bluuebunny/crossref_metadata_2025 huggingface dataset:
Integrating UAV-Based In-Situ and Ground-Based Remote Sensing Observations for Enhanced Aerosol Profiling
Authors: Papetta Alkistis , Kezoudi Maria , Stopford Chris , Thornberry Troy , Sciare Jean , Marenco Franco
Published: July 2024

This study investigates the integration of Unmanned Aerial Vehicle (UAV)-based in-situ aerosol particle size distributions (PSD) with ground-based remote sensing techniques to estimate columnar particle size-distributions and vertically resolved aerosol concentrations. Observations of aerosol vertical distribution are essential for understanding their radiative impact on the atmosphere and interactions with clouds. Ground-based lidar-photometer setups are commonly employed to capture simultaneous information on both columnar and vertical aerosol physical and optical properties, providing complementary insights. For the retrieval of vertically resolved aerosol concentrations from aerosol extinction, one commonly employs an inversion parameter known as the effective radius and which is a product of the measured PSD.Our study focuses on integrating Optical Particle Counters (OPCs, UCASS and POPS) and impactors for collecting high-altitude aerosol samples onto UAVs and conducting flights near co-located lidar and sunphotometers, utilizing the Unmanned Systems Research Laboratory (USRL; https://usrl.cyi.ac.cy) of The Cyprus Institute (CYI). Three diverse atmospheric campaigns in Cyprus, Cape Verde, and Greece are exploited for this purpose. These campaigns target different aerosol compositions and atmospheric conditions, providing a comprehensive dataset for evaluation. We aim to establish the link between the in-situ and remote sensing methods by exploiting scattering computations and retrieval algorithms.Preliminary findings from the Fall Campaign in Cyprus reveal simultaneous observations from lidar,  sunphotometer and UAV in-situ optical particle counters during a dust episode. UAV-based PSDs demonstrate larger particle concentrations within the 1 to 10 μm range compared to AERONET retrievals. This feature is expected to influence the resulting concentrations computed from the lidar, because larger particles contribute the most to mass concentration and extinction, resulting in increased magnitudes for both parameters. This increase affects the effective radius, which (together with the density, the size- and shape- dependent scattering efficiency) is one of the conversion parameters between extinction and mass.Overall, we seek to explore the synergy of UAV-based high-altitude in-situ observations with highly spatiotemporally resolved lidar profiles to enhance our understanding of observations of the  aerosol-intensive and extensive properties at different layers. This integration aims to understand and mitigate remote-sensing-only biases, address uncertainties, and characterize the reliance on assumptions.
Open Data for Impact: how to use the Open Data on Amazon Web Services to advance analysis
Authors: Stoner Chris
Published: July 2024

When data is made accessible in the cloud, you can shorten the time to insights and help build impactful offerings for climate modelers and stakeholders. When data is shared on the Registry of Open Data on AWS, anyone can analyze it and build services on top of it using a broad range of compute and data analytics products, including Amazon EC2, Amazon Athena, AWS Lambda, and Amazon EMR. Sharing data in the cloud lets data users spend more time on data analysis rather than data acquisition. Just putting data into the cloud isn’t enough. In this session, the Amazon Web Services (AWS) Open Data team will discuss different options for creating cloud-optimized versions of data, that can be more efficiently utilized in the cloud. We will discuss the various options and trade-offs, such as when to create a full cloud optimized copy versus when to create a subset for your user community. We discuss how to match up the different options based on different levels of traffic, newness of data, and typical types of processing of the data. We will show how to use high quality climate and environmental datasets with the Registry of Open Data on AWS when they are cloud optimized and when they are not, and discuss the time difference for the data consumer between each option. Additionally, we will also show how to harness these data using low- and no-code machine learning tools such as SageMaker and SageMaker Studio Lab, using cloud-optimized capabilities such as subsetting, and how these capabilities can speed up the time from data to decision.
Probabilistic minute-scale forecasting of solar energyacross Europe
Authors: Meyer Angela , Carpentieri Alberto , Schuurman Kevin
Published: July 2024

Solar energy plays a major role in climate change mitigation. With rising shares of solar power in the grid, short-term forecasts of surface solar irradiance (SSI) are becoming increasingly important for grid operators to enable cost-eDicient supply and demand balancing. Solar nowcast models provide estimates of SSI from minutes to hours ahead. Accurate solar nowcasts are required across spatially extensive areas as most solar power is generated by decentralised photovoltaic systems. Such regional-scale SSI estimates can be derived from geostationary satellites, like Meteosat, that monitor Earth in visible and infrared bands.Existing regional-scale solar nowcast models are usually deterministic, lacking forecast uncertainty awareness, and require satellite Level-2 products of SSI as input obtained from radiation retrievals such as Heliosat. We present the first probabilistic regionalscale solar nowcast models, SolarSTEPS and SHADECast (Carpentieri et al., 2023, 2024), an autoregressive model and a generative diDusion model, that can be applied to regions ranging from tens to several thousand kilometers in extent. Our solar nowcast models improve forecast accuracy and reliability in all cloudiness conditions compared to existing models. SHADECast extends the forecast horizon of our state-of-the-art SolarSTEPS model by 26 minutes at lead times of 15 minutes to 2 hours. We also present a deep-learning-based emulator of Heliosat SARAH-3 (Pfeifroth et al., 2021) that estimates instantaneous SSI across Europe with similar ccuracy as SARAH-3. We demonstrate that the emulator, a convolutional residual network, can even outperform SARAH-3 in SSI accuracy when a subsequent finetuning step is added in which the emulator is retrained on pyranometer stations, resulting in more accurate SSI initialisations for solar nowcast models. The emulator estimates SSI at kilometer-scale and 15-minute intervals based on visible and infrared images of Meteosat's SpinningEnhanced Visible and Infrared Imager. Pyranometers from BSRN, IEA-PVPS and European national weather services were employed for emulator finetuning and testing. Carpentieri, A., S. Pulkkinen, D. Nerini, D. Folini, M. Wild, A. Meyer, 2023, Intraday probabilistic forecasts of surface solar radiation with cloud scale-dependent autoregressive advection, Applied Energy, 351, doi:10.1016/j.apenergy.2023.121775Carpentieri, A., D. Folini, J. Leinonen, A. Meyer, 2024, Extending intraday solar forecast horizons with deep generative models, arXiv:2312.11966, doi:10.48550/arXiv.2312.11966Pfeifroth, U., J. Drücke, J. Trentmann, R. Hollmann, 2021, SARAH-3 - a new satellite-based Cimate Data Record for surface radiation parameters from the CM SAF, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-454, doi:10.5194/ems2021-454
Exploring the climate suitability of Slovenia for the invasive species Spodoptera frugiperda (JE Smith)
Authors: Žnidaršič Zala , Curk Miha , Trdan Stanislav , Pogačar Tjaša
Published: July 2024

In the context of climate change and globalisation, the risks for invasive species are increasing. One example of this is Spodoptera frugiperda (JE Smith), commonly known as the fall armyworm (FAW). The FAW originates from the tropical and subtropical regions of America and has spread to most continents worldwide in recent years. Since the FAW has recently also spread to the Mediterranean regions of Europe, the aim of this study was to analyse the potential susceptibility of Slovenian agriculture to this pest, with specific emphasis on maize (Zea mais). Additionally, the climate suitability was assessed for four possible natural enemies of FAW. The climate suitability of the past and future climate in Slovenia was evaluated in relation to the latest available data on the global occurrence of FAW and its natural enemies. The analysis was carried out on the basis of historical data and climate model projections for temperature and precipitation. The climate projections included 6 sets of regionally downscaled model results from the EURO-CORDEX project for the RCP4.5 and RCP8.5 scenarios. The results show that an increasing probability of FAW occurrence in Slovenia throughout the 21st century, especially in the maize-growing areas of Slovenia with the largest areas under maize cultivation. The natural enemies whose climatological niches were calculated to be the most comparable to those of the FAW are parasiotids Telenomus remus and Trichogramma Pretiosum. The results of this study can serve as a tool for the possibilities of pre-emptive introduction of natural enemies of FAW to Slovenia, which is a method of biological pest control, in which the use of synthetic pesticides is replaced by the introduction and release of natural enemies of the pests.
Opposite spectral properties of Rossby waves during weak and strong stratospheric polar vortex events
Authors: Schutte Michael , Domeisen Daniela I.V. , Riboldi Jacopo
Published: July 2024

This study characterizes and contrasts Rossby wave activity during the 25 sudden stratospheric warming (SSW) and 31 strong polar vortex (SPV) events that occurred in the period 1979–2021. While the events are tied to a decrease or increase, respectively, in background flow, it is less clear how the associated properties of Rossby waves change, e.g., how their phase speed is affected. The goal is to identify the specific tropospheric and stratospheric waves exhibiting anomalous behavior during these events. Applying space-time spectral analysis to ERA5 reanalysis data allows us to assess both the wavenumber and the zonal phase speed of the waves.We find that SSW events are associated with a reduced phase speed of Rossby waves, first in the stratosphere and then in the troposphere, while SPV events are tied to a concomitant increase of phase speed across vertical levels. These phase speed anomalies become significant around the event and persist for 2-3 weeks thereafter. In the stratosphere, both SSW and SPV events are dominated by the change in the background flow, manifested as a systematic reduction or increase, respectively, in eastward propagation of Rossby waves across most wavenumber.In the troposphere, the effect of the background flow is complemented by changes in wave properties, with a shift towards higher wavenumbers during SSW events and towards lower wavenumbers during SPV events. This opposite response between SSW and SPV events is also visible in the meridional heat and momentum flux co-spectra, which highlight from a novel perspective the connection between stratospheric Rossby waves and upward propagation of waves.
Improvements of shallow cloud and boundary layer parameterizations in the AROME-France model.
Authors: Marcel Adrien , Riette Sébastien , Ricard Didier , Lac Christine
Published: July 2024

Shallow convection affects the shape of the Planetary Boundary Layer (PBL) and has a significant impact on low cloud formation, and thus on model computed mean tendencies and radiation budget. Recently, observational studies have been used to compare AROME (the kilometer-scale operational Numerical Weather Prediction model used at Météo-France) versus in situ observations of the radiation budget components. Comparisons with several measurement sites (Meteopole Flux, Sirta) have shown biases of solar radiations in AROME. Extended investigations have attributed a part of these biases to shallow convection scheme representing the PBL with rather poor performance, in particular stratocumulus clouds. The present work aims to investigate and improve the parameterizations used in AROME and Meso-NH models (both models share the same 1D physics package). Inconsistencies and bugs have been corrected, while new parameterization components and updates of the Eddy Diffusivity Mass Flux scheme (EDMF) have been added from the literature. This includes changes in the shallow convection, turbulence, subgrid cloud and microphysics schemes, from the updraft hypothesis to shallow cumulus subgrid precipitation. We use the Single Column Model (SCM) version of AROME (MUSC) to evaluate the adjustments brought to the model on well-known idealized cases of cloud development like cumulus over land (ARMCu) or ocean (RICO), stratocumulus (FIRE), transition cases (SANDU) and dry convection (IHOP, AYOTTE) as well. The Meso-NH research model is used to perform Large Eddy Simulations (LES), in which conditional sampling methods and diagnostics are applied to retrieve resolved quantities that serve as a reference for evaluating parameterizations. In addition, the semi-automatic tool High-Tune Explorer helps us to explore the n-dimensional space of free parameters of the parameterizations, and thus closes the EDMF scheme and then gives us hints on the sensibility over the set of relevant parameters. This complete update of AROME has shown significant improvements especially for stratocumulus cases, triggered precipitation and cloud formation on most of the cases.
Three-dimensional (3D) characteristics of heat waves in EURO-CORDEX climate models
Authors: Plavcová Eva , Lhotka Ondřej , Kyselý Jan
Published: July 2024

Heat waves are regarded as one of the major hazards in relation to climate change but their climate change scenarios are laden with uncertainties due to the inability of RCMs to simulate their driving mechanisms properly. We aim to advance in understanding of processes governing heat waves in climate models by analysing them as three dimensional (3D) phenomena.The ensemble of 9 EURO-CORDEX regional climate models (RCMs) with lateral boundary conditions provided by the ERA-Interim reanalysis is analysed over Middle Europe in the 1989–2008 period. We apply a novel approach to classify heat waves as the 3D phenomena and evaluate capabilities of the RCMs to reproduce their characteristics. Heat waves are defined based on positive mean daily temperature anomalies from the 95th percentile in near-surface, 850 hPa, and 500 hPa levels with temporal and spatial criteria imposed. Based on predominant locations of positive temperature anomalies we classify heat waves into four types: i) near-surface, ii) lower-tropospheric, iii) middle-tropospheric, and iv) omnipresent.We find that all RCMs overestimate the number of heat wave days, especially of the near-surface and lower-tropospheric types. By contrast, they underestimate the occurrence of omnipresent heat waves which indicates their inability to reproduce properly processes leading to the vertically extensive heat waves.Since large-scale atmospheric circulation is an important driver in heat waves development, we examine which circulation types are favourable and non-favourable to heat waves in the RCMs. We also analyse moisture characteristics of air masses linked to the days of heat waves. RCMs outputs are validated against the reference datasets (ERA5, EOBS) and by comparing their atmospheric circulation and humidity characteristics we explain some of the models’ biases in reproduction of the frequency and other characteristics of heat waves types.
A climatological perspective on cyclones and precipitation in the Eastern Mediterranean using potential vorticity-based classification
Authors: Gens Tali Sarit , Raveh-Rubin Shira
Published: July 2024

Eastern Mediterranean Cyclones (EMCs) are a major contributor to extreme weather in this region, including precipitation, strong winds, cold extremes or dust storms, significantly impacting the population and natural environment. Thus, understanding the relationship between EMC variability and associated impacts is key to understanding their predictability and forecasting these hazards. Previous approaches for cyclone classification over the Eastern Mediterranean region involved the cyclones’ location, intensity, or structure. Such approaches do not consider the involved processes and, therefore, result in limited physical interpretations of cyclone variability and associated impacts and predictability. Various processes come together to govern the genesis and development of EMC, and these processes have distinct signatures on the potential vorticity (PV) distribution. Here, we classify EMCs based on their associated upper-tropospheric PV structures into 6 clusters and analyse the impacts of resulting clusters.We find that each cluster has its own signature of precipitation pattern. For each of the seasons, there are dominant clusters that bring extreme precipitation. In particular, two clusters of anti-cyclonic Rossby wave breaking PV patterns dominate the eastern Mediterranean's annual precipitation. Evidently, a strong ridge upstream of the PV trough has a greater impact on precipitation and temperature extremes than the PV pattern with a weak ridge upstream. Moreover, cyclones with low PV values, which correspond to heat-lows in the transition seasons, have an increasing trend in their occurrence frequency. This classification approach enhances our understanding of the link between cyclone variability and their surface impacts in the region through processes reflected in upper-level PV distributions. These findings could benefit strategies for managing the societal and environmental impacts of EMCs at weather and climate timescales.
Climate Change Impacts on Montenegrin Viticulture
Authors: Fernandes Antonio , Kovač Nataša , Fonseca André , Fraga Hélder , Radonjić Sanja , Simeunović Marko , Menz Christoph , Costafreda-Aumedes Sergi , Santos João
Published: July 2024

Montenegro is located on the Balkan Peninsula. This country portrays a complex topography, and so various climate types are found, from alpine conditions in inland mountains to a Mediterranean climate in valleys and near the coast. The Mediterranean conditions allowed Montenegro to develop a viticulture heritage, which is becoming threatened by climate change. Most of the vineyards are located in the vicinity of the capital, Podgorica, where the temperature will certainly increase. In this sequence, the present study aimed to expose the climate change impacts on Montenegrin Viticulture, resorting to a high-resolution dataset CHELSA (≈ 1km). Temperature data from the weather station was compared to the CHELSA dataset for the historical period, revealing that the dataset is accurate. Thermal bioclimatic indices were analyzed for the historical period 1981-2010, and for the future period 2041-2070. Projections were ensembled for 5 climate models, and encompassed 3 Shared Socioeconomic Pathways, SSP1—2.6, SSP3—7.0, SSP5—8.5. The results revealed that, regardless of the SSP scenario, the temperature increase threatens Montenegrin viticulture, as the baseline conditions will be surpassed. Temperature increase will be major during the warmest quarter, which will be around 4 °C. Regarding the growing season, the average temperature from April to October, will reach 24 °C, which is above traditional thresholds for successful grape growing. The mean annual temperature will increase by more than 2 °C, and as a consequence, the growing season will be expanded, by more than 1 month, anticipating phenological events. During the historical period, the Winkler Index is 2530 °C, suitable for wine production, but in future, there will be excessive heat in grapevines, as the Winkler index will become higher than 3000 °C, increasing the risk of thermal and hydric stress. These findings are alarming, serving as a careful alert that adaptation measures should be applied in the short term, to guarantee the long-term sustainability and productivity of Montenegrin Viticulture.Acknowledgments: Research funded by National Funds by FCT – Portuguese Foundation for Science and Technology, under the project UIDB/04033/2020 and LA/P/0126/2020 (https://doi.org/10.54499/UIDB/04033/2020)This research was funded by the MONTEVITIS project “Integrating a comprehensive European approach for climate change mitigation and adaptation in Montenegro viticulture”, funded by the European Union’s Horizon Europe—the Framework Programme for Research and Innovation (2021–2027)—under grant agreement nº 1010594
Extreme precipitation and aridity in Iberian Peninsula: a new high-resolution susceptibility analysis over 1950–2022
Authors: Claro André , Fonseca André , Fraga Helder , Santos João
Published: July 2024

The goal of this research was to assess the aridity of the Iberian Peninsula (IP) and its susceptibility to precipitation extreme events (PEEs) throughout a long historical period of 1950–2022 and a shorter historical period of 1981–2022, by calculating two aridity indices, eight extreme precipitation indices, and two recently-developed extreme precipitation susceptibility indices (EPSIs), namely a composite index and a principal component analysis-based index. The calculations were based on ERA5-Land reanalysis data, previously bias-corrected with observational data from the Iberia01 dataset as a baseline in a 1971–2015 overlapping period, using a quantile-mapping approach. By performing a trend analysis with the Mann-Kendall non-parametric test for the two study periods, annual and seasonal drying trends over southwestern, central and northeastern regions were detected, as well as an annual wetting trend over the southeast. The assessment of PEEs’ contribution to total precipitation presented higher values, of around 24% to 28%, over eastern IP, and showed that it is increasing in several coastal regions during winter, as well as in north-central regions during summer and annually. The most susceptible regions to extreme precipitation events (high to very high susceptibility) are found on the mountains’ Atlantic-facing (western IP mountains) or Mediterranean-facing (eastern IP mountains) side and correspond to approximately 50% of the IP territory. The IP’s inner regions present low to moderate susceptibility. The results achieved agree with previous studies’ results, and give a highly detailed illustration of the PEEs’ susceptibility map of the IP and the recent past trends of all IP regions, which is a novelty comparatively to past studies. These data have a variety of applications, e.g., to improve assessment and mitigation of urban flood risks, mitigate water scarcity in the agro-food industry, or prevent crop destruction during extreme precipitation events.Acknowledgments: Research funded through National Funds by FCT – Portuguese Foundation for Science and Technology, under the project UIDB/04033/2020 and LA/P/0126/2020 (https://doi.org/10.54499/UIDB/04033/2020). The corresponding author thanks FCT and MIT Portugal Program for their support through the grant PRT/BD/154652/2023.
Climatology of near-surface wind speed from observational, reanalysis and high-resolution regional climate model data over the Tibetan Plateau
Authors: Minola Lorenzo , Martin Nuria P. Plaza , Zhang Gangfeng , Ou Tinghai , Kukulies Julia , Curio Julia , Guijarro Jose A. , Deng Kaiqiang , Azorin-Molina Cesar , Shen Cheng , Pezzoli Alessandro , Chen Deliang
Published: July 2024

The Tibetan Plateau is the highest and most extensive upland region in the world, with an average elevation over 4,000 m above sea level. It is referred to as the “water tower of Asia” because it is the source region of several major rivers in Asia, hence providing water resources to almost two billion people. As near-surface wind speed plays a key role in regulating surface evaporation and thus the hydrological cycle, among other implications, it is crucial to explore its spatio-temporal characteristics over this region. However, due to its vast and complex geographical area with steep terrain, high elevations, and harsh environmental conditions, in-situ measurements are scarce and mostly located in valleys, limiting the understanding of wind speed climate across the Tibetan Plateau.This study explores the climatology of near-surface wind speed over the Tibetan Plateau by using for the first time homogenized (i.e., variations caused by non-climatic factors have been removed) observations from 104 measuring stations, together with reanalysis products and regional climate model simulations. By exploring wind characteristics with a focus on seasonal cycle through cluster analysis, three regions of distinct wind regimes are identified: (1) the central Tibetan Plateau; (2) the eastern and the peripheral areas of the plateau; and (3) the Qaidam basin, a topographic depression strongly influenced by the blocking effect of the surrounding mountainous terrain. Notably, the ERA5 reanalysis, with its improvements in spatial and temporal resolution, model physics and data assimilation, demonstrates closer agreement to the measured wind conditions than its predecessor ERA-Interim. However, the newest ERA5-Land product does not show improvements compared to ERA5, most likely because they share most of the parametrizations. Furthermore, the two analyzed dynamical downscalings of ERA5 fail to capture the observed wind statistics and exhibit notable biases and discrepancies also when investigating the diurnal variations.
Analysis of splitting supercell storm based on the Doppler weather radar data
Authors: Dimitrova Tsvetelina , Barakova Denitsa , Georgiev Stefan , Kadiyska Nadezhda
Published: July 2024

The impact of climate change suggests that the environments favorable for severe thunderstorms development, in particular supercells will become more frequent across Europe.Bulgaria is a country with a high frequency of thunderstorms during the warm season. Every year, the development of supercells is observed over its territory, and storm splitting process is not rare. This phenomenon can pose difficulties for the forecasters. For this reason, correct radar analysis and estimation of wind shear are of great importance to the forecasting and the nowcasting.A case of a splitting supercell that developed on the 29-th of May 2022 is analysed. Radar data from S-band Doppler radars were used. All radar products shown throughout the study were generated by the Interactive Radar Information System (IRIS) Analysis of Vaisala.On the 29-th of May 2022 over the territory of Bulgaria there was a 500-hpa trough with the center of the upper level low over the Scandinavian Peninsula. On the surface pressure chart there was a sequence of cold fronts. The flow on the upper and near-ground level converged (from SW, following the circulation of the trough and from SE, following the movement of the Mediterranean cyclone respectively). This leads to strengthening of the vertical wind shear. This synoptic setup was favorable for severe convection development. Data for instability indices and wind hodograph were additionally used.In the afternoon an isolated convective cell first appeared at 15:45 EET in the layer between 5th (-9.4°C) and 10th km (-42.4°C) with maximum radar reflectivity Zmax = 29 dBZ. The cell grew rapidly and after 20 minutes Zmax was 60 dBZ and the storm had characteristics of a supercell. A mesocyclonic vortex was visible on the Doppler radar. The cell acquired a V-shaped structure, and later on it split into two cells, both of which retained high radar reflectivity. Based on the radar data the presence of the typical supercellular radar signatures, such as weak echo region (WER), bounded weak echo region (BWER), hook echo, and TBSS are presented. After splitting, the left-moving (LM) and the right-moving (RM) cells kept high values of radar characteristics, but RM had shorter life time and its intensity was lower.  Both cells produced multiple convective hazards along their paths. The hazards associated with the RM were severe hail (hailstone sizes greater than 2 cm in diameter), strong wind gusts, heavy rain and intense lighting activity. The storm severity and the presence of large hail stones were confirmed by maximum radar reflectivity, VIL (Vertical integrated liquid) and VILD (VIL density). 
Differences in modelled pavement temperature at German Road Weather Stations
Authors: Trepte Sebastian , Schmitz Ralf
Published: July 2024

The Canadian Meteorological Service's road surface model METRo is used by the DWD to simulate road surface temperatures at around 1,500 road weather stations (RWS). The modelled road temperature from METRo, together with other meteorological parameters from Model Output Statistics (MOS), is displayed on operational websites of winter services such as Autobahn GmbH.The MOS provides a combined statistical interpretation of the IFS-HRES (ECMWF) and ICON (DWD) model forecasts at individual weather stations. The technique is based on multiple linear regressions by minimizing the Root Mean Square Error (RMSE). A wide range of model variables are used as predictors, including unobserved variables, as well as surface observations, precipitation radar and lightning detection for nowcasting.The MOS is used to apply forecasted variables, such as air temperature, dew point, and precipitation parameters, to the METRo. The METRo then calculates the pavement temperature for the next seven days.The RWS provide temperature data from federal roads, country roads, and international airports. The data undergo automated plausibility checks and are used to train the MOS and in METRo's data assimilation.The MOS also calculates pavement temperatures. However, it has not been used operationally in the past due to a lack of quality testing. Thanks to years of automatically quality-assured measurement data at the RWS, the MOS can simulate the road temperature fairly well.The RMSE was used to evaluate the accuracy of the METRo and MOS forecasts. It was based on 2.2 million quality-assured measured and modelled pavement temperatures at around 1,100 RWS stations between November 2023 and January 2024.The RMSE was averaged over forecast times covering the next day and night, which is crucial for winter services. METRo's RMSE is 1.5°C, indicating high prediction accuracy given the pavement temperature measurement. MOS predictions are even better, with a calculated RMSE of 1.3°C. The largest differences between the models occur at noon and at night. Both models' forecasts are more accurate in regions of Germany where the data quality and quantity of observed pavement temperature at the RWS is higher.Automated plausibility checks are crucial, as demonstrated by experiences with pavement forecasting in operational and pre-operational mode. If such checks are in place, a MOS can outperform a physical model, even with parameters that are difficult to observe. This is especially relevant for future developments in the field of AI.
Recent progress in downscaling key parameters describing local daily precipitation statistics in the Nordic countries
Authors: Benestad Rasmus , Parding Kajsa M.
Published: July 2024

Shape of mathematical curves describing local weather statistics are systematically influenced by large-scale conditions and geographical factors, and we present results suggesting that it is possible to downscale this kind of information directly. We downscale cumulative probability functions for daily precipitation and intensity-duration-frequency (IDF) curves for estimating return values of intense sub-daily rainfall. Recent progress in reanalyses such as ERA5 has made it possible to use wet-day frequency and mean precipitation intensity as predictors to compute local variations in wet-day frequency and mean intensity respectively, and we use a simple and approximate formula from earlier studies with these two parameters to specify the shape these curves. Downscaling the shape of such curves may be referred to as ‘downscaling climate’ if we regard ‘local climate’ as the statistical description of various weather parameters. This approach is distinct from the more traditional approach ‘downscaling weather’, where one seeks to estimate particular local states for instance on a day-by-day basis. We also present work on downscaling the shapes of pdfs and IDFs for large multi-model ensembles for the application in climate change adaptation efforts. Our eanalysis is accompanied by an evaluation of both methodology and the global climate models' (GCMs) ability to reproduce observed large-scale climatic variability in terms of the salient spatio-temporal covariance structure. When it comes  to providing future regional climate projections, we also emphasise that it is important to combine different strategies for downscaling, e.g. regional climate models (RCMs) and empirical-statistical downscaling (ESD) that are based on different assumptions, for getting robust future regional climate projections.
Advancements in atmospheric nitrous oxide eddy covariance flux measurements
Authors: Bogoev Ivan
Published: July 2024

Nitrous oxide (N2O) is stratospheric ozone depleting, long-lived green-house trace gas with a 100-year global warming potential 298 times greater than carbon dioxide, (IPCC, 2013). Microbial production processes in agricultural soils contribute significant portion of the total N2O emissions. Nitrogen fertilization and manure management are the main drivers for the N2O fluxes at the soil-atmosphere interface, which are highly variable in space and time. The eddy covariance (EC) technique provides a continuous landscape-scale flux estimates with high temporal resolution. The availability of mid-infrared tunable diode lasers (TDL) operating at room temperature allowed the development of high precision fast response gas analyzers suitable for the EC method. In this study we describe the design and evaluate the performance of a novel, field-deployable low-power N2O EC system. The system consists of compact closed-path TDL absorption spectrometer with a 1 m single-pass, small volume optical cell which allows the use of low power DC pump. It features a cyclone-based inertial particle separator acting as a non-barrier filter to prevent contamination of the optical components with minimal attenuation of N2O fluctuations. The effects of absorption line broadening and dilution due to water vapor are minimized using sulfonated tetrafluoroethylene ionomer intake tube acting as water vapor permeable membrane to dry the air sample. The new N2O EC system was deployed in manure fertilized agricultural cornfield 3 m above the canopy and was collocated with an open-path CO2 and H2O infrared gas analyzer and ultrasonic anemometer (IRGASON). Spectral analysis and Ogive functions demonstrated that the new EC system achieves adequate performance and excellent frequency response to measure N2O fluxes under a wide range of meteorological conditions. Further refinements for stable and prolonged unattended operation are described.
Characteristics of dry and hot extreme events in Croatia
Authors: Boras Marijana , Herceg Bulić Ivana
Published: July 2024

This study analyses the characteristics of compound dry and hot (DH) extreme events in Croatia. As in many parts of the world, such events have become more frequent in Croatia in recent decades. In this work, measured data collected at local meteorological stations were used. The analysis covers the period from 1963 to 2021. Croatia’s geographical location includes parts of the Pannonian Basin, the mountainous regions of the Dinarides and the coastal parts of the eastern Adriatic. Therefore, both continental and coastal stations are included in the analysis.    In this study, the temporal and spatial characteristics of DH events are analysed. Precipitation and daily maximum temperature data were used to define DH extremes. The frequency and intensity of DH events in different time periods were analysed. As an additional measure of the dryness of atmospheric conditions, the characteristics of the water vapour pressure deficit during DH events were analysed. The results show an increase in DH events at all stations considered, with the increase being somewhat more pronounced at the continental stations. In addition, an assessment of the compound effect of drought and high temperatures was calculated using cumulative heat. This measure also shows that the characteristics of compound DH events are changing and becoming more intense.  The impacts of compound extremes can have more severe consequences for humans and the environment than the occurrence of individual extremes alone. The change in the characteristics of extremes in terms of their intensification and increasing frequency can cause significant damage to agriculture, the energy sector, etc. Therefore, it is necessary to continue research on DH events to better understand the mechanisms of their occurrence and the atmospheric conditions that support them. With better preparation and adaptation, it may therefore be possible to improve the risk assessment of extreme events and reduce their harmful consequences.
Global Streamflow Seasonal Forecast by a novel two-way AOGCM/Land/River coupling
Authors: Narváez Campo Gabriel Fernando , Ardilouze Constantin
Published: July 2024

In disaster prevention, water management, agriculture, and hydropower generation, an accurate seasonal streamflow forecast (SSF) is crucial, while global approaches become necessary in regions lacking forecast systems. This study evaluates the Météo-France seasonal prediction system (SYS8) skill for global SSF through hindcasts of river discharges. Contributing to Copernicus Climate Change Services (C3S), the SYS8 employs a fully coupled Atmosphere-Ocean General Circulation Model (AOGCM) with an advanced river routing component (CTRIP) interacting with the ISBA land-surface scheme. This research is part of the European project CERISE, which aims to enhance the C3S seasonal forecast portfolio by improving land initialisation methodologies.SYS8 derives land initial conditions from a historical coupled initialisation run where land-river is weakly constrained, while atmosphere/ocean is nudged to the ERA5/GLORYS re-analysis. This study improves the initialisation run by relaxing soil moisture to fields reconstructed from an offline land simulation. Daily streamflow ensemble hindcasts of 25 members are generated in a 0.5° grid, with a lead time of up to 4 months initialised on the 1st of May/August/November between 1993-2017, allowing hindcasting summer (JJA), fall (SON) and winter (DJF) seasons. Forecast skill is assessed against discharge observations in 1608 monitored basins worldwide (with areas > 3000 km²) using deterministic and probabilistic metrics. The classical Ensemble Streamflow Prediction approach (ESP) is a benchmark for evaluating the control SYS8 skill and the additional skill of moisture nudging.Globally, the control SYS8 skill is superior to the ESP, but the bias is higher in dry regions such as northeastern Brazil, western US and some rivers in Spain and Africa. On the other hand, the hindcast with enhanced land surface initial conditions outperformed the control SYS8 and benchmark ESP, especially during summer. Local skill degradation in higher latitudes will be discussed. Still, overall positive results support ongoing efforts to enhance land initialisation through a global land data assimilation system.
Convective and stratiform precipitation in ALADIN-CLIMATE/CZ
Authors: Rulfová Zuzana , Beranová Romana
Published: July 2024

When studying precipitation in mid-latitudes, it is appropriate to focus on the basic types: convective and stratiform. Stratiform precipitation is inherently more crucial for agriculture, and a decrease in its amounts, coupled with higher temperatures, can lead to a greater soil moisture deficit. Conversely, more frequent and intense convective and stratiform precipitation may result in floods and landslides, thus posing additional hazards and causing damages.In this study, we analyze the characteristics of convective and stratiform precipitation (e.g. annual cycle, proportion of convective and stratiform precipitation, trends, and extremes) in the Czech Republic using data from the regional climate model ALADIN-CLIMATE/CZ, operated by the Czech Hydrometeorological Institute. The model has been upgraded to achieve a convection-permitting resolution of 2.3 km, along with the implementation of a non-hydrostatic, fully elastic dynamical core. As the outputs from this model will be utilized to enhance the accuracy of climate change scenarios for the Czech Republic region, it is necessary to evaluate the simulation accuracy of convective and stratiform precipitation.We analyze two datasets of convective and stratiform precipitation from the ALADIN-CLIMATE/CZ model. Firstly, we examine direct model-simulated convective and stratiform precipitation. Secondly, we analyze convective and stratiform precipitation separated from the model's output using a physically based algorithm. While the algorithm is applicable over complex terrain, it exhibits better efficiency over land with smooth topography. Therefore, we believe it is suitable for the Czech Republic region. The characteristics of convective and stratiform precipitation derived from both datasets are compared to each other, as well as to observations from SYNOP stations and radar data
Climate change signals based on different bias-corrected EURO-CORDEX simulations over Hungary
Authors: Simon Csilla , Torma Csaba Zsolt , Kis Anna
Published: July 2024

Climate change is one of the major challenges that humanity faces today. The latest IPCC report highlights that an increase in the frequency and intensity of extreme weather and climate events are likely to occur in the future. Global and regional climate models (GCMs and RCMs, respectively) are key tools for climate research which provide valuable information about future climate change. However, model simulations are subjects to uncertainties of various origins. In order to quantify and reduce these uncertainties it is recommended to evaluate several RCM simulations together, as members of an ensemble, and/or apply a bias-correction method. The implementation of a bias-correction procedure has become a standard step in the process of using climate model outputs, since unbiased data are required for impact studies. During this procedure, the raw simulated meteorological variables are adjusted to measurements, so the use of a high-quality, observation-based dataset as a reference is a crucial point for all bias-correction methods, in addition, the choice of the calibration period is also an important factor.In our research an ensemble of 5 RCMs (CCLM, HIRHAM, RACMO, RCA, REMO) driven by 4 different GCMs are investigated from the framework of EURO-CORDEX with the finer (0.11°) horizontal resolution for the historical (1976–2005) and the scenario (2006–2099) time periods under two radiative forcing scenarios (RCP4.5 and RCP8.5). Our aim is to investigate how the choice of the reference dataset and different calibration periods affects the results of the expected changes based on the different bias-corrected datasets, focusing on Hungary. For this purpose, a bias-adjustment was carried out by applying the internationally widely used percentile-based quantile mapping method on a monthly level for 4 variables: daily minimum- and maximum temperature, mean temperature and precipitation. The most accurate, measurement-based and quality controlled HuClim dataset was used as a reference. Two calibration periods were chosen from HuClim for the bias-correction: an earlier (1976–2005) and a more recent (1993–2022) one, thus creating two, different bias-corrected databases. Beside them, a third bias-corrected database was also analyzed, namely, the same RCMs provided by the EURO-CORDEX community and adjusted using the MESAN reanalysis data based on the 1989–2010 calibration period. In the present study, we analyze and show the results of the different bias-adjusted RCM simulations for two future periods (2021–2050; 2070–2099) focusing on the detected change of climate indices.
A series of major sudden stratospheric warming events in winter 2023/2024
Authors: Vorobeva Ekaterina , Orsolini Yvan
Published: July 2024

During winter months, the high-latitude stratospheric circulation is characterized by a prominent eastward flow, also known as the stratospheric polar vortex. However, this flow pattern is regularly disturbed by planetary waves (PW) originating from the troposphere, that occasionally cause either a temporary weakening or a complete reversal to a summer-like westward flow regime. Such events are called minor and major stratospheric sudden warmings (SSWs), correspondingly.  Major SSW events tend to take place approximately every two years on average.In winter 2023/2024, the stratospheric polar vortex was weak and highly variable. Three major SSWs were detected based on the standard definition at 10 hPa in the period between January and March 2024. Two short duration events (under 9 days) and one long duration event (over 20 days) occurred in mid-January, mid-February and early March, respectively. This number of SSW events within a single winter is notably uncommon. In this study, we investigate the dynamical evolution of the polar stratosphere, the tropospheric forcing and precursors, and the surface impacts throughout the winter, with a focus on the first two short-duration events. The study is mostly based on the fifth generation ECMWF reanalysis (ERA5) data.It has been shown in previous studies that synoptic evolution of blocking highs and anomalous lows in the upper troposphere conditions the duration of SSW events. For the short SSW event in January, we show that blocking highs developed over the Euro-Atlantic region were indeed responsible for enhanced planetary waves activity prior to the SSW central date.  Later an anomalous, westward-propagating High over the North Pacific suppressed the zonal-mean PW activity flux into the stratosphere and caused a quick termination of the event. Despite a similar development timeline, the second short-duration SSW event in February had a different tropospheric forcing. In this case, we investigate the role of snow accumulation in creating an increased meridional heat flux over Ural Mountains. Finally, a canonical long-duration Major SSW developed in early March.
Improving wind and temperature forecast accuracy using eXtreme Gradient Boosting and Quantile Mapping
Authors: Ylinen Kaisa , Tack Andreas
Published: July 2024

A machine learning based bias correction for MetCoOp temperature, wind speed and wind gust forecasts has been implemented at the Finnish Meteorological Institute (FMI). The MetCoOp model, with a horizontal resolution of 2.5 km, serves as the primary source of short-range forecasts at FMI. However, like many numerical weather prediction models, it suffers from systematic errors that can negatively affect forecast accuracy. To address this issue, we utilize eXtreme Gradient Boosting (XGB) to correct these systematic errors in wind and temperature forecasts. Although XGB effectively reduces the mean absolute error of the forecasts, it tends to underestimate high wind speeds. To mitigate this limitation, we additionally apply quantile mapping (QM) bias-correction technique for wind speed and wind gust forecasts.Our model uses 2.5 years of training data containing various weather parameters as predictors, including time-lagged variables. Also station and time dependent features such as forecast hour and month, are used. The model is trained to predict forecast errors at station points and subsequently gridded to the original resolution using the gridpp method. The bias correction information is then applied to the original MetCoOp forecast fields of 10-m wind speed, wind gust, and 2-m temperature, resulting in more accurate forecast fields.The machine learning-based model is currently in pre-operational use at FMI, with the primary aim of reducing the manual editing traditionally performed by meteorologists to correct forecast errors. Preliminary verification results indicate that bias-corrected forecasts have smaller errors on average compared to uncorrected MetCoOp forecasts. Importantly, the model improves the accuracy of wind alerts and warnings, thereby providing significant value in critical decision-making situations.