bluuebunny/crossref_metadata_2025 huggingface dataset:
Harmonia COST Action: Overview of aerosol optical depth calibration and homogenization activities
Authors: Kazadzis Stelios ,   COST action Harmonia Core group
Published: July 2024

Harmonia is a COST Action funded by the European Cooperation in Science and Technology. It deals with networking and scientific activities related with columnar aerosol measurements and involves scientists dealing with measurements, model assimilation , satellite validation but also software and instrument developers. The scope of the presentation is to provide an overview of the global status of aerosol sun photometer networks and the homogenization activities performed towards reducing spatiotemporal aerosol properties uncertainties.Harmonia COST Action aims to establish a network involving institutions, instrument developers, scientific and commercial end users, in order to homogenize aerosol retrievals using mainly solar and sky but also lunar and star photometers from different networks. It aims bridging user needs and the science and technology expertise residing in academia and industry, through:- Increasing the interactions and knowledge exchanges between several atmospheric aerosol measurement networks.- Standardizing and improving of existing products and tools, toward a “harmony” in the aerosol photometry.- Stimulating the communication between operational agencies and academia, increasing the applicability of aerosol related products.- Capacity building towards instrument operators on improving the use of solar, lunar and stellar radiometry/photometry instrumentation.- Encouraging the dialogue between researchers and instrument manufacturers towards instrument technical improvements.The presentation aims to provide an overview of the current methods to measure aerosol columnar properties from the surface and also an overview of the homogenization activities towards better ground-based products towards better representation of the aerosol field and aerosol effects on climate, model assimilation and satellite validation. Including:- The current state of the art of instrument calibration including project results towards establishing a traceability to the SI system of units,- Aerosol sun photometric networks` common international comparison towards measurement homogenization- The status of the Aerosol, Clouds and Trace Gases Research Infrastructure (ACTRIS), Calibration of aerosol remote sensing and links to HarmoniaAcknowledgementThis research has been supported by the This article/publication is based upon work from COST Action HARMONIA, supported by COST (European Cooperation in Science and Technology) . 
A Northern European paradox: both wetting and drying trends complicate water management 
Authors: Newell Matthew , Drews Martin , Payne Mark , Larsen Morten Andreas Dahl
Published: July 2024

The diverging trends in annual soil moisture across Europe are stark: Northern regions are increasingly wet, while Southern areas face growing aridity, largely due to contrasting precipitation patterns between these regions. While several studies have examined the consequences of soil moisture deficits and drought in the south, the implications of increasing soil moisture levels in the north remain less explored. This study leverages reanalysis products, such as ERA5-Land, to delve into the soil moisture dynamics of Northern Europe, examining seasonal and sub-seasonal trends across the period 1951-2020. This research includes both a detailed case study of Denmark, and an analysis focusing on the Northern European domain. A primary hypothesis is that spatiotemporal soil moisture trends are influenced not only by changes in mean precipitation, but also by altered temporal patterns of rainfall at sub-seasonal scales across the domain. Moreover, increased soil moisture persistence during the wet season heightens the risk of complex and compound hydro-meteorological interactions between saturated soils and intense rainfall events. The analysis also uncovers a north-to-south soil moisture gradient within Northern Europe, particularly evident with spring drying (MAM). In the southern parts of Northern Europe (including Denmark, southern Sweden, and the Baltic region) this manifests as a wetting trend in winter (DJF) and a drying trend in spring (MAM), posing challenges for water management across sectors. The findings of this study underscore critical research areas requiring further investigation, such as the impacts of increasing soil moisture and more frequent intense rainfall events on streamflow and flooding. Future research could benefit from employing hydrological models to explore these dynamics in greater detail.  
Vintage Port quality under climate change: A machine learning approach using Twentieth Century Reanalysis and CMIP6 data.
Authors: Fraga Helder , Guimarães Nathalie , A. Santos João
Published: July 2024

The Douro region is renowned for its exceptional wines, notably the renowned Port Wine. Vintage years, occurring approximately 2-3 times per decade, signify outstanding quality, driven by optimal climatic conditions that enhance grape attributes. However, climate change presents challenges as rising temperatures and extreme weather events impact viticulture. This study evaluates the climatic influence on vintage years and forecasts climate change effects for forthcoming decades, utilizing machine learning algorithms. Historical vintage data spanning from 1850 to 2014 was gathered alongside monthly climatic variables including temperature, precipitation, humidity, solar radiation, and wind components from the 20th Century Reanalysis dataset. Diverse machine learning algorithms were deployed for classification, augmented by statistical analysis to pinpoint pertinent climate variables. Model training and evaluation employed cross-validation, followed by hyperparameter tuning for the most effective models. Future climate projections from 2030 to 2099, under various socio-economic scenarios (IPCC SSP2, SSP3, and SSP5), were integrated, with quantile mapping bias adjustment applied to refine future climate data. Historical data revealed vintage years occurring 23.6% of the time, averaging two vintage years per decade, with a slight upward trend. Crucial climate variables influencing vintage year occurrence were identified, including precipitation in March, air temperatures in April and May, humidity in March and April, solar radiation in March, and meridional wind in June. The study found promising results from Logistic Regression, SVC, and XGBClassifier models. This research offers valuable insights into the nexus between climate variables and wine vintage years, empowering winemakers to make informed decisions regarding vineyard management and grape cultivation. The projections underscore the importance of adaptation strategies in confronting the challenges posed by climate change to the wine industry. This research was funded by National Funds by FCT – Portuguese Foundation for Science and Technology, under the projects UIDB/04033/2020 (https://doi.org/10.54499/UIDB/04033/2020) and LA/P/0126/2020 (https://doi.org/10.54499/LA/P/0126/2020).
Atmospheric fronts and their association with day-to-day temperature differences
Authors: Navrátilová Denisa , Kašpar Marek , Huth Radan , Stryhal Jan
Published: July 2024

Advancements in computer technology have facilitated the objective analysis of atmospheric fronts. With objectively defined fronts at hand, it was a relatively short step to producing the climatology of their occurrence. Recent studies have focused on delineating the spatial distribution of fronts across various spatial scales and on elucidating the relationship between fronts and precipitation patterns.Despite the considerable attention given to the link between fronts and precipitation, little emphasis has been placed on exploring their influence on surface temperature dynamics over consecutive days. Traditionally, fronts have been associated with temperature gradients aloft rather than at the surface. However, a number of small-scale (regional and local) processes play an important role in surface temperature dynamics. Surface temperature is modified by a variety of boundary-layer and surface processes, that make its dynamics in the vicinity of fronts and during their passage very complex, and therefore worth investigating. Examining the impact of fronts on surface temperature variability is important task, given their potential contribution to asymmetries in distributions of day-to-day temperature differences – a key indicator of weather variability.This study presents a comprehensive climatological analysis of cold and warm fronts in the Euro-Atlantic region, utilizing the ERA-5 reanalysis dataset spanning from 1961 to 2020. To objectively identify fronts, we utilize the temperature frontal parameter defined as the second derivative of temperature in the direction perpendicular to the front. The change in temperature over a 24-hour period centered on the passage of a front is retained for each gridpoint over which the front passes. Through this approach, we derive the climatology of day-to-day temperature differences associated with the passage of cold and warm fronts, and we compare these results with temperature patterns in the absence of any frontal activity.We found that strong day-to-day cooling events are significantly more likely to be associated with the passage of cold fronts in summer across most of the European continent. However, the passage of a warm front in summer leads, on average, to warming only over approximately one half of the continent, while the opposite effect can be seen for the other half. We will further analyse the whole day-to-day temperature difference distribution and its regional specifics during frontal passages, and investigate links between day-to-day temperature differences and various properties of cloud cover.
A near-global multiyear climate data record of the submicrometer-mode and supermicrometer-mode components of atmospheric pure-dust
Authors: Proestakis Emmanouil , Gkikas Antonis , Georgiou Thanasis , Kampouri Anna , Drakaki Eleni , Ryder Claire L. , Marenco Franco , Marinou Eleni , Amiridis Vassilis
Published: July 2024

Significant is the complex role of atmospheric dust in the climate system, environmental conditions, and human health. However, the impact of dust layers residing in the atmosphere vary strongly based on the mineral dust physical and chemical properties as well as the particle size distribution, ranging from less than 0.1 μm to over 100 μm in diameter. More specifically, it is documented that larger mineral dust particles are more efficiently removed through dry deposition near the source regions and act more efficiently as CCN and/or IN, while fine dust particles are more prone to long-range transport, affecting air quality and human health over distances of thousands of kilometers downwind.Here, a new four-dimensional, multiyear, and near-global climate data record is introduced, established with the overarching objective to decouple the submicrometer and supermicrometer components in terms of diameter of atmospheric dust layers. More specifically, this separation is realized through a combination of (1) the total pure-dust product from the ESA-LIVAS database, and (2) the supermicrometer-mode component of atmospheric dust, as extracted through implementation of the first step of the two-step POLIPHON technique. Accordingly, the submicrometer-mode component of atmospheric dust is derived as the residual between the total pure-dust and supermicrometer-mode component of pure-dust.The decoupling scheme is applied to CALIPSO observations at 532nm, resulting in quality-assured profiles of backscatter coefficient at 532nm, extinction coefficient at 532nm, and mass concentration, for both submicrometer-mode and supermicrometer-mode of atmospheric pure-dust. These datasets are established with original CALIOP resolution along the CALIPSO orbit-path and in averaged profiles of 1ox1o spatial and seasonal temporal resolution, covering over 15 years of Earth Observation. This climate data record is considered unique, providing valuable insight into the characteristics of the atmospheric pure-dust components, thus contributing to a better understanding of their impact on climate, environment, and human health.Acknowledgements The research study was supported by the AXA Research Fund for postdoctoral researchers under the project entitled “Earth Observation for Air-Quality - Dust Fine-Mode (EO4AQ-DustFM)”.
Post-processing for wind and PV power production of hectometric NWP forecasts - which Machine Learning methods are beneficial for sparse data and extreme events?
Authors: Schicker Irene , Papazek Petrina , Gfäller Pascal , Odak Plenkovic Iris , Vujec Ivan , Kann Alexander , Horvath Kristian
Published: July 2024

With the increasing amount of wind and solar energy fed into the European powergrid, despite slowdowns due to social acceptance and regulatory issues, plus thetransition to a fossil fuel-free energy production, accurate predictions includinguncertainties are required for grid operators. Relying heavily on renewable energysources, frequently updated and as accurate as possible predictions for bothhigh-resolution temporal and spatial scales ensure grid management and takingprevention measures in case of extreme meteorological events affecting the powerproduction. Moreover, both extreme events in weather across the nowcasting to weeks aheadtime scale and combined non-necessarily extreme weather factors can notably affect the power production grid. The latter is sometimes a combination of just-above-normal events, such as  high solar penetration, high wind penetration plusdecent hydropower combined with a reduced electricity demand. Thus, on-demandavailable predictions of the expected power production are especially needed. Post-processingmethods enable targeted forecasts of meteorological parameters at site-location andregional level, serving as a baseline for the conversion to power production,particularly a direct conversion of NWP predictions and observations to powerproduction.  However, so far most methods available use NWP forecast with spatial resolutionsbetween 2 and 9 km, and hourly output frequency thus requiring temporal interpolation andspatial downscaling or point interpolation. Recently, NWP upgradestowards sub-km scale and sub-hourly have been done to improve extreme events prediction. These NWP models are onlyavailable for selected extremes and short periods of time and often do not share thesame parametrization throughout. This poses a challenge for post-processing forrenewables, as well as the uncertainties that lie in e.g. wind farmspecifications and solar farm/PV panels. To prepare for the next phase of NWPmodels, and currently developing on-demand extreme digital twin forecasting system, fast andtransferable post-processing methods are needed. Here, we look into differentmachine learning and classical statistical methods, such as the analog method,LSTMs and Random forest, EMOS, to generate post-processed forecasts for extremeevents with a sparse training database. Furthermore, we investigate thetransferability and generalisability of these methods when pre-trained with acoarser NWP model. 
Exploring sea level projections and their return levels around the Baltic Sea with a focus on uncertainties
Authors: Dubois Kévin , Nilsson Erik , Rutgerssoon Anna
Published: July 2024

Coastal floods are one of the main natural hazards which often lead to disasters worldwide including in Sweden. In the Baltic Sea, extreme sea levels (ESL) are driven by different processes such as seiches, pre-conditioning of the Baltic Sea (filling) and storm surges. Predicting those along with accurate uncertainties range is essential for marine coastal management and planning to better mitigate potential impacts. It is well known that Climate Change is currently changing coastal conditions globally and around Sweden. In this context, this study aims to characterize the atmospheric drivers of ESL along the coastline of Sweden and predict return levels of extreme sea heights. Machine Learning tools like Random Forest are then used to predict storm tide time series, characterising extreme sea levels, based on atmospheric drivers such as wind and surface pressure. The model is firstly trained with ERA5 reanalysis and tide gauge observations. It is then used to predict storm tides from 1850 to 2100 using input variables from CMIP6 models as EC-Earth. Predictions are then compared with observation values in a statistical way over the co-occurring time period. Results show that storm tides are driven by different atmospheric characteristics (i.e. wind direction, wind speed and surface pressure) at each station but that stations located close to each other can be grouped accordingly. For instance, strong westerly winds greatly contribute to storm tides on the west coast of Sweden while southernly winds are driving storm tides on the northern Swedish Baltic coast. Our main results include investigating trends in extreme values of storm tides. Based on the EC-Earth historical and ssp245 climate model, a decrease in storm tide of 1 to 4 mm/decade is to be expected along the east coast of Sweden while no trend seems to be noticeable on the west coast of Sweden. 
Compound precipitation and wind extremes in the eastern part of the Baltic Sea region
Authors: Klimavičius Laurynas
Published: July 2024

Compound precipitation and wind extremes (CPWEs) are multivariate compound climate events that have received increasing attention recently. Such extremes can cause damage to buildings and infrastructure that results in extensive socio-economic losses. The main objective of this work is to assess the recurrence and intensity of CPWEs in the eastern part of the Baltic Sea region from 1950 to 2022.This study investigated the region between 53.5° to 59.5° N and from 20° to 28.5° E. Daily precipitation and hourly maximum wind speed data with a grid size of 0.25 x 0.25° were obtained from the ERA-5 reanalysis database to identify CPWEs. Then, for the entire study period and each grid cell within the study area, the 98th percentile of daily maximum wind speed and precipitation were calculated. A CPWE was distinguished when precipitation and maximum wind speed values exceeded the 98th percentile values of the corresponding variable on the same day for at least one point in the study area.Two different approaches were employed to evaluate the intensity of CPWEs. Firstly, terciles of maximum wind and precipitation were calculated, and the values of each variable were allocated into three categories, of which nine intensity classes of CPWEs were formed. While applying the other method, the number of CPWEs, when one or both of these variables exceeded the 99th percentile values in at least one grid cell of the study region, was computed.A total of 797 CPWEs have been distinguished from 1950 to 2022. During the study period, the number of these events significantly increased (when p < 0.05). The highest number of CPWEs occurred in October. During the cold season (from October to March), most of these compound events were obtained in western Lithuania, while during the warm season (from April until September) – in the northeastern and south-eastern parts of the study region.Evaluation of the intensity of CPWEs using the first approach revealed that all events characterized by extremely high amounts of precipitation (> 80 mm per day) occurred in June and September. Meanwhile, 88% of CPWEs, during which the wind speed was higher than 28 m/s, were obtained from October until March. The most severe CPWE occurred on August 7, 1987, in the northeastern part of the study region. However, using the other approach, the most intense CPWE was identified on October 28, 1998, when the values of both maximum wind speed and precipitation exceeded the 99th percentile values of the corresponding variable in 37.8% of the grid cells of the study area. The number of CPWEs when the values of both variables exceeded the 99th percentile slightly increased over the study period.
A geospatial approach for heat risk estimation by integrating remotely sensed and ground-based data in Milan, Italy
Authors: Žgela Matej , Vavassori Alberto , Brovelli Maria Antonia
Published: July 2024

The effects of climate change have never been as pronounced as they are now, however, simultaneously, urban areas are experiencing additional local-scale anthropogenic effects due to the modification of the natural environment. Consequently, urban heat islands form, amplifying the heat load in the cities and heightening the vulnerability of citizens to heat-related issues.In the context of Europe, the fastest-warming continent in the world, significant impacts on living conditions will occur as a result of numerous climate risks. Furthermore, in a recently published European Climate Risk Assessment, heat risk is identified as one of the two main risks for which urgent action is needed. However, implementing mitigation measures in urban areas is often slow, highlighting the need to employ geospatial technologies to aid the process.To address this issue, we identified high heat risk areas and heat-resilient zones in the city of Milan, Italy. High-resolution geospatial datasets, including remotely sensed imagery and official/crowd-sourced in-situ data, were utilised for producing a heat risk index. Main known drivers of heat risk were used as predictors, such as land surface and air temperature, vegetation fraction, surface material composition and others.The produced heat risk maps were overlayed with population data, selecting the locations of public institutions most used by vulnerable populations - schools, retirement homes, and public health institutions. Institutions’ surroundings have been investigated based on urban geometry, considering their micro-urban morphology and at the neighbourhood scale with local climate zones. Moreover, the research examined the frequency of extreme temperatures occurring in various areas of the city over time and put it in relation to vulnerable population data.Through detailed analysis of city-scale heat risk, the study has identified the main hotspots and cool spots within the city. The research findings are critical for vulnerable populations facing high heat risks, enabling targeted and straightforward implementation of appropriate heat mitigation measures. Finally, the utilisation of high-resolution geospatial information and multiscale datasets can aid city planners, providing them with crucial scientific insights for informed decisions.
Does the atmosphere drive summer SST anomalies in southern Bay of Biscay?
Authors: Saenz Jon , Carreno-Madinabeitia Sheila , Ibarra-Berastegi Gabriel , Esnaola Ganix , Fontán Almudena
Published: July 2024

During the last few years, observations show that summer warm Sea Surface Temperature (SST) records are more frequently exceeded than cold ones over the southern Bay of Biscay (SBoB). During warm years over land (for instance, summer 2023), it might be expected that atmosphere-ocean energy fluxes drive SBoB’s SST anomalies.We explore this issue by analyzing satellite-derived (MODIS) and Optimum interpolated SST datasets. We focus on daily data (2000-2023 for MODIS and 1981-2023 for OISST) over spatially-averaged open-ocean data in SBoB, with a mask constructed from satellite pixels with SST higher than or equal to 20 ºC during August and located at least 10 km away from the coast.We intercompare the representation of SST by those datasets. Next, we use data from reanalyses (ERA5 for atmosphere-ocean energy fluxes and Iberian-Atlantic ocean reanalysis from CMEMS for mixed layer depth and salinity) to estimate the linear relationship between the derivative with time of spatially averaged SST and surface energy fluxes. This way, the amount of variance that can be explained by the atmosphere alone is computed.Results show that the MODIS NSST product is colder than SST4 and SST, but during summer, all of them (and OISST) are very similar. The time derivative of SST from MODIS and OISST, however, show differences. The time derivative calculated from OISS, is better suited for the calculation of the relationship with energy fluxes. The application of a multiple linear regression model to the principal components computed from daily averaged surface turbulent latent and sensible heat fluxes and shortwave and longwave radiation shows that the atmosphere is at most able to explain a 29% of the total variance during august between 2000 and 2023. This result suggests that the sea surface warming has been likely driven by a combination of atmospheric forcing and oceanic advective processes.
KAPy – a community-based approach to the production of climate services
Authors: Payne Mark R. , Nangombe Shingirai Shepard , Abbey Peter-William , Ansah Samuel Owusu , Björnsson Halldór , Bredvold Kaja Louise Havig , Boberg Fredrik , Dobler Andreas , Düsterhus André , Ólafsdóttir Anna Hulda , Poropat Lea , Quaye David , Stensballe Julie , Tunheim Ketil , Tómasdóttir Katrín Agla , Zaqout Tarek A. M.
Published: July 2024

Climate services provide tailored information to support climate adaptation at the local level. One common form of climate service is the provision of downscaled climate projections, often bias-corrected using local observations and customised to meet the needs of local society based on extensive stakeholder engagement. Well-established examples of such services already exist in many European countries, while others are currently in the process of developing their own. While each such instance has its own peculiarities, there is also a high degree of overlap and duplication between these services. For example, the Danish “Klimaatlas”, the Norwegian “Climate in Norway 2100” report and the Swedish “Advanced Climate Change Scenario Service” all start from the EURO-CORDEX ensemble, bias-correct against local datasets, and produce comparable indicators, albeit entirely independently of each other. Recognising the potential to reduce duplication, to learn from each other and to enable the development of climate services in new regions, we established the KAPy (Klimaatlases in Python) project and network. KAPy builds on an open-source software stack centred on the Python language, leveraging the extensive tools already developed in this programming community. The use of workflow control tools from the field of bioinformatics enables reproducibility and scalability, while the open-source approach drives both collaboration and transparency. We illustrate the capability of this tool to produce climate service information using, as an example, ongoing work in Ghana, with a detailed analysis of the efforts required to produce climate-service ready indicators starting from scratch: after downloading of data was complete, configuration, bias-correction and indicator production was possible in a matter of a few hours. We also detail experiments showing how bandwidth limitations can be circumvented using cloud computing, further increasing the productivity and enabling implementation in resource limited situations, such low and low-middle income countries. We conclude with an open invitation to all to join the KAPy network as both users and developers, and thereby contribute to making climate services more transparent and widely accessible.
Ireland’s Route to a permanent National Framework for Climate Services
Authors: Scannell Claire , Lambkin Keith , Delmar Jordan
Published: July 2024

The establishment of a National Framework for Climate Services in Ireland followed a low risk, yet high impact “project-first” approach to understand the national relevance and appetite for a coordinated approach to climate services. Rather than begin with a National Framework for Climate Services, and be unsure of its interest or value, this “project-first” approach allowed Met Éireann to identify relevant stakeholders, evaluate engagement and discover potential problems. As projects are time bound, this was a low-risk method of determining appetite for a more permanent national solution to provision and sharing of climate information.Following the publication of the National Adaptation Plan and recommendations from the Irish Climate Change Advisory Council, there was an unprecedented requirement for accessible and robust climate information to support decision making and adaptation planning. At this time the Irish climate services landscape was fragmented and climate information for Ireland was developed in an ad hoc, project-to-project basis. There were no clear mechanisms or structures in place for establishing and maintaining sustainable climate services and partnerships between researchers, providers, policy makers and end users. Met Éireann saw the need for both the development of a common set of climate projections to help Irish society speak the same climate language and the establishment of a permanent national framework for climate services. This led to a national research call (to build national capability), from Met Éireann and in 2021 the TRANSLATE project was founded. TRANSLATE became the seed that led to the establishment of this permanent national framework.National collaboration, knowledge sharing, and co-production of data and services are central to the TRANSLATE ethos. A key challenge of this project was bringing the whole Irish climate science and services community together and subscribe to a collaborative approach. Rather than work in isolation and leveraging Met Éireann’s position as the National Met Service, an organisation that naturally bridges the science-policy-user interface, Met Éireann brought together the national community – (all of whom would be required under a successful national framework) - under the TRANSLATE banner. This “all-Ireland” approach was so effective that it has led to the establishment of a permanent National Framework for Climate Services.Here we describe an “all-Government” approach to building permanent mechanisms and structures for developing and maintaining sustainable and scalable climate services and partnerships across the science-policy-user interface by establishing an inclusive National Framework for Climate Services. We explore the challenges, barriers and successes in establishing and operationalising of a National Framework for Climate Services in Ireland.
Changing drought occurrence and severity affecting agriculture and forestry in Slovakia
Authors: Labudová Lívia , Ivaňáková Gabriela , Faško Pavol , Kajaba Peter
Published: July 2024

Drought is a natural phenomenon that occurred in Central Europe in the past, but it is becoming a more serious problem due to the changes in its occurrence and intensity related to climate change. We analysed changes in air temperature and precipitation amounts in Slovakia from 1931 to 2020 as well as changes in drought occurrence and intensity using the Standardised Precipitation and Evapotranspiration Index (SPEI) since 1961. Further, cluster analysis was used to determine regions with similar drought occurrences. For each of the identified five clusters, drought characteristics were determined and compared between two reference periods 1961–1990 and 1991–2020.While a decrease in the number of months with SPEI-1 below − 1 was identified in autumn, spring and summer months showed a drying tendency. Overall, we can say that we observed a shift in drought occurrence from autumn and winter months to spring and summer months. The clearest tendency in drought events was observed in the western part of Slovakia covering areas with agriculturally intensive land use. Besides prolonging drought events, there was also a higher accumulated deficit for each event. Considering SPEI-12, the severely to extremely dry conditions repeatedly occurred in clusters 1 and 5 since 2011, affecting agriculturally intensively used areas and causing yield losses. These regions also experience the most severe change in water balance concerning the relative SPEI-12. Generally, approximately two-thirds of Slovakia experience a decreasing value of rSPEI-12.Observed drying tendency in spring months is a great problem for the agriculture, because of the seeding and germination of some spring crops such as maize, sunflower or soya. This is also the main cause, why the farmers seed winter crops rather than spring crops. This is a reason, why the cultivation area of spring barley has had a decreasing tendency in south-western part of Slovakia since 2018 (Križanová 2022). The reports published in the last years by the Regional Agricultural and Food Chamber Galanta (south-western part of Slovakia) show that the farmers try to use autumn soil moisture for securing better crop yields next year. Therefore, they prefer rather winter cultivars of crops than their spring cultivars. They even sow spring barley in autumn months. It is a way, how to ensure higher yields and better use of agroclimatic conditions while maintaining malting parameters (Križanová 2022; Križanová 2023).The same situation is in the forestry. According to internal information provided by the Forests of the Slovak Republic, state enterprise, the foresters start to plan the seeding of tree seedlings rather for autumn months, because the success of spring planting is lower due to the drying of seedlings as the result of lower soil moisture in spring.
Wet Bulb Globe Temperature (WBGT) in the Netherlands
Authors: van den Besselaar Else , Koning Matthijs , van der Schrier Gerard , de Baar Jouke , Hartmann Hugo
Published: July 2024

This year, the European Environment Agency published a report in which heat stress is defined as the most dangerous climate risk for the health sector. Society is expected to suffer from increasing heat stress events in the coming years. Moving towards impact-based forecasting, the Royal Netherlands Meteorological Institute (KNMI) has recently adopted the heat stress indicator Wet Bulb Globe Temperature (WBGT) as part of their Early Warning Centre.  

The WBGT presented here is computed analytically from air temperature, relative humidity, wind speed and global radiation using the theoretical Liljegren approximation. These WBGT values are compared with WBGT values as derived from black bulb (globe) and air temperature observations for a specific station in the Netherlands. 

We present a characterization of WBGT based on hourly observations made by Automatic Weather Stations (AWS) in the Netherlands and compare the safety levels as defined by the US Army, the world athletic organization and the Dutch fire brigade. By assessing the trends in WBGT over the past decades, we find a significant increase of hourly intervals where critical threshold values are exceeded for all five investigated stations. The safety levels of these organizations are based on the absolute values of WBGT. The effective WBGT (WBGTeff) also considers the effects of clothing and a person's activity. The total time in a day that the WBGTeff is above a certain threshold can therefore be used for warnings for specific work environments.  

Looking into a case study of a Dutch triathlon (25 June 2023) with a combination of hot weather conditions which resulted in a high amount of heat strokes and hospitalizations, we show spatial variations of WBGT for that day using detailed maps based on AWS and crowd-sourced data.
Characterization of outdoor-indoor air pollution at a city-wide scale in Barcelona using a high-resolution urban air quality model
Authors: Julian Izquierdo Andreu , Carnerero Cristina , Criado Alvaro , Soret Albert , Mateu Armengol Jan
Published: July 2024

Urban air quality presents a significant global environmental challenge, with 77% of the urban population exposed to NO2 concentrations exceeding the 2021 guidelines established by the World Health Organization. Moreover, recent analyses show that people typically spend approximately 90% of their lives indoors. In this context, understanding indoor and outdoor air quality levels is key to better assess population exposure. Despite there is extensive research on modelling and monitoring air quality, studies modelling outdoor and indoor pollution simultaneously remain limited. In this study, we address this research gap by combining NO2 outdoor levels from the air quality model CALIOPE-Urban with indoor-outdoor parametric relations.Bias-corrected NO2 concentration levels at the street scale (20 m x 20 m) are used. This information is based on a kriging data-fusion method that merges monitoring stations and the dispersion model CALIOPE-Urban. Additionally, it integrates a machine learning-driven microscale-Land Use Regression model, which is calibrated using observational data gathered from short, intensive passive dosimeter campaigns.Two different parametric relations for deriving indoor-outdoor air quality levels are assessed. On the one hand, we use infiltration rates, which quantifies the fraction of outdoor pollutants  entering into buildings. On the other hand, we apply indoor-outdoor ratios, which besides infiltration also include indoor sources. Both parameters are obtained from available data in the literature. They depend on socio-demographic data (e.g., building use) and  seasonality (e.g., summer, winter, and spring-autumn) which can affect ventilation patterns.To obtain unprecedented city-wide maps containing both indoor and outdoor NO2 concentrations, bias-corrected NO2 concentration levels and the parametric relations are combined. We present results for the 2019 annual mean of NO2 in the domain of Barcelona. Results are validated using existing indoor and outdoor measurements from various experimental campaigns available in the city. Furthermore, a sensitivity analysis is carried out to quantify  uncertainties associated with parametric relations and their effects upon final results.This comprehensive approach allows us to refine our understanding of both indoor and outdoor air quality dynamics in Barcelona. Such findings hold promise in improving population exposure in urban environments, revealing both indoor and outdoor NO2 hotspots of pollution exposure.
Internal Structure of Giant Hail in a Catastrophic Event in Catalonia (NE Iberian Peninsula).
Authors: Farnell Barqué Carme , Rigo Ribas Tomeu , Martín Vide Javier , Úbeda Cartaña Xavi
Published: July 2024

During the last two years, Catalonia has experienced the most severe hail episodes of the previous three decades. The first one was on August 30th, 2022, and it affected the northeast of the country, resulting in significant urban damage, injuries, and one fatality—a two-year-old girl. The second episode impacted the region's southern part, causing extensive damage, particularly in urban areas. In both instances, giant hail was recorded, with a maximum diameter of 12 cm and 10 cm, respectively.In response to these extraordinary episodes, we conducted exhaustive fieldwork in the core of the affected areas to understand the path and behavior of the thunderstorm over the terrain. Additionally, we collected hailstones preserved in the freezers by observers living in the affected village. In total, we have collected 20 samples during the first event and 12 samples in the second one.We calculated most samples' sizes of the larger, mid, and minor axes, weight, and sphericity index. Furthermore, we underwent analysis through two methods. One of these methods involved employing an innovative technique in this field: Computed Tomography Scanning (CT scanning). This technique enables us to examine the internal structure of hailstones in three dimensions, thereby observing the diverse layers within them and their varying densities. Such analysis offers valuable insights into the processes these stones undergo within thunderstorms, which are influenced by temperature and humidity in various atmospheric layers. The second analysis involved a chemical analysis conducted in the laboratory, which unveiled a clear correlation between the main components and their proximity to the sea.
Improving predictability of Mesoscale Extreme Precipitation events with Convection-permitting models
Authors: Siena Matteo , Ruggieri Paolo , Marsigli Chiara , Gastaldo Thomas , Di Sabatino Silvana
Published: July 2024

Extreme precipitation events are both changing in frequency of occurrence and intensity. Among them, we can identify the Mesoscale Convective Systems (MCSs), which have typical dimensions of the order of ~100 km and consist of large clusters of powerful thunderstorms that produce intense rainfall, large hailstones and, occasionally, dangerous tornadoes. Italy is being affected more and more frequently by such systems.Here, we carried out sensitivity tests using a limited-area setup at 2km horizontal resolution of the ICON numerical model, combined to a double nesting at 1km and 500m to simulate a strong quasi-linear convective system that triggered a huge flooding in central Italy on 15-16 September 2022, causing 13 deaths and 2 billion euros worth of damage. The convergence of warm, humid southwesterly winds (that produced an atmospheric river with peak values of 1000 kg*m-1*s-1) with northwestern atmospheric currents triggered intense convection over the central Apennine regions leading to a destructive V-shaped self-regenerating thunderstorm, despite the absence of very cold air at higher levels. The numerical models underestimated such event and did not reproduce well the spatial extent and intensity of the system. These discrepancies are related to model errors usually due to uncertainties in (i) estimation of moisture in the several layers of the atmosphere, (ii) parameterization schemes for cloud microphysics, (iii) orography representation and (iv) turbulence diffusion. It follows that convection-permitting models are crucial to better solve these phenomena. Thus, we did some sensitivity tests operating on the above-mentioned parameterizations to identify the main physical processes that are the most likely to impact the forecasts’ accuracy.Results showed that the activation of the Smagorinsky 3D turbulence scheme, compared to the COSMO 1D scheme, noticeably improves the forecast accuracy in terms of  precipitation amounts and spatial distribution. Moreover, we employed an ensemble-based approach to enhance predictability, with a focus on extreme percentiles within the ensemble tail of specific variables such as total precipitation. This method offers more valuable insights compared to the traditional analysis of ensemble means, especially when  it comes to rare and unusual weather patterns that lead to extreme precipitation events.
Retrieval of Aerosol Properties from Direct Solar Irradiance Data with High Temporal and Spectral Resolution Using GRASP 
Authors: Karanikolas Angelos , Torres Benjamin , Herreras Giralda Marcos , Momoi Masahiro , Kouremeti Natalia , Gröbner Julian , Doppler Lionel , Kazadzis Stelios
Published: July 2024

Several sun photometer networks (e.g. the Aerosol Robotic Network (AERONET), the Global Atmospheric Watch-Precision Filter Radiometer (GAW-PFR) retrieve aerosol optical depth (AOD). AERONET provides more aerosol properties such as the effective radius and the single scattering albedo through inversion modelling using sky radiance measurements. However, the data availability for such properties is limited due to the low number of sky radiance scans and cloudiness. In contrast, AOD measurements are significantly more frequent (every minute for GAW-PFR). In the past, methods were developed to retrieve aerosol properties using only direct sun scans. Kazadzis et al., 2014 used linear estimation technique to retrieve the effective radius and volume concentration using only AOD measurements. The Generalized Retrieval of Aerosol and Surface Properties (GRASP) uses multi-term least square method to retrieve the AOD, the volume concentration, the median radius and geometric standard deviation separately for fine and coarse mode. Previous studies concern the application of GRASP-AOD to CIMEL sun photometers [Torres & Fuertes, 2021].  In this study, we aim to apply it to GAW-PFR instruments (PFRs) at common locations with AERONET, assess the retrieval homogeneity with AERONET products and investigate the effect of the wavelength differences and AOD uncertainties. Also, we aim to apply the method to the Precision Spectroradiometers (PSR) and investigate the potential improvements using higher spectral range and resolution. Preliminary retrievals in a common GAW-PFR and ARONET station Hohenpeissenberg) show good results for the fine and coarse mode AOD separation and the fine mode median radius (correlation factor with AERONET standard inversions >0.9 for AOD and >0.8 for fine mode radius). For the coarse mode the retrievals are less accurate, mainly for the median radius in which GRASP-AOD shows low sensitivity to its fluctuations. ReferencesKazadzis, S., Veselovskii, I., Amiridis, V., Gröbner, J., Suvorina, A., Nyeki, S., Gerasopoulos, E., Kouremeti, N., Taylor, M., Tsekeri, A., and Wehrli, C. (2014): Aerosol microphysical retrievals from precision filter radiometer direct solar radiation measurements and comparison with AERONET, Atmos. Meas. Tech., 7, 2013–2025, https://doi.org/10.5194/amt-7-2013-2014.Torres, B. and Fuertes, D. (2021): Characterization of aerosol size properties from measurements of spectral optical depth: a global validation of the GRASP-AOD code using long-term AERONET data, Atmos. Meas. Tech., 14, 4471–4506, https://doi.org/10.5194/amt-14-4471-2021.
Frost safety margins: a proposal based on daily minimum temperature 
Authors: Pérez-Sosa Eduardo , Serrano-Notivoli Roberto , Saz-Sánchez Miguel Ángel , Hernández-Navarro María Luz
Published: July 2024

The concept of frost safety margins has been used in previous research as an indicator associated with the period between the occurrence of the meteorological phenomenon and the most sensitive phenological stage to freeze damage in deciduous perennial plants in mid and high latitudes. It is a relevant indicator in agriculture due to its use in agricultural planning and territorial management of productive areas, as it has been linked to damages from late frosts and as a fundamental criterion for false springs events in fruit trees. However, its implementation requires robust phenological records or experimentation under controlled conditions. The aim of this study is to propose a concept of frost safety margins based on daily minimum temperature data (TMIN). To this end, we used TMIN in raster format at a spatial resolution of 1 km² for the Aragón region (northeast Spain). Calculations were performed in the R programming language and the cartography was built using ArcMap 10.7.1® software. The indicator represents a state composed of two elements: a numerical one, without hierarchical order, which incorporates the moment of occurrence; and a character one associated with the difference in days between the penultimate and last frost per year. Once the states were classified, an annual sequence was formed for the historical series and analyzed as a Markov chain. The results show that the margins are persistent and their autocorrelation with lag-1 is statistically significant, especially with increasing altitude. It was also found that, with a greater number of types of margins, the forecast using Markov chains is lower, indicating a more random behavior. In much of the Aragonese territory, the stationary probability of safety margins corresponds to type 4a, i.e., a margin during the month of April with a difference between 1 to 7 days, while in the lowlands it corresponds to type 2a, a margin during the month of March with a difference between 1 to 7 days. The proposed safety margins indicator can be used in conjunction with empirical or modeled phenological data to obtain agroclimatic risk, making it a line of research that allows for expansion with other future research lines.