bluuebunny/crossref_metadata_2025 huggingface dataset:
What caused the increased rainfall during the Pliocene in today’s hyper-arid Atacama Desert?
Authors: Reyers Mark , Fiedler Stephanie , Ludwig Patrick , Shao Yaping
Published: June 2022

The Atacama Desert in Northern Chile is considered to be the driest warm desert on Earth. However, its hyper-aridity was punctuated by more humid phases in the past. A prominent paleoclimate period of increased rainfall is the warm Pliocene (ca. 5.3 to 2.6 Ma before present). The processes leading to this rainfall increase are not well understood. While recent studies propose regions Southwest or East of the Atacama Desert as moisture sources for enhanced rainfall, the tropical South-eastern Pacific has so far been overlooked as a potential source. The goal of this study is to identify potential key drivers for the increased rainfall in the Pliocene. With this aim, we have downscaled PMIP4-CMIP6 mid-Pliocene (3.2Ma) and historical (1985-2014) experiments using the regional climate model WRF with a spatial resolution of 10 km. The mid-Pliocene WRF simulation exhibits increased mean annual rainfall in the hyper-arid core of the Atacama Desert when compared with the historical period consistent with paleo-records. This increase can be attributed to frequent intense rainfall events in austral winter during the mid-Pliocene, often associated with strong upper-level moisture conveyer belts (MCBs) originating in the tropical Southeast Pacific in front of mid-tropospheric troughs. In present-day climate, such MCBs are much weaker and mostly originate from the relatively dry subtropics producing less moisture advection. Our clustering of upper-level moisture fluxes uncovers systematic differences between MCBs in the mid-Pliocene and the present-day climate: for the mid-Pliocene we found clusters of strong MCBs from the tropical Pacific, that do not occur under present-day conditions. This is due to stronger troughs off the Atacama Desert paired with warmer sea-surface temperatures in the tropical Eastern Pacific and along the northwest coast of South America during the mid-Pliocene, which are favourable for the development of MCBs. The winter rainfall amount associated with MCBs in the mid-Pliocene far exceeds the present-day total rainfall amount. We therefore conclude that tropical MCBs are a key driver for increased rainfall in the Atacama Desert and should be assessed for future climate. This will require kilometre-scale simulations to resolve the dynamical processes involved.
Developing Forward-looking Probabilistic Road Weather Forecasts at the Met Office
Authors: Eyles Joe , Lake Alice , Susorney Hannah , Odbert Henry
Published: June 2022

Surface transport forecasting has traditionally focused on deterministically categorising the state - for example, dry, damp, or icy - of a road surface, to provide decision support to winter gritting services. However, with the emerging Connected Autonomous Vehicle (CAV) sector, the ability to accurately describe a broader range of weather conditions, at a much smaller temporal and spatial scale, is becoming equally important to ensuring public safety. Localised conditions such as road spray, rain and fog can all degrade the performance of CAV sensors, whilst settled snow also has the potential to impede navigation by obscuring road markings. The difficulty of deterministically forecasting such precise localised conditions requires us to quantify uncertainty, driving a move towards probabilistic, risk-based forecasting. Therefore, the Met Office is currently developing a new Surface Transport Forecasting (STF) post-processing system, designed to accommodate these future user requirements.

The new STF post-processing system is centred on the Joint UK Land Environment System (JULES); a community model used as the land-surface component of the Met Office Unified Model (UM), but which can also be used – as we do here – as a stand-alone land-surface model driven by forecast output from Numerical Weather Prediction (NWP) models. To produce probabilistic forecasts for locations within the United Kingdom, we are using output from the Met Office regional ensemble model MOGREPS-UK to drive JULES. MOGREPS-UK is a 2.2km resolution 18-member ensemble which provides forecasts out to 5 days. It is generated by time-lagging over 6 hours, initialising three new ensemble members every hour from perturbed initial conditions. By running JULES for each ensemble member, we create a set of possible road forecast outputs. Considering these predictions in aggregate allows us to generate probabilistic forecasts of road weather conditions.

Our ensemble-driven STF system has been verified using standard ensemble verification techniques, including rank histograms and reliability plots. Initial analysis of results, using data from approximately 300 locations in the United Kingdom for which good quality road weather observations are available between 2015 and 2021, indicate that observed road surface states are generally captured within the spread of predictions. The new system has been compared with the current deterministic STF system.  In particular, in challenging meteorological conditions (for example, where there is variable cloud cover, scattered showers, or on marginal temperature nights) the probabilistic approach allows us to quantify the uncertainty in the road state forecast in a way that the deterministic approach does not. Further work will focus on the most effective way to communicate probabilistic forecasts to end-users, ensuring they are able to apply the output to enhance their decision-making.
Recent Hadley circulation strengthening: a trend or multidecadal variability?
Authors: Zaplotnik Žiga , Pikovnik Matic , Boljka Lina
Published: June 2022

This study explores the possible drivers of the recent Hadley circulation strengthening in modern reanalyses. Predominantly, two recent generations of reanalyses provided by the European Centre for Medium-range Weather Forecasts (ECMWF) are used: the fifth-generation atmospheric reanalysis (ERA5) and the interim reanalysis (ERA-Interim). Some results are also evaluated against other long-term reanalyses: ERA-20C, CERA-20C, NOAA-20CR and NCAR/NCEP. To assess the origins of the Hadley cell (HC) strength variability we employ the Kuo-Eliassen equation. ERA5 shows that both HCs were strengthening prior to 2000s, but they have been weakening or remained steady afterwards. Most of the long-term variability in the strength of the HCs is explained by the meridional gradient of diabatic latent heating, which is related to precipitation gradients. However, the strengthening of both HCs in ERA5 is larger than the strengthening expected from the observed zonal-mean precipitation gradient (via Global Precipitation Climatology Project, GPCP). This suggests that the HC strength trends in the recent decades in ERA5 can be explained partly as an artifact of the misrepresentation of latent heating and partly through physical long-term variability. To show that the latter is true, we analyze ERA5 preliminary data for the 1950-1978 period, other long-term (e.g. 20th century) reanalyses, and sea surface temperature observational data. This reveals that the changes in the HC strength can be a consequence of the Atlantic multidecadal oscillation (AMO) and related diabatic and frictional processes, which in turn drive the global HC variability. This work has implications for further understanding of the long-term variability of the Hadley circulation.
Establishment and Application of Platform Based on Downscaling Climate Reanalysis Data
Authors: Li Ren-Yun , Wang Liu-Chen , Lin Tzu-Ping
Published: June 2022

The architectural design is heavily influenced by the base's climatic conditions, such as window opening, daylighting, ventilation, etc.In the past, weather information obtained from the existing climate data may be too far from the base, too wide a range of coverage, or too wide a difference in geographical features, making it difficult to obtain seasonal information.With the increasing degree of urbanization, the heat island effect has intensified, and considering Taiwan's fluctuating terrain, it is even more important to quickly obtain detailed and accurate environmental background information.The TMY3 standard meteorological year data available in Taiwan has been established for only eight out of 23 counties and cities in Taiwan.

Taiwan's historical climate reconstruction data (TReAD) based on the Taiwan Climate Change Estimation Information and Adaptive Knowledge Platform (TCCIP) of the Ministry of Science and Technology (MOCT).The historical simulated climate data can show the climatic background of a location to some extent after verification by the present study and the Central Meteorological Administration of Meteorology.

Based on the climate background data of the base, this study compares TReAD data to a health examination table: A microclimate health checklist is made to determine the climate background of a base based on the data of various meteorological parameters.A website on Taiwan's Historical Climate Map has also been set up to provide a convenient way for users to access climate information and to use it as a reference for architectural design and urban planning.Furthermore, the site may be expanded to understand the relationships and other applications of the various factors affecting the historical climate.

Downscaling, close-knit climate data will help bring in the various stages of the architectural and urban life cycle as background data, such as open windows and airflow simulations.It is hoped that the link between urban and microclimate information can be further enhanced, creating a more pleasant architectural or urban environment, and effectively enhancing green architecture.
Towards the combination of in-situ and satellite-based solar radiation data in near-realtime – the project DUETT at DWD
Authors: Klameth Anna , Brinckmann Sven , Trentmann Jörg
Published: June 2022

As the production of the current solar radiation sensor systems ceased and changes due to automation of the measuring network were necessary, a new concept of solar radiation measurements in Germany was established by Deutscher Wetterdienst (DWD). Aiming at an efficient network with high-quality data, it was decided to enhance the coverage of high-quality pyranometers that measure global and diffuse solar radiation, herewith also allowing the calculation of sunshine duration via the pyranometric method. The use of low to moderate quality instruments to measure solar radiation and sunshine duration will be discontinued in the future. To compensate for the reduction of the number of in-situ measurements and with solar radiation products from satellite data continuously improving in quality, the integration of both data sources was planned within the project DUETT at DWD.  

The project DUETT focuses on the development of merging algorithms for the parameters global radiation and sunshine duration and therefore uses data from the (future) 42 pyranometer stations as well as near-real-time satellite data based on measurements of the METEOSAT SEVIRI instrument with a latency of only a few minutes. Preoperational, merged DUETT products are processed on an hourly basis with a spatial resolution of 2x2 km for Germany and a time delay of 15 minutes after each full (synoptic) hour for both parameters. Here, validation results of the latest version of the merging procedure, based on independent in-situ data, are presented for both parameters for a validation period of one full year; possible future improvements are also discussed.
Sensitivity analysis of the OpenFOAM LES simulations performed on the 3D urban array structure
Authors: Radovic Jelena , Fuka Vladimír , Nosek Štěpán
Published: June 2022

This study presents the outcome of a series of different street scale Large Eddy Simulations (LES) performed on a 3D urban array. The configuration and the setup of these simulations are built upon the results previously published by Nosek et al., 2018, BAE 138; Kluková et al., 2021, JWEIA 208. The LES simulations are focused on the turbulent flow, passive scalar transport, and dispersion within and above the previously mentioned 3D urban array which portrays a typical building setup present in many cities across Europe. This study is connected to the project named “The Role of coherent structures’ dynamics on scalar transport and dispersion in the urban canopy layer” and it is backed up by the experimental data obtained in the wind tunnel measurements.

The LES computations are carried out by the open-source computational fluid dynamics (CFD) software called OpenFOAM.  The simulations differ in terms of resolution and refinement of the computational grid within the street canyons of the urban array, the type of solvers used (e.g., pimpleFoam or a solver based on a projection method), and the boundary conditions imposed (periodic and turbulent inlet boundary conditions). The comparison between the LES and Detached Eddy Simulation (DES) approach is investigated and presented as well.

The findings of this study are serving to a better understanding of how well the different setups and configurations of the OpenFOAM LES simulations are capable of representing the turbulent structures in the street canyons and their influence on the passive scalar dispersion. In addition, the results confirm the optimal OpenFOAM simulation setup for the investigation of these types of problems. 
Introduction to the AQ-WATCH project and its multi-model air quality forecast system 
Authors: Li Cathy Wing Yi , Brasseur Guy , Sofiev Mikhail , Timmermans Renske , Kumar Rajesh , Pfister Gabriele , Mo Dan , Granier Claire , Doumbia Thierno , Basart Sara , Salvi Olivier , Caillard Bastien , Boose Yvonne
Published: June 2022

AQ-WATCH (Air Quality: Worldwide Analysis and Forecasting of Atmospheric Composition for Health) is an international consortium, which co-develops and co-produces tailored products and services derived from space and in situ observational data for improving air quality forecasts and attribution. For this purpose, AQ-WATCH develops a supply chain leading to innovative downstream products and services for providing air quality information. These prototypes are based on existing space and in situ observations of air quality and tailored to the identified needs of international users. The project allows small and medium enterprises to integrate datasets associated with earth’s observations with advanced predictive models. These innovative products and services are aimed at improving public health and optimizing service provided by the energy sector in different regions of the world. The project also creates opportunities for new environmental technologies and enhances the interaction between research institutions, stakeholders and enterprises in support of the world-wide effort to improve air quality.

The AQ-WATCH products and services are organized into 5 modules allowing users to access historical and air pollution data as well as air quality forecasts at a global and regional scale, to compare different air pollution reduction scenarios, to assess the effect of wildfires or fracking activities on air quality, and to derive the changes in solar irradiance due to the presence of dust in the atmosphere. These 5 modules include: (1) Global and regional air quality atlas, (2) Air quality attribution & mitigation, (3) Dust and fire forecasting system, (4) Fracking analysis, and (5) Air quality forecast. In this presentation, apart from an overview of the AQ-WATCH project and its products, extra focus will be put on the last product module, Air quality forecast, in particular on the effort of the consortium to provide air quality forecasts from their hosting regional models and the execution of the AQ-WATCH multi-model air quality forecast system.
Comparison of AI Downscaling Methods on C3S Seasonal Forecasts for Early Warning System Development
Authors: Lin Qing , Heidari Fatemeh , Espitia Sarmiento Edgar Fabián , Vischer Marc , Xoplaki Elena
Published: June 2022

The seasonal climate forecasts at the Copernicus Climate Change Service (C3S) [1] provide longer-term predictions with several variables and forecast systems, serving as an outlook of weather statistics several weeks to months ahead. Hydrological models can assess natural hazards at regional scales when given high-resolution inputs of kilometer scales. Thus, to use the seasonal forecast ensembles for regional damage estimation, it is essential to increase the spatial resolution of these large sets of climate variables. Furthermore, the high-resolution data are adjusted to be physically consistent with the driving model in weather processes. Thanks to the latest advances in artificial intelligence, downscaling to high-resolution local data has become feasible. This study focuses on the comparison of three AI downscaling methods: multiple linear regression [2], artificial neural networks [3], and support vector machines [4]. In AI downscaling, the gridded data is used for model training and hyperparameter tuning. First, climate variables, including temperature, wind speed, precipitation, and solar radiation, are downscaled from 1° to 1 km. The downscaling results are then evaluated using statistical indicators compared with the historical daily station observations. Finally, the best-performing AI downscaling method is implemented to develop an early warning system to detect future climate extreme risks and their impacts on diverse economic activities in Germany.

[1] Seasonal forecast daily and subdaily data on single levels, Copernicus Climate Change Services, (2018). DOI: 10.24381/cds.181d637e.
[2] J. Bedia, J. Baño-Medina, M. N. Legasa, M. Iturbide, R. Manzanas, S. Herrera, A. Casanueva, D. San-Martín, A. S. Cofiño, J. M. Gutiérrez, Statistical downscaling with the downscaleR package (v3.1.0): Contribution to the VALUE intercomparison experiment, Geosci. Model Dev. 13 (3), 1711-1735 (2020). DOI: 10.5194/gmd-13-1711-2020.
[3] K. Ahmed, S. Shahid, S. B. Haroon, X. J. Wang, Multilayer perceptron neural network for downscaling rainfall in arid region: A case study of Baluchistan, Pakistan, J. Earth Syst. Sci. 124 (6), 1325-1341 (2015). DOI: 10.1007/s12040-015-0602-9.
[4] A. Goly, R. S. V. Teegavarapu, A. Mondal, Development and evaluation of statistical downscaling models for monthly precipitation 18 (18), 1-28 (2014). DOI: 10.1175/EI-D-14-0024.1.
Harmonie-Arome on AWS
Authors: Weismann Poulsen Jacob , Yang Xiaohua , Whelan Eoin , Raman Karthik
Published: June 2022

Cloud computing provides on-demand availability of all computer system resources and is, consequently, of  
general interest to National Meteorological and Hydrological Services. However, how does one get started and is true HPC a real option in the cloud?"
In connection with the work on a forecast benchmark for United Weather Centre-West project and the
upgrade of the operational forecast system at the Danish Meteorological Institute (DMI), both of
which are based on the Harmonie-arome 43h2.1 released by the Hirlam-C consortium, we ported and
tested the Harmonie forecast system on the Amazon Web Services (AWS), a public cloud. In these explorations, we
tested both a stand-alone benchmark configuration and a full system configuration. The latter includes
boundary preparation and observation pre-processing, data assimilation and forecast cycling, and the
subsequent postprocessing; i.e. execution of a full NWP cycling dataflow with the required typical
suite of applications using the Harmonie scripting system.
We will share our experiences of porting the Harmonie-Arome weather forecasting system to AWS.There are both differences and the similarities between running
NWP on-premise versus running it on AWS and we will first focus on describing these. Then we will cross-compare
the performance obtained on the on-premise systems versus the ones obtained on AWS. Finally we will describe
our approach to integrate the on-premise clusters with AWS clusters. The approach will be using layered builds
of AMIs (Amazon Machine Images). Moreover, these builds will be launched in a fully automatic fashion using Git
CI/CD pipelines. This approach has the potential to have 24/7 NWP production systems running on our on-premise clusters that
will have AWS sitting in the back 24/7 both as a fallback option and as a bursting option to our on-premise
production. 
A holistic study of the urban climate and thermal bioclimate before, during and after the extreme heat wave of July-August, 2021, in the Athens urban area in Greece
Authors: Giannaros Christos , Agathangelidis Ilias , Papavasileiou Georgios , Galanaki Elissavet , Kotroni Vassiliki , Lagouvardos Konstantinos , Giannaros Theodore M. , Cartalis Constantinos , Matzarakis Andreas
Published: June 2022

Situated in the eastern Mediterranean basin, Greece is identified as a climate change hotspot, characterized by pronounced increasing trends in temperature extremes and heat stress. People accommodated in cities are particularly at heat-related risk due to the amplifying impact of the built-up environment in the human thermal comfort conditions. Recent studies highlight the applicability of the local climate zone (LCZ) concept in investigating the diversified heat exposure within cities. For this, in the frame of the ADAPT2CC research project, the current study presents the first attempt at introducing the LCZ framework into a multi-scale numerical modeling system for examining the urban climate and thermal bioclimate in the Athens urban area (AUA), Greece, before, during and after an extreme heat wave (HW) episode that occurred during July and August of 2021. The modeling system is based on the online coupling between the state-of-the-art Weather Research and Forecasting (WRF) model and the advanced urban scheme BEP/BEM (Building Energy Parameterization/Building Energy Model). Further, the system is coupled offline with the RayMan Pro model for assessing the heat stress conditions in a physiologically consistent manner. The performance of the modeling system is evaluated against ground-based and satellite observations, with the results demonstrating a high capability of the system in reproducing the meteorological and human-biometeorological conditions during the examined period (July 25 to August 08, 2021). The numerical atmospheric analysis underlines the important contribution of physical factors (altitude) and background atmospheric circulations (sea breeze and Etesians) to the spatiotemporal variation of the urban temperatures under both non-HW and HW conditions. Concerning the heat stress conditions, the use of the modified physiologically equivalent temperature (mPET) is suitable for human-biometeorological analysis especially during HW days, when increased solar radiation and low wind speeds prevail. Overall, the long-lasting heat wave of July-August, 2021, put a great heat stress burden to the AUA population, especially in the south and central regions of the study area. In the course of a typical sea breeze day under HW conditions, the local atmospheric circulation lead to lower temperature and mPET values in the south areas of Athens compared to the city’s central regions across all LCZs. However, this cooling effect is not effective to also result in a more comfortable human thermal perception (i.e., from very hot to hot). This outcome, as well as the whole set of results of the study, could be very useful for urban design applications aiming at mitigating the impact of heat waves and urban heat in AUA.
Lightning Simulations over the Boreal Zone: Skill Assessment for Various Land-Atmosphere Model Configurations and Lightning Indices
Authors: Mortelmans Jonas , Brisson Erwan , Lynn Barry , De Lannoy Gabrielle , Van Lipzig Nicole , Kumar Sujay , Bechtold Michel
Published: June 2022

The boreal zone has experienced more severe fires over the last years, often coinciding with years of anomalously high lightning frequencies. These lightning frequencies might increase even further with global warming. Current lightning predictions are however highly uncertain, either relying on empirical relationships derived from present climate, or coarse-scale climate scenario simulations in which the critical process of deep convection is parameterized, and the detailed representation of land-atmosphere interactions is lacking.

 

In this study, we used the NASA Unified-Weather Research and Forecasting (NU-WRF) modeling framework to simulate lightning over a 550,000 km2 domain including the Great Slave Lake in Canada. Simulations were run for the six lightning seasons (June-August; 2015-2020) at both a convection-parameterized (9 km) and convection-permitting (3 km) spatial resolution. Additionally, two microphysics (MP) schemes (Goddard 4ICE and Thompson) were compared at both resolutions. From the simulation output, we derived four diagnostic lightning indices which were evaluated against observations from the Canadian Lightning Detection Network (CLDN). This evaluation was done in terms of the capability of the indices to match the observational spatial pattern (temporally averaged), spatiotemporal frequency distribution, daily and seasonal climatology (spatially averaged), and an event-based overall skill assessment. Our results show that the Thompson MP scheme better predicts the daily climatology than the Goddard 4ICE MP scheme. The Goddard 4ICE MP scheme, on the other hand, predicts the spatial pattern best. Both MP schemes predict the seasonality equally well. Concerning the spatial resolution, a clear improvement when simulating at convection-permitting resolution is only seen for the Goddard 4ICE MP scheme. Regarding the different lightning indices, no clear superior index is found as the relative performance of each index strongly depends on the evaluation criteria. Finally, the study shows that models are in particular poor in reproducing the long-term averaged observed spatial pattern of lightning occurrence. This might be related to an insufficient representation of the land surface heterogeneity in the study area.
Along-valley winds in the Himalayas as simulated by the WRF-model
Authors: Mikkola Johannes , Sinclair Victoria , Bister Marja , Bianchi Federico
Published: June 2022

Along-valley winds in four major valleys in the southern slope of Nepal Himalayas are studied by means of a 5-day long high-resolution Weather Research and Forecasting model simulation. Model evaluation against observations from three automatic weather stations in the Khumbu valley showed a good agreement in the simulated diurnal cycle of wind direction and strength. The characteristics of the daytime up-valley winds are identified in the four valleys and compared to each other. Since all four valleys are under similar large-scale forcing, the differences in the along-valley winds are assumed to be mainly due to differences in the valley topographies. These valleys are separated by their topographic characteristics in two groups: the valleys in the west have a continuous inclination (2–5 degrees) in the valley floor and there is an 1-km high perpendicular mountain barrier between the valleys and the plain. The two valleys in the east have a 40 km portion with a nearly flat valley floor (<1 degree inclination) from the open valley entrance into the valley.

Daytime up-valley winds develop in all of the four valleys and they vary between the valleys and their parts in strength (2–10 m/s) and flow depth (600–1500 m). The night-time along-valley winds are weak and flow mostly in the up-valley direction. During large-scale northerlies, the daily cycle of the along-valley winds is interrupted more compared to the days with large-scale westerlies especially in the heads of the valleys that reach up to 4000 masl. The night-time down-valley winds are found more during the large-scale northerlies, which is most likely due to channelling of above-valley winds into the valley atmosphere.

The daytime up-valley winds are shallower and weaker in the parts of the valleys where the floor inclination exceeds 2 degrees compared to the parts where the valley floor is almost flat. The depth of the surface-based heated layer within the valleys is correlated with the flow depth and is lower in the steeply inclined parts of the valleys. The steep inclination of the valley floor and ridges in along-valley direction may shift the dominant driving mechanism of the along-valley winds from the valley volume effect to the buoyancy mechanism which would explain the shallower and weaker along-valley winds.

The winds at the valley entrances are weaker in the two valleys with the 1-km high barrier between the valleys and the plain, compared to the valleys with open valley entrances. A shallow layer with strong along-valley winds is found on the lee-side slope of the barrier and 20 km after this (i.e. towards the head of the valley), weaker winds are evident. This spatial distribution of the along-valley wind speed resembles the typical structure of a hydraulic jump related to down-slope windstorms.
Effects of large-scale atmospheric circulation on precipitation and river stream flows in Europe during cold seasons
Authors: Naakka Tuomas , Nygård Tiina , Shevnina Elena , Vihma Timo , Karpechko Alexey
Published: June 2022

Winter precipitation remarkably affects river flooding risk in Europe. This study focuses on linkages between large-scale atmospheric circulation, precipitation and river discharge in western and northern Europe during extended winter period (October – April). Compared to other seasons, the effects of large-scale atmospheric circulation on precipitation are strongest in winter, when moisture transport is a major source of moisture for precipitation, and large-scale updraft is an effective mechanism to produce water condensation. This study shows that winter precipitation amount correlates with interannual variation of river stream flow in the month when the annual maximum in river streamflow occurs.

We applied Self-Organizing Maps (SOM) to categorize circulation patterns. The results show that variation of circulation patterns, based on clusters of daily 500-hPa circulation patterns produced by a SOM analysis, were able to explain a relatively large fraction of interannual variation of monthly mean precipitation in western and northern Europe. However, the explained fraction decreases towards eastern Europe. Based on the SOM analysis, four main circulation types, which strongly affect regional interannual variation of precipitation in western and northern Europe, were recognized: (i) “Westerly flow” characterized by an almost zonal circulation pattern in the 500-hPa geopotential field causes the strongest positive precipitation anomalies in a large part of western Europe. (ii) “Wester European blocking” decreased precipitation over a large part of western Europe but increased it in Scandinavia due to a northward moisture transport from the northern North Atlantic. (iii) “North Atlantic blocking” together with an upper-level trough over the Baltic Sea region allows moisture transport from the North Sea to central Europe, causing positive anomalies in precipitation in central Europe. (iv) “Azorean low” centred over the northern side of Azores allows moisture transport from the North Atlantic to reach Iberian Peninsula and the Mediterranean region, increasing precipitation in the region. Even though these circulation types are not able to exhaustively explain the entire interannual variation of precipitation in western and northern Europe, a frequent occurrence of some of these circulation types increases probability of enhanced river stream flow in the region where the circulation type causes positive precipitation anomalies.
Precipitation over coastal regions of southern France and its impacts on the regional hydrological cycle
Authors: Jomaa Fatima , Zolina Olga
Published: June 2022

The hydrological cycle over southern France is an interplay between highly variable precipitation impacting the variability of soil moisture and continental runoff. The regional hydrological cycle experiences strong impacts of the Mediterranean Sea (Gulf of Lion) through the moisture advection. Moreover, the strong coupling between the atmosphere and the land surface is affecting all branches of the regional hydrological cycle, especially due to the specific structure of the orography in the region. Under the climate change scenarios potential intensification of the regional hydrological cycle, resulting in extreme hydroclimate events is a critically important phenomenon for understanding mechanisms of climate variability over the Mediterranean.

We perform the analysis of long-term daily precipitation time series over southern France (300 stations from METEO-FRANCE collection) along with modern era Reanalysis (ERA5, JRA55) and satellite datasets (TRMM, GPCP, and Persian) over the period from 1979  onwards. Diagnostics estimate linear trends and interannual variability in precipitation totals and precipitation extremes and allow for quantifying the differences in precipitation variability patterns across the different datasets, demonstrating their strengths and weaknesses. Then we associated changes in precipitation with tendencies in soil moisture derived from the GLEAM-GRACE datasets, thus quantifying regional responses of soil moisture to precipitation impacts. Finally, we analyze the representation of the observed regional hydrological cycle in historical simulations with regional (Euro-CORDEX) and global (CMIP6) climate models. We, thus, establish a statistical association between precipitation, soil moisture, and continental runoff and quantify the role of regional atmospheric circulation in forming multidecadal changes in the regional hydrological cycle.
How public private partnerships can enable better end user forecasts
Authors: Finney Isla
Published: June 2022

Lake Street's aim is to help companies 'work with the weather'.  Put another way, we aim to translate the user's question into scientific terms, find the best answer available, and translate it into information useful to the user in decision-making.  Along the way, we have come across a host of challenges, which have encouraged public private partnerships, and inter- and transdisciplinary collaboration, to enable progress. 

Using a simple example from agriculture, we will demonstrate how such interactions can enable one to see the differing perspectives of a forecast challenge.  From this new perspective come new ideas, giving potential for both better numerical weather prediction models, and better crop yields.  Whilst the process may seem easy given a theoretical example, the reality is that partnerships and collaborations usually come with a host of challenges, which can seem daunting. 

Trust, open mindedness, acceptance that there is room for improvement are three traits that help - on both sides.  We will mention some practical changes within the weather and climate research communities that we believe would lead to improved engagement with society.  Meanwhile, especially as a small company, Lake Street know that collaboration enable us to both keep delivering quality products, and to feedback to the research community what questions users want answers to.
The role of transient eddies and diabatic heating in the maintenance of European heat waves: a nonlinear quasi-stationary wave perspective
Authors: Ma Qiyun , Franzke Christian
Published: June 2022

European heat waves result from large-scale stationary waves and have major impacts on the economy and mortality. However, the dynamical processes leading to and maintaining heat waves are still not well understood. Here we use a nonlinear stationary wave model (NSWM) to examine the role played by anomalous stationary waves and how they are forced during heat waves. For our study, we use the Japanese Reanalysis (JRA-55) data for the period 1958 through 2017. We show that the NSWM can successfully reproduce the main features of the observed anomalous stationary waves in the upper troposphere. Our results indicate that the dynamics of heat waves are nonlinear, and transient momentum fluxes are the primary drivers of the observed anomalous stationary waves. The contribution from orographic forcing is moderate and mainly through nonlinear interactions with diabatic heating. Further decomposition of the transients indicates that the high-frequency transient vorticity fluxes make dominant contributions. Furthermore, our results reveal that the response to heating located in the tropical Indian Ocean and the west Pacific region is primarily responsible for maintaining the observed anomalous stationary waves linked to European heat waves. This is confirmed by exploring the relationship between heat waves and the Indian Ocean Dipole strength. The heating in the mid-latitude and tropical Atlantic region plays a secondary role. Our results suggest that European heat waves are potentially predictable by considering the nonlinear effects involved in anomalous stationary waves and the heating sources in the nearby and remote tropical region.

Further details refer to: Ma, Q., Franzke, C.L.E. The role of transient eddies and diabatic heating in the maintenance of European heat waves: a nonlinear quasi-stationary wave perspective. Clim Dyn 56, 2983–3002 (2021). https://doi.org/10.1007/s00382-021-05628-9
Understanding the Differences in the Sub-seasonal Predictability of Extreme Stratospheric Events: The extreme wave activity flux events in 2009 and 2018
Authors: Wu Rachel Wai-Ying , Wu Zheng , Domeisen Daniela I.V.
Published: June 2022

Extreme stratospheric events such as sudden stratospheric warmings (SSWs) and strong vortex events can have downward impacts on surface weather that can last for several weeks to months. In subseasonal-to-seasonal (S2S) prediction systems, the predictability of these events strongly differs within events of the same type, and also between event types. The reason for the differences in predictability between events is however not resolved. To investigate this question using a larger sample size, we extend the definition of strong vortex and SSW events to wind acceleration and deceleration events due to their similar dynamics, respectively. Specifically, we use the zonal mean zonal wind at 60°N, 10hPa from ERA-interim reanalysis for the winters of 1998/99 to 2017/18 to identify wind acceleration and deceleration events, which are defined as a wind change over a 10-day window. We then assess the predictability of the identified events using the hindcasts from the ECMWF S2S prediction system. Overall,  wind acceleration events are found to be more predictable than deceleration events. However, when expressing the predictability of deceleration and acceleration events as a function of event magnitude, they qualitatively exhibit the same predictability behaviour; that is, events of stronger magnitude are less predictable. We explain the observed predictability dependence from two perspectives: 1) In a statistical sense, strong magnitude events lie within the tails of the climatological distribution and thus are penalised more heavily than weak magnitude events, and 2) from a dynamical perspective, extreme stratospheric events are associated with strong anomalies in precursors such as wave activity and vortex background state, which are themselves often associated with large ensemble spread and large uncertainties. The model shows a poor predictability of extreme wave activity fluxes in the lower stratosphere. In particular, the split SSW events in 2009 and 2018, which are reported to be associated with anomalously strong wave-2 activity flux, are found to exhibit large prediction errors even at short lead times, suggesting that nonlinear wave dynamics might play an important role.  We suggest that a better representation of extremely strong wave activity in the prediction system may enhance the predictability of stratospheric extreme events, and by extension their impacts on surface weather and climate.
An AI-based approach for bias correction of temperature and precipitation forecasts to develop an early warning system
Authors: Heidari Fatemeh , Lin Qing , Espitia Sarmiento Edgar Fabián , Adakudlu Muralidhar , Vischer Marc , Xoplaki Elena
Published: June 2022

The project DAKI-FWS (Data and AI-supported Early Warning System to stabilize the German Economy), funded by the Federal Ministry of Economic Affairs and Climate Action (Germany), develops an innovative early warning system with a seasonal time horizon to protect and support lives, jobs, land and infrastructure. High-skilled, innovative time and space-dependent bias correction and high resolution downscaling artificial intelligence approaches, such as deep learning and reinforcement learning techniques, are designed and implemented on ensemble seasonal forecast data. A fundamental challenge in bias correction is to preserve climate trends and plausible representation of the physical properties (variables) of the climate data.Thus in this work, a trend preserving AI-based correction approach is implemented. The high quality bias-corrected data can be introduced into the various climate-related practical applications of the overall project, such as the detection of extreme events but also evolution of pandemics or subtropical/tropical diseases and hydrological models. State-of-the-art AI techniques are applied not only for preprocessing and preparation of the climate and sectoral data but also for the analysis and post-processing phases. Weather and climate extremes, such as heatwaves, storms and droughts, and concurrent extremes are identified from the large pool of meteorological and climatological reference datasets, seasonal forecasts as well as event lists.  Such a comprehensive early warning system with seasonal horizon that contributes to the estimation of the outbreak and development of climate and health crises and supports disaster management and risk reduction and mitigation, does not yet exist for Germany, illustrating the importance and potential of this work.
A statistical approach to the spatial analysis of precipitation by combining multiple data sources
Authors: Lussana Cristian , Nipen Thomas N. , Seierstad Ivar A.
Published: June 2022

Precipitation data predicted by numerical models is used in a wide variety of applications. For civil protection, numerical weather prediction provides key information for decision making and as input to hydrological modelling. For climatology, climate bulletins based on reanalyses reconstructing past weather events are increasingly gaining ground. Nevertheless, it is often the case that observed precipitation data from several data sources are available over the domain covered by the numerical simulations. Sometimes the observational data have been used by the numerical models, however usually measurements from observational networks of weather stations or radar-derived precipitation estimates constitute an independent source of information for precipitation. The combination of numerical model output and observational data aiming at a more accurate and precise representation of precipitation has already become a classic of post-processing of numerical models.

The work we will present is based on the application of Inverse problem theory to the spatial analysis of precipitation. Numerical model precipitation is the background field. The observations may have been measured or estimated by more than one observational system, such as weather stations or remote sensing, and they are considered to be more reliable than numerical model precipitation. At the same time, observations are used to add locally more details to the precipitation field, thus increasing its effective spatial resolution. The final product, the combined field of precipitation, has to provide information on the precipitation uncertainty because this is needed for most applications.

Examples of the statistical approaches investigated at the Norwegian meteorological institute for hourly precipitation will be provided. The numerical model is a high-resolution ensemble local area model. The observations are radar-derived estimates from the Norwegian mosaic of weather radars and the rain-gauge measurements used have been collected by an heterogeneous network of traditional and private stations.
Application and evaluation of the WRF-chem modelling infrastructure over Ireland to support greenhouse gas mitigation policies
Authors: Sati Ankur Prabhat , Li Zeting , Obe Benjamin , Demuzere Matthias , Fealy Rowan , Ishola Kazeem , Mills Gerald
Published: June 2022

Managing greenhouse gas (GHG) emissions at a landscape scale requires a framework that can account for local and advected sources. TerrainAI is a funded project that is designed to provide the scientific infrastructure needed to make policy decisions at a national scale to mitigate net GHG emissions from the Irish landscape. The project has established new observation platforms over a variety of natural (e.g., grasslands, peatlands, and forests) and urbanised landscapes and uses satellite and drone technologies to capture detailed spatial and temporal data on the physical properties of land-cover. A core part of the work uses the Weather Research Forecasting (WRF) model coupled with chemistry (WRF-Chem) to simulate the net emissions and transport of GH gases. WRF-Chem has been set up for double nested domain with the entire country of Ireland as inner domain at 1 km resolution; a third nest will be established for the Dublin metropolitan area at a resolution of 250 m. The current model incorporates better resolved model input fields (e.g., land use, meteorology, and emissions) and is evaluated for two 15-day periods in 2018, representing a dry and wet periods dominated by anticyclonic and cyclonic activity respectively. The Dublin nest will employ detailed descriptions of the urban canopy layer and examine their influence on the net emission and dispersion of GHGs. Currently, a WRF-Greenhouse option is used to simulate CO2, CO and CH4 over the study region using the EDGAR emission inventory, which can capture hot-spots and general patterns. More precise emission and sequestration data based on socio-economic profiles will be generated to match the detailed land-cover database used in WRF. These data will permit us to evaluate the efficacy of mitigation policies. 