bluuebunny/crossref_metadata_2025 huggingface dataset:
Rescuing historical data from the ACMAD collection- The importance for climate research in Africa.
Authors: Healion Kevin , Noone Simon , Thorne Peter
Published: July 2024

Although much work has been conducted worldwide to discover, rescue and digitise historical weather observations, there remains a lack of access to weather observations from many regions of the world. One such region is Africa which has a scarcity of historical observations available from the pre satellite era which is preventing important research taking place on past extreme weather events and the climate of the continent. The need for this data becomes more urgent when we consider that the workforce in African countries is disproportionately employed in climate-exposed sectors with over 60% employed in the agricultural sector where crops are completely dependent on rainfall. The ACMAD (African Centre of Meteorological Applications for Development) collection offers an opportunity to improve the temporal and spatial data available across the continent of Africa with data available in some countries as far back as the late nineteenth century.          An inventory has been created of the collection and shows that within it a large amount of unique data exists that has not been previously inventoried by other sources. This includes numerous weather stations that have never been inventoried before. With an estimated four million images within the collection, numerous methods are required to digitise the unique data. The team in ICARUS (Irish Climate Analysis Research UnitS), Maynooth University, launched a project called CliDAR-Africa, where we assisted second year undergraduate geography students to digitise unique data from stations in Madagascar and Guinea. Discussion on the use of AI as a tool to transcribe data have also taken place. Quality issues with the images within the collection also exist and the team have been developing a citizen science project where inferior quality images can be identified. Once identified it is hoped that the quality of these images can be improved at a later date.In my presentation I will discuss the various issues with the ACMAD collection the ICARUS team are attempting to solve, the results following the inventorying of the collection, efforts to digitise data from Madagascar, proposed projects involving Citizen Science and AI, and finally the important need to rescue African meteorological data in order to improve our knowledge of climate change and extreme weather events in Africa.  
Combining ensembles using a new synoptic-scale curvature diagnostic
Authors: Roberts Nigel , Hewson Timothy
Published: July 2024

ECMWF is now running its extended-range 101-member ensemble, with an approximate grid spacing of 36 km, every day from 00UTC, out to 46 days ahead. This is in conjunction with the 9 km medium-range 51-member ensemble that is run 4 times a day (out to 15 days ahead from 00 and 12 UTC, and 6 days ahead from 06 and 18 UTC). It means that there are effectively 152 ensemble members available from 00UTC in the overlap period, and even more than that if time-lagging is used to include earlier forecasts.Now that the two ensembles are run every day, work is underway to investigate the potential benefit from combining them and to get a better understanding of how they differ. Many previous studies have already shown that an increase in ensemble size or a combination of ensembles improves skill, especially for longer lead times as members diverge more.One of the difficulties of blending ensembles at two different resolutions is that each has different biases and representativeness of point observations. Hence the assumption that all members are equally likely is not true since they form two different distributions. That problem could be alleviated by calibrating one to the other or both to observations, which would involve applying statistical methods to a very large ensemble which is not a trivial undertaking.The work presented here introduces a new, very simple, diagnostic that identifies cyclonic and anticyclonic regions using surface pressure or geopotential height, computed over scales ranging between 500 and 6000km. This has many benefits for assessing ensembles as well as more generally. The prediction of cyclonic and anticyclonic regions is one of the most fundamental aspects of a weather forecast to get correct and hence diagnose. The approach removes the need for any calibration because the scales are much larger than the grid spacing of each ensemble, enabling comparison with analyses processed in the same way. It is also possible to apply standard or spatial ensemble verification metrics, detect any systematic differences in the synoptic pattern between ensembles, and measure predictability over different scales. Once the diagnostic has been introduced, highlights of comparative behaviour and verification scores will be presented for the individual and combined ensembles using one year of forecasts, along with any selected cases that are helpful for understanding the overall scores.
Evapotranspiration Partitioning using a Process-Based Model over a Rainfed Maize Farmland in Northeast China
Authors: Chen Nina , Schlaepfer Daniel R. , Zhang Lifeng , Lauenroth William K. , Mi Na , Ji Ruipeng , Zhang Yushu
Published: July 2024

The Northeast China maize belt is one of the three major golden maize belts in the world and has been severely affected by climate changehowever, the evapotranspiration(ET) partitioning is not clear. It is important to study ET and its components under climate change. In this paper, the water balance model SOILWAT2 was used to estimate ET partitioning in drought and humid years, seasons, and maize growth stages from 1989 to 2018 over rainfed maize farmland. The results indicated that the SOILWAT2 model performed well for the prediction of ET and its partitioning compared with eddy covariance measurements. The mean yearly ET, transpiration (T), soil evaporation (Es), and canopy interception evaporation (Int) were 432.3 mm, 197.6 mm, 204.7 mm and 19.2 mm, respectively, over 30 years. Es/ET was 6.3% lower in drought years than in humid years, T/ET was conversely higher (6.2% higher in drought years). There was no clear difference of Int/ET between humid and drought years. In the growing season, T/ET, Es/ET, and Int/ET varied from 40.0% to 75.0%, 22.8% to 55.7%, and 0.7% to 7.0%, respectively. T/ET decreased along with the growth of maize and was greatest at the greening–jointing stage. Es/ET was smallest at the greening–jointing stage. We found a power function relationship between T/ET, Es/ET, and leaf area index (LAI) and above-ground biomass. Our results indicated that for the rainfed farmland, drought may limit maize yield by increasing water loss of maize through increasing T under climate change conditions. Therefore, securing food yield will depend on increases in water-use efficiency and other adaptive strategies, such as drought-resistant varieties, and irrigation.
Mitigating hailstorm impacts: an integrated approach using Machine Learning and physical simulations
Authors: Guerrero-Calzas Iciar , Sanchez-Marroquin Alberto , Barcons Jordi , Tuinenburg Obbe , Cortés Fité Ana , Miró Cubells Josep Ramon
Published: July 2024

Hailstorms and other convective events present significant societal and economic risks, with challenges in assessing these risks due to inconsistent data. The main goal of our research is to help mitigate the impacts of hailstorms by generating high-resolution risk maps. While high-resolution simulations with models like the Weather Research and Forecast (WRF) are key for generating risk maps, these models are computationally expensive. To address this challenge, we have developed a machine learning model, the Convective Days Detector (CDD), which uses ERA5 reanalysis data to identify the days when the probability of deep convection is high. The CDD serves as a starting point for hail event simulations, reducing the number of days to simulate to essential ones, and thereby reducing computational costs. We will provide a comprehensive analysis of the CDD, discussing its performance in detecting convective events and the process we followed to refine its capabilities. Our analysis began with a detailed examination of essential instability variables Convective Inhibition (CIN) and Convective Available Potential Energy (CAPE) from the ERA5. This analysis involves comparing different packages for calculating CAPE and CIN with ERA5 data. Despite finding differences, particularly in high CAPE values, we concluded that these discrepancies are not significant for a machine learning model. Therefore, we use ERA5 CAPE due to its importance in detecting convective events and opted not to use CIN in our model due to some inconsistencies we found. Following this, we performed a feature analysis to further refine our model, reducing the number of variables used to eight essential ones for detecting convection. We validated the model to ensure its functionality and introduced a spatial error margin to assess spatial inaccuracies in convective day detection. This validation process involved verifying the performance of the model against known convective events and evaluating its ability to accurately identify convective days. Performing high-resolution WRF simulations on days with high deep convection likelihood, we aim to provide a more detailed understanding of the dynamics and impacts of extreme weather events. We will discuss how we plan to use WRF to simulate the physical processes of these events and how these simulations can contribute to the generation of more accurate risk maps. By combining insights from meteorological models, machine learning algorithms, and risk mapping, this research aims to provide a comprehensive framework for understanding and predicting and minimise the socio-economic impact of hailstorms across diverse sectors.
Understanding the impact of varying geometry level of detail in multi-direction urban RANS simulations tailored for urban air-mobility viability.
Authors: Patil Akshay , Garcia-Sanchez Clara
Published: July 2024

Wind flow predictions in realistic urban areas are sensitive to a wide range of governing parameters such as building resolution, wind incidence, urban morphology, and underlying topography to list a few. In this study, we quantify the impact of the level of detail (LoD) of the urban built environment and the inflow direction (θ) on wind-safety for urban-air mobility using a Reynolds-Averaged Navier-Stokes (RANS) simulation framework. To isolate the effect of LoD and θ, we chose the TU Delft campus (radius of ~ 1 km) and the city of Den Haag (radius of ~ 1.5 km) as representative urban environments that contain a variety of urban fabric and incident wind conditions.The simulation framework consists of steady state Reynolds-Averaged Navier-Stokes simulations using a second-order accurate finite volume formulation that solves the governing equations using the SIMPLE algorithm at 5-degree resolution (i.e., 72 θ’s over a 360-degree range) with a reference wind velocity of 5 m/s at 10 m above the ground. In addition to varying the inflow wind direction, we also compare two LoD’s, specifically, LoD1.2 (lower quality of building resolution, industry standard for wind engineering simulations) and LoD2.2 (higher quality of building resolution), resulting in a total of 288 simulations.First, we assessed the effect of θ resolution on the prediction capabilities of the wind-rose weighted directionally-averaged peak wind velocities (Ua) and found that when compared to the 5-degree resolution cases with 10-, 15-, and 20-degree resolution, there is a selection bias on how accurately high-wind regions are predicted. Specifically, when the Ua is computed using 5- and 15-degree resolution, there is a better agreement as opposed to the 10- and 20-degree resolution case. These results suggest that even with a minimum resolution of 10-degree’s (for θ) the peak Ua locations can be subject to a selection bias for both, simple and relatively complex wind-roses. Next, we studied the effect of varying LoD’s and found that LoD2.2 shows substantially different peak Ua regions when compared to LoD1.2. Directly comparing these two LoDs, we find that minimally LoD2.2 should be used for cases where peak Ua are of interest at approximately 10-20 m height above the ground which are of most interest in terms of urban air mobility.This work represents a systematic review of the effect of wind incidence direction and level of detail on the flow prediction capabilities in realistic urban environments. We anticipate that our findings will be useful to urban planners and engineers working to improve the air-quality, wind comfort, and explore the possibilities of UAV’s as a potential mode of last mile mobility within the urban-air space, to list a few.
Harmonizing solar photometry calibration methods: A Cross-Network Calibration Study at Izaña Observatory
Authors: González-Sicilia Pablo , Campanelli Monica , Estellés Víctor , Barreto África , Doppler Lionel , Almansa A. Fernando , Álvarez-Losada Óscar , Kumar Gaurav , García Rosa D.
Published: July 2024

Aerosols are a significant factor in the radiative forcing of the Earth, influencing both climate and air quality. Accurate monitoring and measurement of aerosol properties are therefore essential. The spatial and temporal extent of these measurements is critical for analyzing the global distribution of aerosols and detecting shifts in global trends. The World Meteorological Organization’s Global Atmosphere Watch (GAW) program highlights the importance of Aerosol Optical Depth (AOD) as a key metric in studying atmospheric aerosols. To facilitate these studies, several networks of sun-sky-lunar photometers have been established over the years. Prominent among these are the AErosols RObotic NETwork (AERONET), Sky Measurements Network (SKYNET), and the Global Atmosphere Watch - Precision Filter Radiometer (GAW/PFR). Each network employs distinct instruments, calibration techniques, and data retrieval methods, leading to variations in the AOD measurements obtained.The Izaña Observatory, located at 2,373 meters above sea level on the island of Tenerife, Spain (28.3ºN; 16.5ºW), is managed by the Izaña Atmospheric Research Center which is part of the Spanish Meteorological Agency (AEMET). Its elevated position in the free troposphere provides ideal conditions for employing the Langley plot calibration method. This technique benefits from minimal atmospheric interference, which significantly enhances the accuracy of solar and atmospheric measurements. In November 2019, the observatory launched a year-long intercomparison campaign, employing a diverse array of sun-sky-lunar photometers including a Prede POM unit from SKYNET and a Cimel CE318-T model from AERONET, to systematically compare and validate their data.The objective of this study is to leverage the concurrent measurements obtained from both instruments to conduct simultaneous Langley plot calibrations. By doing so, we aim to elucidate and identify the primary differences between the photometers. This analysis will not only highlight discrepancies but also contribute to enhancing the calibration accuracy of both networks. This approach will provide insights into the consistency and reliability of the AOD measurements, facilitating improvements in measurement techniques and data comparability across different networks.
Improved precipitation and temperature over Western Europe due to convection-permitting scales and 3DVAR data assimilation
Authors: Gonzalez-Roji Santos J. , Raible Christoph C.
Published: July 2024

In recent years, using convection-permitting scales in regional climate simulations has become more and more frequent. These scales allow the models to include previously parameterized atmospheric processes, which improve the accuracy of a model in simulating precipitation. Data assimilation techniques can also improve the simulation of precipitation and temperature. However, the number of simulations combining these two options is scarce because of the high computational costs. Hence, it is important to evaluate the effect of data assimilation schemes on such convection-permitting simulations and to determine their added values.In this study, we use the Weather Research and Forecasting model (WRF; version 3.8.1) to dynamically downscale the ERA5 reanalysis over Western Europe for period 2010–2020. We use one year of spin-up to allow the correct initialization of both soil and atmosphere. The spatial resolution of the simulation is set to 3 km and the temporal resolution to 1 hour. 51 vertical levels are employed, up to 20 hPa. Two model configurations are tested: with and without data assimilation (the Da and NoDA experiments, respectively). The former uses the 3DVAR data assimilation (WRFDA), which is run every six hours (00, 06, 12 and 18 UTC – analysis times). Observations are provided by the PREPBUFR dataset (NCEP ADP Global Upper Air and Surface Weather Observations), but only the observations inside a 120-minute window around analysis times are assimilated. Additionally, the DA experiment employs monthly-varying background error covariance matrices. Both experiments share the same parameterization options (e.g., Noah-MP land surface model) and use the daily-varying SST field from the NOAA OI SST v2 dataset instead of SST from ERA5.The results show that both experiments generate monthly precipitation patterns over Europe which are similar to those from observational datasets such as IMERG and CHIRPS, or the reanalysis ERA5. However, in general, and particularly during summer months, DA generates larger amounts of precipitation than NoDA. These amounts are in line with those from CHIRPS. In terms of temperature, the DA experiment shows warmer temperatures than NoDA over central Europe during winter and colder temperatures over most of the domain in the remaining months. The temperatures of DA are again more in line with those from observational data sets such as CRU or EOBS. The datasets employed in the verification were not assimilated in WRFDA, and independent from each other.These results highlight the fact that increasing the spatial resolution to reach convection-permitting scales has a positive impact on the simulation, allowing the generation of reliable precipitation and temperature fields. The use of 3DVAR data assimilation can additionally improve the performance of the regional model due to its effect on the positive feedbacks between soil moisture, air temperature, water vapour content and precipitation.
Aerosol forecast and its effect on surface solar radiation
Authors: Hou Xinyuan , Papachristopoulou Kyriakoula , Masoom Akriti , Kazadzis Stelios
Published: July 2024

Aerosols affect surface solar radiation (SSR) by modifying cloud properties. For solar energy applications, either for photovoltaics (PV) or concentrating solar power (CSP) systems, understanding of spatio-temporal variability of aerosol is essential for short-term forecasts of solar radiation.  This study aims to quantify interday variability of the commonly used proxy for optical properties of aerosols - aerosol optical depth (AOD) at the wavelength of 500 nm and to assess the deviation in direct normal irradiance (DNI) and in global horizontal irradiance (GHI) caused by AOD variation using radiative transfer modeling. The following AOD data are used: AOD forecasts for the next day from the Copernicus Atmosphere Monitoring Service (CAMS); ground-based AOD measurements from the Aerosol Robotic Network (AERONET); ModIs/Terra high spatial resolution data set (MIDAS) which is based on satellite retrievals. We select locations with different aerosol types around the globe based on AERONET stations, where at each site more than 1500 daily values for consecutive days from 2010 to 2020 are available. We adopt the persistence approach using AOD data from AERONET and MIDAS and compare them with CAMS forecasts. The AOD data from AERONET serve as ground truth for validation. Under cloudless conditions, AOD variability informs the deviation of DNI and GHI forecasts from the observation. Furthermore, we attempt a worldwide clustering of aerosol types based on chemical modeling. Interday AOD variability is high in megacities with high aerosol load and in the proximity of highly variable aerosol sources such as desert dust and biomass burning (fires). This work should deepen our knowledge of spatio-temporal variability of aerosol in the context of AOD and SSR prediction, thus contributing to the quality and reliability of solar resource assessment.
Modelling Wind Induced Impacts for Application in a New Weather Warning System
Authors: Anger Falk , Felsberg Anne , Koser Daniel , Riedl Kira , Lambert Andreas , Erhardt Bodo , Leschzyk Dinah-Kristin , Fundel Vanessa , Winterrath Tanja , Feige Kathrin
Published: July 2024

The enhancement in user communication is one of the key aspects in the RainBoW (Risk-based, Application-oriented and INdividualizaBle delivery of Optimized Weather warnings) programme, which encompasses the development of the new weather warning system for the German Meteorological Service (Deutscher Wetterdienst). Within RainBoW, a probabilistic weather warning system is developed that supplies both information to users with special requirements but also generates standardised warnings for the general public. It addresses a number of weather elements, such as wind, precipitation, and thunderstorm. Hence, a good understanding of the issued weather warnings by the recipients plays a crucial role in order to enable both professional forces but also individuals to take appropriate action in affected regions. In RainBoW, one of the means to achieve a better perception is to move impact information into the focus for future weather warnings provided by the German Meteorological Service. In this contribution, we present a study on hazards of wind gusts, which is among the weather elements that show the largest impacts on infrastructure and society. In order to shed light on the severeness of impacting events, we discuss the generation of an impact proxy for Germany, which we model by evaluating historical wind gust data by means of extreme value statistics. Despite the available several years of detailed reanalysis data (e.g. from the COSMO REA6 dataset), one of the main challenges is the sparsely available extreme weather data. One reason for this lies in the very profound nature of extreme value statistics, but wind gusts are also not very well represented in the model data. This is further complicated by the rather short period of sufficiently detailed and consistent data, which is governed by few more or less local extreme weather situations that occured during roughly two decades. We discuss the potential of this approach and motivate its implementation into the future warning system (RainBoW) of the German Meteorological Service. Moreover, we complement this with user generated crowd sourcing data and discuss its suitability for an impact proxy of wind.
Temperature sensitivity to vertical resolutions over tropics in KIM
Authors: Park Ja-Rin , Lee Eunjeong , Bae Soo Ya
Published: July 2024

   Korean Integrated Model (KIM), a global weather prediction model, plans to extend into a variable resolution system covering the short to medium forecast range. To improve forecast skill, we intend to increase vertical resolution from 91 to 137 layers in KIM, which has been updated with an enhanced horizontal resolution of 8 km. The vertical coordinates of 137 layers (L137) followed those of the European Centre for Intermediate Forecasting (ECMWF) Integrated Forecasting System (IFS),  with reduced vertical grid spacing throughout the troposphere and stratosphere compared to the 91 layers (L91). Here, we focus on understanding the sensitivity of performance to vertical resolution in the medium-range forecast. The overall conclusion for increased vertical resolution is that it shows mainly neutral in the northern and southern hemispheres, but also some impacts in the tropics, against the IFS analysis. One of the notable impacts of changes in vertical resolution is a decrease of temperature in the lower and upper troposphere, which results in a considerable degradation of the upper troposphere over the tropics. This is because the physical processes rely on vertical grid spacing, which induces radiative cooling while increasing cloud hydrometeors. The refinement of the vertical grid increased the planetary boundary layer (PBL) height, which enhanced vertical mixing results in cooling/moistening above the PBL and warming/drying within the PBL. This increases the amount of cloud water content, influencing radiative fluxes and contributing to the lower troposphere cooling. The refined vertical resolution increased the amount of cloud ice content remained in cumulus convection, wherever weak or robust in its occurrence. This contributes to the cooling in the upper troposphere over the tropics. Increasing vertical resolution is needed in improvement of physical processes.
First experiences with a seamless weather forecast for severe weather products at MeteoSwiss
Authors: Bessenbacher Verena , Beusch Lea , Bhend Jonas , Mahlstein Irina , Nerini Daniele , Spirig Christoph
Published: July 2024

At MeteoSwiss, a suite of Numerical Weather Prediction (NWP) models is regularly run to provide the data basis for generating weather forecasts and severe weather warnings for the public. These models produce forecasts that differ in spatial resolution and extent as well as initialization frequency and lead time length. We combine those sources into a single probabilistic, gridded weather forecast that is seamless in space and time, without any postprocessing applied. The user can retrieve the seamless forecast at the spatial and temporal resolution, temporal aggregation and spatial extent they need. 

The resulting seamless forecast serves as input data for the Extreme Weather Identifier (EWI), a tool that summarizes the forecasting information focusing on occurrence probabilities of warning levels for different temporal aggregations for severe rain and wind events. 

Since February of this year, the EWI has been delivering three products to the forecasters every time a new seamless forecast becomes available: forecast percentiles (10,50,90), occurrence probabilities of warning levels at grid point level, and for warning regions. Our first systematic feedback collection among forecasters has confirmed that the forecasters appreciate the support by EWI products when deciding whether to issue warnings, with different products being most helpful in different weather conditions.  

We further evaluate the merit of these forecasts quantitatively by analyzing reforecasts of the seamless forecast and the contributing individual models. This happens for a set of past severe weather events over Switzerland. Reforecasts are compared with surface wind and rain observations as well as with a gridded precipitation product that combines radar and rain gauge information.  

In future developments, the EWI will be expanded to produce actual warning proposals for forecasters from this seamless forecast. Furthermore, seamless forecasts are not just desirable in the context of severe weather warnings but also for e.g., drought prediction or hydrological runoff modelling and can facilitate uncertainty communication. Hence, our seamless weather forecast is planned to be disseminated to customers from these sectors as well in the future.
Applying citizen weather data and AI for developing a high-resolution wind speed monitor in the Valencia region (Spain)
Authors: Plaza Nuria P. , Martinez-Roig Marcos , Azorin-Molina Cesar , Andres-Martin Miguel , Navarro Jorge , Gonzalez-Rouco Jesus Fidel , Garcia-Bustamante Elena , Guijarro Jose A. , Piroz Amir A. S. , Chen Deliang , McVicar Tim R. , Zeng Zengzhong , Vicente-Serrano Sergio M.
Published: July 2024

Reliable near-surface wind speed  (NSWS; ~10 m above ground level) data is crucial for assessing the impact of wind changes on various socio-economic and environmental sectors, such as wind energy production or risk assessment. Meteorological stations provide local and realistic observations, but their spatial coverage is limited. Although this limitation can be overcome by using classical geostatistical interpolation methods, the reliability of their results is questionable, especially in regions with complex topography. This has motivated the use of reanalyses or dynamical downscaling of simulations as gridded NSWS products that contain local to regional wind data. However, their uncertainties in reproducing observed trends and their coarse resolutions raise doubts about their reliability for reproducing local NSWS. The use of classical interpolation products is even riskier in regions such as the Valencian Community (Eastern Iberian Peninsula, Spain), a region where both local winds (sea breezes) and extreme winds (westerlies “ponientes” or convective wind gusts “downbursts”) occur at local scales (~3km), impacting the tourism activities and wildfires propagation fatalities, among others.Here, we propose a deep neural network based on partial convolutions as a more reliable spatial interpolation method, capable of learning the wind speed pattern across the Valencian Community observed in a dense observational network.  Observed NSWS from a citizen weather network of up to ~600 stations from the Valencian Association of Meteorology (AVAMET) were used after homogenization, resulting in a high-resolution (3-km) wind speed product. This offers a new tool for both climate and marine research in the framework of the ThinkInAzul project.
Cataloguing global soil moisture droughts since 198
Authors: Řehoř Jan , Brázdil Rudolf , Rakovec Oldřich , Hanel Martin , Fischer Milan , Kumar Rohini , Balek Jan , Trnka Miroslav
Published: July 2024

Droughts at the global scale can be described by many variables, expressing their extent, duration, dynamics and severity. To identify common features in global land drought events (GLDEs) based on soil moisture, we present a robust method for their identification and classification (cataloging). Gridded estimates of root-zone soil moisture from the SoilClim model and the mesoscale Hydrologic Model (mHM) were calculated over global land from 1980–2023. Using the 10th and 20th percentile thresholds of soil moisture anomalies, outputs of the two models were merged into a united dataset of drought affected areas in a 10-day step. OPTICS clustering of the gridded data was then used to identify a total of 736 GLDEs. By utilizing four spatiotemporal and three motion-related characteristics for each GLDE, we established threshold percentiles based on their distributions. This information enabled us to categorize droughts into seven severity categories (ranging from extremely weak to extremely severe) and seven dynamic categories (ranging from extremely static to extremely dynamic). The severity and dynamic categories overlapped substantially for extremely severe and extremely dynamic droughts but very little for less severe/dynamic categories, despite some very small droughts that have occasionally been very dynamic. The frequency of GLDEs has generally increased in recent decades across different drought categories but is statistically significant only in some cases. Overall, the cataloging of GLDEs presents a unique opportunity to analyze the evolving features of spatiotemporally connected drought events in recent decades and provides a basis for future investigations of the drivers and impacts of dynamically evolving drought events.
Sensitivity study on the impact of urban scheme on the simulation of heatwaves in Europe
Authors: Villalba-Pradas Anahí , Machado Crespo Natália , Verma Shruti , Karlický Jan , Huszár Peter , Halenka Tomáš
Published: July 2024

The main goal of the OP JAK Geohazards project is to study in detail the threats in the Earth's upper spheres, to understand the causes of their occurrence and to quantify the possible impacts on human society. Climate models will be used to evaluate the impact of climate change on risk phenomena, such as heatwaves, with analysis of their causes and assessment of their consequences in selected areas of human activity. In that sense, urban environments are hotspots of anthropogenic emissions, affect the warming rate over cities and induce changes in several relevant meteorological variables such as temperature and horizontal wind speed, which in turn affect air quality and human health. Therefore, it is important to identify how urban parameterizations impact the regional-to-local scale processes in regional climate model simulations. In order to evaluate these impacts in the long term, we need to find the “best” configuration possible for our models by choosing different parameterizations and validating them against high-quality station observations. In this study, we present preliminary results from a series of sensitivity tests focusing on a heatwave event during 2015 and over two cities in Europe, namely Paris and Prague. Simulations were performed using two models (WRF and RegCM5) with nested domains at 27, 9 and 3 km resolution. Urban schemes of different complexity are used in WRF and in RegCM. To validate the ouputs of our simulations, we use the daily gridded observational dataset from EOBS. The results show that relevant meteorological variables, such as temperature and horizontal wind speed, depend on the urban canopy scheme used as well as on the horizontal resolution.
Short-term association between hot nights and mortality: a multicountry analysis in 178 locations. 
Authors: Royé Dominic , Sera Francesco , Tobías Aurelio , Hashizume Masahiro , Honda Yasushi , Kim Ho , Armstrong Ben , Iñiguez Carmen ,   MCC Collaborative Research Network
Published: July 2024

The increase in hot nights in the last decades and the projected increase due to climate change makes increasing knowledge about their impact important and critical for measures of public health actions and adaptation planning. However, few studies have addressed the importance of hot nights, which may prevent necessary nocturnal rest. This study estimated the association between hot-night excess (the sum of excess heat during the nighttime above a threshold) and duration (the percent of nighttime with a positive excess) and daily mortality in the warm season over multiple locations worldwide. We fitted time series regression models to mortality in 178 locations across 44 countries using a distributed lag non-linear model over lags of 0-3 days, controlling for daily maximum temperature. Next, we used a multivariate meta-regression model to pool results. We found a positive, increasing mortality risk with hot-night excess and duration. The pooled relative risks of death associated with extreme excess and duration, defined as the 90th percentile with respect to 0 in each index, were both 1.03 (95% CI, 1.02; 1.04). The overall estimated attributable fractions were also observed to be almost similar at 0.59% (95% CI, 0.00; 1.13%) and 0.58% (95% CI, 0.00; 1.10%), respectively. The magnitude of effects ranged from an RR of 1.02 to 1.04, slightly higher in tropical and arid climates.  The highest overall estimated attributable fractions with hot-night excess were found for the arid climate, at 1.11% (95% CI, 0.00; 2.35%), while the largest burden has been estimated for the tropical climate zone, at 1.19% (95% CI, 0.00; 2.38%). This study provides new evidence that hot night indices considering excess and duration were strongly associated with an increase in the risk of mortality. A clear advantage of these exposure metrics is that they more realistically reflect thermal exposure over the entire night period rather than a single-moment temperature, such as minimum temperature.  The use of hourly data allows for a more detailed assessment of the thermal characteristics of warm season nights, making it possible to accurately assess the risk of hot nights for population health and wellbeing. Modeling thermal characteristics' sub-hourly impact on mortality during the night could improve decision-making for long-term adaptions and preventive public health strategies.
High-resolution (250x250m) gridded daily mean wind speed dataset for Austria spanning from 1961 to 2023.
Authors: Klisho Tatiana , Lehner Fabian , Laimighofer Johannes , Formayer Herbert
Published: July 2024

High-resolution gridded climate data derived from in-situ observations play a crucial role in global and regional climatology. The data are a valuable input for further climate impact studies, especially in ecological and energy modeling, and can be subsequently used for the wind power potential analysis.Moreover, policymakers can make informed decisions based on accurate climate information derived from these datasets, enhancing the effectiveness of climate-related policies and interventions.This study explores methods to enhance mean wind speed interpolation techniques over complex topography, resulting in the creation of a high-resolution (250x250m) gridded daily mean wind speed dataset for Austria spanning from 1961 to 2023. A two-step approach is tested, wherein climatologies for each month are computed using the best-performing interpolation technique. Subsequently, the optimal interpolation method would be employed to interpolate the model residuals (in case of machine learning (ML) superiority). In the subsequent stage, the same interpolation approach is applied to interpolate daily residuals to the monthly climatologies. Combining both fields produces the final gridded daily mean wind speed dataset.Various spatial interpolation approaches, including Inverse Distance Weighting (IDW), 3D IDW (an Euclidian method, which accounts for elevation differences), Thin Plate Splines (tp_spline),  Local Polynomial Interpolation (loc_poly), and Kriging approaches (OK, OK_trend, UK, UK_poly) are evaluated. Additionally, the results would be compared to regression models, such as Ridge Regression (RR), Random Forest Regression (RFR), Decision Tree Regression (DTR), and Gradient Boosting Regression (GBR),  as well as ensembles of these models by combining different regressors in a pipeline. Each selected regression model is trained independently on the training data, and the final prediction is obtained by averaging the individual model predictions. Each model has the same set of predictors and is set up for each month separately. Additionally, qualitative comparisons will be conducted with other high-resolution gridded datasets. The dataset will be made publicly available for download.
Advanced Weather Forecasts for Ethiopia by Optimized Initialization using a 3DVAR hybrid approach
Authors: Adgeh Tamene , Schwitalla Thomas , Warrach-Sagi Kirsten , Wulfmeyer Volker
Published: July 2024

Ethiopia is frequently affected by extreme rainfall events with devastating consequences for its society and economy. For example, they caused a deadly flash flood in Dire Dawa town in April 2020, and in Addis Ababa in August 2021. The frequency and severity of extreme rainfall events varies by region and season. Their variability depends mainly on the advection of moist air and the location and intensity of precipitation-bearing systems approaching Ethiopia. The westward propagation of low-pressure systems developing over the Indian Ocean and Arabian Sea as well as southerly moisture flow are widely known precipitation-producing features in this region.Due to the vulnerability to extreme precipitation, especially over the orographically complex regions of Ethiopia, a very high resolution of Numerical Weather prediction (NWP) models with high quality initial conditions are required to enhance the advance warning time. The accuracy of initial states of the NWP models can be enhanced through advanced variational Data Assimilation (DA) techniques. With this aim, we simulated an extreme precipitation event using the Weather Research and Forecasting (WRF) model over the Horn of Africa centering the target region Ethiopia. The model is set up at approx. 2 km horizontal resolution and 100 vertical levels. The impact of DA is also evaluated using single observation tests and 3DVar simulations at three cycles. The 3DVar simulations are compared with the control run (without DA) and observations from Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS). This shows that the DA improves the results in comparison with the control run. Finally, we combined the Ensemble Transform Kalman Filter (ETKF) with the 3-Dimensional Variational DA.The hybrid ETKF-3DVar (3DEnsVar) simulations are performed within a Rapid Update Cycle (RUC) with 6 hourly updates and 15 ensemble members. The 3DEnsVar simulations are compared with observations from Global Precipitation Measurement Mission (GPM), Climate Prediction Center morphing method (CMORPH), and European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) products. Results indicate that the 3DEnsVar method is an appropriate DA method to advance extreme rainfall prediction over Ethiopia.
Projected soil drought development in Czechia (2021–2050; 2051–2080) using high-resolution ALADIN-Climate/CZ regional model.
Authors: Mozny Martin , Vlach Vojtech , Hajkova Lenka , Musilova Adela
Published: July 2024

The ALADIN-Climate/CZ regional climate model, operating at a high resolution of 2.3 km, was utilised to project future occurrences of soil drought in Czechia. GCM ARPEGE-Climate drives the regional climate model. To assess the effects of weather conditions on soil drought, we developed a simple model simulating the soil moisture based on API30 and near-surface air temperature, calibrated against observations. Using simulations of future climate, we predict soil drought. Drought was defined as a soil water content below 30% of the maximum water-holding capacity.According to the medium emissions scenario, there is an expected average rise in the number of days with soil drought in the arable layer by 46% and 106% during 2021-2050 and 2051-2080, respectively, compared to the baseline period of 1991-2020. Additionally, there is a notable increase in the percentage of land affected by drought, with areas experiencing drought for over 50 days increasing by 15.5% and 53.2% by 2050 and 2080, respectively. Higher intensity of soil drought is evident across the country, particularly pronounced at higher elevations due to rising temperatures and reduced precipitation amounts. These observed changes emphasise the urgent need to implement adaptation measures in agriculture and alter agricultural land management practices.This work was supported by the Technology Agency of the Czech Republic (Prediction, Evaluation and Research for Understanding National Sensitivity and Impacts of Drought and Climate Change for Czechia, SS02030040 and Centre for Landscape and Biodiversity, SS02030018).Keywords: soil drought, soil water, precipitation, temperature, climate change, regional climate model, climate change scenario, adaptation measures in agriculture
The Renewable Energy Forecasts from Observations and high-Resolution Modeling (REFORM) project
Authors: de Roode Stephan R. , van Soest Marleen , Frei Max , Jonker Harm J.J. , Verzijlbergh Remco A. , Russchenberg Herman W.J. , MacKenzie Rob , Sourzac Mahaut
Published: July 2024

Renewable energy from resources like the wind and sun comprise a gradually increasing share of the energy mix. Since these resources are fluctuating, forecasts for renewable energy sources are crucial for an efficient operation of the power system. For a large part, forecasting of renewable energy production relies on the output from numerical weather prediction (NWP) models. NWP models operate at coarse mesh sizes that cannot resolve turbulence, which is one of the key processes affecting wind and clouds alike. This leads to systematic biases, most notably an overestimation of solar radiation in the presence of low (stratus) clouds, and a severe underestimation of the wind speed during the night. Both situations are frequently occuring weather conditions.Large-eddy simulation (LES) models apply a high spatial resolution (~10-100m) which is sufficiently fine to resolve turbulence. LES modeling is an established, yet computationally expensive technique. Recently a giant gain in the computational speed was obtained by running LES on a GPU (Graphics Processing Unit), which nowadays allows the TU Delft spin-off company Whiffle to operate it as a high-resolution weather forecast model. The GPU‐Resident Atmospheric Simulation Platform (GRASP) receives the large-scale forcing conditions from the European Weather European Centre for Medium-Range Weather Forecasts (ECWMF) model. The full potential of LES-based forecasts is however not acquired since the initial state, which is also taken from the ECMWF model, contains errors.With REFORM, we aim to improve weather forecasts of solar radiation and wind by introducing a novel, hybrid approach which makes use of GRASP as well as observations to obtain the best possible estimate of the initial atmospheric state in terms of wind, temperature, humidity and clouds. We will capitilize on the recently initiated national observational platform Ruisdael, which includes ground-based in-situ measurements as well as advanced remote sensing retrievals. To fully exploit the capabilities of GRASP the proposed research will strongly focus on turbulent regimes such as the nocturnal stable boundary layer, the clear convective boundary layer, their transitions, and low clouds. 
Challenges in training neural networks for global lightning probability forecasts
Authors: Schröder Guido , Baumgartner Manuel , Primo Cristina , Theis Susanne
Published: July 2024

Forecasting lightning is generally challenging for numerical weather models with parameterized convection. Nonetheless, for the aviation industry reliable lightning forecasts are needed for larger domains for which running convection permitting models is not feasible yet. For models with coarser resolution, parameterizations exist that derive the potential for lightning based on the model's convection scheme. One example is the subgrid-scale lightning potential index (LPI, Schröder et al. 2022). However, this index inherits model biases such as a biased onset of convection.  Furthermore, for ensemble prediction systems, the spread of the LPI is not calibrated either. Baumgartner et al. (2023) have shown that applying a neural network within the European domain can correct model errors like the wrong timing as well as the ensemble spread. Nevertheless, applying this approach to the global domain leads to further challenges, e.g. the different behaviour of the numerical model in different climatological regions of the world. From a technical point of view, a global product needs to be trained on significantly larger datasets. Furthermore, after an update of the numerical model usually only a few months of data are available for (re-)training which is inherently a biased dataset. In this work it is shown how to deal with these challenges by using the global ensemble prediction system of ICON. The global lightning data of Vaisala (GLD360, 3) serves as ground truth. A key component of the neural network is a climate feature that allows the neural network to differentiate between the different climatological regions. This is accompanied by features like solar elevation and local time. The benefits of these new features/predictors are shown using the Brier score. The diurnal cycle, as well as region dependent climatological biases, are corrected. It is also shown that even without the availability of training data for a whole year, the neural network can learn corrections e.g. from the northern hemisphere summer and apply them to the southern hemisphere summer and vice versa.References:(1) Schröder et al., 2022: Subgrid scale Lightning Potential Index for ICON with parameterized convection. Reports on ICON (10), DOI: https://doi.org/10.5676/dwd_pub/nwv/icon_010 .(2) Baumgartner, M., Schröder, G., and Primo, C.: Forecasting lightning probabilities derived from the Lightning Potential Index using neural networks, EMS Annual Meeting 2023, Bratislava, Slovakia, 4–8 Sep 2023, EMS2023-34, https://doi.org/10.5194/ems2023-34, 2023.(3) https://www.vaisala.com/en/products/systems/lightning/gld3