bluuebunny/crossref_metadata_2025 huggingface dataset:
Evaluation of ICON-LAM and COSMO high-resolution simulations with urban parameterisation for Warsaw, Poland
Authors: Jaczewski Adam , Wyszogrodzki Andrzej , Interewicz Witold
Published: July 2024

The rapid development of urban areas and the impacts of global climate change make cities increasingly vulnerable to environmental challenges, including extreme weather and climate events. While operational numerical weather prediction (NWP) models are crucial in supporting emergency management systems, they often lack the necessary detail and do not adequately account for the complex physical interactions between buildings, artificial surfaces, and meteorological processes. Enhancing these models could significantly improve the accuracy of weather predictions in cities, providing better guidance for preparing and responding to weather-related emergencies. This improvement is essential for safeguarding urban populations and infrastructure against the adverse effects of climate change and urbanisation.The ICON-LAM and COSMO models have a bulk urban canopy parameterisation, TERRA_URB, which is additionally implemented with spatially variable urban canopy fields in the COSMO model based on the Local Climate Zones (LCZ) approach. By considering the varied characteristics of urban surfaces, such as their materials, structures, and layouts, we can significantly enhance the precision of urban meteorology models. This comprehensive approach allows a deeper understanding of how cities influence local weather patterns and climate conditions, ultimately leading to more accurate predictions and better urban planning strategies. This work presents the results of evaluating NWP hindcasts at hectometric scales for Warsaw agglomeration. For the test simulation, a period of heat wave and strong convection in the city area was selected, covering the end of June and the beginning of July 2022. The National Hydrological and Meteorological Service's measurement and observation data were used. During this period, on 26-28 June, the maximum temperature in urban areas exceeded 30°C, and on 30 June and 1 July, the highest monthly temperatures of 34°C and 36°C, respectively, were observed. On 1 July, the front passed through with heavy precipitation – the daily total of 11 mm was recorded, and there was a rapid cooling. The comparison study results show a significant enhancement in the accuracy of surface weather predictions. This is due to implementing urban parametrisation with detailed spatial land use characteristics. The findings provide strong evidence for the effectiveness of this approach in weather forecasting.
A NAO-based subsampling approach for winter seasonal prediction 
Authors: Benassi Marianna , Athanasiadis Panos , Borrelli Andrea , Cavicchia Leone , Gualdi Silvio , Hashemi Devin Mehri , Sanna Antonella , Tibaldi Stefano
Published: July 2024

The North Atlantic Oscillation (NAO) represents the dominant mode of atmospheric circulation variability over the North Atlantic, driving winter weather conditions over a large part of the Euro-Mediterranean sector. Seasonal forecast systems have demonstrated some predictive skill for wintertime NAO, related to the enhanced ability of dynamical models to correctly represent possible sources of NAO predictability. However, increasing the predictive skill at the seasonal timescale over the European domain is still considered a major challenge.In this work the aim is to extract the potential hidden skill in a dynamical seasonal forecast ensemble by properly selecting relevant realizations. The idea is to define a reduced ensemble better performing in terms of NAO predictions and to assess the performance of this subsample compared to the full ensemble.Different subsampling criteria have been tested and verified. On the one hand, under the assumption that the ensemble average represents the most predictable path, the members simulating at the beginning of the forecast a NAO state closest to the ensemble mean NAO are selected. On the other hand, the realizations that resemble a reliable and independent estimate of the winter NAO, derived from the autumn conditions of a set of established dynamical predictors, are taken into consideration.The comparison between the results obtained with the full ensemble and these subsampled ensembles reveals the potential for a significant improvement in the prediction of 2m temperature and precipitation anomalies, therefore representing a valuable strategy for possible real-time operational applications both in a multi-model and in a single model framework.
Retrieving global radiation from Meteosat Level-1 with deep learning and pyranometer fine-tuning
Authors: Schuurman Kevin , Meyer Angela
Published: July 2024

Short-term forecasts of solar radiation are gaining importance for power grid operators, providing estimates of surface solar irradiance (SSI) and anticipated photovoltaic (PV) power generation for minutes to hours ahead. Geostationary satellites such as the Meteosat Second Generation equipped with the Spinning Enhanced Visible and Infrared Imager (SEVIRI) provide valuable spectral imagery for deriving SSI across wide geographical areas. While these spectral measurements are foundational for many SSI estimation and forecasting methods, current approaches still use Level-2 products of SSI, which leads to significant delays in the forecasting process. We demonstrate that a convolutional residual network can accurately emulate SSI produced by the SARAH-3 algorithm.A generalized deep-learning model is trained to estimate the instantaneous SARAH-3 SSI from all channels of the SEVIRI imager over a large region in Europe. The SSI emulator shows a similar bias and root mean square error (RMSE) for a large validation pyranometer set across Europe and Northern Africa. The emulator directly infers SSI from the Level-1.5 spectral imagery of SEVIRI within just 15 seconds, strongly reducing the 10+ minutes runtime of non-machine-learning radiation retrievals such as SARAH-3. We present a characterisation of the SSI emulator's performance depending on location and season and quantify the channel importance for different regions and surface albedos. We also present the SSI emulator's performance over snowy surfaces where retrieval algorithms such as SARAH-3 have been struggling to distinguish between snow and clouds. To further improve the emulator, advanced fine-tuning methods are applied based on ground observations without degradation in regional bias. The fine-tuning improves the RMSE on average from 80 to 65 W/m^2 and outperforms the SARAH-3 algorithm. 
AI-based approach for short-term forecasting of wind speed from a weather station network: A Case study in Valencia
Authors: Martinez-Roig Marcos , P. Plaza Nuria , Azorin-Molina Cesar , Andres-Martin Miguel , Chen Deliang , Zeng Zhengzhong , Vicente Serrano Sergio M. , R. McVicar Tim , Guijarro Jose A. , Ali Safaei Pirooz Amir
Published: July 2024

The generation of accurate and reliable forecasts of near-surface (~10 m above ground level) gridded wind speed data, hereinafter called NSWS, is crucial since it influences numerous socioeconomic and environmental fields. For instance, in the face of climate change, wind energy can contribute to the decarbonization of the electricity grid. NSWS, however, is a complex meteorological variable due to its inherent space-time variability, particularly in regions with complex topography like Valencia (Spain).The traditional approach to forecasting NSWS relies on Numerical Weather Prediction (NWP) models, which demand substantial computational resources, specially when high spatial and temporal resolution are required, often necessitating hundred to thousands of CPU hours. As an innovative solution to this pressing issue, the ThinkInAzul project, under Climatoc-Lab, is exploring the use of deep learning for accurate NSWS predictions. We propose an architecturebased on encoder-decoder neural networks composing mixed convolutional and recurrent  (ConvLSTM) layers. This AI-based product, designed as an early warning system, generate high-resolution (3- or 9-km) short-term (i.e.,
HARMONIE-AROME high-resolution experiments in the Destination Earth On-demand Extremes digital twin: an air quality models and end users perspective
Authors: Benacchio Tommaso , Thorsen Søren Borg , Baordo Fabrizio , Yang Xiaohua
Published: July 2024

In the framework of the flagship EU Destination Earth initiative, the On-Demand Extremes digital twin project aims at delivering an event- and user-driven weather-induced engine for selected impact sectors. The core of the project lies in the design and development of an operational capability for an on-demand workflow on Europe's fastest supercomputers, efficiently exploiting large data volumes coming from observations and hectometric limited-area numerical weather prediction (NWP) simulations for the benefit of a variety of users. Within the project, air quality (AQ) is one of the chosen impact sectors to highlight the DT engine capabilities.Poor air quality is a major cause of premature death and disease, and the single largest environmental health risk in Europe. AQ models are routinely run at meteorological and environmental agencies to identify contributions to air quality problems and assist in the design of effective strategies to reduce harmful air pollutants.The talk will showcase the work done during the first phase of the project in the design and development of high-resolution HARMONIE-AROME NWP experiments within the Extremes DT infrastructure Prototype whose output is given as input for AQ model runs. Initially focusing on a historical summer heatwave case, the presentation will highlight the impact of higher resolution and other configuration parameters as well as the steps taken to deliver the NWP output variables to the AQ model developers on the target high-performance computing facilities.Example use cases for air quality end users will be shown and next steps will be discussed in the context of the second phase of the project, which aims at delivering an integrated end-to-end, on-demand NWP-AQ workflow.
Roles of terrain, surface roughness, and cold pool outflows in an extreme rainfall event over the monsoon coast
Authors: Li Huiqi , Huang Yongjie , Hu Sheng , Wu Naigeng , Liu Xiantong , Xiao Hui
Published: July 2024

An extreme rainfall event with maximum 12-h accumulated rainfall of 464.8 mm associated with a quasi-stationary mesoscale convective system occurred over the coastal region of South China (a monsoon coastal area) in June 2017. An observational analysis shows that early convective storms were initiated near the mountains and moved northeastwards. A convergence line between cold northerly winds and warm southerly winds in the lower levels was formed, which favored the development of the quasi-stationary system. Cold northerly winds were associated with land breeze, downslope winds as well as previous rainfall during earlier period, and cold pool outflows during later period. High rain rate was mainly contributed by abundant small- and medium-sized raindrops. Cloud-resolving simulations were performed to examine the roles of terrain, land-sea surface roughness contrast, and cold pool outflows in the formation of heavy rainfall. Results demonstrate that orographic lifting and lee side convergence near the coastal hills helped initiate convection. Land-sea surface roughness contrast and mountains in the middle of Yangjiang and Jiangmen affected the formation of convergence line during earlier period, and cold pool outflows sustained it during later period. Mountains in the middle of Yangjiang hindered the movement of the convective system, without which the associated heavy rainfall would shift farther north. Mt. Tianlu with westward concave morphology was vital for the formation of local convergence and the concentration of heavy rainfall. This study suggests the importance of representing processes associated with complex underlying surface in models for the prediction of heavy rainfall in coastal areas.
Development of eddy accumulation technique for aerosol particle number concentration and ammonia flux measurements
Authors: Weidinger Tamás , Salma Imre , Bozóki Zoltán , Horváth László , Huszár Helga , Gombi Csilla , Torma Péter , Rehák András , Nagy Zoltán , Pintér Krisztina , Szabó Anna
Published: July 2024

Eddy accumulation technique provides an opportunity to quantify the turbulent exchange processes of properties for which fast response measuring devices are not available. The signals of slow response instrument are separated based on the up- (c+) and down-flowing (c–) air concentration by using a long-term averaging process (20-30 minutes). The difference in the average concentration of the up- and down-flow, multiplied by the standard deviation of the vertical wind is proportional to the turbulent flux of the given property. Two applications are presented.i) The particle number concentration was measured by a condensation particle counter (CPC, type A30, Airmodus, Finland) at the ELTE BpART Lab (http://salma. elte.hu/BpART/) in summer 2023. The instrument uses butanol as a working fluid, and detects particles with a diameter >7 nm in single particle counting mode with coincidence correction up to 150  000 cm–3 with a time resolution of 1 s. Fast response (10 Hz) turbulence measurements were accomplished using an EC150 Open-Path CO2/H2O gas analyser combined with CSAT-3 sonic anemometer. The time lag between the two instruments due to air intake was considered. The autocovariance function of the vertical velocity and the particle number concentration were also calculated. The concentration within the 1-s averaging time was i) taken as constant, and ii) the tenth-second values were calculated by linear interpolation. A 10-Hz data series were generated, knowing that the covariance [cov(w,c)] is underestimated. In each case, 30-min averaging period was used. As a check of the method, the flux calculations based on relaxed eddy accumulation (REA) were compared with eddy covariance (EC) measurements for raw vertical fluxes of sonic temperature, moisture, and CO2. The fluxes calculated in these two ways were in good agreement (R2 > 0.83).ii) Another methodological development is a novel approach that involves combination of a photoacoustic ammonia sensor with a CSAT-3 sonic anemometer. The photoacoustic instrument, which is currently in the development phase, yields reliable concentration values with an averaging time of approximately 10 s. Similar to wind parameters, the output signals from the ammonia sensor were collected at a frequency of 10 Hz, acknowledging the presence of significant white noise. These measurements were started in a sunflower field even before sowing and fertilization during spring 2024. Our analysis encompassed: a) assessing the reliability of ammonia concentration measurements, b) studying the daily concentration patterns, c) investigating concentration differences detectable between upstream and downstream air flows. Initial findings from this new field campaign are summarised on the posters.
Supporting mitigation and adaptation to climate change in Serbia through national climate service 
Authors: Tosic Milica , Lazic Irida , Filipovic Lazar , Savic Darko , Djurdjevic Vladimir
Published: July 2024

Online platform Digital climate atlas of Serbia (https://atlas-klime.eko.gov.rs) has been established under the Advancing medium and long-term adaptation planning in the Republic of Serbia. This web platform provides free access to publicly available climate datasets over the country, both observational and projected climate conditions for the future as well as at the sub-national and city/local level. Digital Climate Atlas of Serbia is based on the data provided by CORDEX, Copernicus Climate Change Service and geospatial information system of the Republic of Serbia. Past and future data are gridded data and defined on the same spatial resolution grid of 0.11 degree. For the past condition the EOBS dataset was used, covering the period from 1951 to 2020. For the future projections data that was used is results from eight regional climate models from EURO-CORDEX database covering the period from 1971 to 2100. We selected two climate scenarios, RCP4.5 and RCP8.5 including historical part of model runs. Model outputs were bias-adjusted in order to minimize their error in relation to observed conditions in the past and then they were processed further. Available data are classified into two major groups. The first group includes the essential climate variables (ECVs), minimum, maximum and mean daily temperature and daily precipitation. The second one consists of 22 climate indices derived from ECVs, including some that are selected as a support for high-impact extreme events analysis. Both ECVs and calculated indices can be interactively displayed for different time-slices as maps of variable values and maps that represent changes compared to selected reference time periods. The atlas offers interactive display of relevant time series, enabling users to visualize data dynamically. Users can download any plot displayed on the atlas in digital picture format or as a dataset for further research. Spatial plots are available for download in NetCDF format to share array-oriented scientific data, while time series can be obtained in CSV format. Users can choose spatial levels on which the time series data are aggregated, such as national, regional and the level of municipalities, as well as single grid point time series. Atlas provides time series with daily, monthly, seasonal and annual time resolution.  The main idea behind the atlas development was to provide national climate service and free access to relevant data over Serbia that serve as the basis for all climate-related initiatives and can be used to support decision and policy making and vulnerability assessments.  Acknowledgment: Digital Climate Atlas of Serbia has been developed in cooperation with the Faculty of Physics, Belgrade University and Neopix, under the project “Advancing medium and long-term adaptation planning in the Republic of Serbia” supported by the Green Climate Fund.
Coupled land-atmosphere data assimilation developments in support of the next generation of Earth system reanalyses and seasonal prediction systems: The CopERnIcus Climate Change Service Evolution (CERISE) project
Authors: de Rosnay Patricia , Aires Filipe , Bojarova Jelena , Brookshaw Anca , Calvet Jean-Christophe , Cardinali Carla , Herbert Christoph , Hersbach Hans , Knight Jeff , Pérez-Zanón Núria , Schyberg Harald , Stockdale Tim , Trigo Isabel , Vitart Frédéric , Weston Peter
Published: July 2024

The aim of CERISE is to develop new and innovative coupled land-atmosphere data assimilation approaches and land initialisation techniques to pave the way for the next generations of the Copernicus Climate Change Service (C3S) reanalysis and seasonal prediction systems. These developments are combined with innovative work on machine-learned observation operator to ensure optimal data fusion fully integrated in coupled assimilation systems. The project aims at improving the quality and consistency of the C3S reanalysis systems and of the components of the seasonal prediction multi-system, directly addressing the evolving user needs for improved and more consistent C3S Earth system products.This presentation gives an overview of the objectives of the CERISE project with a focus on developments of coupled land-atmosphere data assimilation to improve the climate consistency of the next generation of C3S Earth system global and regional reanalyses. It describes ensemble-based unified land data assimilation and coupling infrastructure and methodology developments conducted in the first 18 months of the project. Work on machine-learning based observation operator to enhance the exploitation of passive microwave data is introduced, presenting the training databases, the machine learning approaches developed, and results comparing simulated and observed brightness temperature from AMSR2. Results from numerical experiments show the benefits of using ensemble-based land data assimilation for surface and near-surface weather representation both at regional and global scales. The first CERISE global land reanalysis prototype is presented. Its results are compared to state-of-the-art operational reanalysis using a set of newly developed diagnostic tools. Infrastructure, methodology and scientific results presented highlight the feasibility and the added value of the integration of the CERISE developments in the existing C3S core service.
Unveiling seamless climate information benefits for the wine sector: co-development of a case study
Authors: Torralba Verónica , Soret Albert , Terrado Marta , Delgado-Torres Carlos , Duzenli Eren , Solaraju-Murali Balakrishan , Muñoz Angel G.
Published: July 2024

The wine industry is one of the agri-food sectors most highly influenced by climate variability and change at different timescales. In particular, integrating reliable and timely seasonal to decadal climate information in decision-making might help the wine sector better manage climate-related risks such as spring frost or water restrictions. This explains the recent interest of the wine industry in having climate information at these time scales. However, some of the challenges that prevent the users from uptake of climate information are the coarse resolution of the climate model outputs and the lack of coherence between the climate predictions from different sources (i.e., forecast systems at seasonal and decadal time scales). To produce coherent regional climate information for the wine industry, the suitability of different statistical downscaling methods to increase the resolution of user-relevant climate variables and indicators has been assessed for specific locations in Catalonia. Furthermore, a novel methodology for the temporal merging of seasonal and decadal predictions has been implemented to improve the accuracy of the relevant climate variables. This temporal merging method exploits the knowledge from the physical processes relevant to climate predictability at both seasonal and decadal time scales. The new scientific knowledge has been developed in the framework of the EU-funded project ASPECT (Adaptation-oriented Seamless Predictions of European ClimaTe). In this initiative, all the scientific methods are being co-developed in collaboration with representative stakeholders to ensure the climate information produced has actual value for the management of the vineyards and wineries, and the strategic planning for the wine market. 
Reduced Data PV Forecasting Challenges: An Austrian Casestudy  
Authors: Papazek Petrina , Gfäller Pascal , Schicker Irene
Published: July 2024

Solar power/PV installations pose challenges to traditional and AI forecasting technologies. Major difficulties in forecasting PV – particularly aiming to simulate production peaks in high spatial and temporal resolution – include being presented with poorly resolved or accumulated data (e.g.: grid-level data only, no exact location referenced), missing specifications within installations, rapidly changing environments or installation setup, and a high diversity among sites that feed into the power grid. Non-shareable, not continuously recorded or fragmented data-sets can be another obstacle, leading to reduced data-sets. Particularly, AI powered forecasts rely on sufficient, consistent historic data and may fail or underperform under these circumstances. To still provide reliable PV location forecasts tailored approaches are needed. Within this study, we work on tailored machine learning based solutions addressing challenged reduced data locations. We evaluate the effect of impaired data on AI based approaches to investigate the benefit of a data driven approach within an Austrian case study. We follow a multi-step machine learning approach, including the generation of semi-synthetic data, data transformation, and feature selection by exploiting a set of spatial and temporal strongly associated inputs from non-reduced auxiliary data. Data sources used are for instance, satellite data products (e.g.: CAMS by Copernicus) and reanalysis fields (e.g.: ERA-5 reanalysis by ECMWF), PV production records, and high-resolution numerical models (e.g.: AROME). We show the added value of combining different data sources within a post-processing model. Further considered inputs are, for instance, climatology of satellite data and reanalysis, pvlib’s estimation, AROME surface parameter simulations, and inhouse nowcasting models (e.g.: IrradPhyD-Net). This way, we are able to address PV forecasts of trafo level only data and challenged locations.  
Measured and simulated turbulent flux over heterogeneous surface: MOSAI project (Model and Observation for Surface Atmosphere Interactions)
Authors: Lohou Fabienne ,   MOSAI field campaign team
Published: July 2024

The land surface, through its topography, soil moisture, temperature or vegetation activity, impacts the atmosphere from daily to seasonal time scale. An accurate assessment of the Land-Atmosphere (L-A) exchanges, and their correct representation, are therefore essential for weather and climate forecasts. However, Earth System Models (ESM) and Numerical Weather Prediction (NWP) often have large biases in their representation of surface-atmosphere flux when compared to observations. The detailed quantification and reduction of these biases are still on-going efforts in many modelling centres. The Models and Observations for Surface-Atmosphere Interactions (MOSAI) project aims at contributing to this effort, with a focus on the impact of surface heterogeneity. The first step to achieve this objective is to conduct a fair and correct evaluation of the L-A interactions simulated by ESM and NWP. This is based on (i) reliable references against which the simulated L-A exchanges can be evaluated, and, (ii) relevant comparison methods able to point out the ESM and NWP weaknesses. These points define the two first scientific objectives of MOSAI project: (1) to investigate and determine the uncertainty and representativeness of L-A exchanges measured over heterogeneous landscapes. The second scientific objective (2) is to propose and test new methods to evaluate the L-A exchanges in ESM using long-term measurements.The second step of the project concerns the improvement of the L-A exchanges simulated by the ESM and NWP. The coupling between land surface models (LSM) and atmospheric models is based on several simplifications which are different when considering Large Eddy Simulation (LES), weather or climate models. The third scientific objective (3) addresses some of these underlying simplifications in the coupling between LSM and atmospheric models, and their impacts on the simulated L-A exchanges.From an observational prospective, MOSAI is based on long-term reference surface observations of research infrastructures (ACTRIS and ICOS) and on Enhanced Observing Periods (2021-2023) on three different ACTRIS-Fr sites. From the modelling prospectives, several ESM, NWP and LES models are involved. In this presentation, we will state the objectives and strategy of MOSAI, and illustrate them with ongoing fields, works and results. The latter will notably address (1) the representativity of reference long-term measurements in a heterogeneous landscape, (2) the use of Neural Networks for the evaluation of models, (3) the impact of the heterogeneity on the boundary layer structure.
Exploring Uncertainty in Future Changes of Convective Afternoon Rainfall in Taiwan: The Influence of Sea Surface Temperature
Authors: Huang Wan-Ru , Chien Yu-Tang , Cheng Chao-Tzuen , Hsu Huang-Hsiung , Koralegedara Suranjith Bandara
Published: July 2024

The present study investigates the uncertainties in future projections of summer convective afternoon rainfall (CAR), a key meteorological feature in Taiwan that significantly contributes to over 40-50% of the total summer rainfall in Taiwan. The analyses employ a dynamical downscaling approach utilizing the Weather Research and Forecasting Model (WRF) and the High-Resolution Atmospheric Model (HiRAM). The projections were driven by four different sea surface temperature (SST) categories derived from CMIP5 model simulations under the Representative Concentration Pathway (RCP) 8.5 scenario. All projections indicate a consistent decrease in CAR frequency but an increase in intensity by the end of the 21st century. However, the extent of changes in CAR varies across four simulations with different degrees of Pacific SST warming. These variations in potential future changes in CAR frequency and intensity are linked to variations in daytime thermal instability, local inland wind convergence, and moisture flux convergence over Taiwan. The study further investigates the relationship between local thermodynamic conditions and projected large-scale atmospheric circulation patterns, emphasizing a decrease in East Asian monsoonal low and a decrease in subtropical Pacific high in the future. The study also clarifies the potential impact of ENSO on modulating the changes in CAR activities over Taiwan under global warming, where La Niña-like circulation patterns induce more CAR activities over Taiwan compared to El Niño-like circulation patterns. In summary, the study offers valuable insights into the potential implications of climate change on CAR events in Taiwan, highlighting the importance of understanding the underlying physical mechanisms of the factors governing CAR event characteristics.
A WRF-based operational high-resolution numerical weather prediction system in northern Greece
Authors: Pytharoulis Ioannis , Kartsios Stergios , Spyrou Christos , Tegoulias Ioannis , Bampzelis Dimitrios , Zanis Prodromos
Published: July 2024

The region of Central Macedonia in northern Greece has a vital role in the financial life of the country because of its tourism and agricultural production. A primary objective of the Agroray project, which is a collaboration of Raymetrics S.A. and the Laboratory of Meteorology and Climatology of the Aristotle University of Thessaloniki, is the development of an operational high-resolution numerical weather prediction system that focuses on Pieria and its surrounding areas. This system provides timely and valid forecasts and allow the farmers to optimize their activities and protect their production from intense or high-impact weather events. It is based on the non-hydrostatic Weather Research and Forecasting (WRF) model with the Advanced Research dynamic solver. Three model domains, using telescoping nesting, cover: i) Europe, the Mediterranean Sea and northern Africa, ii) a large part of Greece and iii) central Macedonia, part of Thessaly and western Macedonia (central and northern Greece), at horizontal grid-spacings of 9 km, 3 km and 1 km, respectively. In the framework of the Agroray project, the meteorological forecasts become available to the farmers and the public (https://meteoray.com/en/). Kalman filtering is applied to the numerical forecasts at locations with station observations in order to reduce their systematic errors. The aim of this research is to present the meteorological model setup and its operational use, as well as to validate its performance during selected intense weather events (related to frost or intense precipitation) that affected the area of interest. The statistical scores were computed against station measurements and weather radar-estimated precipitation, using point statistics and a neighborhood-based technique, to investigate the impact of different model characteristics and surface input data on the model performance.Acknowledgments: This research was carried out as part of the Agroray project, entitled “Development of a forecasting system and geographical indicators for the agriculture” (project code: KMP6-0078047) under the framework of the action “Investment Plans of Innovation” of the Operational Program “Central Macedonia 2014–2020”, which is co-funded by the European Regional Development Fund and Greece.
Creating tools for the generation of weather-based crop calendars to support climate services
Authors: Aguilar Enric , Boqué-Ciurana Anna , Olano Jon , Agniga Kossi , Grey Sebastian , Gascon Tania
Published: July 2024

The UN's 2030 Sustainable Development Agenda sets a crucial target: eradicating hunger, yet progress has been insufficient. According to the FAO, more than 735 million people still suffer from hunger. This issue isn't evenly distributed globally; it hits hardest in less developed nations where agriculture sustains many livelihoods. Weather patterns, climate variations, and climate change all disrupt crop cycles, complicating agricultural planning and production.To assist farmers, weather-based crop calendars have become invaluable. These calendars utilize historical rainfall data to offer practical insights into optimal planting and harvesting times for various crops and regions. Creating these calendars demands precise rainfall data to pinpoint the start and end of rainy seasons, alongside crop-specific knowledge like drought resistance and growth duration.Determining the onset and end of the rainy season relies on complex threshold-based methods. These methods analyze accumulated rainfall over multiple days, incorporating additional criteria to avoid false alarms. By fitting this data to probability distributions, climatological expectations for early, normal, and late onsets and cessations of the rainy season are evaluated. The resulting crop calendar aligns early, normal, or late rainy season onsets with optimal sowing dates, while harvest dates are calculated based on sowing dates and crop growth cycles.In collaboration with the World Meteorological Organization (WMO) and initiatives like the Climate Risk and Early Warning Systems (CREWS) project in West Africa and the Enhancing Adaptive Capacity of Andean Communities through Climate Services (ENANDES) project in South America, a suite of tools has been developed. Following a comprehensive "from data to service" approach, these tools are accompanied by training events to empower local communities.This process is exemplified through the utilization of rainfall data from Togo. By integrating meteorological insights into practical agricultural guidance, these initiatives strive to enhance food security and resilience in vulnerable regions. Ultimately, these efforts contribute to the broader mission of achieving the UN's Sustainable Development Goal of Zero Hunger by 2030.
Analysis of the Fraction Skill Score for the rainfall verification of high resolution model in KMA
Authors: Jeong Soyeon , Lee Jeongsoon , Lee Eunhee , Kim Seungbum
Published: July 2024

 The Korea Peninsula is surrounded by the sea on three sides and is made up of mountainous areas more than 70% of the land, making it difficult to predict and verify precipitation using numerical models. To overcome these topographical features, a dense observation network and 10 radar site have been operated in Korea Meteorological Administration (KMA). Also, a regional prediction system based on the Korea Intergrated Model (RDAPS-KIM) has been operated since May 2022. RDAPS-KIM covers the East Asia region with a finer horizontal resolution(3km) than the global model KIM(12km) and simulates real complex terrain closely. Traditional skill score to verify models assesses the rainfall prediction performance for the grid closest to single point locations, which has the double penalty problem that the forecast precipitation exhibits the same pattern as the observation but leads to worse prediction evaluations due to missing spatial displacement. This double penalty issue occurs more frequently in regional model when the rainbands were shifted, so it can lead to misconception that regional model underperform global model. In order to avoid this problem, spatial verification methods to evaluate probability of rainfall forecast in the surrounding area have been suggested in the previous study (Ebert, 2008). The Fraction Skill Score (FSS) is one of the spatial verification methods suggested Roberts and Lean (2008), which allows the comparison of predicted precipitation with a spatial truth fields such as radar data. Accordingly, by verifying prediction with probability, it is expected that the shortcomings of verification of high resolution models can be overcome. Also, the FSS is recommended for verification in numerical weather prediction models by World Meteorological Organization (WMO) (JWGFVR, 2013). In this study, the models of KMA (KIM and RDAPS-KIM) are assessed using FSS method compared with different sized neighborhoods and various rainfall threshold for 3h-accumulated precipitation. Analysis indicated how the spatial scale influences the FSS values, showing that FSS increases as neighborhood size increases. The use of selected one proper neighborhood size pointed out that RDAPS-KIM lead to a high FSS than KIM, because it fits the rainbands better. The result shows that RDAPS-KIM is more efficient for heavy precipitation and local rainfall than KIM. In the future, we will operate the higher resolution 1km model to improve prediction performance in simulating severe weather events, and evaluate in combination with traditional techniques and probabilistic forecasts. It is expected that interpretation of accuracy of precipitation forecasts from various perspectives will be possible.
Analysis of the compound impact of heat waves and droughts in the urban environment
Authors: Pongrácz Rita , Dezső Zsuzsanna
Published: July 2024

As a consequence of global warming, heat waves become longer lasting, more frequent, and more intense in extratropical regions. Summer heat stress often occurs simultaneously with drought events causing compound impacts in the affected region. In order to analyze such coincidences in a mid-latitude continental, Central/Eastern European city (i.e., Budapest, the capital of Hungary with 1.7 million inhabitants), local temperature and humidity conditions provided by satellite data, are evaluated. So, the unique 22-year-long time series of continuous measurements from the MODIS instrument on NASA's Terra and Aqua satellites was used to study the surface urban heat island (SUHI) pattern, surface temperature and humidity in detail.A significant warming trend can already be identified in the surface temperature data during the period of 2001-2022. The analysis of summers shows that the SUHI intensity decreases as the rural area around the city becomes warmer, especially in July and August. When less water is available in the rural area in a drought event, the lack of latent heat thus facilitates the warming of the surface temperature in the rural area as well, as in the urban area, because these conditions are unable to reduce the surface temperature via the latent heat as usual. This way, the SUHI intensity is mainly determined by the rural surface temperature. The SUHI is very weak during summers with frequent and intense heat waves and droughts, due to the fact that the land surface temperatures are very high in both urban and rural areas resulting in very little difference between the built-up area and the vegetation-covered surrounding. This phenomenon is analyzed in the present study in detail for the years 2003, 2007, and 2022, when intense heat waves occurred in the region. Such detailed analysis aiming to understand the complex environmental processes in the urban environment is essential in order to develop effective adaptation strategies to the upcoming challenges of climate change, which will probably result in increasing frequency and persistence of heat waves and droughts in the future, with adverse effects to the quality of the life of the urban population.Acknowledgements: Research leading to this study has been supported by the Hungarian National Research, Development and Innovation Fund (under grant K-129162), and the National Multidisciplinary Laboratory for Climate Change (RRF-2.3.1-21-2022-00014). 
Analysis of zonal and meridional differences in projected changes of extreme temperature within Europe using CMIP6 simulation data under different scenarios
Authors: Pongracz Rita , Divinszki Ferenc , Kis Anna
Published: July 2024

The Interactive Atlas of the IPCC AR6 Working Group 1 is used to analyse the geographical differences of climate projections within Europe. For this purpose, the multi-model and multi-scenario ensemble is evaluated from global climate model simulations (available via the CMIP6, i.e. Coupled Model Intercomparison Project Phase 6 of the World Climate Research Programme) including four different SSP-RCP scenario pairs (i.e. from immediate rapid mitigation and effective adaptation, SSP1-RCP2.6, to no mitigation with highly challenging adaptation, SSP5-RCP8.5). The study provides key information so the regional and national adaptation strategies for different socio-economic sectors can be built and/or updated accordingly.This study focuses on temperatures extremes, e.g. the monthly frequency of heat days with daily maximum temperature above 35 °C. The target periods cover two decades on the medium- and long-term, i.e. mid-century (2041-2060) and late-century (2081-2100), respectively, and the reference period defined as the last two decades of the historical simulation period (1995-2014). Several zonal and meridional segments were defined over Europe, along which the projected changes are analysed with a special focus on sea cover, continental, and topography effects. Furthermore, the consequences of different scenarios are also compared. The results clearly show that greater radiative forcing change implies more severe health effects via the more frequent heat stress events. However, substantial differences can also be identified from south to north as well, as from west to east.Acknowledgements: Research leading to this study has been supported by the European Climate Fund (G-2309-66801), the Hungarian National Research, Development and Innovation Fund (under grants PD-138023 and K-129162), and the National Multidisciplinary Laboratory for Climate Change (RRF-2.3.1-21-2022-00014). 
Optimal Transport Tools for Spatial Forecast Verification
Authors: Francis Jacob , Cotter Colin , Mittermaier Marion
Published: July 2024

We formulate a novel spatial forecast verification methodology grounded in the geometric principles underlying optimal transport (OT). In its original form OT seeks to minimise the transport of mass between two distributions in space, providing a cost of transportation. Canonical examples come from logistics, such as finding the optimal route to distribute bread from bakeries to cafes. Since its initial formulation, OT theory has found many varying applications from signal processing and economics to meteorology and machine learning, notably through the famed Wasserstein distance. Its success is first due to Kantorovich’s dual formulation and more recently due to novel algorithms and GPU compute. Which combined allow regularised OT problems to be solved efficiently.  In this work we consider a precipitation field as a measure in 2D space and compute the unbalanced OT distance between a 2D observation and forecast field. By leveraging this geometric formulation, we find a summary metric (the objective), which itself has constituent parts indicating performance. Additionally, the methodology allows us to form transport maps. These maps highlight regions in the field which require the most transport to align with the observation. Alongside providing a visual representation of the error, this provides physical insight into transport error as the map will traverse along geodesics of the underlying space.  This has direct implications for operational forecasters, giving clear, easy to understand illustrations of error, whilst simultaneously providing important information to researchers at forecasting services. This research is supported by UKRI NERC, Imperial’s Grantham Institute and the MET Office UK.  
Seamless significant convection forecast for aviation
Authors: Cheung On-Pong , Ng Yin-Lam , Leung Christy Yan-Yu , Kok Mang-Hin
Published: July 2024

The Hong Kong Observatory (HKO) has been developing severe weather forecast products for the aviation community in order to provide early and reliable alerts for advance flight route, deviation and flow control planning. This study presents an 8-hour seamless significant convection forecast for the Asia-Pacific region, created by blending the extrapolation-based satellite nowcast with precipitation forecast from either the in-house regional HKO-WRF model or the European Centre for Medium-Range Weather Forecasts (ECMWF) high-resolution model, smoothly transitioning to just the NWP models thereafter.Our 1-year verification suggested that the blended forecast benefited from heavy reliance on satellite extrapolation at early forecast hours, progressively shifting towards the NWP models as forecast lead time increased, particularly after the fourth hour when the intensity-corrected NWP model forecast outperformed the extrapolation. This study also demonstrated the advantage of the salient cross dissolve (SalCD) technique, which minimised the misses raised from linear blending. In view of the capability of the NWP models, latitude-dependent weights were developed to capture the more rapidly evolving convection at lower latitudes. A specific profile for tropical cyclones was introduced, considering that NWP models performed better with the spiral bands. The blended forecast exhibited improved accuracy compared to using only satellite extrapolation or the NWP output within the forecast period. The findings revealed the potential benefit of blending to enhance accuracy while allowing a seamless transition to the NWP models for longer lead times. In addition to the deterministic blended forecast, probability prediction was explored by perturbing the weights and combining satellite extrapolation with individual ECMWF ensemble members for supporting the decision-making of aviation users.