bluuebunny/crossref_metadata_2025 huggingface dataset:
The GOME-type Total Ozone Essential Climate Variable (GTO-ECV) data record for climate applications
Authors: Coldewey-Egbers Melanie , Loyola Diego , Heue Klaus-Peter , Lerot Christophe , van Roozendael Michel
Published: June 2022

In this study, we present the satellite-based GOME-type Total Ozone Essential Climate Variable (GTO-ECV) Climate Data Record (CDR), which has been developed in the framework of national and the European Space Agency’s Climate Change Initiative (ESA-CCI+) ozone projects. GTO-ECV covers the past 26 years (1995-2021), and it is regularly extended as part of the European Union Copernicus Climate Change Service (EU-C3S2) ozone project.
The GTO-ECV CDR combines space-based observations from a series of seven nadir-viewing low earth orbit sensors of the GOME-type (Global Ozone Monitoring Experiment). The latest additions were measurements from GOME-2/MetOp-C and from the TROPOspheric Monitoring Instrument (TROPOMI) onboard Sentinel-5P. All instruments measure the upwelling solar radiation reflected or scattered in the Earth's atmosphere and from its surface in the ultraviolet, visible, and near-infrared spectral range. Total ozone columns are retrieved using the GOME Direct Fitting approach (GODFIT version 4) and the inter-sensor consistency is excellent. We combine the individual data sets into one homogenized record that provides monthly means with global coverage and a spatial resolution of 1°x1°.
The data record can be used for various climate applications regarding the long-term evolution of the atmospheric ozone layer including inter-annual variability and decadal trends on global and regional scales, or the evaluation of Chemistry-Climate Model simulations. Of particular interest is the search for signs of ozone recovery. Thanks to the Montreal Protocol the stratospheric concentrations of ozone depleting substances have been declining since the late 1990s and a slow healing of the ozone layer is expected. In this study, we report on the spatial and seasonal distribution of ozone trends and on the possible impact of climate change.
Semi operational real-data large eddy simulations for agricultural applications
Authors: Poll Stefan , Zhang Lijie , Kollet Stefan
Published: June 2022

One of societies greatest challenges is to provide food and energy for an increasing global population while minimizing agricultural production's environmental footprint. To alleviate pollution and halt the reduction in biodiversity, a more productive arable land is needed, and agro-chemical inputs must be reduced. A crucial aspect is precise agricultural management and planning, in which soil-vegetation-atmosphere (SVA) modeling is a major tool to provide forecasts and bridge the gap between in-situ measurements at the point scale and information required at the field scale to fully exploit the potential of spatiotemporal information.

We are using the scale-consistent Terrestrial System Modeling Platform (TSMP), which is composed of an atmospheric model (Icosahedral Nonhydrostatic - ICON or Consortium for Small-scale Modeling - COSMO), a land surface model (NCAR Community Land Model - CLM), and a subsurface flow model (ParFlow) coupled via the OASIS3-MCT library. In this contribution, we show a real-data, semi-operational, field-scale large eddy simulation (LES) application from the atmosphere to the subsurface. We outline the setup of the agricultural domain located in the western part of Germany and introduce the nested model chain from pan-European scale over region and local scale to the target resolution at field-scale of ∆x=10m. Demonstrating the added value of O(101m) simulations, we provide a basis for improved insights into scale-dependent processes based on real-data LES, which may lead to improved operational forecasting capabilities in the future.
Impact of the improved urban fraction on rainfall simulation using the WRF model over Kampala, Uganda
Authors: Umer Yakob , Ettema Janneke , Jetten Victor , Steeneveld Gert-Jan
Published: June 2022

Urbanization affects the initiation and intensification of convective activities by changing local meteorological variables, which alters the atmosphere's convective processes. Therefore, proper urban surface information is required to model the energy partitioning pattern and its contrast with neighboring grid cells. In this study, the mesoscale weather research and forecasting (WRF) model is configured with satellite-derived urban fraction for optimal rainfall simulation and to evaluate its impact on the simulated rainfall over Kampala, Uganda. The WRF urban parameter values associated with the considered urban fraction are adjusted based on the literature reviews. The satellite-derived urban fraction represents the more realistic extent and intensity of the urban class with a more representative urban fraction. Three different simulations are performed to distil the impact of changing urban fractions as well as of adjusting urban parameters: (1) DUF_DUP, which uses the default urban fraction and default urban parameter values, (2) DUF_AUP, which uses the default urban fraction with adjusted urban parameter values, and (3) SUF_AUP, which uses the satellite-derived urban fraction and adjusted urban parameter values. A single extreme rainfall event, which caused a flood hazard in Kampala on 25 June 2012, is used for all three simulations. The simulated peak rainfall and its spatial distribution over the Kampala catchment are evaluated using observed rainfall data from gauging stations and satellite data from Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS). Results indicate that the simulated rainfall is overestimated compared to CHIRPS and underestimated when comparing gridcell values with gauging station records. However, the SUF_AUP simulation shows relatively better results with a lower absolute relative error score compared to the other two simulations. Compared with the default urban fraction, the satellite-derived urban fraction represents the more realistic urban extent and intensity. As a result, SUF_AUP results in a more realistic rainfall simulation compared to when using the default urban fraction. Rainfall analysis for both 24-hour and 2-hour indicates that the presence of an urban landscape alters both the structure and propagation of high-intensity rainfall over the city, mainly due to the impact of the urban landscape on the different meteorological variables leading to modifying mechanisms associated with rainfall.

Keywords: rainfall, Default urban fraction, Kampala, urban parameter, Updated urban fraction, and WRF model
Measurement data availability within EnerSHelF
Authors: Yousif Rone , Kimiaie Nicola , Meilinger Stefanie , Bender Katja , Abagale Felix K. , Ramde Emmanuel , Schneiders Thorsten , Kunstmann Harald , Diallo Belko , Salack Seyni , Denk Steven , Bliefernicht Jan , Sawadogo Windmanagda , Guug Samuel , Rummeny Silvan , Bohn Paul , Chaaraoui Samer , Schiffer Sebastian , Abass Mohammed , Amekah Edward
Published: June 2022

Proposal of a poster for the EMS2022

Intention:

Within the research project EnerSHelF (Energy-Self-Sufficiency for Health Facilities in Ghana), i. a. energy-meteorological and load-related measurement data are collected, for which an overview of the availability is to be presented on a poster.

Context:

In Ghana, the total electricity consumed has almost doubled between 2008 and 2018 according to the Energy Commission of Ghana. This goes along with an unstable power grid, resulting in power outages whenever electricity consumption peaks. The blackouts called "dumsor" in Ghana, pose a severe burden to the healthcare sector. Innovative solutions are needed to reduce greenhouse gas emissions and improve energy and health access.

The aim of the project is therefore to develop PV-based energy solutions for healthcare facilities and to improve the reliability and integrability of such systems in the local electricity grid.

The work is based on a measurement campaign that has been running since 2020 at three hospitals spread across the country. The variables measured include:
Global tilted irradiance (GTI)
Soiling ratio and temperature of the PV panels
All-sky camera recordings
Load measurement aggregate (grid node)
Load measurement sub-distribution (departments and devices)

In addition, weather stations are operated at the sites to improve weather forecasts.

These datasets can be used to follow different approaches to managing the harsh conditions caused by dry and rainy seasons, and to design and control PV hybrid systems appropriately.

According to the World Bank (2017) only 3% of the population in West Africa and the Sahel can currently access PV power through off-grid systems. As an important catalyst for sustainable development, access to a reliable source of clean energy is vital for inclusive economic development, improved human health, wellbeing and security. As such, EnerSHelF can contribute to Sustainable Development Goals (SDG) of health (SDG 3), energy (SDG 7) and partnerships (SDG 17).
Latest developments on Vaisala broadband DIAL with examples
Authors: Tuononen Minttu , Lehtinen Raisa , Tuominen Pekko , Roininen Reijo
Published: June 2022

Continuous water vapor profiling within the atmospheric boundary layer is a major unmet measurement requirement for real-time monitoring of humidity profiles and improving numerical weather prediction. Water vapor mixing ratio profile information can be used in mesoscale numerical models for severe weather prediction, flash flood prediction, energy management, and other applications. Vaisala has been developing broadband Water Vapor Differential Absorption Lidar (DIAL) to meet with the need of lacking humidity profile observations. Vaisala broadband DIAL not only reports water vapor mixing ratio in the boundary layer, but also serves as an atmospheric profiler offering attenuated backscatter profile to obtain information on scatterers in the atmosphere including clouds, precipitation, and aerosols.

In this presentation, the latest hardware and software improvements on our broadband DIAL are presented with case examples. In recent years, we have been continuously testing our prototypes with different scientific partners in different climates as well as on our test field. Based on the vital feedback from our scientific partners we have been able to focus and develop solutions to improve the prototype performance incrementally. We have been developing features based on the user feedback, as well as improving data format and data visualization. Most importantly, the performance of the instrument has been improved, including the reduction of instrument-related biases in various situations. Our target is to meet the needs of the community to better serve the field of numerical weather prediction and use of broadband DIAL in networks.

In addition, we will present our vision of the finalized product that can help with meteorological applications from numerical weather prediction improvements to atmospheric boundary layer research and unattended operation in all-weather situations.
Latent Dirichlet Allocation: a new machine learning tool to evaluate CMIP6 climate models atmospheric circulation
Authors: Malhomme Nemo , Faranda Davide , Podvin Bérengère , Mathelin Lionel
Published: June 2022

Climate models aim at representing as closely as possible the observed state of the climate components such as the atmosphere or the ocean. This is a fundamental requirement to correctly project changes in their dynamics due to anthropogenic forcing. In order to evaluate how closely models match observations, we need algorithms capable of selecting, processing and evaluating relevant dynamical features of the climate components. This has to be reiterated efficiently for large datasets such as those issued from the Coupled Model Intercomparison Project 6 (CMIP6). In this work, we use Latent Dirichlet Allocation (LDA), a statistical learning method initially designed for natural language processing, to extract synoptic patterns from sea-level pressure data and evaluate how close the dynamics of CMIP6 climate models are to the state-of-the-art reanalyses datasets such as ERA5 or NCEPv2.

LDA allows for learning a basis of decomposition of maps into objects called "motifs". Applying it to sea-level pressure data, reanalysis or simulation, robustly yields motifs that are known relevant synoptic objects, i.e. cyclones or anticyclones. Furthermore, LDA provides their weight in each of the maps of the dataset, their most probable geographical position and their possible changes due to internal variability or external forcings. LDA decomposition is efficient because most of the information of a given sea-level pressure map is contained in about 5 motifs, making it possible to decompose any map in a limited number of easy-to-interpret synoptic objects. This allows for a variety of new angles for statistical analysis.

We look at the dominant motifs and their distributions either on entire datasets or conditionally to particular extreme events, such as cold or heat waves, and compare results between reanalysis data and historical simulations. This enables us to assess which models can or cannot reproduce statistical properties of the observations, and whether or not there are properties that no model yet demonstrates. We find that models can capture the statistical synoptic composition of sea-level pressure data in general, but that some drawbacks still exist in the modelling of extreme events. LDA can also be applied separately to each dataset, and the two resulting synoptic bases can be compared. We find the sets of motifs from reanalysis and historical simulations are very similar, even if different spatial resolutions are used.
Wind Turning in the Atmospheric Boundary Layer from Differences in Turbulence Parameterization and its Impact on Climate Modelling Results 
Authors: Þórarinsson Páll Ágúst , Svensson Gunilla , Wallin Stefan
Published: June 2022

The wind turning in the atmospheric boundary layer (ABL), the angle between the surface friction and wind just above the ABL, is greatly affected by the interaction between turbulence, Coriolis effects and density stratification within the layer. The wind-turning angle can be used as an indicator of cross isobaric mass flow which is notable in the formation and lifespan of synoptic scale cyclones (Holton & Hakim 2013, Beare 2007). It is therefore also good an indicator of how parameterization of sub-grid processes can affect outcome in climate and numerical weather prediction models.

By examining different ways of modelling turbulence in the ABL and boundary conditions we aim to find an explanation for why many state-of-the-art numerical weather prediction and climate models have such different results for the wind turning amongst themselves and compared to observations and large eddy simulations (LES). As a benchmark for our study, we use the first GEWEX Atmospheric Boundary-Layer Study (GABLS1) (Svensson & Holtslag 2009).

We anticipate to find variations in the representation of the turning of the wind by running suites of experiments by exploring parameter variations using a single column model of the ABL. We vary surface boundary conditions and internal turbulence parameterization parameters, and different temporal and vertical resolutions.

In the study we will use LES data (Sullivan et al. 2016), supersite observations, worldwide dataset of radio soundings (the Integrated Global Radiosonde Archive), and climate models.

References

Beare, R. J., 2007, ‘Boundary Layer Mechanism in Extratropical Cyclones’, Quarterly Journal of the Royal Meteorological Society, 133 (623), 503-515.

Holton, J. R. & Hakim, G. J, 2013, An Introduction to Dynamic Meteorology, 5th edn Academic Press, Oxford.

Svensson G. & A. A. M. Holtslag, 2009, ‘Analysis of model results for the turning of the wind and related momentum fluxes in the stable boundary layer’, Boundary Layer Meteorology, 132 (2), 261-277.

Sullivan, P. P., Weil, J. C., Patton, G., E., Jonker, H., J., J. & Mironov, D., V., 2016, ‘Turbulent Winds and Temperature Fronts in Large-Eddy Simulations of the Stable Atmospheric Boundary Layer’, Journal of the Atmospheric Sciences, 73 (4), 1815-1840.
Atmospheric fronts climatology over Central Europe
Authors: Wypych Agnieszka , Ustrnul Zbigniew , Bochenek Bogdan , Kubacka Danuta
Published: June 2022

Variability of weather conditions in the temperate zone is mainly related to the atmospheric circulation, including the presence of atmospheric fronts. Especially extreme weather phenomena such as wind gusts, heavy precipitation, hail, thunderstorms, and many others are usually occurred with passing of weather front. Climatology of weather fronts is difficult, since they are usually drawn manually by forecasters on maps, therefore the database of them is limited and the process itself is very subjective in its nature. In this study front analysis climatology over Central Europe (5°–30° W and 45°–60° N) is presented. It is based on an objective method.

The key issue in the study was to develop a method for the detection of atmospheric fronts. After many attempts and analyzes, the method based on based on random forest machine learning technique was used to distinguish the fronts. On the basis of a 3-year testing sample (2019-2021) of digitized synoptic maps developed by DWD, a system was built using the AI method.

The digitized weather fronts were used for further analysis based on data from an ERA5 meteorological reanalysis. We used surface data as well as data from 4 pressure levels (925, 850, 750, and 500 hPa) and 16 available meteorological fields that can indicate changes in weather conditions in the selected area. Due to relatively high dependency between original frontal information and automatically detected fronts the next step was performed, i.e., the preparation of fronts database for the entire 30-years 1991-2020. Finally, it enabled to create fronts climatology for the area of interest.

Therefore, the frequency of the occurrence of fronts over Central Europe was assessed. Due to methodological conditions, the analysis covered the number of days with the front over the investigated area. The study was made for an individual sub-areas with the size of 5x5° for the period 1991-2020. The analysis showed a slightly higher frequency of the number of days with a front in the year and in individual seasons in the NW part of the study area. The analysis of the variability of the number of days with fronts in the analyzed multiannual period did not show any significant differences between individual years. Larger differentiation was found only when analyzing the number of days with fronts in individual months.
Model development for the estimation of seasonal urban air temperature patterns using MODIS satellite data, a case study in Szeged
Authors: Guo Yuchen , Unger János , Gál Tamás
Published: June 2022

Urban air temperature (Tair) is an essential variable for a variety of urban issues, and analyzing the spatial patterns of Tair is of great importance for urban planning and management. However, it is difficult to obtain Tair data with a high spatial resolution because of the absence of weather stations within a heterogeneous city. In this research, Tair predictive models were developed in Szeged by the help of an urban climate monitoring system which is a part of COST FAIRNESS project. Our investigation focused on the whole year instead of just the summer which has been the most studied season for urban heat issues. Two statistical mthods, multiple linear regression and random forest regression, were used for developing Tair estimation models. Tair data obtained from in situ meteorological stations from 2014 to 2017 were used as the dependent variable for models training. Land surface temperature data from numerous MODIS satellite images and 7 auxiliary variables were used as the independent variables. The auxiliary data are open source, including local climate zone (LCZ) lassification data containing a wide range of urban surface information and atmospheric parameters in the urban boundary layer associated with near-surface urban climate. The atmospheric parameters calculated from the ERA5 reanalysis data. We calculated RMSE based on 10-fold cross-validation to valadite the models. The results indicated that the random forest models performed better than multiple linear models with lower RMSE in four seasons. According to the importance analysis in random forest, both LCZ classification and atmospheric parameters are effective in reducing model errors. LCZ parameters affect the models significantly during the day, while atmospheric parameters affects the models significantly at night. Both effects of these two auxiliary variables showed their maximum in summer. In the end of this research, we used the final models to estimate Tair and mapped the seasonal average Tair patterns from 2018 to 2019. Our overall aim is to develop a generalized methodology using globally available land surface temperature and auxiliary data capable to estimate the urban air temperature.
Machine learning in a probabilistic framework can improve the prediction of lightning ignited fires
Authors: Di Giuseppe Francesca
Published: June 2022

Human activity is widely recognised as the primary cause of wildfires ignition. Still lightning-ignited fire are common and the majority of burned areas in remote regions of Boreal and arctic biomes have this as origin. In 2021, the availability of lightning forecasts at ECMWF led the development of a data driven modelling framework to provide a quantitative measure to identify those lightning flashes that are conducive of fires. Different binary classifiers were developed to attribute a 'yes' or 'no' ignition label to lightning flashes. Under certain assumptions, the classifiers worked remarkably well reaching an overall accuracy up to 78%.

 

The modelling framework had simplifications which posed the limit to what achievable by those models when applied to real events and it was highlighted that the lack of skill in the lightning forecast could play a crucial role. Accounting for forecast errors is traditionally done using ensemble prediction systems (EPS) where several simulations are performed starting from slightly different initial conditions and model configurations. In some application EPS have proven more skilful  and, also, more helpful   than a single deterministic outcome.

 

This work stems from Coughlan et al (2021)  main conclusions and explores the advantages of using the EPS to provide a probabilistic metric in comparison to a deterministic outcome provided by a binary classifier to improve the prediction of lightning ignited fires. Indeed, it is shown that there is an optimal probability threshold that allows to maximise the correlation between prediction and ignitions and that the use of probability of ignitions instead that a yes\/no outcome provides the best value for money when a cost-loss analysis is performed, and a potential economic value is attributed to the forecast.

 
Generation of a high resolution climatological precipitation product for Belgium
Authors: Goudenhoofdt Edouard , Delobbe Laurent
Published: June 2022

Accurate and homogeneous high resolution precipitation datasets are needed for many advanced weather and climate applications. A new automatic system for generating such a climatological product has been developed for Belgium. It is based on radar volumetric data from Belgium, France and Germany. This means that the data are quite heterogeneous with different scanning strategy, sampling resolution, signal filtering and post processing. The precipitation estimation is obtained after a complex processing of the volumetric data. This begins with a careful quality control of the radar data. Each radar is dynamically recalibrated based on the average of the daily median bias over 2 months. The reflectivity level with systematic contamination by non-meteorological signals is identified for each measurement bin. The remaining clutter is identified by comparing with satellite images and looking at the reflectivity field texture or vertical gradient of reflectivity. Ground rainfall estimation is obtained by a dynamic model of vertical profile of reflectivity including the identification of melting snow. Single radar rain rates are combined into a composite by taking the maximum value of the three closest radars in the convective season (May–August). In the other months, the composite is based on all values weighted based on the distance to the radar. In a last step the radar estimation is combined with measurements from dense automatic rain gauge networks. The radar-gauge merging is obtained by a single bias correction or by kriging with external drift. The method is based on the operational realtime product, which benefits from many years of quality control and continuous improvement. An independent verification is performed for the years 2013-2021 with the computation of various scores for different applications. The product is also analysed for the July 2021 extreme flood event.
High temporal resolution monitoring of precipitation in the Basque Country
Authors: Gaztelumendi Santiago , Otxoa de Alda Kepa , Ruiz Raul , Orue Judit , Egaña Joseba , Maruri Mercedes , Aranda Jose Antonio
Published: June 2022

Precipitation remains as one of the most difficult meteorological variables to measure. In the Basque country case there are two main ways of performing point measurements; with the pluviometers network that has been developed during more than thirty years, and with the new disdrometer network that is being implemented during  these last years.

Here we present the main characteristics of the Basque disdrometer network. This network is based on OTT Parsivel disdrometer, a laser-based optical instrument for simultaneous measurement of PARticle SIze and VELocity of all liquid and solid precipitation. This new instrumentation isincluded in the Basque multipurpose network, providing information for rain-drop distribution, velocities and other derived quantities on a 1-minute basis for different locations all around the territory.  

In this work, we also present the visualization and analysis tool developed for an easy and intuitive exploitation of disdrometer derived data in real time together with information from the network of rain gauges and remote sensing systems (mainly radar). Such tool allows us to analyze in real and in deferred time all the information available in the territory for precipitation characterization at each observation point.

Finally, some aspects of interest are introduced in relation to different measurements obtained with the OTT Parsivel disdrometers at different points of the network during some interesting events. These disdrometers are equipped with two heads, one of which is an infrared laser emitter and the other a receiver that measures the attenuation produced in the light beam by the particles that pass through it. From this information, size, velocity and typology of the meteors, based on a diameter-velocity relationship are derived. Intensity of precipitation, the volume of water per volume of air, visibility or radar reflectivity, among others, are also available.

The effort to increase the understanding of the precipitation characteristics, that affects our territory, and its quantification through the new network of disdrometers, will allow an improvement in impact weather capabilities with a better surface precipitation fields characterization, integrating new data with that coming from radars or satellites. Many others areas of interest such as edafology, hydrology or climate change at local level, would also be positively affected.
Daily-resolved lightning climatology of the eastern Alpine region at the kilometer scale
Authors: Simon Thorsten , Mayr Georg J.
Published: June 2022

Lightning flashes are locally rare albeit hazardous events. Cloud-to-ground lightning strikes may injure or kill people and damage infrastructure or start wild fires. Having reliable climatologies of lightning thus aids the assessment of all these risks.

Despite this scarcity, generalized additive models (GAMs) succeed in producing a climatology of lightning occurrence for the eastern Alps and surrounding lowlands at an unprecedented resolution of 1km2 for each day of April through September with data from the Austrian lightning detection and information system (ALDIS) and the digital elevation model TanDEM-X.  ALDIS provides data for eleven years 2010-2020.  The GAM adds the effects of seasonality, jaggedness of the terrain, and seasonally varying effects of elevation and region, thus combining information from analysis cells sharing similar characteristics. Therefore the GAM approach enables more flexibility and the inclusion of more explanatory information than the commonly used "cell-count" method that allows only for smoothing over cells which are close in space and/or time to each other.

The results of the GAM climatology are summarised as follows: At the beginning of the season the probability of a cloud-to-ground discharge over 1km2 on a given day is typically less than 1% with a rapid increase in spring, followed by a plateau and a gentler tapering-off in fall. Probabilities are lower at high elevations early in the season but increase once their snow cover is gone.  Regional patterns of lightning also vary with season with an overall southward shift later in the year but more complex details. Grid cells with jagged topography have a higher probability of lightning.
Irrigation Impact on Precipitation Forecasts During the LIAISE-2021 Field Campaign
Authors: Udina Mireia , Bech Joan , Peinó Eric , Mercader Jordi
Published: June 2022

The LIAISE (Land surface Interactions with the Atmosphere over the Iberian Semi-arid Environment) field campaign was designed to study the effects of irrigation on a semi-arid area in NE Spain  (Boone et al. 2019). Within the framework of LIAISE, the WISE-PreP project was conceived to examine precipitation processes, on the one hand collecting high resolution data using Parsivel disdrometers and Micro-Rain Radars complementing operational rain-gauge and C-band Doppler weather radar observations and on the other one, carrying out numerical simulations to improve our understanding of physical processes involved. In this presentation we explore the irrigation impact on precipitation in Weather Research and Forecasting (WRF) model simulations during the intensive period of the LIAISE field campaign (15-30 July 2021). We quantify the precipitation accumulation and distribution by including the irrigation parameterization (Valmassoi et al 2020) and varying its parameters (days of irrigation, amount of irrigated water, hours of irrigation, etc.). First results indicate that fractional area of precipitation is greater if the irrigation parameterization is activated and if the irrigated amount is greater as well. Finally, we explore differences in stratiform vs convective fractions of precipitation. This work was partly funded by the project “Analysis of Precipitation Processes in the Eastern Ebro Subbasin” (WISE-PreP, RTI2018-098693-B-C32, MINECO/FEDER) and the Water Research Institute (IdRA) of the University of Barcelona.

References

Boone A, Best M, Cuxart J, Polcher J, Quintana P, Bellvert J, Brooke J, Canut-Rocafort G, Price J (2019). Land surface Interactions with the Atmosphere over the Iberian Semi-arid Environment (LIAISE). Gewex News, February 2019.

Valmassoi A, Dudhia J, Sabatino SD, Pilla F (2020). Evaluation of three new surface irrigation parameterizations in the WRF-ARW v3. 8.1 model: the Po Valley (Italy) case study. Geoscientific Model Development, 13(7), 3179-3201.
Engaging schools to explore meteorological observational gaps
Authors: Caluwaerts Steven , Top Sara , Mesuere Bart , Vergauwen Thomas
Published: June 2022

Today, the vast majority of meteorological data are collected in open, rural environments to comply with the standards set by the World Meteorological Organization. However, these traditional networks lack local information that we need for example for high-resolution numerical weather prediction or to assess climate adaptation measures. The citizen science project called VLINDER uses a novel approach for addressing this observational gap based on an intense collaboration with high schools. The collaboration resulted in the establishment of a region-wide climate monitoring network of about 70 accurate weather stations in a wide variety of locations across northern Belgium e.g. in city centers, forests, lakes, industrial environments,... The stations measure temperature, relative humidity, precipitation, wind and pressure and data are transferred every 5 minutes via IoT-technology. The resulting data can be consulted in realtime on the project’s dashboard (https://vlinder.ugent.be/dashboard/) which contains an API to extract the data. To guarantee a sustainable and mutually valuable collaboration, the schools and their students are involved at all stages, ranging from proposing measurement locations, building the weather stations, and even data analysis. The approach received overwhelming enthusiasm from high schools and students and resulted in a high-accuracy monitoring network with unique locations offering novel insights. The data collected by the stations are used both by schools who received educational material to explore the data in the classroom and by scientists e.g. to evaluate hectometric NWP runs. With this presentation we would like to share our experiences and the lessons learnt to encourage and help colleagues in exploring and setting up collaborations with schools.
Assessment of atmospheric stability measurements from microwave radiometer observations for wind energy applications
Authors: Cimini Domenico , Gandoin Rémi , Fiedler Stephanie , Wilson Hector , Pospichal Bernhard , Martinet Pauline , Balotti Andrea , Gentile Sabrina , Romano Filomena
Published: June 2022

Atmospheric stability is a measure of atmospheric status which determines whether thermodynamically perturbed air will rise, sink, or be neutral. Atmospheric stability has a major impact on the evolution of wind turbine wakes and thus on the yield and performance of offshore wind parks. For estimations of wind park power output and for improving analyses of offshore wind park wakes, a crucial parameter was found to be profiles of atmospheric temperature and stability metrics. Atmospheric temperature profiles can be measured in-situ by balloon-borne sensors, but also estimated from the ground using remote sensing observations.

Ground-based microwave radiometer (MWR) units operating in the 22-30 GHz and 50-60 GHz bands are commonly used to estimate atmospheric temperature and humidity profiles. A handful of MWR profiling types are nowadays available as off-the-shelf commercial products, and a MWR network is currently being established in the framework of EUMETNET E-PROFILE programme (Rüfenacht et al., 2021). This presentation reviews the stability metrics useful for monitoring wind park performances and provides a quantitative assessment of the value of MWR observations to estimate these stability metrics from near surface, either over land or ocean. Results from three different MWR instruments, representing the most common available on the market, will be presented, as obtained during at least three field experiments, both onshore and offshore.

This contribution presents the main outcomes of the Radiometry and Atmospheric Profiling (RAP) scoping study, carried in the framework of the COST Action PROBE (https://www.probe-cost.eu/) and funded by Carbon Trust and the partner companies of the Off-shore Wind Accelerator (OWA) program: (in alphabetical order) EnBW, Equinor, Orsted, RWE, Scottish Power Renewables, Shell, SSE Renewables, Total Energies, Vattenfall.
Assimilation of 3D radar information in convective scales at Deutscher Wetterdienst (DWD)
Authors: Khosravian Kobra , Stephan Klaus , Blahak Ulrich , Neef Lisa , Vobig Klaus , De Lozar Alberto , Potthast Roland , Schraff Christoph , Welzbacher Christian A.
Published: June 2022

The study of radar data assimilation (reflectivity, radial wind and cell object) in NWP models and its effect particularly on short-term forecast has been intensified recently at DWD. In particular, the seamless Integrated ForecastiNg sYstem (SINFONY) project, which develops a short-term forecasting system with a focus on convective events from minutes up to 12 hours ahead, shows clearly the benefit of radar data assimilation in improving the short-term forecast. This system integrates Nowcasting techniques for radar data with numerical weather prediction (NWP based on the new ICON-model) in a seamless way with initial focus on severe summertime convective events and associated hazards such as heavy precipitation, hail and wind gusts.

Besides, radar data assimilation is being operationally used in the short-range ensemble numerical weather prediction (SRNWP) system (ICON-D2-KENDA LETKF system) at DWD since 2020 (radial wind starting in March 2020 and reflectivity starting in June 2020). This is in addition to the traditional Latent Heat Nudging (LHN) of 2D radar-derived precipitation rates. For both systems, SRNWP and SINFONY, the usage of 3D radar data is not only advantageous but crucial to improve the forecast skills related to convection and precipitation.

We will present the latest results of our research in radar assimilation at DWD including the application of radar data assimilation together with a more sophisticated cloud microphysiscs parameterization (a 2-moment bulk scheme) and in combination with the LHN in the SINFONY forecasting system. We also study to assimilate radar information in the alternative form of convective cell objects. Of particular interest are for example the specification of the radar observation error, but also other topics related to the improvement of short-term forecasts.
Teleconnection-driven extreme events: Relevant case studies
Authors: Domeisen Daniela I.V. , White Christopher J. , Afargan-Gerstman Hilla , Antoine Salomé , Ardilouze Constantin , Batté Lauriane , Camargo Suzana J. , Collins Dan , Ferranti Laura , Infanti Johnna M. , Janiga Matthew A. , Kolstad Erik W. , LaJoie Emerson , Magnusson Linus , Strazzo Sarah , Vitart Frédéric , Wulff C. Ole
Published: June 2022

Extreme weather events have devastating impacts on human health, economic activities, ecosystems, and infrastructure. It is therefore crucial to anticipate extremes and their impacts to allow for preparedness and emergency measures. There is indeed potential for probabilistic subseasonal prediction on timescales of several weeks for selected cases of extreme events that are linked to remote drivers and large-scale teleconnections. We here present a range of case studies, including heatwaves, cold spells, and tropical cyclones, where precursors and global linkages may have improved sub-seasonal predictability. These linkages include teleconnections from the tropics as well as the stratosphere, in addition to circumglobal teleconnections. For example, cold extremes can be induced by stratospheric or tropical teleconnections, while the predictability of tropical cyclones tends to be increased when an active Madden-Julian Oscillation signal is present. Prediction can further be improved through increased physical process understanding of the drivers of extremes, an improved representation of these processes in prediction systems, as well as the development of post-processing techniques that improve model output. While the case studies presented only represent some of the major types of extreme events, they provide an informative overview of the current ability of prediction models to benefit from teleconnections. These case studies clearly illustrate the potential for event – dependent advance warnings for a wide range of extreme events globally.The subseasonal predictability of extreme events allows for an extension of warning horizons, can provide advance information to impact modelers, and informs communities and stakeholders affected by the impacts of extreme weather events.
An institutional approach to translating and synthesising science for different audiences
Authors: Williams Daniel , Dunbar Tyrone
Published: June 2022

The Met Office Hadley Centre has a dedicated stategic area, known as Knowledge Integration, which focuses on translating and communicating science from across its research. Made up of people with expertise across both science and communications, the team works directly with scientists and a wide variety of end users to co-create tailored outputs for specific needs. These outputs synthesise information from many different programmes of work from both the Met Office, as well as from a range of partners both across the UK and internationally . This includes working with collaborators across multiple disciplines, for example taking on best practice from social science approaches to communication (and the team is also working to help contribute to that evidence base). The outputs take the form of anything from written policy briefs, to infographics, to workshops and teach-ins – with the final package of media determined by the needs and preferences of users.

While the work of the team is primarily focused at policy and decision-making audiences, they provide support to those who work directly with the media and public by sharing their work and expertise to ensure scientific rigour and credibility. Our work also feeds into a programme of education and outreach which reaches a large and diverse audience.

In this session members of the Knowledge Integration team will explain how they work with scientists and end-users to maximise the potential value and impact of research findings. We will talk about the role we play at an institutional level, how we operate, who we work with, how we learn lessons to keep learning and evolving, and how we monitor the impact of what we do.
Creating and validating high-resolution wind atlases
Authors: Floors Rogier , Olsen Bjarke Tobias , Davis Neil
Published: June 2022

Wind-atlases are vital for planning of the massive transition towards renewable energy that is required over the next decades. Reanalysis products like ERA5 are frequently used to obtain winds at hub heights, but coarse reanalyses products often lack crucial details that are needed to represent the microscale flow. To circumvent this aspect, wind-atlases use a procedure of generalizing and downscaling mesoscale model outputs to predict the flow down to the microscale (~250 m grid spacing). The methods to do this are based on the geostrophic drag law and the logarithmic wind profile, which both need information from the mesoscale model to represent for example the wind profile in each wind-direction sector. However, one also needs high-resolution roughness and elevation maps to represent microscale speed-up effects. Combining these large-scale and microscale effects is notoriously difficult

Due to lack of measurements it has been difficult to validate the accuracy of these wind atlases at the hub heights of modern turbines. In this presentation we briefly discuss recent improvements in the model chain related to stability and surface roughness modelling, but mostly focus on a validation of these new wind atlases at more than 60 tall masts around the world. Three python packages have made it much easier to generate and validate these wind atlasses. The first package contains wind-related data structures including geospatial information that make your data self-explanatory. A second python package to make wind validations easier to perform is also introduced. It contains a work flow to validate data and create simple reports to analyze them. Finally, flow modelling is done using PyWAsP, which contains submodules for roughness, stability and wake-modelling.
The Global-to-Regional ICON Digital Twin
Authors: Marsigli Chiara , Potthast Roland ,   the GLORI Team
Published: June 2022

The Global-to-Regional ICON (GLORI) Digital Twin is a project made possible by the tri-lateral cooperation between Germany, Italy and Switzerland. The project is fostered by the ICON cooperation and by the long-lasting collaboration in the COSMO Consortium and in the CLM Community. The partnership includes national weather and climate services as well as research institutes, which bring together a broad and transdisciplinary expertise. Project partners are the Deutscher Wetterdienst (DWD), the Forschungszentrum Jülich (FZJ) and the Karlsruher Institut für Technologie (KIT) in Germany, the Centro euro-Mediterraneo sui Cambiamenti Climatici (CMCC), the Agenzia per la meteorologia e climatologia ItaliaMeteo (IM), the HydroMeteoClimate Service of Arpae Emilia-Romagna (Arpae-SIMC) and the Servizio meteorologico dell'Aeronautica Militare (MeteoAM) in Italy and the Swiss Federal Office for Meteorology and Climatology (MeteoSwiss) in Switzerland. GLORI is a configurable on-demand global-to-regional short-range high resolution digital twin based on the prediction capability of ICON. The twin is fit to run on heterogeneous GPU-CPU architecture at high resolution. It provides short-range global storm-resolving km-scale (~3 km horizontal) predictions using hybrid variational ensemble data assimilation. GLORI offers on-demand high resolution (~ 500m -1 km horizontal) predictions for the selected regions, like GLORI-Alps, the twin on the Alpine region. It includes uncertainty estimation through ensemble forecast for global and regional. Model parametrisations are developed to take into account higher-resolution and the complexity of the land-surface interface. It is possible to carry out on-demand predictions of selected atmospheric composition elements such as mineral dust for energy applications and pollen for health applications. The digital twin also includes interfaces to application layers in the hydrological area. The skill of the twin in forecasting precipitation at high-resolution will be also demonstrated on catchments by using a hydrological model as user-oriented verification tool.
Non-CO2 Forcers and Their Climate, Weather, Air Quality and Health Impacts – New Project FOCI
Authors: Halenka Tomas , Sokhi Ranjeet
Published: June 2022

While overall the global warming with the causes and global processes connected to well-mixed CO2, and its impacts on global to continental scales are well understood with a high level of confidence, there are knowledge gaps concerning the impact of many other non-CO2 radiative forcers leading to low confidence in the conclusions. This relates mainly to specific anthropogenic and natural precursor emissions of short-lived GHGs and aerosols and their precursors. These gaps and uncertainties also exist in their subsequent effects on atmospheric chemistry and climate, through direct emissions dependent on changes in e.g., agriculture production and technologies based on scenarios for future development as well as feedbacks of global warming on emissions, e.g., permafrost thaw. In addition to the atmospheric radiative forcing (gaseous or aerosols), albedo changes connected to land use and land cover can play a role, depending on the adaptation or mitigation measures included in different scenarios.

Thus, the main goal of the new EC Horizon Europe project FOCI (accepted within the call HORIZON-CL5-2021-D1-01-0 Improved understanding of greenhouse gas fluxes and radiative forcers, including carbon dioxide removal technologies), which will be presented, is to assess the impact of key radiative forcers, where and how they arise, the processes of their impact on the climate system, to find and test an efficient implementation of these processes into global Earth System Models and into Regional Climate Models, eventually coupled with CTMs, and finally to use the tools developed to investigate mitigation and/or adaptation policies incorporated in selected scenarios of future development targeted at Europe and other regions of the world. We will develop new regionally tuned scenarios based on improved emissions to assess the effects of non-CO2 forcers. Mutual interactions of the results and climate services producers and other end-users will provide feedbacks for the specific scenarios preparation and potential application to support the decision making, including climate policy.