bluuebunny/crossref_metadata_2025 huggingface dataset:
Gridding of Hourly Surface Air Temperature – Application of a Spatio-Temporal Statistical Model in Complex Terrain
Authors: Frey Louis , Frei Christoph
Published: July 2024

At present, most operational climate grid datasets are available with a temporal resolution of one day. This can be limiting in applications that rely on an explicit representation of the diurnal cycle, such as when modelling temperature-dependent environmental processes (e.g. transpiration). In this study we propose a methodology for the construction of hourly datasets of surface air temperature and present results from its application over Switzerland. The method uses a spatio-temporal (ST) statistical model. Unlike classical interpolation, the ST approach exploits data from a period of times simultaneously, and therefore allows to formally represent the connection of spatial and temporal variations. This is particularly desirable in complex terrain, where the diurnal cycle of temperature has marked topographic imprints. As ST model we use a dynamic linear model (DLM), which is a conceptual extension of kriging with external drift (KED), and offers high flexibility to be configured to the specifics of a region. In our application, for example, the DLM incorporates model components for cold-air pooling, basin-scale inversions, and the lake- and valley effects.We present results from an application of the method to several multi-day episodes with specific weather conditions and from a continuous application over several months. The results illustrate that the method is capable to represent complex spatio-temporal variations, including the build-up and decay of an inversion and distinct diurnal variations in valleys, the flatland and at lake shores. Comparison of the spatio-temporal DLM to a spatial-only KED shows added value of DLM in terms of enhanced temporal continuity of trend coefficients. However, cross-validation statistics are not significantly different between the two approaches. Further experiments suggest that this is due to the dense station coverage in Switzerland. In regions / times where data is sparser, or when model complexity is substantially increased, spatio-temporal modelling is expected to provide a measurable improvement over spatial-only modelling. Our presentation will illustrate the results of our application in films of the temperature evolution. We will also introduce the extensions, both methodological and technical, implemented for the continuous multi-month application, and quantify the predictive performance of the method in this setting.Our study provides valuable insight on extending a familiar interpolation concept (KED) into a spatio-temporal model, suitable to produce sub-daily temperature grid datasets.
Impacts of Combining Various Types of Initial Perturbations on Convection-Permitting Ensemble Forecasting over South China during the Rainy Season
Authors: Zhang Xubin
Published: July 2024

Precipitation forecasting for heavy-rainfall events over South China in the rainy season is still challenging due to large uncertainties. Convection-permitting ensemble forecasting is expected to address such uncertainties to improve forecasts of heavy rainfall. However, it is not yet clear how to optimally design convection-permitting ensembles by implementing perturbations in initial conditions (ICs). This study investigates the impacts of various IC perturbation methods on convection-permitting ensemble forecasting over South China in the rainy season. Specifically, downscaling, ensemble of data assimilation, time-lagging, and their combination were used to generate IC perturbations for 12-h convection-permitting ensemble forecasting for heavy-rainfall events over South China during the rainy season in 2013–2020. These events were classified as weak- and strong-forcing cases based on synoptic-scale forcing during the presummer rainy season and as landfalling tropical cyclone (TC) cases. Various IC perturbation methods represented different-source IC uncertainties and thus differed in multiscale characteristics of perturbations in vertical structures, horizontal distributions, and time evolution. Combination of various IC perturbation methods evidently increased perturbations or spreads of precipitation in both magnitude and location and thus improved the forecast-error estimation. Such an improvement was most and least evident for TC cases during the early and late forecasts, respectively, and was more evident for strong- than weak-forcing cases beyond 6 h. The growing spatial similarity among various IC perturbations caused the damping of the added value of combining various IC perturbations over each individual type of perturbations. The added value was attributable much more to the increasing magnitude of initial perturbations than to the increasing sources of IC uncertainties. However, the impacts of combining various sources of IC uncertainties on the added value was still nonnegligible although case-dependent for precipitation perturbations in terms of both magnitude and location. Combination of various IC perturbation methods generally improved both the ensemble-mean and probabilistic forecasts with case-dependent improvements. For heavy rainfall forecasting, 1–6-h improvements were most prominent for TC cases in terms of discrimination and accuracy, while 7–12-h improvements were least prominent for weak-forcing cases in terms of reliability and accuracy. In particular, the improvements in predicting weak-forcing cases increased with spatial errors. In contrast, for strong-forcing cases, the improvements were least and most prominent before and beyond 6 h, respectively.
Estimation of particle concentrations from tyre and brake wear in an urban environment
Authors: Samland Mailin , Badeke Ronny , Grawe David , Matthias Volker
Published: July 2024

Air pollution is threatening human health worldwide, especially in urban areas. Legislative actions have successfully decreased pollutant concentrations in European countries. However, while exhaust emissions from road traffic have decreased over the last decades, non-exhaust emissions remain and tend to increase. In this study, tyre and brake wear emissions are quantified applying a bottom-up model for the city of Hamburg in 2018. Their dispersion and contribution to total particulate matter (PM) concentrations are investigated with the urban scale chemistry transport model EPISODE-CityChem. For this purpose, EPISODE-CityChem 1.8 has been extended to include six new particle components. These are tyre and brake wear in the three size classes: PM2.5, PM2.5-10 and PM10+, airborne particles with a diameter of over 10 µm. The emission factors for PM10 for tyre and brake wear from the National Atmospheric Emission Inventory of the UK are used as a starting point to derive the emission factors for the new particle classes. These are combined with the mass size distribution of the total suspended particles from EMEP.PM concentrations at traffic stations show a higher monthly mean contribution of tyre and brake wear to the total PM2.5 and PM10 than at urban background stations. The contribution of tyre and brake wear to the total PM2.5 concentrations varies throughout the months between 9% and 16% at traffic stations and between 2% and 6% at urban background stations.The particle concentrations from tyre and brake wear vary locally and seasonally, which could be a difficulty in adhering to the recommended guideline values for particle concentrations, especially since the inner city of Hamburg experiences considerable PM concentrations caused by tyre and brake wear emissions.The results of this study can be transferred to other large European cities with high traffic volumes and can help to understand the problem's scope, as measurements rarely differentiate between particles caused by exhaust vs. non-exhaust emissions.
Forecasting across timescales by crossing “bridges ofopportunity”
Authors: Muñoz Ángel G.
Published: July 2024

Stakeholders in all socio-economic sectors require reliable forecasts at multiple timescales as part of their decision-making processes. Although basing decisions mostly on a particular timescale (e.g., weather, subseasonal, seasonal) is the present status quo, this approach tends to lead to missing opportunities for more comprehensive risk-management systems (Goddard et al. 2014).Seamless operational climate prediction --ranging from hours to multiple decades-- is a sound theoretical possibility that in practice has not been fully realised  yet. While today a variety of forecasts are produced targeting distinct timescales in a routine way, these products are generally presented to the users in different websites and bulletins, often without an assessment of how consistent the predictions are across timescales.Because different models and strategies are used at different timescales by both national and international decadal, seasonal and subseasonal forecasting centers (e.g. Kirtman et al. 2014, Kirtman et al. 2017, Vitart et al. 2017,Mahmood et al., 2021), and skill is different at those timescales, it is key to guarantee that a physically consistent "bridging" between the forecasts exists, and that the cross-timescale predictions are overall skilful and actionable, so decision makers can conduct their work. As recent research suggests, forecasts at different timescales can be merged to mimic the idea of seamless forecasts (e.g. Befort et al., 2022). Here, a method for merging forecasts across timescales (Muñoz et al., 2023) is discussed and illustrated. The approach is based on the identification of "bridges of opportunity" via a cross-wavelet spectral analysis of causal information flows between prediction systems. The new method enables to formally calculate the timing and length of the windows of opportunity, and the optimal temporal aggregation to conduct the bridging of the forecast systems. The approach is illustrated for operational (a) subseasonal-to-seasonal, and (b) seasonal-to-interannual forecast systems.
Variability of atmospheric circulation over Central Europe in the light of manual classifications
Authors: Wypych Agnieszka , Ustrnul Zbigniew , Řehoř Jan , Mika János
Published: July 2024

Various classifications of circulation types have long been used to assess changes and variability of atmospheric circulation. As a result, several dozen different manual classifications were created, which were based on various methodological assumptions. Although they are now increasingly replaced by automatic classifications (often having the characteristics of objective classifications), the mentioned classifications are still used for many analyses. And sometimes, due to the subjectivity of the assessment of synoptic situations, they better explain specific weather conditions, including extreme ones. This also applies to Central Europe, where due to its location, weather and climate, it is characterized by great variability and transience. This was also one of the reasons why several classifications were created for this region.The work attempts to assess the variability of atmospheric circulation over Central Europe for a long period of 120 years (1901-2020) based on available manual classifications of circulation types.Five traditional classifications of Osuchowska-Klein, Niedźwiedź, CHMU, Pecely and the well-known Grosswetterlagen classification were considered here. Simultaneously the automatic classifications developed by Lityński, Ustrnul and Řehoř were also taken into account independently. A comparison of the occurrence of circulation types or groups of circulation types between the analyzed classifications was made.The results sometimes showed large discrepancies between individual types of circulation defined in the analyzed classifications. The analysis showed that their causes should be sought in the diversity of mesoscale circulation conditions in Central Europe, but also in a different methodological approach taken into account in individual classifications. Taking into account other synoptic materials used in the development of individual classifications may also have some impact on the results obtained.
Climate Change and Aviation: The Increasing Impact of Heatwaves on European Airports
Authors: Guerrini Federica , Trentini Laura , Venturini Marco , Dal Gesso Sara , Calmanti Sandro , Petitta Marcello
Published: July 2024

The aviation sector is experiencing the impacts of climate change on both in-flight and ground operations. One meaningful example is that of heatwaves, as temperature extremes reduce aircraft lift and takeoff performance, requiring lighter loads—including fewer passengers and less cargo— and increasing noise pollution due to shallower climb angles. Heatwaves also deteriorate runway asphalt and compromise comfort inside airport terminals.In this work, we analyze the occurrence of summer heatwaves at 30 European airports by comparing historical data from 1985-2014 with projections for 2035-2065, using data from 10 CMIP6 models across three Shared Socioeconomic Pathways (SSP126, SSP370, SSP585). We apply a dedicated bias correction and downscaling technique to model data to improve spatial resolution and address systematic distributional biases. Heatwaves are identified and measured using a Standardized Heatwave Indicator (SHI), enabling the assessment of any trends in the frequency, length, and intensity of this phenomenon at each airport.Our findings reveal a significant increase in the frequency, duration, and intensity of summer heatwaves across all the airports considered for the period 2035-2065 compared to historical data. Heatwaves are anticipated to become an annual phenomenon, with Southern European airports expected to face the most severe conditions. These airports could in fact experience an average of up to four heatwaves each summer, lasting up to 14 days and reaching intensities exceeding the 99.97th percentile of historical events.This work underscores the urgent need to identify and implement heatwave-aware adaptation measures and operational improvements in aviation, enhancing the climate resilience of this sector to ensure safety and efficiency in operations.
Aerosol optical depth retrieval using ground-based solar irradiance measurements and machine learning
Authors: Logothetis Stavros-Andreas , Salamalikis Vasileios , Kosmopoulos Georgios , Kazantzidis Andreas
Published: July 2024

Aerosol optical depth (AOD) is a key parameter for many scientific fields like air pollution and climate modelling. Ground-based AOD measurements outperform the other datasets in terms of data accuracy but still lack spatiotemporal resolution, while satellite/reanalysis encompasses the opposite behaviour. The overreaching goal of this study is to present a retrieval technique for cloud-free conditions that can be used to reproduce the AOD at the high temporal resolution of radiometric instruments. For the purposes of this analysis, reference measurements of aerosols (AERONET) and solar irradiance (BSRN) have been used. The retrieval technique uses various machine learning (ML) algorithms (e.g., gradient boosting machines, random forests, neural networks, etc.) by testing different components of ground-based solar radiation measurements in order to retrieve AOD at the 1-min temporal resolution of AERONET retrievals. In particular, each ML algorithm includes as an input parameter the optical air mass (m), water vapor (WV), and the direct or global clearness index. The WV is accessed through the Copernicus Atmosphere Monitoring Service Reanalysis, operated by ECMWF. For each ML algorithm, a randomized cross-validation searching method is applied to retrieve the optimal ML model architecture.The ML-derived AOD retrievals are compared against reference ground-based (AERONET), satellite (MODIS), and reanalysis (MERRA-2, CAMS) AOD retrievals at 26 AERONET-BSRN stations under different aerosol and climatic conditions from 2004 to 2019. We validated the MLA-based AODs against reference AERONET retrievals, finding root mean square error (RMSE) values ranging from 0.01 to 0.15, irrespective of the underlying climate and aerosol environments. Among the different ML algorithms, the artificial neural networks outperformed the other algorithms in terms of RMSE at 54% of the measurement sites. The overall performance of ML-based AODs against AERONET revealed a high coefficient of determination (R2 = 0.97), a mean absolute error of 0.01, and an RMSE of 0.02.
Clear Air And Wind Shear Modes On The Solid-state Mobile Radar
Authors: Najman Filip , Pitaš Ondřej , Staněk Miloslav , Bartel Jakub , Horák Jan
Published: July 2024

Our solid-state C-band mobile weather radar has undergone testing with two distinct scanning strategies. The initial setup prioritized windshear detection during episodes of deep moist convection proximate to the radar. Subsequently, a secondary scanning strategy was developed to detect wind shear in clear-air conditions, devoid of precipitation in the radar's vicinity. This strategy aimed to enhance radar sensitivity during these situations.In this poster presentation, we present a comparative analysis of these two scanning strategies, detailing their respective advantages and limitations. Through empirical data and case studies, we illustrate the efficiency of each strategy in detecting wind shear phenomena.The first strategy, focused on convective conditions, demonstrates robust windshear detection capabilities during active weather events. However, it is limited in its applicability during periods of clear-air turbulence. Conversely, the second strategy, tailored for clear-air scans, showcases wind shear features in non precipitation events. We provide examples of measured data from both scanning strategies to underscore their performance in detecting wind shear events.This poster offers insights into the operational utility of these scanning strategies for enhancing wind shear detection capabilities on solid-state mobile radar systems.
Rapid Update NWP Postprocessing with AI and Real-Time Measurements
Authors: Choma Matej , Murín Matej , Bartel Jakub , Troller Milly , Šimánek Petr , Najman Michal
Published: July 2024

Effective short-term weather forecasting is vital for informed decision-making during severe weather events to mitigate their impact. Traditional numerical weather prediction (NWP) models often face challenges in accurately predicting rapidly evolving weather phenomena. This study introduces an innovative approach that uses artificial intelligence (AI) to post-process NWP forecasts for the near future with respect to the latest available weather measurements. In the scope of this work, our solution leverages real-time synoptic scale meteorological station measurements, radar reflectivity data, and satellite imagery to post-process Global Forecast System (GFS) predictions for the Central Europe area. By fusing these diverse data sources, both the accuracy and resolution of the input GFS predictions are enhanced, offering an increase in prediction step resolution from 3 hours to 1 hour and an update of the forecasts with the most recent measurements every 30 minutes. Our solution internally uses a deep neural network trained to post-process GFS predictions to mimic ERA5 reanalysis as closely as possible. The predicted variables are total accumulated precipitation, temperature 2 meters above the ground, and wind gusts. However, in theory, the presented approach is not limited to the abovementioned set of input data or target variables. The model achieves up to 2.5 times lower mean absolute error compared to baseline forecasts, showcasing its effectiveness in capturing real-time weather dynamics. Moreover, the model exhibits the capability for rapid updates as new weather measurements become available, continuously refining predictions. This dynamic adaptability ensures that forecasts remain relevant and accurate, even in rapidly changing weather conditions. Alongside the quantitative evaluation against the ERA5 data, we will present a case study showcasing the usefulness of the post-processed forecasts in specific weather situations.
Cold City Microclimates: Enhancing Understanding and Communication
Authors: Miles Victoria , Ezau Igor , Pettersson Lasse
Published: July 2024

Rapid urbanization and climate change have significantly altered the microclimate of northern cities, leading to unfavorable anomalies that threaten cities' sustainability and residents' comfort. Although it is crucial to address these anomalies, the impact of large public buildings on the microclimate needs to be addressed, with assessments of the surrounding morphology taking precedence. This study aims to fill this gap by conducting land surface temperature (LST) and geographic information systems (GIS) analyses in four northern cities: Fairbanks, Tromsø, Bergen, and Nadym. We use remote sensing techniques and GIS tools to assess the fragmentation of urban morphology and distinguish between large and small buildings to evaluate their impact on LST variations. Understanding the microclimate impacts of large public buildings is necessary to optimize spatial configurations and find a balance between vegetation and buildings to mitigate the effects of local heat islands.The study highlights the importance of effective communication in translating scientific findings into practical ideas. To achieve this, we examine the potential of two innovative communication strategies, web-based geographic information system (GIS) applications and storytelling, to raise awareness of urban climate issues. WebGIS is a powerful tool that combines stories, visualizations, engagement tools, briefings, and surveys to present content in an easy-to-understand format for all users. This framework integrates impact analysis, risk assessment, and options exploration, allowing the assimilation of scenarios and the attribution of trends to changes in society and the land surface. WebGIS effectively communicates complex scientific results to a broad audience by combining model output with thematic city layers. Storytelling techniques complement WebGIS by providing narrative context that makes scientific data more accessible and relevant to non-experts. Framing scientific evidence into compelling narratives promotes understanding, empathy, and action by highlighting the human dimensions of urban problems. In conclusion, effectively disseminating science is crucial for solving urbanization's problems. By utilizing the power of WebGIS and storytelling, we can make complex scientific data accessible and meaningful, facilitating informed decision-making and driving positive change in urban environments.
Exploring within-subject associations with mixed effects models: How does weather affect physical activity?
Authors: Tunn Siiri , Keller Jan , Becker Nico , Knoll Nina , Rust Henning
Published: July 2024

Physical activity is one of the most important factors for human health, but around 28 % of the world's population does not meet the World Health Organisation's recommendations. Physical activity can be increased in everyday life, for example through exercise or walking, with weather being a facilitator or a barrier. Due to climate change, extreme weather events are becoming more frequent, highlighting the relevance of further research into the associations between weather and physical activity. The future increase in heat waves may become a problem, particularly for vulnerable groups. Current research shows that higher air temperatures, more daylight hours, no precipitation and low wind speeds are associated with more physical activity. The present study aims to investigate these associations at the within-subject level (i.e., differences within individuals over time) by further exploring individuals’ fitness level as a moderating factor.The Days in Motion study examined 676 participants (aged 18-80) from 338 heterosexual couples living in Berlin (Germany) at a baseline week and a 12-month follow-up week between 2013 and 2015. This secondary analysis used data from a sub-sample of 228 participants who did not receive an intervention, provided longitudinal data, and did not report to be ill, injured or on vacation during the assessment period. Minutes of moderate-to-vigorous physical activity were measured using accelerometers. Hourly physical activity data were linked to meteorological variables (air temperature, relative humidity, precipitation, wind speed, sunshine duration and cloud cover) from a central weather station in Berlin. Physical fitness level was assessed objectively by the 2-km indoor walking test. Mixed effects models were used for the analysis, with the following data structure: hours nested within days, weeks, and persons. Between-subject (i.e., differences between individuals) and within-subject associations can be analysed in mixed effects models.Significant between-subject associations occurred only for wind speed, with higher mean wind speed reducing physical activity. Positive significant within-subject associations with physical activity were found for air temperature and sunshine duration, whereas a significant negative association was found for precipitation and no association for cloud cover. However, the effects were moderated by the fitness index. In the future more data is needed during heat waves to be able to adequately analyse when physical activity might start to decrease again, or when physical activity in heat might have a detrimental effect on health.
European Collaborative Convection Forecast for Air Traffic Management - Experiences from Croatian MET Provider
Authors: Jurković Jadran , Šoljan Vinko
Published: July 2024

The Cross-Border Convective Forecast (CBCF) is a collaborative forecast that provides the Network Manager (NM) at EUROCONTROL and participating air navigation services a forecast of convective weather across European Airspace. Eurocontrol, as a primary user, uses this information to coordinate with impacted Air Traffic Control Centres (ACCs) across Europe and implement mitigation measures to minimise flight delays and improve aircraft safety. Currently, Eumetnet established CBCF as a module within the Aviation Support Program. The user-oriented forecast started in 2018 and is still developing. Since 2020, aviation weather forecasters of all services who participate simultaneously produce the forecast on a collaborative web tool. From 2022, 24 organisations are participating - from Portugal and Ireland up to Poland and Greece. The number of forecasters included in production of forecast during a year is more than 200, which is probably unique worldwide. To ease dissemination of the product, the lead forecaster coordinates the final product and issues and communicates with the user.CBCF is a probabilistic forecast of polygons in the domain in 3-hour periods for tomorrow and the current day.  Local meteorological service providers create polygons for their area of responsibility, harmonised across state borders. The risk matrix is a function of the likelihood and extent/severity of convective weather with additional CB-top height information. Looking at the European domain, Croatia and its neighbouring area have frequent thunderstorm activity. In a presentation, we will share experiences from a forecaster's perspective in Croatia. The product is easy to produce, fast, and flexible for editing, and the chat is easy to use. Despite the country's unique shape, collaboration and coordination with neighbours are mostly very positive.
Improving the Accuracy and Coverage of the NSRDB
Authors: Sengupta Manajit , Habte Aron , Benton Brandon , Xie Yu , Buster Grant , Foster Michael
Published: July 2024

The National Solar Radiation Database (NSRDB) provides global solar resource data at a high temporal and spatial resolution. This data is primarily used in solar energy modeling and is updated on a regular basis. The NSRDB uses a physical approach to satellite-based solar modeling. The underlying Physical Solar Model (PSM) computes cloud-properties using satellite remote sensing and subsequently solar radiation using radiative transfer models. The retrieved cloud properties include cloud-mask, cloud-type, cloud optical depth and cloud droplet size. The radiative transfer models require additional input parameters such as aerosol optical properties (AOD), preciptable water vapor, surface albedo, temperature and pressure to accurately model solar radiation. While cloud properties are obtained directly from the geostationary satellites other inputs are obtained from additional source such as the National Aeronautical and Space Administration (NASA) Modern Era Retrospective Analysis for Research and Applications version 2 (MERRA2), the Interactive Multisensor Snow and Ice Mapping System (IMS) model data from the U.S. National Ice Center and NASA’s polar orbiting satellites such as the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments on the Aqua and Terra Platform. In 2022 the NSRDB was updated to include improved surface albedo and gap-filling of cloud properties. Further, significant new updates have been included in 2023. This includes the use of the new FARMS DNI model under cloudy sky situations which results in a more accurate decomposition of the GHI in direct and diffuse. With the expansion of the NSRDB to provide data from the region covered by Meteosat, the coverage is fully global at this point.While standard data from the GOES continues to be served at an hourly 4km x 4km resolution, full resolution data has also been made available to the user. The user is provided significant flexibility for downloading data depending on the amount of data required. Data can be downloaded using either the web-interface, an Application Programming Interface or directly from the cloud using Amazon Web Services. Services such as spectral data use on-demand computation and delivery.Evaluation of the NSRDB was conducted for 18 stations and the Mean Bias Error (MBE), Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) were computed for both GHI and DNI. The evaluation was conducted for the 1998-2023 period. Generally, the MBE lies within plus or minus ±5% for GHI and ±7% for DNI. The RMSE is less than 25% for GHI and 35% for DNI. There are additional plans to include cloud fraction in cloudy sky situation to improve the accuracy of the NSRDB. This presentation will provide users with the latest information about the NSRDB as well as plans for future development and updates.
Neural network and geostatistical interpolation of observed near-surface atmospheric variables for snow cover numerical model simulation
Authors: Raparelli Edoardo , Tuccella Paolo
Published: July 2024

The snowpack plays a fundamental role in regulating the global climate thanks to its high albedo and thermal insulation properties, and for many regions of the world it also has very local and important impacts. Indeed, the snow is an important water reservoir, storing the water in solid state during cold months, and releasing it in liquid state during warmer months. But the snow is also the necessary condition for the development of rural places which base their economy on winter sports. However, a certain risk is always associated with snow when it deposits on the ground, since the snow can slide down, creating avalanches which may cause several damages to the local flora, fauna, buildings and infrastructures. Typically, the conditions that allow the occurrence of snow avalanches span from the point scale to the slope scale, and depend on the snowpack properties. Kilometer-resolution numerical models are not able to reproduce the slope-scale variability of the snowpack properties because of the complex interaction between the atmospheric flows and the topography at finer scale, nevertheless running atmospheric models at sub-kilometer resolution is highly computational demanding. To face this limitation, sub-kilometer gridded weather data can also be generated using interpolation and downscaling methods of in situ or remote observations, which require less computational resources compared to numerical weather models. Thus, we develop and validate several algorithms based on geostatistical an machine learning techniques to produce a sub-kilometer gridded dataset of the most important near-surface atmospheric variables. Finally, we use the generated dataset to force a snow cover model and we evaluate the model skills in reproducing the observed snowpack properties.
Design and acceleration of new procedures for the automated identification of atmospheric blocking situations
Authors: Hernandez Victor , Migallon Hector , G. Orza Jose A.
Published: July 2024

Atmospheric blocking is associated with high-impact weather events in mid and high latitudes. The specific location of the blocking system is of major importance for the type of meteorological extreme developed. The studies on blocking and its effects in the European continent are based on the differences in atmospheric mass between high, middle, and low latitudes and the strong reduction of the zonal winds. They are mainly based on the Tibaldi and Molteni (1990) blocking index or adapted versions of it, in which meridional gradients of geopotential height fields at 500 hPa integrated in longitudinal intervals are calculated.In this work, we consider an alternative to those approaches, focusing on identifying the atmospheric centers of action and their relative positions and intensities using the geopotential height at 500 hPa. After filtering the local minima and maxima using a gradient criterion, the remaining maxima and minima are grouped according to proximity and intensity. The identification of omega and dipole (Rex) blocks at any instant is made by a selection algorithm that uses the relative positions of the action centers as well as the geopotential contours, identifying the blocking type, shape, and area of influence.We have analyzed some of the most well-studied heat-wave events over Europe (August 2003, July 2019) and a blocking situation over the western Eurasian border which caused one of the strongest African dust outbreaks impacting Europe (March 2022).Algorithm performance is crucial in various computational applications, from scientific simulations to real-time data processing. In this work, we focus on improving the performance and speed of the algorithms through parallelization techniques aimed to produce a climatology of blocking situations in the Northern Hemisphere. Specifically, we are exploring the use of OpenMP and MPI libraries for C, widely used in high-performance computing (execution time and scalability efficiency.
Response of Global Sea Temperature to Solar Radio Flux and Its Influence on Precipitation
Authors: Yan Song , Zhou Yaqing
Published: July 2024

Using global ocean vertical temperature anomaly data, we identified that a significant response of the sea temperature anomaly (STA) to the solar radio flux (SRF) exists. We found that the STA exhibited a significant correlation with Asian summer and winter precipitation, among which the response from the Qinghai–Tibet Plateau (the QTP) was particularly noticeable. Based on NCEP/NCAR reanalysis data, the latent heat flux (LHF) anomaly, which plays a key role in winter precipitation in China, especially over the QTP, showed a significant response to the SRF in the Pacific. The results demonstrated the bottom-up mechanism of impact of solar activity (SA) on the plateau snow through sea–air interaction. Meanwhile, a top-down mechanism was also present. When the SRF was high, the stratospheric temperature in the low and mid-latitudes increased and the temperature gradient pointed to the pole to strengthen the westerly wind in the mid-latitudes. The EP flux showed that atmospheric long waves in the high altitudes propagated downward from the stratosphere to the troposphere. A westerly (easterly) wind anomaly occurred in the south (north) of the QTP at 500 hPa and the snowfall rate over the QTP tended to increase. When the SRF was low, the situation was the opposite, and the snowfall rate tended to decrease. The model results confirmed that when total solar irradiance (TSI) became stronger (weaker), both of the solar radiation fluxes at the top of the atmosphere and the surface temperature over the QTP increased (decreased), the vertical updraft intensified (weakened), and the snowfall rate tended to increase (decrease) accordingly. These conclusions are helpful to deepen the understanding of SA’s influence on the snow cover over the QTP. Keywords: solar activity; snow cover; Qinghai–Tibet Plateau; bottom-up mechanism; top-down mechanism; model simulation
A novel approach to generate very high resolution climate scenario for coastal areas
Authors: Giaiotti Dario , Minigher Alessandro , Gianesini Elena , Pittis Moira
Published: July 2024

Climate change is evident in many aspects, among them there is the response of the sea to the progressive increase of the atmospheric temperature, the changes in the inland precipitation regimes and the surface wind field pattern. In this frame, the Mediterranean area is a hot spot, where atmosphere and sea interact with a high degree of coupling, and there is deep interest for its evolution.Future climate scenarios for the Mediterranean are already available form a rich set of regional climate simulation, both for atmosphere and the sea. Anyway, stakeholders require information on future climate having a spatial resolution higher than that characterizing the regional scale. Furthermore, many local process of interaction between atmosphere, hydrosphere and sea, especially along the coasts and in shallow waters, have a poor representation in the available model outputs. Attempting to fill in the gap, downscaling is applied to regional climate and basin scale simulations.In this work, we present a methodology to generate ensembles of high resolution downscaled sea state to the local scale, for the three main climate scenarios, namely RCP2.6, RCP4.5 and RCP 8.5. Using boundary conditions from EURO-CORDEX, MED-CORDEX ensembles, hydrological projections derived from inland precipitation scenarios, runs of a shallow water numerical model (SHYFEM) have been conducted to explore time windows for each decade in the future up to the end of the XXI century.Starting from preliminary sensitive tests on the response of small spatial domains to the forcing and boundary conditions, we conclude that the effort to run very high resolution models, up to 10 m of horizontal resolution for a full and continuous secular time range, is not an efficient approach.Instead, the quick response of the coastal system to the boundary and the forcing allows to generate a large set of local area sensitivity studies, with computational resources and times that are worth to be considered useful besides efficient.This approach explores decadal time windows of many future scenarios with the spatial resolution required by a large spectrum of stakeholders who require details on future temperature, salinity and level along the coasts, in the lagoons and in the open sea facing the coastal areas.In addition to the methodology, we present a summary of the results of its application to the northern Adriatic Sea, including the northeastern most lagoon. Furthermore, we show how those results become inputs for case studies on climate change impacts at the local scale and they support the definition of risk minimization actions, as requested by climate adaptation plans.This work has been conducted with the contribution from the EU co-financing and the Interreg Euro-MED Programme, in the frame of MedSeaRise Project, exploiting the results of the Interreg IT-HR AdriaClim project.
Impact of Environmental Conditions on Long-Memory of Surface Air Temperature
Authors: Lalic Branislava , Firanj Sremac Ana
Published: July 2024

The memory of dynamic processes refers to the characteristic of dynamic systems where historical states influence their current and future behaviour. In modelling dynamic systems, it is crucial to ensure that the model design allows simulations that consider the system’s history and its impact on future dynamics beyond the integration interval. An important step toward this goal is identifying the characteristic time scale of the memory, i.e. memory longevity. In this research, we use the atmosphere as a dynamic system and air temperature at 2 m height as a variable that can carry memory information. To assess the impact of environmental conditions on memory characteristics, we selected meteorological data measured at climate stations and data from rural areas near Novi Sad and Vrsac (Serbia) measured at automatic weather stations (AWS) as part of the Forecasting and Warning Service for Plant Protection of Serbia (PIS) meteorological monitoring system.Following Caballero et al. (2002), we analyzed the power spectral density (PSD) and autocorrelation functions (AC) of three air temperature time series. According to results of previous studies, processes represented with PSD following power law function, S(w) at low frequencies (w), [S(w) = w-2d] are collectively referred to as “long memory” processes if 0
Analyzing climate variability of surface solar radiation parameters from the CM SAF SARAH-3 climate data record 
Authors: Pfeifroth Uwe , Trentmann Jörg
Published: July 2024

The EUMETSAT Satellite Application Facility on Climate Monitoring (CM SAF) generates and distributes high quality long-term climate data records (CDR) of energy and water cycle parameters, which are freely available.The incoming surface solar radiation is an essential climate variable as defined by GCOS. Long term monitoring of this part of the earth’s energy budget is required to gain insights on the state and variability of the climate system.The CM SAF SARAH-3 climate data record is based on satellite observations from the first and second METEOSAT generations and provides various surface radiation parameters, including global radiation, direct radiation, sunshine duration, photosynthetic active radiation and others. The SARAH-3 climate data records are accompanied by a corresponding near-realtime processing (so-called interim climate data records). With that, SARAH-3 covers the time period since 1983 with a timeliness of 1 day and offers 30-minute instantaneous data as well as daily and monthly means on a regular 0.05° x 0.05° lon/ lat grid. SARAH-3 enables a wide range of applications – from operational climate monitoring and model verification to renewable energy assessments.The presentation will include validation and climate analysis of the SARAH-3 surface solar radiation parameters. A focus will be on the assessment of the temporal stability of the data record by comparison to alternative data records and surface reference measurements. Further, the climate variability and trends of the surface solar radiation will be analyzed. SARAH-3 reveals that there is a positive trend of surface solar radiation in Europe during the last decades, superimposed by decadal and regional variability. Also, potential causes for the found variability and trends (clouds, aerosols) will be discussed. Finally, an outlook on planned further developments will be given.
Upgrade and assessment of the on-site calibration methods used in SKYNET
Authors: Kumar Gaurav , Momoi Masahiro , Campanelli Monica , Estellés Victor , Garcia Meritxell
Published: July 2024

The improved Langley method (ILP) is an in-situ calibration method developed for Prede POM Sun/sky radiometers. The so-obtained calibration is used to retrieve the aerosol optical depth (AOD) in SKYNET from direct irradiance data, and other optical properties. As opposed to the standard Langley method where we need a pristine high-altitude site to calculate calibration value, ILP is used at any site with varying aerosol optical depth. In the ILP, the Skyrad pack 4.2 version has been used for years to invert the radiance data, followed by strict screening criteria to derive the calibration. In the process of performing ILP, radiance data between the scattering angle of 3o-30o is selected. From the previous studies, it is well established that the ILP has weak dependence on imaginary refractive index and Single Scattering Albedo (SSA) in this range, whereas ILP has major dependence on real refractive index. Apart from this, it is also assumed that the refractive index remains almost constant during the period of observation.However, this assumption is not always respected in real conditions. With the change of seasons, the observation sites can face different weather and atmospheric situations. For sites like Valencia (Spain), which is affected by urban pollution but also dust intrusions during summer, the type of dominant aerosol can change during the observation time. In the present study, we aim to check the dependencies of ILP method, by using multiyear data of POM01 and POM02 instruments located at Valencia. The idea behind this is to use different assumptions of real, imaginary refractive index instead of fixed values. In previous analysis of this kind, assumed values were changed keeping the other inputs constant. However, we plan to do this sensitivity analysis by using different combinations of the input values. Other strategies will be also explored in order to estimate more accurate calibrations, as calibration transfers from pre and post calibrated Cimel instruments, belonging to AERONET, to be used as reference. Improving the accuracy in calibration value will help us in reducing the uncertainity associated with the calculation of aerosol properties in the SKYNET network.Keywords: Improved Langley Plot, Calibration, SKYNETAcknowledgements: The current analysis has been done in the frame of the COST Action CA21119 HARMONIA, supported by COST (European Cooperation in Science and Technology). The Spanish Ministry of Economy and Competitiveness also fund the research through project PID2022-138730OB-I00, PID2021-123881OB-I00, TED2021-129185B-I00 and the Valencian Autonomous Government project AICO/2021/341. The participation of G. Kumar has been supported by the Santiago Grisolia program fellowship GRISOLIAP/2021/048. We thank AERONET, PHOTONS and SKYNET for their scientific and technical support
Quantitative Sub-Ice and Marine Tracing of Antarctic Sediment Provenance (TASP v1.0)
Authors: Marschalek Jim , Gasson Edward , van de Flierdt Tina , Hillenbrand Claus-Dieter , Siegert Martin , Holder Liam
Published: July 2024

Abstract. Ice sheet models must be able to accurately simulate palaeo ice sheets to have confidence in their projections of future polar ice sheet mass loss and resulting global sea-level rise, particularly over longer timescales. This requires accurate reconstructions of the extent and flow patterns of palaeo ice sheets using real-world data. Such reconstructions can be achieved by tracing the detrital components of offshore sedimentary records back to their source areas on land. For Antarctica, however, sediment provenance data and ice sheet model results have not been directly linked, despite the complimentary information each can provide on the other. Here, we present a computational framework (Tracing Antarctic Sediment Provenance, TASP) that predicts marine geochemical sediment provenance data using the output of numerical ice sheet modelling. The ice sheet model is used to estimate the spatial pattern of erosion rates and to trace ice flow pathways. Beyond the ice sheet margin, approximations of modern detrital particle transport mechanisms using ocean reanalysis data produce a good agreement between our predictions for the modern ice sheet/ocean system and seabed surface sediments. These results show that the algorithm could be used to predict the provenance signature of past ice sheet configurations. TASP currently predicts neodymium isotope compositions using the PSUICE3D ice sheet model, but thanks to its design it could be adapted to predict other provenance indicators or use the outputs of other ice sheet models.