bluuebunny/crossref_metadata_2025 huggingface dataset:
Argentina expanding Climate Services Knowledge Frontiers: capacity building for local stakeholders.
Authors: Cimolai Caterina , Boqué-Ciurana Anna , Olano Pozo Jon , Cherini Oriana , Aguilar Enric
Published: July 2024

The construction of local capacities related to climate change adaptation, such as climate indicators, is crucial to generate effective strategies against climate hazards. Along these lines, a "Workshop on Co-creation of Climate Indicators" has been conducted in the municipality of Posadas (Misiones, Argentina), adapting the co-creation methodology developed by Font et al. (2021). The purpose was to develop local capacity building, including the provision to participants with WMO competencies for delivering Climate Services. To achieve this, participants acquired skills and abilities that help them think, discuss, define, and compute climate indices to assist decision-making in climate-dependent sectors.The workshop was carried out in collaboration with a local partner, REDAPPE, enabling a comprehensive diagnosis of the territory and all stakeholders involved. Local work and outreach facilitated the participation of stakeholders from the private, public, and civil society sectors. Furthermore, both the provincial government (Misiones Province) and the local government (Posadas municipality) engaged with the process, actively participating and providing support.During the workshop, co-created indicators were generated in different working groups. Additionally, each participant conducted individual work where they explained why they consider the development of such capacities important and how they would apply them in their work. Finally, networks of collaboration and work have been established among participants, facilitating the exchange of knowledge and information among them during and after the workshop. We understand this as a fundamental strength for the construction of effective and coordinated actions in the territory; therefore, part of the process is to ensure the support of the technical team with the participants and their work, as well as the promotion and facilitation of tools for the development of new projects.
Wind speed maps for Austria: An artificial-intelligence approach part 2
Authors: Tilg Anna-Maria , Schicker Irene , Lexer Annemarie , Andre Konrad
Published: July 2024

The crucial role of wind power in the future energy generation demands to investigate the influence of climate change on wind speed and the associated consequences for wind power. Especially in areas with complex terrain, there are still open research needs for wind energy in future climate (Clifton et al., 2022). To assess future wind climate, one requirement is the availability of a climatological dataset of wind speed with high spatial and temporal resolution to downscale wind speed from global and regional climate models (GCMs, RCMs) to a local scale. So far, no such dataset has been available for Austria. To fill this gap, a wind speed atlas for ideally the past 30 years (1991 – 2020) is compiled for the diverse landscape of Austria, having flat and mountainous terrain, using a set of artificial intelligence (AI) methods.A comparison of gridded wind speed data considering three baseline methodologies will be presented: The ‘Integrated Nowcasting through Comprehensive Analysis’ (INCA) model (Haiden et al., 2011) combining ERA5 and station observations, a generalized additive regression model (GAM) and a deep neural network (DNN) model with adapted loss function. All approaches are used to create a wind speed analysis for the years 1991 to 2020 with a spatial resolution of 1 km to 1 km for the territory of Austria. Furthermore, they consider the same station-observation dataset of 10-m wind speed measurements of the national weather service in Austria. Last year’s shown first results of the GAM and DNN baseline were promising. The uncertainty in interpolation given through the methodologies is, so far, within the expected range. This year, preliminary final results of the gridding and validation of the different methods used will be presented.The availability of a wind atlas for Austria allows downscaling of climate projections and thereby the investigation of the climate change impact on future wind speeds and future wind power potential in Austria.References:Clifton, A., Barber, S., Stökl, A., Frank, H., and Karlsson, T.: Research challenges and needs for the deployment of wind energy in hilly and mountainous regions, Wind Energ. Sci., 7, 2231–2254, https://doi.org/10.5194/wes-7-2231-2022, 2022. Haiden T, Kann A, Wittmann C, Pistotnik G, Bica B, Gruber C. 2011. The Integrated Nowcasting through Comprehensive Analysis (INCA) System and Its Validation over the Eastern Alpine Region. Weather and Forecasting, 26/2, 166-183, doi: 10.1175/2010WAF2222451.1
A comparison of remote sensing evapotranspiration products over agriculture crops
Authors: Ontel Irina , Angearu Claudiu-Valeriu , Irimescu Anisoara , Chitu Zenaida , Irasoc Adrian
Published: July 2024

Evapotranspiration is an essential element in the water cycle, being estimated through various methods such as the Penman-Monteith equation, the Priestley-Taylor equation, the FAO-56 Penman-Monteith equation, the Surface Energy Balance Algorithm etc. With the advent of satellite imagery, constant efforts have been made to improve the spatial resolution of ET estimates through the integration of high-resolution remote sensing data. Evapotranspiration estimated based on MODIS data utilizes the Penman-Monteith equation, resulting in a product at a resolution of 500 meters over a period of over 20 years (2001-2023). Recently, new estimation tools for ET based on Sentinel 2 and Sentinel 3 data have been developed, such as Sen-ET, which uses the Two-Source Energy Balance (TSEB-PT) model and a Data Mining model to bring the product to a resolution of 20 meters. Furthermore, Python libraries developed for the Google Earth Engine platform provide the capability to estimate ET at 30 meters based on Landsat data using the Surface Energy Balance Algorithm. In this study, a comparison of ET will be performed, based on products derived from the integration of satellite imagery obtained from Sentinel 2 and Sentinel 3, Sentinel 2 and Landsat 8, Landsat 8 alone, as well as the ET product from MODIS. The analysis extends over a period of 8 years between 2016 and 2023, which is the common timeframe for all datasets. The study area is located in southeastern Romania, where extensive agricultural fields frequently suffer from drought. The results obtained from satellite imagery will be compared with those obtained from meteorological data, as well as with other satellite products capable of highlighting moisture deficits or drought.Acknowledgments: This study has received funding from the European Union Agency for the Space Programme under the European Union’s Horizon Europe research and innovation programme under grant agreement No. 101082189 (MAGDA project).
Uptake of the ECMWF Open Data Programme
Authors: Parodi Ilaria , Coughlan Ruth , Kuilman Maartje , Pidduck Emma , Bennett Victoria , Yang Xiaobo , Modigliani Umberto
Published: July 2024

ECMWF is a leading provider of weather information, and its products and weather data make contributions to a huge range of activities in service provision, research and for the protection of life and property via National Weather Services and humanitarian agencies. Since 2020, ECMWF has been implementing an open data policy, recognising that open data under Creative Commons licensing is an essential tool in contributing to the development of new meteorological methods and is encouraging innovation and progress in the weather and climate domains.ECMWF plans to achieve this goal by 2026. A number of steps have been taken to increase the open data available to users and in the last year ECMWF has built on this further. The data currently released as open data are a subset of ECMWF Real-time catalogue at 0.25 degree resolution, from both ECMWF Integrated Forecasting System (IFS) and Artificial Intelligence/Integrated Forecasting System (AIFS).The usage statistics on ECMWF open data for 2023 show a significant increase of user adoption with respect to the previous year, indicating the users are engaging with ECMWF open data offerings.ECMWF Open Data are provided through different mechanisms, all of which have seen a notable growth in usage in 2023:Open Charts Service, which provides chart visualisations of ECMWF products. Some of the increased uptake can be attributed to the introduction of charts based on Machine learning products in mid-2023.
The Open Data Portal (FTP service) is available since January 2022 and shows a continuing increase in numbers of requests. ECMWF predicts even higher usage with the addition of new Open Data of IFS products at higher resolution and additional parameters, plus AIFS products (machine learning-based) in early 2024.
WMO Essential and WMO Additional datasets (also provided via FTP service) are still increasingly popular. ECMWF will, in the future, optimise this provision, possibly merging it with the open data offering.
Since January 2022, ECMWF Open Data is also redistributed by 3rd ECMWF has partnered with different cloud providers as part of their public dataset program (e.g. Amazon, Google and Microsoft), but the data has also started being provided by smaller independent users (e.g. Open-meteo.de) and in research archives (e.g. NCAR). The benefit is not only a good user uptake but also that data can be shared with a larger user community with no additional load on ECMWF systems.
ECMWF is committed to moving forward to achieve the goal of an open data policy over the next few years. Creative Commons licences will be applied to further datasets, while reducing and eventually removing the cost of data. The main objective of these changes is to continue to encourage collaboration, innovation and progress within the meteorological and climate science communities.
Robust ML-nowcasting of solar irradiance from satellite derived features
Authors: Gfäller Pascal , Schicker Irene , Papazek Petrina
Published: July 2024

With the increasing shift to renewable energy sources, their predictability is becoming more of a concern. While some renewables follow more stable power output patterns, solar irradiance and thereby photovoltaic power production can experience significant shifts in shorter timeframes. The great potential of solar irradiance as a power source should although not remain underused, as it globally provides orders of magnitudes more energy to the earth than currently or foreseeable required. Solutions lie in forecasts for different timescales, with the short-term and nowcasting domains being able to provide the most accurate insights to volatility introduced by atmospheric phenomena. These forecasts are not only relevant to determine the expected economic impact but also to maintain an equilibrium in electrical grids with the goal of minimizing the waste of potential power production from solar irradiance.Large-grid-nowcasts of solar irradiance can substitute forecasting of solar power potential for individual sites, which are typically derived from the sites’ measurements. With models using satellite data instead, forecasts for large-areas are available, which are useful to approximate the solar intensity for a range of increasingly spatially distributed photovoltaic power stations. Satellite data is in contrast to ground-based data sources or NWP model estimates less reliant on the proper workings of a wide range of externalities and readily available in near-real-time.Based on a study of multiple convolutional-recurrent neural network architectures, deriving nowcasts from a single solar irradiance satellite-data-product, further research is undertaken to determine the limitations and benefits of single-irradiance-feature nowcasts and counteract potential detriments.A known issue with this kind of pipeline lies in its main benefit: The easy near-real-time access to a single dynamic feature can bring the whole model to a halt if an issue occurs with this single externality. To gather insights and provide a practical solution to this problem a further study on model robustness to missing data is undertaken, leading to the technique Timestep-Dropout. Via probabilistic removal of irradiance frames during training, neural networks can learn to expect missing frames in inference and still derive forecasts from the remaining valid frames.Possible benefits in forecast accuracy through the introduction of further features from satellite data or data from other sources may outweigh the additional burden of requirements, and provide overall improvements. To estimate these effects a comparison to the multi-irradiance-feature trained model will provide insights into a reasonable balance of irradiance-feature requirements.
A radar analysis of two giant hailstones in Catalonia
Authors: Rigo Tomeu , Farnell Carme
Published: July 2024

Hail events are common in Catalonia (NE of the Iberian Peninsula). The first two giant hailstones (with diameters equal to or exceeding 10 cm) occurred in this region in 2022 and 2023. These occurrences align with the hypothesis that extreme severe weather events have increased with rises in air and sea surface temperatures. This behavior coincides with the pattern observed in most of regions around the World.The research presented here focuses on the thunderstorm characteristics of both cases as observed by the weather radar network of the Servei Meteorologic de Catalunya. Regarding the volumetric structure, the giant hail-bearing storms exhibited dimensions unprecedented in Catalonia, as it is observed from the analysis comparing with a set of more than 200 different cases.We have compared these two events with the rest of the hailstorms set in terms of affected area, duration, dimensions, or life cycle evolution. The study was conducted using a high-resolution 3D grid of 1 km per 1 km per 0.5 km. This grid enabled the identification of the most relevant elements associated with the reflectivity fields in each case.Furthermore, we sought general parameters through initial automatic identification and characterization algorithms, allowing us to determine reflectivity distribution along the vertical, which has been compared also with lightning data. Additionally, we conducted more in-depth analyses for certain life cycle instants with particular elements, such as a change in the nature of the storm, the inclination, or the observation of concrete elements such as the three-body scatter spike, the boun weak echo region, or the hook echo.
Added value of a multi-model ensemble of convection-permitting rainfall re-forecasts over Italy
Authors: Giordani Antonio , Ruggieri Paolo , Di Sabatino Silvana
Published: July 2024

The interest towards the development of regional high-resolution retrospective datasets, allowing an enhanced representation of past meteorological states, has been progressively increasing. Convection-permitting (CP) datasets (i.e., hindcasts, based on historical model integrations, or reanalyses, including the additional assimilation of historical observational data) have demonstrated to improve the representation of precipitation compared to convection-parameterized counterparts. The benefits involve the spatial structures of rainfall fields, the timing and peak of the diurnal cycle of summer precipitation, and the frequency of wet days/hours. This is of particular relevance for enhancing the characterization of severe precipitation events with the aim to prevent and minimize their impacts on terrestrial ecosystems, and on human and animal life. However, the simulation of convective-related phenomena is highly model-dependent, implying the inability to sample the full range of natural variability with single-model experiments. This is exacerbated for km-scale simulations owing to the intrinsic chaotic behavior underlying convective processes.Recently, the development of Multi-Model Ensembles (MMEs) of CP regional climate models over Europe has demonstrated to efficiently tackle this issue and reduce the simulation error associated with single model outputs. This approach could benefit also retrospective estimates in order to retrieve a complete, homogeneous, and optimized assessment of past atmospheric states. In case of precipitation, this could be valuable also for potential downstream modeling applications such as forcing hydrological forecasting systems to obtain improved historical series of high-impact flood events.This work presents the first MME of retrospective CP datasets over Italy based on four reanalyses/hindcasts recently produced, with the aim to assess the added value of their joint employment. The datasets are obtained by dynamically downscaling the global reanalysis ERA5 using different numerical models: MERIDA-HRES (based on WRF-ARW), the hindcast based on the model MOLOCH, and SPHERA and VHR_REA-IT (both based on COSMO). The reference dataset for comparison is GRIPHO, the first Italian pluviometer-based hourly analysis. The investigation over a decade (2007-2016) includes various aspects such as the annual and seasonal dependence of daily and hourly mean rainfall intensity and frequency, heavy precipitation occurrences, and their summer diurnal cycles. The results indicate that a superior dataset performing always best is not detected, while large inter-model variability characterizes summer precipitation. The ensemble aggregation systematically improves rainfall estimates over single datasets, resulting in more adherent spatial fields and lower root-mean-squared errors and relative biases when compared to the observations, at the expense of reduced spatial variability of the distributions.
Impact of cool roofs on thermal comfort and air quality at street level during a heat wave episode in Madrid
Authors: Rivas Ramos Esther , Martilli Alberto , Santiago del Río José Luis , Meier Fred , Sánchez Sánchez Beatriz , Martín Llorente Fernando
Published: July 2024

Cool roofs have higher solar reflectance and thermal emissivity than conventional roofs, decreasing the temperature of buildings during sunlight hours. This benefits the buildings (reducing the cooling loads) and the environment (improving the outdoor thermal comfort) during summer. However, cool roofs' impact on outdoor air quality is not well known.Computational Fluid Dynamic (CFD) models allow the evaluation of environmental improvement measures at very high spatial resolution. Nevertheless, microscale studies combining outdoor thermal comfort and air quality at district or city scales have been scarce in the literature due to the computational cost involved.The objective of this work is to estimate the impact of cool roofs on outdoor thermal comfort and air quality at the district scale. For this purpose, a CFD model is used considering:atmospheric flows through a URANS (Unsteady Reynolds-Averaged Navier-Stokes) approach
traffic-related NOX dispersion as a passive scalar
thermal loads using a complete radiation model (solar radiation, radiation from the environment, transmission through non-opaque surfaces, and emission from non-transparent surfaces)
thermal and optical properties of the building envelope
energy storage in walls, floors and roofs (in glazing is negligible) through a non-steady state conjugate heat transfer model between the outdoor and indoor
Firstly, some scenarios of the COSMO experiment (Kawai et al., 2007) are simulated to evaluate the model performance. Finally, the cool roof impact is estimated during 24 hours of a heat wave episode in a district of Madrid (Spain), characterized by a regular morphology (aligned blocks of H=15 m and H/W=1).Results show that cool roofs modify the urban meteorology (mean radiant temperature, air temperature, wind speed and turbulent kinetic energy), decreasing the Universal Thermal Climate Index, UTCI, at pedestrian height, especially upstream and during hours of higher irradiance. However, depending on the wind speed, cool roofs can generate thermal inversions at building height affecting the pollutant dispersion within the streets. This fact increases the pollutant concentration at pedestrian height.
The role of gap-filling observational data in air quality data-fusion methods: a case study with CALIOPE
Authors: Barrantes Ada , Carnerero Cristina , Mateu Armengol Jan
Published: July 2024

Reliable air quality data are vital for informed decision-making, enabling evidence-based mitigation strategies to improve public health and sustainability. Although monitoring stations are essential for assessing air quality, they have limited spatial representativeness, leaving large extensions of areas without appropriate observational data. On the other hand, numerical air quality systems provide full spatial coverage. Nevertheless, modeled data are affected by persistent uncertainties, mainly due to emission inventories inaccuracies and the complexity of atmospheric processes involved in pollution transport. Data-fusion methods offer bias-corrected air quality maps with full spatial coverage. There is, however, a strong dependence on observational data availability to ensure reliable results of data-fusion methods. In this study, we quantify the impact of imputing missing observational data in data-fusion methods. We focus on PM2.5 for the region of Catalonia (Northeastern Spain) during 2019, for which data availability is strongly limited. We first present straightforward gap-filling methodologies, such as linear interpolation and persistence (repetition of the previous available value). We then compare these techniques with a state-of-the-art artificial intelligence gap-filling method based on the Gradient Boosting Machine algorithm trained with several years of data (2019-2022). To assess gap-filling methodologies, we generate random gaps of varying characteristics identifying the optimal technique for each gap size and frequency. Finally, we study how these methods affect the data-fusion process applied to the mesoscale air quality model CALIOPE. The output of this system has a horizontal spatial resolution of 1 km x 1 km on a daily scale. The data-fusion method uses universal kriging, a geostatistical technique based on a regression model and the spatial correlation between the model and observational data. Data-fusion results show significant improvement when using gap-filling observational data. Notably, the method’s effectiveness depends on observation availability, performing better with GBM-filled data.
The Distribution and Evolution of Multi-scale Moist energy within the Typhoon "Meranti"
Authors: Huang Hong , Wang Ju , Wang Tianju , Wang Zhen , Jiang Chao , Fan Yin , Wang Xuezhong
Published: July 2024

The release of condensation latent heat associated with water phase changes is the most important non-adiabatic energy source in tropical cyclones (TCs) besides sea surface temperature, and will affect the energy conversion process inside TCs. Based on the derived moist atmospheric energy tendency equation in cylindrical coordinates,  the numerical simulation results were used in this paper to analyze the distribution and evolution characteristics of the multi-scale energy of Typhoon "Meranti".The results showed that the high-value center of symmetric kinetic energy was always located at the Radius of maximum wind(RMW), distributed in a columnar shape and centrifugally inclined, whereas the high-value areas of asymmetric kinetic energy at the vortex scale and sub-vortex scale were mainly located in the strong updraft area within the eyewall and the low-level inflow area. At the strongest stage of the typhoon "Meranti", there also existed large kinetic energy in the middle-level inflow layer and the high-level outflow layer away from the vortex center. The high values of symmetric moist available potential energy were mainly distributed in the warm core and wet core within the eyewall, and their vertical stretching increased with the enhancement of the warm core structure and low-level moisture transport.The moist available potential energy of symmetric vortices and asymmetric perturbations at each scale was generated by the non-adiabatic heating process at the corresponding scale, and was further converted into kinetic energy at the corresponding scale through vertical heat transfer.The research in this paper will help to analyze how the multi- water phase affect the energy and intensity of tropical cyclones from the energy perspective.
Irrigation impact on boundary layer and precipitation in WRF model simulations (LIAISE-2021)
Authors: Udina Mireia , Peinó Eric , Polls Francesc , Mercader Jordi , Guerrero Iciar , Valmassoi Arianna , Paci Alexandre , Bech Joan
Published: July 2024

The Land surface Interactions with the Atmosphere over the Iberian Semi-arid Environment (LIAISE) campaign examined the impact of anthropization on the water cycle in terms of land-atmosphere-hydrology interactions. The objective of this study is to assess the effects of irrigation on the atmosphere and on precipitation in WRF model simulations during the LIAISE Special Observation Period in July 2021 (LIAISE-2021 SOP). Comparisons between simulations and observations show better verification scores for air temperature, humidity and wind speed and direction when the model included the irrigation parameterization, improving the model warm and dry bias at 2 m over irrigated areas. Other changes found are the weakening of the sea breeze circulation and a more realistic surface energy partitioning representation, where the latent heat flux is prevailing over the sensible heat flux. The boundary layer height is lowered in the vicinity of irrigated areas, causing a decrease in the lifting condensation level and the level of free convection, which induce increases in convective available potential energy (CAPE) and convective inhibition (CIN).Precipitation differences between simulations become relevant for smaller areas, close to the irrigated land. When convection is parameterized, simulations including irrigation tend to produce a decrease in rainfall (negative feedback) while convection-permitting simulations produce an increase (positive feedback), although the latter underestimates substantially the observed precipitation field. In addition, irrigation activation decreases the areas exceeding moderate hourly precipitation intensities in all simulations. There is a local impact of irrigated land on model-resolved precipitation accumulations and intensities, although including the irrigation parameterization did not improve the representation of the observed precipitation field, as probably the precipitation systems during LIAISE-2021 SOP were mostly driven by larger scale perturbations or mesoscale systems, more than by local processes. Results reported here not only contribute to enhance our understanding of irrigation effects upon precipitation but also demonstrate the need to include irrigation parameterizations in numerical forecasts to overcome the biases found.This research has been funded by projects WISE-PreP (RTI2018-098693-B-C32), ARTEMIS (PID2021-124253OB-I00) and the Institute for Water Research (IdRA) of the University of Barcelona.
Marine air particle trajectories into the Asia-Africa monsoonal system
Authors: Dalu Giovanni , Baldi Marina
Published: July 2024

Monsoons are composed by a lower cyclone coupled to an upper anticyclone by an intense mid-tropospheric updraft, surrounded by a larger region of weaker downdraft. In this work, the dynamics of the South Asia monsoon (SAM) and of the West African monsoon (WAM) are analyzed using Gill's tropospheric model (GTM). Since their dynamics are fueled by the latent heat released by the marine air masses, which cyclonically spiral inland from nearby tropical oceans in the warm season, the focus of this work is on the impact of the presence and in the absence of an Ekman frictional layer (EFL), of the Somali mountains and of the desert heal lows (DHLs) on these trajectories.Results show that the addition of a lower Ekman frictional layer (EFL) to the GTM brakes the antisymmetry between the upper and the lower layer, making the lower cyclone deeper and more compact than the upper anticyclone; hence, the marine air particles reach the monsoons with tighter spirals. In addition, the presence of Ekman pumping weakens the low level subsidence about the monsoons, widening the monsoonal updraft region. In the absence of the East Africa mountains, particles from the Atlantic ocean can reach SAM, and particles from the Indian ocean can reach WAM. The Somali mountains separate WAM's catch basin of marine air from SAM's catch basin in the EFL; thus, the Atlantic and the Mediterranean air particles reach WAM, whilst the Indian ocean air particles reach SAM; while, the two monsoons are still coupled in the upper levels.The Atlantic air particles sink over the Azores, the Mediterranean air particle sink over the Eastern Atlantic. The particles originating from the Indian ocean sink over the Arabian and Persian deserts. The Saharan-Arabian desert heat lows (DHL) deform the air particle trajectories and the intertropical front (ITF) over Africa; the Western Sahara heat low displaces the ITF northwards; whilst, the eastern Saharan-Arabian heat low displaces it southwards.
Analysis of flood compound events in the Spanish Peninsular Mediterranean coast
Authors: Llasat-Botija Montserrat , Llasat Maria Carmen , Marcos-Matamoros Raül , Aguilera Vidal Maria , Castán Salvador , Jimenez José A.
Published: July 2024

Understanding the compounding threats of floods and sea storms in the Mediterranean is crucial in the context of climate change. For this reason, a holistic analysis of their interaction and its implications for coastal resilience is needed. According to Zscheischler et al (2020), compound events are defined as the combination of multiple drivers and/or hazards that contribute to societal or environmental risk and that can be responsible for many of the most severe weather and climate related impacts. To manage these situations more effectively, it is essential to identify and prioritize elements that can be controlled, such as infrastructure planning and early warning systems, while also improving the resilience of those elements that cannot be controlled, such as natural ecosystems. Through the integration of scientific research, stakeholder collaboration, and management and policy interventions, coastal communities can enhance their capacity to mitigate risks and adapt to the impacts of compound hydrometeorological events.In the framework of the C3Riskmed project, multidisciplinary research has been conducted to analyze compound events on the Spanish Peninsular Mediterranean coast. This communication introduces the compound events of floods, wind and sea storms recorded in the coastal municipalities covering a total 1,609 km of coastline between the Spanish-French border and Gibraltar. Their economic impact is analysed using Insurance Compensation Consortium (CCS) data and this is compared with the impact of non-compound events. A remarkability criterion, which integrates physical and socio-economic variables, and used to select the most important events, is introduced. Finally, a paradigmatic compound event such as the January 2020 storm "Gloria" is presented. Through detailed analysis of meteorological and impact data, key aspects of the dynamics and consequences of this compound event are shown. The lessons learned from Storm Gloria can be considered for the improvement of early warning systems, infrastructure planning, and community preparedness to mitigate the impacts of future extreme weather events.This research has been done in the framework of the C3Riskmed project, Grant PID2020-113638RB-C22 funded by MCIN/AEI/10.13039/501100011033.References:Zscheischler, J. et al., 2020, A typology of compound weather and climate events. Nature Reviews.
Quantitative assessment of Methane concentration using WRF-GHG model over Middle East and Iran 
Authors: Karbasi Samira , Abdi Amir Hossein , Malakooti Hossein
Published: July 2024

Methane (CH4) is the second most important greenhouse gas after carbon dioxide (CO2). It helps to increase the mixing ratio of these gases and accounts for 18% of the atmospheric greenhouse effect. Based on all measurements (both ground and satellite) the concentration of greenhouse gases such as CO2 and CH4 are increasing in the world. For an accurate evaluation of the significance of their dispersion, quantitative monitoring of greenhouse gas emissions in areas with urban and human sources is essential.The WRF-GHG model is used in this work to better understand the contributions of different methane sources in Iran.  In the simulation we have considered the Middle East as the first domain with a resolution of 30 km and Iran as the second domain with a resolution of 10 km.The main sources of methane are taken into consideration: burning biomass, anthropogenic emissions, and emissions from wetlands. The warm (Aug) and cold (Feb) seasons in 2010 are contrasted in order to examine the impacts of the seasonal variations of natural sources and meteorological conditions.Surface observations from synoptic stations are used to evaluate the simulated meteorological fields, showing that the model can accurately represent variations in wind, relative humidity, and surface temperature over time.Compared to GOSAT data, the average bias error for methane concentration simulations in the warm and cold seasons, according to the results, is -24.99 and 7.50 ppb, respectively., the predicted methane concentration is often underestimated in August and overestimated in February. The WRF-GHG model performs statistically better in the cold season than in the summer season.The monthly average of methane fluxes in Iran, shows the maximum methane occurs throughout the late night to early in the morning hours in February and August. Iran contributes significantly to the Middle East's methane emissions production. According to an analysis of the spatial distribution of emission sources for the months of February and August, the presence of oil refineries and rice-growing wetlands in the north and west of Iran contributes significantly to the concentration of gasses in the center of Iran.
Socioeconomic and Environmental Determinants of Heat Vulnerability in Prague, Czechia
Authors: Dogan Tugba , Urban Aleš
Published: July 2024

Climate change is highly likely to increase both the frequency and severity of heatwaves, posing a significant challenge to global health. Heatwaves are associated with an increased risk of human mortality, particularly during extreme events that exceed local acclimatization thresholds. However, the impact of heatwaves is not uniformly distributed across all populations. Certain demographic groups, especially the elderly and children, are at a higher risk due to physiological and socioeconomic factors. Moreover, the literature indicates that environmental and socioeconomic factors, such as access to green spaces and income level, also play a crucial role in determining vulnerability to heatwaves. These factors are known to affect thermal comfort, thereby influencing the ability of individuals to cope with extreme heat. Therefore, to mitigate heat-related health outcomes, it is necessary to evaluate the heat vulnerability of districts in Prague and identify where mitigation measures and interventions are most urgently required. We use geographically weighted principal components analysis (GWPCA) to determine the role of meteorological (mean summer air temperature, mean number of heatwave days), environmental (proportion and type of greenery, urban coverage ratio), and socioeconomic factors (demographic structure, unemployment rate, mean income) on heat vulnerability (daily heat-related mortality and ambulance call outs spanning the period 2001–2023) in 22 districts of Prague, Czechia. To investigate the influence of air temperature, we utilize a novel ALADIN/CLIMATE-CZ reanalysis with high spatial resolution (2.3 x 2.3 km). Finally, a multivariate meta-regression model is used to determine the risk of mortality and ambulance call-outs associated with high temperature in each district, taking into account the modifying effect of the district characteristics (i.e., principal components from the GWPCA).Our findings will provide new insights to the heat vulnerability assessment in Prague and will enable us to determine the most at-risk areas of Prague regarding its population structure and environmental conditions. The study contributes to a comprehensive understanding of the underlying drivers of heat vulnerability in Prague and informs targeted interventions to mitigate the impacts of extreme heat on vulnerable populations.
Unraveling the dynamics behind future changes in El Niño teleconnections impacting Antarctica
Authors: Jin Emilia Kyung , Lee Hyun-Ju
Published: July 2024

Understanding the evolving dynamics of El Niño teleconnections is crucial due to its profound global impact. While current climate models consistently predict a shift in these teleconnections towards the east and poles, the mechanisms behind this shift remain unclear. Surprisingly, previous studies have overlooked the role of barotropic Rossby waves, which are fundamental to teleconnections. This investigation aims to fill this gap by examining the changes in these waves through spectral analysis, measuring circulation anomaly distances, and exploring their dispersion relationship.The results indicate that as the climate warms, the wavelength of teleconnection-forming waves is expected to increase, with a greater prevalence of zonal-wavenumber-2 waves. This shift in wavelength suggests alterations in the propagation characteristics of Rossby waves, potentially influencing the spatial distribution and intensity of teleconnection patterns associated with El Niño events. Additionally, changes in the mean state, such as the strengthening of westerlies in high-emission scenarios, lead to shifts in wave frequencies. In particular, the Southern Hemisphere exhibits a more pronounced response due to the smaller inter-model spread of the mean state compared to the Northern Hemisphere.Consequently, El Niño's influence is forecasted to extend towards higher latitudes in both hemispheres, impacting regions that may not have experienced significant El Niño-related effects in the past. In the Southern Hemisphere, where the impacts of climate change are already evident, this shift in El Niño teleconnections could have far-reaching consequences. For instance, warming oceans near West Antarctica and increased moisture transport towards Antarctica, driven by El Niño events, are projected to shift eastward under high-emission scenarios. These changes could have implications for regional climate variability, sea ice dynamics, and ecosystems in the Antarctic region, highlighting the interconnected nature of global climate systems.These findings shed light on the complex interplay between climate change and El Niño teleconnections, offering valuable insights into future climate patterns. Understanding these dynamics is crucial for policymakers, stakeholders, and communities to better prepare for and adapt to the impacts of climate change, particularly in regions like Antarctica where the effects can be significant and wide-ranging. By elucidating the mechanisms driving changes in El Niño teleconnections, this research contributes to our broader understanding of how global climate patterns may evolve in a warming world, informing strategies for mitigation and adaptation efforts.
Assessing the risk of unprecedented flooding with event-based storylines 
Authors: Bianco Elena , Ruggieri Paolo
Published: July 2024

Hydrometeorological extremes cause widespread socio-economic disruption and damage worldwide. Assessing the probability and impact of such events is critical for improving risk preparedness and emergency management. Ensemble climate simulations allow to extend the sample size of plausible weather extremes and estimate the probability of disruptive events that may be unprecedented in the historical record. This approach, commonly known as UNSEEN (UNprecedented Simulated Extremes using ENsembles) has demonstrated a comparative advantage over traditional approaches that rely solely on observations for risk assessment. In this work, we apply the UNSEEN methodology to estimate the probability of river flooding in European medium-size catchments, using ensemble simulations of seasonal forecasts provided by the European Flood Awareness System (EFAS). Simulated values of river streamflow during rare events can be used as input for risk models to yield estimates of the socio-economic loss resulting from unprecedented floods. This framework promotes the co-design of flood risk storylines that can assist stakeholders and policy-planners in the development of mitigation and adaptation strategies to manage disasters. 
The role of wave coupling from the mesoscale to the sub mesoscale in a sting jet windstorm
Authors: Brumer Sophia E. , Pantillon Florian , Pianezze Joris
Published: July 2024

Windstorms associated with extratropical cyclones are destructive natural hazards. Processes governing the formation of near-surface winds are crucial for their societal impact but are not well understood and too small scale to be explicitly represented in numerical weather prediction models. The explosive cyclone Alex made landfall in southern Britany in October 2020 causing extensive wind damage in Belle-Ile and further inland. We investigate the mechanisms allowing high momentum air to descend to the surface and the influence of wave driven air-sea interactions from the mesoscale O(100km) to the sub-mesoscale O(100m). For this purpose we run a series of stand-alone atmospheric simulations and coupled wave-atmosphere simulations using the Meso-NH and WAVEWATCH-III models incrementally decreasing the horizontal grid spacing by two from 1.6 km to 100 m. The high resolution allows explicit representation of shallow convection and of the most energetic turbulent eddies in the atmospheric boundary layer (Large Eddy Simulations).  Online trajectory calculations allow for a Lagrangian air mass tracking in Meso-NH. In the 1.6 km simulations, these reveal the presence of 3 distinct airstreams responsible for the strongest winds. The evolution of state parameters along these trajectories helps match the airstreams to the classical conceptual model for extra tropical cyclones. Evidence hints to the presence of a rare sting jet associated with Alex’s extreme winds, along with the more common cold conveyor belt and dry intrusion. In the Large Eddy Simulations, the same Lagrangian approach shows how the high momentum air in the airstreams is brought down by coherent boundary-layer structures. The vertical momentum transport is further controlled by wave coupling, which influences the stability of the boundary layer and the surface drag. The impact of wave coupling and resolution on extreme winds is discussed for the different mesoscale airstreams.  The results show that the representation of both sub-mesoscale processes and air-sea interactions constrains the formation of near-surface winds under storm conditions.
Gap-filling and quality control in biometeorological modelling: overview of best practices
Authors: Petrić Mina , Lalić Branislava , Marsboom Cedric
Published: July 2024

Biometeorological models simulating the dynamics and spread of insect species coupled with spatial decision support systems (SDSS) are becoming an important component of human and animal disease risk assessment and mitigation planning. To accurately simulate the dynamics of insect species and the anticipated spread of vector-borne disease (VBD), complex mathematical models are being developed which require precise and continuous micrometeorological forcing representative of the vector habitat, provided in near-real time. For this reason, reanalysis product such as ERA5-Land are not always suitable, and environmental wireless sensor networks (WSN) are often employed.Regardless of the purpose of the observations, the quality of the recorded data is determined by the accuracy and completeness of the measurements, the spatial and temporal representativeness of the records, and the representativeness with respect to the goal of the observation. With the increasing use of autonomous environmental sensors employing different data-storage, transmission, and communication protocols, particularly in low-power/low-cost applications, the risk of data loss and measurement error grows, emphasising the importance of properly implementing quality planning and assurance.In this paper we provide an outline of quality control (QC) and gap-filling methods for dealing with spatial and temporal gaps in meteorological measurements for different classes of biometeorological applications with a focus on: (i) mathematical vector population dynamics models; (ii) machine learning vector distribution models; and (iii) mechanistic vector distribution models.Establishing a comprehensive quality assurance (QA) and gap-filling protocol for micrometeorological data in the field of biometeorological modelling will improve the understanding of the types of errors and limitations expected during analysis, as well as provide an overview of best-practices for a broader interdisciplinary audience.
Low-frequency variability of Arctic sea ice in CMIP6 historical simulations: model uncertainty and links to northern hemispheric ocean variability 
Authors: Bianco Elena , Blanchard-Wrigglesworth Edward , Materia Stefano , Ruggieri Paolo , Iovino Dorotea , Masina Simona
Published: July 2024

The variability of Arctic sea ice extent (SIE) on interannual and multi-decadal timescales is examined in 29 models with historical forcing participating in phase 6 of the Coupled Model Intercomparison Project (CMIP6) and in 20th-century sea ice reconstructions. Results show that during a period of low external forcing (1850-1919), interannual SIE variability (IVSIE) is generally well-represented in CMIP6, whereas multi-decadal sea ice variability (MVSIE) exhibits substantial inter-model spread. Specifically, models disagree on the contribution of sea ice variability in the sub-polar North Atlantic to pan-Arctic MVSIE variability. We find that this is associated with differences in models’ sensitivity to northern hemispheric sea surface temperatures. Additionally, we show that while CMIP6 models are generally capable of capturing Arctic SIE trends from the mid-20th century to present day, they tend to underestimate the sea ice decline during the Early Twentieth-Century Warming (ETCW; 1915-1945) relative to observations. These results suggest that model uncertainty on low-frequency timescales originates mainly from the representation of processes coupling Arctic sea ice and northern hemispheric climate variability. Thus, an improved characterization of these processes and their interactions should be an element of priority for the development of next-generation climate models.  