosols model to reduce model bias and alleviate ensemble spread deficiency. Cycled experiments show that assimilation of AOD retrievals reduces bias and rootmean-square error of simulated AOD, and improves agreement of global aerosol analyses and forecasts with aerosol reanalyses from NASAand ECMWF. Experimentation of leveraging ensemble forecasts at varying valid times to populate background ensemble suggests enhanced
background ensemble error-spread consistency and further improves global aerosol analyses and forecasts.

Wednesday, 15 Sep 2021 at 11UTC - room GREEN

New approaches to DA II
The Effective Use of Anchor Observations in VarBC in the Presence of Model Bias
Francis, Devon(1); Fowler, Alison(1); Lawless, Amos(1); Eyre, John(2); Migliorini, Stefano(2)(1)

University of Reading, (2)United Kingdom Met Office [Exeter]

Satellite data have a significant impact on forecast skill, but the data must be bias corrected prior to use. At many operational centres, the bias
correction is performed using Variational Bias Correction (VarBC), however, VarBC relies on unbiased observations that are not bias corrected(anchor observations) in order to constrain any model bias that is present within the system. As the number of satellite observations increases,
the proportion of unbiased observations to biased observations decreases, so it is important to use these anchor observations effectively. In
order to understand how anchor observations impact the bias correction, we derive analytical expressions to show the sensitivity of the bias
correction to the anchor observations. We find that the ability of the anchor observations to correct the observation bias is dependent on theinformation shared between the biased observations and the unbiased observations via the background error covariance matrix. We show that
it is necessary for the anchor observations to observe the regions of significant model bias, in order to reduce the effect of model bias on theestimate of the observation bias. We demonstrate these results in a series of idealised numerical experiments to show the implications on thecurrent network of anchor observations in the use of VarBC for operational NWP.

Newly developed impact diagnostics for cross-validating the consistent use of different observation types
Stiller, Olaf(1)(1)

Deutscher Wetterdienst [Offenbach]New cross-validation diagnostics have been derived by partitioning the established impact diagnostic originally introduced by Langland andBaker (2004). They give rise to consistency relations, the most prominent of which indicates whether the first guess departures of a given

observation type pull the model state into the direction of the verifying data (when processed with the employed model error covariances). Thisis a fundamental condition for getting a beneficial impact from these observations. As verifying data we use well established observations as,e.g., radiosonde measurements, and the new diagnostic relationship is exploited to quantify the consistency of other observation types withthese data in different locations (e.g., at different latitudes or altitudes) or under some otherwise specified conditions. To obtain a sensitive
statistical tool, a normalization is provided which renders results largely independent from the total number of observations and the closenessof their collocation and, also, an indicator of statistical significance. While the model error covariances employed in this work are estimatedfrom the DWD's localized transform ensemble Kalman filter, we expect results to be highly relevant also for our hybrid EnVar system which
also makes use of the ensemble estimated covariances. As the quality of the assessment strongly depends on the suitability of the ensemble
covariances, in a first step the method is applied to the cross-validation of two well established types of in situ observations whose results givesome indication of the quality (and some limitations) of these covariances and can be taken as a benchmark when applying the method to more
complex observations (like, e.g., satellite radiances).

State, global and local parameter estimation using ensemble Kalman filters for model error correction
Bocquet, Marc(1); Malartic, Quentin(1); Farchi, Alban(1)(1)École des Ponts ParisTech

We investigate data-driven techniques based on an ensemble Kalman filters (EnKF) in order to either learn the dynamics or a model error
parametrisation thererof. The (local) EnKF can be used to gradually learn both the state and the dynamics as observations are collected. The
surrogate model, which represents the dynamics learned in the process, is parametrised (typically as a neural network) and the update is a
two-step process: a state update, possibly localised, and a parameter update consistent with the state update. In this framework, the parameters of the surrogate model are assumed to be either global, local or both. To that end, we show how to rigorously extend the classicalaugmented state parameter EnKF method to the case where the surrogate model admits both global and local parameters. This results in acollection of useful new algorithms, based on either covariance localisation or domain localisation, applied to the state and/or to the parameters. Finally, these algorithms are implemented and tested with success on the chaotic 40-variable Lorenz model. The method is also illustrated
on a more challenging two dimensional non-homogeneous multi-layer Lorenz model with radiance-like non-local observations, where a hybrid domain and covariance localisation is implemented.

Wednesday, 15 Sep 2021 at 11UTC - room BLUE

Ground-based remote sensing II
Long-term assessment of Doppler lidars for an operational use in a future network
Kayser, Markus(1); Lehmann, Volker(2); Knist, Christine(1); Löffler, Moritz(3); Leinweber, Ronny(1); Klink, Stefan(4)(1)

Deutscher Wetterdienst [Lindenberg], (2)Deutscher Wetterdienst [Lindenberg], (3)Deutscher Wetterdienst [Potsdam], (4)Deutscher Wetterdienst[Offenbach]Fibre-optic based Doppler wind lidars (DL) are widely used for both meteorological research and in the wind energy sector. These compact
systems are able to obtain vertical profiles of the mean horizontal wind vector from the atmospheric boundary layer as well as from opticallythin cloud layers in the free troposphere with high spatio-temporal resolution. It is therefore likely that especially short-term forecasting would
benefit from assimilating their data. However, their potential is currently not yet employed operationally. Here, we present results from a longterm assessment of DLs at Lindenberg, starting in 2012, in the context of the DWD's effort to evaluate ground-based remote sensing systems
for their operational readiness, called “Pilotstation”. The tests focus on aspects such as technical reliability, uncertainty characterization, scanning strategies, and the verification of the retrieved mean wind speed and direction with the help of independent reference data from a 482
MHz radar wind profiler and 6-hourly radiosonde ascents. Through this assessment, we developed a standardized retrieval assuring a highquality Level-2 product, which is compatible with the EUMETNET's E-PROFILE observation program. Further, a software implementation of theretrieval is freely accessible and continuously improved to account for different scanning strategies and additional products.

Assessment of microwave radiometers for operational network deployment and its observational value forforecasting models
Knist, Christine(1); Löffler, Moritz(2); Vural, Jasmin(3); Schomburg, Annika(3); Kayser, Markus(1); Lehmann, Volker(1); Klink,
Stefan(3)(1)

Deutscher Wetterdienst [Lindenberg], (2)Deutscher Wetterdienst [Potsdam], (3)Deutscher Wetterdienst [Offenbach]

The DWD currently runs the project “Pilotstation” to evaluate ground-based remote sensing instruments at dedicated testbed sites for theiroperations, readiness for network deployment, and forecast benefits. One of the maturing technologies that are tested are ground-based
microwave radiometers (MWRs). This complements well the efforts of the EUMETNET's E-PROFILE observation program towards an operational
MWR network by 2023. MWRs have shown their potential for a 24/7 monitoring of brightness temperature (BT) and retrievals such as temperature and water vapour under all-weather conditions except rain, which can be assimilated into numerical weather prediction systems. However, the most common challenges we identified are gaining reliable absolute calibration results, identifying hydrometeors on the hygroscopic
window and the need to routinely quality-assure the observations for forecast applications. Additionally, the use of MWR-derived BTs for data
assimilation requires developments for the implementation in the data assimilation system, e.g. the forward operator RTTOV-gb, in combination with the ICON limited area model (ICON-LAM). This work introduces our developments for the technical monitoring and assessing data
quality and stability in near real time, using e.g. observations minus background statistics, and presents results of the first assimilation experiments. This knowledge is important to make the MWR applicable to routine network operation.

Expected Benefit of Cloud Radar and Microwave Radiometer Observations for Future Data Assimilation During Fog
Conditions
Bell, Alistair(1); Martinet, Pauline(1); Caumont, Olivier(1); Vie, Benoit(2); Burnet, Frédéric(1); Borderies, Mary(1); Delanoë, Julien(3);Vishwakarma, Pragya(3)(1)

Centre national de recherches météorologiques, (2)Groupe d'étude de l'atmosphère météorologique, (3)Laboratoire Atmosphères, Milieux, Observations Spatiales
Fog is still poorly represented in even the highest resolution operational NWP models. Continuous transmission, 95 GHz cloud radars (CR),sensitive to cloud droplets, open up the possibility of retrieving vertical profiles of fog microphysical properties with unprecedented capabilities. Additionally, ground-based microwave radiometers (MWR) can provide information on the thermodynamics and total liquid water path.

This work aims at combining ground-based MWR and CR measurements with a one-dimensional variational approach (1D-Var) as a first step
to 1D-Var + 3D-Var data assimilation. Developments were made on an existing 1D-Var algorithm designed to retrieve temperature andhumidity from MWR brightness temperatures. This algorithm, which uses the operational model AROME information as the background, wasextended to make use of radar reflectivities to directly retrieve liquid water content profiles in addition to temperature and humidity profiles.
The expected benefit to improvements in the AROME analysis is shown firstly by using synthetic observations. The benefit of the synergistic
method will be discussed by comparing 1D-Var retrievals made with only one instrument to the dual-instrumental approach. The 1D-Var
algorithm will then be applied to real measurements from the SOFOG-3D field campaign. The CR and MWR were collocated at two sites wherein-situ observations of cloud microphysics and temperature/humidity profiles during intensive observation periods were made. Retrievals willthus be validated using these in-situ measurements. The improvement brought to the AROME model analysis during fog events will be discussed through case studies and a statistical evaluation throughout the campaign.

Wednesday, 15 Sep 2021 at 12UTC - room RED

New approaches to DA III
Contributions of the parametric Kalman filter in practical and theoretical data assimilation
Pannekoucke, Olivier(1,2,3); Ménard, Richard(4); Bocquet, Marc(5); Fablet, Ronan(6); Perrot, Antoine(7); Ricci, Sophie(8); Thual,
Olivier(2)(1)

Centre national de recherches météorologiques, (2)CERFACS [Toulouse], (3)INPT-ENM, (4)Environnement et Changement climatique Canada,
Centre d'Enseignement et de Recherche en Environnement Atmosphérique, (6)Laboratoire des sciences et techniques de l'information, de lacommunication et de la connaissance [Lorient], (7)Centre national de recherches météorologiques, (8)Cerfacs(5)

The parametric Kalman filter (PKF) is a novel implementation of the Kalman filter (KF) which approximates the covariance dynamics by theparametric evolution of a covariance model all along the analysis and the forecast steps. In this talk we review the ideas behind this new
approach and show some applications when the covariances are parameterized from the error variance and local anisotropy tensor. We firstdescribe the update of the covariance parameters during the assimilation step in a 2D domain. Then, for the forecast step, we explain thedesign of the evolution equations by using an automatic symbolic computation tool, SymPKF, which calculates the second-order Gaussian filter
equations. The PKF provides a low cost computation of the covariance dynamics but often needs a closure. An example of analytical closure is
introduced, then generalized by the use of IA combining the physical equations and an automatic generation of a neural network architecture(PDE-NetGen). A multivariate prediction is shown for a simplified non-linear chemical model in 1D domain. While the PKF provides a practicalimplementation of the KF, it also offers some new theoretical tools to tackle difficult issues, such as the characterization of the model-error
covariance due to the discretization. In particular, we characterized the loss of variance due to the model error which occurs in the discretization of the advection as encountered when using an ensemble Kalman filter in air quality.

What does the spread amongst ensembles tell us about forecast errors?
Lonitz, Katrin(1); Healy, Sean(1)(1)

European Centre for Medium-Range Weather Forecasts

In 2020 a new data source of GNSS-RO (GNSS radio occultation) observations from COSMIC-2 and Spire became available, adding about 9000
additional occultations per day to the ECMWF data assimilation system. This dataset enables us for the first time to investigate how the relationship between the Ensembles of Data Assimilations (EDA) spread values and the forecast error statistics from Observing System Experiments(OSEs) changes as a function of number of "real" assimilated GNSS-RO measurements. At 100hPa in the Tropics, the addition of COSMIC-2
and Spire shows the largest effect on reducing the EDA spread by about 13% and 7%, respectively. In this region the reduction in ensemble
spread behaves linearly with the reduction in forecast error for an increase in GNSS-RO data numbers. In other geographical areas and heightlevels the relationship is not linear. However, it is important to highlight that it is not useful to derive a quantitative relationship between thetwo measures as limitations in the EDA experiments and OSEs can have a profound impact on those numbers. Nevertheless, the reduction in
EDA spread can be used as a relative measure to estimate the reduction in forecast errors when adding GNSS-RO data.

"Twin-analysis" verification: a new verification approach that alleviates pitfalls of "own-analysis" verification
when applied to short-range forecasts
Hotta, Daisuke(1); Kadowaki, Takashi(2); Yonehara, Hitoshi(2); Ishibashi, Toshiyuki(1)(1)

Meteorological Research Institute, Japan Meteorological Agency, (2)Numerical Prediction Develpment Center, Japan Meteoroglogical Agency

In operational NWP, forecast verification against analysis from the same experiment is part of the standard evaluation practice. This "ownanalysis" verification is beneficial in providing complete spatial coverage but is known to suffer from overly optimistic scores when applied toshort-rage forecasts due to the inevitable positive correlation between the forecast and analysis errors. This issue is particularly problematicwhen a new development involves assimilation of new observations since the more observations we assimilate, the less correlated the background and the analysis tend to be, leading to apparent degradation in the score which makes interpretation a delicate task. To alleviate thisproblem, we propose "twin-analysis" verification in which we produce "twin analyses" by running an independent cycle using the same NWP
system as the one used to produce the forecasts, but initializing from an independent first guess and then verify the forecasts against thesetwin analyses. This way the error correlation between the forecasts and analyses should be reduced while preserving the statistical propertiesof the analyses, hopefully enabling a clearer interpretation of verification. In this talk we will report the results of comparison between "twinanalysis" and "own-analysis" verification scores obtained for the JMA's global NWP system. The two scores disagree up to two days, suggestingthat "own-analysis" verification is unreliable for such short ranges.

Local Ensemble Transform Kalman Filter with Cross-Validation
Buehner, Mark(1)(1)

Environment and Climate Change Canada

Many ensemble data assimilation (DA) approaches suffer from the so-called “inbreeding” problem. As a consequence, there is excessive reduction in ensemble spread by the DA procedure, causing the analysis ensemble spread to systematically underestimate the uncertainty of theensemble mean analysis. The stochastic EnKF algorithm used for operational NWP in Canada largely avoids this problem by applying crossvalidation, that is, using an independent subset of ensemble members for updating each member. The goal of the present study is to evaluate
new variations of the local ensemble transform Kalman filter (LETKF) that also incorporate cross validation. The new LETKF approaches areevaluated using both idealized experiments and in the context of real NWP systems. In idealized numerical experiments, the new LETKF approaches are shown to produce reliable analysis ensembles such that the ensemble spread closely matches the uncertainty of the ensemblemean, without requiring any ensemble inflation. In the context of a regional numerical weather prediction system, experiments are performed
with the new LETKF-based approaches with cross validation, the standard LETKF, and the stochastic EnKF. All approaches with cross validation
produce similar ensemble spread, though the amplitude of the changes to the individual members is much larger with stochastic approaches.
Results from pre-operational testing of the LETKF with cross validation in the global ensemble prediction system show a statistically significant
improvement over the operational system when no recentering on a 4D-EnVar analysis is performed. The new LETKF approach will replace theoperational stochastic EnKF during the next system upgrade.

Wednesday, 15 Sep 2021 at 14UTC - room RED

Ocean DA I
A new global ocean ensemble system at the Met Office: Assessing the impact of hybrid data assimilation andinflation settings
Lea, Daniel(1); While, James(1); Martin, Matthew(1); Weaver, Anthony(2); Storto, Andrea(3); Chrust, Marcin(4)(1)

United Kingdom Met Office [Exeter], (2)CERFACS [Toulouse], (3)ISMAR, (4)European Centre for Medium-Range Weather Forecasts

We have developed a global ocean and sea-ice ensemble forecasting system based on the operational FOAM (Forecasting Ocean Assimilation
Model) system run at the Met Office. The ocean model NEMO and the CICE sea-ice model are run at 1/4 degree resolution and the systemassimilates data using NEMOVAR. This is primarily a variational data assimilation (DA) system, but it can now also perform hybrid ensemble/
variational assimilation. A 36 member ensemble of hybrid-3DEnVars with perturbed observations (values and locations) has been set-up, witheach member forced at the surface by a separate member of the Met Office Global-Regional Ensemble Prediction System (MOGREPS-G). The
system includes stochastic model perturbations and a Relaxation to Prior Spread (RTPS) inflation scheme. The system has an additional unperturbed or deterministic member with higher resolution Met Office Numerical Weather Prediction (NWP) atmospheric forcing. A control run of
the system using an ensemble of 3DVars is shown to be generally reliable for sea level anomaly (SLA), temperature and salinity (the ensemble
spread being a good representation of the uncertainty in the ensemble mean), although the ensemble is under spread in eddying regions. In
this system the optimal results, in terms of short-range forecast error and ensemble reliability statistics, are obtained with hybrid-3DEnVar pure 3DVar and pure 3DEnVar do not perform as well. The inflation scheme is beneficial in producing an appropriate ensemble spread in
response to hybrid DA. Work has started to include the ocean ensemble system in a coupled NWP system.

Quantitative Observing System Design within ECCO's 4DVar ocean data assimilation framework
Loose, Nora(1); Heimbach, Patrick(2)(1)

University of Colorado, Boulder, (2)University of Texas at Austin

We leverage Hessian-based uncertainty quantification (UQ) within the ECCO (Estimating the Circulation and Climate of the Ocean) data assimilation framework to explore a quantitative approach for ocean climate observing systems. Here, an observing system is considered optimal if
it minimizes uncertainty in a set of investigator-defined design goals or quantities of interest (QoIs), such as oceanic transports or other usefulclimate indices. Hessian UQ unifies three design concepts: (1) An observing system reduces uncertainty in a target QoI most effectively when

it is sensitive to the same dynamical controls as the QoI. The dynamical controls are exposed by the Hessian eigenvector patterns of the modeldata misfit function. (2) Orthogonality of the Hessian eigenvectors rigorously accounts for complementarity versus redundancy between distinct members of the observing system. (3) The Hessian eigenvalues determine the overall effectiveness of the observing system, and arecontrolled by the sensitivity-to-noise ratio of the observational assets (analogous to the statistical signal-to-noise ratio). We illustrate Hessian
UQ and its three underlying concepts in a North Atlantic case study. Sea surface temperature observations inform mainly local air-sea fluxes.
In contrast, subsurface temperature observations reduce uncertainty over basin-wide scales, and may therefore inform transport QoIs overlarge distances. This research provides insight into the design of effective observing systems that maximally inform the target QoIs, such asones related to the Atlantic Meridional Overturning Circulation (AMOC), while being complementary to the existing observational database.

Forecast Sensitivity to Observations in an Analysis-Forecast System of the California Current Circulation
Moore, Andrew(1); Drake, Patrick(1); Edwards, Christopher(1); Arango, Hernan(2); Wilkin, John(2); Tajallibakhsh, Tayebeh(3);Powell, Brian(4)(1)

Department of Ocean Sciences, (2)Department of Marine and Coastal Sciences, (3)RPS North America, (4)Department of Oceanography

The U.S. Integrated Ocean Observing System (IOOS) forms the backbone of real-time ocean analysis-forecast systems of U.S. territorial waters.
In addition to satellite remote sensing, the IOOS is augmented with in situ observations from a variety of platforms including Argo floats, buoys
and gliders. In addition, remote sensing observations of surface currents are also available from an extensive national network of coastal HF
radars. Maintenance of these observing systems is obviously labor-intensive and costly. Routine monitoring of the impact of the data fromeach element of the observing array on ocean analysis-forecast systems is therefore recognized as an important activity, not only for maintaining the array and demonstrating its value, but also as an aid for planning future expansions of the observing network. This talk will focus
on current efforts to quantify forecast sensitivity to observations (FSO) in an ocean analysis-forecast system of the California Current System(CCS) along the U.S. west coast. The real-time system is based on ROMS-4D-Var, and FSO has been applied to metrics of forecast skill thattarget important features of the CCS circulation along the central California coast. On average, ~50-60% of all observations assimilated into
the model were found to yield improvements in the forecast skill. An adjoint-based approach for Observing System Experiments has also beenused in combination with FSO to quantify the extent to which different components of the observing network support each other.

Wednesday, 15 Sep 2021 at 15UTC - room RED

Ocean DA II
The Estimating the Circulation and Climate of the Ocean (ECCO) “Central Estimate”: a Multi-decadal, Coupled
Ocean Reanalysis
Fenty, Ian(1); Fukumori, Ichiro(1); Heimbach, Patrick(2)(1)

Jet Propulsion Laboratory, (2)University of Texas at Austin

The Consortium for Estimating the Circulation and Climate of the Ocean (ECCO) has been producing dynamically- and kinematically-consistentglobal ocean state estimates for nearly two decades. Our current focus is Version 4 of the “Central Estimate”, a data-constrained global, 1degree, coupled ocean, sea-ice, and thermodynamic ice-sheet model that spans the period 1992-present. Our coupled model is made consistent with a diverse in-situ and remotely-sensed ocean, sea-ice, and ice-sheet data in a least-squares sense by iteratively adjusting a set ofcontrol parameters using the gradient of an uncertainty-weighted model-data misfit cost function. The cost function gradient is provided by
the adjoint of the model (MITgcm). By construction, ECCO state estimates satisfy the laws of physics and thermodynamics encoded in the
numerical model and therefore conserve heat, salt, mass, and momentum. Our philosophy of strict adherence to conservation principles ensures that ECCO reanalyses are useful for investigating the causal origins of observed ocean climate variability. However, because of the enormous scale of the nonlinear optimization problem, strictly obeying conservation laws involves a trade-off with goodness-of-fit; on the whole,
ECCO reanalyses are unlikely to reproduce observations as well as ocean reanalyses that allow incremental adjustments to their state vectors
through time. Here we summarize our recent efforts and challenges associated with (i) coupling to the sea-ice and thermodynamic ice-sheet
models, (ii) adding novel data constraints such as ocean bottom pressure from GRACE and GRACE-FO, and (iii) increasing the spatial resolutionof the state estimation system to achieve eddy-resolving scales.

Assimilation of satellite total surface current velocities in global ocean forecasting systems
Jennifer, Waters(1); Martin, Matthew(1); King, Robert(1); Elisabeth, Remy(2); Mirouze, Isabelle(3); Gaultier, Lucille(4); Ubelmann,
Clement(5)(1)

Met Office, (2)Mercator Ocean International, (3)Cap Gemini, (4)OceanDataLab, (5)OceanNext

Accurate prediction of ocean surface velocity is important for various applications such as search and rescue, marine plastic tracking andcoupled forecasting. Observations of ocean velocities are currently limited and are not routinely assimilated in global operational ocean forecasting systems. This may change with proposed new satellite missions designed to observe ocean surface velocities. The ESA Assimilation of
Total Surface Current Velocities (A-TSCV) project aims to investigate the design, implementation and impact of assimilating synthetic TSCV
data in global ocean forecasting systems. The project will use observing system simulation experiments (OSSEs) to test the assimilation
methodology and provide feedback on the observation requirements for future satellite missions. Synthetic observations of the standard observing network along with synthetic observations of new satellite total surface current velocities are being generated from a high-resolution
nature run. The assimilation of these observations will be tested in the Met Office FOAM and the Mercator Ocean forecasting systems. The
OSSEs are not yet complete, but we will present an overview of the project, the design of the experiments and the data assimilation developments being made to effectively assimilate the surface velocity data into these systems.

Impact of superobbing high resolution marine glider and HF radar data in regional marine JEDI data assimilation
systemLiu, Ling(1); Kim, Jong(1); Kleist, Daryl(2); Mehra, Avichal(2); Book, Cameron(1); Li, Yongzuo(1); Sluka, Travis(3); Bhargava, Kriti(3);Vernieres, Guillaume(3); Kim, Hyun-Sook(4); Kang, HeeSook(4); Kalina, Evan(5); Turuncoglu5, Ufuk(5)(1)

I.M. Systems Groups, (2)NOAA/NWS/NCEP/EMC, (3)JCSDA/UCAR, (4)NOAA/AOML, (5)CIRES

Ocean data assimilation is important to improve coupled numerical weather and climate prediction. Joint Effort for Data Assimilation Integration (JEDI) infrastructure has been developed and is being testing for coupled (ocean and atmospheric) forecast systems by NOAA. Use of ocean
observations for initialization is critical for coupled Hurricane forecasts using Marine JEDI. High-resolution ocean glider data are useful forproviding details of vertical mixing process during Hurricanes at sub-surface levels due to their continuous high vertical resolution; Highfrequency radar surface radial velocity data provide broader spatial coverage over Hurricane passage comparing to ocean glider data. The
assimilation of both will help us advance our understanding of data assimilation systems especially during Hurricane season. Experiments set
up during Hurricane Isaias show that when glider data were superobbed to a similar vertical resolution to that of regional model background,data assimilation produces optimal analysis and forecast skill. Our results also indicate superobbing oceanglider data provides fuller details ofvertical mixing than thinning because of rapid hydraulic changes in the surface mixed layer, whereas thinning might skip or miss these details.
HF Radar radial velocity obtained over deployment at US Virgin Island with 6 km resolution are also assimilated into 10 km resolution regional
marine JEDI during Hurricane Isaias. The forward operator H(x) and observation minus background, observation minus analysis are analyzed
for HF radar data. Details on superobbing and observation error correction is also discussed to compensate for the observation errors causedby optimal interpolation of the radar data.

Wednesday, 15 Sep 2021 at 15UTC - room GREEN

Satellites
All-sky microwave humidity sounder assimilation in the Korean Integrated Model forecast system
Lee, Sihye(1); Song, Hyo-Jong(2); Chun, Hyoung-Wook(3); Kwon, In-Hyuk(1); Kang, Jeon-Ho(1)(1)

Korea Institute of Atmospheric Prediction Systems, (2)Myongji University, (3)Korea Meteorological Administration

The Korean Integrated Model (KIM) and hybrid data assimilation system were extended to assimilate all-sky radiance from microwave satellite
sensors. Initially, RTTOV-SCATT (version 11.3) was implemented to assimilate the Microwave Humidity Sounder (MHS) 183 GHz channels over
the ocean. While there is no cloud/precipitation control variables in our system, temperature and humidity profiles are improved in the all-sky
assimilation. In the cycled analysis and forecast experiments, an assimilation of the MHS in a cloudy region shows globally substantial benefits;
a 1.11% reduction in the humidity root-mean-square-error occurred in the background field compared to clear-sky radiance assimilation as
verification against the ECMWF IFS analysis. Even though the MHS all-sky assimilation's impact on the Northern Hemisphere mid-latitudes is
not remarkable, it nonetheless produces a highly realistic humidity analysis increment for heavy rainfall events over East Asia. This improvement is consistent from the analysis initial time to 120 h forecasts.

Evaluation and Assimilation of Geostationary Hyperspectral InfraRed Sounders (GeoHIS) : Progress and Challenges
Han, Wei(1); Knuteson, Robert(2); Yin, Ruoying(3)(1)

Joint Center for Satellite Data Assimilation, (2)Space Science and Engineering Center [Madison], (3)National Meteorological Center of CMA

High temporal geostationary (Geo) hyperspectral IR sounder (GeoHIS) radiance measurements enable continuous sounding of the atmospherictemperature and moisture, and thus capture the temporal and spatial variability for high impact weather or rapid changing weather events. On
10 December 2016, the successful launch of China's Fengyung FY-4A satellite into geostationary orbit initiated a new era in Earth observationby providing the first time-continuous observations of the upwelling thermal infrared at high spectral resolution with the Geostationary Interferometric Infrared Sounder (GIIRS). A subset of GIIRS longwave temperature sounding channels has been assimilated in China's global NWP

system GRAPES (Global/Regional Assimilation and PrEdiction System) since December 2018 and improve the forecast over East Asia , especiallyfor high impact weather forecasting, such as Typhoons and cold air outbreaks. The European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) is developing an operational advanced GEO hyperspectral IR sounder (IRS) as a part of Meteosat Third Generation(MTG-3) in the mid 2020's. Based on the evaluation and assimilation of the real GeoHIS data from GIIRS, this talk will discuss the recent
progress , current major challenges of GeoHIS assimilation. The opportunities include targeted observing for high impact weather, and improvement in convective storm forecasts, including tornadoes and hurricanes. The challenges include accurate spectral and radiometric calibration, considering the possible diurnal variation, fast radiative transfer model for large satellite zenith angles, and continuous data assimilation for application to high temporal observations.

Improving cloud and radiation forecasts by assimilating visible satellite images
Scheck, Leonhard(1,2); Geiss, Stefan(2); Bach, Liselotte(1); De, Lozar Alberto(1); Weissmann, Martin(3)(1)

Deutscher Wetterdienst [Offenbach], (2)Hans Ertel Centre for Weather Research / LMU Munich, (3)Institute of Meteorology and Geophysics [Vienna]
As the share of renewables in electricity generation is increasing, there is also a rising interest in improved radiation forecasts that allow for abetter prediction of the uncontrollable and intermittent generation of photovoltaic power. Convective scale radiation forecasts can be improvedby assimilating cloud-related observations. A promising type of observation for this purpose are satellite images in the visible spectral range,
which contain high-resolution information on the location, water content and microphysical properties of clouds. Here we report on assimilation experiments using the ICON-D2 regional model and the local ensemble transformation Kalman filter implemented in the data assimilation
system of the German Weather Service. A forward operator based on the Method for FAst Satellite Image Synthesis (MFASIS) and accounting forslant rays and 3D radative transfer effects is used to generate model equivalents for 0.6 micron Meteosat SEVIRI satellite images. Systematicdifference between observed and systematic images and the benefits of using visible channels for model evaluation are discussed. Assimilation
results for test periods of several weeks indicate that errors in the cloud distribution and the surface radiation can be significantly reduced byassimilating visible satellite images. Some beneficial impact is still present after 24 hours and also the agreement with most conventional
observations is improved.

An observation operator for geostationary lightning imager data assimilation in storm-scale numerical weather
prediction systems
Combarnous, Pauline(1,2); Erdmann, Felix(3); Caumont, Olivier(1); Defer, Eric(2)(1)

Centre national de recherches météorologiques, (2)Laboratoire d'aérologie, (3)Royal Meteorological Institue of Belgium

The Lightning Imager (LI) onboard the Meteosat Third Generation (MTG) satellite will provide total lightning observations. The assimilation ofsuch data could improve the thunderstorm prediction accuracy. To prepare the assimilation of the flash extent accumulation (FEA) measured
by LI in the French storm-scale regional AROME NWP system, a lightning observation operator is required to convert the model variables into
a product comparable to the observations. Since LI FEA observations are not available yet (launch planned in the forthcoming years), pseudo-LI
FEA observations were generated from a ground-based lightning detection system (Erdmann et al., in revision for JTECH). This study focuseson the evaluation of different FEA observation operators from various proxies encountered in the literature and calculated from the outputs of
1 h AROME-France forecasts for 27 storm days in 2018. The data are processed as distributions over the whole domain and time period sincea pixel-to-pixel comparison exhibits a rather poor correlation. Different regression techniques, linear regression as well as machine learningmodels, are used to relate the synthetic FEAs and the modeled proxies. The training of observation operator is performed on 25 days of thedataset and 2 days are used for validation. The observation operator is finally evaluated by computing Fraction Skill Scores for simulated FEAs.
The performance of a principal component analysis is also examined.

Wednesday, 15 Sep 2021 at 15UTC - room BLUE

Non-Gaussian DA
4D-Localized Particle Filter Method in KENDA for ICON-LAM
Schenk, Nora(1,2); Walter, Anne(3,2); Potthast, Roland(3,2)(1)

Institute of Mathematics [Goethe University Frankfurt], (2)Deutscher Wetterdienst [Offenbach], (3)Department of Mathematics and Statistics[University of Reading]Nonlinear data assimilation methods like particle filters aim to improve the numerical weather prediction in a non-Gaussian setting. The localized adaptive particle filter (LAPF), introduced by R. Potthast, A. Walter and A. Rhodin in 2019, overcomes filter collapse in a high-dimensionalframework. This particle filter was further developed by Walter et al. (2021) to the local mixture coefficients particle filter (LMCPF) which wastested within the global ICON model. In the LMCPF method the background distribution is approximated by Gaussian mixtures. After a classicalresampling step, Bayes' formula is carried out explicitly under the assumption of a Gaussian distributed observation error. Furthermore, theparticle uncertainty can be adjusted which affects the strength of the shift of the particles toward the observation. Lastly, Gaussian resamplingis employed. As for the LETKF, all steps are carried out in ensemble space. We explore the potential of the LMCPF in the kilometre-scaleensemble data assimilation (KENDA) system with the limited area mode of the ICON model (ICON-LAM) and compare the particle filter method
to the localized ensemble transform Kalman filter which is operationally used at the German Weather Service (DWD). Both methods describe
four-dimensional data assimilation schemes if the observation operators are applied during the model forward integration at the exact observation times and not only at analysis time. This leads to four-dimensional background error covariance matrices at times and locations of theobservations which are employed to derive the analysis ensemble. We present experimental results for the LMCPF in comparison with the LETKF
method in KENDA used at DWD for the ICON-LAM model.

Improving the stability of the Local Particle Filter and Its Gaussian Mixture Extension: Experiments with an
Intermediate AGCM
Kotsuki, Shunji(1); Oishi, Ken(2); Miyoshi, Takemasa(3)(1)

CEReS, Chiba University, (2)Chiba University, (3)RIKEN Center for Computational Science

Penny and Miyoshi (2015) developed the local particle filter (LPF) in a form as the ensemble transform matrix of the Local Ensemble Transform
Kalman Filter (LETKF). Potthast et al. (2018) applied the LPF in the German weather service's operational LETKF system and reported a stableperformance. Walter and Potthast (2021) improved their LPF as a Gaussian mixture filter (LPFGM), what they call the LMCPF (Local Mixture
Coefficients Particle Filter). Kotsuki et al. (2021) implemented the LPF and LPFGM with an intermediate global circulation model known as theSimplified Parameterizations, Primitive Equation Dynamics (SPEEDY), and reported that the LPFGM outperformed the LETKF in sparsely observed
regions. However, performances of the LPF and LPFGM were sensitive to tunable parameters such as inflation and resampling frequency. This
study aims to explore methods for improving the stability of the LPF and LPFGM. First, we revised the way to compute the posterior weights
for the LPFGM. Walter and Potthast (2018) approximated the posterior weights of the LPFGM by those of the LPF. We introduced the exact
posterior weights without approximation for the LPFGM, and showed that the exact weights improved the stability of the LPFGM in terms of
the inflation parameter. Second, we implemented the optimal transport (OT) for constructing the resampling matrix from the posterior weights.
Farchi and Bocquet (2018) reported that the OT was optimal for constructing the resampling matrix in the LPF through a series of experimentswith the 40-variable Lorenz-96 model. However, our experiments revealed that the use of the OT in SPEEDY generally results in underdispersive posterior particles.

A new way to infer non-Gaussian observation errors based on ensemble innovations
Hu, Chih-Chi(1); Van, Leeuwen Peter Jan(1,2)(1)

Department of Atmospheric Sciences, Colorado State University [Fort Collins], (2)University of Reading

The proper specification of the observation error is critical to a well-performing data assimilation system. The observation error can be divided
into the measurement error, which is an inherent part of the results of the measurement, and the representation error, which is the uncertainty
in the forward operator. The measurement error is usually well-known based on the design of the instrument, while the representation error
is often less understood and often dominant. Previous studies have infered the observation error from the innovation statistics, often assumingthat the background error in observation space and the observation error are Gaussian. However, this Gaussian assumption can be problematic,
especially for nonlinear forward operators. This study infers a non-parametric observation error pdf based on a background ensemble without
any assumption on the shape of the background error or observation error pdfs. The only assumption is that each ensemble member is statistically indistinguishable from the truth. Since the innovation pdf is the convolution of observation error and background error pdfs, the observation error pdf can be found by expressing the pdfs as histograms and solving a linear system. Experiments with toy systems and real cloudy
observations show that this new method is able to retrieve non-Gaussian observation pdfs, even multimodal pdfs, demonstrating the potentialof this method for complex representation errors in real atmospheric observations.

Non-Gaussian Hybrid Variational Data Assimilation
Fletcher, Steven(1); Goodliff, Michael(2); Hossen, Md Jakir(1); Kliewer, Anton(3); Zupanski, Milĳa(1); Wu, Ting-Chi(4)(1)

Cooperative Institute for Research in the Atmosphere, (2)Cooperative Institute for Research in Environmental Sciences, (3)Cooperative Institutefor Research in the Atmosphere, (4)RIKEN Center for Computational Science [Kobe]
With the advancement of non-Gaussian based variational techniques the need to extend this to hybrid ensemble-variational techniques is thenext step towards operational viability. However, the problem lies in the Gaussian assumptions that are made in the derivation of the Kalmanfilter. In this presentation we shall show a lognormal, and Gaussian-lognormal based Kalman filter which is then approximated with the Maximum Likelihood Ensemble Filter (MLEF) approach. The MLEF is a hybrid scheme that utilizes Kalman filter equations for the analysis and forecast error covariance matrices but solves a variational-based cost-function projected in the ensemble space. The hybrid nature makes MLEF a
suitable system for our non-Gaussian applications. As a theoretical study, we shall present results of these new formulations with the Lorenz
1963 model.

Thursday, 16 Sep 2021 at 11UTC - room RED

DA for atmospheric composition II
Aerosol data assimilation as a tool to detect model errors
Tsikerdekis, Athanasios(1); Schutgens, Nick A. J.(2); Hasekamp, Otto(1)(1)

SRON Netherlands Institute for Space Research, (2)Vrĳe Universiteit Department of Earth Science

An ensemble Kalman-filter smoother (LETKS) is used to estimate aerosol emission in the global climate/aerosol model ECHAM-HAM by assimilating retrievals from the multi-angle polarimeter POLDER. The assimilated observations (aerosol optical depth, angstrom exponent and single
scattering albedo) provide a wealth of information in order to correct the aerosol amount, size and composition simultaneously. The emissions
are estimated per species (dust, sea salt, organic carbon, black carbon, sulfates and sulfate precursor gases), per sector (biomass burning andfossil fuel) and by size (Aitken, Accumulation and Coarse). An evaluation of the data assimilation experiment reveals that the model errors are
reduced for all the assimilated observables in most areas over the globe. Surprisingly, in the biomass burning outflow area of South Atlantic
the aerosol optical depth error increases. This increase cannot be attributed to emission errors, since the model error (for all observables) overthe biomass burning African sources is very low after the assimilation. Hence, we use the new corrected emissions and tune various model
processes (e.g. emitted particle size, wet growth, emission height, removal processes) in order to improve the aerosol optical depth in theoutflow and not affect it negatively in the sources. This application shows how data assimilation results can be used to highlight “hidden” model
errors and promote future model development.

Assimilation of Aerosol Optical Depth (AOD) retrievals and PM2.5 in NCEP's Next-Generation Regional Air Quality
Forecasting SystemWang, Hongli(1,2); Weygandt, Stephen(2); Pągowski, Mariusz(1,2); Li, Ruifang(1,2); Montuoro, Raffaele(1,2); Liu, Quanhua(3); Dang,
Cheng(4); Ma, Yingtao(5,3); Kumar, Rajesh(6); Kondragunta, Shobha(3); Martin, Cory(7); Huang, Jianping(8); Mcqueen, Jeffery(9);Stajner, Ivanka(9); Hughes, Brian(8)(1)

Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, (2)NOAA Global Systems Laboratory, (3)NOAA
NESDIS STAR, (4)UCAR Joint Center for Satellite Data Assimilation, (5)CSU CIRA, (6)NCAR RAL, (7)Redline at NOAA/NWS/NCEP/EMC, (8)IMSG at NOAA/
NWS/NCEP/EMC, (9)NOAA/NWS/NCEP/EMC
Wildfires provide a major source of emissions contributing to poor air quality in the United States. Current operational models at the National
Centers for Environmental Prediction (NCEP) show large uncertainties in the analysis and prediction of wildfire emissions with respect to emission strength, composition, duration, diurnal evolution, as well as to what altitude smoke plumes rise. This presentation describes efforts toimprove wildfire smoke forecasting capabilities for air quality applications by assimilating satellite retrievals of Aerosol Optical Depth (AOD)
and PM2.5 in-situ measurements into NCEP's Next-Generation Regional Air Quality Forecasting System, which consists of a Limited Area Modelversion of FV3 (FV3LAM) coupled with the Community Multi-scale Air Quality Model (CMAQ) to provide inline atmospheric chemistry. The
ability to assimilate AOD and PM2.5 has been developed for FV3LAM-CMAQ within the Gridpoint Statistical Interpretation (GSI) 3-dimensionalvariational data assimilation system (3DVar). The Community Radiative Transfer Model (CRTM) AOD module is used as an observation operator
for AOD assimilation. The control variables are individual aerosol species, with associated background error statistics derived via the NMC

method. The diurnal variations of background error statistics are examined and discussed. The VIIRS smoke and dust mask products are used
to help produce physically reasonable aerosol analysis increments in smoke and dust areas. The performance of the developed GSI 3DVar data
assimilation system will be presented and limitations will be discussed.

Thursday, 16 Sep 2021 at 11UTC - room GREEN

Regional reanalysis I
A high resolution reanalysis for the Mediterranean Sea
Escudier, Romain(1,2); Clementi, Emanuela(2); Omar, Mohamed(2); Cipollone, Andrea(2); Pistoia, Jenny(2); Drudi, Massimiliano(2);Grandi, Alessandro(2); Lecci, Rita(3); Aydogdu, Ali(2); Masina, Simona(2); Coppini, Giovanni(3); Pinardi, Nadia(4)(1)

Mercator Océan International, (2)Centro Euro-Mediterraneo per i Cambiamenti Climatici [Bologna], (3)Euro-Mediterranean Center on Climate
Change, (4)Alma Mater Studiorum University of Bologna
Here we present a new physical reanalysis of the Mediterranean Sea at high resolution, developed in the Copernicus Marine Environment Monitoring Service (CMEMS) framework. The hydrodynamic model is based on NEMO combined with a variational data assimilation scheme (OceanVar). The model has a horizontal resolution of 1/24° and 141 vertical z* levels and provides daily and monthly 3D values of temperature,salinity, sea level and currents. Hourly ECMWF ERA-5 atmospheric fields force the model and daily boundary conditions in the Atlantic are takenfrom the global CMCC C-GLORS reanalysis. 39 rivers model the freshwater input to the basin plus the Dardanelles. The reanalysis covers
33-years, initialized from SeaDataNet climatology in January 1985, getting to a nominal state after a two-years spin-up and ending in 2019.
In-situ data from CTD, ARGO floats and XBT are assimilated into the model in combination with satellite altimetry data. This reanalysis has
been validated and assessed through comparison to in-situ and satellite observations as well as literature climatologies. The results show an
overall improvement of the skill and a better representation of the main dynamics of the region compared to the previous, lower resolution(1/16°) reanalysis. Temperature and salinity RMSE is decreased by respectively 12% and 20%. The deeper biases in salinity of the previous
version are corrected and the new reanalysis present a better representation of the deep convection in the Gulf of Lion. Climate signals show
continuous increase of the temperature due to climate change but also in salinity. The new reanalysis will allow the study of physical processes
at multi-scales, from the large scale to the transient small mesoscale structures.

IMDAA regional reanalysis over the Indian monsoon region
Rani, S Indira(1); George, John P(1); Rajagopal, E N(1); Arulalan, T(2); Renshaw, Richard(3); Maycock, Adam(3); Barker, Dale(4)(1)

NCMRWF, MoES, (2)IMD, MoES, (3)Met Office, (4)Centre for Climate Research Singapore

Indian Monsoon Data Assimilation and Analysis (IMDAA) is the high resolution (~ 12 km), long-term (40 years, 1979-2018, extended to 2020),
satellite-era regional reanalysis over the Indian monsoon region, covering the area 30ºE to 120ºE, and 15ºS to 45ºN. IMDAA reanalysis is a
result of the collaboration between the National Centre for Medium Range Weather Forecasting (NCMRWF), India and the U. K. Met Office,funded by the National Monsoon Mission (NMM) project of the Indian Ministry of Earth Sciences. The reanalysis was prepared using the 4D-Var
data assimilation method and the UKMet Unified Model. This is presently the highest resolution atmospheric reanalysis available for the Indianmonsoon region. Conventional and satellite observations from different sources are used, including Indian surface and upper air observations,of which some were not used in any previous global or regional reanalyses. IMDAA used the meteorological observations collated by the European Centre for Medium range Weather Forecasts (ECMWF) for reanalysis as the main dataset. Lateral boundary conditions for the IMDAA runs
are provided by ECMWF global reanalysis of ERA-Interim. The hourly and three hourly IMDAA variables are released to the international researchers through the NCMRWF reanalysis portal (https://rds.ncmrwf.gov.in/). This presentation is intended to provide the details of the IMDAA reanalysis, including the observations used and their quality control procedures, data assimilation system, the forecast model, post-processing, and the comparison of various IMDAA estimates during different seasons over India with IMD gridded observations and ERA5 global
reanalysis.

Performance of a 5-km regional reanalysis over Japan with respect to summer precipitation
Fukui, Shin(1); Soga, Daiki(1); Ohara, Ryota(1); Usui, Ken(1); Takiguchi, Kaito(1); Ito, Junshi(1); Iwasaki, Toshiki(1); Yamazaki,
Takeshi(1); Saito, Kazuo(2,3,4); Seko, Hiromu(3)(1)

Tohoku University, (2)Japan Meteorological Business Support Center, (3)Meteorological Research Institute, (4)The University of Tokyo

We are conducting a regional reanalysis with 5 km grid spacing over Japan for investigating regional climate change. The reanalysis is produced
using a system developed by Fukui et al. (2018). The reanalysis system is a one-way double nesting system consisting of regional model (Japan
Meteorological Agency Nonhydrostatic Model) and local ensemble transform Kalman filter. The inner reanalysis covers Japan with a grid spacingof 5 km (RRA-5km). The outer reanalysis covers East Asia with a grid spacing of 25 km (RRA-25km), nested in the Japanese 55-year reanalysis(JRA-55), whose resolution is approximately 55 km. The assimilated data are limited to conventional observations, such as in-situ surfacepressure observations and aerological sonde observations, and tropical cyclone center position data, in order to keep the long-term consistency in the analysis quality through the total reanalysis period. Focusing on precipitation in summer seasons, we checked the performance of
RRA-5km comparing with JRA-55 and RRA-25km. Higher resolution reanalysis improves representation of precipitations. RRA-5km fits to therain-gauge data the best, and followed in order by RRA-25km and JRA-55, in terms of frequencies of heavy precipitation and spatial distribution of total precipitation. However, some problems still remain that it underestimates the frequencies of precipitations over 10 mm h-1 andoverestimates precipitations in the mountainous area in central Japan.

Thursday, 16 Sep 2021 at 11UTC - room BLUE

AeolusValidation and Impact assessment of Aeolus Doppler Wind Lidar Observations at the German Weather Service
Cress, Alexander(1)(1)

Deutscher Wetterdienst [Offenbach]

Validation and Impact assessment of Aeolus Doppler Wind Lidar Observations at the German Weather Service Alexander Cress; Alexander.Cress@dwd.de Deutscher Wetterdienst, Frankfurter Strasse 135, 63067 Offenbach am Main ABSTRACT The first European Space Agency (ESA)
Earth Explorer satellite mission Aeolus was launched successfully in August 2018, providing globally distributed profiles of horizontal line-of-

side (HLOS) wind information. From the beginning, the Deutscher Wetterdienst (DWD), as part of the German project team EVVA (Experimental
Validation and Assimilaton of Aeolus data), investigated statistics of differences of the Aeolus winds relative to the global ICON model system
of DWD, the observation error characteristic and bias behaviour of the HLOS winds, for both Laser A and Laser B, on a routinely basis and giving
contributions to the CAL/VAL activities of ESA and for internal use. Several impact studies were conducted for both, Laser A and Laser B,
showing a significant positive impact of the Aeolus HLOS winds in our data assimilation system for both hemispheres and the Tropics. The
impact is largest in the tropical upper troposphere and lower stratosphere and over polar areas. All the activities accumulated in the operationaluse of the Aeolus HLOS winds in May 2020, only 1 and a half year after the first arrival of the winds in our observational data base system. Thiswas only possible by an excellent collaboration between a variety of partners, including ESA, ECMWF, the DISC team and the national EVVA
team. In my talk, I will give an overview of the activities and impact assessments, mainly for Laser B, leading to the successfully operational
usage of the Aeolus HLOS winds in the data assimilation and foresting system of DWD.

Impact Assessment of Aeolus Wind on NOAA Global NWP Analyses and Forecasts
Ide, Kayo(1); Liu, Hui(2); Garrett, Kevin(3); Lukens, Katherine(2); Hoffman, Ross(2)(1)

University of Maryland, (2)NOAA and University of Maryland, (3)NOAA

The Aeolus mission, launched by the European Space Agency in August 2018, provides the first observations of vertical wind profiles from aspace-borne Doppler wind lidar (DWL). The Aeolus Atmospheric LAser Doppler Instrument (ALADIN) measures both molecular and Mie(e.g., aerosol, hydrometeor) backscatter to derive Horizontal Line of Sight (HLOS) winds throughout the troposphere and lower stratosphere.
Since the data have been made available to the Aeolus cal/val teams in December 2018, NOAA has been evaluating the quality of the HLOS
winds, characterizing random and systematic errors, integrating the observations into the NOAA Finite-Volume Cubed-Sphere Global Forecast
System (FV3GFS) numerical weather prediction (NWP) model, and iterating on refinements to maximize the impact on medium-range forecasts. Several Observing System Experiments (OSEs) have been performed for different periods of the Aeolus record, and with various optimizations including the reprocessing of the Aeolus Level 2B data using the FV3GFS background and refinement of the observation operator
components. In each case, the assimilation of Aeolus HLOS winds has shown positive impact on FV3GFS forecasts. In this study, we will provide
a summary of the optimized data assimilation configuration for the FV3GFS including approaches to quality control, bias correction, and observation error specification, and draw some preliminary conclusions on the overall impact of Aeolus on NOAA global forecasts.

Validation of Aeolus L2B Wind Product with ECCC Short-Range Forecasts and ERA5 over the Arctic
Chou, Chih Chun(1); Kushner, Paul(1); Laroche, Stéphane(2); Mariani, Zen(3); Rodriguez, Peter(3)(1)

University of Toronto, Department of Physics, (2)Environment and Climate Change Canada, (3)Environment and Climate Change Canada

In August 2018, the European Space Agency launched the Aeolus satellite, the first spaceborne Doppler Wind Lidar, that measures global
horizontal line-of-sight (HLOS) wind profiles by analyzing the Doppler frequency shift of backscattered signal from air molecules (Rayleigh)and aerosol and cloud (Mie). This mission has the potential to improve numerical weather prediction over the Arctic where wind observations
are sparse. In this study, the Aeolus Level-2B wind product is compared to the ECCC's short-range forecast (ECCC-B) and the reanalysis product, ERA5, from the ECMWF in the Arctic during the early FM-A (2018-09 to 2018-10), early FM-B (2019-08 to 2019-09), and mid-FM-B
periods (2019-12 to 2020-01). Notably, consistency is found in the Rayleigh channel from the troposphere to the stratosphere and in the Miechannel from the PBL to the lower stratosphere. The correlations between the analyses and observations are greater than 0.7 and their RMSD

are within one normalized standard deviation in the higher atmosphere for Rayleigh winds and lower atmosphere for Mie winds. The reprocessed product, 2B10, has no significant improvement compared to the near-real time product, 2B06. Nevertheless, this reprocessed productdoes not need further model-based bias correction which could ease the inter-comparison of experiments across different weather centers.
ECMWF's estimated error product for Aeolus is found to be coherent with the differences between Aeolus and the other datasets, and thus canbe used as a guide for expected consistency. Thus, the new Aeolus product provides a valuable addition to current wind products in regions
such as the Arctic Ocean sector where few direct wind observations have been available to date.

Thursday, 16 Sep 2021 at 12UTC - room RED

Global reanalysis I
JRA-3Q: Japanese Reanalysis for Three Quarters of a Century
Kobayashi, Shinya(1); Kosaka, Yuki(1); Chiba, Jotaro(1); Tokuhiro, Takayuki(1); Harada, Yayoi(2); Kobayashi, Chiaki(2); Naoe,
Hiroaki(2)(1)

Office of Earth System Modeling / Numerical Prediction Division / Japan Meteorological Agency, (2)Meteorological Research Institute / Japan
Meteorological Agency
Reanalysis has been playing a crucial role in JMA's climate services and related activities, such as climate monitoring, seasonal forecast modelling and climate research underpinning them, by providing long-term, high-quality climate data. To further improve the quality of reanalysis
data, thereby contribute to advancing climate services and applications, JMA is currently conducting the third Japanese global atmosphericreanalysis, called the Japanese Reanalysis for Three Quarters of a Century (JRA-3Q). It covers the period from the late 1940s to present and theinitial release of the data for the 1990s onwards is planned around the end of 2021. JRA-3Q is being produced with the TL479 resolution
version of JMA's operational data assimilation system as of December 2018, which was extensively improved since the JRA-55 reanalysis was
conducted. In JMA's Global Spectral Model, for example, biases of radiation budget, surface sensible/latent heat fluxes and precipitation havebeen significantly reduced due to extensive improvements of parameterisations for physical processes. A boundary condition over the ocean
for the mid-1980s onwards is also replaced with satellite-based high-resolution (0.25-deg) SST data to improve the representation of atmospheric processes around the western boundary currents and associated SST fronts. This presentation provides an overview of JRA-3Q and an
initial evaluation of its characteristics and performance.

The ERA5 reanalysis: a detailed record of the climate and weather for the past 70 years.
Hersbach, Hans(1); Bell, Bill(1); Berrisford, Paul(1); Horanyi, Andras(1); Nicolas, Julien(1); Radu, Raluca(1); Munoz, Sabater Joaquin(1);Soci, Cornel(1); Schepers, Dinand(1); Simmons, Adrian(1)(1)

European Centre for Medium-Range Weather Forecasts

At the European Centre for Medium-Range Weather Forecasts (ECMWF), reanalysis is a key contribution to the Copernicus Climate Change
Service (C3S) that is implemented at ECMWF on behalf of the European Commission. The most recent ECMWF global reanalysis, ERA5, provides
hourly snapshots of the Earth's atmosphere, land surface and ocean waves from 1950 with updates with a latency of 5 days. It is highly popularand used for a wide range of applications. The focus will be on the ERA5 back extension from 1950 to 1978 that supplements the previously
published segment from 1979 onwards. It features the assimilation of many conventional observations and improved use of early satellite data.
The fidelity of the extension is illustrated by the accurate depiction of the North Sea Storm of 1953, and the events leading to the first discovery
of Sudden Stratospheric Warmings in 1952. Time series of ERA5 global surface temperature anomalies show temperatures to be relatively
stable from 1950 until the late 1970s, in agreement with the other contemporary full-input reanalyses and independent datasets. The evolutionof upper air temperatures, humidities and winds show smoothly varying behaviour, including tropospheric warming and stratospheric cooling,
modulated by volcanic eruptions. The Quasi Biennial Oscillation is well represented throughout. Despite these good characteristics, the ERA5
back extension did use historical observations for tropical cyclones in a sub-optimal way, which, given their sparsity is a challenging subject
in itself. The back extension was published as a separate, preliminary data set and the production of an improved version is currently inprogress. The presentation will end with an outlook on future ECMWF reanalysis plans.

A synoptic to decadal evaluation of the 20th Century Reanalysis Version 3
Slivinski, Laura(1,2); Compo, Gilbert(1,2); Sardeshmukh, Prashant(1,2); Whitaker, Jeffrey(2)(1)

Cooperative Institute for Research in Environmental Sciences, (2)NOAA Physical Sciences Laboratory

The performance of a new historical reanalysis, the NOAA–CIRES–DOE Twentieth Century Reanalysis version 3 (20CRv3), is evaluated via comparisons with other reanalyses and independent observations. This dataset provides global, 3-hourly estimates of the atmosphere from 1806
to 2015 by assimilating only surface pressure observations and prescribing sea surface temperature, sea ice concentration, and radiative forcings. Comparisons with independent observations, other reanalyses, and satellite products suggest that 20CRv3 can reliably produce atmospheric estimates on scales ranging from weather events to long-term climatic trends. Not only does 20CRv3 recreate a ʻʻbest estimate'' of theweather, including extreme events, it also provides an estimate of its confidence through the use of an ensemble. Surface pressure statistics
suggest that these confidence estimates are reliable. Comparisons with independent upper-air observations in the Northern Hemispheredemonstrate that 20CRv3 has skill throughout the twentieth century. The skill of analyzed 500-hPa geopotential heights from 20CRv3 for
1979– 2015 is comparable to that of modern operational 3–4-day forecasts.

Thursday, 16 Sep 2021 at 14UTC - room RED

Regional reanalysis II
Regional reanalysis activities at DWD: review and outlook
Kaspar, Frank(1); Borsche, Michael(1); Niermann, Deborah(1); Ostermoeller, Jennifer(1); Roesch, Thomas(1); Spangehl, Thomas(1);Keller, Jan(1); Potthast, Roland(1)(1)

Deutscher Wetterdienst [Offenbach]In 2011, the development of a regional reanalysis based on the COSMO model began within the Hans-Ertel-Centre for Weather Research. The
Meteorological Institute of the University of Bonn and the Institute of Geophysics and Meteorology of the University of Cologne focused on thequestion of whether it is possible to produce a long-term regional reanalysis for applications in the field of climate analysis and climate services

by using DWD's weather forecast model. A regional reanalysis for Europe (COSMO-REA6, 6 km spatial resolution, available from 1995) andCentral Europe with Germany in the center (COSMO-REA2, 2 km spatial resolution, currently available for 2007-2013) was produced with the
COSMO model and the quality has been evaluated with various methods. The data product is widely used by a large and diverse community of
researchers, companies, and governmental institutions alike. In this presentation, we summarize the background and current status of DWD's
reanalysis activities and present quality estimates and evaluation results. We provide an overview over recent studies as performed at DWD andat the Hans-Ertel-Centre in combination with REA6. Examples are shown addressing renewable energy applications with a focus on offshore
wind speeds as well as for the analysis of weather and climate extremes. Finally, we provide an outlook on our near future plans, which include
the preparation of an ICON based reanalysis.

Copernicus European regional reanalysis
Schimanke, Semjon(1); Isaksson, Ludvig(1); Edvinsson, Lisette(1); Ridal, Martin(1); Berggren, Lars(1); Hopsch, Susanna(1); Le,
Moigne Patrick(2)(1)

Swedish Meteorological and Hydrological Institute, (2)Météo-France

The Copernicus European regional reanalysis (https://climate.copernicus.eu/regional-reanalysis-europe) is produced as part of the Copernicus Climate Change Service (C3S). We will give an overview on the service main objectives as well as we will present produced data. In the firstphase of the service, systems inherited from the FP7 project UERRA (Uncertainties in Ensembles of Regional ReAnalyses, http://www.uerra.eu)
were applied extending the UERRA-HARMONIE as well as the MESCAN-SURFEX datasets. These datasets contain analyses of the atmosphere,
the surface and the soil. Data are available for the period 1961 – July 2019 through Copernicus Climate Data Store (CDS). The next generationof the regional reanalysis for Europe comprises three components: - CERRA (5.5 km horizontal resolution) - CERRA-EDA (10-member ensemble at 11 km resolution) - CERRA-Land (5.5 km horizontal resolution) In addition to the higher resolution, CERRA is more sophisticatedthan UERRA. For instance, more observations are assimilated into CERRA, in particular remote sensing data. The production of CERRA, CERRAEDA and CERRA-Land will complete in September/October 2021 and data will become available in the CDS shortly thereafter. The quality of
the regional reanalysis in comparison to ERA5 will be shown with results of the standard HARMONIE-verification package as well as based on
certain case studies. For instance, the winter storm Gudrun (January 2005, southern Sweden) will be investigated.

The Copernicus Arctic Regional Reanalysis
Schyberg, Harald(1)(1)

Norwegian Meteorological Institute

The Copernicus Climate Change Service (C3S) is now implementing an Arctic climate reanalysis. ECMWF organizes this service on behalf of theEuropean Commission, and the work is led by MET Norway with the other Nordic and the French national meteorological services as partners.
Due to Arctic climate warming on average faster than the rest of the globe, there is enhanced user focus on change processes in this area. The
Copernicus Arctic Regional Reanalysis service (where the data set is abbreviated as CARRA) produces its regional reanalysis on two Arctic
subdomains of interest for change processes and economic activities. The production is ongoing and the reanalysis covers the period 1991 2021 with a horizontal resolution of 2,5 km. Recently the first batch of the data covering the period 1998-2019 was made publicly available.
Additionally a proof-of-concept for a next generation pan-Arctic reanalysis system will be provided for the period September 2017 - September 2018, to cover part of the Year of Polar Prediction. In this presentation we will focus on how the data set verifies and compares to otherdata sets. We demonstrate how the Arctic reanalysis adds value versus the global reanalysis ERA5 both statistically and for special weatherevents. This value adding is obtained both by using a higher-resolution model and by using fine scale regional input data not used in the global
system. (A companion presentation by Xiaohua Yang provides more detail on the development and the performance of the assimilation system.)Thursday, 16 Sep 2021 at 15UTC - room RED

Innovative observations
Usage of crowd-sourced meteorological car data for new real time road weather forecast
Acevedo, Walter(1); Paschalidi, Zoi(1); Riede, Hella(1); Steinbach, Maximilian(1); Heyer, Lisa(1); Hellweg, Meike(2,3); Nachtigall,Jens(3); Kratzsch, Thomas(1); Potthast, Roland(4,1)(1)

German Weather Service, (2)The Institut für Mess- und Regelungstechnik / The Department of Measurement and Control, (3)AUDI AG, (4)University of Reading
New mobility approaches like autonomous driving require high precision weather solutions, such as real time weather maps and warnings. The
development of such demanding products is becoming possible due to different emerging technologies like Internet of Things, 5G mobile
networks and the growing availability of high resolved meteorological data from automobiles. So, the Flotten-Wetter-Karte (FloWKar) project,a joint work of the German Meteorological Service (DWD) and the car manufacturer AUDI AG, explores how environmental data from car sensors, can be used within DWD's forecast system, respecting data protection regulations. As the standard assimilation systems cannot achieve
the needed exceptionally fast assimilation cycles, an ultra-rapid data assimilation (URDA) method is developed. URDA achieves an update rateof the order of minutes, using only a reduced version of the state variables in an existing model forecast and different kind of observation data
available. However, the meteorological data collected by moving vehicles suffer from errors and an almost real time improvement is needed.
Thus, a series of quality control and bias correction algorithms has been built for the correction of the raw observations, employing among
others artificial intelligence techniques. The corrected measured variables of the mass-produced vehicle-based sensors match well with theʻground truth' and real time maps can be produced after the assimilation of the high resolved project data.

The potential of assimilating wind power data for future reanalysis
Kelbch, Alexander(1); Keller, Jan(1)(1)

Deutscher Wetterdienst [Offenbach]Reanalyses are continuously developed towards smaller grid spacing. Thus, the representation of processes influenced by surface heterogeneity is expected to improve but accompanied by a growing impact of model errors. This affects downstream applications, e.g. renewable energy,
where boundary layer (PBL) data are crucial. Recent studies also suggest to further improve near-surface winds in NWP products. While DA canhelp to attenuate errors, it relies on the availability of long time series of relevant observations. The FAIR project, in which the German Meteorological Service (DWD) participates, aims at better utilizing weather and climate data. Specifically, DWD benefits from using third party datafor product improvement. In this context, our goal is to investigate the potential of wind power output DA for improving future reanalysis

products. Wind power output data are provided by BayWa re GmbH with record lengths of up to 20 years. These data may provide the necessary
retrospective information on wind speeds in the lower PBL. The data is assimilated into DWD's operational NWP framework (ICON-LAM, LETKF)
for Central Europe. To successfully assimilate wind power data the construction of a forward operator is crucial. We can rely on specific power
curves also provided by BayWa. We present comparisons and evaluations of identical twin experiments stretching over several weeks long
periods in different seasons to assess the benefit for reanalysis.

Assimilation of surface observations from citizen weather stations into a regional weather prediction system
Paschalidi, Zoi(1); Acevedo, Walter(1); Sgoff, Christine(1); Ulbrich, Sven(1); Kratzsch, Thomas(1); Potthast, Roland(1,2)(1)

German Weather Service, (2)University of Reading

The new unprecedented massive amount of near-surface meteorological measurements from crowd sourced observation networks offers various new possibilities for improved weather forecast on regional and local scale, as well as for the development of real time weather products.
That is why in the German Meteorological Service (DWD) we study the impact of assimilating observations from the private weather station's
network NETATMO, within the regional weather model ICON-LAM. A comparative study takes place, where temperature and humidity observations from NETATMO over Germany are assimilated in addition to their synoptic analogues and a full set of available measurements, such as
2m temperature, surface pressure, 10m winds, observations from airplanes, wind profilers and radiosondes. As the crowd-sourced measurements suffer from strong biases and high noise, a bias correction approach is applied, based on the diurnal cycle of temperature and humidity
variables. The preliminary results show that the assimilation of NETATMO observations profits always from the bias corrections and it can keep
up with the positive effect of assimilating temperature and humidity synoptic observations. The model's cold bias in the lower atmosphere canbe reduced by the assimilation of NETATMO surface temperature observations, while the corresponding humidity observations give a neutral
error reduction.

Exploring the characteristics of a vehicle-based temperature dataset for convection-permitting numerical
weather prediction
Bell, Zackary(1); Dance, Sarah L.(1); Waller, Joanne A.(2)(1)

University of Reading, (2)Met Office@Reading

Crowdsourced vehicle-based observations have the potential to improve forecast skill in convection-permitting numerical weather prediction(NWP). Our aim is to explore the characteristics of vehicle-based observations of air temperature. We describe a novel low precision vehiclebased observation dataset obtained from a Met Office (UK) proof-of-concept trial. In this trial, observations of air temperature were obtained
from built-in vehicle air-temperature sensors, broadcast to an application on the participant's smartphone and uploaded, with relevant metadata, to the Met Office servers. We discuss the instrument and representation uncertainties associated with vehicle-based observations andpresent a new quality-control procedure. It is shown that, for some observations, location metadata may be inaccurate due to unsuitablesmartphone application settings. The characteristics of the data that passed quality-control are examined through comparison with km-scale
NWP data and nearby independent observations of air temperature. Our results show that the uncertainty associated with vehicle-based observation minus-model comparisons is likely to be weather-dependent and possibly vehicle-dependent. Despite the low precision of the data,
vehicle-based observations of air temperature could be a useful source of spatially-dense and temporally-frequent observations for NWP.

Thursday, 16 Sep 2021 at 15UTC - room GREEN

New approaches to DA IV
Assimilation of microwave radiances over the rainbands of tropical cyclones
Moradi, Isaac(1); Evans, Frank(2); Mccarty, Will(3); Gelaro, Ronald(1)(1)

Global Modeling and Assimilation Office, (2)University of Colorado, (3)Global Modeling and Assimilation Office, NASA Goddard Space Flight
CenterMicrowave satellite observations are one of the largest datasets assimilated into Numerical Weather Prediction (NWP) models. However, thepotential of microwave observations in improving the weather forecasts is limited by the accuracy of all-sky radiative transfer calculations. We
introduce a novel Bayesian Monte Carlo technique to improve the assimilation of microwave observations over the rainbands of tropical cyclones. The BMCI technique eliminates the need for a forward model in the data assimilation system. The technique includes three steps, (i)generating a comprehensive dataset using in-situ cloud measurements and atmospheric profiles, (ii) generating synthetic ATMS observations
from the training dataset, and (iii) using real observations to estimate the geophysical variables over the rainbands of tropical cyclones. The
retrieved profiles of temperature, relative humidity, and cloud water content as well as surface information such as SST were then assimilated
into the model. The results show that assimilating the BMCI retrievals can influence the dynamical features of the cyclone, including a stronger
warm core, a symmetric eye, and vertically aligned wind columns.

Hilbert curves for data thinning and application to aircraft data
Su, Xiujuan(1); Purser, R. James(1)(1)

IMSG at NOAA/NCEP/EMC

The space-filling Hilbert curve associates points on a parameterized interval (the Hilbert parameter) with points in a spatial domain of higher
dimensions in a continuous way. Point observations scattered in space can be mapped, and serially sorted, onto the Hilbert parameter efficiently. When the data density is high on the Hilbert curve, it is necessarily high in space. This suggests that when there is a need to thin data,such as aircraft observations, which are very dense in compact locations (airports) distributed sporadically, we can perform this thinning relatively easily on the Hilbert curve with the assurance that the effects of this thinning will be reflected in physical space. We shall discuss our
experience using this method, and will discuss the impacts with application of hilbert curve method to aircraft data.

Evaluation of background error models for JEDI-based data assimilation with GFS and GEOS.
Holdaway, Daniel(1); Ménétrier, Benjamin(1,2); Thomas, Catherine(3)(1)

Joint Center for Satellite Data Assimilation, (2)Institut de recherche en informatique de Toulouse, (3)NOAA National Centers for Environmental
Prediction
The Joint Center for Satellite Data Assimilation's (JCSDA's) Joint Effort for Data assimilation Integration (JEDI) will be used to initialize futureversions of NOAA's Global Forecast System (GFS) and NASA's Goddard Earth Observing System (GEOS). FV3-JEDI will form the interface between
the atmospheric component of these models and the JEDI system, performing data assimilation with JEDI algorithms directly on the cubed-

sphere grid. One of the difficulties when developing any data assimilation system is in constructing a suitable background error model, andusing the native grid introduces a number of novel challenges. In this presentation we will discuss the work that is being done at the JCSDA toconstruct the background error model for GFS and GEOS. The work utilizes the infrastructure that the JCSDA has put in place for enabling useof a range of background error models with any grid. We will show some scientific evaluation for the various flavors of static background error
model, including different balance operators and covariance models. We will also demonstrate the use of the models for producing a standalone analysis. Localization of ensemble covariance in JEDI can be done with fixed length scales or dynamic length scales diagnosed from anensemble. We will show some comparison of these approaches for GFS and GEOS and discuss the tuning of both localization and static background errors.

Are we minimizing the appropriate errors in data assimilation for weather forecasting?
Fabry, Frederic(1); Glazatov, Valentina(1)(1)

Department of Atmospheric and Oceanic Sciences, McGill University

Data assimilation approaches and algorithms used for weather forecasting generally seek to minimize errors on model fields at analysis timeand at the resolution of the model to help reduce forecast errors. While this may be the best assimilation strategy to improve analyses, does it
lead to the best forecasts? If not, what error minimization criteria should be used for smaller initial condition errors to result in improved
forecast skill? Four error-minimization criteria were considered, together with their combination: lower errors at analysis time, at two times,on smoothed fields, and on time tendencies. Four months of existing forecasts from the Canadian Global Ensemble Prediction System wereanalyzed in a simulation context. We determined the extent with which pairs of members with smaller differences considering different typesof initial condition errors led to smaller differences in forecasts. At short forecast times, minimizing errors on initial values still provides thebest forecasts; for medium-range forecasts, also minimizing errors on initial tendencies provide additional value as both types of errors are
not 100% correlated. This suggests that, for best forecast results, we may want to adjust assimilation approaches to also minimize errors on
tendencies.

Thursday, 16 Sep 2021 at 15UTC - room BLUE

Global reanalysis II
The ERA5-Land Global land surface reanalysis
Munoz, Sabater Joaquin(1); Dutra, Emanuel(2); Agusti-Panareda, Anna(1); Albergel, Clément(3,4); Arduini, Gabriele(1); Balsamo,
Gianpaolo(1); Boussetta, Souhail(1); Choulga, Margarita(1); Harrigan, Shaun(1); Hersbach, Hans(1); Martens, Brecht(5); Piles, Maria(6);Gonzales, Miralles Diego(5); Rodriguez-Fernandez, Nemesio(7); Zsoter, Ervin(1); Buontempo, Carlo(1); Thepaut, Jean-Noel(1)(1)

European Centre for Medium-Range Weather Forecasts, (2)Instituto Português de Investigação do Mar e da Atmosfera, (3)European Centre forSpace Applications and Telecommunications, (4)Centre National de Recherches Météorologiques, (5)Hydro-Climate Extremes Laboratory, (6)Department of Thermodynamics, Faculty of Physics, (7)CESBIOECMWF has produced the ERA5-Land dataset, a dynamical downscaled version of the land component of the ERA5 global reanalysis. The production of this dataset has been motivated, primarily, to serve the land surface community, and other communities focused on land applications, requiring a multi-decadal consistent dataset at resolutions higher than those currently offered by atmospheric reanalysis. ERA5-Land isa unique dataset of its kind that provides a global scale description of the most important land variables through a single simulation driven bynear-surface atmospheric fields from ERA5, with thermodynamical orographic adjustment of temperature. The global projected horizontal
resolution is 9 km (around 4 times finer resolution than ERA5), matching the ECMWF TCo1279 operational grid, and therefore providing consistent input for Numerical Weather Prediction and climate studies involving land water resources, but also for accurate hydrological and agricultural modeling. Owing to its relatively low computational cost, forefront model developments can also be quickly tested before the production phase. ERA5-Land is currently available in the Copernicus Climate Data Store from 1981 to present, with 2-3 months delay with respect
to the real time. However, the synchronization with ERA5T mode will also make it possible to provide close to NRT updates. The production of
the back-extension from 1950 to 1980 is completed and is currently under verification before made it available. This presentation will provide
an overview of the ERA5-Land dataset and the main findings of a substantial evaluation exercise against in-situ data from different networks
around the world and global satellite-based reference datasets.

Preparations for assimilating rescued and reprocessed satellite sounding observations in the next generation
global atmospheric reanalysis at ECMWF - ERA6
Bell, William(1); Hersbach, Hans(1); Berrisford, Paul(1); Horanyi, Andras(1); Munoz, Sabater Joaquin(1); Nicolas, Julien(1); Radu,
Raluca(1); Schepers, Dinand(1); Simmons, Adrian(1); Soci, Cornel(1); Schulz, Joerg(2); John, Vĳu(2); Hanschmann, Timo(2); Poli, Paul(2);Prunet, Pascal(3); Klonecki, Andrzej(3); Standfuss, Carsten(3); Mittaz, Jon(4); Hall, Tom(4); Six, Bruno(5); Vidot, Jerome(6); Barbossa,
Silveira Bruna(6); Roquet, Pascale(6); Turner, Emma(7); Saunders, Roger(7)(1)

European Centre for Medium-Range Weather Forecasts, (2)European Organisation for the Exploitation of Meteorological Satellites, (3)Sté SPASCIA, (4)University of Reading, (5)University of Lille, (6)Météo France, (7)Met Office
An extension of the fifth generation atmospheric reanalysis at ECMWF, ERA5, back to 1950 has recently been completed. ERA5 makes use ofearly infrared sounding data from VTPR, carried on NOAA-2 through-5 from 1973-1979. Preparations are now underway for the next generation of reanalysis, ERA6, due to start in early 2024. These preparations aim to address some aspects, reviewed here, in the assimilation ofsounding data evident in ERA5, including: drifting biases for the advanced IR sounding data due to the handling of evolving CO2 concentrations; large inter-satellite biases in MSU data; and discontinuities in analysed stratospheric temperatures as a result of discontinuities andinter-satellite biases in SSU data. In addition, ERA6 will make use of several reprocessed radiance datasets produced by EUMETSAT as part of
the first (2015-2021) and second (2021-2027) phases of the EU's Copernicus Climate Change (C3S) programme. Plans currently include theassimilation of FCDRs for ATMS, MHS, MWHS-2, HIRS, SSM/T, SSMIS and Japanese geostationary satellite radiances. This element of C3S also,
under current plans, aims to produce FIDUCEO-type uncertainty analyses for MSU, AMSU-A and ATMS. ERA6 will also make use of several
recently rescued early (1970s) satellite datasets, including radiances from SI-1, SMMR, SSH, IRIS, SIRS, PMR, MRIR, NEMS, SCAMS, ESMR and
SCR. Preparations to date have included the generation of new RTTOV coefficients and evaluation of the quality of these radiances relative to
ERA5 using analysis departures computed off-line. These evaluations have uncovered several biases in these datasets which are under investigation.

The NASA GMAO retrospective analysis for the 21st Century GEOS-R21C
El, Akkraoui Amal(1,2); Takacs, Lawrence(1,2); Wargan, Krzysztof(1,2); Sienkiewicz, Meta(1,2); Chattopadhyay, Mohar(1,2); Jin,
Jianjun(1,2); Bloecker, Christine(1,2); Mccarty, Will(2); Gelaro, Ron(2); Pawson, Steven(2)(1)

Science Systems and Applications, Inc. [Lanham], (2)NASA Global Modeling and Assimilation Office

Building on the success of MERRA, and its successor MERRA-2 (released in 2009 and 2015 respectively), the GMAO continues its incremental
effort towards a decadal goal of an Integrated Earth System retrospective analysis, MERRA-3 (~2025), coupling atmosphere, ocean, chemistry,land, and ice. With aspects of the coupled Goddard Earth Observing System (GEOS) still under development, an intermediate reanalysis featuring recent advances in the GEOS atmospheric component is planned as a stepping-stone towards MERRA-3. The GEOS-5 Retrospective analysis
for the 21st Century, GEOS-R21C, will be a hybrid 4D-EnVar atmospheric reanalysis at 25km, covering the period 2000-onwards, and featuring
the NASA's EOS and post-EOS satellite observations. MERRA-2 by contrast covers 1980-present and uses 3DVar at 50km. Other planned features in GEOS-R21C include All-sky radiances, new/re-processed data, and new radiance and aircraft bias correction strategies. Efforts to
reduce field discontinuities involve alternative treatment of ozone data and an adjustment to the frequency of the Incremental Analysis Update(IAU) in the initialization process. The output fields from GEOS-R21C are planned to drive an off-line chemistry reanalysis (GEOS-R21C-Chem)
and a high-resolution downscaling for the polar regions (PolarMERRA). GEOS-R21C is currently in the pre-production stages. This talk presents
the GMAO strategy for upcoming reanalyses and focuses on GEOS-R21C configuration details and preliminary results from prototype-R21C
with comparisons with MERRA-2 and ERA5. Along with an overall review of the new features, a reassessment of MERRA-2 strategy for dry-massconservation is presented as well as a new diagnostic tool for GEOS-R21C.

The CMCC Global Ocean Reanalysis System (C-GLORS): a series of consolidated eddy-permitting ocean reanalyses
Banerjee, Deep Sankar(1); Cipollone, Andrea(1); Iovino, Dorotea(1); Masina, Simona(1)(1)

Centro Euro-Mediterraneo per i Cambiamenti Climatici [Bologna]

In the last decade, CMCC has been dedicating a constant effort in the production of up-to-date global ocean reanalyses that can be suited fordiverse purposes, from climate studies to initialization of forecasting systems and downscaling applications. The robustness of the C-GLORS
series on key oceanic variables has been confirmed by the participation to several reanalysis inter-comparison projects through these years
and is continuously monitored being part of the GREP ensemble product in the CMEMS catalogue. The last consolidated version is the CGLORSv8 and covers the altimetry era 1993-present. While the spatial resolution is similar to the previous v7, the new version employs thelatest hourly atmospheric Reanalysis ERA5 as boundary forcing. The optimization of the sea-ice ocean parametrizations and an improved
version of SST and SSS surface nudging scheme largely reduced the underestimation of sea ice extension and volume that affected the v7. A
cold temperature bias affecting the water column at mid-latitude (-0.1°C) is also removed. The assimilation of SLA data has been revised with
the inclusion of a new unbiased scheme, thus reducing the error with respect to SLA observations. The new configuration shows a slightincrease in the heat and volume transport at mid-latitudes. An in-depth evaluation of skills of the new Reanalysis is currently underway.

Friday, 17 Sep 2021 at 11UTC - room RED

Convective scale DA I

Representation of model error in convective scale data assimilation
Janjic, Tĳana(1); Zeng, Yuefei(2); Yuxuan, Feng(2,3); Ruckstuhl, Yvonne(1); De, Lozar Alberto(4); Blahak, Ulrich(5); Seifert, Axel(4)(1)

Ludwig Maximilians University Munich, (2)Meteorologisches Institut, Ludwig-Maximilians-Universität, (3)Key Laboratory of Meteorological Disaster of Ministry of Education/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University ofInformation Science & Technology, (4)Deutscher Wetterdienst [Offenbach], (5)DWD
The convection-permitting numerical weather prediction models are able resolve highly nonlinear dynamics and physics. However, they often
fail to capture processes that trigger convection or miss intensity and location of convective storms. Due to the very short-scale and stochastic
properties of convection, as well as boundary condition errors, it is extremely difficult to parameterize the model error for convection permitting models. We investigated a variety of methods for treating the model error in ensemble based convective scale data assimilation. The
experiments are done using the convection-permitting models and data assimilation system KENDA of German weather service. Conventionaland radar observations are assimilated hourly by the LETKF. The model error due to unresolved scales and processes, is represented withsamples coming from the difference between high-resolution model run and low-resolution experiment, through warm bubble initialization,
stochastic boundary layer perturbations, perturbation in microphysics variables as well as through parameter estimation. It is shown that theadditive noise approach, contributes greatly to ensemble spread, and considerably improves the skill of precipitation forecasts up to six hours.
Combining the additive noise with time evolving estimates of model error, for example, either the stochastic boundary layer perturbations,parameter estimation or the warm bubble technique even further improves the skill of the precipitation forecasts.

Assimilation of Nowcast Objects in the Regional Forecast Model ICON-LAM
Neef, Lisa(1); Potthast, Roland(2,3); Welzbacher, Christian(2); Blahak, Ulrich(2)(1)

Deutscher Wetterdienst [Offenbach], (2)Deutscher Wetterdienst [Offenbach], (3)University of Reading

Data assimilation methods are designed to update gridded model variables based on their statistical correlation to observations at other locations. This framework, while reliable and well-established, does not entirely take into account the coherent nature of convective systems but
rather relies on modeled correlations to account for the fact that observations at neighboring gridpoints may be part of a coherent system. As
assimilation updates are becoming more frequent (from several hours to hourly or less), data assimilation is starting to enter the world of
nowcasts, i.e. rapidly updated short-term forecasts based on recent radar composites. On these time scales is makes sense to assimiliate notjust gridded fields of observed precipitation, but also information about convective systems as a whole, e.g. the area of a convective cell or thecell-based Vertically Integrated Liquid Water. At the German Weather Service, cell attributes are computed from radar composites using the
KONRAD3D cell detection and gridding system. We have implemented a method for assimilating these cell attributes into to the ICON-LAM
regional forecast model, by generating model-equivalent radar fields that are then fed into the cell-detection and subsequently counted up on
a model grid. This talk will explain the approach and show some first experiments that illustrate the extra information that can be obtained byconsidering objects.

Comparison of 4D-EnVAR and 4D-LETKF when running with 1000 ensemble members
Duc, Le(1,2); Kawabata, Takuya(1); Saito, Kazuo(2,1); Oizumi, Tsutao(2,1)(1)

Meteorological Research Institute, (2)Japan Meteorological Business Support Center

The typical number of ensemble members in ensemble data assimilation is of the order of 100 in almost all forecast centers. The increase of
the number of ensemble members in one order from 100 to 1000 is expected to better produce ensemble analyses, since uncertainties aremuch better represented with 1000 members. In this study, three 1000-member ensemble data assimilation schemes are compared using thesame observation dataset as the operational meso-scale data assimilation system of the Japan Meteorological Agency: (1) the hybrid fourdimensional variational-ensemble assimilation 4D-EnVAR, (2) the four-dimensional local ensemble transform Kalman filter 4D-LETKF, and thefour-dimensional local diagonal ensemble transform Kalman filter 4D-LDETKF. Vertical localization is turned off in all schemes to retain significantly vertical correlations, which also helps to reduce computational costs considerably. It is found that 4D-EnVAR produces better analyses and therefore better forecasts than the two ETKF variants, which can be attributed to the use of climatological error covariances besideensemble error covariances in 4D-EnVAR.

Friday, 17 Sep 2021 at 11UTC - room GREEN

Deep learning in DA I
Model error correction with data assimilation and machine learning
Farchi, Alban(1); Bocquet, Marc(2); Bonavita, Massimo(3); Laloyaux, Patrick(3)(1)

CEREA, (2)CEREA, (3)ECMWF

The idea of using machine learning (ML) methods to reconstruct the dynamics of a system is the topic of recent studies in the geosciences,in which the key output is a surrogate model meant to emulate the dynamical model. In order to treat sparse and noisy observations in arigorous way, ML can be combined to data assimilation (DA). This yields a class of iterative methods in which, at each iteration a DA step
assimilates the observations, and alternates with a ML step to learn the underlying dynamics of the DA analysis. This framework can be used
to correct the error of an existent, physical model. The resulting surrogate model is hybrid, with a physical and a statistical part. In practice,
the correction can be added as an integrated term (i.e. in the model resolvent) or directly inside the tendencies of the physical model. The
resolvent correction is easy to implement but is not suited for short-term predictions. The tendency correction is more technical, in particular
it requires the adjoint of the physical model, but also more flexible and can be used for any prediction. In this presentation, we start by a proof
of concept for the use of joint DA and ML tools to correct model error. We use the resolvent correction with simple neural networks to correct
the error of a two-dimensional, two layer quasi-geostrophic model. We then illustrate the difference between the resolvent and the tendency
correction with the two-scale Lorenz system. Finally, we show that the tendency correction opens the possibility to make online model error
correction, i.e. improving the model progressively as new observations become available. We compare online and offline learning using thesame twin experiment with the two-scale Lorenz system.

high-resolution Ensemble Kalman Filter with a low-resolution model using a machine learning super-resolution
approach
Barthélémy, Sébastien(1); Brajard, Julien(2); Bertino, Laurent(2); Counillon, Francois(2)(1)

University of Bergen, (2)Nansen Environmental and Remote Sensing Center

Going from low- to high-resolution models is an efficient way to improve the data assimilation (DA) process in three ways: it makes better useof high-resolution observations, it represents more accurately the small scale features of the dynamics and it provides a high-resolution fieldthat can further be used as an initial condition of a forecast. Of course, the pitfall of such an approach is the cost of computing a forecast witha high-resolution numerical model. This drawback is even more acute when using an ensemble DA approach, such as the ensemble Kalmanfilter (EnKF), for which an ensemble of forecasts is to be issued by the numerical model. In our approach, we propose to use a cheap lowresolution model to provide the forecast while still performing the assimilation step in a high-resolution space. The algorithm is based on amachine learning approach: from a low-resolution forecast, a neural network (NN) emulates a high-resolution field that can then be used toassimilate high-resolution observations. This NN super-resolution operator is trained on one high-resolution simulation. This new DA approach denoted "Super-resolution data assimilation" (SRDA), is built on an EnKF algorithm. We applied SRDA to a quasi-geostrophic model
representing simplified ocean dynamics of the surface layer, with a low-resolution up to four times smaller than the reference high-resolution.
We show that this approach outperforms the standard low-resolution DA approach and the SRDA method using standard interpolation insteadof a neural network as a super-resolution operator. For the reduced cost of a low-resolution model, SRDA provides a high-resolution field withan error close to that of the field that would be obtained using a high-resolution model.

Deep-Learning
Autoencoders

Augmented

Data

Assimilation:

Reconstructing

Missing

Information

With

Convolutional

Wang, Yueya(1); Shi, Xiaoming(2,1); Fung, Jimmy(3,1)(1)

Division of Environment and Sustainability [HKUST], (2)Department of Civil and Environmental Engineering [HKUST], (3)Department of Mathematics [HKUST]
Remote sensing data play a critical role in improving numerical weather prediction (NWP), especially for regions with no in situ observations
available. However, Data voids often exist in physical space (e.g., no-precipitation region for radar reflectivity) which undermines the accuracyof initial conditions from data assimilation, negatively impacting NWP. Here, we use the barotropic vorticity equation (BVE) to demonstrate thegreat potential of deep learning (DL) in augmenting data assimilation, by reconstructing spatially complete observation fields from incomplete
observations. The training data set for deep learning is a long-term BVE simulation on a coarse resolution (T63). By training a convolutional
autoencoder (CAE), we obtained a deep-learning approximation of a 'reconstruction operator,' which maps spatially incomplete observation toreconstruction with full spatial coverage. The reconstruction operator was tested with the streamfunction generated from a high-resolution(T85) simulation and exhibited satisfactory performance. We further evaluated the impact of DL reconstruction on assimilation and forecastwith four groups of observations from the T85 benchmark simulation, including ones with complete coverage (FullObs), incomplete observation (PartObs), DL reconstruction (RecObs), and a combination of PartObs and RecObs (MixObs). The data assimilation using RecObs andMixObs generates significantly more accurate initial conditions than those using PartObs, close to the accuracy when using FullObs. Predictions
based on the initial conditions from the DL-augmented data assimilation are also considerably better than the predictions relying on digesting
spatially incomplete observations only.

Friday, 17 Sep 2021 at 11UTC - room BLUE

Regional reanalysis III

A new temporally flow-dependent EDA to estimate B-matrix in Copernicus European Regional Reanalysis
El-Said, Adam(1); Brousseau, Pierre(1); Ridal, Martin(2); Randriamampianina, Roger(3)(1)

Meteo-France, (2)Swedish Meteorological and Hydrological Institute, (3)Norwegian Meteorological InstituteA new Ensemble of Data Assimilations (EDA) framework to estimate the background error covariances (B-matrix) has been developed andtailored to the new Copernicus European Regional Re-Analysis (CERRA). CERRA is produced by the HARMONIE NWP system with ALADIN
physics, 3DVAR to analyse upper-air fields and Optimal Interpolation (MESCAN) to analyse surface fields, cycling every 3 hours. CERRA spans
40 years and has 5.5km resolution. Background errors are assumed isotropic, homogeneous and non-separable, meaning isometric horizontal
breadth and vertical depth in 3D space. Linearised geostrophic and hydrostatic balances are incorporated as multivariate relationships, coupling vorticity and geopotential extended to wind, mass and specific humidity fields via f-plane approximation. We augment the estimation of
background errors to be temporally dynamic, accounting for `errors of the day', seasonal and weather regime variation (CERRA-EDA). CERRAEDA has 10 members cycled 6-hourly. CERRA-EDA comprises 2 components. The first component is 'seasonal' (5.5km), with respective summer and winter periods. A new augmentation governs the uptake of summer and winter differences at a given time of year. The second component is 'daily' (11km). B-matrix computation occurs every 2 days, with a fixed split of 80-20% seasonal-daily. Temporal flow dependence
enables a degree of real-time tethering to meteorological situations as they arise. We show how the EDA accounts for weather regime change
by case study. We also show the impact of varying EDA-component weighting on the statistics, and analysis and forecast quality, with improvements worth considering.

A 20-year High resolution Red Sea Reanalysis using a Hybrid ensemble data assimilation
Sanikommu, Siva Reddy(1); Langodan, Sabique(1); Zhan, Peng(1); Hoteit, Ibrahim(1)(1)

King Abdullah University of Science and Technology

A 20-year (2001-2020) eddy resolving reanalysis is generated for the Red Sea by assimilating satellite and in situ observations of sea surface
temperature, sea surface height, ocean temperature and salinity profiles into a 4 km Massachusetts Institute of Technology general circulation
model (MITgcm). The assimilation is performed using a Hybrid system combining a monthly varying quasi-static ensemble and a flow-dependent ensemble from the ensemble adjustment Kalman filter (EAKF) of the Data Assimilation Research Testbed (DART). The flow-dependent
ensemble integrates 50 different MITgcms to account for uncertainties in initial conditions, model physics, and atmospheric forcing. The system is forced with 50-member atmospheric ensemble from the European Center for Medium Range Weather Forecast (ECMWF) available at 50
km every 6 hours, and open boundary conditions from the Mercator ocean reanalysis. The generated reanalysis is compared against available
global ocean reanalyses products to show its relevance. This high resolution long-term reanalysis of the Red Sea is important not only for
exploring the circulation of this historically sparsely observed basin, but to also deepen our understanding of its biological processes.

Evaluation of European anthropogenic trace gas and aerosol emissions using 4D-var: First results of a full-year
re-analysis for 2016
Franke, Philipp(1); Lange, Anne Caroline(1); Elbern, Hendrik(2)(1)

Forschungszentrum Jülich, (2)Rhenish Institute for Environmental ResearchIn densely populated areas as in many parts of Central Europe, anthropogenic emissions are the key driver of air pollution. Emission inventories
use yearly, reported emission data to provide estimates of spatially distributed emission maps, which include large uncertainties for several
reasons: statistical data as proxy for spatial emission distribution, fixed time profiles for temporal distribution, lack of resilience to regionalsocietal or meteorological effects (e. g. change of road transport in dependence on weather conditions). In order to analyze the emission
inventory for Europe and for Germany in particular, the four dimensional variational data assimilation system of the EURopean Air pollution
Dispersion – Inverse Model (EURAD-IM) is used to validate the emission data of CO, SOx, NOx, NMVOC, NH3, PM10, and PM2.5 against groundbased and space-borne observations. The analysis has been performed for the year 2016 on 15 km (Europe) and 5 km (Central Europe) horizontal resolution. The results provide insights into the spatial and temporal distribution of anthropogenic emission corrections across Europe.
Areas of large deviations between emission-driven model data and observations are identified and reasons for the large emission correction
factors are discussed. Further, the results will be discussed in view of implications for emission inventories.

Friday, 17 Sep 2021 at 12UTC - room RED

Deep learning in DA II
Combining Data Assimilation and Machine Learning to Estimate Parameters of a Convective-Scale Model
Legler, Stefanie(1); Janjic, Pfander Tĳana(1)(1)

Ludwig Maximilians University of Munich

Parametrization of microphysics, as well as parametrization of processes in the surface and boundary layers, typically contain several tunable
parameters that are only crudely known, leading to model errors in the representation of clouds in convection-permitting numerical weather
prediction models. Traditionally, the numerical values of these model parameters are chosen by manual model tuning. More objectively, they
can be estimated from observations by the augmented state approach during the data assimilation. Alternatively, in this work, we look at the
problem of parameter estimation through an AI lens by training two types of artificial neural networks to estimate several parameters of the
one-dimensional modified shallow-water model as a function of the observations or analysis of the atmospheric state. We show that Bayesianneural networks and Bayesian approximations of point estimate neural networks are able to estimate model parameters and their relevant
statistics. The estimation of parameters combined with data assimilation for the state decrease the initial state errors even when assimilating
sparse and noisy observations. The sensitivity to the number of ensemble members, observation coverage, and neural network size is shown.
Additionally, we use layer-wise relevance propagation to gain insight into how the neural networks are learning and discover that they naturally
select only a few grid points that are subject to strong winds and rain to make their predictions.

Surrogate Tree and Model Forest Extensions to the Multifidelity Ensemble Kalman Filter
Popov, Andrey A(1); Sandu, Adrian(1)(1)

Department of Computer Science at Virginia Tech

Recently, the idea of using a hierarchy of models for sequential data assimilation has been gaining traction. We present an extension of thisidea to surrogate trees (and model forests thereof). We present a formal theory of such methods, through a rigorous extension of the theory

of linear control variates, and present a hypothetical framework for non-linear coupling.We showcase this methodology on the multifidelity
ensemble Kalman filter with surrogate derived from data-driven methods such as POD-Galerkin, and neural-network based autoencoders. We
thus aim to provide a convincing argument that such methods can be applied not only on toy models but for operational data assimilation as
well.

Data-Driven Methods for Weather Forecast
Acevedo, García Felipe José(1); Niño, Ruiz Elias David(1)(1)

Universidad del Norte, Barranquilla

Numerical weather forecasts are of extreme importance in different aspects of life, particularly in scenarios wherein human lives can be compromised (i.e., forecasts of storms, floods, hurricanes, and tsunamis). Numerical models are commonly employed to mimic the behavior of
actual system dynamics, for instance, the ocean and the atmosphere. Since numerical models are computationally demanding, high-performance computing is necessary to produce forecasts within reasonable computational times, especially for high-resolution grids. We propose
efficient and practical data-driven methods for weather forecasts. We exploit the information brought by historical weather datasets to build
machine-learning-based models. For instance, the National-Centers-for-Environmental-Prediction Department-of-Energy (NCEP-DOE) Reanalysis II is a data set that holds meteorological information since 1979 onto global grids at varying resolutions. These models are employed
to produce numerical forecasts, which can be improved by injecting additional data via data assimilation. Our approaches' general idea is asfollows: given a set of time snapshots of some dynamical system, we group the data by time across multiple days. These groups are employed
to build first-order Markovian models that reproduce dynamics from time to time. Our numerical models' precision can be improved via sequential data assimilation. Experimental tests are performed by using the NCEP-DOE Reanalysis II dataset. The results reveal that numerical
forecasts can be obtained within reasonable error magnitudes in the L_2 norm sense. Even more, observations can improve forecasts by orderof magnitudes, in some cases.

Learning UFS State-Dependent Systematic Errors from the Analysis Increments
Chen, Tse-Chun(1,2); Penny, Stephen G.(1,2); Whitaker, Jeffrey(1); Frolov, Sergey(1,2); Pincus, Robert(1,2); Tulich, Stefan(1,2)(1)

Physical Sciences Laboratory, NOAA ESRL, (2)Cooperative Institute for Research in Environmental Sciences

Systematic errors in the state-of-the-art NWP models degrade the accuracy of forecasts made with these models. Past studies showed that arunning average of analysis increments from data assimilation can reveal part of these systematic errors in seasonal and diurnal time scales.
Applying corrections based on these simple estimates improves the model forecast skill. However, in addition to stationary seasonal anddiurnal time scales, the analysis increments also contain information about state-dependent errors. Using the GFSv16b parallel runs, a varietyof Neural Networks (NN) are trained to predict the corresponding increment from a forecast, with the goal to capture the underlying statedependent systematic model errors. We will show that in addition to the obvious diurnal and seasonal signals in the analysis increments (accounting for 30% of the total variation) the NNs can predict on average more than 50% of the signal in the increments at any given point, using
only the model forecast as an input. Adding both neighboring points from the forecast and background fields from the previous DA cycles
further improves the skill of the NNs in predicting the analysis increments. We will also demonstrate a sensitivity measure of the NN prediction
to the input information.

Friday, 17 Sep 2021 at 14UTC - room RED

Convective scale DA II
Big Data Assimilation: Real-time Demonstration Experiments of 30-second-update Forecasting in Tokyo in 2020
and 2021
Miyoshi, Takemasa(1,2,3,4); Honda, Takumi(1); Amemiya, Arata(1); Otsuka, Shigenori(1); Maejima, Yasumitsu(1); Taylor, James(1);Tomita, Hirofumi(1); Nishizawa, Seiya(1); Sueki, Kenta(1); Yamaura, Tsuyoshi(1); Ishikawa, Yutaka(5); Satoh, Shinsuke(6); Ushio,
Tomoo(7); Koike, Kana(8); Hoshi, Erika(8); Nakajima, Kengo(9)(1)

RIKEN Center for Computational Science [Kobe], (2)RIKEN Cluster for Pioneering Research, (3)RIKEN Interdisciplinary Theoretical and Mathematical Sciences Program, (4)Japan Agency for Marine-Earth Science and Technology, (5)National Institute of Informatics, (6)National Institute of Information and Communications Technology [Tokyo, Japan], (7)Osaka University, (8)MTI, Ltd., (9)Information Technology Center, The University ofTokyo
The Japan's Big Data Assimilation (BDA) project started in October 2013 and ended its 5.5-year period in March 2019. Here, we developed anovel numerical weather prediction (NWP) system at 100-m resolution updated every 30 seconds for precise prediction of individual convective
clouds. This system was designed to fully take advantage of the phased array weather radar (PAWR) which observes reflectivity and Doppler
velocity at 30-second frequency for 100 elevation angles at 100-m range resolution. By the end of the 5.5-year project period, we achieved
less than 30-second computational time using the Japan's flagship K computer for past cases with all input data such as boundary conditions
and observation data being ready to use. The direct follow-on project started in April 2019. We continued the development to achieve real-time
operations of this novel 30-second-update NWP system for demonstration at the time of the Tokyo 2020 Olympic and Paralympic games. The
games were postponed, but the project achieved successful real-time demonstration of the 30-second-update NWP system at 500-m resolution during July 31 and August 7, 2020 using a powerful supercomputer called Oakforest-PACS operated jointly by the Tsukuba University andthe University of Tokyo. This presentation will summarize the real-time demonstration in 2020 and early results from the planned 2021 experiment during the Tokyo 2021 games using the world's leading supercomputer Fugaku.

Evaluating errors due to unresolved scales in convection permitting numerical weather prediction
Waller, Joanne(1); Dance, Sarah(2); Lean, Humphrey(1)(1)

Met Office, (2)University of Reading

In numerical weather prediction (NWP), observations and models are quantitatively compared for the purposes of data assimilation and forecast
verification. The spatial and temporal scales represented by the observation and model may differ and this results in a scale mis-match error
which may be biased and correlated. Here we investigate the structure of representation error in convection-permitting NWP models for four
meteorological variables: temperature, specific humidity, zonal and meridional wind. We use high resolution data from the experimental Met
Office London Model (approximately 300 m grid-length) to simulate perfect observations and lower resolution model data. The scale mismatch error and its bias, variance and correlation are calculated from the perfect observation and low-resolution model equivalents. Our newresults show that the scale mis-match bias is significant in the boundary layer for temperature and specific humidity, whereas the variance issignificant in the boundary layer for all analysed variables. Furthermore, they are shown to be related to the mismatch in the high- and lowresolution orography. Contrary to previous studies using low-resolution, (km-scale) data, horizontal correlations are shown to be insignificant.
However, all variables exhibit considerable vertical representation error correlation throughout the boundary layer; for temperature a significant positive vertical correlation persists for all model levels in the troposphere. Our results suggest that significant biases and vertical correlations exist that should be accounted for to give maximum observation impact in data assimilation and for fairness in model verification andvalidation.

Localization on convective scales: What can we learn from a 1000-member ensemble?
Necker, Tobias(1,2); Hinger, David(1); Honda, Takumi(2); Weissmann, Martin(1)(1)

Universitaet Wien, (2)RIKEN Center for Computational Science [Kobe]

Finding a proper localization is crucial for the success of ensemble data assimilation systems. Localization mitigates sampling errors anddamps spurious correlations, which arise when modeling ensemble-based background error covariances using small ensembles. However,choosing an optimal localization is very challenging, especially for non-local satellite observations. Our goal is to improve localization techniques on convective scales by applying a novel convective-scale 1000-member ensemble simulation. The data set covers several forecasts ina high-impact weather period in summer 2016 (Necker et al. 2020a & 2020b). Our latest study analyzes correlations and optimal localizationin the vertical. We focus on 40-member subsamples and assume the 1000-member ensemble as truth. We present estimated optimal localization length scales based on the often-applied Gaspari-Cohn tapering function. Besides, we discuss optimal localization functions for different variables and observations in model and observation space. That includes the open question on how to optimally localize visible andinfrared satellite observations. A distance-based localization can be suboptimal for non-local satellite observations considering domain localization. A possible solution offer statistical sampling error correction approaches.

Friday, 17 Sep 2021 at 15UTC - room RED

Convective scale DA III
Further development of simultaneous multiscale data assimilation in EnVar to improve convective scale weather
prediction
Wang, Yongming(1); Wang, Xuguang(2)(1)

School of Meteorology, University of Oklahoma, (2)School of Meteorology, University of Oklahoma

Accurate prediction of convective scale weather requires accurate analysis of the storm as well as its larger-scale environment. In this study,a simultaneous multiscale data assimilation (MDA) approach is further developed and implemented in the EnVar. This approach allows all
resolved scales including both the storm and its embedded environment to be updated simultaneously with all available observations. Thisapproach enables both model space scale-dependent localization and variable-dependent localization. Three experiments are designed toevaluate the impact of simultaneous MDA on radar observation assimilation. Experiments are performed on the 8 May 2003 OKC tornadic

supercell storm with assimilation of both radar reflectivity and radial velocity. The simultaneous MDA outperforms the experiment with scale
unaware uniform localization (UNI) in increasing the coverage of low-level reflectivity forecast, better capturing of the hook-echo structure,and enhancing the midlevel updraft. It is found that the improved forecasts in simultaneous MDA result from the enhanced storm ambient
convergence. This result states that although only the radar observations are assimilated, the simultaneous MDA is capable of updating not
only the storm but also the storm environment. Additionally applying the state variable-dependent localization favors to reduce the spurious
reflectivity and further enhance the midlevel updraft.

Experiments with a continuously cycling 3-km ensemble Kalman filter over the entire conterminous United States
for convection-allowing ensemble initialization
Schwartz, Craig(1); Romine, Glen(1); Dowell, David(2)(1)

National Center for Atmospheric Research [Boulder], (2)NOAA Earth System Research Laboratory

Using the Weather Research and Forecasting model, 80-member ensemble Kalman filter (EnKF) analyses with 3-km horizontal grid spacing
were produced over the entire conterminous United States (CONUS) for 4 weeks using 1-h continuous cycling. For comparison, similarly-configured 15-km EnKF analyses were also produced. The EnKF analyses initialized 36-h, 3-km, 10-member convection-allowing ensemble forecasts. Additionally, forecasts were initialized from operational Global Ensemble Forecast System (GEFS) initial conditions (ICs) and experimental“blended” ICs produced by combining large scales from GEFS ICs with small scales from EnKF analyses using a low-pass filter. Precipitation
forecasts initialized from 3-km EnKF analyses were better than those initialized from downscaled GEFS and 15-km EnKF ICs through 12–18
and 6–12 h, respectively. Conversely, after 18 h, GEFS-initialized precipitation forecasts were better than EnKF-initialized precipitation forecasts. Blended 3-km ICs reflected the respective strengths of both GEFS and high-resolution EnKF ICs and yielded the best overall performance:
blended 3-km ICs led to short-term forecasts with similar or better skill and reliability than those initialized from unblended 3-km EnKF
analyses and 18–36-h forecasts possessing comparable quality as GEFS-initialized forecasts. In addition to describing these results, this presentation will discuss challenges that were overcome to produce the 3-km EnKF analyses; this work likely represents the first time a convection-allowing EnKF has been continuously cycled over a region as large as the entire CONUS. Furthermore, sensitivity studies regarding assimilation of radar observations will be briefly described.

Reduced non-Gaussianity by 30-second rapid update in convective-scale numerical weather prediction
Ruiz, Juan(1,2); Lien, Guo-Yuan(3); Kondo, Keiichi(4); Otsuka, Shigenori(5); Miyoshi, Takemasa(6,7,8,9,10)(1)Consejo Nacional de Investigaciones Científicas y Técnicas, (2)University of Buenos Aires [Argentina], (3)Central Weather Bureau, (4)Meteorological Research Institute, (5)RIKEN Center for Computational Science [Kobe], (6)RIKEN Center for Computational Science [Kobe], (7)RIKEN Cluster for
Pioneering Research, (8)RIKEN Interdisciplinary Theoretical and Mathematical Sciences Program, (9)University of Maryland, College Park, (10)Japan
Agency for Marine-Earth Science and Technology
Non-Gaussian forecast error is a challenge for ensemble-based data assimilation (DA), particularly for more nonlinear convective dynamics.
This study investigates the degree of non-Gaussianity of forecast error distributions at 1-km resolution using a 1000-member ensemble
Kalman filter, and how it is affected by the DA update frequency and observation number. Regional numerical weather prediction experiments
are performed with the SCALE (Scalable Computing for Advanced Library and Environment) model and the LETKF (Local Ensemble Transform
Kalman Filter) assimilating every-30-second phased array radar observations. The results show that non-Gaussianity develops rapidly withinconvective clouds and is sensitive to the DA frequency and the number of assimilated observations. The non-Gaussianity is reduced by up to
40% when the assimilation window is shortened from 5 minutes to 30 seconds, particularly for vertical velocity and radar reflectivity. Also,
short-range forecasts confirm the beneficial impact of reducing the length of the assimilation window to assimilate phased-array radar observations.

Large error correction in storms at convective scales by “grafting” look-alike modelled storms from otherensemble backgrounds
Sodhi, Jagdeep Singh(1); Fabry, Frederic(1)(1)

Department of Atmospheric and Oceanic Sciences, McGill University

Traditional assimilation approaches assume the existence of a linear relationship between field errors, a condition typically found where errors
are small. This, however, makes it difficult to correct large errors that grow highly non-linearly, such as in storms at convective scales. Here amethod we call “grafting” is proposed. In areas where observation errors are high, the ensemble members are searched for a storm that looks
like the observations (such as radar reflectivity). Upon a successful match, that near-twin storm, with its adjacent environment is transplanted
in other members with high observation errors, hence the name grafting. The hypothesis is that a storm that looks similar to the observed
storm is likely to have similar dynamics. The hope is that large errors are reduced at the location of the storm. The grafting method sharessome similarity with bogus DA that has been historically used to improve larger-scale hurricane forecasts. We experimented with grafting in
an identical-twin setup using WRF at convective scales. Early results show that the storm placed in a member with this method lasts longer andevolves more closely to the real storm than one where the analysis is generated via EnKF. This method is only applied in the region (i.e., storm)
where the errors are large, while in the rest of the domain the observations are assimilated with EnKF. The proposed method is complimentary
to other traditional assimilation methods and benefits from the use of information from the available members. Current and future work includes minimizing model shock after grafting and generating perturbations for the grafted storm to increase ensemble spread.

Friday, 17 Sep 2021 at 15UTC - room GREEN

Observation Impact II
Implementation of Ensemble Forecast Sensitivity to Observations (EFSO) on a operational-like CFSv2 model andmodifications for reanalysis
Chang, Chu-Chun(1); Chen, Tse-Chun(2); Kalnay, Eugenia(1)(1)

Department of Atmospheric and Oceanic Science, University of Maryland, College Park, (2)Physical Sciences Laboratory, NOAA ESRLThe EFSO (Kalnay et al., 2012) is a powerful tool to identify the beneficial or detrimental impact of every observation used in ensemble-based
data assimilation (DA). Encouraged by the exciting success in the EFSO/PQC application on the operational GFS (Ota et al., 2013, Chen and
Kalnay, 2020), we implemented the EFSO, for the first time, on the operational-like coupled system CFSv2-LETKF (Sluka et al., 2018). With theimplementation of EFSO on the ocean components, the scientific questions we investigate are: (1) Does the EFSO work for the coupled DA
system with real observations? (2) Ocean has a significantly longer timescale which is associated with depth. How long should the lead time beto capture meaningful signals? (3) How do the different characteristics of ocean observations (i.e., distribution, density, and platforms) affectthe observation impacts? This study aims to provide more insights into how to optimize the use of observations and improve the forecasts andanalysis within a coupled system. A series of experiments are conducted with the weakly coupled assimilation of the preBUFR data for atmosphere and the ocean profiles and the NOAA Level 4 SST for the ocean. Preliminary result shows that the EFSO can estimate the cross-components (i.e., atmosphere-ocean) observation impacts. We found that assimilating the sea temperature and the salinity observations provides
significant beneficial effects on the ocean analysis, especially for the regions with large SST variability. Moreover, EFSO captures the significant
beneficial impacts of SST, which is associated with tropical cyclones. This result gives a piece of clear evidence on how the SST WCDA canimprove the ocean components perturbed by air-sea interactions.

Impact of Losing Aqua and Legacy POES and of Gaining Radio-Occultation Observations
Todling, Ricardo(1,2); Mccarty, William(1)(1)

NASA Goddard Space Flight Center, (2)Global Modeling and Assimilation OfficeThis work summarizes the findings of a GMAO Observing System Simulation (OSE) study to evaluate the impact of Aqua leaving the A-Trainand the termination of access to the Legacy Polar Operational Environmental Satellites (POES) for NWP applications. The summary also provides
a brief assessment on the potential for new data sources to compensate for the loss of Aqua-POES; a case study is developed here that looks
at the impact of adding COSMIC-2 to the main observation denial experiment. Overall, the impact of losing the observations from Aqua-POES
is found to be large and particularly detrimental to forecasts, especially in the range of a few hours to about three days. Introduction of COSMIC-2 is seen to largely compensate for the loss of Aqua-POES in the Tropics, but the Extratropics are found not to recover to the level of
performance of the control experiment. Ultimately, results from the GMAO OSEs suggest that loss of Aqua-POES would come to considerable
detriment to the quality of its NWP system. In the hopes of trying to regain lost skills in the Extratropics, this work looks further into studying
the impact of commercial RO being added to the mix. This presentation will show the highlights of this investigation.

Assessment of observation impact on the low troposphere in the GMAO GEOS system
Zhu, Yanqiu(1); Todling, Ricardo(1); Arnold, Nathan(1); Errico, Ronald(2); Chattopadhyay, Mohar(3); Conaty, Austin(3); Holdaway,
Dan(4); Mccarty, Will(1)(1)

Global Modeling and Assimilation Office, NASA Goddard Space Flight Center, (2)USRA/GMAO, (3)SSAI/GMAO, (4)UCAR/GMAO

The planetary boundary layer (PBL) is an important interface between the earth and its atmosphere, core to understanding the flux balancesacross the earth system components. However, due to various limitations in the existing observations and their usages, no single observing
system is able to provide adequate information to characterize the PBL structure, therefore advanced data assimilation systems play a criticalrole by combining a wide range of observations with model physics in a consistent and coherent manner. The GMAO GEOS hybrid EnsembleVariational (EnVar) data assimilation system assimilates about 4.5 million observations at each analysis cycle. The purpose of this study is toassess the impacts from various observation types on the low troposphere and constraining the PBL in terms of analysis and short-term andmedium-range forecasts. A set of Observing System Experiments (OSE) is performed, where different observation types including microwave
and infrared radiances and conventional observations are removed respectively, to evaluate the roles of different observation types. To complement the OSE assessment, Forecast Sensitivity to Observation diagnostics are performed. In this study, the dissipative aspect of the model
PBL schemes is also discussed.

Impact of Dropsondes from the Atmospheric River (AR) Reconnaissance Program on Forecast Skill of ARs and theSatellite Radiance Assimilation
Zheng, Minghua(1); Delle, Monache Luca(1); Ralph, F. Martin(1); Tallapragada, Vĳay(2); Cornuelle, Bruce(3); Haase, Jennifer(3); Wu,
Xingren(4); Subramanian, Aneesh(5)(1)

Center for Western Weather and Water Extremes, (2)NOAA National Centers for Environmental Prediction, (3)Scripps Institution of Oceanography, (4)I. M. Systems Group Inc., (5)University of Colorado
Atmospheric rivers (ARs) over the western US are responsible for ~30-50% of the annual precipitation, and their accurate forecasts are essential
for aiding water management decisions and reducing flood risks. Sparse coverage of conventional observations over the Pacific Ocean can limit
the improvement of forecast skill for these events. A targeted field program called AR Reconnaissance (Recon) was initiated in 2016 to better
understand and reduce forecast errors of landfalling ARs at 1-5–day lead times. During the winter seasons of 2016, 2018-2020, 32 Intensive
Observation Periods (IOPs) sampled the upstream conditions for landfalling ARs. This study assesses the impact on forecast accuracy of assimilating these dropsonde data. Data denial experiments with and without dropsonde data were conducted using the WRF model with the GSI
hybrid 4DEnVar system. Comparisons between the paired experiments demonstrate that dropsondes reduced the root-mean-square error in
integrated vapor transport (IVT) and inland precipitation for more than 70% of the IOPs, averaged over all forecast lead times from 1 to 6 days.
Dropsondes have improved the spatial pattern of forecasts of IVT and precipitation in all 15 IOPs in 2016, 2018-2019. IOP sequences (i.e.,
back-to-back IOPs every other day) show the most improvement of inland precipitation forecast skill. Additional data denial experiments fromJanuary 23 to March 13 in 2020 were performed to investigate the anchoring impact of dropsondes with the NCEP GFS model. Radiance counts
and total bias corrections show significant differences between the GFS paired experiments. The anchoring impact of dropsondes for radiancedata are found to be far beyond the North Pacific and the western US.

Friday, 17 Sep 2021 at 15UTC - room BLUE

Reanalysis development
Towards an enhanced regional atmospheric reanalysis for Australia
Su, Chun-Hsu(1); Rennie, Susan(1); Dharssi, Imtiaz(1); Smith, Andy(1); Black, Mitchell(1); Stassen, Christian(1); Steinle, Peter(1);Jakob, Doerte(1); Jones, David(1)(1)

Australian Bureau of Meteorology [Melbourne]

The Bureau of Meteorology is now embarking on developing the next regional ensemble-based atmospheric reanalysis, to improve upon its
first deterministic reanalysis – Bureau of Meteorology Atmospheric Regional high-resolution Reanalysis for Australia (BARRA) – completed in
2019. This will lead to a foundational dataset to provide a consistent, detailed characterisation of historical hazards and continuous intelligence. It is a part of a strategic initiative to establish a single authoritative source of information, analysis and expertise on climate and natural
disaster risks that is focused on relief, recovery and resilience. The enhanced reanalysis is expected to span the era of modern meteorologicalsatellites from 1979 and will be kept up to date as an operationally supported system. The reanalysis will use the same modelling components
as those in other modelling systems in the Bureau including nowcasting, weather forecasting, seasonal forecasting and regional climate pro-

jections, reflecting the Bureau's strategy for a common modelling approach across all time scales from historical analysis to multi-decadal
outlooks to support seamlessness in services. Here we report on the quality of BARRA and the development of the enhanced reanalysis, andbenchmarking results against the Bureau's global numerical weather prediction system and the global reanalyses. We also describe how BARRA
is disseminated to users and used to inform climate risk.

Developing Aerosol Reanalysis at NOAA Version 1.0: Methodology and Results
Pagowski, Mariusz(1); Da, Silva Arlindo(2); Huang, Bo(1); Lu, Sarah(3); Wei, Shih-Wei(4)(1)

CIRES, CU Boulder and NOAA/ESRL/GSL, (2)NASA/GMAO, (3)JCSDA and SUNYA, (4)SUNYAIn collaboration with the Global Modeling and Assimilation Office at NASA and State University of New York at Albany, NOAA is developing
capability to assimilate observations to create the first ever aerosol reanalysis at this institution. The observations include Aerosol Optical
Depth (AOD) derived from Moderate Resolution Imaging Spectroradiometer (MODIS) and AErosol RObotic NETwork (AERONET) direct sun measurements. The model relies on Finite-Volume Cubed-Sphere (FV3) dynamical core, Global Forecast System (GFS) physics and the aerosolparameterization based on the Goddard Chemistry Aerosol Radiance and Transport (GOCART). The assimilation tools are from the Joint Effort
for Data assimilation Integration (JEDI): the forward operator uses aerosol scattering tables from NASA/GMAO and the assimilation approachcombines the variational solver and the Local Ensemble Transform Kalman Filter (LETKF). Reanalysis for year 2016 is compared to NASA's
Modern-Era Retrospective analysis for Research and Applications Two (MERRA-2) and ECMWF's Copernicus Atmosphere Monitoring Service
interim Reanalysis (CAMSiRA) and evaluated against multiwavelength observations from AERONET, attenuated backscatter from Cloud-Aerosol
Lidar and Infrared Pathfinder Satellite Observation (CALIPSO), and other independent satellite AOD retrievals.

Development of kilometer scale regional data assimilation for Copernicus Arctic Regional Reanalysis
Yang, Xiaohua(1); Bojarova, Jelena(2); Schyberg, Harald(3); Randriamampianina, Roger(3); Dahlgren, Per(3); Amstrup, Bjarne(1);Nielsen, Kristian Pagh(1); Homleid, Mariken(3); Palmason, Bolli(4); Dahlbom, Mats(1); Lindskog, Magnus(2)(1)

Danish Meteorological Institute, (2)Swedish Meteorological and Hydrological Institute, (3)Norwegian Meteorological Institute [Oslo], (4)Icelandic
Meteorological Office
Copernicus Arctic Regional Reanalysis covers the recent three decades for the European arctic at 2.5 km grid. The region is characterised byextensive areas with complex orography, cold surfaces and weather with strong local variability. It has a sparse conventional observation
network. The reanalysis system is adapted from the HARMONIE-AROME NWP system, focusing on improved treatment of cold surfaces andenhancement on data input for physiographic databases, ocean and sea ice, snow and use of remote sensing data. A large amount of surface
observations, including those over icecap, has been collected with quality control, resulting in significantly more use of surface data than in
ERA5. High resolution, gap free albedo data over permafrost regions in the region have been used to improve physical realism. Efforts havebeen spent to enhance representation of the background error covariance model, and for optimal use of large scale information from the lateralboundary model in the analysis. From verification intercomparison, it appears that high resolution and the above mentioned enhancement inenhancement on data input, assimilation algorithm and treatment for cold surface have all contributed to the added values in the reanalysis.
In this talk, we also discuss about weakness of assimilation system as found in some of the analysis bust cases. The work is part of the Copernicus Climate Change Service. ECMWF implements this Service on behalf of the European Commission.

A novel approach to surface reanalysis
Wahl, Sabrina(1,2); Figura, Clarissa(1,2); Keller, Jan D.(3,2)(1)

Institute for Geoscience, Meteorology Department, University of Bonn, (2)Hans Ertel Center for Weather Research, (3)Deutscher Wetterdienst[Offenbach]An alternative to running an NWP model and data assimilation scheme for generating reanalysis is a so-called surface reanalysis. Here, anexisting reanalysis is used as prior information (for the near-surface state). This first guess is then corrected in a DA step, often performed
only for a single parameter at at time and employing simple DA methods, e.g., optimal interpolation. In such a scheme, an additional downscaling is often performed to enhance the spatial representation. The aim of a surface reanalysis is to provide enhanced climate data of surface
parameters relevant to a large number of users and downstream applications. Here, we show results from a more complex approach to surface
reanalysis. As background data, we use COSMO-REA6, the operational regional reanalysis at the German Meteorological Service (DWD) with ahorizontal resolution of about 6km. The target resolution is 1km with the DA step being performed using DWD's LETKF scheme. As COSMOREA6 is a deterministic reanalysis, we generate a synthetic ensemble as input by employing the analog ensemble technique. The aim of thisapproach is that the generated data set is still consistent among its parameters as in a full reanalysis. Thus, the approach requires a multivariate DA step in contrast to previous surface reanalysis efforts. We start with temperature and humidity and successively extent the set of parameters. In addition, the approach is not restricted to the surface but allows the estimation of multiple layers. Therefore, it provides a reanalysis for the lower boundary layer which is of potential interest to application such as renewable energy. Further, the approach provides uncertainty information which can also benefit downstream applications.

Monday, 13 Sep 2021

Poster session P1 - General DA
Improving Met Office predictions of Artic sea ice through assimilation of CryoSat-2 and SMOS thickness
data
Carneiro, Davi(1); Martin, Matthew(1); Fiedler, Emma(1); Blockley, Ed(1)(1)
Met Office
Arctic sea ice is one of the most rapidly and visibly changing components of the global climate system. Although global analysis and forecasting
systems have been used successfully for mid-latitude ocean prediction for some time, their application to Arctic sea ice is less mature, sinceobservations are much less abundant and data assimilation techniques less advanced in the polar regions than at lower latitudes. In this work,
we aim at implementing sea-ice thickness data assimilation from satellite measurements within the Met Office Forecast Ocean Assimilation
Model (FOAM), specifically: CryoSat-2 and Soil Moisture and Ocean Salinity (SMOS). The FOAM data assimilation scheme is NEMOVAR, a threedimensional variational data assimilation (3D-Var) with the first guess at appropriate time (FGAT). We derive sea-ice thickness from alongtrack sea-ice freeboard measurements in CryoSat-2, focusing on the assimilation of thicker ice, whereas SMOS assimilation is particularly
focused on the thinner ice. Therefore, we will show results of FOAM runs with additional assimilations of CryoSat-2 and SMOS individually, as
well as combined.

Partial analysis increments as diagnostic for LETKF data assimilation systems
Diefenbach, Theresa(1); Weissmann, Martin(2); Scheck, Leonhard(3,4)(1)
Meteorologisches Institut München / LMU Munich, (2)Institute of Meteorology and Geophysics [Vienna], (3)Deutscher Wetterdienst [Offenbach],(4)Hans Ertel Centre for Weather Research / LMU Munich
For convective-scale data assimilation there is potentially a vast amount of information available from ground-based remote-sensing instruments, various satellites and also human and economic activities e.g. smartphones, weather cameras, renewable energy production. However,
the assimilation of such complex observations is non-trivial and requires better understanding of the processes and effects of the data assimilation system. For this purpose we developed an efficient method for the quantification and visualization of the analysis influence of observations in the convective-scale data assimilation system of the German Weather Service, which is based on a Local Ensemble Transform Kalman
Filter (LETKF). Previous studies analysed the integrated influence of observations as a scalar quantity. A key aspect of our work is to investigate
the influence in 3-dimensional space, which seems to be crucial for better understanding the effects of observations. The latter include thevertical and horizontal propagation of information but also the generation of potential unphysical gradients due to spurious correlations. Our
method is based on the computation of partial analysis increments from standard LETKF output variables. The method was validated by single
observation experiments in which spatio-temporally close radiosonde - and satellite observations were assimilated. Further we discuss potential applications of our method and its potential to optimize LETKF data assimilation systems.

Investigation of the potential factors that caused the July 2020 Kyushu heavy rain using a 1000-member ensemble simulation
Duc, Le(1,2); Kawabata, Takuya(1); Saito, Kazuo(1,2); Oizumi, Tsutao(1,2)(1)
Meteorological Research Institute, (2)Japan Meteorological Business Support Center
The potential factors that caused the July 2020 Kyushu heavy rain have been investigated with the help from a 1000-member ensemble simulation. The 1000 ensemble simulations are produced from the ensemble analyses of a 1000-member ensemble Kalman filter. The data assimilation method is the four-dimensional local diagonal ensemble transform Kalman filter 4D-LDETKF, which is chosen to ensure relatively
independence between ensemble members. Three multivariate statistical techniques are used to identify the critical factors: (1) ensemble sensitivity analysis, (2) canonical correlation analysis, and (3) clustering with the generative topographic mapping algorithm. The heavy rain isshown to be strongly associated with mesoscale frontal depressions along the Baiu front.

Evaluating block methods for Ensemble Data Assimilation in JEDI
Gas, Clementine(1)(1)
University Corporation for Atmospheric Research
In the context of Ensemble Data Assimilation, many similar identical optimization problems are solved. So far, these problems have been solved
independently of each other, using for example a Lanczos algorithm. However, it is possible to use information from all the members to construct a better approximation of the eigen-structure of the matrix at the heart of the optimization problem and accelerate the convergence.
The block Lanczos algorithm is one of the block methods that exist to solve an Ensemble of Data Assimilation. This presentation describes theblock Lanczos algorithm implementation in the JEDI data assimilation framework, and presents results of the EDA experiments with GFS model,
focusing on comparisons of eigenvectors produced by both the regular and the block Lanczos algorithms.

Test and evaluation of data assimilation algorithms and configurations to improve the Rapid Refresh Forecast
System for convection forecasts
Hernandez, Banos Ivette(1); Hu, Ming(2); Ge, Guoqing(3,2); Mayfield, Will(4); Sapucci, Luiz Fernando(5)(1)
Graduate Program in Meteorology, National Institute for Space Research, (2)NOAA Global Systems Laboratory, (3)Cooperative Institute for Research in Environmental Sciences, CU Boulder, (4)National Center for Atmospheric Research, (5)INPE Center for Weather Forecasting and Climate
Studies
The regional/convective application of the United Forecast System, or Rapid Refresh Forecast System (RRFS), is under development and aims
to replace the NOAA operational suite of regional models in 2023. In order to achieve skillful convection forecasts comparable to operationalmodels, each component needs to be exhaustively tested and the best configuration determined. RRFS currently includes the FV3 Limited Area
Model with the Common Community Physics Package, the Unified Post-Processing system, and data assimilation capability using the Gridpoint
Statistical Interpolation (GSI) analysis system. In this work, the RRFS capability to simulate convection was investigated through a case study
on a squall line that occurred over Oklahoma during the afternoon of 4 May 2020. Various analysis algorithms in GSI were evaluated along withdifferent analysis grid ratios, supersaturation removal, planetary boundary layer pseudo-observations, vertical localization length scales, andvarious ensemble background error covariance weights in hybrid analysis. Observation impact experiments were conducted and cold start runs
from HRRR and GFS were also evaluated. Hourly cycled experiments ran from 00Z on 4 May 2020 to 06Z on 5 May 2020 with 18-h forecasts
launched at each cycle. Forecast verification was performed using MET. Results obtained in this research may inform RRFS developers on theperformance of the system and will be presented during the symposium.

Ensemble Data Assimilation and Probabilistic Forecast with 1000 Members Coupled with a Hydrological Model
Using the Supercomputer “Fugaku” Aiming to the Impact-Based Forecast
Kawabata, Takuya(1,2); Le, Duc(1,2); Oizumi, Tsutao(1,2); Saito, Kazuo(1,2)(1)
Meteorological Research Institute [Tsukuba], (2)Japan Meteorological Business Support Center
We started the supercomputer Fugaku project "Large Ensemble Atmospheric and Environmental Prediction for Disaster Prevention and Mitigation" in April 2020, in Japan. The Theme 1 "Short-range regional prediction" of the project aims “the impact-based forecast” for convective
scale disasters and their predictions. To realize this concept, we developed a probabilistic forecast system coupled with a hydrological model
based on a 1000-member ensemble. The objective is to show the probability of disaster itself such as flooding and landslide. This large ensemble simulation is conducted on the supercomputer Fugaku, which is the Japanese flagship supercomputer ranked in the top seat of TOP500,
HPCG, HPL-AI, and Graph500. As the first attempt, we ran NHM-LETKF with 1000 members at a 5-km resolution and downscaled-into a 2-km
resolution. The target event is a quasi-stationary line-shaped rainband case in July 2020, whose scales in space and time were about 200 km
in east-west and 12 hours, respectively. In this event, over 80 lives were claimed by a flood of the Kuma river in Kyushu. By assimilating
ordinary observations, the deterministic and ensemble forecasts of NHM-LETKF outperformed the JMA operational mesoscale forecasts 12-h
before the flooding. By applying the ensemble forecast results to a hydrological model, we obtained amazing 60% of probability of the occurrence of flooding hazard. In this presentation, as well as this case study for the impact-based forecast, we will show the objective of the project,and other applications.

WRF- 3DVAR application for Georgia
Kutaladze, Nato(1)(1)
National Environmental Agency
Accurate weather forecast is very valuable, but also a very challenging for Georgia. Territory of country lies between of the Major Caucasian
Ridge and the Lesser Caucasus mountains. About 85 percent of the total land area occupies complex mountain ranges divided with river's
valleys and ravines of different exposition. Peculiarities of locally developed weather phenomena at any time a year are often characterized withdiversity and extremality. Two lams run at the NHMS of Georgia (WRF and COSMO), mother domain centered on South Caucasus territory andcovers black and Caspian seas, the territory, which is less overlapped by other regional and local domains from neighboring NHMSs and consortia. Convective scale forecast (deterministic and ensembles) very demanding. We run WRF taking boundaries from GFS assimilating surface
parameters and radar's reflectivity with 3DVAR in the pre-operational mode. At the current moment, we are working to utilize ground base GPSdata for the assessment of the amount of water vapor and perceptible water in the atmosphere profile. These are very important meteorologicalparameters determining such essential processes as precipitation and latent heat release. The forecasting of these fields by NWPs are still
problematic as well as their proper verification. These data together with the satellite data are very important for the Caucasus region, because

of the shortage of the sounding observations. Similar synthetic analysis of observation data at a region scale from the conditions of atmospheric processes and the mechanisms of interaction allow the atmospheric physics specialists to carry out its absolutely deep understanding.

The enhancement of usage of the aircraft based observations in the KIAPS data assimilation system
Kwon, Hui-Nae(1); Kang, Jeon-Ho(1); Kwon, In-Hyuk(1)(1)
Korea Institute of Atmospheric Prediction Systems
The aircraft based observations such as AMDAR and AIREP are one of the key observations that are used as an anchor observation, being
assimilated without any bias correction, in the Korea Meteorological Administration (KMA) operational Numerical Weather Prediction (NWP)
system. Our sensitivity tests showed that increasing the number of well-qualified observations to be assimilated enhances the positive impacts.
In this study, we compared the quality control methods of the KIM (Korean Integrated Model) Package for Observation Processing (KPOP) tothat of the Meteorological Assimilation Data Ingest System Aircraft-Based Observation (MADIS ABO) data-producing process. We didn't findsomething specifically different in categorical methods of Quality Control (QC) through this comparison study. But it was clear that the QC
methods should be more sophisticated for the KPOP such as putting vertically separated more tightened tolerance to the gross-error QC fortemperature or winds. Our efforts to put a more sophisticated QC process for the KPOP and to enhance the usage of those qualified observations to the Korea Institute of Atmospheric Prediction Systems (KIAPS) Data Assimilation (DA) system by controlling the observation errors will
be presented.

Comparison of JEDI Unified Forward Operator and GSI Observer Using Rapid Refresh Forecast System Background
Liu, Shun(1); Martin, Cory(2); Lei, Ting(3); Carley, Jacob(4); Zhang, Xiaoyan(1); Liu, Emily(5); Mahajan, Rahul(5); Kevin, Dougherty(6); Kleist,
Daryl(4); Ming, Hu(7); Thompson, Greg(8)(1)
IM Systems Group @ NCEP/EMC, (2)Redline at NOAA/EMC, (3)IM Systems Group at NOAA/EMC, (4)Environmental Modeling Center, NOAA National
Centers for Environmental Prediction, (5)NOAA National Centers for Environmental Prediction, (6)IM Systems Group @ NCEP/EMC, (7)Global Systems Laboratory, NOAA, (8)Joint Center for Satellite Data Assimilation
The Joint Effort for Data assimilation Integration (JEDI) is the next generation data assimilation system for research and operational use. JEDI
may be potentially implemented to improve data assimilation in the Rapid Refresh Forecast System (RRFS), which is the next generation regional
convection-allowing model forecast system under development for the National Weather Service. One of the key components in RRFS's data
assimilation system is to simulate observations with RRFS's background using Gridpoint Statistical Interpolation (GSI) observer. The Unified
Forward Operator (UFO) is being developed under the JEDI project and plays the same role as the GSI observer. The initial effort is to compare
GSI observer and UFO to identify if they are equivalent in science using backgrounds from RRFS. The comparison includes the simulated observations from both systems, the forward operators, observation data pre-processing and quality control methods. The comparison will focus
on the observations used in RRFS. The computational resource usage for GSI and JEDI will be compared as well, based on the project's progress.
The result will be reported at the conference.

Whole Atmosphere-Ionosphere Data Assimilation and Ensemble Forecasting System
Matsuo, Tomoko(1,2); Hsu, Chih-Ting(3); Cantrall, Clayton(1); Kubaryk, Adam(4); Maruyama, Naomi(4); Fuller-Rowell, Timothy(4); Pedatella,
Nicholas(3); Shao, Hui(5)(1)
Ann and H.J. Smead Department of Aerospace Engineering Sciences, University of Colorado Boulder, (2)Department of Applied Mathematics,
University of Colorado Boulder, (3)High Altitude Observatory, National Center for Atmospheric Research, (4)Cooperative Institute for Research in
Environmental Sciences, University of Colorado Boulder, (5)Joint Center for Satellite Data Assimilation
Accurate forecasting of the ionosphere-thermosphere conditions is critical to radio communication, navigation, positioning, and satellite
tracking. Located at the intersection between geospace and the Earth's atmosphere, the ionosphere-thermosphere is exposed to drivers fromboth space and terrestrial weather that operate over a wide range of spatial and temporal scales. Recent observational and modeling studies
have revealed clear signatures of the effects of terrestrial weather far beyond the mesosphere into the topside ionosphere, motivating theextension of an operational forecasting system to the ionosphere-thermosphere. With an increased availability of geospace data from recent
NASA Heliophysics missions such as GOLD and ICON as well as COSMIC-II, the use of a single seamless system, extending from the ground togeospace, to assimilate observations from both Earth and geospace observing systems is becoming a real possibility. This presentation willreport on recent progress with ongoing research efforts to develop a capability to assimilate geospace satellite data into a coupled wholeatmosphere-ionosphere-plasmasphere model as an integral part of the NOAA National Weather Service's operational ensemble forecasting anddata assimilation systems.

The WMO Global Air Quality Forecast and Information System (GAFIS) project
Ménard, Richard(1)(1)
Environnement et Changement climatique Canada
GAFIS in a new WMO-GAW initiative to support the transition of science and prototype applications of air quality information system to operational and user driven services. The overall objective is to provide harmonized and standardize air quality forecasting and services worldwide,and promote chemical data assimilation. The activities of this effort ranges from building capacity development, good practices of air quality
forecasting information systems, the observations for air quality information systems and the operational and scientific synergies between air
quality forecasting and numerical weather prediction. Examples of such synergies will be shown from the experience at ECCC on other nodes
will be presented.

ACTRIS/EARLINET pilot for NRT provision of aerosol remote sensing profiles to CAMS
Mona, Lucia(1); D'amico, Giuseppe(1); Gagliardi, Simone(1); Amato, Francesco(1); Amodeo, Aldo(1); Sergio, Ciamprone(1); De, Rosa Benedetto(1);Ripepi, Ermann(1); Summa, Donato(1); Alados-Arboledas, Lucas(2); Amiridis, Vassilis(3); Baars, Holger(4); Komppula, Mika(5); Mattis, Ina(6);Nicolae, Doina(7); Pietras, Christopher(8); Stachlewska, Iwona(9); Letertre-Danczak, Julie(10); Peuch, Vincent Henri(10)(1)
National Research Council of Italy, (2)IISTA-CEAMA, (3)National Observatory of Athens, (4)Leibniz Institute for Tropospheric Research, (5)Finnish
Meteorological Institute, (6)DWD, (7)National Institute of Research and Development for Optoelectronics, (8)CNRS, (9)University of Warsaw, (10)European Centre for Medium-Range Weather Forecasts

A pilot NRT provision of ACTRIS/EARLINET lidar-derived aerosol optical properties profiles to CAMS will be introduced. Setting up this service
required implementation of new solutions for automation of data processing, transfer and quality control with an aim at improving the retrievability of data products in NRT. Starting from very heterogeneous lidar stations in terms of system set-up and data processing, large efforts
were done in ACTRIS/EARLINET over the years for gaining in harmonization of the data processing and provision. Nowadays, ACTRIS/EARLINET
has reached a degree of maturity that makes it suitable for near real time data provision. New modules and submodules of the ACTRIS/EARLINET Single Calculus Chain (SCC) have been designed for the provision. In particular, optimized algorithms for cloud screening have been
implemented. Moreover, additional procedures for improving the quality of NRT Level 2 data products (delivered after 6-months) were realized.
The pilot data provision started in October 2020 at the test site in Potenza. In January 2021, the provision started for a group of 9 stations
located over Europe, which are seen as representative for the whole network in terms of instrumental capability, but also ensuring a good
geographical coverage of the European continent.

An Overview of Atmospheric Data Assimilation at the Naval Research Laboratory
Satterfield, Elizabeth(1); King, Sarah(1); Baker, Nancy(1); Ruston, Benjamin(1); Campbell, William(1); Christopherson, Hui(1); Crawford,
William(1); Langland, Rolf(1); Pauley, Patricia(1); Stevens, Bailey(1); Swadley, Steve(1); Tsu, Justin(1); Tyndall, Dan(1); Zhao, Allen(1)(1)
Naval Research Laboratory
This talk will overview current work in data assimilation (DA) being conducted at the Naval Research Laboratory Marine Meteorology Division.
In this presentation, we will highlight four major areas of research and development. These include: • Developing a new hybrid 4DVar global
data assimilation system for the Navy's next generation unified model NEPTUNE using the JCSDA/JEDI DA infrastructure. This new NEPTUNEJEDI infrastructure will efficiently utilize tens of thousands of cores, facilitate rapid integration of new observations and community-developed
software, and be adaptable to support both basic DA research as well as future coupled and limited area applications. • Maintaining NRL's
utilization of atmospheric sounding observations though the addition of the sensors onboard NOAA-20, MetOp-C and through the observations of COSMIC-2. • Integrating new observations by assimilating GNSS-RO sensors of opportunity (KOMPAT-5, PAZ and commercial weatherdata), GNSS Zenith Total Delay and Aeolus winds, and evaluating small satellite microwave observations. • Continuing to enhance our model
verification through community partnerships with MET/METplus.

Developing Unified Forward Operator (UFO) for JEDI at the Joint Center for Satellite Data Assimilation (JCSDA)
Shao, Hui(1,2); Thompson, Greg(1,2); Han, Wei(1,2); Liu, Emily(3); Zhangz, Hailing(1,2); Diniz, Fabio(1,2); Honeyager, Ryan(1,2); Vandenberghe, Francois(1,2); Schweiker, Rachel(1,2); Johnson, Benjamin(1,2); Tremolet, Yannick(1,2); Auligne, Thomas(1,2)(1)
Joint Center for Satellite Data Assimilation, (2)University Corporation for Atmospheric Research, (3)Environmental Modeling Center, NOAA National Centers for Environmental Prediction
The Joint Effort for Data assimilation Integration (JEDI) is a unified and versatile data assimilation system for Earth System Prediction. It is a joint
project by the Joint Center for Satellite Data Assimilation (JCSDA) and its main stakeholders (NOAA, NASA, DoD, UKMO). The goal is to enable
collaborative development, sharing of resources and faster transfer of research to operations. The JCSDA stakeholders are committed to using
JEDI to develop their next-generation prediction systems. The use of observations in JEDI is generally handled by the Unified Forward Operator(UFO) software component. The function of the UFO is to represent, as well as possible, the precise relationship between what is observed andwhat is modeled, so that the information provided by the observations can be used to improve the model forecasts. For the past couple ofyears, the UFO has rapidly evolved and now includes observation operators for most of the operational observation types (radiance, conven-tional, GNSS-RO, satellite retrievals, etc.). Some of the observation types even have multiple operators to choose from based on the existing
operational capabilities (e.g., NOAA, UKMO, NRL, etc.). This paper will introduce the joint development efforts at JCSDA and its stakeholders
and current status of UFO. This paper will also present the working efforts to evolve UFO as an integral part of applications supported by JCSDA.
UFO validation through such a JEDI application testbed (e.g., GDAS) will be demonstrated.

Coupled data assimilation development with Joint Effort for Data assimilation Integration
Shlyaeva, Anna(1); Tremolet, Yannick(2)(1)
Joint Center for Satellite Data Assimilation, (2)Joint Center for Satellite Data Assimilation
Joint Effort for Data assimilation Integration (JEDI) is a unified data assimilation software framework developed for research and operationaluse. Data assimilation algorithms in JEDI (including a wide range of variational algorithms and Local Ensemble Kalman Filter-type algorithms)
are written in a generic way, allowing their use with any model implementing a JEDI interface. This generic approach simplifies development ofcoupled data assimilation algorithms. In this presentation we will describe the coupled data assimilation software approach in JEDI.

New Variational Quality Control scheme and application in GSI
Su, Xiujuan(1); Purser, R. James(1)(1)
IMSG at NOAA/NCEP/EMC
The original implementation of variational quality control in NOAA's Grid-point Statistical Interpolation (GSI) assumed the effective measurement error distribution to be a mixture of a dominant Gaussian plus a minor uniform contaminant. But the effective distribution of errors
appears to conform more to a distribution such as the Logistic distribution, or variants of it, whose log-probability densities, being convex or
only very weakly concave, have the advantage of avoiding, or substantially mitigating, the problem of multiple minima in the cost function
minimization. The shape of the assumed error distribution is controlled by two main parameters: one determines the broadness of the tails,
and the other determines the convexity of the logarithm of those tails. We discuss the application in GSI and forecast impacts with new variational QC.

Developments Towards a Unified Naming Convention for Observations Used in a Data Assimilation System
Thompson, Gregory(1); Honeyager, Ryan(1,2); Herbener, Stephen(1); Shao, Hui(1)(1)
University Corporation for Atmospheric Research, (2)Joint Center for Satellite Data Assimilation
The Joint Center for Satellite Data Assimilation (JCSDA) has a framework for storing and reading observations used in the JEDI (Joint Effort for
Data assimilation Integration) software for data assimilation. JEDI's Interface for Observation Data Access (IODA) bridges the external observational data to the components that utilize the data during assimilation. The various agencies that comprise JCSDA typically have a variety ofvariable names and store their own data individually in numerous formats, many of which diverge from their true origins. Typical steps of DA
systems are conversion from raw format type such as BUFR, derivation of new variables, bias correction, diagnostics like model departures,and quality control. While IODA handles data ingest, the Unified Forward Operators (UFO) handle most of the remaining steps. To facilitate
these linked steps, the JCSDA has organized a common set of conventions for variable and dimension names as well as storage of I/O files that
lend themselves to rapid diagnostic tools for post-processing and DA-related investigations. While NetCDF Climate and Forecast (CF) conven-tion has had great success establishing a standard for numerical model data dissemination, the conventions do not adequately describe thebreadth of observational variables. The JCSDA has created a draft version of naming conventions for most existing DA-related observationalvariables and metadata that can assist in the quality control and forward operator steps.

The Joint Effort for Data assimilation Integration
Tremolet, Yannick(1)(1)
Joint Center for Satellite Data Assimilation
The long term objective of the Joint Effort for Data assimilation Integration (JEDI) is to provide a unified data assimilation framework for research and operational use, for different components of the Earth system, including coupled systems, and for different applications. The
project also aims at reducing or avoiding redundant work within the community and increasing efficiency of research and of the transition fromdevelopment teams to operations. The key concept in modern software development for complex systems is the separation of concerns. In thisproject, the concept of models is clearly separated from the observation handling with clear interfaces. The data assimilation algorithms arethemselves separated from the model space and observation space components. Generic code is used wherever possible to further reduce
duplication of effort. The JEDI project is based on modern programming technology, it is also a joint effort between the JCSDA partner agencies.
Very rapid progress has been possible through the use of a new approach to development. However, these tools imply changes in working
habits and practices, affecting individual and collaborative work. In this talk, the new development practices will be described. An overview ofthe system and the current status of the implementation of the various data assimilation components for use in atmospheric, marine, land,
atmospheric composition and coupled systems will be presented.

Satellite radiance data assimilation within NOAA's prototype Rapid Refresh Forecast System
Zhang, Xiaoyan(1); Carley, Jacob(2); Liu, Emily(2); Liu, Haixia(1); Rogers, Eric(3); Lei, Ting(1); Liu, Shun(1); Martin, Cory(4); Weygandt, Steve(5); Lin,
Haidao(6)(1)
IMSG at NOAA/NWS/NCEP/EMC, (2)NOAA/NWS/NCEP/EMC, (3)NOAA/NWS/NCEP/EMC, (4)Redline at NOAA/NWS/NCEP/EMC, (5)NOAA/OAR/GSL,(6)
CIRA/CSU and NOAA/OAR/GSL
NOAA's next-generation Rapid Refresh Forecast System (RRFS), a unified hourly-updated, convective-scale ensemble data assimilation andforecasting system, is currently under development and testing. Satellite radiance data from both polar orbit and geostationary satellites havebeen assimilated in this system at an hourly cadence. New satellite instruments and new methods of assimilating radiance data are also continuously tested and updated within the RRFS. This presentation will provide a general overview of the assimilation of satellite radiances in the
RRFS and address recent progress in evaluating the ABI radiance data, DBNet radiance data, and higher peaking channels from microwave
radiance data.

Monday, 13 Sep 2021

Poster session P1 - Reanalysis
SPI-based drought forecasting data assimilation by using ARIMA models
Arshad, Adnan(1); Sundari, Ristina-Siti(2)(1)
PODA-Pakistan & China Agricultural University, (2)University of Perjuangan Tasikmalaya
Drought always has more severe impacts on the agriculture sector and adversely affects many people's livelihood than any other natural hazard. Pakistans' economy mainly revolves around the agriculture sector, and nearly 62 percent of the country's population depends on this
sector that primarily depends on rainfall water. Therefore, an effective monitoring system is indispensable in developing countries like Pakistan
to mitigate the negative impacts of drought. This study aimed to create the seasonal stochastic (SARIMA) models to investigate and forecast
the meteorological drought in Pakistan. Meteorological data from 20 rain gauge stations over 1951 – 2017 were used to calculate the SPI at
the 12-months timescale. The Box-Jenkins approach was employed to build SARIMA models for SPI of various stations and was then utilized
to predict Pakistan's drought conditions over the next three years, from 2018 to 2020. SPI-12 month time scale results indicate that all thefour provincial capitals experienced frequent severe droughts (SPI ) for different months of the year during the period 1951-2017.Furthermore,
the SARIMA model used for modeling and predicting SPI-12 time series for each rain gauge station was reasonably found. Moderately dry
periods are expected for Karachi and Lahore stations, whereas the Quetta station is expected to have an arid period by 2020-2060. This study's
findings can be important for Pakistan as a comprehensive valuation of drought features at the national level is indispensable for frequent
droughts management.

Examining the model parameters of COSMO-CLM in 11 selected extreme events over West Bengal (WB), India
Bal, Sourabh(1); Kirchner, Ingo(2)(1)
1 Department of Physics, Swami Vivekananda Institute of Science & Technology, (2)Institut für Weltraumwissenschaften [Berlin]
The performance of the regional climate model COSMO-CLM driven by Era-Interim over West Bengal domain (81.25° E - 94.64 ° E and 15.87°
N – 27.76° N) on 11 extreme events have been obtained by performing 3-hourly simulations for 11 days (5 days before and after central date)with different model parameter values (25 tunning parameters). The purpose of this study was to ascertain model performances with respect
to changes in tuning parameters which are mainly related to turbulence, surface convection, cloud parameterization. The model performances
are evaluated on the ability of reproducing temperature, precipitation and cloud cover for all the 11 extreme events. The metric used forinvestigating the COSMO-CLM parameters uncertainty is Performance Score computed from Performance Index. Additionally, model parameters for different areas of the domain are also investigated using Skill Score and expressed with respect to the area of the reference simulation(setting all the tuning parameters as default value). The model is particularly sensitive to a subset of all the tested parameters. The model withthe largest effect on model performance is qi0, the cloud ice threshold for automatic convection.

Assessment of past and present human biometeorological environment over WB, India based on observations and
Era-InterimBal, Sourabh(1)(1)
1 Department of Physics, Swami Vivekananda Institute of Science & Technology
Research works are scanty on the validation of long-term trend of human thermal stress over West Bengal (WB), India. Therefore, this study
focuses on the past and present human bio-meteorological environment in WB reproduced from Era-Interim and observed station data respectively. To account for the prolonged thermal stress periods, the duration of three UTCI stress categories such as strong heat stress, very strong
heat stress, and extreme heat stress are counted as an event. An event is classified as a strong or very strong event if the corresponding stress
category turns up in consecutive days for more than or equal to five days and less than twelve days. Extreme events are numbered when theextreme stress class renews for more than or equal to 2 days and less than five days. No extreme heat stress events/year are identified for anyof the study stations in WB for past (Era-Interim) and present (IMD) bio-climate conditions. For strong heat stress, number of events/year
ranges from (41-48) past 40 years (Era-Interim) at 12 hr for all the stations except Darjeeling. In the same stress category, for the last two
years, observed station data (IMD), at 11:30 am, computes (23-53 events/year) for all the stations except Darjeeling. At 12 hr, the highest
number for very strong heat stress class occurs in Alipore and Dum Dum for last four decades ranging from 2-3 events/year whereas observed
station data identifies Diamond Harbour (11.5 events/year) for the last two years and no other observed station identifies such events/year at
11:30 am.

Evaluation of multiple reanalyses in reproducing temperature and precipitation indices over southern SouthAmerica
Balmaceda-Huarte, Rocio(1); Olmo, Matias Ezequiel(2); Bettolli, Maria Laura(2); Poggi, Maria Mercedes(3)(1)
Department of Atmospheric and Ocean Sciences, Faculty of Exact and Natural Sciences, University of Buenos Aires, (2)Department of Atmospheric and Ocean Sciences, Faculty of Exact and Natural Sciences, University of Buenos Aires, (3)National Weather Service
Several temperature and precipitation indices, with special focus on extremes, were analysed in different sub-regions of southern South America (SSA) during 1979-2017 using multiple reanalyses, the CPC gridded dataset and the most extended network of meteorological stations
employed in regional climate studies up to date. Reanalyses generally well represented the spatial patterns of the indices, although they
showed some differences in extreme indices over large portions of SSA and tended to overestimate precipitation maximums, especially insouthern Chile. Furthermore, ERA-Interim presented clear difficulties in reproducing precipitation near the Andes mountains, exhibiting thelargest overestimations. This seemed to be improved in ERA5. When evaluating the long-term changes, most of the datasets agreed in generalwarming conditions, stronger and more homogeneous for the maximum temperature. NCEP1 and NCEP2 showed contrary temporal changes
in almost all the temperature indices. Precipitation indices exhibited less consistent changes among reanalyses, notwithstanding, most of thedatasets agreed in drier conditions in the arid region of Argentina as reflected by significant positive trends for dry spells and negative trends
for the total annual precipitation. In terms of the inter-annual correspondence, reanalyses presented good correlations to the stations reference in the regional series. The use of reanalyses data to perform regional climate studies should consider the existent differences among
them. Using multiple sources of information is strongly recommended to account for observational uncertainty, especially in regions like SSA,
where data availability and its resolution are often limited.

Assimilation strategies of sea-ice remotely-sensed observations for ocean Reanalysis
Cipollone, Andrea(1); Banerjee, Deep Sankar(1); Aydogdu, Ali(1); Iovino, Doroteaciro(1); Masina, Simona(1)(1)
Centro Euro-Mediterraneo per i Cambiamenti Climatici [Bologna]
Despite the first sea-ice thickness observations from satellite date back to 2000s, multivariate assimilation strategies for sea-ice are still in an
early stage, due to the highly non-gaussian distributions of related uncertainties and the low accuracy of such measurements especially during
summer season. In literature, several sea-ice comparison studies, starting from ORA-IP, has shown large variability in crucial sea-ice parameters among diverse state-of-the-art ocean/sea-ice reanalyses, despite a general agreement in the sea-ice extension. To better constrain thesea-ice, the CMCC ocean 3dvar scheme (called OceanVar), employed in the routinely production of global/regional ocean reanalysis and forecasts, has been recently extended to ingest sea-ice concentration and thickness via gaussian anamorphosis operator. This operator is used tocovary thickness and concentration by transforming the probability density function of sea-ice variables into Gaussian ones. The investigationof different sea-ice assimilation strategies (univariate/multivariate) is underway and will be presented together with the impact on the sea-ice
variability and on several integrated climatic indexes that are of crucial importance in reanalysis applications. The sea-ice DA scheme is meant
to be coupled with the ocean DA in the future.

Using reanalysis to assess ʻdesign-level' wind events with the potential for infrastructure damage in the built
environment
Morris, Michael(1); Kushner, Paul(1); Mercan, Oya(2)(1)
Department of Physics, University of Toronto, (2)Department of Civil and Mineral Engineering, University of Toronto
Extreme surface wind speeds in urban regions can cause significant damage to the built environment. Understanding how extreme winds,
which are used in infrastructure design, may change under climate change may be an important consideration for climate change adaptation. Climate model projections of future extreme winds are highly uncertain, in part because models do not adequately capture turbulent
boundary-layer processes at small scales. Still, large-scale dynamics in the free troposphere could provide reliable constraints on local extreme
winds. To investigate, we produce composites of ERA5 reanalysis fields during observed extreme wind events in selected Canadian cities. We
include only the strongest 100 events in each season, robust across multiple weather stations in the city, to isolate “design-level” winds withpotential for causing damage. In the winter and shoulder seasons, when most extreme wind events occur in these cities, composites show
synoptic wave patterns and mesoscale frontal systems centred near the city. Typically, extremes at the stations occur in ERA5 10 m wind speeds
within a two-day window of the observed event. Compositing separately across events that appear in both datasets and those not captured by
ERA5 within this window reveals weaker wave amplitude and smaller frontal zones during the extremes found in the station data only. Thissuggests that these events may be driven by processes at smaller scales, challenging to capture within reanalysis.

Evaluating extreme wind speed in regional and global reanalysis products
Niermann, Deborah(1); Spangehl, Thomas(1); Borsche, Michael(1); Kaspar, Frank(1)(1)
Deutscher Wetterdienst [Offenbach]Due to continuous higher spatial and temporal resolutions, reanalyses are used by a steadily growing group of users. The development of new
applications requires a comprehensive quality assessment focusing on different temporal scales, parameters and geographical areas. Withinthe framework of the joint project ClimXtreme (https://www.climxtreme.net/), which is funded by the German Federal Ministry of Education
and Research (BMBF), Deutscher Wetterdienst (DWD) evaluates the quality of its current reanalysis product COSMO-REA6 concerning the representation of extreme events like heat waves, windstorms and heavy precipitation. Therefore, COSMO-REA6 is evaluated and compared toknown global reanalysis products such as ERA5 and several measurements (e.g. station based and gridded products). Reanalyses enable acomprehensive assessment of extreme events and relevant processes. Here we are aiming at an objective and systematic evaluation approachof extreme wind events in order to document related uncertainties and guide potential users.

Exploring Information Exchange in Climate System Applications.
Pothapakula, Praveen Kumar(1); Ahrens, Bodo(1)(1)
Goethe University Frankfurt am Main
Quantifying Information exchange (IE) between sub-components in climate systems often assists in understanding the underlying system
dynamics. We present various information theory estimators (linear and non-linear) and their application to idealized systems and climate
applications. As expected the linear estimators work for linear systems but fail for strong non-linear systems. Among the non-linear estimators, TE-kraskov proved to be reliable than kernel and discrete binning estimators. However, all the non-linear estimators are sensitive to thefree tuning parameters which are to be tuned and tested while applied. We apply these estimators to real-world climate system applications
such as the interaction between the Indian and Pacific Oceans. An expected bi-directional IE is observed between these two sub-components.
However, while these estimators are applied to the interaction between NAO and European Surface air temperatures, an unexpected bi-directional IE is observed. This hints of a possible hidden driver. We also applied these estimators to the Vb-events precipitation and its possible
moisture sources. We observe that the northwest Mediterranean sea supplies moisture to the extreme Vb events. In a three climate systemcomponent interaction example, we observed a synergistic information exchange between IOD and ENSO to the monsoon precipitation during
the summer season over central India. Furthermore, we also evaluated various global and regional climate simulations. We conclude that the
information theory framework provides insights into the system dynamics in observations and also can be useful in model verification provided
their limitations and sufficient time series length are taken into account.

Use of remote sensing retrievals of evapotranspiration based on reanalysis data for assessment of forested landscape drying
Stoyanova, Julia(1); Georgiev, Christo(1); Kulishev, Andrey(1)(1)
National Institute of Meteorology and Hydrology
A multi-sensor satellite data fusion approach to study the response of conifer forest evapotranspiration (ET) to drought and disease is applied.
The work concerns a real forestry problem with health disturbance and patch wilting of conifers in the lower forest belt in Bulgaria (Eastern
Mediterranean). It is accent on a specific case of progressive mass wilting after injuries by a snow storm in 2015 when broken trees become
affected by a disease and subsequent desiccation. Ten years period (2011-2020) of reanalysis ET data from MSG reprocessing chain are used
for diagnoses of forest cover health after the storm. For diagnosis, the Evapotranspiration Stress Index (ESI) is used to designate the intensityof water stress and correspondingly of disturbed ecosystems functionality. This is calculated on the bases of EUMETSAT LSA-SAF products ofreference ET and actual ET. Using monthly means values, evaluation is based on the comparison of ESI on a site scale for a selected health andill forest, and then this ESI difference is used as a reference for the whole target region. Georeferenced ESI information is used for constructing
time series maps indicating the severity of conifer landscape injuries, and their spatial allocation, which appears to be maximal for July, August

2016 and 2017. The results are analysed to capture signals of drought, disease disturbance and subsequent recovery after restored water cycle
at stand scale, and consequently are confirmed by using higher spatiotemporal resolution remotely sensed data of NDVI obtained by PROBA V
environmental satellite. Analyses demonstrate differential response to water stress of conifers in health and infected areas, and the utility ofMeteosat based information for water use change monitoring.

Regionalization of MERRA-2 50-m wind speed over the United States for Energy Applications
Thomas, Natalie(1,2); Bosilovich, Michael(1); Dezfuli, Amin(1,3)(1)
NASA GSFC GMAO, (2)USRA, (3)Science Systems and Applications, Inc. [Lanham]
Climate regionalization is a tool for dividing a large region into smaller regions that are coherent with respect to a chosen climate variable.
Here we use climate regionalization on 50-meter wind speed over the United States from NASA's Modern-Era Retrospective Analysis for Research and Applications 2 (MERRA-2) to determine appropriate regions for studying wind resources. Identified regions are then examinedfurther to assess drivers of inter-annual variability, including modes of climate variability. We will then explore how these results may be usefulfor predictability of wind resources over the United States.

Usage of reanalysis data for wind energy expansion in the North Sea and Baltic Sea
Spangehl, Thomas(1); Borsche, Michael(1); Niermann, Deborah(1); Kaspar, Frank(1); Tinz, Birger(1)(1)
Deutscher Wetterdienst
The exploitation of offshore wind energy is an essential part of the German energy transition (Energiewende). The regional reanalysis COSMOREA6, operated by the German Meteorological Service (Deutscher Wetterdienst, DWD), and the global reanalysis ERA5, produced by the European Centre for Medium Range Weather Forecasts (ECMWF), are suited to support offshore wind farm planning in the German Exclusive Economic Zone of the North- and Baltic Seas. DWD delivers reanalysis data and statistical evaluation results to Federal Maritime and Hydrographic
Agency (Bundesamt für Seeschifffahrt und Hydrographie, BSH) to facilitate offshore site tenders. Data and a report have been published in
February 2021 as part of the tenders for 2021 (https://pinta.bsh.de/). The presentation gives an overview on the data and evaluation results.
Examples are shown for two offshore sites in the North Sea and one in the Baltic Sea. The sites are located nearby FINO1 and FINO2 (Researchplatforms in the North Sea and Baltic Sea, https://www.fino-offshore.de/en/index.html) which enables a comprehensive evaluation of the windspeed and direction near turbine hub heights.

Tuesday, 14 Sep 2021

Poster session P2 - Observations
Assimilating atmospheric infrasound data to constrain atmospheric winds in a two-dimensional gridAmezcua, Javier(1,2)(1)
National Centre for Earth Observation, (2)University of Reading
Infrasound waves travelling through atmospheric channels are affected by the conditions the encounter in their path. The shift in the backazymuth angle of a wave front detected a reception site depends on the cross-wind it encountered. Estimating the original field from this integrated measurement is an (ill-posed) inverse problem. By using a prior, this can be converted into a Bayesian estimation problem. In this work
we use the (ensemble) Kalman filter to tackle this problem. In particular, we provide an illustration of the setup and solution of the problem ina two-dimensional grid, depending on both across-track distance and height, which has not been done in previous works. We use a synthetic
setup to discuss the details of the method. We show that one of the effects of along-track averaging (something done in previous studies tosimplify the problem) is to overestimate the magnitudes of the analysed values, and propose that this should a source of model error. We also
illustrate the process with real data corresponding to nine controlled ammunition explosions that took place in the summer of 2018.

Assimilation of Web Camera Derived Estimates of Horizontal Visibility
Carley, Jacob(1); Matthews, Michael(2); Morris, Matthew(3); Pondeca, Manuel(4); Colavito, Jenny.colavito@faa.gov(5); Yang, Runhua(6)(1)
NOAA Environmental Modeling Center, (2)MIT Lincoln Laboratory, (3)SRG at NOAA/Environmental Modeling Center, (4)IMSG at NOAA/Environmental Modeling Center, (5)Federal Aviation Administration, (6)IMSG at NOAA/Environmental Modeling Center
Conditions associated with limited horizontal visibility account for a disproportionate number of aviation related accidents in Alaska. This is inpart due to the relatively sparse observing network in the region. In order to supplement traditional visibility observations the U.S. Federal
Aviation Administration (FAA) maintains a network of high quality web cameras throughout the state. This work explores the potential forderiving automated estimates of horizontal visibility via image processing and assimilating the resulting estimates in NOAA's Real Time
Mesoscale Analysis System. Results suggest that the web camera-derived estimates of visibility are effective for capturing visibilities associatedwith the most restrictive flight categories. The assimilation of these estimates can improve the resulting analysis. Implications for future studies
and operations are discussed.

Ensemble Forecast Sensitivity to Observations applied to a regional data assimilation system over Argentina
Casaretto, Gimena(1,2,3); Dillon, Maria Eugenia(3,2); García, Skabar Yanina(2); Ruiz, Juan(4); Sacco, Maximiliano(2); Lien, Guo-Yuan(5)(1)
Departamento de Ciencias de la Atmosfera y los Oceanos, (2)National Meteorological Service, (3)Consejo Nacional de Investigaciones Científicas
y Técnicas [Buenos Aires], (4)Consejo Nacional de Investigaciones Científicas y Técnicas, (5)Central Weather Bureau
Observations that are assimilated into numerical weather prediction systems are conformed by numerous data sets and the impact of theobservations must be objectively evaluated. The Forecast Sensitivity to Observation (FSO) provides an efficient impact evaluation of each observation on forecasts. This study proposes applying the simpler ensemble formulation of FSO (EFSO, Kalnay et al 2012) to the Weather Re-

search and Forecasting model coupled with the Local Ensemble Transform Kalman Filter in Argentina (Dillon et al 2019). The experiment iscarried out during 25 days of the intensive observing period of the RELAMPAGO-CACTI field campaign that was conducted during the
2018-2019 austral warm season in the center of Argentina (Nesbitt et al 2021). Analyses were obtained every 6-h with a 20-km resolution,
assimilating data from soundings, aircrafts, satelite, AIRS and surface and automatic stations. 6-hour forecast sensitivity is computed for eachobservation type. The results evidence that wind, temperature and humidity from automatic stations have nearly neutral impact on the forecast
skill. Sounding, aircrafts and atmospheric infrared sounder observations present a larger positive impact. Also the spatial distribution of thesensitivity documents the beneficial impact of observations in the forecasts for the central area of Argentina. It is shown that the EFSO method
can efficiently suggest data selection criteria.

Deriving observation impact measures through the FV3-JEDI interface
Diniz, Fabio(1); Todling, Ricardo(2); Holdaway, Daniel(3); Vandenberghe, Francois(3); Kleist, Daryl(4)(1)
UCAR Joint Center for Satellite Data Assimilation, (2)NASA Goddard Space Flight Center, (3)UCAR Joint Center for Satellite Data Assimilation,(4)
NOAA National Centers for Environmental Prediction
The Joint Center for Satellite Data Assimilation (JCSDA) and its partners have been interfacing a variety of models to the Joint Effort for Dataassimilation Integration (JEDI). These models include the NOAA's Global Forecast System (GFS) and NASA's Goddard Earth Observing System(GEOS). Efforts are presently on the way to complete implementation and testing of a full resolution cycled data assimilation for these twomodels. One feature of interest to be added to the implementation is the capability to evaluate the contribution of the observations to reduce
forecast errors using the Forecast Sensitivity-based Observation Impact (FSOI) technique. We have added a preliminary, model agnostic, implementation of FSOI to the Object-Oriented Prediction System (OOPS) layer of JEDI. The present work applies this OOPS capability to generate
FSOI for both GEOS and GFS. The presentation will discuss practical aspects of the implementation and provide preliminary results.

Status of Atmospheric Motion Vectors use in the NCEP GFS data assimilation system
Genkova, Iliana(1); Liu, Haixia(1); Thomas, Catherine(2); Kleist, Daryl(2); Daniels, Jaime(3); Apodaca, Karina(4); Santek, Dave(5)(1)
IMSG, (2)NOAA/NCEP, (3)NOAA/NESDIS, (4)NOAA/AOML, (5)CIMSS/UWisconsin
The Global Observing System's upper-air observations component has improved with the launch of a number of next generation weather
satellites. Advanced Baseline Imager (ABI) - class imagers with surpassing spatial and spectral capabilities are now on board the Himawari and
GOES satellites. In addition, the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on board the Meteosat Second Generation (MSG) Meteosat-8/11 has many of the spatial and spectral features as ABI. These technological advancements have led to new algorithm development
and improved Atmospheric Motion Vector's (AMVs) data products from EUMETSAT, JMA and NOAA/NESDIS. New wind products are thoroughly
studied, evaluated and tested before inclusion in the NCEP's Global Data Assimilation System (GDAS) as they become available. We will present
results from recent model implementations allowing the use of Meteosat-8 and 11, Himawari-8, Goes-16/17 and S-NPP/NOAA-20 VIIRS
winds, preparation for Metop-B/C AVHRR winds, and evaluation of LeoGeo and Aeolus winds. We will illustrate that improved handling of theAMVs leads to the development of a more robust forecasting system.

Estimates of radiosonde impact and their implications
Ingleby, Bruce(1)
(1)
ECMWF
The impact of each assimilated data value is estimated in the ECMWF system using Forecast Sensitivity to Observation Impact (FSOI). Aggregated statistics provide useful information on the relative impact of different variables, levels and geographic locations. For radiosondes
and aircraft the wind FSOI is more than twice the temperature FSOI and reports in data sparse areas have larger impact than those in data
dense areas.
Recent work has shown that the impact of stratospheric radiosonde data north of 45N
is much stronger in winter than insummer. This has possible implications for the targets set for radiosondes (numbers of profiles reaching 30 hPa or 10 hPa etc) and should
encourage the use of larger balloons in winter - on average balloons burst at lower altitude in winter unless such measures are taken.

Assimilation of GK-2A clear sky radiance products in the KIM DA system
Jeong, Han-Byeol(1); Chun, Hyoung-Wook(2); Kang, Jeon-Ho(1)(1)
Korea Institute of Atmospheric Prediction Systems, (2)Korea Meteorological Administration
The Korea Meteorological Administration (KMA)'s operational global numerical weather prediction (NWP) system, named Korea Integrated
Model (KIM), was developed by the KIAPS (Korea Institute of Atmospheric Prediction Systems) and it started to run in April 2020. GEO-KOMPSAT-2A (GK-2A, located in 128.2E) Clear Sky Radiance (CSR) data assimilation (DA) system that assimilates 3 water vapor channels has beendeveloped as a part of the KIM DA system. The Advanced Meteorological Imager (AMI) onboard the GK-2A satellite, Korea's second geostationary satellite, is composed of 16 channels. The GK-2A's CSR product is defined as the output of the area-averaged brightness temperature fora given pixel in the clear sky area observed by the satellite. To better understand the products and enhance the quality control for the GK-2A
CSR in the KIM Package for Observation Processing (KPOP), we had retrieved the main features of it compare to other CSR products such as
Himawari-8 CSR and investigated the impact of its data assimilation. In this study, the quality control process for GK2A CSR will be described.
And, preliminary results from the GK2A CSR data assimilation will be examined around South Korea.

An Evaluation for Impacts of Ocean Observing System in the NCEP GODAS
Lee, Hyun-Chul(1); Kleist, Daryl(2)(1)
IMSG at NOAA/NCEP/EMC, (2)NOAA/NCEP/EMC
The current National Centers for Environmental Prediction (NCEP) Global Ocean Data Assimilation System (GODAS) assimilates in situ profile
data from EXpendable BathyThermograph (XBT) and Conductivity Temperature Depth (CTD), stationary fixed moorings, autonomous Argo
floats, and remotely sensed sea surface temperature using a variational method of 3D-var in a daily cycle. The ocean model of GODAS is MOM3
with one degree horizontal resolution with 40 vertical levels. With GODAS, these data from ocean observing systems are fundamental to NCEP's
operational efforts not only for monitoring the ocean state but also for forecasting multi-week to seasonal variability in the NCEP Coupled
Forecasting System (CFS). In order to evaluate the impact of the observation system in the NCEP operational products, a series of observing
system experiments (OSE) of 5-day/10-day cycle runs have been carried out, and the observational innovations and the analysis increments
associated with individual ocean observations in NCEP's GODAS are calculated from the OSE runs. The impact of individual and regional obser-vations, based on Assimilation Impacts of Observing Systems and Forecast Impacts of Observing Systems from the OSE runs, were estimatedand applied to monitor the spatio-temporal impacts of ocean observing systems in the current operational NCEP GODAS.

Sea Surface Temperature analysis within the NCEP GFS
Li, Xu(1); Derber, John(2); Collard, Andrew(2); Kleist, Daryl(3)(1)
IMSG at EMC/NCEP/NOAA, (2)IMSG at EMC/NCEP/NOAA, (3)NOAA/NCEPThe Sea Surface Temperature (SST) analysis within the NCEP GFS, referred to as NSST (Near-Surface Sea Temperature), became operational in
July 2017. In the NSST, the SST, with the foundation temperature as the analysis variable, is analysed together with the atmospheric analysis
variables, by Grid Statistical Interpolation (GSI), the atmospheric assimilation system in the NCEP GFS. The satellite observations are used asradiances, instead of retrievals, as in other SST stand-a-lone analysis systems. The background is persistence, but modified with diurnal warming and skin-layer cooling simulated by the NSST model and with a monthly SST climatology constraint. The NSST has undergone several
upgrades since becoming operational in the NCEP GFS. With the initial implementation, it was found that small-scale spatial features are notwell resolved and the cloud contamination of the partly clear AVHRR radiance cannot be discriminated well with the available GSI cloud detection scheme. To address these issues, a few NSST updates have been developed and tested with GFSv15.2 and GFSv16 recently. They are: theinclusion of VIIRS radiances, new background error correlation length scales, a smaller thinning box size for AVHRR and VIIRS radiances andexclusion of AVHRR partly-cloudy radiances. Experimental results have shown the NSST analysis can be improved significantly through theaforementioned modifications. Next steps include the addition of the NSST functionality in the Joint Effort for Data Assimilation Integration(JEDI) system and the use of the NSST in coupled data assimilation in the Unified Forecast System (UFS).

Observing System Simulation Experiments in the Brazil Current using SWOT synthetic data with HYCOM+RODAS
Pires, Leonardo(1,2); Pereira, Janini(3,1,2); Tanajura, Clemente(3,1,2); Costa, Filipe(1,2); Vidal, Vitor(2)(1)
Graduate Program in Geophysics, (2)Oceanographic Modeling and Observation Network, (3)Department of Earth and Environmental Physics
The assimilation of SWOT altimetry data will play a key role in the understanding of ocean energy and mesoscale processes. In the present
work, Observing System Simulation Experiments (OSSEs) were performed with the Hybrid Coordinate Ocean Model + Oceanographic Modelingand Observation Network Data Assimilation System (HYCOM+RODAS) with 1/24° of horizontal resolution and 21 vertical layers. The synthetic
data of SST was obtained from a free run using ROMS, the SSH to nadir and SSH type SWOT were obtained from a simulator by Gaultier et al(2017). The objective of the work is to evaluate the impact of assimilating synthetic SWOT altimetry data with HYCOM+RODAS on the Brazil
Current kinetic energy (11° - 34° S). Three experiments were carried out to achieve this goal: expt 1.0, assimilating only synthetic SST data;
expt 1.1, assimilating synthetic SST and nadir SLA data; and expt 1.2, assimilating synthetic SST, nadir SLA data, and SWOT SLA data. The resultof the generation of synthetic SSH data showed very high SWOT errors in the regions of the outer edges of each band of the satellite groundtrack. However, the system managed to assimilate this data as it was evident in the analysis increment along the SWOT tracks. Mean Kinetic
Energy (MKE) and Eddy Kinetic Energy (EKE) fields improved both on the surface and in depth when SWOT synthetic data were assimilated in
HYCOM+RODAS. The features in the MKE and EKE fields in expt 1.2 were more similar to those in the ROMS run. The best correlations for the
SSH fields, zonal and meridional velocities with respect to ROMS were found in in expt 1.2. RMSE of SST with respect to ROMS in these threeexperiments was reduced by more than 50% when compared to the control run.

Influence of Snow Representation in Operational Seasonal Prediction Systems
Risto, Danny(1); Fröhlich, Kristina(2); Ahrens, Bodo(1)(1)
Institute for Atmospheric and Environmental Sciences, Goethe University Frankfurt, (2)Deutscher Wetterdienst
Recent seasonal forecast systems have difficulties predicting temperature over continental regions, whereas for some regions with maritime
influence their performance is better. For continental regions, the land surface can become a more important source of predictability on (sub)seasonal time scales. Snow is a crucial component of the land surface as it stores water and modulates the earth's radiation budget each winterseason. A snow-covered land surface leads to local temperature decreases in the overlying air (snow-albedo effect and high emissivity) andmelting snow cools the surface air and contributes to soil moisture and river water. We compare the snow representation in seasonal forecast
systems from four European weather/climate services and their performance in predicting snow, temperature and precipitation. The goal is to
identify the impact of the snow initialisation and snow modelling from the four forecasts systems. The first results show that the predicted
anomalies of 2m temperature over continental regions correlate with reanalyses only for the first forecasted month, whereas anomalies in snow
water equivalent can be predicted up to several months. While the biases among the forecast systems differ, the correlation skills are similar
for the same variable, with precipitation having the lowest correlation skills. Furthermore, we will investigate the causal relationships between
snow and 2m temperature with time-lagged correlation or similar methods.

A virtual network of ground-based microwave radiometers for monitoring of atmospheric stability and its potential impact in synergy with hyperspectral satellite observations.
Toporov, Maria(1); Löhnert, Ulrich(2)(1)
University of Cologne, (2)University of Cologne
In this contribution, we show the potential of a network of ground-based MWR to complement observations of the future Infrared Sounder(MTG-IRS) and to provide information on atmospheric stability. The last is described in terms of CAPE and Lifted Index. We present a neuralnetwork retrieval of CAPE and LI from simulated IRS and MWR measurements based on the COSMO-REA2 reanalysis as truth. To assess thespatial representativeness of observations of a single ground-based MWR and to estimate the required network density the retrieval is applied
to a 150*150km reanalysis domain. The impact of a MWR network was investigated in two ways. First, using spatial statistical interpolation,
the fields of CAPE/LI retrieved from IRS observations were merged with the CAPE/LI values from MWR. Within this method, the contribution ofground-based network consisting of a varying number of radiometers was shown to be significant under cloudy conditions. The second approach mimics the assimilation of satellite and ground-based observations in the space of retrieved CAPE/LI fields. Assuming the persistenceof atmospheric fields for a period of six hours, the CAPE/LI fields calculated from reanalysis were taken as a first guess in an assimilation step.
Observations, represented by CAPE/LI fields obtained from satellite and ground-based measurements with +6 hours delay, were assimilated
by spatial interpolation. Within this method, the added value of MWR observations is highly dependent on the current weather situation, cloudiness, and the position of MWR. As the next step, an observing system experiment is planned and will be performed to show the possible benefit
of assimilation of ground-based MWR observations into the regional high-resolution ICON model.

Estimating the impact of commercial observations with an Ensemble of Data Assimilations approach
Vandenberghe, Francois(1); Zhang, Hailing(1); Gas, Clementine(1); Auligne, Tom(1)(1)
Joint Center for Satellite Data Assimilation
The assessment of the impact of individual components of the observing system on forecast quality is traditionally conducted through Observing System Experiments (OSE) or Forecast Sensitivity - Observation Impact experiments (FSOI), when the real observations are available, orObserving System Simulation Experiments (OSSE) when observations must be simulated. When an ensemble forecast is available, an alternative
approach has been shown to be a good alternative, at least qualitatively. This approach estimates the analysis uncertainty from the spread
between the ensemble members. The change in spread between two different ensembles, one with and one without the observing system ofinterest, provides useful information about the impact of that observing system on forecast accuracy. This method was demonstrated using
simulated data from the AEOLUS mission, and later applied at ECMWF with simulated GNSSRO measurements. Using the JEDI's Ensemble Data
Assimilation function, JCSDA has conducted similar experiments with the GNSSRO commercial data acquired during NOAA Data Order 1 period(Dec. 15 - Jan. 15) using NOAA FV3-GFS operational ensemble 6-hours forecasts. In this preliminary study, we looked at the ensemble spread
reduction after analysis and no forecast was performed. The spread reduction resulting from the assimilation of commercial observations canclearly be identified and its magnitude is consistent with spread reductions that have been previously observed at ECMWF with synthetic data.
Those preliminary results give confidence that the method provides useful qualitative information about observation impact on analysis accuracy, and we are planning to expand the method as more commercial weather data are available.

Impact of Aircraft High-Density Observations on GFSv16 Tropical Cyclone Forecasts
Wu, Xingren(1); Kleist, Daryl(2); Tallapragada, Vĳay(2); Yang, Fanglin(2); Sippel, Jason(3)(1)
IMSG at EMC/NCEP/NWS/NOAA, (2)NOAA Environmental Modeling Center, (3)NOAA/AOML/Hurricane Research Division
High-density observation (HDOB) data are assimilated in the operational Hurricane Weather Research and Forecast (HWRF) model. HDOBs include flight-level wind, temperature, and moisture measurements, and SFMR-derived surface wind speeds from the NOAA P-3, NOAA G-IV andAir Force Reserve Command C-130 aircraft. The assimilation of HDOB data in HWRF showed a considerable forecast benefit for tropical cyclone
forecasts. Changes to the NCEP Global Forecast System (GFS) have been driven by the successes in HWRF data assimilation. In this study the
NCEP pre-operational GFS version 16 (GFSv16) was used to examine the impact of HDOB data on the GFS forecast for hurricanes. GFSv16 isbased on GFS version 15, with the finite volume cubed-sphere dynamical core and microphysics from GFDL. Changes in GFSv16 include increasing the vertical resolution from 64 to 127 levels and moving the model top to 80 km height, improved physics, using a Local Ensemble
Kalman Filter with model space localization and linearized observation operator, and employing the 4-Dimensional Incremental Analysis Update technique (4D-IAU) for data assimilation (DA). The experiments to assimilate HDOB data were set up and run using the same GFSv16 preoperational version for reforecast with a 6-hourly DA cycle, over selected periods with available HDOB data. Track and intensity have been
analyzed and compared to the GFSv16 reforecast. Improvements are observed with less track error and an increase in track forecast skill for
the Atlantic and West Pacific when the HDOBs data are used. For the Eastern Pacific basin, the impact is neutral on the track forecast and there
is slight degradation in the intensity. Based on most of the positive impacts, the HDOB was added to GFSv16 for operations.

Impact of Atmospheric River Reconnaissance Dropsonde Data on GFS Precipitation Forecasts: A Case Study
Wu, Xingren(1); Wu, Keqin(2); Tallapragada, Vĳay(3); Ralph, F. M.(4)(1)
IMSG at EMC/NCEP/NWS/NOAA, (2)NGI at EMC/NCEP/NWS/NOAA, (3)EMC/NCEP/NWS/NOAA, (4)CW3E, Scripps Institution of Oceanography,
Atmospheric rivers (ARs) are long narrow corridors of water vapor transport that serve as the primary mechanism to advect moisture into midlatitude continental regions, including the U.S. West Coast. They are responsible for most of the horizontal water vapor flux outside of thetropics and a source of precipitation. Although the advancements in satellite data assimilation has greatly improved global model forecast skill,including the NCEP global forecast system (GFS), forecasting the AR features remains a challenge due in part to their formation and propagation
over the ocean, where in-situ and ground-based observations are extremely limited. The AR Reconnaissance (AR Recon) Campaigns provides
additional data by supplementing conventional data assimilation with dropsonde observations of the full atmospheric profile of water vapor,temperature, and winds within ARs. In this study we used NCEP GFS version 16 (GFSv16) to examine the impact of the AR supplemental observations dropsonde data on GFS forecast.The dropsonde data used were from the AR Recon 2021 campaigns, including 29 intensive observation
periods (IOPs) from Jan 17 to Mar 18. Global denial experiment was conducted near real-time by denying the dropsonde data in the GFSv16
from January 17 to March 25 for both DA and model forecast. Preliminary analysis indicates that there is significant improvement for the
precipitation prediction over California during January 27-29 for an AR landfall event when the dropsonde data were used. This is a first case
AR Recon provides six consecutive IOPs from January 23 to 28.The AR supplemental observations have helped to fill the data gap that is needed
for the data assimilation to provide better GFSv16 model initial condition.

Interpreting estimated Observation Error Statistics of Weather Radar Measurements using the ICON-LAM-KENDA
SystemZeng, Yuefei(1); Janjic, Tĳana(1); Yuxuan, Feng(1,2); Blahak, Ulrich(3); De, Lozar Alberto(3); Bauernschubert, Elisabeth(3); Stephan, Klaus(3); Min,
Jinzhong(2)(1)
Meteorologisches Institut, Ludwig-Maximilians-Universität, (2)Key Laboratory of Meteorological Disaster of Ministry of Education/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science & Technology, (3)DWD
Assimilation of weather radar measurements including radar reflectivity and radial wind data has been operational at the Deutscher Wetterdienst, with a diagonal observation error (OE) covariance matrix. For an implementation of a full OE covariance matrix, the statistics of the OE
have to be a priori estimated, for which the Desroziers method has been often used. However, the resulted statistics consists of contributions
from different error sources and are difficult to interpret. In this work, we use an approach that is based on samples for truncation error inradar observation space to approximate the representation error due to unresolved scales and processes (RE) and compare its statistics with
the OE statistics estimated by the Desroziers method. It is found that the statistics of the RE help the understanding of several importantfeatures in the variances and correlation length scales of the OE for both reflectivity and radial wind data and the other error sources from themicrophysical scheme, radar observation operator and the superobbing technique may also contribute, for instance, to differences among
different elevations and observation types. The statistics presented here can serve as a guideline for selecting which observations to assimilate
and for assignment of the OE covariance matrix that can be diagonal or full and correlated.

Tuesday, 14 Sep 2021

Poster session P2 - Methodology
Breakdown of the equivalence between two common preconditionnings in multi-incremental variational data assimilation
Baillot, D'etivaux Nicolas(1); Gurol, Selime(2); Ménétrier, Benjamin(3); Michel, Yann(1)(1)
Centre national de recherches météorologiques, (2)CERFACS [Toulouse], (3)Institut de recherche en informatique de Toulouse
Variational Data Assimilation (DA) schemes are often used to adress high dimensional non-linear problems in operational applications in the
NWP domain. Because of the high computational cost of such minimization problems, various methods can be applied to improve the convergence at a reasonable numearical cost. One of these methods currently applied in operational DA schemes is the multi-incremental approachthat consists in solving a succession of linearized versions of the original non-linear problem in several outer loops, by using well known
algorithms (such as Lanczos) to ensure the convergence of the linear problem at the inner loop level, and using the solution of the inner loops
to update the problem at each outer loop. In order to save computational cost, the multi-incremental multi-resolution method consists instarting the minimization at a lower resolution than the original one, and increasing it at the outer loop level until the full resolution of the
problem. On the other hand, the conditionning of NWP problems is often poor, and one can use preconditionning techniques in order toimprove the convergence. We have applied the multi-incremental multi-resolution scheme to a simplified problem in order to study the equivalence of two well known preconditionnings ("full" or "square root") in such a scheme and also present a new alternative method to update theproblem at the outer loop level. We illustrate the differences with the standard method currently used and compare those two methods to thetheoretical result. Some equivalence conditions between the updating methods and the two preconditionnings are drawn according to the way
the resolution change is realised at the outer loop level.

Impact of assimilating SST vs nudging in an atmosphere ocean coupled model
Bhargava, Kriti(1); Chang, Chu-Chun(2); Kalnay, Eugenia(2); Da, Cheng(2); Sluka, Travis(1)(1)
University Corporation for Atmospheric Research, (2)University of Maryland
In the operational CFS, the surface sea temperature (SST) and surface sea salinity(SSS) at the surface layer is constrained by nudging to the
Reynolds OI SST and the annual salinity climatology, respectively. However, this nudging towards SST leads to the alleviation of the SST biases
in the CFS analysis. This influences the air-sea flux estimation and leads to a biased forecast. To address this issue, we replace the SST nudging
with the Weakly Coupled Data Assimilation of the level 2 (L2) and level 4 (L4) SST products (Canada Meteorological Center). We ran a series of
1-month WCDA and nudging experiments using the CFSv2-LETKF. Our results show that, unlike nudging, WCDA can efficiently correct theexisting SST biases and produce a more accurate SST analysis. This also improves the SST 1 to 10 days forecasts. WCDA of SSTs also reduces
the RMS error in the atmospheric forecast variables not only at the surface but also at higher model levels.

JEDI application in Assimilation and Evaluation of GTS Synoptic Snow Depth Observations into NCEP Operational
FV3GFS System
Dong, Jiarui(1); Kleist, Daryl(2); Barlarge, Michael(2); Fox, Andy(3); Draper, Clara(4); Gichamo, Tseganeh(4)(1)
IMSG at NOAA/NCEP/EMC, (2)NOAA/NCEP/EMC, (3)JCSDA/UCAR, (4)PSL
The land analysis in the NOAA's Global Forecast System with FV3 dynamical core (FV3GFS) was conducted with the Global Land Data Assimilation System (GLDAS) using the Noah LSM (Land Surface Model) to evolve land states and to compute surface fluxes. The land states are updated
using a “semi-coupled” approach, where these states are generated from a parallel GLDAS driven by observed precipitation and with nearsurface forcing from the parent atmospheric data analysis system. However, assimilation of in-situ and remotely-sensed estimates of landsurface states such as soil moisture and snowpack are not supported yet in the operational GLDAS. The Joint Effort for Data assimilation Integration (JEDI) is a unified and versatile data assimilation (DA) system contributing to the Numerical Weather Prediction. NOAA/NCEP working
with NOAA/PSL and JCSDA will set up necessary infrastructure to replace GLDAS with JEDI based land data assimilation system for use ininitializing the NCEP operational FV3GFS forecast. A set of experiments using 3DVar and JEDI-based OI were conducted to evaluate the impactof assimilating the synoptic snow depth data. The qualities of the GTS synoptic snow depth data were assessed to flag or filter out the poor
observations. Optimal parameters are determined by assimilating a single point observation into FV3GFS background setup through specificparameter tuning. We will explore the impact of the snow assimilation on FV3GFS forecasts and compare to the PSL's offline OI (optimal interpolation) benchmark tests.

A Comparison of Two Local Moment-Matching Nonlinear Filters: Local Particle Filter (LPF) and Local Nonlinear
Ensemble Transform Filter (LNETF)
Feng, Jie(1); Wang, Xuguang(2); Poterjoy, Jonathan(3)(1)
Department of Atmospheric and Oceanic Sciences, Fudan University, (2)School of Meteorology, University of Oklahoma, (3)Department of Atmospheric and Oceanic Science, University of Maryland
The local particle filter (LPF) and the local nonlinear ensemble transform filter (LNETF) are two moment-matching nonlinear filters to approximate the classical particle filter (PF). They adopt different strategies to alleviate filter degeneracy. LPF and LNETF assimilate observations in apartially sequential and a simultaneous manner, respectively. In addition, LPF applies the resampling step, whereas LNETF applies the deterministic square root transformation to update particles. Both methods preserve the posterior mean and variance of the PF. LNETF additionallypreserves the posterior correlation of the PF for state variables within a local volume. These differences lead to their differing performance infilter stability and posterior moment estimation. LPF and LNETF are systematically compared and analyzed here through a set of experimentswith a Lorenz model. Strategies to improve the LNETF are proposed. The original LNETF is inferior to the original LPF in filter stability andanalysis accuracy, particularly for small particle numbers. This is attributed to both the localization function and particle update differences.
The LNETF localization function imposes a stronger observation impact than the LPF for remote grids and thus is more susceptible to filter
degeneracy. The LNETF update causes an overall narrower range of posteriors that excludes true states more frequently. After applying thesame localization function as the LPF and additional posterior inflation to the LNETF, the two filters reach similar filter stability and analysis
accuracy for all particle numbers. The improved LNETF shows more accurate posterior probability distribution but slightly worse spatial correlation of posteriors than the LPF.

Updating and Testing the Snow Data Assimilation in the Unified Forecast System (UFS) Land surface model Noah
Gichamo, Tseganeh Z(1); Draper, Clara(2)(1)
CIRES/NOAA ESRL PSL, (2)NOAA, ESRL, PSL
Within the NOAA National Weather Service Unified Forecast System (UFS), land processes are simulated by Noah land surface model (LSM).
Currently, snow depth and snow cover observations are assimilated in to the Noah model using rule-based approaches to update the snow
states on a daily time step using the Air Force Weather Agency (AFWA) gridded snow depth analysis product (SNODEP) and snow cover datafrom NOAA/NESDIS (National Oceanic and Atmospheric Administration's National Environmental Satellite, Data, and Information Service) Interactive Multi-sensor Snow and Ice Mapping System (IMS). While the approach used at NCEP improved the forecasts over its predecessors, it now
needs updating. Other global forecast centers use more advanced data assimilation approaches, compared to the one at the NCEP UFS, toassimilate ground observations of snow depth and snow cover data. In this study, we updated the snow data assimilation in UFS by implementing the 2D Optimal Interpolation (OI) method to assimilate daily snow depth observations from the Global Hydrologic Climatology Network(GHCN) and the NESDIS IMS snow cover data. The data assimilation scheme employed here is based on ECMWF's implementation, although wehave made some changes to suit our particular implementation. Evaluation of the new OI DA in UFS shows marginal improvement of snow depth
and significant increase in snow cover skills. The improved snow cover leads to reduced error in near surface air temperature. Following a
successful demonstration of these improvements, the OI will be transitioned into operational use at NCEP.

Continuum Covariance Propagation for Understanding Variance Loss in Advective Systems
Gilpin, Shay(1,2); Matsuo, Tomoko(2,1); Cohn, Stephen E.(3)(1)
Department of Applied Mathematics, University of Colorado Boulder, (2)Ann and H.J. Smead Department of Aerospace Engineering Sciences,
University of Colorado Boulder, (3)Global Modeling and Assimilation Office, NASA Goddard Space Flight Center
At the heart of modern data assimilation schemes is covariance propagation. Loss of variance experienced in large-scale applications such as
numerical weather prediction is problematic, and the development of auxiliary methods to mitigate this issue is an active research area. The
focus of this work is to understand the root causes of variance loss and show that for advective dynamics, the covariance propagation by itself
typically causes significant, spurious loss of variance, even at full rank. To demonstrate this, we first study continuum covariance propagation
by analyzing the covariance evolution equation for advective dynamics. The behavior of this evolution equation changes abruptly as the correlation length tends to zero, for example in the vicinity of sharp gradients in the advection field. This happens because the diagonal of the kernelof the covariance operator is a characteristic surface for advective dynamics. Our numerical experiments then confirm that the variance lost
during numerical propagation greatly exceeds that due to numerical dissipation alone. The variance loss is driven primarily by inaccurate
variance propagation resulting from standard, full-rank covariance propagation schemes, which have difficulty capturing the abrupt change indynamics as the correlation length tends to zero. These results suggest that developing local covariance propagation methods may prove
useful in ameliorating the variance loss observed in data assimilation schemes.

A new way to infer non-Gaussian observation errors based on ensemble innovations
Hu, Chih-Chi(1); Van, Leeuwen Peter Jan(1,2)(1)
Department of Atmospheric Sciences, Colorado State University [Fort Collins], (2)University of Reading

The proper specification of the observation error is critical to a well-performing data assimilation system. The observation error can be divided
into the measurement error, which is an inherent part of the results of the measurement, and the representation error, which is the uncertainty
in the forward operator. The measurement error is usually well-known based on the design of the instrument, while the representation error
is often less understood and often dominant. Previous studies have infered the observation error from the innovation statistics, often assumingthat the background error in observation space and the observation error are Gaussian. However, this Gaussian assumption can be problematic,
especially for nonlinear forward operators. This study infers a non-parametric observation error pdf based on a background ensemble without
any assumption on the shape of the background error or observation error pdfs. The only assumption is that each ensemble member is statistically indistinguishable from the truth. Since the innovation pdf is the convolution of observation error and background error pdfs, the observation error pdf can be found by expressing the pdfs as histograms and solving a linear system. Experiments with toy systems and real cloudy
observations show that this new method is able to retrieve non-Gaussian observation pdfs, even multimodal pdfs, demonstrating the potentialof this method for complex representation errors in real atmospheric observations.

An Adaptive R Estimator with a Storm-Scale Particle Filter
Kawabata, Takuya(1); Ueno, Genta(2)(1)
Meteorological Research Institute [Tsukuba], (2)The Institute of Statistical Mathematics
Ueno and Nakamura (2016) proposes the adaptive R estimator (ARE) with an ensemble Kalman filter for estimating flow-dependent observational errors in sea-surface-height observations by TOPEX/POSEIDON. ARE is a Bayesian estimator with a likelihood estimated with an ensemble and a prior R with a Wishart distribution. Kawabata and Ueno (2020) have developed a storm-scale particle filter with the JMA nonhydrostatic model, and ARE has been implemented in the filter, called as NHM-RPF (NonHydrostatilce Model R-estimating Particle Filter). NHM-RPF
succeeded to represent non Gaussianity of initiation and development stages of a Cb with 1000 ensemble and showed the origin of the non
Gaussianity was the updraft on the top of the boundary layer in an OSSE. For this success, ARE played an essential role in stable filtering. In thecase that ARE was switched off, NHM-RPF illustrated worse scores than the no data assimilation experiment. The estimated R seemed to bereasonable and to follow the stages and course of the Cb. In this presentation, we will discuss detailed impacts of ARE in the experiment andalso show another case with actual observations.

Local Ensemble Transform Kalman Filter Experiments with Hybrid Background Error Covariance: A Case with an
Intermediate AGCM
Kotsuki, Shunji(1); Bishop, Craig(2)(1)
CEReS, Chiba University, (2)U. Melbourne
Recent operational numerical weather prediction (NWP) systems have achieved significant forecast improvements by using hybrid backgrounderror covariance (HBEC) that linearly combines climatological and ensemble-based error covariance. The HBEC has been used mainly in variational data assimilation systems to use the flow-dependent error covariance in addition to static error covariance. This study explores using
the HBEC within the local ensemble transform Kalman filter (LETKF). The standard LETKF approximates the error covariance matrix by sample
estimates using the flow-dependent ensemble perturbations. This study followed the Kretchmer et al. (2015)'s approach and implemented the
HBEC by adding a collection of climatological perturbations to the forecast ensemble mean to boost the rank of the background error covariance. For HBEC, we propose a new localization (Z-localization) that attenuates ensemble perturbation instead of inflating observation error
variance (R-inflation). The Z-localization enables multi-scale localization by applying different localization scales to flow-dependent and cli-matological perturbations. We tested the HBEC and new localization with an intermediate atmospheric model known as the SPEEDY (Simplified
Parameterizations, Primitive Equation Dynamics). Our preliminary results are promising, and the HBEC successfully reduced analysis errors
compared to the standard LETKF. This presentation will include the most recent progress up to the time of the symposium.

Numerical discretization causing error variance loss and the need for inflation
Ménard, Richard(1); Skachko, Sergey(2); Pannekoucke, Olivier(3)(1)
Environnement et Changement climatique Canada, (2)Environment and Climate Change Canada, (3)CNRM
The effects of model discretization errors on the propagation of error covariance are different and more complex than the effect of model
discretization errors of a state variable. The analysis is carried out for the advection transport equation, where the continuous (space/time)
propagation of the related error covariance function can be written, solved and compared with the discrete model applied on the covariancematrix. The numerical analysis of the problem is carried with a 1D-problem, but is also illustrated with a 3D-chemical transport model used
for chemical data assimilation of satellite observations. It is shown that variance loss (compared to the continuous propagation solution) depends on the covariance function itself as well as the numerical discretization scheme. The variance loss is particularly sensitive to the correlation length and model discretization. In a simple first-order discretization an analytical expression is obtained and is used to obtain ananalytical expression for inflation. Experiments shows that for example that right after an analysis using a dense network of observations (withspatially uncorrelated errors) results in a very significant variance loss, over the observed region. This effect occurs between analysis times
during time propagation. With the (variance) inflation scheme, we are able to restore the variance loss at each grid point, at each time steps,
during the whole time of integration. The variance inflation scheme applied to an EnKF can be formulated to change the variance spread of theensemble or directly on the state variables.

Assimilation of Nonlinear Observations with the Maximum Likelihood Ensemble Filter
Nakashita, Saori(1); Enomoto, Takeshi(2)(1)
Graduate School of Science, Kyoto University, (2)Disaster Prevention Research Institute, Kyoto University
We investigate the performance of the Maximum Likelihood Ensemble Filter (MLEF) in assimilation of nonlinear observations. MLEF is a variational-ensemble data assimilation method, and can treat differentiable or non-differentiable nonlinear observation operators. In this study,
we compare MLEF with the Ensemble Transform Kalman Filter (ETKF) in assimilation experiments with a one-dimensional Burgers model. The
ETKF analysis with a certain formulation of nonlinear operators diverges when the observation nonlinearity is strong and the observation error
is small. This divergence is found to be associated with an extra rank of ensemble perturbation matrix. Optimization in MLEF can improve theanalysis to the level comparable to or better than ETKF. In addition, the smaller observation error is, or the stronger observation nonlinearity
is, MLEF with the nonlinear operators can assimilate observations more effectively than MLEF with the tangent linear operators. However, thestrong nonlinearity hinders convergence. We found that re-evaluation of the Hessian preconditioning matrix can alleviate such poor convergence. These encouraging results indicate that MLEF can incorporate nonlinear effects and evaluate observations appropriately.

A hybrid nonlinear-Kalman ensemble transform filter for data assimilation in systems with different degrees ofnonlinearity
Nerger, Lars(1)(1)
Alfred Wegener InstituteA hybrid nonlinear-Kalman ensemble transform filter (LKNETF) algorithm is build by combining the second-order exact particle filter NETF
(nonlinear ensemble transform filter) with the local ensemble transform Kalman filter (LETKF). The hybrid filter combines the stability of the
LETKF with the nonlinear properties of the NETF to obtain improved assimilation results for small ensemble sizes. Both filter components are
localized in a consistent way so that the filter can be applied with high-dimensional models. The degree of filter nonlinearity is defined by ahybrid weight, which shifts the analysis between the LETKF and NETF. Since the NETF is more sensitive to sampling errors than the LETKF, thelatter filter should be preferred in linear Gaussian cases. An adaptive hybrid weight can be defined based on the nonlinearity of the system sothat the adaptivity yields a good filter performance in both linear and nonlinear situations. In particular the skewness and kurtosis of theensemble can be applied to quantify the non-Gaussianity. The filter behavior is exemplified based on experiments with the chaotic Lorenz-63
und -96 models, in which the nonlinearity can be controlled by the length of the forecast phase. In these experiments the hybrid filter can yieldan error reduction of up to 28% compared to the LETKF.

CAFE60v1: The CSIRO Climate retrospective Analysis and Forecast Ensemble system: version 1: System design,model configuration and data assimilation.
O'kane, Terence(1); Sandery, Paul(1); Sakov, Pavel(2); Kitsios, Vassili(1); Chamberlain, Matthew(1); Collier, Mark(3); Fiedler, Russell(4); Moore,
Thomas(4); Chapman, Christopher(4); Sloyan, Bernadette(4); Matear, Richard(4)(1)
Terence O'Kane, (2)Bureau of Meteorology, Australia, (3)CSIRO Oceans and Atmosphere, (4)CSIRO Oceans and AtmosphereWe detail the system design, model configuration and data assimilation of the CSIRO Climate retrospective Analysis and Forecast Ensemble
system: version 1. CAFE60v1 has been designed with the intention of simultaneously generating both initial conditions for multi-year climate
forecasts and a large ensemble retrospective analysis of the global climate system from 1960 to present. Strongly coupled data assimilation(SCDA) is implemented via an ensemble transform Kalman filter in order to constrain a general circulation climate model to observations.
Satellite (altimetry, sea surface temperature, sea ice concentration) and in-situ ocean temperature and salinity profiles are directly assimilated
each month, whereas atmospheric observations are sub-sampled from the JRA-55 atmospheric reanalysis. Strong coupling is implemented via
explicit cross domain covariances between ocean, atmosphere, sea ice and ocean biogeochemistry. Atmospheric and surface ocean fields areavailable at daily resolution and monthly resolution for the land, subsurface ocean and sea ice. The system produces 96 climate trajectories(state estimates) over the most recent six decades as well as a complete data archive of initial conditions potentially enabling individual forecasts for all members each month over the 60 year period. The size of the ensemble and application of strongly coupled data assimilation leadto new insights for future reanalyses.

Assimilation of SMAP Brightness Temperature Observations in the GEOS Land-Atmosphere Data Assimilation
SystemReichle, Rolf(1); Zhang, Sara Q.(2,1); Liu, Qing(3,1); Draper, Clara S.(4); Kolassa, Jana(3,1); Todling, Ricardo(1)(1)
NASA Goddard Space Flight Center, (2)Science Applications International Corporation, (3)Science Systems and Applications, Inc. [Lanham],(4)
NOAA Earth System Research Laboratories
Errors in soil moisture adversely impact the modeling of land-atmosphere water and energy fluxes and, consequently, near-surface atmospheric conditions in atmospheric data assimilation systems (ADAS). To mitigate such errors, a land surface analysis is included in many such
systems, although not yet in the currently operational NASA Goddard Earth Observing System (GEOS) ADAS. This study investigates the assimilation of L-band brightness temperature (Tb) observations from the Soil Moisture Active Passive (SMAP) mission in the recently developed GEOS
weakly-coupled land-atmosphere data assimilation system (LADAS) during summer 2017. The SMAP Tb analysis improves the correlation vs.
in situ measurements of LADAS soil moisture by ~0.1-0.26 over that of the ADAS; the unbiased root-mean-square error (ubRMSE) of LADAS
soil moisture is reduced by 0.002-0.008 m3 m-3 from that of ADAS. Furthermore, the global average RMSE vs. in situ measurements of LADAS
screen-level air specific humidity (q2m) and daily maximum temperature (T2mmax) is reduced by 0.05 g kg-1 and 0.04 K, respectively, fromthat of ADAS. Regionally, the RMSE of LADAS q2m and T2mmax is improved by up to 0.4 g kg-1 and 0.3 K, respectively. Improvements in
LADAS specific humidity extend into the lower atmosphere (below ~700 mb), with relative improvements in q2m bias and ubRMSE of 15-25%
and 1-3%, respectively. LADAS air temperature bias slightly increases but ubRMSE is reduced relative to that of ADAS. Finally, the root-meansquare of the LADAS Tb observation-minus-forecast residuals is smaller by ~0.1 K than in a land-only assimilation system, which corroborates
the positive impact of the Tb analysis on the modeled land-atmosphere coupling.

Towards the assimilation of microwave vegetation optical depth into global land surface models
Scherrer, Samuel(1); Dorigo, Wouter(1); Heyvaert, Zdenko(2); De, Lannoy Gabrielle(2); Gruber, Alexander(2); Büechi, Emanuel(1); Calvet, JeanChristophe(3)(1)
Technische Universität Wien, (2)Catholic University of Leuven, (3)CNRM
Only a few recent studies have considered assimilating microwave vegetation optical depth (VOD) to improve estimates of vegetation states inland surface models (LSMs) with dynamic vegetation. These studies showed that VOD assimilation can improve estimates of GPP, ET, and CO2
concentrations. We intend to assimilate VOD from merged products, as well as from multiple individual passive and active microwave sensors,
using the NoahMP and SURFEX-ISBA LSMs. This poses several challenges, in particular developing observation operators for VOD for the different sensors, or characterizing model and observation error structures. We will present various linear and nonlinear approaches to map variables
simulated by the models to VOD observations. These will be evaluated with respect to their performance with merged VOD products from theVOD Climate Archive (VODCA) for different bands (Ku/X/C/L), and on single-sensor VOD retrievals from LPRMv6. Additionally, we will assess
the added value of including plant water status related variables compared to using only optical vegetation indices as predictor variables for
VOD. Acknowledgements: This work is part of the projects CONSOLIDATION, funded by the Austrian Science Fund (FWF), and the CCN1 CCI+
Soil Moisture Scientific Evolution, funded by ESA.

A variational particle filter
Subrahmanya, Amit N(1); Popov, Andrey A(1); Sandu, Adrian(1)(1)
Department of Computer Science at Virginia Tech
Particle filters or Sequential Monte Carlo techniques are used in data assimilation for state estimation. Unlike other Kalman-like methods,
particle filters work with fewer assumptions and are generic in nature. Standard variational particle techniques involve minimizing the KL Divergence between the prior and a Bayesian posterior via kernel embeddings. However, these methods require rigorous tuning in a high-dimensional setting. In this work, we propose to perform the aforementioned minimization without kernel embeddings. We perform a sequence oftransformations to push the particles towards the posterior. With this in mind, we have developed methods for both filtering and smoothing.
We have run experiments on Lorenz '63, Lorenz '96 and the quasi-geostrophic equations that show promising results qualitatively(rank histogram) and quantitatively(RMSE) when compared to the Mapping Particle Filter, the Ensemble Transform Particle Filter and the Ensemble
Kalman Filter.

Wednesday, 15 Sep 2021

Poster session P3 - General DA
High-Resolution Regional Ocean Data Assimilation in JEDI-SOCA framework: Hurricane Supplemental Project at
NOAA-EMC
Book, Cameron(1); Liu, Ling(1); Vernieres, Guillaume(2); Sluka, Travis(2); Bhargava, Kriti(2); Kang, Heesook(3); Kim, Hyun-Sook(4); Kim, Jong(1);Mehra, Avichal(5)(1)
IMSG at NOAA-NWS-NCEP, (2)JCSDA/UCAR, (3)University of Miami/NOAA/AOML, (4)NOAA/AOML, (5)NOAA-NWS-NCEP
Preliminary regional ocean data assimilation (DA) and initialization experiment results using the Modular Ocean Model Version 6 (MOM6) arepresented based upon the Joint Effort for Data assimilation Integration - Sea Ice Ocean and Coupled Assimilation (JEDI-SOCA) framework. Our
goal is to support the UFS-based coupled Hurricane Analysis and Forecast System (HAFS) development with improved, realistic initial ocean
conditions. Along with various in-situ and satellite retrieval data sets, high-density marine observations including glider and HF-radar are
assimilated using a 3DVar method. Cycled experiments have been performed for the 2020 Hurricane season with a specific focus on diagnostic
evaluation of ocean conditions during Hurricane Isaias. Our analysis results are cross-validated against various independent ocean analysis
products, such as the Real-Time Ocean Forecast System - Data Assimilation (RTOFS-DA), Navy Coupled Ocean Data Assimilation (NCODA), andthe Operational Sea Surface Temperature and Ice Analysis System (OSTIA). Further, we conducted preliminary observation data denial experiments to investigate the impact of specific marine observation datasets on initial ocean states. In our studies, we focused on ocean dynamics
in both the surface and subsurface layers along the hurricane track. As a future goal, we seek to use these MOM6 JEDI-SOCA results to better
constrain ocean properties such as mixed layer temperature and salinity, as well as ocean heat content and heat fluxes for improved coupledhurricane forecasts.

Assimilation of Aerosol Observations in the NASA GEOS model
Buchard, Virginie(1,2); Da, Silva Arlindo(1); Holdaway, Dan(3,1)(1)
NASA Goddard Space Flight Center, (2)Universities Space Research Association, (3)University Corporation for Atmospheric Research
In the GEOS near real-time system and in the latest MERRA-2 reanalysis produced at NASA's Global Modeling Assimilation Office, the assimilation of aerosol observations is performed using a so-called analysis splitting method. In this scheme, a 2D analysis of AOD is first performed,
using error covariances derived from innovation data. Then, the 3D analysis increments of aerosol mass concentration are computed using an
ensemble formulation for the background error covariance. The prognostic model underlying this data assimilation system is based on the
GEOS earth system model radiatively coupled to GOCART aerosol module. Leveraging progress made in the JCSDA-JEDI framework, the GMAO
is transitioning its aerosol data assimilation scheme to a hybrid ensemble-variational scheme. This new scheme brings the flow-dependence
in the background error specification inherent in ensemble methods but retains some of the flexibility of variational methods, permitting
assimilation of multi-spectral passive and active aerosol measurements. Within this new system, we continue to employ an analysis splitting strategy, this time upgrading the control variable to vertically resolved aerosol extinction. In addition, the observing system is being
expanded to include observations of multi-wavelength AOD from geostationary sensors. In this talk, we will examine the impact of replacing
the control variables in the analysis splitting scheme, going from single-wavelength AOD to extinction profiles at multiple wavelengths. The
analysis step is also being upgraded from a 2D PSAS-based analysis to a JEDI- based hybrid approach. We will discuss the impact of including
multi-wavelength AOD retrievals, and the impact of geostationary aerosol data.

Studying Causal Mechanisms of the Ocean with the ECCO Estimate: Beaufort Sea Sea-Level and Freshwater-Content Change
Fukumori, Ichiro(1); Wang, Ou(1); Fenty, Ian(1)(1)
Jet Propulsion Laboratory
The “Estimating the Circulation and Climate of the Ocean” (ECCO) consortium provides physically-consistent multi-decadal model-data syntheses of the complete, three-dimensional, time-varying, global ocean state and the underlying processes governing its dynamical evolution.
In this talk we demonstrate how ECCO's strict adherence to conservation principles and the availability of its model's adjoint allow interrogationof these processes to gain novel insights into the causal mechanisms driving observed ocean climate variability — here, the rapid sea-level riseand freshwater accumulation in the Western Arctic's Beaufort Sea over the last two decades. Using the adjoint model, we calculate the sensitivity of Beaufort Sea's variation with respect to its forcings and employ it as a kernel to expand the variation in terms of those forcings, viz.,surface heat, freshwater, and momentum fluxes, so as to quantify their relative contributions in space and time. After showing that wind stressand surface freshwater flux anomalies explain the observed sea-level and freshwater content rise, we then analyze the region's property budgets and quantify that wind-driven convergence and melting of sea ice dictate the freshwater flux and are as important as Ekman ocean volumetransport for the observed Beaufort Sea variation. The suitability of the ECCO estimate and its adjoint model for investigating causal mechanisms of other types of ocean dynamical variation is briefly described.

Impact of COVID-19 measures on the air quality, monitored for the state of Himachal Pradesh: A Google EarthEngine Based Study
Galodha, Abhinav(1); Prakash, Dr. Chander(2)(1)
Indian Institute of Technology Delhi, (2)NIT Hamirpur
The World Health Organization (WHO) declared the COVID-19 outbreak a pandemic on March 11, 2020, and advised countries to take immediate and concerted action. Governments of India and Himachal Pradesh carried out preventive and precautionary steps to minimize the spreadof coronavirus disease. In this study, the impact of a sudden halt in human activity on air quality was investigated by looking at changes insatellite imagery using a remote sensing approach. The concentrations of the gaseous contaminants studied (CO, SO2, NO2, and C6H6) show
a significant decrease during the lockdown, which is consistent with findings published in many reputed journals where studies were carriedout in foreign cities and metropolitan areas. The average concentrations of particulate matter (PM10 and PM2.5) differed significantly fromthose of gaseous emissions, meaning that particulate matter has a greater effect occurring from anthropogenic activities.NO2 concentrations
and NOx emission variations were tracked for rural/town areas around Himachal Pradesh and major urban cities of India. Daily top-down NOx
emissions were measured using the TROPOspheric Monitoring Instrument (TROPOMI), which assisted in retrieving NO2 from the steady-statecontinuity equation. The emissions of NOx from rural, urban, and power plants were compared before and after the lockdown. The research
was done in a phased manner to account for the second wave of COVID-19 for our studies. The levels of NO2, Ozone (O3), and sulfur dioxide(SO2) were monitored, using Sentinel-5P imagery using the GEE platform and photochemical processes being the cause of O3 formation concluding our study.

Evaluation of ECCO Currents in the Pacific Equatorial Undercurrent
Halpern, David(1); Le, Megan(2); Smith, Timothy(3)(1)
Scripps Institution of Oceanography, (2)University of Texas at Austin, (3)University of Texas at Austin
The representation of horizontal currents produced by the Estimating the Circulation and Climate of the Ocean (ECCO) global ocean state
estimate, an inverse modeling framework constrained with nearly all available in-situ and satellite ocean observations, was evaluated withmoored acoustic Doppler current profiler (ADCP) measurements recorded on the equator in the Equatorial Undercurrent (EUC) at 165E, 170W,
140W and 110W. ADCP data were not assimilated. Record lengths of 1-h data were at least 10 years. Artificial gaps in ECCO matched ADCP
gaps. The average ADCP EUC core speed was nearly 100% larger than that computed with ECCO; the average ADCP standard deviation was 30%
higher than ECCO. The average ADCP-ECCO correlation coefficient was higher than 0.71, indicating that more than 50% of the variance waslinearly related. At each site the mean depth of the ADCP core speed was greater than with ECCO. The upward slope towards the east of mean
ADCP core speeds from 165E to 170W was double that of ECCO and only slightly smaller compared to ECCO from 170W to 140W and 140W to
110W. The ADCP and ECCO EUC transports per unit width (TPUW) computed for currents greater than 0.2 m/s indicated an inflow of mass into
the EUC as it flowed from 165E to 170W to 140W and then an outflow from 140W to 110W, with different magnitudes for ADCP and ECCO. As
the EUC flowed eastward, the average correlation coefficient between ADCP and ECCO TPUWs was above 0.71. These and other results will be
discussed.

Atmospheric radiance variation: On the basis of atmospheric aerosols in different locations IranKhoshsima, Masoud(1); Haghshenas, Javad(1)(1)
SATELLITE RESEARCH INSTITUTE, IRANIAN SPACE RESEARCH CENTER, TEHRAN,IRAN
In this paper, variability of atmospheric radiance based on some fundamental atmospheric parameters was considered. Atmospheric opticalindices in near infrared and visible channels including aerosol optical depth, Angstrom Exponent, albedo and visibility are studied. Subsequently, atmospheric radiance and its variability were calculated using Modtran algorithm. The incoming data are achieved from MERRA, GLDAS
and space-borne observing platforms for 10 different locations including urban, continental, polluted, industrial, rural and dusty over Iranduring 2000 to 2020. Results show that the maximum variability for NIR and VIS albedo is about 14% and 4.4%, respectively. Moreover, total
aerosol extinction varies 22 percent in different locations. Consequently, atmospheric radiance varies 13% in visible region. Results indicatethat the variation in aerosol optical indices can affect the atmospheric radiance and despite the improvements in knowledge about aerosol
forcing, a great deal remains uncertain. The results also exhibited that urban locations have a significant role in variation of radiance.

Assimilating TROPOMI Nitrogen Dioxide Retrievals in NOAA's Next-Generation Regional Air Quality Forecasting
SystemMartin, Cory(1); Carley, Jacob(2); Huang, Jianping(3); Kleist, Daryl(2); Kondragunta, Shobha(4); Lin, Hsin-Mu(3); Mcqueen, Jeff(5); Montuoro,
Raffaele(6); Stajner, Ivanka(7); Wang, Hongli(6)(1)
Redline at NOAA/EMC, (2)Environmental Modeling Center, NOAA National Centers for Environmental Prediction, (3)IMSG @ NOAA NWS NCEP
EMC, (4)NOAA National Environmental Satellite, Data, and Information Service, (5)NOAA National Centers for Environmental Prediction, (6)CIRES /NOAA GSL, (7)NOAA/NCEP/EMC
Current estimates of annual mortality from poor air quality are over 100,000 per year in the United States, far exceeding all other weather
hazards combined, which are lower than 600 per year. At the National Centers for Environmental Prediction (NCEP), progress is being made to
improve global and regional air quality and aerosol forecasting through collaborative efforts on both model improvements and enhanced data
assimilation capabilities. As part of the Unified Forecast System (UFS) effort, a new regional, convection-allowing modeling system, the Rapid
Refresh Forecast System (RRFS), is being developed. Within the RRFS, a new regional air quality prediction system is also being developed, using
the Limited Area Model (LAM) version of FV3 coupled with the Community Multiscale Air Quality (CMAQ) model to provide inline atmosphericchemistry. Here we present results demonstrating the ability to assimilate tropospheric columns of nitrogen dioxide from TROPOMI into the
LAM-CMAQ model, using a Joint Effort for Data assimilation Integration (JEDI) based 3DVar system. Sensitivity to observation thinning andquality control are shown. Limitations of the current system are also discussed, including those associated with limited observation availability,
as well as the influence lateral and surface boundary conditions have on the results.

Ocean data assimilation for ICON-Seamless
Pohlmann, Holger(1); Sgoff, Christine(2); Fröhlich, Kristina(2); Brune, Sebastian(3); Baehr, Johanna(3)(1)
Deutscher Wetterdienst, (2)Deutscher Wetterdienst, (3)Universität Hamburg
The method of the "Parallel Data Assimilation Framework" (PDAF) "Ensemble Kalman Filter" (EnKF) is used here as a test case for the assimilationof observed ocean 3-d temperature and salinity fields into the coupled climate model "Icosahedral Nonhydrostatic" (ICON) for seasonal todecadal climate prediction. This ocean-only assimilation serves as a first step towards a weakly coupled data assimilation with ICON. An assimilation run over the period 1960-2014 is produced and analyzed in terms of the degree of realism of its climate variability. This assimilation
run is used to initialize an ensemble of 10 hindcast simulations. The hindcasts are analyzed in terms of prediction skill and used to identify
problems emerging from this method.

Validation of MODIS, MISR and OMI aerosol optical depth with globally distributed AERONET data over the Middle
East region
Sabetghadam, Samaneh(1)(1)
Institute of Geophysics, University of Tehran
In order to ensure the reliability of satellite-based observation, this study validated the aerosol optical depth (AOD) data from three different
instruments including MODIS, MISR, and OMI, against the Aerosol Robotic Network (AERONET) stations across the Middle East from 2012 to2017. The daily level 2.0 AERONET is used for the validation of satellite retrievals at six stations across the Middle East. Errors of aerosol
products were examined using the Pearson correlation coefficient, the relative mean bias (RMB), and the root-mean-square error (RMSE). Comparison between the daily averaged AOD values derived from each instrument and AERONET derived AOD values indicates that all studied
platforms show the annual variation of AOD over the region. MODIS and MISR AOD show a reasonable correlation with AERONET data almost
all over the Middle East, i.e. the average Pearson correlation coefficient for both of these sensors is about 0.7. The differences in the correlation
coefficients in each station may be caused by different aerosol species. It should be mentioned that MODIS has the highest number of available
daily data, while MISR has the least. However, the smallest average RMSE values of about 0.15 belong to the MISR data while the largest errors
were found for MODIS, where the average RMSE value is about 0.3. Results also show that there is a general overestimation of all satellitederived AOD in six sites, i.e. the RMB values are more than 1. On the other hand, two sites namely KAU and CAI, in Saudi Arabia and Egypt,respectively, show underestimation in AOD values, which is likely attributed to the inaccurate assumptions of aerosol mixtures in the aerosol
algorithm and the contribution of coarse mode aerosols in these two stations.

Mesoscale and wind-driven intra-annual variability in the East Auckland Current
Santana, Rafael(1,2); Suanda, Sutara(3); Macdonald, Helen(2); O'callaghan, Joanne(2)(1)
Department of Marine Science, University of Otago, (2)National Institute of Water and Atmospheric Research [Wellington], (3)University of North
Carolina [Wilmington]
Intra-annual variability in the East Auckland Current (EAuC) was studied using a year-long timeseries of in situ and remotely-sensed velocity,temperature and salinity observations. Satellite-derived velocities correlated well (r > 0.75) with in situ observations and well-represent thelong-term (> 30 days) variability of the upper ocean circulation. Four mesoscale eddies were observed during the year (for 260 days) which
generated distinct flows between the continental slope and rise. The EAuC dominated the circulation in the continental shelf break, slope andrise for 110 days and generated the most energetic events associated with wind forcing. Current variability on the continental slope was coherent with along-slope wind stress (wind stress curl) at periods between 4 and 12 days (16 and 32 days). We suggest that along-slope winds
generated offshore Ekman transport, uplift on the shelf-break, and a downwind geostrophic jet on the slope. In contrast, positive wind stresscurl caused convergence of water, downwelling, and increased the current speed in the region. Bottom Ekman transport, generated by the
EAuC, was suggested to have caused the largest temperature anomaly ( −1.5°C ) at the continental shelf-break. Numerical simulations wereconducted to provide a 3D picture of the EAuC and its wind-driven variability. Moreover, data assimilation of satellite and in situ observations
is being implemented into the model to generate more realistic simulations.

Impact of ocean observation systems on ocean analyses and subseasonal forecasts in the Indo-Pacific region
Subramanian, Aneesh(1); Vitart, Frederic(2); Balmaseda, Magdalena Alonso(2); Wei, Ho-Hsuan(1); Karnauskas, Kris(1); Du, Danni(1)(1)
Department of Atmospheric and Oceanic Sciences [Boulder], (2)European Centre for Medium-Range Weather Forecasts
We evaluate the relative merits of different ocean observation systems (moored buoys, Argo, satellite, XBTs, and others) by their impact on
ocean analyses and subseasonal forecast skill of weather and climate phenomena in the Indo-Pacific region. Several ocean analyses were performed where different ocean observation platforms were withheld from the assimilation in addition to one ocean analysis where all observations were assimilated. We then use these ocean analyses products for initializing a set of subseasonal forecasts to evaluate the impact ofdifferent ocean analyses states on the forecast skill. We use the European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble
prediction system for the twenty-year sub-seasonal hindcast experiments. Results from these hindcast experiments will be presented to highlight changes in the ocean analysis states and their impact on the forecast skill in the Indo-Pacific region. Coupled air-sea interaction processes relevant to intraseasonal variability in the earth's climate system are inadequately represented in regional and global coupled models.
These inaccuracies could be related to either poor parameterization of model physics or insufficient model resolution to resolve the criticalprocesses. New efforts in observations, process understanding, and translation into weather and climate models are necessary for improvements in simulation and prediction of the intraseasonal variability and associated weather events. We will discuss the merits of different ocean
observation platforms in this context and also future observation and model improvement pathways for better understanding and simulationof climate variability in the Indo-Pacific region.

Assimilation of AOD retrievals in GEFS-Aerosols using a JEDI-based 3D-EnVar Hybrid System
Tangborn, Andrew(1); Martin, Cory(2); Pagowski, Mariusz(3); Huang, Bo(3); Wang, Siyuan(4); Brock, Charles(5); Kondragunta, Shobha(6); Stajner,
Ivanka(7)(1)
IMSG@NOAA/NCEP/EMC, (2)Redline at NOAA/EMC, (3)CIRES, CU Boulder and NOAA/ESRL/GSL, (4)CIRES, CU Boulder and NOAA/ESRL/CSL,(5)
NOAA Chemical Sciences Laboratory, (6)NOAA National Environmental Satellite, Data, and Information Service, (7)NOAA/NCEP/EMC
We have undertaken a series of assimilation experiments using the hybrid 3d-EnVar system, which is being developed as part of the Joint Effort
for Data Assimilation Integration (JEDI). It uses the Finite-Volume Cubed-Sphere Dynamical Core (FV3) combined with an aerosol parameterization from the Goddard Chemistry Aerosol Radiance and Transport (GOCART) model. This is part of the development work for the NCEP Global
Ensemble Forecast System - Aerosols (GEFS-Aerosols) operational forecasting system. The assimilation uses forecast error covariance estimates weighted between a forecast ensemble and a static background (B), which is parameterized using the B matrix on an Unstructured Mesh
Package (BUMP) algorithm. This system assimilates aerosol optical depth (AOD) retrievals from the Moderate Resolution Imaging Spectroradiometer (MODIS) on board the Aqua and Terra satellites, and the Visible Infrared Imaging Radiometer Suite (VIIRS), on the Suomi NPP satellite.
The assimilation allows for indirect comparisons from these sensors through the observed minus forecast (O-F) resultant changes. Further
comparisons are made with in situ measurements from the NASA airborne Atmospheric Tomography (ATom-1) campaign in July-August, 2016.

Biases at the base of the mixed layer induced by 3DVar assimilation of sea surface temperature observations inocean models.
While, James(1); Martin, Matthew(1); King, Robert(1)(1)
United Kingdom Met Office [Exeter]
Propagating sea surface temperature (SST) information to depth is a non-trivial problem in ocean data assimilation as the vertical correlationsof temperature are complex. At the Met Office a parameterised function is used to specify the vertical length scales. At the surface the verticallength scale is the mixed layer depth before reducing to twice the grid scale at the base of the mixed layer. While producing accurate SST
analyses/forecasts, this methodology can produce undesirable features near the base of the mixed layer. A complex interaction between themixed layer depth and the increments often leads to positive increments being projected deeper on average than negative increments, leading
to a positive temperature bias below the mixed layer. Another issue is the generation of excess mixing across the mixed layer. We have been
investigating ways to reduce these issues, and we show results from experiments testing different techniques. Experiments have been conducted using both a 1-D model and a global ocean system. Assimilation was done with a 3DVar methodology. None of the methods investigated eliminated the vertical problem, but some did reduce it. In one key test It was found that applying a compensating gradient to salinity
increments across the mixed layer, such that the increment to density has zero gradient, can be effective. This came at the cost of increased
salinity error. Future work will look at ensemble methods to resolve the issue.

Wednesday, 15 Sep 2021

Poster session P3 - Methodology
Reconstructing the dynamics of the outer electron radiation belt by means of the standard and ensemble Kalmanfilter with the VERB-3D code
Castillo, Angelica(1,2); De, Wiljes Jana(3); Shprits, Yuri(1); Aseev, Nikita(1)(1)
German Research Centre for Geosciences, (2)University of Potsdam, (3)University of Potsdam, Institute for Mathematics
Reconstruction and prediction of the state of the near-Earth space environment is important for anomaly analysis, development of empiricalmodels and understanding of physical processes. Accurate reanalysis or predictions that account for uncertainties in the associated model andthe observations, can be obtained by means of data assimilation. The ensemble Kalman filter (EnKF) is one of the most promising filtering tools
for non-linear and high dimensional systems in the context of terrestrial weather prediction. In this study, we adapt traditional ensemble based
filtering methods to perform data assimilation in the radiation belts. We use a one-dimensional radial diffusion model with a standard Kalmanfilter (KF) to assess the convergence of the EnKF. Furthermore, with the split-operator technique, we develop two new three-dimensional EnKF
approaches for electron phase space density that account for radial and local processes, and allow for reconstruction of the full 3D radiation
belt space. The capabilities and properties of the proposed filter approximations are verified using Van Allen Probe and GOES data. Additionally, we validate the two 3D split-operator Ensemble Kalman filters against the 3D split-operator KF. We show how the use of the split-operator
technique allows us to include more physical processes in our simulations and offers computationally efficient data assimilation tools that

deliver accurate approximations to the optimal solution of the KF and are suitable for real-time forecasting. Future applications of the EnKF todirect assimilation of fluxes and non-linear estimation of electron lifetimes are discussed.

Online nonlinear bias correction in ensemble Kalman filter to assimilate GOES-R all-sky radiances for the analysis
and prediction of rapidly developing supercells
Chandramouli, Krishnamoorthy(1); Wang, Xuguang(1); Johnson, Aaron(1); Whitaker, Jeffrey(2); Otkin, Jason(3)(1)
School of Meteorology, University of Oklahoma, (2)NOAA Earth System Research Laboratory, (3)Space Science and Engineering Center [Madison]
Past studies have shown the superiority of online bias correction in the context of clear-sky radiance assimilation. However, so far only theoffline method is implemented to remove complex bias present in all-sky radiance observations for convective scale data assimilation. The
current study is the first to explore online non-linear bias correction for GOES-R all-sky radiances in a convective scale rapid cycling EnKF data
assimilation system. Experiments are designed and performed for rapidly developing supercell cases with RADAR reflectivity observations
served as the anchoring observations. The online nonlinear bias correction approach is compared with an equivalent offline nonlinear approach. The results show that by using the unbiased anchoring RADAR observations, the online approach results in a less biased backgroundand analysis compared to the offline approach. It is also found that the online nonlinear bias correction method results in physically consistentaddition of humidity and hydrometeors over observed storm location compared to offline bias correction during the rapid cycling data assimilation. Subsequent forecasts of two long track supercells using the online approach are significantly improved.

Improving Tropical Cyclone Predictions by Assimilation of Satellite-Retrieved Surface Precipitation with GaussianTransformation
Da, Cheng(1); Kalnay, Eugenia(2)(1)
Department of Atmospheric, and Oceanic Science, University of Maryland, College Park, (2)Department of Atmospheric and Oceanic Science,
University of Maryland, College Park
Assimilation of precipitation is challenging because: a) errors associated with precipitation are not Gaussian, and b) forcing the model to rain
where observed by moistening or drying the atmosphere, does not change the main dynamical variable (potential vorticity) and thus is forgotten once the forcing of the "assimilation of moisture" ceases. Lien et al. (2013, 2016) and Kotsuki et al. (2017) developed a methodology that
addressed these problems: They performed Gaussian transformation (anamorphosis) and transformed precipitation into variables with more
Gaussian-like errors. Their ensemble-based assimilation approach directly adjusted the model dynamic variables, thus creating an analysis
that is closer to having the right dynamics. As a result, Lien et al. (2016) showed improved forecast skill up to five days. We extend this methodology for the tropical cyclone (TC) predictions. Experiments with assimilation of GPM surface precipitation retrievals are conducted over theWestern Pacific for four typhoons in 2015. The results show that assimilation of precipitation can effectively improve the sea level pressure andhydrometeor analysis of TCs. Verification against the GPS RO wet profiles reveals that precipitation assimilation reduces the root mean squareerrors for temperature and humidity analysis (forecast) near the TC center. Overall, the track forecasts are all improved over three days, andthe minimum sea level pressure forecasts are improved beyond 36 hours due to precipitation assimilation.

Multi-layer Observation Localization for Nonlocal Observations in the LETKF
Da, Cheng(1); Kalnay, Eugenia(2); Chen, Tse-Chun(3)(1)
Department of Atmospheric, and Oceanic Science, University of Maryland, College Park, (2)Department of Atmospheric and Oceanic Science,
University of Maryland, College Park, (3)Physical Sciences Laboratory, NOAA ESRL
Assimilating nonlocal observations within the Ensemble Kalman Filter is known to be a nontrivial problem. Unlike conventional observations
with well-defined locations when observed, nonlocal observations (e.g., radiances, altimeter observations) are intrinsically integrated measurements in space, and thus have no single well-defined observation location. Nevertheless, a single-layer vertical localization (SLVL) approach
widely used to handle these nonlocal observations, is to vertically localize them at their weighting function (WF) peaks with symmetric Gaussian-shape localization functions. While this approach can properly assimilate those observations with narrow symmetric WFs, it has difficulties
handling observations with broad asymmetric WFs, or multiple WF peaks, which are typical for clear-sky radiance observations from infrared
hyperspectral sounders. We developed a multi-layer vertical localization (MLVL) method for nonlocal observations in the Local Ensemble
Transform Kalman Filter. The traditional SLVL can be viewed as a particular case of our newly-formulated MLVL. We compared the performanceof MLVL and SLVL by assimilating simulated nonlocal observations with a multi-layer quasi-geostrophic model under three scenarios: assimilating nonlocal observations with (a) narrow WFs, (b) broad WFs, and (c) multiple WF peaks. The results show that MLVL has comparable performance as SLVL when assimilating narrow-WF observations, and superior performance than SLVL when assimilating observations with broad
WFs or multiple WF peaks.

Evaluation of Observation Impact and Low-Skill Forecasts in the NCEP Global Forecast System/Global Data Assimilation System using Ensemble Forecast Sensitivity to Observation Impact
Eichmann, Andrew(1,2); Lin, Liao-Fan(3,4); Ellis, Travis(1,2); Thomas, Catherine(1); Mahajan, Rahul(1); Kleist, Daryl(1)(1)
NOAA National Centers for Environmental Prediction, (2)I.M. Systems Group, (3)NOAA Global System Laboratory, (4)Cooperative Institute for
Research in the Atmosphere
Ensemble Forecast Sensitivity to Observation Impact (EFSOI) is a technique which derives the positive or negative impact of individual assimilated observations on the skill of a numerical weather forecast using the difference in errors against a verifying analysis between two 24-hour
ensemble forecasts, one initialized with an analysis and the other initialized with the corresponding background (Kalnay et al. 2012). EFSOI has
been implemented in the research version of the Global Data Assimilation System (GDAS) that produces the ensemble background componentof the analysis used to initialize the newly operational v16 Global Forecast System (GFS) at NCEP, and is evaluated using an ensemble with reduced C192 (50 km) resolution and 128 vertical levels. Comparisons of sensitivities in satellite and conventional observation sources in GFS/
GDAS relative to other forecasting centers, including those using adjoint-based methods, will be presented. EFSOI may also help identify assimilated observations by source, geographical location, instrument channel, or other subset that potentially contribute to low-skill forecasts
through the analyses used to initialize the forecasts, and case studies of low-skill forecasts and the sensitivities and impacts of the observations assimilated for the initializing analyses of these forecasts will be examined.

Maximum Likelihood Ensemble Filter with Exact Newton Optimization
Enomoto, Takeshi(1); Nakashita, Saori(2)(1)
Disaster Prevention Research Institute, Kyoto University, (2)Graduate School of Science, Kyoto University
Optimization of a cost function is a key component in variational data assimilation determining the quality of the analysis. In spite of quadratic
convergence, the Newton method to operational systems is prohibitive because of difficulty in calculating the Hessian matrix explicitly. The
Maximum Likelihood Ensemble Filter optimizes the cost function and enables computation of the Hessian matrix in ensemble space, providing
an opportunity to use the Hessian in optimization. Thus, application of the exact Newton optimization is attempted in a single wind speed
assimilation test. With the linear observation operator, where a wind vector instead of wind speed observation is assimilated, the optimization
converges in a single step with little wind speed error as expected from the quadratic cost function. In the wind speed assimilation, the analysis
converges to the solution and the analysis ensemble members are clustered along the circles of the wind speed with observation error. Comparison with other optimization and assimilation methods will also be given.

Local volume solver with the static covariance model: LETKF-OI
Frolov, Sergey(1)(1)
NOAA Physical Science Laboratory
Lack of effective ways to include static error covariance in the framework of the local volume solver (e.g. the local ensemble-transform Kalmanfilter (LETKF)) remains an outstanding problem in data assimilation. Here, we develop the new family of algorithms: LETKF-OI and LGETKF-OI.
These algorithms are similar to the traditional optimal interpolation (OI) algorithm in that they use a static error covariance to update each of
the local volume solutions. However, unlike the traditional OI that scales poorly as the number of observations increases, new algorithms
achieve linear scalability by either using the observational-space localization strategy of the traditional LETKF algorithm or the modulatedensemble strategy of the gain-form LETKF. Using a simple univariate test case, we show that the new algorithms achieve close approximation
to the 3DVAR and OI solutions. Extensions to the multivariate assimilation that includes physical balances is discussed.

Improvement of Accuracy of Global Numerical Weather Prediction Using Refined Error Covariance Matrices
Ishibashi, Toshiyuki(1)(1)
Meteorological Research InstituteFor accurate numerical weather prediction (NWP) and reanalysis, accurate error covariance matrices (ECMs) in data assimilation are essential.
However, current ECMs used in atmospheric analysis strongly depend on empirical tunings. The objective of this study is to estimate ECMs of
all observations and background variables using combining multiple sampling statistics methods, and improve global NWP accuracy and validity by using them. This study presents the first results of such entire ECM refinement (Ishibashi 2020, MWR). ECM diagnostics and numericalexperiments were performed on the JMA global NWP system, where diagonal components of all ECMs and off-diagonal components of radianceobservations were refined. The ECM diagnostic results show: 1) the diagnosed error standard deviations (SDs) are generally much smaller thanthose of the JMA operational system (CNTL) ; 2) interchannel error correlations in humidity-sensitive radiance are larger than 0.2; and 3)

horizontal correlation distances of AMSU-A are about 50 km. The numerical experiments show: 1) the diagnosed ECMs generally improveforecast accuracy over CNTL even without additional tunings; 2) one supplemental parameter, a deflation factor (0.6 in SD) for direct observations, improves forecast accuracy; 3) 10 times high-density assimilation of AMSU-A is better than CNTL; and 4) ECMs estimated using boreal
summer data improve winter forecast accuracy. We will also present flow dependent nature of ECMs.

Ensemble Singular Vectors: Optimizing Convective Scale Uncertainties for Data Assimilation
Keller, Jan(1,2); Vamassoi, Arianna(3,2); Hense, Andreas(3)(1)
Deutscher Wetterdienst [Offenbach], (2)Hans-Ertel-Center for Weather Research, Monitoring and Diagnostic group, (3)University of Bonn
Inherent shortcomings in the representation of smaller scale features become apparent with the continuous decrease of horizontal grid spacing
in NWP models. One of the phenomena affected is convective precipitation. Here, we present an approach with two major aims: (a) enhance therepresentations of uncertainties of convective events, and (b) use these estimates in DA to improve forecasts for convection. We employ theoperational limited area model of the German Meteorological Service (DWD), i.e., ICON-LAM together with the existing LETKF implementation.
Our approach is based on the ensemble singular vector technique which focuses on an optimized uncertainty representation. Here, we will use
this method to (i) estimate fast growing error modes, and then (ii) apply these as initial conditions for the first guess forecast. We show, thatin this way, we are able to target the uncertainty to specific scales, regions or parameters.

Including the spatial observation error correlation in data assimilation of AMSU-A radiances
Koji, Terasaki(1); Miyoshi, Takemasa(2)(1)
RIKEN Center for Computational Science [Kobe], (2)RIKEN Center for Computational Science [Kobe]
Recent developments in sensing technology increased the number of observations both in space and time. It is essential to effectively utilize
the information from observations to improve numerical weather prediction (NWP). It is known to have correlated errors in observations measured with a single instrument, such as satellite radiances. The observations with the horizontal error correlation are usually thinned to compensate for neglecting the error correlation. This study explores to explicitly include the horizontal observation error correlation of Advanced
Microwave Sounding Unit-A (AMSU-A) radiances using a global atmospheric data assimilation system NICAM-LETKF, which comprises theNonhydrostatic ICosahedral Atmospheric Model (NICAM) and the Local Ensemble Transform Kalman Filter (LETKF). This study performs the data
assimilation experiments at 112-km horizontal resolution and 38 vertical layers up to 40 km and with 32 ensemble members. We estimate thehorizontal observation error correlation of AMSU-A radiances using innovation statistics. The computation cost of inverting the observation
error covariance matrix will increase with non-zero off-diagonal terms. We assume uncorrelated observation errors between different instruments and observation variables, so that the observation error covariance matrix becomes block diagonal with only horizontal error correlations included. The computation time of the entire LETKF analysis procedure is increased only by up to 10 % compared with the case using the
diagonal observation error covariance matrix. The analyses and forecasts of temperature and zonal wind in the mid- and upper-troposphere
are improved by including the horizontal error correlations.

Ensemble Kalman filtering with colored observation noise
Raboudi, Naila(1); Ait-El-Fquih, Boujemaa(1); Ombao, Hernando(1); Hoteit, Ibrahim(1)(1)
King Abdullah University of Science and Technology
The Kalman filter (KF) is derived under the assumption of time-independent (white) observation noise. Although this assumption can be reasonable in many ocean and atmospheric applications, the recent increase in sensors coverage and the launching of new constellations ofsatellites with high spatio-temporal coverage will provide high density of observations that are expected to be time-dependent (colored). In
this situation, the KF update has been shown to generally provide overconfident estimates, which may degrade the filter performance. Different
KF-based schemes accounting for time-correlated observation noise were proposed for small systems by modeling the colored noise as a firstorder autoregressive model driven by white Gaussian noise. This work introduces new ensemble Kalman filters (EnKFs) that account for coloredobservational noises for efficient data assimilation into large-scale oceanic and atmospheric applications. We first consider the case where theparameters describing the observational noise time-correlations are known, and follow the standard and the one-step-ahead smoothing formulations of the Bayesian filtering problem to derive colored observational noise-aware EnKFs. We then extend these schemes to jointly estimate the time-correlations parameters along with the system state. We demonstrate the relevance of the proposed colored observational
noise-aware EnKFs and analyze their performances through extensive numerical experiments conducted with the Lorenz-96 model.

Preliminary Testing of a Multigrid Beta Filter Scheme for Modeling Background Error Covariances in NCEP's GSI
Rancic, Miodrag(1); Pondeca, Manuel(2); Purser, R. James(2)(1)
Miodrag Rancic, (2)IMSG at NOAA/NCEP/EMC
The multigrid beta filter, a new scheme for modeling of the background error covariances, is under development at the NOAA's Environmental
Modeling Center (EMC). The new method is synthesizing quasi-Gaussians derived by convolution of a beta-distribution-like filter at different
scales, and is intended to replace the recursive filter, which, being essentially a sequential operator, is difficult to efficiently parallelize in anenvironment with increasingly massive computational parallelism. This development is part of a 3D Real-Time Mesoscale Analysis (3D RTMA)
project, whose goal is to provide analyses at high horizontal resolutions (~2.5 to 1.25 km) at frequent time intervals (~15 min). Our paper will
briefly describe the new technique, but the main focus will be on the presentation and discussion of the first preliminary test results derived
within the Grid-point Statistical Interpolation (GSI), the data assimilation system of EMC, including a comparison with the respective results
derived with the recursive filter.

Effects of misspecified time-correlated model error in the (ensemble) Kalman SmootherRen, Haonan(1)(1)
Data Assimilation Research Centre [Reading]
We investigate what effect inaccurate model errors, in particular, an inaccurate time correlation, can have ondata assimilation results, with a
KS and the EnKS. Furthermore, we use the EnKS to estimate this decorrelationtimescale encoded in the model errors. With a simple linear model,
we generate the prior with a guessedtimescale ωg which differs from the real timescale ωr in the true state of the system. Both analyticaland numerical results are produced using the KS with a 1D system. Then, we extend our numerical experiments to a higher-dimensional
system with the EnKS. To evaluate the performance of the data assimilation with auto-correlated model errors, we calculate the ratio of RMSE
over the spread of ensemble members. The results show that the best performance of the EnKS (KS) is when the guessed decorrelation
timescale matches the real timescale in the system. We find that the results are highly dependent on the observation frequency, especially theposterior variance. With a single observation, the posterior variance has a maximum at a certain ωg value. When we increase the number ofobservations, the posterior variance becomes a monotonic decreasing function of ωg. The results for the posterior MSE with asingle observation show that it increases with both ωr and the mismatch between ωg and ωr. It means if we don't have a fair estimate of the correlation
timescale, the actual posterior error will be larger. For the estimation of the actual decorrelation timescale, we find that when the observation
density is high, the state augmentation is sufficient to obtain converging results. However, with only one observation in a time window, theproblem becomes too nonlinear and the estimation process is slow or does not even converge.

Effects of suppressing supersaturation in a variational data assimilation system
Sawada, Ken(1); Honda, Yuki(2)(1)
Meteorological Research Institute, (2)Japan Meteorological Agency
The reproducibility of precipitation in the early stages of forecasts, often called a spin-down or spin-up problem, has been a significant issue
in numerical weather prediction. This problem is caused by moisture imbalance in the analysis data, and in the case of the Japan Meteorological
Agency (JMA) mesoscale data assimilation system JNoVA, we found that the imbalance stems from the existence of unrealistic supersaturated
states in the minimal solution of the cost function in JNoVA. Based on the theory of constrained optimization problems, we implemented an
exterior penalty function method for the mixing ratio within JNoVA to suppress unrealistic supersaturated states. The results of twin data
assimilation cycle experiments conducted for the Heavy Rain Event of July 2018 over Japan show that—with the new method—unrealistic supersaturated states are reduced successfully, negative temperature bias to observations is alleviated, and a sharper distribution of the mixing
ratio is obtained. These changes help to initiate the development of convection at the proper location and improve the fractions skill score (FSS)of precipitation in the early stages of the forecast.

Application of Data Assimilation to Reconstruct the State of the Near-Earth Space Environment and Issue Space
Weather Predictions into the Future.
Shprits, Yuri(1); Kondrashov, Dmitri(2); Cervantes, Villa Sebastian(1); De, Wiljes Jana(3); Castillo, Angelica(1); Michaelis, Ingo(1); Wutzig,
Michael(1); Drozdov, Alexander(4)(1)
German Research Centre for Geosciences, (2)Atmospheric and Ocean Sciences, (3)University of Potsdam, Institute for Mathematics, (4)Earth,Planetary and Space Sciences, University of California, Los Angeles, CA, USA
The near-Earth radiation environment can be hazardous to satellites and humans in space. Understanding, predicting, and prescribing theradiation environment is complicated by the fact that a single spacecraft provides only measurements at one location in space at a given time.
Data assimilation can help reconstruct the state of the environment from various measurements with observational errors. Data assimilation
allows creating new empirical models, issue real-time predictions into the future that can help with anomaly resolution and can help identify
missing physical processes. We present a real-time data assimilation forecast of the outer electron radiation belts. We also present the initial
results of ring current data assimilation and show how data for the newly developed VERB-4D code can be used to reconstruct ring current

fluxes and now-cast, and predict surface charging. We also present the long-term reconstruction of the historical state of the radiation belts.
We also show how the analysis of the innovation vector helps identify missing physical processes.

Development of the tangent linear and adjoint models of the MPAS-Atmosphere dynamic core and applicationsin adjoint relative sensitivity studies
Tian, Xiaoxu(1); Zou, Xiaolei(2)(1)
University of Maryland [College Park], (2)Nanjing University of Information Science and Technology
This study develops and tests a version of the Python-driven, non-hydrostatic Model for Prediction Across Scales – Atmosphere (MPAS-A)
dynamic model, as well as its tangent linear and adjoint models. The non-linear, non-hydrostatic dynamic core of the MPAS-A is restructured
to have a Python driver for the convenience of parsing namelists, manipulating matrices, controlling simulation time flows, reading model
inputs, and writing outputs, while the heavy-duty mediation and model layers are retained in Fortran for computational efficiency. Under thesame Python-driving structure, developed are the tangent linear and adjoint models for the dynamic core of the MPAS-A model with verified
correctness. The case of Jablonowski and Williamson's baroclinic wave is used for demonstrating the approximation accuracy of the MPAS-A
tangent linear model and the applicability of the MPAS-A adjoint model to relative sensitivity studies. Numerical experimental results show thatthe tangent linear model can well approximate the temporal evolutions of non-linear model perturbations for all model variables over a fourday forecast period. Employing the MPAS-A adjoint model, it is shown that the most sensitive regions of the 24-h forecast of surface pressure
are weather dependent. An interesting westward vertical tilting is also found in the relative sensitivity results of a 24-h forecast of surfacepressure at a point located within a trough to model initial conditions. This functionality of the MPAS-A adjoint model is highly essential inunderstanding dynamics and variational data assimilation.

Soil moisture assimilation system for multilayer soil model
Travova, Svetlana(1); Tolstykh, Mikhail(2,1)(1)
Hydrometcenter of Russia, (2)Institute of Numerical Mathematics, RAS
This research presents the future soil moisture analysis system for the global atmospheric model SL-AV. It's based on a point-wise Simplified
Extended Kalman Filter (SEKF). The analysis scheme is developed within an offline version of the land surface scheme ISBA and multilayer soil
scheme INM RAS for the initialization of soil water content in numerical weather prediction model. This system is compared with a fully coupledland data assimilation system. It is shown, by comparing the observation operator Jacobians of both analysis schemes, that the assumption of
linearity of these operators estimated by finite differences fits offline version better. Another advantage is the reduction in computing time
while running the land surface scheme outside the atmospheric model, which makes SEKF approach compatible with operational requirements. Elements of the Jacobian matrix of the observation operator for the two layers of soil and the spatial structure of the analysis increments
are represented. The performance of the offline assimilation system is evaluated against the open-loop system and in-situ data. The assessing
period from June till August 2014 was taken. Comprehensive estimations of analysis data against conventional screen-level temperature andrelative humidity observations, non-conventional soil temperature and moisture measurements are represented. It's demonstrated that using
SEKF improves medium-range forecasts of the SL-AV model.

Initializing high-resolution deterministic forecasts in hybrid-gain and hybrid-covariance ensemble DA systems
Whitaker, Jeffrey(1)(1)
NOAA Physical Sciences Laboratory
Most operational centers calculate analysis increments at a lower resolution than that of the deterministic forecast model. In NOAA, a hybridcovariance ensemble-variational system is used in which the analysis increment is computed at the ensemble resolution, using a high-resolution background from a single determinstic forecast. The analysis ensemble, after being updated by an EnKF, is recentered around the upscaled deterministic analysis. In this project I demonstrate an alternative strategy in which the cycling of the high-resolution deterministic
forecast is de-coupled from the ensemble update. As before, the analysis increment is computed at the ensemble resolution using either
hybrid-covariance or a hybrid-gain ensemble-variational approach, but using the ensemble mean as a background instead of a single highresolution forecast. The determinstic forecast is obtained by 'replaying' (Takacs et al, https://doi.org/10.1175/MWR-D-18-0117.1) the highresolution model to the ensemble-mean analysis using the incremental-analysis update capabilitiy of the forecast model. This approach for
initializing high-resolution forecasts is more amenable to the hybrid-gain ensemble-variational framework and produces forecasts that fit
observations slighlty better than the current approach. It also allows the high-resolution deterministic forecasts to be run offline, possibly withdifferent forecast models, since the replay approach only requires the archived ensemble mean analysis states.

Including observation error correlation for ensemble radar radial wind assimilation and its impact on heavy rainfall prediction
Yeh, Hao-Lun(1); Yang, Shu-Chih(1); Terasaki, Koji(2); Miyoshi, Takemasa(2)(1)
Department of Atmospheric Sciences [Taoyuan City], (2)RIKEN Center for Computational Science [Kobe]
An assumption of uncorrelated observation errors is commonly adopted in conventional data assimilation. For this reason, high-resolution
data is re-sampled with strategies like superobbing or data thinning. This also sacrifices the advantage of high temporal and spatial resolution
observations that can provide essential detailed structures. However, assimilating the high-resolution data, such as radar radial wind, without
considering the observation error correlation can lead to overfitting and thus degrade the performance of data assimilation and forecast. This
study uses the radar ensemble data assimilation system, which combines the Weather Research and Forecasting model and Local Ensemble
Transform Kalman Filter (WRF-LETKF), to assimilate the radar radial velocity and reflectivity data. We present a strategy to include the error
correlation of the Doppler radar radial velocity in the WRF-LETKF radar assimilation system and examine their impact on short-term precipitation prediction based on the heavy rainfall case on 2nd June 2017 in Taiwan. Including the correlated observation error for radar radial winds
leads to more small-scale features in the wind analysis corrections compared to the experiment using the independent observation assumption. Consequently, the modification on wind corrections generates stronger convergence, accompanied by higher water vapor content, andenhances local convections, resulting in more accurate simulated reflectivity and short-term rainfall prediction. In particular, such advantage
is identified for the threshold of extreme heavy rainfall at small scales according to probability quantitative precipitation forecast and fractions
skill score.

Thursday, 16 Sep 2021

Poster session P4 - Reanalysis
Data rescue of national and international meteorological observations at Deutscher Wetterdienst
Andersson, Axel(1); Kaspar, Frank(2); Tinz, Birger(1)(1)
Deutscher Wetterdienst [Hamburg], (2)Deutscher Wetterdienst [Offenbach]Historic observational data records are an important contribution for climate reconstructions and analysis of past weather events. Particularly
in remote and data sparse regions, such as the open ocean, newly rescued data can significantly improve the knowledge about weather andclimatic conditions in earlier decades and centuries. Deutscher Wetterdienst (DWD) holds several collections of original historical weather
records from land stations and ships. They comprise not only observations from Germany, but also of the global oceans and land stations inmany parts of the world. All German state-owned meteorological observations beginning with the Prussian Meteorological Institute in 1848
are collected in the main archive of DWD in Offenbach. DWD's branch office in Hamburg holds the marine archive starting with the collectionsof the German Naval Observatory, 'Deutsche Seewarte', which existed from 1874 to 1945. It includes marine data records from ships, as wellas land stations in many parts of the world (e.g. from former German colonies) and signal stations situated at the coasts of the North and Baltic
Sea. The documentation, digitisation and quality check of the enormous quantity of handwritten journals of all four data archives is still ongoing. The digitised data will be freely accessible to all interested scientists and are also continuously submitted to international data archives,
such as ICOADS and ISPD. Through these data sets, the data are also an important input for regional and global reanalyses. The presentation
will give an overview of the historical archives of Deutscher Wetterdienst and will show the recent progress of the digitization efforts andongoing analysis of the data.

Modeling impact of climate warming on cotton growth and phenology in Pakistan from 1961 to 2010 based onprovincial data
Arshad, Adnan(1); Pourshirazi, Shabnam(2); Zhang, Yue(3); Ashraf, Muhammad(4)(1)
PODA-Pakistan & China Agricultural University, (2)Gorgan University of Agricultural Sciences & Natural Resources, (3)China Agricultural University, (4)University of Balochistan
The general circulation models (GCM) estimated an average increase in the global surface temperature of about 2.9-5.5°C by the end of thiscentury. The increases in temperature and shift in the rainfall cycle affect cotton growth and threaten cotton production and quality in theregion. The rise highly influences cotton growth and development in weather and changes in rainfall cycles. Climate data (1980-2020) wereused as model input resulted that the escalating temperature increases evapotranspiration rates, sometimes causing severe water stress andfruit abscission, thus reduce the plant growth and yield. APSIM-cotton model projections of 15-agrometeorology stations showed that thegrowing duration during the sowing-boll opening stages and drilling-harvesting were reduced by was reduced 2.30-5.66 days decade-1 and4.23 days decade-1. Temperature rise has advanced the planting dates, sowing-emergence, 3-5 leaves, budding-anthesis, full-bloom, cleftboll, boll-opening, boll-opening filling by 24.42, 26.19, 24.75, 23.28, 22.62, 15.75, 14.58, 5.37, 2.85, 8.04 days. Further, our findings exhibited that climate stop-growing becomes 2.16 days premature, and the time-scale has been delayed for 8.2, 2.4, and 5.3 days in the 1970s,
1980s, and 1990s. APSIM and DSSAT quantification revealed that sowing-emergence and anthesis-maturity stages were negatively linked with

temperature -2.03, -1.93, -1.09, and -0.42 days °C1. The negative impact might be mitigated by adaptation to the climate-smart cotton
production system by improving agrotechnological services.

Regional Water Cycle Consistency in Atmospheric Reanalysis
Bosilovich, Michael(1); Robertson, Franklin R.(2); Roberts, Brent(2)(1)
NASA GSFC, (2)NASA MSFC
Interannual variations of global ocean -to- land transport of water in reanalyses are generally consistent with evaporation minus precipitation(E-P) derived from observationally-constrained land data assimilation systems (LDASs) and global hydrologic models (GHMs). In the present
study, we evaluate the regional water budgets consistency across several regions to determine the degree that this applies at sub-continental
scales. Preliminary results for CONUS shows for MERRA-2 and ERA5 the regional atmospheric convergence of water from the atmosphericreanalysis is more consistent with the LDAS E-P, than is the reanalyses E-P. The water vapor increment (or imbalance) from the data assimilation is more related to the reanalyses E-P. This work will also extend to comparisons over regions with smaller amounts of assimilated observations (especially radiosondes). In addition, we will diagnose recycled precipitation in comparison to inflowing water vapor relative to thedivergence term.

Assessment of temperature extremes based on departures from long-term reanalysis and high-resolution ensemble forecasts over Indian region
Chakraborty, Paromita(1,2); Shankar, Gauri(1); Singh, Harvir(1); Sarkar, Abhĳit(1); Bhatla, R.(2); Mitra, A.k.(1)(1)
National Center for Medium Range Weather ForecastingNational Center for Medium Range Weather Forecasting, (2)Department of Geophysics,
Institute of Science, Banaras Hindu University
Extreme weather prediction requires the resolution of the models to be sufficiently high in order to resolve the small-scale features of theevents. Medium range ensemble prediction of extreme events helps assess the forecast uncertainty well ahead in time, enabling risk assessment and effective planning for disaster management. The global ensemble prediction system at NCMRWF (NEPS-G) features one control and22 perturbed members with 12 km horizontal resolution and 70 vertical levels. NEPS-G is used for predicting extreme temperature events(heatwaves, coldwaves, warm nights and cold days) in the year 2019-2020 in this study. The daily gridded surface maximum (Tmax) andminimum (Tmin) temperature dataset developed by India Meteorological Department (IMD) have been used to verify the probabilistic temperature forecasts. A high-resolution regional atmospheric reanalysis data named the Indian Monsoon Data Assimilation and Analysis (IMDAA) is
recently generated by NCMRWF and Met Office, UK, in collaboration with IMD under the National Monsoon Mission project. The daily climatology for Tmax and Tmin is calculated from IMDAA reanalysis data between 1988 and 2018. It has been used for calculating departures for
identifying the temperature extremes over Indian regions. The probabilistic skill of the EPS medium-range forecasts of extreme day-time andnight-time temperatures is examined with reference to long-term climatology from IMDAA reanalysis. The continuous ranked probability score(CRPS) shows a better correspondence between forecasted and observed cumulative distribution in the summer season than that in the winterseason.

Early results of the evaluation of the JRA-3Q reanalysis
Harada, Yayoi(1); Kobayashi, Shinya(2); Kosaka, Yuki(2); Chiba, Jotaro(2); Tokuhiro, Takayuki(2)(1)
Meteorological Research Institute / Japan Meteorological Agency, (2)Office of Earth System Modeling / Numerical Prediction Division / Japan
Meteorological Agency
The Japan Meteorological Agency (JMA) is conducting the third Japanese global atmospheric reanalysis named Japanese Reanalysis for Three
Quarters of a Century (JRA-3Q) using the JMA operational data assimilation system that has been upgraded and improved since the Japanese
55-year Reanalysis (JRA-55) was conducted. For the evaluation of the JRA-3Q, several kinds of observational datasets based on satellite observation are used: precipitation provided by Global Precipitation Climatology Project (GPCP), radiation fluxes from the clouds and the Earth's
radiant energy system project (CERES), spectral latent heating from the Global Precipitation Measurement (GPM) mission by Japan Aerospace
Exploration Agency (JAXA), and water vapor retrieved from Global Navigation Satellite System Radio Occultation (GNSS-RO) and the Earth Observing System Microwave Limb Sounder (MLS). The early results show that both overestimation of precipitation in the tropics and dry bias in
the middle troposphere are diminished compared with those in JRA-55, and the representation of diabatic heating rate is also improved. In
addition, biases of surface heat fluxes and radiation fluxes at the top of the atmosphere are also reduced.

The Panama Bight Index: a new index for the Eastern Tropical Pacific
Herrera, Carmona Julio Cesar(1); Selvaraj, John Josephraj(1); Giraldo, Alan(2)(1)
National University of Colombia, (2)University of Valle
This paper presents the Panama Bight Index (PBI), a new climate variability index for the Eastern Tropical Pacific, based on the Sea Surface
Temperature (SST) anomaly of an area of the central Panama Bight (PB). The PB includes the waters of Costa Rica, Panama, Colombia, andEcuador. The extent to calculate the PBI was defined as the homogeneous region between -1 and 1 standard deviation of the first Empirical
Orthogonal Function (EOF) of an analysis of the regionalization of the Remote Sensing Reflectance (Rs 412 and Rs 488 Modis-Aqua,
2002-2020; Rs 412 and Rs 490 SeaWiFS, 1997-2010). The SST from the ERA5 reanalysis dataset (1950-2020), of the European Centre for
Medium-Range Weather Forecasts (ECMWF), was used to generate the PBI. For calculating the SST anomalies, we used the NOAA's Climate
Prediction Center for El Niño region indices (Niño 4, Niño 3.4, Niño 3, Niño 1+2 y ONI). The PBI is dominated by the interannual variations of
the SST associated with El Niño and La Niña (EN/LN), which presents a high correlation with El Niño 1+2 index and the ONI Index, and allows
identifying the mode of variability associated with the annual cycle. Although there are other metrics to monitor ENSO, they do not adequately
fit the PB's processes in the oceanic zone. Therefore, this PBI will allow the study of the interannual climatic variability of the POT associatedwith EN/LN and evaluate the occurrence and intensity of the Eastern Pacific El Niño.

A kriging method for a gridded quantitative precipitation estimate over Alaska with uncertainty bounds
Hoover, Brett(1); Otkin, Jason(1); Petrescu, Eugene(2); Niebuhr, Emily(2)(1)
Space Science and Engineering Center [Madison], (2)NOAA National Weather Service
In collaboration with the National Weather Service (NWS) Alaska and the Alaska-Pacific River Forecast Center, a gridded quantitative precipitation estimate (QPE) has been developed to provide a precipitation analysis for Alaska, which is a challenge because of Alaska's size, sparse
population, and limited access to observations. Gridded quantitative precipitation forecasts (QPF) from high-resolution numerical weather

prediction models are merged with rain gauge data through kriging - a geostatistical interpolation technique which provides the best linear
unbiased estimate at each gridpoint along with an estimated error variance. The error variance is further inflated and refined through crossvalidation to produce an empirically tuned 5% and 95% confidence bounds on QPE. A 6-hour accumulated QPE was produced for the period
August 01 2019 - July 31 2020, and skill scores are presented for multiple formulations of QPE to identify the value-added by a QPF first-guesscompared to a climatological first-guess.

Analysis of the Wind Fields Based on Radar Network in the East Asia Reanalysis System
Liang, Xudong(1); Yin, Jingfang(1); Xie, Yanxin(1); Li, Feng(1)(1)
The State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beĳing, China
The V-IVAP method (Liang et al., 2019) were proposed to retrieve wind fields based on the radial wind of weather radar. The V-IVAP method
is insensitive to the alias of radial wind. Using the V-IVAP method, the wind fields were analyzed using the observations of the radar network.
The weather radar network of China (CINRAD: China New Generation Weather Radar）includes 224 radars of S or C band. The wind fields havehorizontal resolution of 0.12°×0.12°, vertical resolution of 0.5 km from 0.5km to 7.5 km. A dataset of wind fields from 1st Jan 2008 to
31st Dec 2018 were established based on the radar observations with time interval of 3 hours. The errors of the winds were analyzed using
the radiosonde observations. The analyzed winds have a good correlation with the radiosonde observations. The RMSE of u and v are 4~15 ms1 and 4~9 ms-1 respectively from 0.5 km to 7.5 km height. Reference Liang Xudong, Y. Xie, J. Yin, Y. Luo, D. Yao, and F. Li, 2019: An IVAPbased dealiasing method for radar velocity data quality control. J. Atmos. Oceanic Technol., 36: 2069-2085.

The 26-Year Black Sea Reanalysis
Lima, Leonardo(1); Ciliberti, Stefania Angela(1); Aydoğdu, Ali(1); Masina, Simona(1); Escudier, Romain(2); Cipollone, Andrea(1); Azevedo, Diana(1); Peneva, Elisaveta(3); Lecci, Rita(1); Causio, Salvatore(1); Clementi, Emanuela(1); Jansen, Eric(1); Ilicak, Mehmet(4); Cretì, Sergio(1); Stefanizzi, Laura(1); Palermo, Francesco(1); Coppini, Giovanni(1); Pinardi, Nadia(5)(1)
Centro Euro-Mediterraneo sui Cambiamenti Climatici, (2)Mercator Ocean International, (3)Sofia University “St. Kliment Ohridski”, (4)Istanbul
Technical University, (5)University of Bologna
Ocean reanalyses are becoming increasingly important to reconstruct and provide an overview of the ocean state from the past to the presentday. In the scope of the Copernicus Marine Environment Monitoring Service (CMEMS), the Black Sea reanalysis (BS-REA) is produced by using
an advanced variational data assimilation method to combine the best available observations with a state-of-the-art ocean general circulation
model. The hydrodynamical model is based on Nucleus for European Modeling of the Ocean (NEMO), implemented for the Black Sea (BS) domain
with horizontal resolution of 1/27° x 1/36°, and 31 vertical levels. NEMO is forced by ECMWF ERA5 atmospheric reanalysis and climatologicalprecipitation. The model SST is relaxed to daily objective analysis fields from CMEMS SST TAC. The model is online coupled to OceanVar, a 3DVar ocean data assimilation scheme, to assimilate sea level anomaly (SLA) along-track observations from CMEMS SL TAC and available in situ
vertical profiles of temperature and salinity from both SeaDataNet and CMEMS INS TAC products. Temperature fields present a continuous
warming in the layer between 25-150 m, within which the BS Cold Intermediate Layer resides. SST shows a basin-wide positive bias. The root
mean square difference (RMSD) can reach 0.75 ºC along the Turkish coast in summer. SLA has the largest RMSD close to the shelf due to thehigh mesoscale activity along the Rim current. The system has produced very accurate estimates which makes it suitable for understanding the
BS physical state in the last decades. Nevertheless, in order to improve the quality of the BS-REA, new developments in ocean modelling anddata assimilation are still important, and sustaining the BS ocean observing system is crucial.

Evaluation of the latest Japanese Reanalysis for three quarters of a century (JRA-3Q) during a pre-satellite era
Naoe, Hiroaki(1); Kobayashi, Shinya(2); Kosaka, Yuki(2); Chiba, Jotaro(2); Tokuhiro, Takayuki(2); Harada, Yayoi(1); Kobayashi, Chiaki(1)(1)
Meteorological Research Institute / Japan Meteorological Agency, (2)Office of Earth System Modeling / Numerical Prediction Division / Japan
Meteorological Agency
This study evaluates the latest Japanese Reanalysis for Three Quarters of a Century (JRA-3Q) conducted by the Japan Meteorological Agency(JMA), focusing on a semi-period of pre-satellite era of 1960s and 1970s. JRA-3Q, which is based on the JMA's operational system with 6hourly 4D-Var data assimilation as of December 2018, is the third Japanese global atmospheric reanalysis spanning late 1940s onwards, using
an atmospheric model with a reduced horizontal resolution of TL479 and 100 vertical layers up to 0.01 hPa. Because only few global-covered
observational datasets during the pre-satellite era are available, the JRA-3Q is mainly evaluated in reanalysis intercomparison and about temporal consistency and spatial homogeneities. Emphasis of this evaluation during the non-satellite era is placed on the representation of tropicalcirculation, the time consistency of reanalysed fields between the pre-satellite and satellite eras, and the quality of the stratospheric ozone
and water vapor. The surface circulation over the tropical Africa is improved by reducing spurious anticyclonic circulation anomalies found in
JRA-55. Stratospheric ozone is also improved by incorporating adequate ozone depletion substances, sea-surface temperature as well as thedevelopment of the ozone model. The quasi-biennial oscillation is not as good as that in JRA-55 with a shorter period of around one year in
the middle stratosphere and diminished amplitude in the lower stratosphere.

Advances in the Downscaling of Extreme Hydro-Events in South America
Nunes, Ana M. B.(1); Barros, Isabella E.(1); Pereira, Rafael A. G.(1); Camarinha, Camila(1)(1)
Federal University of Rio de Janeiro
Reconstructions of climatological features in the tropical areas of South America may be proven challenging due to the scarcity of reliable,
long-term data records. To address this problem, retrospective analyses – aka (re)analyses – are regularly used to provide spatially continuous,
long-term time series of several atmospheric and land-surface variables in studies of South American climate. Overall, reanalysis comprehends
forecast models and data assimilation systems. Data assimilation in a global reanalysis comprises computationally expensive techniques togenerate initial conditions used in the embedded forecasting systems. Global reanalysis products also offer a wide range of opportunities tomonitoring atmospheric conditions at regional scales, for instance, providing initial and boundary conditions to regional downscaling. The
downscaling of a global reanalysis in the tropical-to-subtropical South America by a regional numerical model may as well be enhanced bymeans of empirical methods, such as spectral (dynamic) nudging and satellite-based precipitation assimilation, both employed in the present
study. Examples of the combined application of the two methodologies in regional downscaling will be illustrated through the reconstructionof extreme events that occurred in the continental South America depicted by the interannual variability of South American monsoon precipitation, and the severe weather episodes near the Brazilian coastline.

Evaluation of (regional) reanalysis data using the Free Evaluation System Framework
Ostermöller, Jennifer(1); Niermann, Deborah(1); Thomas, Spangehl(1); Borsche, Michael(1); Kaspar, Frank(1); Lucio-Eceiza, Etor E.(2); Kadow,
Christopher(2)(1)
Deutscher Wetterdienst [Offenbach], (2)Deutsches Klimarechenzentrum [Hamburg]
COSMO-REA6 is a regional reanalysis for Europe based on the COSMO model. The data set is widely used, especially in the sector of renewableenergies. A new version of the regional reanalysis is currently under development with boundary conditions from ERA5. To assess the qualityof the data, the output can be compared to the former version and to global reanalyses as well. Among different evaluation methods, thecomparison of the regional reanalysis data with station data (of the DWD observation network) is part of the evaluation suite. To make thesoftware and data available to a larger user group, we take advantage of the Free Evaluation System Framework (Freva) which is currently in
use at DWD, FU Berlin and DKRZ. In this way, the software is available for various applications and projects. Freva was developed at FU Berlinand DKRZ to provide an infrastructure for cooperative and efficient use of standardized data and tools. REALISTIC (“Reanalysis Station Comparison”) is developed as a plugin within the Freva framework. The tool compares reanalysis or other gridded data with station data by different
metrics (e.g. bias, correlation, MAE). REALISTIC is based on CDO (Climate Data Operators) that are used to find the nearest neighbor of thestation locations in the model grids to make pointwise comparisons. The results are plotted on a map, showing the values of the metrics at thedifferent station locations.

The stratospheric Brewer-Dobson circulation in ERA5 and ERA-Interim reanalyses
Plöger, Felix(1); Diallo, Mohamadou(1); Konopka, Paul(1); Riese, Martin(1)(1)
forschungszentrum jülich
The stratospheric Brewer-Dobson circulation (BDC) is a key element in the climate system as it controls variations in ozone and other tracegases which impact the radiation budget. We investigate the BDC in the new ERA5 meteorological reanalysis and compare with results from its
predecessor ERA-Interim, based on residual circulation dignostics and on simulations of stratospheric age of air with the transport model
CLaMS. Our results show a substantial uncertainty in the representation of the BDC in reanalyses regarding both the climatology and trends.
In particular, the BDC is significantly slower in ERA5 than in ERA-Interim, manifesting in weaker tropical upwelling, diabatic heating rates andlarger age of air, mainly related to weaker subtropical gravity wave drag. In the tropical lower stratosphere, heating rates are 30-40% weakerin ERA5 than in ERA-Interim, likely correcting a bias in ERA-Interim. At 20km and in the NH stratosphere, ERA5 mean age values are aroundthe upper margin of the uncertainty range from historical tracer observations, indicating a somewhat slow-biased BDC. The stratospheric ageof air trend in ERA5 over 1989-2018 is negative and is related to an increase in tropical upwelling. However, the age decrease is not linear but
steplike, potentially caused by multi-annual variability or changes in the observations included in the assimilation. Particularly regarding trends
on decadal time scales, the different reanalyses can largely differ.

Development and Quality Evaluation of an Operational Ensemble-based Regional Reanalysis System
Rösch, Thomas(1); Borsche, Michael(1); Kaspar, Frank(1); Potthast, Roland(2,3)(1)
Deutscher Wetterdienst [Offenbach], (2)German Weather Service, (3)University of Reading
In 2011 the development of a regional reanalysis began at DWD together with partners from the Universities of Bonn and Cologne within the
HErZ initiative. In the meantime, the continuous processing has been transferred to DWD. The regional reanalysis system COSMO-REA6 wasused to produce data sets covering the period 01/1995 – 08/2019 and Europe in a spatial resolution of 6 km. The data sets have proven highquality as shown within different activities, in particular in the project UERRA or in applications by external users especially from the sector ofrenewable energies. Despite the good results, COSMO-REA6 represents no longer the state of the art and will be superseded in the next years
by an ensemble-based reanalysis system based on ICON and up-to-date data assimilation schemes. The new system was designed to run botha global and a regional reanalysis together in a coupled approach. In regular time intervals, the system is initialized by IFS fields from ERA5.
We are going to apply reprocessed observations from ECMWF. Several production sites are taken into account. Here we present the concept of
the new system and the current state of the development.

Assessment of ERA-5 wave characteristics with in-situ measurements in Southern Baltic
Sapiega, Patryk(1); Marosz, Michał(1)(1)
Institute of Meteorology and Water Management
Commonly used methods of obtaining wave characteristics data, such as in-situ measurements, satellite or cruise (r/v) data, are becoming
insufficient in the era of contemporary climate change and the resulting need for more accurate recognition of the spatial and temporal variability of the sea state. In such a context, reanalysis data may constitute a valuable source of information.The research aimed to assess andcompare the ability of ERA5 to reproduce wave characteristics for two selected points of in-situ measurements in the Baltic Sea, both of differing oceanographical characteristics (offshore and nearshore). The temporal scope of the research covered a period from June 2018 until June
2020 (nearshore) and from 2017 until 2020 (offshore) with 3 hourly temporal resolution. Two wave characteristics were taken into account:significant wave height and mean wave period. Significant wave height from ERA5 shows a high correlation (r = 0.86) for the point representing the deepwater (offshore) zone (Petrobaltic rig) where data are acquired with an AWAC device. In a nearshore location (Pomeranian Bay), thecompliance is lower with a correlation coefficient of 0.67. For the mean wave period, the concordance is much lower.The analysis shows that
ERA5 reanalysis, under some conditions, can be a reliable source of information in the long-term, large-scale marine analyses and may serve
as a supplementary source of data on hydrodynamic parameters.

Ocean Data Impacts on the Reanalysis of Atlantic Meridional overturning circulation in the Next Generation Global
Ocean Data Assimilation System (NG-GODAS)Teng, Yi-Cheng(1); Jong, Kim(1)(1)
NOAA/NWS/NCEP/EMC
The Atlantic Meridional Overturning Circulation (AMOC) involves a northward movement of warm upper waters accompanied by a southward
movement of cold waters at depth, and carries up to 25% of the northward global atmosphere–ocean heat transport in the northern hemisphere. The natural variations in the AMOC can affect climate over decadal timescales, so there is an obvious need for better, more quantitative,
forecasts of the future behavior of the AMOC. An accurate AMOC prediction system could potentially provide a valuable early warning of imminent climate change. As part of ongoing efforts to improve forecasting, the NCEP's Environmental Modeling Center (EMC) is developing theprototype version of the Next Generation Global Ocean Data Assimilation System (NG-GODAS). The NG-GODAS uses JEDI-based SOCA (Sea-ice
Ocean Coupled Assimilation) as its ocean data assimilation component, and an advanced ocean model (MOM6) is used. The following satellite
and in-situ observation data are assimilated in current system: satellite sea surface temperature/sea surface salinity, in-situ temperature andsalinity, absolute dynamic topography, and sea ice concentration. The AMOC at 26.5N will be investigated using the porotype NG-GODAS, anda comparison would be presented of ocean reanalyses with different observing networks, to the observations and transport estimates from the
RAPID mooring array across 26.5◦ N in the Atlantic.

Intercomparison of surface temperature estimates from IMDAA reanalysis with ERA5 and in-situ observations atselected locations over India
Vishal, Jisha K(1); Indira, Rani S.(2)(1)
Department of Atmospheric Sciences, (2)National Centre for Medium Range Weather Forecasting
Indian Monsoon Data Assimilation and Analysis (IMDAA) is the currently available highest resolution (12 km), long-term (40 years, 1978-2018,
extended to 2020), satellite era regional reanalysis over south Asian monsoon region. National Centre for Medium Range Weather Forecasting(NCMRWF), Ministry of Earth Sciences, Government of India distributes the IMDAA reanalysis through reanalysis web portal, https://rds.ncmrwf.gov.in/. This study illustrates location specific verification of surface temperature estimates from IMDAA and its comparison with ERA5
global reanalysis at the selected locations over Indian landmass for 19 years during the 21st century, from 2000 to 2018. Fourteen stations
selected, such that (i) they are representatives of different homogenous temperature regions of India, and (ii) continuous in-situ surface observations are available during the period of study with minimum gap. Verification of surface temperature estimates (maximum, minimum andmean) shows that, quality of IMDAA is better over the Tropics and coastal regions (south of 20°N), whereas ERA5 outperforms over extratropics (north of 20°N). This could be due to better representation of global circulation in the global reanalysis ERA5, and local effects more
effectively in the regional reanalysis IMDAA. High resolution feature of IMDAA (12 km) compared to ERA5 (31 km) better captures surface
temperature over the orographic region. Surface maximum and minimum temperature estimates show that IMDAA has a comparatively hotsummer and cool winter over north India, whereas the reverse in the ERA5. In general, ERA5 shows better correlation with in-situ observations
than IMDAA; however, the mean of the three datasets differs significantly (p < 0.05)

Evaluation of multi-parameter dependencies in reanalyses
Wahl, Sabrina(1,2); Keller, Jan D.(3,2)(1)
Institute for Geoscience, Meteorology Department, University of Bonn, (2)Hans Ertel Center for Weather Research, (3)Deutscher Wetterdienst[Offenbach]Reanalyses are an unparalleled source of data for the evaluation of climate and its variability providing 4-dimensional reconstructions of multiple meteorological parameters describing the atmopsheric's system state. Yet, the vast majority of evaluation studies focus on the evaluationof single parameters in time and space without looking at the statistical dependence between two parameters. But this is necessary especially
if you are interested in specific events where two or more parameters are involved, i.e., so called compound events. Recent studies havetherefore investigated the representation of natural hazards such as wildfires, heat stress, droughts by evaluating corresponding indices basedon two or more parameters. We employ a more sophisticated approach by using copula theory. With this method, we aim at evaluating themultivariate statistical distribution between two parameters separately from their marginal distributions. In a second step, we can related
misrepresentations of meteorological indices in model simulations to deficiencies of the model to represent either the margignal distribution
or the interdependencies between the contributing variables. We will present results for a joint copula-based evaluation of temperature andhumidity related to the natural hazards mentioned above.

The NCEP Reanalysis Observation Archive Contents and Formats
Woollen, Jack(1)(1)
NOAA National Centers for Environmental Prediction
Beginning in 1992, NCEP has been conducting reanalysis projects with ever evolving observation capacity and complexity. Many historicalarchives of weather observations have been supplied to NCEP by NCAR, NCDC, NCEI, ECMWF, JMA, among other organizations. NCEP now has
BUFR archives for land surface data back to the 1930's, for marine and upper air data sets the archive reaches back to 1948. This report will
detail the contents of the archive, and to document the chain of processing which prepares and integrates observations from all different
sources into a common form of the NCEP BUFR dumpfile format. For conventional data types, these files are inputs to make prepbufr files for
NCEP DA reanalysis processing. Ocean surface and subsurface observations are especially important for running coupled reanalysis. A focus
on archive development is increasing the historical holdings for this type of data. CFSR used subsurface ocean data back to 1979, but there
was much less data in the 1980's and 1990's then there is now. Combining ocean obs from several reanalysis development streams could help
ocean subsurface coverage after Y2K. Finding subsurface data prior to 1980 is challenging. With the volume of the satellite datasets dominating
the last twenty years of DA observations, compact and compressed datasets like the dumpbufr format are convenient for storage. The satellite
datasets are by far the biggest members of the archive, starting from several NOAA-n platforms in the early 1970's, all the way to now. The
NCEP satellite observation archive contains big data.

The ORAP6 ocean and sea-ice reanalysis: description and evaluation on climate and forecasts
Zuo, Hao(1); Balmaseda, Magdalena Alonso(1); De, Boisseson Eric(1); Tietsche, Steffen(1); Mayer, Michael(2,1); De, Rosnay Patricia(1); Johnson,
Stephanie(1); Roberts, Christopher(1)(1)
European Centre for Medium-Range Weather Forecasts, (2)University of Vienna
Ocean and sea-ice are two essential components of Earth system models. By providing initial conditions of these two system states, ocean andsea-ice analysis play a vital part in the coupled forecasting system of NWP service. Ocean and sea-ice reanalyses also provide invaluable information for climate monitoring, and for long-term prediction such as decadal or climatic projections. The Ocean ReAnalysis Pilot system-6(ORAP6) is a new ocean and sea-ice reanalysis that has been developed based on the ECMWF operational OCEAN5 system. ORAP6 uses ERA5
Atmospheric forcing and is produced with the most up-to-date reprocessed observation datasets. The data assimilation system has been
updated, including: i) assimilation of L3 sea-ice concentration data; ii) a new flow-dependent SST nudging scheme; iii) refined off-line bias
correction term for both temperature and salinity. In addition, observation error covariance settings have been revised. Production of ORAP6
for the full ERA5 period (1979-2019) has been completed. Preliminary evaluation suggests that, in a general sense, ocean and sea-ice states
are improved in ORAP6 w.r.t to its predecessor ORAS5, partially due to its more realistic large-scale overturning circulations in the pre-Argo
period. The ORAP6 sea-ice performance is better in the sense of both climate signals and spatial distributions of sea-ice thickness and concentration. The ocean heat content tendency in ORAP6 also correlates better with variations of global net energy input derived from independently observed TOA radiation data. Initializing from ORAP6 instead of ORAS5 leads to slightly improved performance in the ECMWF seasonal
forecasting system-S5.

Thursday, 16 Sep 2021

Poster session P4 - Observations

Accuracy assessment of TRMM precipitation product in different Agro-Climatic Zones of Tamil Nadu, India
Venkadesh, Samykannu(1)(1)
Tamil Nadu Agricultural University [Coimbatore]
In this study, the accumulated precipitation of TRMM data over different Agro-climatic Zones of Tamil Nadu was analyzed using statisticalanalysis, which showed an accountable variation during the study period from 2015 to 2017. Minimum precipitation of 144.31, 34.40, and75.01 mm was recorded with TRMM during NEM of 2015, 2016, and 2017, respectively. The corresponding maximum values were 1400, 251,
and 687 mm whereas the automatic weather station (AWS) recorded a minimum of 151.65, 31.82, and 73.29 mm during the NEM of 2015,2016, and 2017, respectively. Maximum values of 1755.31, 450.39, and 939.58 mm were recorded for the corresponding years. TRMM data
was found to have higher R2 values of more than 0.8 in all the North East Monsoon (NEM) seasons of 2015, 2016, and 2017 irrespective of theAgro-climatic zones assessed. During NEM 2016, TRMM estimated maximum rainfall in High Altitude and Hilly Zone (HAHZ) and low rainfall
in North Western Zone (NEZ), which revealed that the TRMM product performance was high and dependable for use. Even though RMSE values
were found to be high in HAHZ and NEZ and the other Zones recorded less value, the agreement of the data with (AWS) values was found to be
more than 80 percent which indicated the high correlation of the data with ground truth. It concludes TRMM product provides a lot of scopein climate research studies. Overall, the performance of the rainfall-based satellite product TRMM – 3B42 over Tamil Nadu is promising for
further application in the supplement of rain gauge station.

Variational quality control of Aeolus satellite wind LiDAR observations
Apodaca, Karina(1,2); Cucurull, Lidia(1); Genkova, Iliana(3); Purser, R. James(4); Su, Xiujuan(4); Bucci, Lisa(1); Marinescu, Peter(5,1); Liu, Hui(6,7);
Garrett, Kevin(6)(1)
NOAA Atlantic Oceanographic and Meteorological Laboratory, (2)Cooperative Institute for Marine and Atmospheric Science, (3)IMSG at NOAA/
EMC, (4)IMSG at NOAA/NCEP/EMC, (5)Cooperative Institute for Research in the Atmosphere, (6)NOAA National Environmental Satellite, Data, andInformation Service, (7)Cooperative Institute for Satellite Earth System Studies
Given the positive impact by the assimilation of Aeolus horizontal line of sight wind profiles particularly in the tropics at several operational
centers around the world, we want to specifically investigate how beneficial this new observation type is for the initialization of tropical cyclones(TCs) in NOAA's Finite Volume Cubed Sphere Global Forecasting System (FV3GFS).To maximize the benefits of Aeolus, several key aspects thatmake data assimilation in the vicinity of TCs complicated, need to be addressed. By having inaccurate a-priori estimates of the state of theatmosphere under complex flow structures combined with the use of suboptimal quality control (QC) procedures can have detrimental impacts
to the analysis. Static QC and background checks, which are based on blacklisting and fist-guess rejections can make it arbitrary to takedecisions, such as keeping observations with initially large departures from the model background or how much weight should be given to an
observation during the analysis update stage. To address suboptimal quality controls, we implemented the assimilation of Aeolus Mie-cloudy
and Rayleigh-clear observation regimes with additional Variational Quality Control (VarQC) on NOAA's FV3GFS. This VarQC algorithm can assign adaptive weights and address non-Gaussianity aspects of Aeolus observations. VarQC can also be beneficial to TC analysis and forecast
as it considers information about the local TC flow, the a-priori estimates of relevant sources of error, and the analysis state in a synergisticmanner. In this presentation, we describe the benefits of applying VarQC to the Aeolus observations for improving the quality of the analysis
and forecast in NOAA's FV3GFS during TC activity.

Assessment and Evaluation of Commercial GPS Radio Occultations in the NCEP Global Forecast System
Bathmann, Kristen(1); Thomas, Catherine(2); Kleist, Daryl(2); Wu, Wanshu(2); Vandenberghe, Francois(3); Shao, Hui(3)(1)
IMSG@NOAA/NCEP/EMC, (2)NOAA Environmental Modeling Center, (3)JCSDA / UCARThe United States Weather Research and Forecasting Innovation Act of 2017 permits the National Oceanic and Atmospheric Administration(NOAA) to purchase weather data from the private sector. Through Delivery Order 1 (DO-1), NOAA obtained 30 days of commercial spacebased radio occultations (RO) from Spire and GeoOptics, and through Delivery Order 2 (DO-2), NOAA obtained six months of RO observations
from GeoOptics. Impact experiments were performed with data from DO-1 to test configurations for the operational assimilation of commercial RO from DO-2. Assimilation of commercial RO is planned to become operational in the Global Forecast System (GFS) in May 2021. An
initial experiment was run, applying the operational GNSS-RO quality control and observation errors to the commercial RO. Two follow-up
experiments tested alternate quality control and observation errors for the commercial RO observations. This presentation will detail the assessment of the quality of the commercial RO data, as well as the impacts of their assimilation in the GFS.

Extending a forward operator for visible satellite channels by near-infrared and aerosol capabilities
Baur, Florian(1,2); Scheck, Leonhard(3,2); Gindl, Stephan(1); Köpken-Watts, Christina(2); Potthast, Roland(2)(1)
Hans-Ertel-Center for Weather Research / Ludwig-Maximilians-Universität, Munich, Germany, (2)Deutscher Wetterdienst [Offenbach], (3)HansErtel-Center for Weather Research / Ludwig-Maximilians-Universität, Munich, Germany
Satellite images in the solar spectrum provide high resolution cloud and aerosol information and thus present a promising observation typefor data assimilation and model evaluation. While visible channels contain information on the cloud distribution, cloud optical thickness andcloud structure, near-infrared channels are in addition more sensitive to cloud microphysical properties and can be used to distinguish between water and ice clouds. Moreover, solar channels are sensitive to aerosols, so their assimilation can be expected to improve forecasts ofcloud and aerosol distribution, and thus also solar radiation. However, mainly due to a lack of sufficiently fast forward operators for visible
and near-infrared radiances, operational data assimilation systems so far use only the thermal channels. With recent development of MFASIS,
a 1D radiative transfer (RT) method that is similarly accurate but orders of magnitude faster than conventional RT solvers for the solar spectrum, it has become possible to utilize visible channels.Here we discuss MFASIS's limitations preventing it from simulating near-infrared channels accurately and present a solution increasing the accuracy significantly for near-infrared channels. Furthermore, it will be demonstrated
that replacing MFASIS's look-up table by a neural network reduces computational costs significantly, thus allowing for additional input parameters. Those parameters enable us to describe the vertical aerosol distribution for multiple species. This extends the application of MFASIS
towards the assimilation of aerosol-affected radiances. The new approaches presented are tested using IFS and ICON model output.

Impact of Satellite Radiance data assimilation on the prediction of extreme rain events in the haor basin area
Das, Mohan Kumar(1,2)(1)
National Oceanographic And Maritime Institute, (2)Institute of Water and Flood Management, BUET
An attempt has been taken is to investigate the impact of satellite radiance assimilation in simulating the extreme rainfall formed over the haorbasin region. In this study, the Weather Research and Forecasting (WRF) model and three-dimensional variational (3D-Var) data assimilation
system is used. The Global Forecast System (GFS) model of the National Centers for Environmental Prediction (NCEP) data has used as an initialcondition.WRFDA is assimilated with Advanced Microwave Sounding Unit-A (AMSU-A) radiance data and NCEP Global surface and upper-air
observations (NCEP PREBUFR). Overall, the results suggest that assimilating both NCEP PREBUFR together with Radiance observations into themesoscale model enhanced the initial condition. The results also indicate that assimilating both NCEP PREBUFR together with Radiance observations into the WRFDA system reliability is better than the control experiments.

Bayesian Inference of Oil Spill Source Parameters from Image Contours
El, Mohtar Samah(1); Ait-El-Fquih, Boujemaa(1); Knio, Omar(2,1); Lakkis, Issam(3); Hoteit, Ibrahim(1)(1)
King Abdullah University of Science and Technology, (2)Duke university [Durham], (3)American University of Beirut [Beyrouth]
Oil spills at sea pose a serious threat to the coastal environment. To control and limit unreported spills, it is essential to identify pollution
sources, and satellite imagery can be an effective tool for this purpose. We present in this work a Bayesian inference approach to identify thesource parameters of a spill from contours of oil slicks detected by satellite images. The approach adopts an observation error model basedon a non-local measure of the dissimilarity between the predicted and observed contours. A Markov chain Monte Carlo (MCMC) technique isthen employed to sample the posterior distribution of five parameters of interest: the x and y coordinates of the source of release, the timeand duration of the spill, and the quantity of oil released. To make the estimation of the posterior distribution computationally feasible, aPolynomial Chaos-based surrogate of the oil spill model is used within MCMC. To that end, a feature-based object localization method basedon image moments is proposed to approximate contours, or binary images, in the form of integral quantities, for which surrogate models canbe built. Two synthetic experiments of a spill released from a fixed point source are investigated, where a contour is completely observed in
the first case, while two contours are partially observed at different times in the second case. In both experiments, the proposed framework isable to provide good estimates of the source parameters along with a level of confidence reflected by the uncertainties within. In the case ofpartial observations, the estimated parameters can be used to reconstruct the missing parts of an observed slick from which an oil spill model
can be initiated to better forecast the spread of oil.

Radar-measured near-surface refractivity: a rare representative constraint on the lower boundary layer
Fabry, Frederic(1)(1)
Department of Atmospheric and Oceanic Sciences, McGill University
In addition to measuring properties of hydrometeors and their motion, radars are capable of measuring the average refractive index between
the radar and ground targets, or rather its temporal change. This measurement, physically similar to the GNSS refractivity, is focused on thefirst tens of meters of the atmosphere and on a few tens of kilometers around the radar, and are available under all weather conditions. Radarmeasured refractivity data are mind-boggingly precise and are very sensitive to humidity changes, as well as being representative of conditions
at meso-beta scales instead of at a point. Because refractivity measurements are difficult to interpret by people, they have not seen much use
in the forecaster-centric world of operational radars. But they have the potential of being particularly useful to constrain surface properties
and humidity in the lower boundary layer. In this poster, examples of measurements and applications of radar-measured refractivity will bepresented.

Operational direct assimilation of radar reflectivity volumes with KENDA at Arpae-SIMC
Gastaldo, Thomas(1); Poli, Virginia(1); Marsigli, Chiara(2,1); Paccagnella, Tiziana(1); Cesari, Davide(1); Alberoni, Pier Paolo(1)(1)
Hydro-Meteo-Climate Structure of the Regional Agency for Prevention, Environment and Energy of Emilia-Romagna region in Italy,(2)
Deutscher Wetterdienst [Offenbach]In convective-scale data assimilation, the high resolution of numerical weather prediction (NWP) models demands dense observations at asuitable temporal and spatial resolution. On this respect, radar reflectivity volumes are of great value since they provide a large amount ofobservations of a vast volume of atmosphere, typically every 5 or 10 minutes. In most operational NWP models, these observations are assimilated indirectly, employing estimated precipitation, e.g. via latent heat nudging (LHN), latent heat tendencies or relative humidity profiles. The
direct assimilation of reflectivity volumes in an operational framework has been achieved just recently. This was done, first, at Deutscher
Wetterdienst (DWD) and at the Hydro-Meteo-Climate Structure of the Regional Agency for Prevention, Environment and Energy of Emilia-Romagna region (Arpae-SIMC) in Italy, using the local ensemble transform Kalman filter (LETKF) as assimilation scheme. Arpae-SIMC runs theConsortium for Small-scale Modeling (COSMO) model at 2.2 km horizontal resolution, while DWD employs the icosahedral nonhydrostatic
weather and climate model (ICON) at the same resolution. In both cases, reflectivities are assimilated in combination with fields of estimated
precipitation via LHN. The implementation adopted at Arpae-SIMC will be described here, reporting verification results for precipitation andfor upper-air and surface variables.

Evaluation of multiple GNSS radio occultation observation operators with JEDI
Zhangz, Hailing(1); Vandenberghe, Francois(1); Shao, Hui(1)(1)
JCSDA/UCAR
Multiple observation operators for Global Navigation Satellite System (GNSS) Radio Occultation (RO) data are provided in the Unified Forward
Operator (UFO) component of the Joint Effort for Data assimilation Integration (JEDI) system, JEDI-FV3 1.0.0, that was released in October 2020.
JEDI is developed by the Joint Center for Satellite Data Assimilation (JCSDA) and its stakeholders ((NOAA, NASA, DoD, and UKMO). Specifically,
UFO includes a refractivity operator, a one-dimensional bending angle operator replicating the NCEP Bending Angle Model (NBAM) used inoperations at NOAA, as well as a one-dimensional and a two-dimensional bending angle operators incorporated through the Radio Occultation
Processing Package (ROPP) adopted by ECMWF and NRL in their operations. Thanks to the unified feature of the JEDI UFO, for the first time,
GNSS RO data impacts can be evaluated using multiple operational operators within the same framework. In this study, we will conduct a seriesof one-month numerical experiments by applying these operators for GNSS RO data assimilation to the NCEP operational FV3-GFS forecasts.
We will compare the performances of the operators and evaluate RO data impact in terms of a number of metrics. The fitting of model background and analysis to RO observations in observation space, such as COSMIC2 and other RO data; as well as a series of month-long statisticalresults will be presented.

Ensemble-Based Data Assimilation of GPM DPR Reflectivity into the Nonhydrostatic Icosahedral Atmospheric
Model NICAM
Kotsuki, Shunji(1); Terasaki, Koji(2); Satoh, Masaki(3); Miyoshi, Takemasa(2)(1)
CEReS, Chiba University, (2)RIKEN Center for Computational Science, (3)The University of Tokyo
This study aims to improve the precipitation forecasts from numerical weather prediction models through effective assimilation of satelliteobserved precipitation data. We have been developing a global atmospheric data assimilation system NICAM-LETKF, which comprises the Nonhydrostatic ICosahedral Atmospheric Model (NICAM) and Local Ensemble Transform Kalman Filter (LETKF). This study pioneers to assimilate
radar reflectivity measured by the Dual-frequency Precipitation Radar (DPR) onboard the Global Precipitation Measurement (GPM) core satellite
into the NICAM. We conduct the NICAM-LETKF experiments at 28-km horizontal resolution with explicit cloud microphysics of a single-moment 6-class bulk microphysics scheme. To simulate GPM DPR reflectivity from NICAM model outputs, the Joint-Simulator (Hashino et al. 2013;
JGR) is used. Our initial tests showed a better match with the observed reflectivity by assimilating GPM DPR reflectivity into NICAM forecasts.
However, the results from a 1-month data assimilation cycle experiment showed general degradation by assimilating GPM DPR reflectivity. For
better use of GPM DPR reflectivity data, we estimated a model cloud physics parameter corresponding to snowfall terminal velocity by data
assimilation. Parameter estimation reduced the snowfall terminal velocity, and successfully mitigated the gap between simulated and observed
Contoured Frequency by Altitude Diagram (CFAD). The estimated parameter also improved temperature and humidity fields in the mid- tolower troposphere, and precipitation forecasts.

DWD pilot station – Evaluating ground-based remote sensing systems for future observing networks
Lehmann, Volker(1); Klink, Stefan(2); Knist, Christine(3); Vural, Jasmin(2); Kayser, Markus(3); Schomburg, Annika(4); Leinweber, Ronny(3);Löffler, Moritz(5); Görsdorf, Ulrich(3)(1)
Deutscher Wetterdienst [Lindenberg], (2)Deutscher Wetterdienst [Offenbach], (3)Deutscher Wetterdienst [Lindenberg], (4)Deutscher Wetterdienst[Offenbach], (5)Deutscher Wetterdienst [Potsdam]
The current surface-based observing network has an insufficient density to fully characterize atmospheric processes at scales of Meso-β (andbelow). It is therefore unable to capture many relevant meteorological phenomena, especially in the boundary layer. Space-based measurements are unlikely to resolve this deficiency. A new generation of ground-based remote sensing instruments, often called “profilers”, has
meanwhile become commercially available. These instruments are able to provide continuous measurements of kinematic, thermodynamic andcloud/aerosol particle related variables, mostly in the form of vertical profiles. Benefits of assimilating such data were recently seen in field
campaigns. It is therefore timely to ask whether such profilers can also be used successfully in an operational setting. The presentation will
give an overview of the project "Pilotstation" at DWD, which is investigating various options for a qualitative extension of the surface-based
observing network. A testbed approach is employed to assess data availability, quality, observation impact as well as operational sustainability
for the following profilers: Doppler lidar, microwave radiometer, water vapor broadband-DIAL, cloud radar and Raman lidar.

Validation of the active microwave sensor module within the RTTOV-SCATT radiative transfer model
Mangla, Rohit(1); Chambon, Philippe(1); Borderies, Mary(1); Geer, Alan(2)(1)
Météo-France, Toulouse, (2)European Center for Medium-range Weather Forecast
Space-borne radar observations are currently emerging has an observation kind important to consider within Numerical Weather Prediction
applications. Like for the forward simulation of passive microwave observations, radar data simulations require to make multiple assumptions
including on the scattering properties of hydrometeors. With the objective of simulating both active and passive microwave instruments within
a single framework using the same radiative transfer assumptions into a widely-used tool in the NWP community, a first version of active sensor
module has recently been released within Version 13 of the RTTOV software by the EUMETSAT NWP SAF. This initial version supports thesimulation of both the GPM/Dual frequency Precipitation Radar and the Cloudsat/Cloud Precipitation Radar. Simulations of the GPM/DPR, performed with RTTOV V13 and the ARPEGE global model running operationally at Météo-France will be shown. Comparisons will be performed
with observations, both on a case study as well as on a large number of samples. In particular, a sensitivity of the simulations to the hydrometeor fraction profile specifications will be discussed.

3D Precipitation Nowcasting: RESNet applied to Highly Dense PAWR Data
Mdini, Maha(1); Miyoshi, Takemasa(1); Otsuka, Shigenori(1)(1)
RIKEN Center for Computational Science [Kobe]
Sudden heavy rain may lead to disasters like flooding and loss of life and property. To reduce the risk, predicting sudden downpours is of key
importance. However, predictability of such events is limited to only for a very short range within an hour or shorter because of their abruptness. In this case nowcasting is an effective approach. Detecting sudden heavy rain even 10 minutes before it occurs can reduce the damage
drastically. Precipitation nowcasting is the process of short-range prediction based on observation data. In the case of sudden rainfalls, thisprocess is difficult due to the fast evolution of the rain and its chaotic nature. Therefore, we need innovative techniques. The novel PhasedArray Weather Radar (PAWR) offers dense 3D images of reflectivity every 30 seconds. We took advantage of this big data to perform nowcasting
using neural networks. We use Residual Neural Networks (RESNet) to compress the images and extract information relevant for the prediction.
Next, we use a Convolutional Neural Network (CNN) to make the prediction. Afterwards, we use the same RESNet to map the forecast to theoriginal domain. The RESNet and the CNN are trained jointly for the compression to maximize the prediction accuracy. Our first results showthat in most cases we can predict precipitations up to 30 minutes, with an error rate (false positives + false negatives) of 8%. The use of the
RESNet allowed to alleviate the memory load and the computational complexity of the prediction. Moreover, training the RESNet and the CNN
jointly reduced immensely the prediction noise in non-precipitation regions and improved the accuracy in precipitation regions.

Impact of ground-based water vapour and temperature lidar profiles on short-range forecast skill by means ofhybrid 3DVAR-ETKF data assimilation
Thundathil, Rohith(1); Schwitalla, Thomas(1); Behrendt, Andreas(1); Lange, Diego(1); Späth, Florian(1); Wulfmeyer, Volker(1); Leuenberger,
Daniel(2); Haefele, Alexander(2); Arpagaus, Marco(2); Giovanni, Martucci(2)(1)
University of Hohenheim, Institute of Physics and Meteorology, (2)Federal Office of Meteorology and Climatology MeteoSwiss
Assimilation of ground-based thermodynamic lidar observations has augmented numerical weather prediction capabilities from nowcasting to
the very short-range, short-range, and medium-range. In this study, temperature and water vapour profiles obtained from the temperature
Raman lidar and the water vapour differential absorption lidar, respectively, of the University of Hohenheim are assimilated into the Weather
Research and Forecasting (WRF) model through a new forward operator. The operator directly incorporates the water vapour mixing ratio,
avoiding undesirable cross sensitivities to temperature, enabling complete observation concerning the water vapour contents to be propagatedinto the model. The assimilation was performed with the three dimensional variational DA system and with the hybrid 3DVAR Ensemble Transform Kalman Filter approach at a convection-permitting resolution. The 3DVAR-ETKF experiment resulted in a 50% smaller temperature andwater vapour RMSE than the 3DVAR experiment. The planetary boundary layer height (PBLH) of the analyses also showed improvement compared to available ceilometer data. A single lidar vertical profile impact spreads over a 100 km radius, promising future assimilation of water
vapour and temperature data from operational lidar networks. Forecast improvement with respect to PBLH was observed for about 7 hours,
while an improvement of integrated water vapour lasts for 4 hours. We also present some significant collaborative effort with the Raman lidar
for meteorological observation (RALMO) from the MeteoSwiss. Also, some initial results from the assimilation Atmospheric Raman Temperatureand Humidity Sounder (ARTHUS) data will be shown.

A statistical evaluation of Bayesian inversions from infrared and microwave cloudy observations for future instruments MTG-FCI, MSG-MWI and MSG-ICI
Villeneuve, Ethel(1); Chambon, Philippe(2); Fourrie, Nadia(3)(1)
Centre National de Recherches Météorologiques, (2)Météo-France, Toulouse, (3)Centre National de Recherches Météorologiques
Infrared (IR) and Microwave (MW) satellite observations are widely used in data assimilation for Numerical Weather Prediction. The so-called"1D-Bayesian + 4D-Var" assimilation method (Duruisseau et al., 2019) is used for the all-sky MW observations at Météo-France. It consists ofa Bayesian inversion of the brightness temperatures, that retrieves atmospheric profiles. The resulting relative humidity is then assimilated in
the 4D-Var of the global model ARPEGE. This study focuses on future instruments onboard MTG-I (Meteosat Third Generation) and MSG-B(MetOp Second Generation): the Flexible Combined Instrument (FCI), the MicroWave Imager (MWI) and the Ice Cloud Imager (ICI). Due to theirdifferent spectral ranges, they are sensitive to various and complementary quantities within clouds and precipitation. The objective is to identify the key components of the assimilation system for reaching a synergistic use of these observations in an all-sky context. The ability of theBayesian inversion to provide complementary information is quantified with simulations from radiative transfer model RTTOV v.13 and lagged
forecasts for the observations and the background. Statistical results based on a wide sample of profiles from the ARPEGE forecast model will
enable to build a global evaluation for IR and MW observations, and thus measure the degree of consistency and the differences in the retrievalsfrom IR and MW observations. Two steps are followed: (i) considering a perfect forward model to understand the source of differences fromthe retrieval method and the spectral range ; (ii) introduction of errors in the radiative transfer model to understand the differences introducedby the hypotheses used. Preliminary results will be shown.

Friday, 17 Sep 2021

Poster session P5 - General DA
Exploring the potential of nested EnVAR in the global-to-regional ensemble system at DWD
Burba, Mareike(1,2); Ulbrich, Sven(1); Hollborn, Stefanie(1); Anlauf, Harald(1); Schraff, Christoph(1); Potthast, Roland(1); Knippertz, Peter(2)(1)
Deutscher Wetterdienst [Offenbach], (2)Karlsruhe Institute of Technology
The German Weather Service (DWD) operationally runs ensemble predictions systems for the global and regional Numerical Weather Prediction(NWP). The global system deploys the NWP model ICON with an EnVAR+LETKF (Ensemble variational + localized Ensemble Transform Kalman
Filter) data assimilation at a resolution of 40 km for the ensemble and 13 km for the deterministic run. The global setup is run with a two-way
nest in central Europe (ICON-EU). This provides the lateral boundary conditions for the regional system which consists of ICON-LAM (ICON
Limited Area Mode) run at a resolution of 2.1km (model configuration ICON-D2) and a 4D-LETKF scheme called KENDA (Kilometre-scale Ensemble Data Assimilation). We are investigating a number of different setups for creating a regional EnVAR analysis for the deterministic forecast. The model is ICON-LAM, but the ensemble members for the ensemble covariance matrix and the boundary conditions for the deterministic run originate from different resolutions of the ICON model, e.g. ICON-D2, ICON-EU or ICON global. We focus on a EnVAR data assimilation
based on the KENDA ensemble to improve the forecasts on the convective scale.

Forecast Evaluation of a Deep Convection Case During Relampago Assimilating Conventional and Satellite Observations with the WRF-GSI-LETKF System
Corrales, Paola(1,2,3); Ruiz, Juan(1,2,3); Galligani, Victoria(1,2,3)(1)
Universidad de Buenos Aires [Buenos Aires], (2)Consejo Nacional de Investigaciones Científicas y Técnicas [Buenos Aires], (3)Centro de Investigaciones del Mar y la Atmósfera
We present forecast evaluation results from the first attempt to use the WRF-GSI-LETKF system in Argentina. The impact of assimilating highresolution surface networks and satellite observations on a forecast from the analyses is evaluated. This study also represents the first steps
to assimilate satellite radiances in a regional context in South America. We focus on a case study corresponding to a huge mesoscale convective
system (MCS) developed over central and north-eastern Argentina during November, 22th, 2018. Analyses with 10-km horizontal grid spacing

were produced assimilating observations every hour from 11/20 18Z to 11/23 12Z. We used a 60-members ensemble which at the first assimilation cycle is initialized from the deterministic GFS run adding random perturbations with climatological covariance. A multiphysics approach is also used to represent model errors, using different physics configurations. Four assimilation experiments were conducted using
increasing sets of observations: CONV assimilates conventional observations from prepBUFR, AUT uses CONV observations plus automatic
stations, SATWND add satellite-derived winds and RAD also includes satellite radiances from AMSU, HIRS, MHS, ATMS, AIRS and IASI. We run
two ensemble forecasts initialized from the analyses at 00Z and 06Z 11/22 to evaluate the impact of the different observing networks onforecasts. Comparing the forecasts with the observed precipitation (IMERG Final Run), the representation of precipitation is shown to improvefor forecasts initialized from AUT, SATWND and RAD. Moreover, the impact continues for many hours after its initialisation. Complete results
and conclusions from these experiments will be presented at the symposium.

Assimilation of solar reflectances in a pre-operational online system with a local ensemble Kalman filter
Deppisch, Thomas(1); Bach, Liselotte(1); Scheck, Leonhard(2); Stumpf, Christina(1); Geiß, Stefan(2); De, Lozar Alberto(1); Schraff, Christoph(1);Koepken, Christina(1); Faulwetter, Robin(1)(1)
Deutscher Wetterdienst [Offenbach], (2)Ludwig Maximilian University [Munich]Since spring 2021, the convection-permitting ensemble data assimilation system (ICON-D2 KENDA) of Germany's National Weather Service(DWD) assimilates solar reflectances from the SEVIRI 0.6 micron channel inside the pre-operational SINFONY system for seamless prediction
from nowcasting to NWP. Assimilating solar reflectances allows for deeper insight into the physical dynamics of atmospheric radiation andmicrophysical processes. Better tracking of clouds and convective processes may also improve the skills of short-term NWP, especially for
precipitation. This work is based on the incorporation of the fast and accurate forward operator for solar reflectances MFASIS into the NWP
system at DWD. Data assimilation of these reflectances further demands careful study of the underlying biases introduced by both model andforward operator. We therefore give an overview of the steps undertaken during the development of this system as observational error modelling, quality control and an adaptive bias correction. Extensive case studies and impact experiments in different seasonal conditions showthat data assimilation of solar reflectances reduces the forecast error of cloud and precipitation, precipitation intensity, global radiation andscreen-level variables.

Hydrometeor control variables in the AROME-France 3DEnVar assimilation scheme
Destouches, Mayeul(1); Montmerle, Thibaut(2); Michel, Yann(3); Caron, Jean-François(4)(1)
CERFACS, (2)Météo-France, (3)CNRM, Université de Toulouse, Météo-France, CNRS, Toulouse, France, (4)Environnement Canada
Initialization of variables related to condensed water is a topic of active research for convective-scale weather forecasting. We present in thistalk the work that has been performed at Météo-France to add hydrometeors as control variables of a three-dimensional ensemble variational
scheme (3DEnVar) for the regional model AROME-France. Hydrometeor variables are added in the forecast error covariance matrix B estimatedfrom an Ensemble of Data Assimilations. As a first approximation, no variable transform nor static hydrometeor covariance matrix areused. Common localization and Scale-Dependent Localization (SDL) are tested. Even without any direct assimilation of hydrometeor observations, hydrometeor analysis increments can be produced via covariances with observed variables in B. Cycled forecast-analysis experiments
in near-operational conditions have been performed over a 3-month summer period. Three configurations were compared, (i) a control experiment without hydrometeor control variables, (ii) a test experiment with hydrometeor control variables but without cycling the resulting forecasts, and (iii) an experiment with hydrometeors and cycled forecasts. Compared to the control experiment, both hydrometeor experiments

show a positive impact in the first 7 hours of the forecast in terms of cloud cover, and in the first hour for precipitations. The forecasts display
an extended short-term predictability window for precipitation, with skill scores decaying beyond a reference threshold after 1 h 14 min on
average, instead of 1 h in the control experiment. The added value of hydrometeor control variables persists independently of the chosen
localization method, thus confirming the robustness of the results.

1000-member ensemble forecasts for extreme events: the 2019 typhoon Hagibis and the July 2020 Kyushu heavy
rain
Duc, Le(1,2); Kawabata, Takuya(2); Saito, Kazuo(1,2); Oizumi, Tsutao(1,2)(1)
Japan Meteorological Business Support Center, (2)Meteorological Research Institute
Forecast performances of two extreme events (the 2019 typhoon Hagibis and the July 2020 Kyushu heavy rain) have been revisited with theaim of improving the forecasts for these events. Our approach is to better quantify forecast uncertainties by running data assimilation systems
with 1000 ensemble members to produce ensemble analyses. The two data assimilation methods to be used are the four-dimensional local
ensemble transform Kalman filter 4D-LETKF and the hybrid variational-ensemble assimilation 4D-EnVAR. Verifications show that the resulting
forecasts outperform the operational forecasts both in deterministic and probabilistic forecasts.

Enhancement of Variational Assimilation of High-Frequency and High-Resolution Radial Winds
Fujita, Tadashi(1); Seko, Hiromu(1); Kawabata, Takuya(1); Sawada, Ken(1); Hotta, Daisuke(1); Ikuta, Yasutaka(1)(1)
Meteorological Research Institute, Japan Meteorological Agency
This study is aimed at effectively utilizing radial winds from Doppler radars densely distributed in time and space in data assimilation to initialize a forecast model. An appropriate handling of the observation error correlation is firstly important to consistently assimilate high-resolution data without applying severe thinning. Handling of detailed flow-dependency in data assimilation scheme is also essential to extract
information from the high-resolution observations as a time evolution in line with the model dynamics. A hybrid 4D-Var equipped with anobservation error of radial wind correlated in time and space is implemented based on the former operational Meso-scale Analysis of Japan
Meteorological Agency applying the JNoVA 4D-Var (JMA 2019, Honda et al. 2005), introducing the flow-dependent background error generated
by the Ensemble of Data Assimilation (EDA; Isaksen et al. 2010) with extended control variables. A case study shows the flow-dependent
background error along with the correlated observation error contributes to give promising results lasting into forecast. Investigation is carriedout on sensitivity of the flow-dependent background error to the EDA perturbations, including uncertainties from the random sampling of
perturbations added to observations. An enhancement of the ensemble control variables also is tried to take into account the scale dependent
profile of the background error.

Using a cost-effective approach to increase background ensemble size in EnVar to improve radar analyses andforecasts of convective systems
Gasperoni, Nicholas(1); Wang, Xuguang(1); Wang, Yongming(1)(1)
School of Meteorology, University of Oklahoma
The valid time shifting (VTS) method is explored for the GSI-based EnVar system extended for convective scales with direct assimilation ofradar reflectivity. VTS is a cost-efficient method to increase ensemble size or reduce current cost by including subensembles before and after
the central analysis time. Additionally, VTS addresses common time and phase model error uncertainties within the ensemble. VTS is examined
here in a HRRRE-like continuous hourly analysis system over 1900-0000 UTC on 1-2 May 2019. The VTS application is compared against the
36-member control experiment (ENS-36) to increase ensemble size (3x36 VTS) and as a cost-savings method (3x12 VTS), with three timeshifting intervals t = 15, 30, and 45-min. The 3x36 VTS experiments increased the ensemble spread overall, with larger subjective benefits inearly cycles during convective development. The 3x12 VTS experiments capture analysis with similar accuracy as ENS-36 by the third hourly
analysis. Control forecasts launched from hourly EnVar analyses show skill increases in 1-h precipitation over ENS-36 out to hour 12 for 3x36
VTS experiments. Experiment VTS-3x12t45 captures similar level of skill to ENS-36 out to forecast hour 16, with at times subjectively better
structure of the bowing line. The 3x36 VTS experiments add a computational cost of 35-56%, compared to the near tripling of costs when
directly increasing ensemble size, while 3x12 VTS experiments save about 45-55% costs over ENS-36.

Spin-up time from switching the microphysics scheme within the assimilation cycle and impacts on the precipitation forecast quality
Ulbrich, Sven(1); Welzbacher, Christian(1); Khosravianghadikolaei, Kobra(1); Hoff, Michael(1); De, Lozar Alberto(1); Bach, Liselotte(1); Stephan,
Klaus(1); Schraff, Christoph(1); Blahak, Ulrich(1); Potthast, Roland(1,2)(1)
Deutscher Wetterdienst [Offenbach], (2)University of Reading
The SINFONY project at DWD aims to produce seamless forecast products from minutes up to 12 h focusing on convective precipitation events.
While the forecasts early on are typically produced from nowcasting procedures using radar data, numerical weather prediction (NWP) aims at
forecasting longer time ranges. However, the latest available forecast is usually too old to merge with nowcasting data for reliable seamless
predictions. At DWD, forecasts with lead times beyond 2 h are produced by a short-range numerical weather prediction system (SRNWP) using
ICON. A continuous assimilation cycle is used with relatively long cutoff times and a 1-moment microphysics. To reduce differences in predicted precipitation and nowcasting, 3 actions are taken on the NWP side. First, we reduce the latency of forecasts by using short assimilation
cycles with shorter data cutoff and increasing the frequency of forecasts. This is the Rapid Update Cycle (RUC). Second, the RUC uses newobservation systems, e.g. radar or all-sky satellite data, to capture and represent strong convection. Third, we introduce a 2-moment microphysics scheme into ICON, improving the simulated radar reflectivities. To keep the model state similar to that of the SRNWP, the RUC branches
off from the SRNWP assimilation cycle at several pre-defined times. The introduction of the 2-moment scheme leads to a spin-up affecting
both assimilation cycle and short forecasts. The resulting effects are assessed by a comparisonto the SRNWP with the 1-moment scheme.Theresults are compared regarding quality of precipitation forecast and other observations. It is shown how far the resulting improvements are
related to the assimilation and microphysics scheme, or to the higher forecass frequency.

The predictability of the moist convection over different mountain sizes and environmental flow conditions
Wu, Pin-Ying(1); Takemi, Tetsuya(1)(1)
Disaster Prevention Research Institute, Kyoto University
The predictability of moist convection and the accompanying rainfall is limited to a few hours since the high nonlinearity of moist convective
processes could lead to rapid initial error growth. On the other hand, the topography is considered to decrease the nonlinearity of the atmosphere and increase the predictability by providing a stationary forcing. It has been shown that topography can increase the predictability ofrainfall from a practical perspective. However, the impact of topography on intrinsic predictability hasn't been well investigated. By addressing
the impact of topography on the initial error growth of moist convection, it is expected to be helpful in improving the weather prediction anddata assimilation strategy in areas affected by topography. Here, identical twin experiments with different single Gaussian shape mountains
and vertical wind shear are conducted with the Weather Research and Forecasting (WRF) model in an idealized framework. The initial error
growth is evaluated by a metric referred to as moist difference total energy (MDTE), which considers the differences in horizontal wind, temperature, water vapor, and surface pressure between the two simulations. Comparing to the experiment without the mountain, the initial error
growth estimated by the MDTE is smaller for the moist convection occurring over the mountain in the morning. The reduction effect of themountain on the error growth is more distinct in the water vapor than in the horizontal wind or temperature. When the mountain slope is
steeper, the effect also becomes more pronounced. It also shows a sensitivity to the stability and different flow conditions of the environment.

Improving dynamical balance and mass convervation in convective-scale data assimilation
Zeng, Yuefei(1); Janjic, Tĳana(2); De, Lozar Alberto(3); Ulrich, Blahak(4); Seifert, Axel(5)(1)
Ludwig-Maximilians-University, (2)Ludwig-Maximilians-University, (3)DWD, (4)DWD, (5)DWD
An idealized framework for radar data assimilation has been developed based on the COSMO-KENDA system (Zeng et al. 2021a), It is shownthat the data assimilation could cause significant biased increases in divergence and vorticity as well as in total specific mass of microphysicalvariables. To reduce the bias, two methods have been developed: First, a new integrated mass-flux adjustment filter, which uses the analyzed
integrated mass-flux divergence field to correct the analyzed wind field (Zeng et al. 2021b). It is found that the new filter considerably diminishes spurious mass-flux divergence and the high surface pressure tendency and thus results in more dynamically balanced analysis states.
Second, a weakly constrained LETKF on mass conservation and nonnegativity of microphysical variables, which improves the mass conservation
property of microphysical variables of analyses (Zeng and Janjic 2021). Both methods lead to better forecasts.

Friday, 17 Sep 2021

Poster session P5 - Methodology
Assimilating Disorganized Crowdsourced Imagery Data for Machine Learning-based Geomorphological Change
Detection ResearchChen, Thomas(1)(1)
Academy for Mathematics, Science, and Engineering
As open source data becomes more ubiquitous, the involvement of citizen scientists has increased. The collection of large quantities of relevant
data and respective labels through crowdsourcing on online platforms has yielded many exciting opportunities for machine learning applications. In geomorphology, multitemporal imagery, much of which is captured through crowdsourcing, is especially useful for training deeplearning models for change detection in landscapes. This is relevant in terms of natural hazards that occur, including endogenous types likevolcanoes and neotectonics, exogenous ones such as floods, karst collapses, sedimentation, erosion, tsunamis, and avalanches, as well asclimate change or land use-induced hazards like permafrost and desertification. However, a challenge when harnessing crowdsourced imagery
is the disorganized and “unclean” fashion in which it often presents itself. Cleaning data prior to training neural network-based computer
vision models is key to success in any geomorphology change detection research. We discuss approaches such as manual techniques, image
restoration and denoising, and image duplication reduction. The goal is to assimilate a diverse range of data collected from many sources tosuccessfully train machine learning algorithms. In a broader sense, this research has the potential to save lives by detecting possibly destructive and dangerous geomorphological change, and to conserve environments that have been affected severely.

Assimilating various environmental features to train machine learning algorithms for sea ice drift prediction in
the Arctic as a key geophysical parameter for understanding climate change
Chen, Thomas(1)(1)
Academy for Mathematics, Science, and Engineering
The movement of sea ice is influenced by a number of factors, from winds to ocean currents. As climate change continues to occur rapidly,
understanding sea ice drift in the Arctic is a key parameter to understanding the effects of rising temperatures in the region. Recent literature
has shown that the Arctic and the Antarctic are most affected by global warming, which raises questions regarding climate justice, as most of
the carbon emissions causing anthropogenic climate change are produced in other regions. To analyze this impact, we employ artificial intelligence to predict sea ice drift velocity based on external features. Machine learning is the process of computers gaining insights by seeing andcorrelating large quantities of data. Using external parameters, including wind speed, and drift velocity ground truth as the inputs of the model,
we train multiple different architectures and compare the results. Particularly, we experiment with a convolutional neural network (CNN), a
random forest (RF), and a support vector machine (SVM). We also experiment with various model specifications. This research leads to a greater
understanding of the Arctic's response to climate change.

Timely allocation of resources after natural disasters: deep learning as a tool for damage assessment and saliency
mapping
Chen, Thomas(1)(1)
Academy for Mathematics, Science, and Engineering
Natural disasters ravage the world's cities, valleys, and shores on a monthly basis. Having precise and efficient mechanisms for assessing
infrastructure damage is essential to channel resources and minimize the loss of life. Using a dataset that includes labeled pre- and postdisaster satellite imagery, the xBD dataset, we train multiple convolutional neural networks to assess building damage on a per-building basis.
In order to investigate how to best classify building damage, we present a highly interpretable deep-learning methodology that seeks to explicitly convey the most useful information required to train an accurate classification model. We also delve into which loss functions best
optimize these models. Our findings include that ordinal-cross entropy loss is the most optimal loss function to use and that including thetype of disaster that caused the damage in combination with a pre- and post-disaster image best predicts the level of damage caused. The
highest accuracy percentage on the testing set that we achieve is 74.6%; the non-optimal nature of this is largely attributed to the limited
discernibility between the major and minor damage categories. We also make progress in the realm of qualitative representations of whichparts of the images that the model is using to predict damage levels, through gradient class-activation maps. Our research seeks to computationally contribute to aiding in this ongoing and growing humanitarian crisis, heightened by climate change. Specifically, it advances moreinterpretable machine learning models, which were lacking in previous literature.

Using Machine learning techniques to switch background error distributions to improve data assimilation
Hossen, Md Jakir(1); Goodliff, Michael(1,2); Fletcher, Steven(1)(1)
Cooperative Institute for Research in the Atmosphere, (2)Cooperative Institute for Research in Environmental Sciences
With the development of non-Gaussian based data assimilation in the variational formulation, and the understanding that the underlying distribution can change dynamically, we need techniques that allow us to “switch” between the distributions to ensure a consistent backgrounderror model. To address this question, we have used different machine learning techniques with different criteria to determine if the behaviorof the variable has switched between Gaussian and lognormal, but also back to Gaussian. In this presentation, we shall present results using
a support vector machine technique to determine when the z component of the Lorenz 1963 model switches between Gaussian, lognormal,
and back, through using skewness changing from 0 to determine this change and show that by switching between the distribution, the analysis
error is improved compared to just assuming a Gaussian all the time. We shall also present results from the Lorenz 96 model as well.

A Deep Learning approach for error correction of numerical weather prediction simulation data
Karozis, Stelios(1); Klampanos, Iraklis(1); Sfetsos, Athanasios(1)(1)
National Center for Scientific Research "Demokritos"
Meteorological data are produced in various spatial and time sizes, depending on the application they will be used. The data are the result of
Numerical Weather Prediction (NWP) simulations, e.g. the solution of mass & energy balance equations, concerning the fluids of the atmos-

phere. The uncertainty of the solution (time-series of domain) becomes higher as the prediction goes further to the future, thus, limiting theapplicability of hour per hour resolution of such models, to 2 to 5 days ahead. In the current study, a deep learning approach, based on
convolutional autoencoders (CAEs), is explored in order to effectively correct the error of the simulation result, hence performing a result
similar to statistical downscaling methods. The global seasonal forecast (6 month ahead) Meteo France Seasonal data was used for the Greecearea, alongside the reanalysis dataset NCEP FNL, that incorporates observations, satellite imaging etc and it has better spatial resolution. During the training of the model, external information is used as evidence transfer, concerning the time conditions (month, day, season) and thesimulation characteristics (initialization of simulation). The study was performed for the temperature variable at 2m from the ground and it
was found that the CAEs help improve the resolution of the seasonal data and successfully correct the error of NWP data for 6 month ahead
forecasting. Interestingly, the season evidence yields the best results which indicates a seasonal (winter, spring, summer, autumn) dependenceof the performance.

Accelerating Climate Model Computation by Neural Networks: A Comparative Study
Mdini, Maha(1); Miyoshi, Takemasa(1); Shigenori, Shigenori(1)(1)
RIKEN Center for Computational Science [Kobe]
In the era of modern science, scientists have developed numerical models to predict and understand the weather and ocean phenomena basedon fluid dynamics. While these models have shown high accuracy at kilometer scales, they are operated with massive computer resources
because of their computational complexity. In recent years, new approaches to solve these models based on machine learning have been put
forward. The results suggested that it be possible to reduce the computational complexity by Neural Networks (NNs) instead of classical numerical simulations. In this project, we aim to shed light upon different ways to accelerating physical models using NNs. We test two approaches: Data-Driven Statistical Model (DDSM) and Hybrid Physical-Statistical Model (HPSM) and compare their performance to the classical ProcessDriven Physical Model (PDPM). DDSM emulates the physical model by a NN. The HPSM, also known as super-resolution, uses a low-resolution
version of the physical model and maps its outputs to the original high-resolution domain via a NN. To evaluate these two methods, we measured their accuracy and their computation time. Our results of idealized experiments with a quasi-geostrophic model show that HPSM reduces
the computation time by a factor of 3 and it is capable to predict the output of the physical model at high accuracy up to 9.25 days. The DDSM,
however, reduces the computation time by a factor of 4 and can predict the physical model output with an acceptable accuracy only within 2
days. These first results are promising and imply the possibility of bringing complex physical models into real time systems with lower-costcomputer resources in the future.

Using a Neural Network to choose amplitude and anisotropy parameters of an adaptive background error covariance
Purser, R. James(1); Rancic, Miodrag(1); Pondeca, Manuel(1)(1)
IMSG at NOAA/NCEP/EMC
As part of the development of the new 3D Real-Time Mesoscale Analysis (3D RTMA) we have developed a multigrid background covariancescheme using local superpositions of quasi-Gaussian numerical filters based on compact-support beta distributions. We plan to use a machinelearning approach, specifically a neural network, to guide the optimal construction of the multigrid's amplitude scale-weights, and anisotropydetermining aspect tensor, in response to smoothed diagnostics from terrain, background and ensemble, so that the analysis can be madedynamically adaptive. Given the tensorial characterization of anisotropy, we extract from the background and ensemble fields suitable tensor

diagnostics which a neural network has only to combine additively. The quasi-Gaussian character of the covariance contributions allows someinter-observation covariances to be well approximated by direct evaluation. Thus, we construct a quadratic quality criterion measuring thatcovariance deviation from the corresponding sample innovation statistics that subsets of observation pairs provide, and train the neural network over multiple archived cases, to optimize the covariance parameters, and hence the analysis.

Towards Developing Radio Occultation Machine Learning Forward Operators
Stefanescu, Razvan(1); Steward, Jeff(1); Zupanski, Dusanka(1); Rothstein, Mathew(1); Gorbunov, Michael(1,2,3); Irisov, Vladimir(1); Hei,
Matthew(1)(1)
Spire Global, (2)A. M. Obukhov Institute of Atmospheric Physics, Russian Academy of Sciences, (3)Hydrometeorological Research Centre of
Russian Federation
Spire Global is a space-to-cloud data and analytics company that generates highly impactful earth observations and weather forecasts. Currently, Spire collects ~10,000 radio occultation soundings a day globally via the company's large nanosat constellation; these soundings, alongwith other data, are assimilated into a global weather model to produce the Spire operational forecast. Our main goal of developing machinelearning (ML) forward operators is to improve the skill of our data assimilation analyses and subsequent weather forecasts by enhancing theimpact of the radio occultation profiles. More precisely, we plan to improve over existing physics-based operators by using more accurate
implicit data driven interpolation operators, and superior handling of data biases. We developed two ML operators based on the random forestand deep neural network (DNN) methods. The random forest model is used as an exploratory tool for feature importance to deal with the large
number of predictors obtained from numerical weather prediction forecasts and radio occultation profiles. The first implementation of the DNN
model employs a reduced number of features and takes advantage of an embedding layer to differentiate between various satellites sourcescharacterized by categorical information. The next step is to make use of all geophysical variables as required by the data assimilation scheme
and implement DNNs equipped with either a convolutional or SVD layer. Bending angles resulting from the ML forward operators will be compared against the physics-based 1D operator ROPP outcomes. The performance will be assessed based on statistics of departures from thereal measurements.