bluuebunny/crossref_metadata_2025 huggingface dataset:
Applying a regression model to estimate honey production from weather variables
Authors: Vincze Csilla , Leelőssy Ádám , Mészáros Róbert
Published: July 2023

Beekeeping is a special agricultural field that provides essential resource to other agriculture through pollination services; while it creates value with honey production. Beekeeping is also one of the most weather-sensitive agricultural field. Weather influences honey production in three ways: (1) with seasonal scale weather patterns influencing phenological growth; (2) short-term (daily) weather influence on nectar production and (3) direct weather influence on bee flight.
In this study, a linear time series model is presented to predict honey production describing weather as external variable. Honey production time series was derived from hourly values of total bee and honey weight per hive, measured with hive scales located in apiaries in Debrecen, Hungary in the black locust blooming periods of 2021–2022. Data was pre-processed to remove the seasonal blooming pattern and the typical two-peak diurnal pattern of gathering by bees. Meteorological data was obtained from nearby monitoring sites of the Hungarian Meteorological Service. Mean and maximum temperatures, as well as global radiation was used as external variable in the time series regression model. Consecutive 1-day hindcasts of honey production were evaluated against observation data. Model RMSE was found to be approximately 0.6 sigma (in standardized units) over a range of model settings. However, the fitted regression coefficients had large error and were barely significant.
The method presents a statistical approach to create hive-specific daily forecasts of honey production for hives equipped with scales. The definition of the seasonal pattern (i.e. phenology) is a crucial step to create a stationary time series for predictive modeling.
Cloud patterns clustering in geostationary satellite imagery: Investigating traditional and Deep-Learning-based feature extraction techniques for solar energy forecast
Authors: Chea Nicolas , Cros Sylvain , Guillon Sébastien , Badosa Jordi , Tuomiranta Arttu , Haeffelin Martial
Published: July 2023

Forecasting solar energy is crucial for enabling its better integration into the energy mix. Short-term forecasts (up to 6h) have various applications, including grid integration and management, energy trading, energy storage management, and microgrid management. Photovoltaic (PV) production is highly variable and directly depends on solar irradiance reaching the Earth's surface. The stochastic component of solar irradiance variability is primarily related to its attenuation by the clouds.
Currently, cloud motion analysis methods applied to geostationary meteorological satellite images yield better results than Numerical Weather Prediction (NWP) models for short-term irradiance forecasting. Yet, their performance is limited in particular situations, such as convective or unstable cloud conditions. Deep Learning methods may overcome these limitations because of their ability to automatically extract complex spatiotemporal cloud patterns.
Prior to implementing a Deep Learning method, it is essential to analyse the data in order to understand its spatiotemporal characteristics. Clustering cloud patterns might allow us to better characterize cloud cover evolutions in distinct weather situations, thereby enabling the development of more accurate short-term solar energy forecasting models. Our research establishes a foundation for partition-based forecasts using multiple models, each model being adjusted to the distinct traits of each group.
In this work, we conduct a comparative analysis between traditional feature extraction techniques (e.g., statistical, textural, and temporal features) and Deep-Learning-based feature extraction (e.g., autoencoding) to evaluate their ability to discriminate specific cloud pattern evolutions (e.g., advections, convection, multi-layer, appearance/disappearance). Additionally, we assess and compare several clustering methods (e.g., K-Means and its variations, Hierarchical Clustering, Gaussian Mixture Model) using the extracted features as the input. Then, we evaluate the quality of the final partitioning using quantitative criteria and studying its physical consistency. Finally, we conduct an extensive study of the partitioning results, highlighting useful insights about the data such as the average duration of a given meteorological phenomenon. For this study, we use cloud albedo images derived from the HRV channel of the Meteosat Second Generation’s (MSG) SEVIRI instrument and focus on the Paris area.
The proposed feature extraction method enables us to perform clustering analysis that effectively distinguishes identifiable meteorological situations and cloud pattern evolutions. This is preliminary work for the development of a partition-based Deep Learning model for short-term solar energy forecasting, with the goal of addressing complex and diverse forecast scenarios using multiple models.
Modeling the drivers of urban fine PM pollution over Central Europe: contributions and impacts of emissions from different sources
Authors: Bartík Lukáš , Huszár Peter , Karlický Jan , Vlček Ondřej
Published: July 2023

Air pollution represents the most significant environmental health risk in Europe, which significantly impacts the health of the European population, especially in urban areas. The pollutants with the most critical threat to human health in European urban areas include fine particulate matter (PM), a mixture of aerosols with an aerodynamic diameter less or equal to 2.5 μm. To better understand the composition of fine PM in any urban area of Europe, it is important to identify and quantify the sources of its components. Such knowledge can be further used to propose effective strategies for reducing this type of pollution in this urban area. One of the commonly used ways to source attribution analysis of fine PM is to use sophisticated chemical transport models that can rigorously control: (1) the evolution of primary fine PM, (2) the formation of secondary inorganic and organic fine PM from gaseous precursors and their subsequent development, and (3) aqueous aerosol chemistry (e.i., all relevant processes connected with fine PM).
 
In this work, we utilized an offline coupled modeling framework consisting of the Weather Research and Forecast (WRF) Model version 4.0.3 and the Comprehensive Air quality Model with Extensions (CAMx) version 7.10 on the Central European domain with a horizontal resolution of 9 km for the period covering the years 2018 and 2019 to investigate the relationships between emissions from a wide range of anthropogenic activity sectors and the total concentrations of fine PM in six large cities of this region (Berlin, Munich, Prague, Vienna, Budapest, and Warsaw) from two different approaches. First, we analyzed the contributions of emissions from anthropogenic activity sectors to the total concentrations of fine PM using the Particulate Source Apportionment Technology (PSAT) tool implemented in the used version of the CAMx model. Second, using the zero-out method, we examined the impacts of emissions from the same anthropogenic activity sectors used in the source apportionment analysis on the total concentrations of fine PM. In this case, we additionally considered a dual implementation of organic aerosol chemistry/partitioning in the CAMx model by performing two sensitivity studies determining the impacts described above, each using one of the implementations. As part of the analysis, we compared the identified contributions with the impacts and discussed cases of mutual similarity between the two approaches.
Hybrid statistical-dynamical seasonal prediction of summer extreme temperatures over Europe
Authors: Famooss Paolini Luca , Ruggieri Paolo , Pascale Salvatore , Brattich Erika , Di Sabatino Silvana
Published: July 2023

Several studies show that the occurrence of summer extreme temperatures over Europe is increased since the middle of the twentieth century and is expected to further increase in the future due to global warming. Thus, due to the impacts that extreme temperatures have on socio-economic and environmental systems, predicting heat extremes and their statistics several months ahead is crucial.
State-of-the-art dynamical prediction systems show low skills in predicting European heat extremes on seasonal timescale. This is deemed to be due to the combination of the internal chaotic nature of the atmosphere and the underestimation of predictable component of the climate variability in the model ensemble. Recent studies have shown that our skills in predicting extratropical climate can be largely improved by refining the ensemble-based dynamical prediction systems with statistical post-processing techniques. Such techniques are based on sub-sampling the model ensemble by selecting only members that verify specific conditions.
The present study assesses the prediction skill of summer extreme temperatures over Europe on seasonal timescale in the Copernicus Climate Change Service (C3S) multi-systems seasonal forecasts for the period 1993—2016. Then, a hybrid statistical-dynamical prediction system is presented, where the model ensemble is sub-sampled by retaining only a subset of members which predict the summer extreme temperature statistics over Europe in agreement with a teleconnection-based statistical prediction (following Dobrynin et al. 2022). This approach relies on predictors of European heat extremes during spring season (e.g. sea surface temperature in the North Atlantic and tropical Pacific, soil moisture and sea-ice concentration) and thus allows us to retain only those members with a reasonable representation of summer heat extreme teleconnections.
Results on the skill of the hybrid statistical-dynamical prediction system and its potential applications for the health-sector are discussed.
Spatio-temporal measurements of turbulence in the ABL with a fleet of small uncrewed aircraft systems
Authors: Wildmann Norman , Kistner Johannes , Alexa Almut
Published: July 2023

Exchange and transport processes in the atmospheric boundary layer (ABL) are driven by turbulence on a wide range of scales. Atmospheric turbulence is also a critical parameter for the determination of wind loads on structures such as wind turbines. Measuring turbulence with single point measurements requires assumptions about the stationarity, isotropy and homogeneity of the flow. The same applies if turbulence is retrieved from remote sensing instruments such as lidars. It is however well known that realistic turbulence does appear in more complex three-dimensional structures. This is the case in the ABL in stable and convective regimes, but even more so in complex terrain flow or in disturbed flow such as in the wake of wind turbines.Without the infrastructure of meteorological masts and above applicable heights for such masts, in-situ point measurements of the three-dimensional wind vector are hardly available. We present a system to achieve simultaneous spatial measurements with a fleet of multirotor unmanned aircraft systems (UAS). The major benefit of this approach is, that true simultaneous measurements can be obtained without the need of expensive infrastructure. The SWUF-3D (Simultaneous Wind measurement with Unmanned Flight systems in 3D)  fleet was first deployed at the Meteorological Observatory Lindenberg - Richard Aßmann-Observatory (MOL-RAO) of DWD in association to the FESSTVaL campaign. With more than 1000 single flights, the system was validated against sonic anemometers at the 99-m mast in 2020 and 2021 and was able to provide reliable measurements of the wind vector with a RMSE below 0.3 m/s. We showed that turbulent eddies can be resolved with a time resolution of up to 2~Hz, unless the overall TKE level is below the noise threshold of the UAS measurements, which can be the case in stable ABL conditions. The results are verified by measurements in the wind tunnel of the University Oldenburg with an active turbulence grid under laboratory conditions in 2022. First measurements in the near wake of a wind turbine were also conducted in 2022. Additionally to the wind vector estimation, pressure, temperature and humidity sensors are carried by each UAS. Most recently, a fine-wire resistance thermometer was added to the setup, which measuring temperature much faster than with standard sensors. Fast response measurements of temperature and vertical wind allow the calculation of sensible heat flux additionally to the Reynolds stresses.Within the project ESTABLIS-UAS (Exposing Spatio-temporal structures of Turbulence in the ABL with In-Situe Measurements by UAS, funded by the European Union), the SWUF-3D fleet will be enhanced to allow operation in larger areas and in complex terrain. A fleet of up to 100 UAS shall be deployed during TEAMx (Multi-scale transport and exchange processes in the atmosphere over mountains – programme and experiment) in 2025.
Providing urban climate analyses to support climate sensitive urban planning and climate change adaptation
Authors: Kainz Astrid , Hahn Claudia , Zuvela-Aloise Maja , Reinwald Florian , Thiel Sophie , Zimmermann Daniel
Published: July 2023

Urban areas are particularly affected by climate change due to continuing urban development and an increased occurrence of extreme weather events like heat waves that lead to increasing heat stress on urban population. Furthermore, the occurrence of heavy rain events and dry periods are expected to rise, posing additional challenges to cities because sealed surfaces inhibit the infiltration of water into the soil and thus increase storm water runoff and reduce the water availability for plants. The project GreenAdaptation, funded by the Austrian Climate Research Program (ACRP), examines crucial steps necessary to support climate change adaptation and to develop urban planning recommendation and climate analysis maps for cities and municipalities, as they provide an important tool to support urban planners and local administrations towards decision making and to facilitate future urban planning processes.
Urban climate analyses represent an essential component in the development of urban planning recommendation maps. Here, we focus on already existing climatological datasets as well as urban climate modelling tools. To gather pre-existing knowledge regarding temperature and precipitation change, a set of climate indices with respect to heat, heavy rainfall and drought are selected together with practitioners implementing adaptation measures. The climate indices are calculated using available observational datasets from the Austrian semi-automatic meteorological station network (TAWES) and Austrian climate scenarios (OEKS15) to assess the past and indicate future development for the chosen municipality. Urban climate simulations carried out by the urban climate model MUKLIMO_3, developed by DWD (German Meteorological Service), are used to analyze overheating and to identify areas particularly affected by heat, taking into account city-specific structures and land use information, as well as meteorological conditions. Furthermore, a digital elevation model is analyzed to identify areas potentially prone to flooding. Merging the derived maps will indicate critical zones prone to extreme weather impacts, but also areas with a high synergy potential for climate adaptation.
The methodological framework for the consolidation and integration of the analyses into urban planning recommendation maps will be demonstrated and results of the urban climate analysis will be shown for the Municipality of Perchtoldsdorf, Lower Austria.
Characterising the effect of the transition zone of clouds and aerosol on solar surface irradiance using a dense pyranometer network
Authors: Witthuhn Jonas , Kalesse-Los Heike , Deneke Hartwig
Published: July 2023

Solar energy is an important source of renewable energy, and understanding its variability due to clouds and aerosols is critical for efficient and reliable solar energy systems. The interaction between clouds and aerosols can have a significant impact on surface solar irradiance during broken cloud conditions. Reflections at cloud edges and changing aerosol properties in the vicinity of clouds affect the surface solar irradiance during broken cloud conditions. However, the boundaries between clouds and clear skies with aerosols are not always well defined, and quantifying the frequency and spatial extent of the transition zone between these regions is challenging1. This study uses a unique dataset of observations from the TROPOS dense pyranometer network to detect and characterize the transition zone between clouds and clear skies with aerosols.
The TROPOS pyranometer network consists of up to 100 individual stations. Datasets from two campaigns are used for this study: 99 stations were distributed over an area less than 10 km² operated during the HOPE2 measurement campaign in 2013 in Jülich, Germany; 60 stations were distributed over an area of about 6 km² during the S2VSR3 measurement campaign in 2023 in Oklahoma, USA. The surface solar irradiance is measured at each station with a time resolution of 10 Hz. The transition zone is detected and characterized by applying modified clear sky detection algorithms4 to the data. To quantify the spatial characteristics of the transition zones, a method based on optimum averaging/spatio-temporal Kriging is introduced. A main component of this method is the determination of the cloud motion vector. It is determined whether this can be reliably estimated from ground-based measurements, by using cross-correlation techniques, or whether additional information from satellite data is required. First results utilizing the HOPE and S2VSR dataset are presented.
The study aims to quantify the small scale effects of the transition zone on surface solar irradiance and potential photo-voltaic yield. This information is valuable for photo-voltaic site planning and provides scientifically relevant insights into the interaction between clouds and aerosols. Furthermore, the study provides initial considerations for modelling the identified relationships and insights into small-scale surface irradiance variability not captured by current high-resolution satellite observations.
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1 e.g., Calbó et al. 2017, https://doi.org/10.1016/j.atmosres.2017.06.010

2 Macke et al. 2017, https://doi.org/10.5194/acp-17-4887-2017

3 https://www.arm.gov/research/campaigns/sgp2023s2vsr

4 Bright et al. 2020, https://doi.org/10.1016/j.rser.2020.109706
The past, present, and future of the observatory at Skalnaté Pleso, High Tatras, Slovakia
Authors: Buchholcerová Anna , Onderka Milan , Lukasová Veronika
Published: July 2023

The observatory at Skalnaté Pleso, Slovakia, serves as a facility for both astronomical and meteorological research in the High Tatra Mountains. It is located at an altitude of 1778 meters a.s.l. on the south-eastern slopes of the Lomnický Peak near the Tatranská Lomnica municipality. The observatory was established in 1943 by Dr. Antonín Bečvář, a Czech astronomer and meteorologist. His well-known works, the famous sky charts (i.e. Atlas Coeli) and photographs of mountain clouds were based on observations made at the Skalnaté Pleso Observatory. Nowadays, astronomical measurements are made under the supervision of the Astronomical Institute of the Slovak Academy of Sciences, which also operates another observatory located at the Lomnický Peak (2634 m a.s.l.) and two telescopes at the Stará Lesná Observatory (810 m a.s.l.). These observatories are used to study interplanetary matter, solar, and stellar physics. Apart from astronomical research, the Skalnaté Pleso Observatory provides standard climatological measurements including measurements of air temperature, air pressure, relative humidity, snow cover, precipitation, sunshine duration, wind speed, and wind direction since as early as 1943. With only minor modifications, the methodology of meteorological observations remained the same up until today. In 1962, the meteorological observatory was incorporated into the Slovak Academy of Sciences, currently the Earth Science Institute, and the mission of the station was extended to cover research mostly in the field of energy and radiation balance. Later, measurements of ozone concentrations were initiated, and an automatic weather station was installed. This enabled the extension of ozone research to its phytotoxicity on vegetation and beyond. The advance in automatic weather stations has introduced important questions regarding the comparability of automatic measurements with standard (conventional) measurements conducted by observers.
Ensemble analysis of spatiotemporal attributes derived from objectively identified three-dimensional atmospheric fronts
Authors: Beckert Andreas , Rautenhaus Marc
Published: July 2023

Ensemble simulations have become a standard in numerical weather prediction (NWP). However, ensemble simulations generate large amounts of data, and their comprehensive analysis remains challenging. We introduce a feature-based NWP ensemble analysis method based on the well-established conceptual model of atmospheric fronts. Recent developments in front detection techniques have enabled a reliable, robust, and objective identification of three-dimensional (3-D) frontal structures in NWP data.We advance detection of individual front features towards front-feature-based time series analysis and ensemble clustering. We track 3-D fronts of a selected cyclone system to create time series of frontal attributes. These frontal attributes characterize properties of the tracked front, for example, the maximum strength of the temperature gradient across the frontal zone, the average slope of the 3-D frontal structure or associated upward motion. The obtained time series provide a compact overview of the development of front characteristics. For ensemble analysis, we generate such time series for the cyclone system as represented in the different ensemble members. Time series distance measures including dynamic time warping can then be utilized to analyze similarities and differences of frontal development in the ensemble, for example, for front-feature-based ensemble cluster analysis. Also, a time window similarity search enables users to select a specific event of interest (for example, a sudden increase in frontal strength) in one of the ensemble members and to search for similar events in other members.Integrated in the 3-D visual analysis framework Met.3D, our approach facilitates a comprehensive analysis of the spatiotemporal development of 3-D atmospheric fronts and thus contributes to the challenge of rapidly analyzing large ensemble weather predictions.  
Cloud cover estimation at various sites in Antarctica using different methods based on broadband radiation measurements
Authors: Frangipani Claudia , Ahn Seo-Hee , Choi Taejin , Cordero Raul , Gulisano Adriana M. , Lupi Angelo , Mazzola Mauro , Ochoa Hector A. , Rowe Penny , Vitale Vito
Published: July 2023

Surface observations in Antarctica have always been challenging but cloud observations are particularly scarce due to the lack of observers and instruments and the strict limitation caused by the polar night. This work aims at testing and finding methods that can fill the gap of information on cloud cover, based on solar or terrestrial broadband radiation measurements. In particular, the results from Long et al.[1] method, exploiting solar radiation measurements, and Dürr and Philipona[2] APCADA algorithm and Town et al.[3] methods, both based on terrestrial radiation measurements, will be shown. The methods are chosen to make use of shortwave and longwave radiation components, specially the latter because it can yield information throughout the year and not only during months of daylight, as methods based on shortwave radiation do. Studied data sets are from different coastal sites: Marambio (64°14’50’’S - 56°37’39’’W), Professor Julio Escudero (62°12’57’’S - 58°57’35’’W) and Jang Bogo (74°37’38’’S - 164°14’16’’E). Another analysed data set comprises measurements taken at DomeC (75°05’59’’S – 123°19’57’’E). Since stations can be equipped with different instruments that could record only some of the broadband radiation components, investigating different methods allows to adapt to the actually available data. It also means that is possible to cross-check results when more than one method can be used, like in the case of Marambio. Before data sets are used as input, they undergo quality check controls[4] as recommended by the Baseline Surface Radiation Network[5], when applicable. Preliminary results show that Marambio and Escudero stations thought to be similar, for their latitude and close distance, actually differ from one another when it comes to frequency of occurrence of clear and cloudy sky conditions.
Bibliography
[1] Long et al. (2006): “Estimation of fractional sky cover from broadband shortwave radiometer measurements”, J. Geophys. Res. 111, doi: 10.1029/2005JD006475[2] Dürr and Philipona (2004): “Automatic cloud amount detection by surface longwave downward radiation measurements”, J. Geophys. Res. 109, doi: 10.1029/2003JD0041823[3] Town et al. (2007): “Cloud Cover over the South Pole from Visual Observations, Satellite Retrievals, and Surface-Based Infrared Radiation Measurements”, Journal of Climate 20, doi: 10.1175/JCLI4005.1[4] Long and Shi (2008): “An automated quality assessment and control algorithm for surface radiation measurements”, Open Atm. Science J. 2, doi: 10.2174/1874282300802010023 [5] https://bsrn.awi.de/
A barycenter-based approach for the multi-model combination of ensemble forecasts: an application to the sub-seasonal forecasts of 2m-temperature over Europe
Authors: Le Coz Camille , Tantet Alexis , Flamary Rémi , Plougonven Riwal
Published: July 2023

Multi-model ensemble (MME) methods, i.e. combining ensemble forecasts produced by different models, have been shown to improve the skill of prediction at different scales. One important research question is how to best construct such multi-model ensemble. This is one of the challenges addressed by the subseasonal-to-seasonal (S2S) project for the subseasonal scale, and also the focus of this study. To answer this question, we compare two methods based on barycenters.
The main idea is to consider the ensemble forecasts as discrete probability distributions, and to use a barycentre to combine them. We compare two barycenters based on different distances, the L2 and the Wasserstein distance. Applying an L2-barycentre on ensemble forecasts is equivalent to concatenating their members, i.e. to the well-known MME method known as “pooling”. The Wasserstein distance corresponds to the cost of the optimal transport between two distributions and has interesting properties in the distribution space. The two methods potentially lead to very different multi-model ensembles. The barycenters also allow us to attribute weights to the models. These weights are learned from the data (using cross-validation on the forecasts) in order to optimize the skill of the barycenter-ensembles.
In order to investigate the benefits of these two multi-model ensemble methods, both methods are applied to the combination of two models from the S2S forecasts, namely the European Centre Medium-Range Weather Forecasts (ECMWF) and the National Centers for Environmental Prediction (NCEP) models. The skill of the two barycenter-ensembles are evaluated for the prediction of weekly 2m-temperature over Europe for seven winters with respect to different metrics. Although the ECMWF model has an overall better performance than NCEP, the barycenter-ensembles are generally able to outperform both. However, the best ensemble depends on the chosen metric and on the location. Focusing on the combination of two models has allowed us to investigate the impact of the model’s weights on the performance of the barycenters. It also provides encouraging results for the next step, the combination of several models.
Precipitation over coastal regions of southern France and its impacts on the regional hydrological cycle
Authors: Jomaa Fatima , Zolina Olga
Published: July 2023

The hydrological cycle in southern France is influenced by highly variable precipitation, which impacts the variability of soil moisture and continental runoff. The regional hydrological cycle is also significantly impacted by the Mediterranean Sea (Gulf of Lion) through moisture advection, and the strong coupling between the atmosphere and land surface affects all branches of the regional hydrological cycle, particularly due to the region's special orographic structure. The potential intensification of the regional hydrological cycle under climate change scenarios, leading to extreme hydroclimate events, is a critical factor for understanding the mechanisms of climate variability in the Mediterranean region.
In this study, long-term daily precipitation data from 1979 to 2014 for southern France was analyzed using various datasets, including data from 300 stations in the METEO-FRANCE collection, modern-era reanalysis (ERA5, JRA55), and satellite datasets. Diagnostic analysis was conducted to assess linear trends and interannual variability in total precipitation and precipitation extremes using multiple datasets. The results of this analysis allowed for the quantification of differences in variability patterns of precipitation among the datasets, highlighting their respective strengths and weaknesses. The study also examined the regional responses of soil moisture to precipitation using data from the GLEAM-GLDAS datasets besides 14 SMOSANIA stations. Finally, the representation of the observed regional hydrological cycle in historical simulations with regional (Euro-CORDEX) and global (CMIP6) climate models are analyzed. Thus, a statistical association between precipitation, soil moisture, and continental runoff is established and the role of regional atmospheric circulation and atmospheric rivers in forming multidecadal changes in the regional hydrological cycle is quantified.
Characterization and applicability of online and offline monitoring systems for reactive trace gases using TD-GC-FID/MS analysis
Authors: Marcillo Lara Andrea Carolina , Grasse Achim , Wedel Sergej , Roska Milan , Alwe Hariprasad Dilipkumar , Kiendler-Scharr Astrid , Gkatzelis Georgios I. , Tillmann Ralf
Published: July 2023

Continuous long-term in-situ measurements of reactive trace gases in observational and environmental chamber platforms is essential to identify their impact on future air quality under climate change and the switch to renewable energy. Despite new and more advanced technologies for online and/or offline monitoring using pre-concentration and thermal desorption (TD), followed by gas chromatography (GC) coupled to mass spectrometry (MS) and/or flame ionization detection (FID), the complexity of such measurements is still challenging.
Within the framework of the ACTRIS Centre for Reactive Trace Gases in Situ Measurements CiGAS, this work presents the characterization and orthogonal comparison of two modern systems for online and offline sampling, respectively, coupled to GC dual-columns (Deans Switch heart cut approach) and FID/MS detection. We evaluate the advantages and limitations of these systems regarding the versatility of the sample inlets during collection (e.g., direct line, canisters, and multi-bed TD tubes) as well as automated calibration strategies (e.g., variable sample volume through MFC and loop mode), providing differences in compound coverage, signal responses and sorbent background. We evaluate their performance in terms of sensitivity, reproducibility, linearity, and relative recovery by performing, (1) laboratory calibrations using high purity certified mixtures of non-methane hydrocarbons and monoterpenes and, (2) ambient measurements using the characterized and calibrated analytical methods with particular emphasis on urban emissions from cooking, cleaning, and traffic. In addition, we compare results from the TD-GC-FID/MS to a high time resolution chemical ionization mass spectrometer, called VOCUS, and present the resulting volatile profiles, their variability, and interferences among the different measurements methods.
Ireland’s Climatological Standard Normals (CLINO) 1991-202
Authors: Curley Mary , Coonan Barry , Ryan Ciara , Ruth Conall E.
Published: July 2023

Climatological Standard Normals (CLINO) are the mean or average values of a climate variable over a standard reference period. The World Meteorological Organization (WMO) established that the length of the reference period should be 30 years, with a recommendation to update the climate normals every 10 years, to provide representative reference values for recent climatic conditions. Climate normals are used for two principal purposes. They serve as a benchmark against which recent or current observations can be compared, including providing a basis for many anomaly-based climate datasets. They are also widely used as a reference baseline to provide context for future climate projections.By applying the WMO data requirements and criteria, Met Éireann has compiled a set of climate normals for the period 1991-2020 for a range of parameters including temperature, precipitation, solar radiation, wind and pressure. Annual, seasonal, and monthly normal values for the period 1991-2020 were compiled using quality assured data obtained from Met Éireann’s observation network. Values are averaged for each month over the 30-year period to obtain the long-term average. Where there are gaps in data, estimates are made using data from neighbouring stations. Long-term averages for stations are then used to generate maps and gridded data at a 1km resolution.A comparison of the most recent 30-year period with the 1961-1990 period shows an increase in annual mean temperature of approximately 0.7°C. All seasons experienced a rise in mean temperatures, with Spring displaying the greatest differences between the two periods.On an annual basis, averaged over the country, there has been an increase of approximately 7% in rainfall totals between the two normal periods (1961-1990 and 1991-2020), with the greatest increases observed in the western half of the country. All seasons show an overall increase in rainfall but there are regional variations.Here we present the data and methods used to produce the latest set of climate normals for Ireland as well as an assessment of trends between the two normal periods, 1961-1990 and 1991-2020.From September 2023, weather and climate statistics will reference the new long-term average period 1991-2020, unless otherwise stated. These will replace the 1981-2010 long-term averages that are currently in use. The historical baseline period of 1961-1990 will be retained for use in climate change assessments.
Object-based Ensemble Prediction System KONRAD3D-EPS
Authors: Josipovic Lukas , Pante Gregor , Brechtel Andreas , Blahak Ulrich
Published: July 2023

The precise forecast of convective cells is essential for meteorological services as they can be accompanied by life-threatening severe hail, wind gusts, or heavy rain.
However, state-of-the-art NWP models usually possess update frequencies of several hours so that forecasters must use predictions that are outdated when new thunderstorm cells develop. NWP models do often accurately simulate the intensity of convective cells, but with shifts in space and time.
Object-based nowcasting algorithms with higher update frequencies became necessary to deliver information on the evolution of convective storms for the first two hours since observation. Furthermore, the combination of nowcasting and model data enables the relocation of simulated cells towards observed cells.
 
Many deterministic object-based nowcasting tools as DWD’s KONRAD3D algorithm assume that detected cells will have persistent intensity.
Within the SINFONY (Seamless INtegrated FOrecastiNg sYstem) project at DWD, we aim at modelling the life-cycles of storm cells in a truthful way and capturing the uncertainties of object-based nowcasts. Hence, we extended our nowcasting algorithm towards an ensemble prediction system called KONRAD3D-EPS. Each ensemble member is initialized by drawing from parameterized distributions of storm lifetime and maximum severity. Inspired by previous studies, e.g. Wapler (2021), KONRAD3D-EPS uses a set of horizontally flipped parabolas to model the life-cycle of convective cells in terms of their severity. In case of redetection of a convective cell, the algorithm corrects the previously estimated lifetime and severity maxima. Thus, the parabolas can be adapted individually for any convective storm in any weather condition.
 
Besides life-cycle predictions, KONRAD3D-EPS delivers information on the probability of thunderstorm occurrence for the next 2 hours depending on detected cells and their severity. In order to condense the ensemble data, we also provide the representative member for each convective cell. This is done by applying the pseudomember algorithm by Johnson et al. (2020) to the ensemble data.
 
We will give an overview of our probabilistic object-based nowcasting algorithm KONRAD3D-EPS and present its predictions for prominent example cases. Moreover, we will show first verification results.
Analysis of extreme Winter Warm Spell and Ocean Heat Content Anomaly during 2021 and 2022 over Western Mediterranean Sea.
Authors: Redaelli Gianluca , Ferretti Rossella , Sangelantoni Lorenzo , Carniel Cosimo Enrico , Zardi Dino
Published: July 2023

From December 23rd 2021 to January 4th 2022 and from December 20th to January 8th 2023, two “Extreme Warm Spell” (EWS) episodes have occurred in the Mediterranean Basin and central-western Europe. These phenomena led to several and long-lasting record-breaking atmospheric temperatures in the impacted areas. Combining both intensity and duration, these two episodes can be identified as extremely warm winter events. In the first one (2021-2022), 850hpa temperatures over 15°C were recorded near Spanish, Italian and French coasts and the Western Mediterranean (WM) widely. In the other hand SST anomaly up to 1°C in December over the WM basin.
The second event (2022-2023) was characterized by a zonal development, affecting the Balearic Sea, Northern Africa coasts and Italy, with 850hPa temperatures that, in different impulse, reached 12-16°C and a strong SST anomaly, up to 3°C over WM.
In this work, the two EWSs are statistically analyzed by comparison with the climatology data set from both atmospheric and oceanic point of view using EOF analysis, and last 40-year trend is assessed. Using satellite, Argo float, buoy and model dataset we characterize the 6 months preceding the EWS, exploring the Surface Temperature, Mixed Layer Depth (MLD) and its dynamic, estimating the Ocean Heat Content (OHC) which is more representative than the SST of the amount of energy contained in the sea, and available to the atmosphere. For this reason, we investigated the impact of SST anomalies and Mixed Layer Depth on the atmospheric structure, and vice-versa, using WRF-ARW (Weather Research and Forecasting System) numerical model at 5km resolution, SST provided by CMEMS-GOS 1 km and MDL from CMEMS Reanalysis at 4.5 km and 1D ocean model (SLAB ocean model). Results show two distinct conditions, despite the EWSs are driven by similar large-scale forcing and atmospheric patterns but very different preconditioning features of SST and OHC. Preliminary result show that SST/OHC anomaly impacts especially humidity and ground temperature, up to 850 hPa pressure level. Nevertheless, the main driver is the synoptic atmospheric circulation. On the other hand, result underline a lower heat flux, compared to climatology, during December–January (usually period of strong cooling in the WM) retaining a large part of OHC in the MDL, with possible role next weeks atmospheric dynamics.
Machine learning forecasts of storm damages, forest fires, pedestrian slippings, and vehicle accidents for operational early warnings of weather impacts
Authors: Kämäräinen Matti , Punkka Ari-Juhani , Láng-Ritter Ilona
Published: July 2023

Accurately predicting the impacts of severe weather events is crucial for improving meteorological warnings and aiding weather-vulnerable organizations in decision making. However, achieving a high-quality estimate of impacts is difficult because of complex and detail-sensitive interactions between the weather and the impacted quantity. For instance, forest damages caused by a storm are influenced not only by the strength of gusts but also by factors such as ground frost, tree leaf maturity, and wind direction. 
 
To address this challenge, the SILVA project developed a nation-wide weather-impact database with over 10 individual impact datasets, which were then utilized as targets for machine learning modeling. Storm damage clearance tasks, wildfire fighting tasks, traffic accidents, and pedestrian slipping accidents stored in the database were aggregated to the counties of Finland, and a separate gradient boosting model was fitted between the historical ECMWF HRES weather forecasts and the impact data in each county.
 
Subsequently, a reliable production system was set up to produce forecasts automatically twice per day. Four risk classes were determined based on the extremeness of the forecasted incidents, taking into account the seasonal differences, and the Meteoalarm-like four step color scheme was used to visualize the forecasts as traditional warning maps and as time series. Two of the products primarily describe weather risks in summer (storm damage clearance tasks and wildfire fighting tasks), while the other two are more useful for warning the risks in winter (traffic accidents and pedestrian slipping accidents). 
 
The impact forecasts were tested by numerous end-users during a seven-month pilot phase. Both the feedback from the pilot participants and the numerical validation results clearly indicate the value of the products. The selected modeling method, gradient boosting, was found to be effective in taking into account the nonlinear and complex interactions when explaining the variability of the impact data. 
 
The forecasting system is currently undergoing further development in the Europe Horizon CREXDATA project, with efforts focused on exploring possibilities for expanding the geographical areas of application and incorporating new impact datasets.
Skillful deep learning-based precipitation nowcasting based on new AI-synthetic radar data
Authors: Wang Rui , Chi-Hung Fung Jimmy , Kai-Hon Lau Alexis
Published: July 2023

Accurate and timely rainfall nowcasting is important for protecting the public from heavy rainfall-induced disasters. In recent years, deep-learning models have been demonstrated to significantly outperform traditional methods in heavy rainfall nowcasting. However, the performance of existing deep-learning-based nowcasting models is still limited by short effective prediction time (< 3 hours), insufficient training data, and the rapid growth of blurriness increases in forecast time. In this work, we propose a novel heavy rainfall nowcasting model based on an innovative task-segmented architecture, namely the TS-RainGAN, consisting of two modules: the MaskPredNet predicts the spatial coverage of different rainfall categories to provide bounding for rainfall with various intensities, and the IntensityGAN predicts the intensity of rainfall based on the rainfall coverage produced by the MaskPredNet. To overcome the data scarcity, we develop another novel model called RainMaker which can generate huge amounts of new radar data based on limited observed radar data. In addition, in order to improve the typhoon-induced precipitation nowcasting, we design a new typhoon structure segmentation method, which distinguishes the typhoon structure into typhoon eye, principal rainband, and outer rainband. The TS-RainGAN training with new AI-synthetic radar data produced by RainMaker can accurately capture the spatiotemporal features and evolutions of rainfall systems and provide skillful precipitation prediction with high skill scores compared with the results of the widely used baseline models. The performance of typhoon-induced precipitation nowcasting shows significant improvement by our innovative typhoon structure segmentation method. Meanwhile, the blurriness of the predicted images is significantly reduced. This enables district-level heavy rainfall nowcasting with competitive forecast skills for up o 6 hours.
Aerosol and radiation observations and model forecasts
Authors: Filipitsch Florian , Doppler Lionel , Wacker Stefan , Becker Ralf
Published: July 2023

Atmospheric aerosols modify the incoming solar radiation by scattering and absorption and through their impact on the formation and dissolution of clouds and additionally change their optical properties. Currently, no NWP model can fully account for these very complex effects. Especially during weather conditions affected by high aerosol concentrations, as in the event of Saharan dust outbreaks or wildfires, this can lead to errors in the forecast. One important factor to overcome this shortfall of NWP models, aerosols need to be treated as a prognostic variable.
At Deutscher Wetterdienst (DWD) and Karlsruhe Institute of Technology (KIT) project "PermaStrom" aims to improve radiation forecasts during such events. Using the ICON-ART modeling system emission, transport, and deposition of mineral dust, black carbon from vegetation fires, and sea salt are explicitly simulated. To achieve the project goals and to examine the effect of Saharan dust on solar radiation, accurate and extensive measurements of the Saharan dust in the atmosphere and the ground reaching solar radiation are needed. Within the measurement network of the DWD, we currently have 35 Stations equipped with two secondary standard pyranometers. One to measure the global radiation and one shaded to measure the diffuse incoming solar radiation at the surface with a temporal resolution of 1 minute. At the Meteorological Observatory Lindenberg, we also develop a retrieval algorithm to estimate the AOD at 550 nm from those measurements. With the additional information on the vertical distribution of the dust cloud from Ceilometer measurements in Germany, we obtain a detailed picture of aerosols and radiation.
In our contribution, we will give an overview of how accurate measurements at the surface could help to identify deficiencies in the ICON-ART modeling system and how the development process could benefit. We will focus on the direct and indirect aerosol effects and the benefit of the information of two radiation components.