bluuebunny/crossref_metadata_2025 huggingface dataset:
Extended-range warnings for heatwaves in Switzerland
Authors: Büeler Dominik , Pyrina Maria , Chavez Valérie , Imamovic Adel , Liniger Mark A. , Moret Lionel , Spirig Christoph , Vicedo-Cabrera Ana Maria , Lehning Michael , Domeisen Daniela I. V.
Published: July 2023

Heatwaves in Switzerland have various impacts on human health and ecosystems. Moreover, heat extremes might act as final triggers for high-Alpine hazards such as glacier break-offs and rockfalls, because they can accelerate the slowly growing disturbance of the Alpine permafrost layer due to climate warming. Heatwaves often occur concurrently with drought, which can impact agriculture, reduce lake and river shipping due to low water levels, and reduce nuclear power generation due to shortage of cooling water. As heatwaves have become and are expected to become even more common with climate change, it is crucial to predict their occurrence ahead of time and to issue warnings for stakeholders and the general public. The goal of this interdisciplinary project is, therefore, to assess the potential of heatwave prediction and warnings for Switzerland on timescales up to several weeks. The project consists of two parallel branches: on the one hand, we investigate if the forecast skill horizon for Switzerland can be extended by predicting heatwaves via statistical downscaling from larger-scale weather patterns compared to the prediction based on direct model output on a grid point level. On the other hand, we evaluate the potential benefits of early warning products for sectors that are directly linked to human lives and livelihoods. One focus is on predicting heat-related mortality by coupling a statistical temperature-mortality model to extended-range temperature forecasts. Another focus is on better understanding and predicting the penetration of heatwaves into Alpine glaciers, rocks, and sediments, which might ultimately support early-warning systems for heat-related high-Alpine hazards. The aim of our presentation is to introduce the multifaceted project in more detail and to provide some first results from the different branches.
KNMI Data Platform - A cloud-based data platform for searching and accessing FAIR and Open Data using APIs
Authors: Derks Rosina , Brandsema Joske
Published: July 2023

The KNMI Data Platform (KDP) is a cloud-based data platform that provides a central location for finding and accessing open and FAIR data related to weather and climate. KDP brings a user-friendly and searchable data catalog based on metadata, as well as the option to browse through the files of a dataset, and if applicable a Web Mapping Service (WMS) preview.  
The data is accessible through multiple APIs. In addition to our file-based API and WMS API, we released an alpha version of our Environmental Data Retrieval EDR API in October 2022. Both the data and the APIs adhere to internationally recognized standards by for instance the Open Geospatial Consortium (OGC), allowing interoperability and easy integration of the data into applications.  
The platform is built with cloud-based technologies resulting in high scalability and reliability. Not only is the data stored on Amazon Web Services (AWS), but the whole infrastructure containing the platform and the APIs are hosted on AWS. This ensures large amounts of data are stored and processed while always being available to users.  
KDP has been operational since 2019, evolving into a robust and highly available platform. This opens up room for community building where we exchange experience and knowledge. Currently, KDP facilitates the community by connecting researchers, organizations, and companies from different fields beyond the traditional meteorological user base. The result is better user feedback to KNMI and an exchange of code between companies about the interpretation of KNMI’s data.  
The aim of this contribution is to present the KNMI Data Platform along with an overview of ongoing advancements and forthcoming features. This will be shown by using a representative dataset to introduce our different APIs, demonstrating this dataset can be accessed and visualized using our platform. Additionally, we hope to discuss the opportunities and challenges surrounding building an API-based Open Data Platform using cloud technology. 
Association of temperature and total precipitation anomalies with West Nile Virus human cases in Central Macedonia, Greece
Authors: Angelou Anastasia , Kioutsioukis Ioannis
Published: July 2023

West Nile Virus (WNV) belongs to the flavivirus genus, and it is a neurotropic mosquito-borne virus. The virus transmitted among amplifying hosts, such as birds, transferred through the bites of mosquitoes and incidentally humans and other mammals may become infected. In elderly and immunocompromised people, symptomatic infections can result in neurologic diseases. There is currently no specific treatment or vaccine available for WNV. The transmission dynamics of WNV are complex and affected by various environmental factors, including temperature and total precipitation. Understanding the relationship between the environmental factors and WNV transmission is crucial for predicting and preventing outbreaks of the disease., while the prediction of an infectious disease outbreak is critical for reducing the potential impact on human health. A large proportion of WNV infections in humans present either asymptomatically or with some non-specific clinical symptoms and are unrecorded.
The aim of this study is to explore the association between climatic factors and the occurrence of West Nile fever (WNF) in humans at a finer than NUTS-3 spatial scale (LAU). The investigation area is the region of Central Macedonia in Northern Greece, analyzing a unique dataset that includes meteorological data from ERA5 (European Centre for Medium-Range Weather Forecasts) and epidemiological data from the Hellenic National Public Health Organization for the period 2010-2022. The research focuses on this region because it is an area of great epidemiological interest. Specifically, at least one WNV human case has been recorded at all LAUs, while in 26% of the LAUs an extremely high number of cases have been recorded during the period of research. The analysis shows a strong correlation between the number of annual human cases of WNV and temperature and precipitation patterns in the months leading up to the outbreak. The results reveal the augmented forecast potential from temperature and precipitation anomalies in virus spread prediction models.
Acknowledgments
This research has been co‐financed by the European Regional Development Fund of the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation, under the call RESEARCH – CREATE – INNOVATE (project code: Τ2ΕΔΚ-02070).
Analysis of UTCI in the Czech Republic: a comparison of two reanalyses
Authors: Novak Martin , Hynčica Martin , Procházková Simona
Published: July 2023

The universal thermal climate index (UTCI) is a widely used index evaluating stress caused by meteorological conditions on humans. The index combines the four meteorological elements: temperature, humidity, wind speed, and mean radiant temperature. The UTCI index can be gained from observed data, however, its calculation is also incorporated in several reanalyses. Here, the comparison of the index for the Czech Republic is made between two reanalyses, that is, the ERA5 and Aladin reanalyses. The ERA5 reanalysis is a global dataset produced in a much coarser resolution (approximately 27 km), whereas the Aladin reanalysis is adjusted for the Czech Republic with the finer resolution of 2.7 km. Hence, we analyze and compare both datasets between 1989 and 2020 for the Czech Republic. Firstly, both reanalyses are interpolated on the same grid with a 7.1 km grid step. Subsequent comparison reveals a large similarity between both reanalyses, probably due to the fact that ERA5 is a control dataset for the Aladin reanalysis. The general climatology of UTCI for the Czech Republic is presented in both reanalyses. Furthermore, trends of UTCI over the 30year period are calculated for the whole country as well as for selected interesting sites. A detailed examination is given to the differences between eastern and western parts of the country to evaluate impact of the rising continentality of climate toward east. The dependence of UTCI on altitude in both reanalyses is determined as well. Finally, trends between rural and urban areas are compared to asses trends of UTCI in urban heat islands. The results of this work will be used in the future for bioclimatological mapping of the Czech Republic.
Developement of a land cover map for hectometric numerical weather prediction using machine learning
Authors: Bessardon Geoffrey , Rieutord Thomas , Gleeson Emily
Published: July 2023

Developing hectometric scale numerical weather prediction (NWP) models requires an accurate, high-resolution physiography dataset to represent surface heterogeneity in calculating surface-atmosphere exchanges. Land cover is one of the main components of physiography datasets. Unfortunately, the resolution of land cover datasets currently used in NWP is too coarse to fulfil the needs of hectometric NWP.
High-resolution remote sensing imagery and machine learning techniques have enabled the emergence of 10 m resolution global land cover maps such as  the Environmental Systems Research Institute 2020 (ESRI2020), European Space Agency (ESA) WorldCover, and the second generation Coordination of Information on the Environment land cover  (CLC+). These dataset labels are too generic to be directly implemented into NWP. Other high-resolution datasets can provide information about specific themes, such as the Copernicus dominant leaf type (DLT) 2018 and the Global map of Local Climate Zones, or national/regional information, such as the Swedish national land cover database 2018. While plenty of high-resolution physiography resources exist, none cover NWP needs.
This work aims at developing a high-resolution version of ECOCLIMAP-SG, the land cover map used operationally at Met Éireann, using supervised machine learning techniques. Supervised machine-learning techniques are widely used for land cover mapping but require a reference dataset. Here we propose to develop a reference dataset using multiple data sources and bagging multiple decision trees. We will complement this dataset with artificial (or synthetic) data using Geometric Synthetic Minority Oversampling Technique (G-SMOTE). Eventually, we will compare supervised machine-learning techniques such as convolutional neural networks or random forests.
A drought monitoring and forecasting system for Switzerland
Authors: Humphrey Vincent , Bircher-Adrot Simone , Imamovic Adel , Spirig Christoph , Croci-Maspoli Mischa
Published: July 2023

The intensity and frequency of dry spells in Switzerland have increased in recent years and are likely to intensify in the future. Meanwhile, increases in water use and competition between different actors also place a greater pressure on existing water resources. Because drought has been identified as one of the main risks for various economic sectors in Switzerland, a unified monitoring and forecasting system is to be established through the joint efforts of three different agencies (federal offices for environment, meteorology and climatology, and topography). The project also includes contributions from Swiss research institutions and actively involves stakeholders in its development.
In this contribution, we introduce the Swiss national drought project with a particular focus on in situ and satellite-based monitoring and its integration with long-term forecasts. Current efforts include the creation of a national in situ soil moisture monitoring network with approximately 30 stations, the  development of meteorological and agricultural drought products and indices, as well as the establishment of near real time, downscaled, sub-seasonal forecasts derived from existing systems (local area model, IFS-ENS-EXT). Integrating these highly heterogeneous data streams into seamless products ranging from historical data to sub-seasonal forecasts, all within a consistent climatological baseline, is expected to represent both a major technical challenge but also a significant step forward that will greatly benefit downstream user applications. This meteorological basis will directly feed into impact-relevant drought indices and hydrological models, with the aim of better supporting an early warning system that has to take in consideration the needs of a very diverse user community, such as hydropower production, fluvial navigation, agriculture, forestry, or artificial snow production.
Reconstruction of Emissions from Pottery Kilns in the Roman Period
Authors: Drüe Clemens , Michael Herdick , Holger Schaaff
Published: July 2023

 Reconstruction of Emissions from Pottery Kilns in the Roman Period
Clemens Drüe (1), Michael Herdick (2) and Holger Schaaff (2) 
1) Universität Trier, Umweltmeteorologie / environmental meteorology, Trier, Germany (druee@uni-trier.de)2) Leibniz-Zentrum für Archäologie (LEIZA), Mainz and Mayen, Germany
Thanks to modern filtering techniques, the loading of exhaust gases from industrial plants in Europe has been steadily decreasing over the last sixty years. Prior to that, emissions had been steadily increasing since the Middle Ages. Although the effects of industrial metal production in ancient Rome and China can be traced worldwide, most emissions before the modern era were negligible on a global scale. On a regional or local scale, however, pollution may have been severe, as historical sources suggest. Given the complete lack of data, however, it is not clear a priori whether the small size and production volume of the historical industry or the lack of pollution control combined with the high number of small sources is the predominant factor.
Roman pottery kilns provide a rare opportunity to shed light on this question. Experimental studies in reconstructed kilns of the Leibniz Center for Archaeology in Mayen, Germany, provide data on productivity and fuel consumption in this important industry. Such pottery kilns were located throughout the Roman Empire. However, production in Mayen, Germany, was of particular importance because of the high thermal shock resistance of the goods produced. Another important pottery production place was in and around Herforst (municipality of Speicher) near Trier. Recent studies have suggested that there existed  more than 200 roman potteries. This makes it one of the largest known industrial areas of antiquity.
We have used these experimental data to predict one year's emissions. Based on the resulting source strengths, we simulated the pollutant load using AUSTAL, Germany's regulatory model for emission forecasting.  Various kilns in different locations in and around the city of Mayen as well as several of the scattered kilns in the Roman industrial area near Herforst were simulated. Initial results show a strong dependence on night-time weather conditions and suggest that air pollution was not negligible and may have influenced the potteries' choice of location.
 
Beyond Monin-Obukhov Similarity Theory and the Implications for Turbulence over Complex Terrain
Authors: Stiperski Ivana , Calf Marc , Charrondiere Claudine , Mosso Samuele
Published: July 2023

Turbulent exchange of mass, momentum and heat at the Earth’s surface is the crucial component of the climate system. Correctly representing this interaction is therefore of fundamental importance in weather, climate and air pollution models. Parametrizations of turbulent exchange between the Earth's surface and the atmosphere in virtually all numerical models of atmospheric flows are based on Monin-Obukhov Similarity Theory (MOST). MOST identifies surface fluxes of momentum and heat, and height above the surface, as key parameters fully describing all turbulence exchange. MOST is, however, strongly limited by its assumptions of horizontally homogeneous and flat terrain, not representative of the majority of the Earth’s surface. In addition, MOST does not recognize the anisotropic nature of turbulence forcing, nor does it cover the strongly stable and unstable stratification, often encountered in real world applications. Thus, the limitations of MOST, and its use beyond its intended range of validity contribute to large uncertainty in weather, climate, and air-pollution models, particularly in polar regions and over complex terrain, both experiencing unprecedented warming.
Here we explore why the directionality of turbulence exchange (anisotropy of the Reynolds stress tensor) is fundamental in understanding surface-layer atmospheric turbulence, and present how its inclusion into MOST can provide a first generalized extension of MOST encompassing a wide range of realistic surface and flow conditions. The novel theory shows that the constants in the classical MOST, are actually functions of anisotropy, allowing a seamless transition between classical MOST and the novel generalization. The new scaling relations, based on a large ensemble of measurement datasets ranging from flat to highly complex mountainous terrain, show substantial improvements in scaling under all stratifications. The results also highlight the role of anisotropy in explaining general characteristics of complex terrain and strongly unstable and stable turbulence, adding to the mounting evidence that anisotropy fully encodes the information on the complexity of the boundary conditions.
Probabilistic storm forecasts for wind farms in the North Sea
Authors: Smet Geert , Van den Bergh Joris , Termonia Piet
Published: July 2023

In the last few years there has been a significant increase in Belgian offshore wind energy production, with a 2.26 GW total installed capacity completed at the end of 2020. Storm events over the North Sea can impact many of these wind farms at roughly the same time, because they are situated relatively close together in a narrow zone in the North Sea. Each wind turbine has a characteristic cut-out speed, above which they will decrease production rapidly or shut down as a protection measure. In case of a major storm, many wind turbines can shut down simultaneously, which can lead to large imbalance risks in the electricity grid. To better understand and predict such events, the Royal Meteorological Institute of Belgium (RMI) was involved in the development of a dedicated storm forecast tool for Elia, the Belgian transmission system operator for high-voltage electricity. The aim is to forecast large storm events, several days up to seven days ahead, and associated cut-out events, a day ahead and up to two days, making use of weather models that generate wind speed forecasts at turbine height and location. Due to the uncertainty in the precise location, timing and intensity of a forecasted storm, and the fact that cut-out events are sensitive to whether or not a high wind speed threshold is exceeded, a probabilistic forecast approach was taken. Moreover, Elia also required high temporal resolution forecasts (output every 15 minutes), so that a combination of a high resolution deterministic model and lower resolution ensemble weather prediction model was used. This allows both detailed forecasts and a good estimation of the uncertainty in the forecasts, thereby helping end users in their decision making process. The storm forecast tool developed at the RMI makes use of the deterministic ALARO model (4 km resolution) combined with the ENS ensemble forecasts (18 km resolution) of the European Centre for Medium Range Weather Forecasting (ECMWF). The storm forecast tool has been operational at the RMI since November 2018. We give an overview of the current status of the storm forecast tool, together with its performance over the past years, and present some ongoing and planned future developments. These include ensemble calibration with historical wind speed measurements, the inclusion of wake effects using fast wake models and NWP wind farm parameterizations, and the prediction of ramping events.
Characterising sub-seasonal weather persistence -- an illustration on summer European circulation
Authors: Martius Olivia , Tuel Alexandre , Pappert Duncan , Banderier Hugo
Published: July 2023

The long-lasting hot and dry conditions in summer 2022 in Europe are an example of episodic persistence of surface weather on a sub-seasonal timescale that resulted in high impacts. Similarly, the series of atmospheric rivers that reached the US west coast in winter and spring 2023 are an example of recurrent circulation features which also resulted in persistent surface weather on a sub-seasonal timescale. We will first present a short overview and a classification of methodological (statistical and machine learning) approaches that capture and quantify these two flavours of persistence – stationarity and recurrence. We will illustrate the application of some methods to the European summer circulation and discuss their strengths and limitations.
We will then discuss the large-scale circulation features associated with persistent hot spells and recurrent heavy precipitation for selected areas in Europe using the ERA-5 data set.  The first example are three-week hot spells over both northern Europe and western Europe. They are associated with a change in the jet latitude upstream over the Atlantic prior to the events from a northward shifted to a strong zonal jet at the beginning of the events. The second example are recurrent heavy precipitation events in summer over the Western Atlantic. They occur in the left exit region of the jet and are associated with a positive cyclone frequency anomaly, a trough over the western Atlantic and significant positive IVT anomalies. Both examples illustrate an important role of the jet stream (and hence the circulation) over the North Atlantic for sub-seasonal persistence in summer in Europe prior to and during the events.        
Atmospheric fronts detection over Europe – methodological approach
Authors: Wypych Agnieszka , Bochenek Bogdan , Ustrnul Zbigniew , Suri Dan
Published: July 2023

Weather conditions, including the threat of extreme weather, in the temperate zone are mainly related to the atmospheric circulation, including the presence of atmospheric fronts.
Despite many studies conducted on the distinction of atmospheric fronts in recent years, there is still no entirely satisfactory objective method of their discrimination with respect to traditional synoptic maps. Here we propose a method of distinguishing atmospheric fronts based on cluster analysis methodology treating border zones between air masses as fronts and testing the output against operational analyses
The basic data used for the calculations were meteorological fields taken from the ERA5 database for the period 1951-2020 in hourly time steps across a domain given by coordinates 30°W to 50°E and 30°N to 80°N. The following elements were taken into account: temperature, specific and relative humidity, geopotential height on pressure levels from 925 to 700 hPa as well as temperature and wind speed near the ground.
Using methods such as k-means and dbscan, division into 3, 4 and 5 clusters was conducted over Europe. This analysis was conducted for the whole period, as well as for specific months and seasons, and included considerations of spatial diversity and temporal changes in different regions of the domain.
Validation of the results, including comparative studies with traditional synoptic maps from DWD and the Met Office, confirms the 3-classes distinction of air masses as the most reliable. The proposed method seems to be effective and self-dependent (contrary to, for example, supervised machine learning methods). This approach enables a method to be developed allowing calculations of severity of fronts and trends in the behaviour of fronts with time.
Analysis of surface air temperature and precipitation trends and climate indices in mountain areas in Spain
Authors: Viloria Ramón , Tricio Verónica
Published: July 2023

Several studies and observations suggest that global warming processes are more prevalent in mountain areas, showing higher rates of warming and more pronounced changes in precipitation than in average land data. Snow and ice are highly sensitive to variations in climate. The importance of mountain areas as water reservoirs for the surrounding land and valleys at lower altitudes justifies paying special attention to this type of behaviour and to the changes brought about by global warming. This interest is enhanced by the special environmental sensitivity of mountain ecosystems, and the difficult balance between these fragile ecosystems and their use as tourist resources or winter resorts.
In this paper, climate data collected at mountain weather stations in Spain in time series up to 80 years are analysed. Stations in mountain areas are not numerous and are sometimes very scattered; nevertheless, we have selected the available data on temperatures, precipitation and other meteorological variables at stations located in the various mountain ranges throughout the Iberian Peninsula. For the selected stations, trends in temperatures (mean, maximum and minimum) have been studied and a seasonal analysis has been carried out. In addition, the data were processed with RClimDex, statistical and climatic software package, to evaluate Climate Extreme Indices. Cooling and warming patterns have been detected, and changes in precipitation have been analysed, trying to address the distinctive characteristics of mountain areas in the studies conducted. Monthly and seasonal assessments have also been carried out to detect changes in behaviour patterns. In general, good agreement with previously published data has been obtained, although not many studies have been conducted systematically in Spain, except in the Pyrenees area.
Analysis of Western Mediterranean cyclones and their precipitation zones
Authors: Dolgos Emília , Pongrácz Rita , Bartholy Judit
Published: July 2023

The weather and climate conditions of the Mediterranean region is greatly influenced by mid-latitude cyclones. The region is considered as one of the most vulnerable areas to climate change, and Mediterranean cyclones and their characteristics might change in the future. We selected the western part of the Mediterranean region as our study area. In this area we aim to identify cyclones and get an understanding of their characteristics on the basis of ECMWF reanalysis data and the historical simulations of global climate models from the CMIP6 project. In the study we identify the potential areas of low pressure systems based on mean sea level pressure, with temporal resolution of 6 hours, and then determine their trajectories. Mediterranean cyclones affect other parts of Europe, including Hungary and its vicinity. We aim to analyze what portion of the annual precipitation is connected to Mediterranean cyclones and their frontal systems, and how it will change in the future. For this purpose, we use precipitation data from the above-mentioned databases. This study is focused on the present and historical conditions. Our findings are validated by comparing the results of reanalysis and CMIP6-simulation data. The study continues with the evaluation of future trends based on different climate scenarios (RCP and/or SSP scenarios upon availability), which represent different anthropogenic impacts and mitigation efforts. The results can serve as essential input for further impact studies, and then for decision makers and stakeholders when building the long-term strategies of their corresponding sectors.
Acknowledgements: The study contributes to the COST CA19109 action (MEDCYCLONES). Research leading to this study has been supported by the following sources: the Hungarian National Research, Development and Innovation Fund (under grants K-120605 and K-129162), and the National Multidisciplinary Laboratory for Climate Change (RRF-2.3.1-21-2022-00014).
Preliminary analysis of the vertical wind profiles measured using Acoustic Wind Profiler Sodar MFAS installed at the Split airport (Croatia)
Authors: Jakić Jure , Sepic Jadranka
Published: July 2023

The Scintec Acoustic Wind Profiler Sodar MFAS was installed at Split Airport (on the Adriatic coast, Croatia) in December 2022. However, due to power consumption issues, the instrument was not fully operational until June 2023, when it was connected to the regular power grid. Five months of measurements, from June to October 2023 were analysed: during this period, the time step of the measurements was set to 20 minutes, resulting in 20-minute averages of horizontal and vertical wind at heights ranging from 30 m to a maximum of 400 m, with a 10-m vertical step. The top height of the measurements was strongly dependent on the background atmospheric conditions.
From June to October 2023, several characteristic winds were observed: (1) sea and land breezes; (2) sirocco (Cro. "jugo") - strong, steady southeast wind bringing moisture; and (3) bora (Cro. "bura") - cold, strong, and gusty northeast wind. Since the measurements were made during the warmer part of the year, the sea and land breezes were the predominant winds, which were then further analysed. The mean daily vertical profile of the sea and land breezes was estimated: the breezes rotated clockwise during the day, starting as weak easterly to southeasterly winds with a speed of 2-3 m/s in the early morning hours, turning to weak southerly winds around noon, and then to faster southwesterly winds with a speed of ~5 m/s around 1 pm local time. The sea breezes reached their maximum speed around 4 pm local time and maintained their southwesterly direction. After 4 pm local time, the breezes generally began to weaken and change direction, first to westerly and then to northwesterly winds, which ceased around 8 pm local time. Easterly to northeasterly land breezes typically started couple of hours later, reaching up to ~5 m/s. The vertical structure of the sea and land breezes was further analysed: Preliminary analyses indicate that during the daytime hours, the horizontal winds were fairly uniform in the first 400 m and maintained their direction, but gained some speed with height. On the other hand, a reversal of circulation was frequently observed during the nighttime hours, from weaker easterly winds in the lowest 200 m to stronger northwesterly winds at 200-400 m altitude.
The synoptic conditions favourable for the occurrence of sea and land breezes were then extracted using the ERA5 Global Reanalysis dataset.
Additional Sodar MFAS measurements will be used to analyse sirocco and bora, i.e., winds that occur more frequently during the colder part of the year.
Prediction and predictability of hydrological extreme events in the region Berlin-Brandenburg for risk assessment in the project SpreeWasser:N
Authors: Hauke Clara , Ulbrich Uwe , Rust Henning
Published: July 2023

SpreeWasser:N aims at developing strategies to gain a better understanding of the water cycle and associated hydrological extremes in the region Berlin-Brandenburg and create action plans to reduce related future risks. The goal is to develop long-term concepts regarding drought management, integrated water resources management and improved water storage systems together with water users and policy makers to pave the way for a sustainable and interdisciplinary water resource management course.
 
The predictability of hydrological extreme events is assessed on time scales ranging from near-term to decadal and predictors acting as potential indicators of imminent risk are inferred from statistical analyses, modeling and literature. Climate projections for different emission scenarios for Brandenburg provide a bigger picture of how climate variables will shift in the future and how this will affect the hydrological balance in the region. Ensemble methods are a helpful tool to assist in some of these tasks, including estimating uncertainties for forecasts and projections. Downscaling methods are used with convection-permitting models to provide data which can be used in hydrological models to improve the forecast of hydrological impacts. In conjunction with the project partners drought warning systems and adaptation strategies are developed. A drought forecast based on k-nearest neighbor regression is being developed using an algorithm that automatically selects those meteorological variables and regions yielding the largest forecast skill as input predictor variables during a hindcast period using reanalysis data. This machine learning approach supports the discovery of underlying physical links in atmospheric phenomena. Using weather patterns as an additional predictor variable, connections between certain states of the atmosphere and hydrological extreme weather events can be detected. Another research focus is the succession of certain weather patterns and its impact on precipitation.
Understanding Cold Climate Cities: Implications for Urban Sustainability
Authors: Miles Victoria , Esau Igor , Miles Martin
Published: July 2023

Studying urban climate in cold climate cities is essential for understanding the implications for urban sustainability. Recent research has found that urban areas in high northern latitudes experience intense and persistent positive temperature anomalies, known as urban heat islands (UHI). Heat accumulation from year to year creates a cumulative UHI effect that fundamentally changes the environment and soil properties, affecting vegetation productivity and the length of the growing season. The warmer urban climate has significant implications for the urban economy, environment, and human health, particularly in light of global climate change in the cold climate regions. This study analyzes land surface temperature data in the seven largest cities in the European Arctic, ranging in population from 50,000 to nearly 300,000 and spanning four countries and three bioclimatic zones. The results indicate persistent temperature anomalies every season in the 1–5 ◦C range, with the largest city, Murmansk (Russia), showing the highest values. The study also finds a strong inverse relationship between surface UHI intensity and temporal variability. The more substantial the surface UHI, the more stable it is and the lower its temporal variability. We found no general direction of surface UHI change in the long term. The study also suggests that compact and dense urban infrastructure has a more significant impact than the geographical setting of the city on UHI. Understanding the intensity and variability of UHI in these regions is crucial for developing sustainable urban planning and management strategies to mitigate the negative impacts of UHI on the environment and human health.
Seasonal prediction of droughts over European Russia using artificial intelligence methods
Authors: Chechin Dmitry , Akperov Mirseid , Timazhev Alexander
Published: July 2023

Among natural disasters, droughts cause the most damage and losses to the agricultural sector in Russia. Measures to minimise the damage caused by droughts depend on timely and accurate prediction of droughts. Seasonal prediction of droughts is challenging due to the non-linearity of processes in the coupled atmosphere-land system. For the same reason, it is expected that Artificial Intelligence (AI) methods may be superior to traditionally used statistical methods for drought prediction. The aim of this work is to apply and evaluate different methods of AI for seasonal drought prediction over European Russia. Several indices are used to identify droughts, such as the Standardised Precipitation Index (SPI), the Standardised Precipitation Evapotranspiration Index (SPEI) and the Selyaninov hydro-thermal coefficient (HTC). The latter index is traditionally used in agricultural meteorology in Russia and its relation to the production of various crops is well established. The indices are calculated for the period 1958-2022 using ERA5 reanalysis data. A set of predictors includes several indices characterising the large-scale atmospheric circulation over northern Eurasia (e.g. NAO, AO and others), as well as the ENSO index and the sea surface temperature anomaly over the North Atlantic. Several AI methods are used to predict the values of the drought indices based on the set of predictors for different lead times ranging from two weeks to several months. Prediction is made separately for each of the administrative regions in the European Russia with a special focus on those regions where droughts cause most damage. Another focus of the study is the relationship between droughts and atmospheric blocking events and heat waves. The application of the used AI methods for heat wave prediction is discussed.
Assessing Practical Predictability of Winter Storms using NWP and Ensemble Data Assimilation
Authors: Greybush Steven , Seibert Jon , Zhang Yunji , Kumjian Matthew
Published: July 2023

Winter storms remain a prediction challenge, from the synoptic-scale evolution of the associated mid-latitude cyclone to the formation, location, and intensity of mesoscale snowbands, to the transitions in precipitation type, to terrain-enhanced and lake-effect processes.  Analyzing the predictability of NWP model simulations gives insights to the growth of forecast errors and the underlying dynamical mechanisms for these extreme events.  The recent NASA IMPACTS field campaign, with two coordinated aircraft as well as ground observations across field operations in 2020, 2022, and 2023, provides an unprecedented dataset with which to evaluate winter storm simulations.   In order to analyze the practical predictability of east coast winter storms, operational models as well as convection-allowing ensemble WRF simulations that assimilate conventional and field campaign observations are assessed for several cases during the IMPACTS period.  These simulations are evaluated against conventional surface and radar observations as well as field campaign in-situ thermodynamic conditions and EXRAD radar onboard the ER2. A discussion of optimizing the performance of the data assimilation techniques, as well as evaluating the benefit of assimilating various types of observations is planned. Techniques such as ensemble neighborhood probability can illuminate areas that favor heavy precipitation such as mesoscale snowbands, and predictability horizon diagrams illustrate the timescales over which modeling systems converge on a solution. Finally, errors in precipitation amounts, locations, and types can be related to the representation of dynamical and physical processes in the models, including the thermodynamic environment and hydrometeor evolution in the microphysics scheme.  While the study focuses on examples from the eastern United States, techniques and insights are expected to have broader applicability to winter storms in other regions.
Spatial uncertainty of codling moth biofix caused by adverse weather events
Authors: Marcic Milena , Lalic Branislava
Published: July 2023

Meteorological conditions significantly impact the biofix, i.e., the time of the first flight of the first overwintering generation of codling moth (Cydia pomonella). In general, the flight activity of codling moths is influenced by a combination of temperature, humidity, and light duration and intensity, while precipitation and strong wind, particularly wind gusts, can stop insects from flying. Our research also considered the impact of atmospheric pressure on codling moth flight. 
Data describing biofix used in this study come from the codling moth monitoring in the framework of the PIS observing system. For sixteen locations during the 2012-2022 period, only 101 data sets were analyzed for the locations with the same traps and female pheromones. Since the traps were located in production orchards, to avoid the impact of insecticides on possible errors in the first detection of adults, only locations with a high insect population (with over 100 adults per trap during the season) were selected. Counting is performed daily from the beginning of April each year. Meteorological data for the study are provided from two sources: a) the PIS network of automated weather stations located in the orchards (air temperature and humidity, precipitation), and b) the synop reports from the synoptic stations (atmospheric pressure and wind gust).
Our research aims to measure biofix's uncertainty caused by adverse weather conditions (from the codling moth point of view) or events. Starting backward from when an adult is found in the trap, we analyzed the presence of weather conditions that can affect codling moth flight. As a measure of uncertainty, we use the days between trap ketch day and the last day before that when weather conditions allowed adults to fly.
Ensemble-based regional reanalysis system for Central Europe: Observation data converter, setup, test results and outlook
Authors: Kelbch Alexander , Valmassoi Arianna , Spangehl Thomas , Borsche Michael , Imbery Florian
Published: July 2023

The development of regional reanalyses aims at the provision of high-resolution data sets that are suitable for climate applications and climate services. As the desired high-resolution information can barely be provided by either synoptic or remote sensing observation data, a growing interest in high-quality regional reanalyses is recognisable. Particular demand arises from the renewable energy sector. Further quality gains are expected by using an ensemble approach, e.g. by making available the desired uncertainty information when moving towards higher resolution. Within the framework of the Innovation Programme for applied Researches and Developments (IAFE) at Germany's national meteorological service (DWD) our project aims to develop and evaluate an operational ensemble-based regional reanalysis system incorporating the current NWP model of DWD (ICON). One final goal of the project is to provide a basic framework for user-oriented verification.  
For our new reanalysis system we plan to use observation data sets or subsets of the ECMWF ERA5 reanalysis. Thus, a converter has been developed providing the ERA5 observation data (odb files) to the operational DWD data assimilation scheme. Numerous test experiments have been performed to 1) assess the specifications for our future reanalysis system and 2) evaluate the performance of the observation data assimilation. For testing we use the Basic Cycling Environment (BACY) being characterized by its modularity, robustness, user-friendlyness and high complexity. Here we present the resulting set of "frozen" BACY specifications such as model resolution, number of ensemble members, domain size, choice of output variables as well as results of the assimilation experiments.
Towards seasonal prediction of extreme temperature indices
Authors: Castino Fabiana , Mannig Birgit , Geiger Tobias , Pasternack Alexander , Paxian Andreas , Kreienkamp Frank
Published: July 2023

The variability of meteorological extreme events in Europe is strongly affected by climate change. In particular, it has been shown that temperature extremes have increased in frequency, duration and intensity, becoming one of the natural disasters with the most severe socio-economic impacts for European communities. As climate change continues, seasonal forecasts represent a valuable tool for predicting upcoming high-risk climate conditions in advance (up to several months) to support decision-makers in the implementation of preventive measures. Recently, several studies assessed the predictive skill of decadal climate forecasts, while only few investigations evaluated the ability of climate forecasts in predicting climate extremes at seasonal time scale. This study analyses the performances of the German Climate Forecast System (GCFS) in forecasting selected temperature extreme indices, including the number of hot days and warm spells, which are key for the evaluation of health and mortality risks. The skill of the GCFS is estimated for Germany using a statistically downscaled bias-corrected hindcast-ensemble system with high spatial resolution (5 km) for the period between 1990 and 2020 at daily time scale. Forecasts of the climate indices for the summer months are evaluated for different lead-months (i.e., subintervals of the forecast period) using metrics such as the anomaly correlation coefficient and the ranked probability skill score. To this aim, we compare the hindcast ensemble with two daily observational datasets: ERA5 (9 km spatial resolution) and HYRAS (5 km regular grid covering the German drainage basins, including headwaters in the neighbouring countries). This analysis contributes to an improved understanding of the performances of seasonal forecast systems in order to effectively support decision-makers adopting proper risk-mitigation actions against climate extremes impacts.
The new CAMS Radiation Service v4.5 – method improvements with a special focus on solar energy user needs
Authors: Schroedter-Homscheidt Marion , Azam Faiza , Lezaca Jorge , Lefevre Mireille , Mantelier Nicolas , Saint-Drenan Yves-Marie
Published: July 2023

The Copernicus Atmosphere Monitoring Service (CAMS) provides open data access to solar irradiances through its CAMS Radiation Service (CRS). Data is accessible via both the CAMS Atmospheric Data Store and user-specific Python libraries as the PV-LIB.
Observations from the MSG meteorological satellites are combined with modelled aerosols, water vapor, and ozone from the CAMS Integrated Forecasting System. Irradiance time series in 1 min, 15 min, hourly and daily temporal resolution are produced 'on-the-fly' on user request - using the most recent method and input datasets at the desired location inside the domain of the MSG satellite view. In addition to the standard access as time series, a gridded dataset in 0.1° and 15 min for the years 2004-2022 is available for land surfaces of Europe and Africa. 
CRS v4.5 was released in 2022 and uses APOLLO_NG for deriving cloud information based on a probabilistic cloud detection. Its usage in Heliosat-4 follows a detection approach that is highly optimized for solar energy needs. The method is operationally applied for Europe, Africa and the Middle East, but has also been tested for Asia/Australia, and North and South America.
The derivation of the radiation at the earth’s surface was extended by a parameterization of circumsolar radiation and provides a more accurate validation of the direct radiation with pyrheliometers. Besides concentrating solar power technologies, accurate direct radiation is needed to provide direct/diffuse ratios e.g. for tilted irradiances at solar module planes.
CRS v4.5 switched to CAMS reanalysis as input. This allows improved accuracy for all years 2004 – 2020 compared to the usage of the CAMS IFS in its various versions over time. This accuracy gain is larger for years before 2020 with their older CAMS IFS versions used in the CRS before v4.5.
The bias correction methods used so far have compensated for offsetting errors due to aerosols and clouds, obscuring the opportunity for improvement in both the cloud and aerosol algorithms. Various method improvements have now eliminated the need for operational bias correction in the CRS.
Typical improvement from CRS v3.2 to CRS v4.0 (new cloud scheme, bias correction active) to CRS v4.5 (new clouds, new CAMS reanalysis, no bias correction active) will be discussed in the presentation. Most recent evaluations done for Himawari-8 Field of View will be added.
Furthermore, the operational CAMS integrated forecast system (IFS) provides radiation forecasts in hourly resolution for up to 5 days. These forecasts are evaluated against ground based observations of radiation and compared against the spatially higher resolved ECMWF HRES forecasts. 