bluuebunny/crossref_metadata_2025 huggingface dataset:
Characterization and Understanding of the spatial variability of precipitation over Paris area observed by radars in the last 25 years. 
Authors: Zouzoua Pierre Maurin , Bastin Sophie , Viltard Nicolas , Lalonde Morgane , David Grégoire , Barthes Laurent , Pauwels Nicolas
Published: July 2024

With a current population of over 12 million (Eurostat 2021), the Ile-de-France region is particularly vulnerable to climate change, air-quality and water scarcity. Socio-economic development is leading to a degradation of the urban environment as a result of all human activities and the artificialization of land. Mitigating these adverse effects requires the implementation of specific adaptation policies to make the region more resilient to extreme events (such as heat waves, floods, droughts, pollution episods), but also to better manage water resources. To achieve this, we need to better characterize and understand the dynamics of precipitation in this region.The presence of urban areas modifies the interactions between the surface and the atmosphere through the modifications of energy and water budgets inducing the urban heat island effect, an increased surface roughness, and anthropogenic aerosols emissions.  Several studies investigated the impact of these urban areas on precipitation with various conclusions due to lots of disparity (methods, datasets, cities' characteristics, climate) between the studies (e.g Lalonde et al., 2023; Liu and Niyogi, 2019).  By using a unique methodology and a radar product covering the whole Continental USA at 4km resolution or the whole Europe at 2km resolution over tens of years, Lalonde et al. (2024) still highlighted a large diversity of urban impacts between cities, concluding on the necessity of specific analysis for each city.In this study, we use the recently available long-term 1 km/hourly radar product COMEPHORE (Tabary et al., 2012) which provide precipitation rates over France since 1997 to characterize the spatial variability of precipitation over Ile de France with a special focus on the impact of urban area. We complement this dataset with vertical profiles of reflectivity and vertical velocities provided by one radar located in a southwest suburb of Paris (most of the time upwind from Paris city center) and another one located in the city center to detect potential differences in microphysical properties of precipitation between urban and upwind environments.In the next step, we aim to assess the role of aerosols and urban form in this variability.  
Approaches for making ensemble forecasting based services more suitable to end-users needs
Authors: Joly Bruno , Ponzano Matteo , Beau Isabelle
Published: July 2024

Most end-users are keen to exploit ensemble forecast products for decision-making, such as workflow planning, human resources management, preventive actions. An improper use of a probabilistic product can lead to taking bad decisions, which may result in consistent economic loss. Stake-holders using ensemble forecast product may be classified into two categories: the ones who integrate probabilistic products into decision-making models or leverage human expertise to provide valuable information, the ones who are not able to exploit probabilistic information and need a translation to binary forecasts. One of the current objectives of Meteo-France is to enhance the current range of products and services offered by the public institution by integrating uncertainty, reliability and probabilistic forecast information in order to meet the specific needs of each end- user. Some new approaches are presented that aims at exploring ensemble based binaries forecasts synthetising vulnerability and uncertainty to build simple and adapted decision making tools. One has been experimented to categorize wave heats risk at one day ahead. Another one has been held for vineyards protection against spring frost and developed the use of costs and losts economic balance related to the event. Both have in common that the end user impact associated to the event is the issue that is followed. The ensemble forecasting part of information that could help to determine when mitigation decision has to be taken can then be disciminated. Standardizing these approaches is also investigated in order to adress a large number of commercial and institutional sectors that are left behind the use of probabilistic forecasting today.
Interdecadal Changes in the Boreal Summer Tropical-Extratropical Teleconnections Occurred Around Mid-to-late 1990s
Authors: Lee June-Yi
Published: July 2024

This study investigates robust features of interdecadal changes in the Northern Hemisphere summer tropical-extratropical teleconnection that occurred around the mid-to-late 1990s by analyzing four different reanalysis data for atmospheric circulation and temperature, two precipitation reconstructions, and two sea surface temperature (SST) data during the satellite observation era of 1980-2023. For the last 44 years, there has been a significant increasing trend in anticyclonic circulation at lower and upper troposphere and 2m air temperature with wavenumber-5 Rossby wave structure in the Northern Hemisphere (NH) extratropics. The increase has been accompanied with the significant weakening and northward shift of jet stream over Eurasia and the North Pacific. It is further found that there has been a significant interdecadal shift occurred around the mid-to-late 1990s in the two distinct modes of tropical-extratropical teleconnection: Western Pacific-North America (WPNA) and circumglobal teleconnection (CGT) pattern. After the mid-to-late 1990s, the WPNA has played a more important role in modulating the extratropical atmospheric circulation and surface climate, which has preferentially occurred during the El Niño-Southern Oscillation (ENSO) decaying or transition summers such as 1998, 2010, and 2016. During these summers, severe heat waves occurred over many parts of the NH extratropics due to the combined effect of the increasing trend in the barotropic anticyclonic circulation and the significant WPNA across the NH. Although weakened, the CGT also contributed to hot summers over many parts of the NH extratropics, such as 1999, 2000, 2008, 2011, 2012, 2021, and 2022, when weak to moderate La Niña persisted.  This talk will also discuss the seasonal predictability and prediction for the two teleconnection modes in the state-of-the-art climate models. 
Physical drivers of marine heat waves intensification in the presence of atmospheric heat waves in the Mediterranean
Authors: Paredes-Fortuny Laura , Pastor Francisco , Khodayar Samira
Published: July 2024

Atmospheric and marine heatwaves (AHW and MHW, hereafter) are extreme temperature events occurring in an extensive area and sustained in time. In the Mediterranean region, these events have become more frequent in the last decades. Tracking the spatiotemporal evolution of such events is key for the implementation of adaptation measures to cope with their associated impacts both on the population and the environment. The growing frequency of both types of events leads to a higher concurrence of those events, understood as their spatiotemporal co-occurrence. Despite the growing interest in the scientific community about the co-occurrence of AHW and MHW, the details of the physical interaction between such extreme events are not fully described. Several studies pointed out that in the record-breaking European heatwave of 2003, the simultaneous occurrence of AHW and MHW contributed on the exacerbation of the individual events. 
Our hypothesis is that under extreme temperature conditions heat fluxes can change and that probably these changes have an impact in the AHW and/or MHW characteristics. The aim of this work is to investigate the physical mechanisms behind these changes with a special focus on concurrent AHW and MHW events (CHW). Our results show that there is a heat flux intensification during CHW influencing the temperature anomalies. For this study we investigate the AHW, MHW and their concurrence in the Mediterranean basin during the extended summers (May to September) from 1940 to 2022 using ERA5 reanalysis data to characterize the heat fluxes, surface wind and sea level pressure fields, cloud cover, sea surface and 2-meter air temperatures.  
Unlocking Indonesia’s Resilience: A Good Practice of a Collaborative Journey to Strengthen BMKG Capacity for Weather and Climate Preparedness
Authors: Rodríguez Edith , Florida Riama Nelly , Arumsari Fitriany Anni , Pilli-Sihvola Karoliina , Lilley Megan Megan
Published: July 2024

Indonesia is a sprawling state archipelago with more than 275 million inhabitants that faces persistent urban and long-distance transport challenges. Indonesia is one of the world's most exposed countries to natural hazards, situated on the Pacific Ring of Fire (an area with a high degree of tectonic activity). Indonesia must cope with the constant risk of volcanic eruptions, earthquakes, floods, and tsunamis. Meanwhile, extreme dry and wet conditions (El Niño or La Niña phenomenon) can hamper harvests, trigger inflation, and put severe financial pressure on the poorer segments of the Indonesian population, further reducing people's capacity to cope.Since 2023, the Finnish Meteorological Institute (FMI) and the Agency for Meteorology, Climatology, and Geophysics of the Republic of Indonesia (BMKG) have been cooperating through the INDFIN project that aims to Enhance BMKG Capacity on Weather Climate and Air Quality Forecast and Warnings to Better Serve the Indonesian Society. It is a three-year project supported by Finland's Ministry of Foreign Affairs (MFA) through the Institutional Cooperation Instrument (ICI). The ICI instrument is a mechanism developed by MFA to finance development cooperation projects where the Finnish government agency cooperates with developing country government agencies in capacity building and organisational development.In this presentation, the project's results and best practices will be shared. The presentation will highlight the partnership approach, including cooperation with the Finnish Red Cros, which was piloted to focus on the fulfilment of the cross-cutting objectives proposed by the MFA, extending the impact of the INDFIN project from the NHMA level to the people facing increasing climate shocks.   
Observational evidence differentiating Siberian High variability from the Ural-Siberian pattern
Authors: Santolaria-Otín María , García-Serrano Javier , Mori Masato , Okajima Satoru , Nakamura Hisashi , Martineau Patrick , Wegmann Martin
Published: July 2024

Most studies on atmospheric variability in the Northern Hemisphere (NH) have traditionally focused on the North Atlantic and North Pacific regions, due to the prominence of two main teleconnection patterns: the North Atlantic Oscillation (NAO) and the Pacific-North America (PNA). Despite being relatively overlooked compared to those two regions, Eurasia has gained an emerging interest due to its role in Arctic-mid latitude linkages, particularly in the so-called Warm Arctic-Cold Eurasia (WACE) pattern. The dynamics underlying this latter covariance seem to include an anticyclonic circulation over the Siberian coast whose identification has been ambiguous in literature. Here, we have diagnosed and characterized two distinct atmospheric variability modes, namely the Ural-Siberian (USib) pattern and the Siberian High (SH). The Ural-Siberian pattern, sometimes referred to as the Scandinavian (SCAND) pattern at upper levels, is identified as a regional mode of circulation variability maintained by transient-eddy activity and with a barotropic wave-like structure associated with Rossby wave propagation. Whereas Siberian High variability is mainly driven by radiative processes, i.e. the cooling effect of snow cover, and displays a baroclinic structure. While the signature on surface temperature of both atmospheric modes exhibits a dipole that can be identified as the WACE pattern, our results show clear spatial differences among them, being actually in quadrature (out-of-phase). The USib-related temperature signal shows positive anomalies over the Barents-Kara Seas associated with sea ice reduction and negative anomalies over the whole Eurasian continent related to northerly cold advection from the Arctic. In contrast, the SH signal depicts positive anomalies over northern Eurasia linked to warm advection from the south and negative anomalies around the core of the Siberian High due to an increase in snow cover. Further, we find that the vertical extension into the stratosphere of both patterns is distinct, with only the USib signal being consistent with an impact on the polar vortex.
Agriculture can contribute to carbon dioxide removal from the atmosphere – but what information do regional practitioners need from research? 
Authors: El Zohbi Juliane , Rechid Diana
Published: July 2024

Practitioners in the agricultural sector are being given a special role in the discussion about CO2 removal (CDR) from the atmosphere. They are expected to apply practices that remove CO2 from the atmosphere and contribute to achieving climate neutrality. 
The question is how familiar actors from the agricultural sector are with the concept of CDR and what information they need to implement such practices is an unexplored field of research.
Farmers can apply practices to their land that can change the balance of carbon inputs and losses. Through these practices, also referred to as soil carbon sequestration, the equilibrium level of soil organic carbon content can increase. Such practices include, for example, planting in periods where no plants have been cultivated to date e.g. the cultivation of cover crops. Another method is to shift a plain field into an agroforest. Agroforestry combines crop cultivation, trees and sometimes livestock in one area. By linking arable land, forest, meadows and pastures, CO2 uptake in plants can be increased. A novel method is that farmers add biochar to their ﬁelds. Biochar is produced when biomass is burned with very little oxygen. Added to soil it can store carbon for long periods of time and can have positive impacts on the soil. 
We have conducted 29 interviews with actors of the agriculture sector in Northern Germany in 2023. We talked to farmers, biochar producers, farmers associations, non-governmental associations, bodies of the local administration to explore the following questions:
How familiar are agricultural actors with the concept of CDR?
What do agricultural actors want to know and who needs to know what?
Which formats are considered best for implementing research findings into practice?
 

We discovered that there is a gap between research findings and its transfer into practice. We will present the informational needs and suitable formats to improve the implementation of CDR options. In addition, we will share our experiences on how to improve knowledge transfer from research to agricultural practice. 
Precipitation extremes in Ukraine from 1979 to 2019: Climatology, large-scale flow conditions, and moisture sources
Authors: Agayar Ellina , Aemisegger Franziska , Armon Moshe , Scherrmann Alexander , Wernli Heini
Published: July 2024

Understanding extreme precipitation events (EPEs) and their underlying dynamical processes and moisture transport patterns is essential to mitigate EPE-related risks. In this study, we investigate the dynamics of 82 EPEs (≥ 100 mm∙day-1) over the territory of Ukraine in the recent decades (1979-2019), of which the majority occurred in summer. The EPEs are identified based on precipitation observations from 215 meteorological stations and posts in Ukraine. The atmospheric variables for the case study analysis of selected EPEs and for climatological composites and trajectory calculations were taken from ERA5 reanalyses. Moisture sources contributing to the EPEs in Ukraine are identified with kinematic backward trajectories and the subsequent application of a moisture source identification scheme based on the humidity mass budget along these trajectories. The large-scale atmospheric circulation associated with EPEs was studied for a selection of representative EPEs in all seasons and with the aid of composites of all events per season. Results show that EPEs in summer occur all across Ukraine, but in other seasons EPE hotspots are mainly in the Carpathians and along the Black and Azov Seas. All EPEs were associated with a surface cyclone, and most with an upper-level trough, except for the winter events that occurred in situations with very strong westerly jets. Isentropic potential vorticity anomalies associated with EPEs in Ukraine show clear dipole structures in all seasons, however, interestingly with a different orientation of these anomaly dipoles between seasons. The analysis of moisture sources revealed a very strong case-to-case variability and often a combination of local and remote sources. Oceanic sources dominate in winter, but land evapotranspiration accounts for 60-80% of the moisture that rains out in EPEs in the other seasons. Taken together, these findings provide novel insight into large-scale characteristics of EPE in Ukraine, in a region with a unique geographical setting and with moisture sources as diverse as Newfoundland, the Azores, the Caspian Sea, and the Arctic Ocean. 
Wintertime Arctic Sea Ice Decline Related to Multi-Year La Niña Events
Authors: Zhong Wenxiu
Published: July 2024

Arctic sea ice has undergone a significant decline in the Barents–Kara Seas since the late 1990s. Previous studies have shown that the decrease in sea ice caused by increased poleward moisture transport is modulated by tropical sea temperature changes (mainly referring to La Niña events). The occurrence of multi-year La Niña events has increased significantly in recent decades, and their impact on Arctic sea ice needs to be further explored. In this study, we investigate the relationship between sea ice variation and different atmospheric diagnostics during multi-year La Niña and other La Niña  years. The decline in BKS sea ice during multi-year La Niña winters is significantly stronger than that during other La Niña years. It is because the multi-year La Niña tends to accompany the warm Arctic-cold continent pattern with a barotropic high-pressure blocked over the Ural region. Consequently, more frequent northward atmospheric rivers intrude into the BKS, intensifying long-wave radiation downward to the underlying surface and melting sea ice in the Barents–Kara Seas. However, in the early mature winter of other La Niña cases, negative North Atlantic Oscillation presents in the North Hemisphere high latitudes, which obstructs the atmospheric rivers to the south of Iceland. We infer that such a different response of sea ice decline in Barents–Kara Seas to different La Niña is related to stratospheric processes. Considering likely future climate change, more frequent multi-year La Niña events may account for substantial Arctic sea ice loss in recent decades. In particular, it was proposed that the multi-year La Niña probably occur more frequently in a warming climate.
Leveraging Generative Models for ASI-based Solar Nowcasting
Authors: Fabel Yann , Schnaus Dominik , Nouri Bijan , Wilbert Stefan , Blum Niklas , Zarzalejo Luis F. , Kowalski Julia , Pitz-Paal Robert
Published: July 2024

Short-term variations in PV power are an increasingly important challenge for solar energy integration. By anticipating sudden changes in irradiance caused by passing clouds, all-sky imager-based solar nowcasting can help address this challenge. However, the utility of nowcasting systems is highly dependent on the quality of the forecast. While recent data-driven models have shown great potential in standard forecast metrics such as root-mean-square error (RMSE) and forecast skill, they tend to produce smoothed forecast curves and may not be well suited to detect ramps. An alternative data-driven approach lies in generative modeling. Instead of forecasting solar irradiance directly from available data, like radiometer measurements or sky images, we propose a two-step method to predict cloud dynamics and irradiance separately.Using novel denoising diffusion models [1], we show that realistic sequences of sky images can be generated. By conditioning video prediction on the latest acquired sky images, plausible future sky conditions are produced. In contrast to traditional methods that only predict cloud motion, changes in cloud shape can also be represented. Another advantage of diffusion-based video prediction is the versatility of possible outcomes. By introducing samples of random noise during inference, the model generates different outputs that vary depending on the conditioned input.In the second step, we apply an irradiance model to the generated synthetic sky images. Each image is processed independently and returns a corresponding irradiance value. Thus, an irradiance distribution can be obtained from the samples of synthetic sky images for each lead time. As a result, the uncertainty of the forecast can be estimated, since a larger variation of synthetic sky images will lead to a larger distribution of corresponding irradiance.We evaluate our novel generative nowcasting approach not only on standard forecast metrics, but especially on its ability to detect ramp events. Preliminary results already indicate that such a generative video prediction on sky images in combination with an irradiance model can overcome the problem of smoothed forecast curves [2]. Furthermore, the intermediate results of synthetic sky images enhance interpretability, and the generation of varying scenarios enables probabilistic forecasting. [1] Ho, Jonathan, Ajay Jain, and Pieter Abbeel. "Denoising diffusion probabilistic models." Advances in neural information processing systems 33 (2020): 6840-6851.[2] Paletta, Quentin, Guillaume Arbod, and Joan Lasenby. "Benchmarking of deep learning irradiance forecasting models from sky images–An in-depth analysis." Solar Energy 224 (2021): 855-867.
The Weather Generator and The Regional Climate Model: The Rivals or The Allies?
Authors: Dubrovsky Martin , Meitner Jan , Skalak Petr , Plavcova Eva
Published: July 2024

In climate change (CC) impact studies, various weather-dependent models (e.g. crop growth models or rainfall-runoff models) are employed to assess possible CC impacts on crop yields, river flows, as well as on various climatological and other indices. To perform such experiments, one has to decide what meteorological data (representing present & future climate) will be used as inputs. Two most commonly used approaches at hand are Regional Climate Models (RCMs) and Stochastic Weather Generators (WGs). While one might say, that these two approaches are the rivals which compete for getting the main role in providing the weather data for CC impact studies, we want to demonstrate that these two approaches could be rather allies  which could be very effective while being used together.In our presentation, we show three ways of such co-operation: (1) To produce weather series representing the future climate by the WG, WG parameters must be modified by CC scenarios, which may be derived by comparing RCM simulations of present vs. future climates. (2) To assess separate effects of changes (projected by RCMs) in individual weather variables (e.g. temperature or precipitation) and their statistical characteristics (means, variabilities, spatial and/or temporal correlations), WG may be used: only selected WG parameters representing chosen variable and its statistical characteristic may be modified before producing the synthetic series. (3) To assess statistical significance of the changes derived from a given RCM simulation, WG may be also used: the significancy of projected changes may be based on analyzing the spread of the changes derived by comparing multiple pairs of synthetic time series produced by WG calibrated with RCM simulations for future vs. present time slices.In the experiment, we use: (a) our multi-site multi-variate parametric weather generator SPAGETTA, (b) E-OBS data to calibrate the generator for the present climate conditions in 8 European regions, and (c) outputs from ensemble of 19 RCM simulations for present and future climate (CORDEX database) in these regions.Acknowledgement: The experiment was made within the frame of the PERUN project funded by the Technological Agency of the Czech Republic (project no. SS0203004000).
Design precipitation - new results for Norway
Authors: Dyrrdal Anita Verpe , Lutz Julia , Roksvåg Thea , Lussana Cristian , Thorarinsdottir Thordis , Grinde Lars , Nilsen Irene Brox
Published: July 2024

In August 2023, the extreme weather event “Hans” moved into Scandinavia from the southeast, bringing large amounts of rainfall. It led to large-scale floods, a number of landslides and forced evacuations across large parts of southeastern Norway, and ended up costing more than 150 million Euros.In Norway, water poses the largest physical climate risk, and adaptation to prepare for such events is crucial. Successful adaptation relies on accurate design values for any location, area (such as a catchment), as well as for a future climate.We will show results from a project focusing on all the aforementioned aspects. Specifically, we will present a user-friendly web tool offering design values for short-duration rainfall at any location in Norway, based on a spatial model with observations and gridded covariates as input (Dyrrdal et al., 2015),
a framework for updating recommended climate change allowances based on user needs, to account for a changing climate in long-term planning and infrastructure design, and 
estimated Areal Reduction Factors (ARFs), which convert point estimates of extreme precipitation to estimates of extreme precipitation over a spatial domain, often used in flood risk estimation (Lutz et al., 2024). This analysis considers regional and seasonal dependence in ARFs for extremes of different durations based on gridded data products and provides preliminary recommendations for updated ARFs in Norway
 References:Brox Nilsen, I., Bratlie, R., Dyrrdal, A.V., Engeland, K., Hisdal, H., Lawrence, D., and Øye Leine, A.-L., 2023: Brukerbehov for justerte anbefalinger om klimapåslag (“User needs associated with adjusted recommendations of climate change allowance”; in Norwegian”), VANN, 03/2023Dyrrdal, A.V., Lenkoski, A.,Thorarinsdottir, T.L., and Stordal, F., 2015: Bayesian hierarchical modeling of extreme hourly precipitation in Norway. Environmetrics, 26(2), 89-106, https://doi.org/10.1002/env.2301Lutz, J., Roksvåg, T., Dyrrdal, A.V., Lussana, C., Thorarinsdottir, T., 2024: Areal reduction factors from gridded data products, Journal of Hydrology, in print. Available at https://doi.org/10.1016/j.jhydrol.2024.131177
Regional PV estimation based on a ground station network as a meteorological grid
Authors: Good Garrett
Published: July 2024

This study demonstrates a new regional PV estimation system in Germany using historical data from a network of around 100 meteorological stations. This is compared to the satellite or numerical weather data usually used in the same, variable-resolution physical model. The PV estimation with the ground stations performs better for Germany than satellite data, despite its far lower resolution, though the satellite data maintains the advantage for small, substation regions.The solar prediction system (SPS) used in this study uses physical models to probabilistically simulate plants of many orientations and other properties at all points of a weather data grid and works modularly with various numerical weather predictions and satellite data. It takes a somewhat opposite approach of a typical upscaling, as instead of averaging several reference input values, it simulates nonlinear effects for all possible reference locations and aggregates only probabilistically at the end.Here, we test something new with the SPS, treating the meteorological stations of the German Weather Service (DWD) like an irregular weather data grid that is comparably sparse, with the hypothesis that it may nevertheless have positive features due to the higher quality of its observations of global and diffuse horizontal irradiance. We evaluate the results against German PV meter data as well as via correlations to vertical load time series from TSO substations.The results question the conception that machine learning outperform physical PV modelling, so long as the meteorological inputs are truly accurate. Closing this gap has practical consequences, as physical models otherwise hold several advantages, including far superior computational efficiency, e.g. for realizing digital twin models today, as well as for distinguishing between PV production, storage, and feed-in for grid operation, which empirical models based on reference plant feed-in cannot. In addition to improving PV estimation, the results thus motivate future research to improve irradiance modelling.
Machine learning approach to reconstruct fog history for solar parks
Authors: Peinke Isabel , Agard Vince
Published: July 2024

Fog or low stratus clouds can reduce solar power production significantly. A not forecasted fog event can lead to huge power misses, even at country scale and thus lead to high imbalance prices in energy trading. This presents a financial risk to the solar park owner, who bears the balancing responsibility in the European power markets. Reducing this risk makes solar investments financially more interesting and thus can help for the achievement of a climate neutral Europe. More accurate solar forecasts can help to achieve this goal, and for this an accurate historical data of fog occurrence at the park level is important. It is not straightforward to get this information for several reasons: Fog can be a very local phenomenon, and observations only rarely exist close to solar parks. Reanalysis data such as ERA5 do not have visibility information and numerical weather prediction models (NWP) often poorly predict fog, in part because their grid is too large to model fog formation.  In this study, we will present a machine learning model to reconstruct fog history at solar park level. To build our model we use solar parks, which have weather observation in close vicinity. The weather observation will give us information about the true fog events and thus the target data, i.e. the data we want to predict. We use visibility and low cloud measurements from observation stations from the German weather operator DWD, Deutscher Wetterdienst. Fog was defined to include fog and mist (visibility < 5km) and to low clouds, which are lower than 2 km. To train the model, we will use reanalysis data from ECMWF (ERA5) and solar production. Part of the work was to find the best features to represent fog formation. We used data such as cloud cover, temperature (at surface and gradients) and humidity. Then we trained a classifier to predict the fog history at park level. This model showed good improvements of fog detection compared to NWP fog forecast. Our model achieved an accuracy of 94% and Brier score of 0.05, while the NWP fog forecast of the closest grid point to the solar park shows respective values of 86% and 0.17. This shows that our model was able to produce accurate historical fog data at the solar park level.
On the assessment of temperature and precipitation extremes in Central Europe in gridded observation datasets and reanalysis 
Authors: Wypych Agnieszka , Zahradníček Pavel , Sulikowska Agnieszka , Štěpánek Petr , Łanecki Filip Oskar
Published: July 2024

As the climate continues to warm, patterns of air temperature and precipitation in Central Europe (CE) are changing, affecting the frequency, intensity, and spatial extent of extremes in this region. Studying and understanding these variations is crucial due to natural hazards such events trigger and is essential for refining climate predictions. However, the reliability of climate reanalysis or gridded datasets regarding extreme weather is still uncertain at varying temporal and spatial scales, especially in areas of diversified geography like CE. In this study we aim to evaluate the performance of two reanalysis, ERA5 and ERA5-Land, and two gridded observation datasets, EOBS and newly developed high-resolution data for CE, alongside in-situ observations, regarding the occurrence, spatial extent, and intensity of extreme temperature and precipitation events. The study is based on daily maximum and minimum temperatures (Tmax, Tmin), and precipitation totals (RR) for the 1961-2023 period, covering CE defined as the area encompassing Poland, Czechia, Slovakia, and eastern Germany. The extreme events are defined based on absolute as well as local percentile thresholds. The newly introduced daily gridded dataset for CE is based on quality-controlled and homogenised station data from approx. 500 climatological stations and approx. 1000 rain gauges, with a spatial resolution of 1×1 km and additionally 0.5×0.5 km in the mountainous areas to better capture details of complex terrain.Gridded datasets validation procedure comprises of the comparison of in-situ data with the results extracted from the nearest grid points as well as from the predefined areal buffer. The co-occurrence of extremes between the datasets will be examined using statistics based on a contingency table, such as the hit rate, while the other extreme events’ characteristics, i.e. their spatial extent and intensity, will be studied employing the most common statistical measures, i.e. correlation coefficients, bias estimation, cross validation.The results indicate that the data accuracy regarding the representation of the temperature and precipitation extremes in CE depends strongly on the subregional topography features as well as varies with the intensity and severity of the events. These conclusions highlight the necessity of local datasets development, alongside with already existing regional or global databases, especially when specialised environmental modelling is taken into consideration.
Winter-long Atlantic-African jet merging and the effect on extreme weather
Authors: Suresan Sohan , Harnik Nili , Caballero Rodrigo
Published: July 2024

Variability in the jet stream can have a significant influence on the distribution of extreme weather, with winter-long anomalies leading to extreme seasons. During winter of 2009/2010, the Atlantic and African jets were anomalously merged for most of the winter, resulting in a persistent zonally oriented single jet. At the same time, intense and prolonged negative phase of the North Atlantic Oscillation (NAO) and unusually cold and extreme weather conditions were reported over the Northern Hemisphere. Such a merging was only observed to occur for a whole winter during the winters of 1968-69 and 1969-70. Preliminary results indicate that such persistent winter merged jets could be more frequent in a future global warming scenario and thus it is important to understand this rare dynamical jet state and its effects on the weather patterns. In this study, we explore the associated distributions of extreme weather during merged-jet winter months, in comparison to the distribution of extremes during negative NAO months. We show that merged jet winter months have a signature weather pattern distribution that is different from the negative NAO phase, affecting Northern Africa, Europe and south-west Greenland, which is associated with synoptic storms tending to either propagate zonally towards the Mediterranean, with a secondary northward then westward branch propagating to Greenland. We further find a complex relation to Arctic-Eurasian temperature anomalies. Once we remove the NAO-related high-latitude cold surface temperature anomalies, we find that Atlantic-African jet merging, especially the winter-long persistent ones, occur during winters with unusually warm Arctic and cold Eurasia. This is consistent with the theory that winter-long jet merging represents a dynamical regime transition to a merged jet, which is more likely to occur when mid-latitude baroclinicity is weaker.
Solar radiation modeling under 3D reconstructed cloud fields
Authors: Logothetis Stavros-Andreas , Panagopoulos Orestis , Tzoumanikas Panagiotis , Kosmopoulos Georgios , Kazantzidis Andreas
Published: July 2024

The distribution of diffuse sky radiance over the sky hemisphere is frequently required in many fields and applications, like predicting the solar irradiance on sloped planes or titled solar collectors, quantifying the spectral angular effects on photovoltaics (PV) device performance, etc. Atmospheric clouds constitute the most dominant factor for solar radiation attenuation in the atmosphere, and their accurate representation in radiative transfer models is more than necessary. However, cloud information in radiative transfer models remains a challenging process characterized by uncertainties due to the incomplete knowledge of cloud optical properties, vertical structure, and their high spatiotemporal variability, reflecting high uncertainties in solar resource estimations. These uncertainties frequently stem from the use of two-dimensional (2D) information about clouds, like pixel-level image information from geostationary satellites. Radiative transfer calculations cannot neglect the inherent three-dimensional (3D) cloud structure in order to alleviate the uncertainties in the estimation of solar resource. In this study, we investigate the potential to derive the 3D morphology of clouds with a network of all-sky imagers. Their spatial displacement and synchronized exposure times provide the necessary information for a 3D reconstruction within the area around the cameras. The main objective is to combine, under different cloud conditions, the 3D reconstructed cloud fields and radiance measurements from all-sky imagers in radiative transfer simulations. In particular, the 3D reconstructed cloud fields will be used as input to 3D radiative transfer simulations (Monte Carlo code for the physically correct Tracing of photons In Cloudy atmospheres (MYSTIC) algorithm, included in the libRadtran software package) in order to calculate solar irradiance maps.
Long-term trends in extreme air temperature indices in Ireland
Authors: Mateus Carla
Published: July 2024

Ireland has a rich heritage of long-term air temperature observations. The analysis of long-term historical instrumental records is essential to assessing extreme air temperature indices and examining modern climate warming within a historical context. This presentation highlights the methodologies for data rescue, quality control, homogenisation, and assessment of trends in extreme air temperature indices back to the 19th century.This research assessed the frequency, duration, intensity and geographical distribution of the daily extreme air temperature indices recommended by the ETCCDI (Expert Team on Climate Change Detection and Indices) based on long-term rescued, quality-controlled and homogenised maximum and minimum air temperature series across Ireland.Significant increasing trends were assessed in the seasonal and annual maximum and minimum air temperature series. Additionally, significant increasing trends were assessed in the warm nights (+7.5 nights), warm days (+6.8 days), warm spell duration index (+3.9 days), growing season length (+22 days), coldest night (+2.7 °C) and coldest day (+1.5 °C) in the period 1885–2018 in Ireland. Significant decreasing trends were identified in the frost days (−13.7 days), cold days (−9.3 days), cold nights (−7 nights), cold spell duration index (−6.9 days) and diurnal air temperature range (−0.1 °C) in the same period in Ireland. ReferencesMateus, C., Potito, A. and Curley, M., 2020. Reconstruction of a long‐term historical daily maximum and minimum air temperature network dataset for Ireland (1831‐1968). Geoscience Data Journal, 7(2), pp.102-115.Mateus, C., 2021. Searching for historical meteorological observations on the Island of Ireland. Weather, 76(5), pp.160-165.Mateus, C., Potito, A. and Curley, M., 2021. Engaging secondary school students in climate data rescue through service‐learning partnerships. Weather, 76(4), pp.113-118.Mateus, C. and Potito, A., 2021. Development of a quality-controlled and homogenised long-term daily maximum and minimum air temperature network dataset for Ireland. Climate, 9(11), p.158.Mateus, C. and Potito, A., 2022. Long-term trends in daily extreme air temperature indices in Ireland from 1885 to 2018. Weather and Climate Extremes, 36, p.100464.
Tropical Indian Ocean Mixed Layer Bias in CMIP6 CGCMs Primarily Attributed tothe AGCM Surface Wind Bias
Authors: Feng Jie
Published: July 2024

The relatively weak sea surface temperature bias in the tropical Indian Ocean (TIO) simulated in the coupledgeneral circulation model (CGCM) from the recently released CMIP6 has been found to be important in model simulationsof regional and global climate. However, the cause of the bias is debated because the bias is strongly modeldependent and shows marked seasonality. In this study, we separate the bias in CGCMs into bias arising from oceanicGCMs (OGCMs) and bias that is independent of OGCMs using a set of CMIP6 and OMIP6 models. We found thatOGCMs contribute little to mixed layer bias in the CGCMs. The OGCM-independent bias exhibits a large-scale coldmixed layer bias in the TIO throughout the year, with an unexpectedly high degree of model consistency. By conducting aset of OGCM experiments, we show that the OGCM-independent mixed layer bias is caused mainly by surface wind biasin the utilized CGCMs. About 89% of surface wind bias in the CGCMs is due to the inability of atmospheric GCMs(AGCMs), whereas atmosphere–ocean coupling in the CGCMs has only a minor influence on surface wind bias. The biasin surface wind is also found to be the cause of subsurface temperature bias besides the ocean dynamics such as verticalmixing and vertical shear in currents. Our results indicate that correcting TIO mixed layer bias in CGCMs requires improvementin the capability of AGCM in simulating the climatological surface winds. The results improve our understanding of the cause of the bias in the IndianOcean and show that our method of bias separation is effective for attributing the source of bias to different proposedmechanisms.
Analysis of Predictability Improvement due to the Enhancement of Observation Error for the GK-2A Infrared Channels in Data Assimilation
Authors: Min Ki-Hong , Jo Seo-Youn
Published: July 2024

The All-Sky Radiance (ASR) data from geostationary satellites are important for improving initial conditions in numerical modeling through data assimilation, as it provides dense spatio-temporal atmospheric information over a wide area. Accurately applying the error information inherent in observations is essential for enhancing its effectiveness of satellite data assimilation. In this study, we calculated an observation error model for the ten infrared radiation channels of the Advanced Meteorological Imager (AMI) on the GEO-KOMPSAT-2A (GK-2A) for the summer season using the standard deviation of the brightness temperature observation minus background (O-B) as a function of the cloud impact parameter (Ca). The normalized brightness temperature of O-B probability density function is scaled such that it more closely approximates a normal distribution. For data assimilation experiments, we used the Community Radiative Transfer Model (CRTM) as the satellite observation operator and applied the 3-dimensional variational data assimilation method of the Weather Research and Forecasting Model Data Assimilation. When applying the adjusted observation error model for summer precipitation cases in the Korean peninsula, both the analysis and forecast fields improved compared to a prescribed constant error value. The best rainfall forecast performance was observed in the linear model, which followed the normal distribution better than the high-order regression observation error model. This is thought to be due to the observation error in the linear model saturates more gradually, allowing for consideration of a wider variability of Ca, i.e., a more detailed spatial distribution of cloud impact. Meanwhile, the assimilation results of Clear-Sky Radiance (CSR), excluding cloud area information, were compared to analyze the additional effects of cloud-precipitation area information during ASR assimilation. Further, we plan to assimilate both the water vapor channel ASR and the surface-sensitive channel CSR to improve the cloud detection algorithm, quality control, and refine surface parameter estimates for enhanced predictability.Key words: GK-2A infrared channels, data assimilation, observation error, precipitation forecast※ This research was supported by the National Research Foundation of Korea (No. 2022R1A2C1012361) funded by the Ministry of Science and Technology, and also was funded by the Korea Meteorological Administration Research and Development Program under Grant RS-2023-00237740. Additional support was provided by the BK21 FOUR project funded by the Ministry of Education.
Experiences with Doppler Lidar based routine turbulence measurements
Authors: Päschke Eileen , Detring Carola , Becker Claudia
Published: July 2024

At the Lindenberg Meteorological Observatory - Richard Aßmann Observatory (MOL-RAO), StreamLine Doppler Lidar (DL) systems from the manufacturer HALO Photonics (nowadays HALO Photonics by Lumibird) are tested for a routine application to derive profiles of turbulence variables within the atmospheric boundary layer (ABL). Information from these profile measurements is useful to get a better understanding of ABL processes and to provide enhanced insights into the ABL vertical structure under different weather conditions and over the course of a diurnal cycle (e.g. stable ABL, convective-mixed ABL, transitions between different ABL states). Further possible applications of turbulence profiles from DL measurements can be found in the area of ​​validation and/or development of turbulence closure schemes used in weather or climate models and in the wind energy sector.As an example, results for the turbulence kinetic energy (TKE) derived by means of a DL scanning and turbulence retrieval method introduced by Smalikho and Banakh (2017) are presented. For the retrieval of TKE profiles this method provides two correction terms. One corrects for an underestimation of TKE due to the averaging of radial velocity over the pulse volume and the other is used to prevent an overestimation of TKE if turbulence is very weak. The long-term measurements at MOL-RAO are used to evaluate the performance of these correction terms. It is shown that the corrections can have a significant positive effect on the precision and accuracy of the retrieved TKE, provided that noise-free DL radial velocity measurements are used as input data for the turbulence retrieval and the inherent assumptions of the retrieval method are fulfilled. To verify the latter, an additional data quality control (QC) scheme was developed at MOL-RAO and applied after the retrieval procedure. Insights into the main test criteria of this QC scheme will be presented and the results of their application will be discussed.  Finally, comparisons of DL based TKE retrievals are shown between seven different DL systems which were operated side-by-side with an identical scan configuration under same atmospheric conditions. The DL systems exhibited differences in their pulse lengths and thus the size of their pulse volumes. The associated differences in the correction terms and their effect on the final TKE retrieval results will be discussed.     ReferencesSmalikho, I. N. and Banakh, V. A.: Measurements of wind turbulence parameters by a conically scanning coherent Doppler lidar in the atmospheric boundary layer, Atmos. Meas. Tech., 10, 4191–4208, https://doi.org/10.5194/amt-10-4191-2017, 2017. 