bluuebunny/crossref_metadata_2025 huggingface dataset:
Climate modulation of external forcing factors on air quality change in Eastern China: implications for PM2.5 seasonal prediction
Authors: Ma Jinghui
Published: July 2024

Meteorological conditions significantly affect the frequency and duration of air pollution events. A large number of studies have shown that atmospheric circulation driven and regulated by various external forcing factors can affect haze pollution by influencing meteorological variables related to haze pollution. Since changes in external forcing factors usually precede circulation, they can be used as precursors to predict haze pollution. This study analyzed the spatiotemporal variations of PM2.5 concentration anomalies over the past 39 years (1980–2018) in winter (November to January) over eastern China based on the empirical orthogonal function (EOF) method. Regression analysis is conducted on external forcing factors such as sea ice, sea temperature, and snow cover in the pre-autumn (September to October) using the time series of the first three modes. Nine key factors were selected, which further led to establishing a model for predicting winter PM2.5 concentration in eastern China using the long short-term Memory deep learning algorithm (LSTM). Independent verification revealed that the predicted and observed PM2.5 concentration distributions were consistent, with the absolute value of deviation within 15 μg·m−3 between 2016 and 2018. The correlation coefficients between the predicted and observed values were between 0.42 and 0.93 over eight key cities in the past 10 years (2009–2018). The contribution rates of the nine factors to PM2.5 concentration were calculated to explore their impact on PM2.5 concentration during winter. The Arctic sea ice (ASI) was found to be the key contributor to the winter PM2.5 concentration in eastern China. The predictors can be monitored in real time; hence, the model provides a real-time predictive tool, improving the prospects of predicting seasonal PM2.5 pollution, especially in vulnerable regions such as eastern China.
Co-production of multi-annual climate services to support food and wine production resilience
Authors: Delgado-Torres Carlos , Octenjak Sara , Marcos Raül , Doblas-Reyes Francisco J. , Donat Markus G. , Milders Nadia , Pérez-Zanón Núria , Soret Albert , Terrado Marta , Torralba Verónica , Bojovic Dragana
Published: July 2024

Nature and society are experiencing unprecedented challenges due to impacts of climate change. These impacts compromise the security of several sectors such as agriculture, biodiversity, water management, food production and economy. Changes in temperature trends, precipitation regimes and increased frequency of extreme climate events due to climate change affect crop production. Besides, natural climate variability at annual to decadal time scales also affects crop yields and alters the optimal crop seasons and varieties. Examples of climate events that affect agricultural productivity are shifts in the rainy season, more intense and frequent heatwaves, prolonged droughts and floods.Decision-makers could use information on climate change and variability years in advance to develop adaptation policies to reduce risk and losses. Potential adaptation strategies are investment in new irrigation technologies, sustainable water management, selection of crop varieties, sustainable agricultural practices, purchase of pesticides, agroforestry and livelihood diversification. To support their decisions, climate information can be extracted from decadal climate prediction models, which have been recently shown to be skilful in predicting mean and extreme temperature and precipitation and drought conditions over several land regions.To illustrate the climate services co-production based on multi-annual predictions, we present the implementation of two services. The services aim to support food security in Tanzania and Malawi, and wine sector activities in Catalonia, as part of the FOCUS-Africa and ASPECT projects. The climate information on multi-annual time scales has been provided through an interactive online website where the climate predictions and their quality can be accessed. In addition, co-designed multi-annual bulletins summarise the predictions in an accessible language and show maps of the most relevant variables and periods. Using these documents during in-person meetings helped involve participants, facilitate discussion, and concretise feedback. Co-production is an ongoing process of improving and tailoring climate information for its usability in decision-making processes.
A Study on the Development of Wind Disaster Impact Model Based on Weather GIS Big Data
Authors: Choo Kyung-Su , Choi Seung-Chul , Kim Byung-Sik
Published: July 2024

The frequency and intensity of natural disasters, including high winds, floods, and droughts, are being significantly altered by climate change. This increase in activity not only causes immediate damage but also increases the long-term socioeconomic risks associated with these extreme events. As a result, the field of impact-based forecasting has emerged as an important area of research. In contrast to traditional weather forecasting, which is primarily concerned with meteorological elements such as temperature, precipitation, and wind speed, impact-based forecasting aims to predict the potential socioeconomic impacts of weather events in order to better prepare for and mitigate risks. Currently, the majority of research on impact forecasting is focused on major natural disasters such as floods and typhoons, which can cause significant damage to infrastructure and communities. However, there is still a significant gap in research on the impact of high winds on vehicles, particularly in the transportation sector. Although damage in this sector is generally low compared to more catastrophic events, the increasing frequency of high winds due to climate change requires a deeper understanding and assessment of the risks to vehicle safety. In response to this need, we developed three specific indices to assess the risk to vehicles in high wind conditions. These indices were carefully calculated and analysed to identify areas vulnerable to wind-related vehicle accidents. The results effectively recreated areas where these accidents have occurred in the past, confirming the usefulness of the indices in real-world scenarios. Through this research, we hope to lay the groundwork for an objective, data-driven assessment of the risks that high winds pose to drivers, which can better inform policy decisions, improve driver safety measures, and ultimately reduce the number of road accidents and fatalities caused by severe weather. AcknowledgementThis research was support by a (2022-MOIS63-002(RS-2022-ND641012)) of Cooperative Research Method and Safety Management Technology in National Disaster funded by Ministry of Interior and Safety(MOIS, Korea). 
Operational multi-model ensemble postprocessing: key challenges and lessons learned
Authors: Bhend Jonas , Spirig Christoph , Nerini Daniele , Schaer Mathieu , Moret Lionel
Published: July 2024

MeteoSwiss has recently introduced multi-model ensemble postprocessing for the automatic production of its local forecasts. While this has allowed to streamline forecast production and resulted in improved forecasts compared with the legacy system, the inclusion of data-driven approaches in the forecasting process also poses unique challenges:The statistical postprocessing produces probabilistic forecasts that are well calibrated. This often implies that forecast spread is increased compared with the underlying NWP forecasts requiring adaptation of the forecast visualizations and products. For example, producing deterministic weather symbols (pictograms) to summarize the weather evolution throughout the day is a challenge in particular during convective situations when spatio-temporal uncertainty in the forecast is very pronounced even a few hours ahead. A careful retuning of the decision tree to produce weather symbols was a key requirement for the successful introduction of postprocessed forecasts of precipitation and cloud cover.Initially, the statistical postprocessing has been optimized for general-purpose rather than high-impact weather. In cases of weather warnings there may be notable inconsistencies between the automated forecast based on statistical postprocessing and the official warnings produced by the forecasters. Often, the statistical postprocessing underestimates the intensity of the event whereas the high-resolution NWP better reflects the observed situation. While we are currently further refining our postprocessing to adapt it to severe events, discrepancies between automated forecasts and manually tailored information will remain a communicative challenge. Finally, the operational  and organizational challenges of running data-driven approaches are not to be underestimated. Data-driven approaches have to be constantly maintained and monitored to avoid adverse impacts of erroneous observations and other sources of data drift. Furthermore, NWP development cycles and reforecasts for testing need to be co-designed to provide sufficient data for re-training of statistical approaches. Also, the added complexity poses a challenge when seeking to understand unexpected forecast behavior and respond to end-user feedback. While NWP characteristics are quite well known by all development and forecasting teams, knowledge on the specifics of the postprocessing is less well developed in the organization. While statistical postprocessing is a key component in automated forecasting, careful design of the system with a keen eye on operational constraints is necessary to manage the additional complexity. With the recent advent and proliferation of data-driven approaches in weather forecasting, we expect such considerations to become increasingly important.
How synoptic weather activity and interaction with the extratropical wave guide matter for the prediction of blocking and associated extremes
Authors: Grams Christian M. , Oertel Annika , Quinting Julian F. , Magnusson Linus , Deinhard Moritz , Dorrington Joshua , Hauser Seraphine , Wandel Jan , Balmaseda Magdalena , Vitart Frédéric
Published: July 2024

Blocking anticyclones embedded in highly amplified Rossby waves and flanked by stationary cut-off cyclones can cause devastating extreme events. However, often the magnitude of such extremes is difficult to predict on medium- to subseasonal lead times. In this presentation we discuss the synoptic evolution of two recent extreme events: The heat wave in western North America in June 2021 and the extreme rainfall in the Eastern Mediterranean in September 2023. For both cases we show that the downstream development triggered by the interaction of synoptic weather systems with the upper-tropospheric wave guide in regions far upstream ultimately lead to highly amplified Rossby Waves. Thereby, the interaction of diabatic outflow due to latent heat release in ascending air streams with the jet is key and the exact phasing and timing of this interaction represents a predictability barrier with regard to the magnitude of downstream ridge and associated extreme events. Therefore, probabilistic weather forecasts are only able to predict the extremity of the events once the complex interaction of synoptic activity is captured. Thus, the sequence of individual weather events limits the predictability of the magnitude of extremes linked to highly amplified Rossby waves. We conclude that a correct assessment of highly amplified Rossby waves for weather prediction and climate projection requires large ensembles in order to capture the rare sequence of interactions causing such extremes. Also, an accurate representation of the physical and dynamical processes across spatiotemporal scales in particular of moist processes on synoptic weather scales would help, however, there are indications that the predictability barrier is intrinsic in nature.
Long-term validations of annual wind speeds by the local forecast model and the microwave scatterometer around Japan
Authors: Takeyama Yuko
Published: July 2024

This study investigated the interannual variability (IAV) of sea surface wind speeds in the Northwest Pacific Ocean with the local forecast mode (LFM) and the satellite-borne microwave scatterometer. The IAV of wind speed is an essential parameter for evaluating the reliability of annual mean wind speeds (MWSs) during wind resource assessment. In previous study, the distribution of standard deviations (SDs) of annual MWSs at 10 m height above the sea level from the Meteorological Operational Satellite Program of Europe (MetOp) -A, -B and -C/Advanced SCATterometer (ASCAT) from 2009 to 2020 showed some areas with high SDs above 5% around Japan (120 – 160 °E, 14 - 49°N). It is indicated that there are two factors for high SDs, 1) the characteristics of the microwave scatterometer, and 2) the effect of sea surface temperature (SST). High SST leads to unstable atmospheric stability conditions, and the vertical wind shear decreases and vice versa. Accordingly, the IAV of offshore wind speeds increases in areas with current meander or transition zones between warm and cold currents. In this study, the SD with the LFM wind speed provided by the Japan Meteorological Agency with about 2 km spatial resolutions is shown in addition to the wind speed from the scatterometer to clarify clearly separate these two factors. Furthermore, an effect of tropical cyclones to the IAV of wind speed from the scatterometer is shown. The observation period of the scatterometer is extended from the previous study, and the analysis is conducted using a total of 15 years of data until 2023.
Combining high-resolution wind downscaling with numerical weather prediction models
Authors: Wenzel Caspar , Pierotti Nicola
Published: July 2024

High resolution wind speed forecasts are crucial for a range of applications, including the management of onshore wind power generation. Conventional wind speed forecasting is bound to the coarse spatial resolution of NWP models of 2-30 km. The wind speed complementarity model (WiCoMo)* is a high-resolution wind downscaling model that provides distributions of annual wind speeds across Germany at a horizontal resolution of 25 m x 25 m. This work aims to combine high resolution wind downscaling with numerical weather prediction (NWP) models to improve accuracy and resolve local effects, particularly in complex terrain. Quantile mapping was used to derive a transfer function at each 25 m x 25 m grid cell based on annual historical wind speeds calculated by WiCoMo and the NWP models respectively. The function was then applied to hourly time series of NWP models to simulate downscaled predictions. In addition, power curves of wind turbines were used to calculate the onshore wind power output of Germany from the high-resolution forecast. Validation metrics were used to compare the performance of the WiCoMo-enhanced NWP models with raw NWP outputs. The analysis demonstrates that the WiCoMo-enhanced NWP models outperform raw NWP across all tested models. For the year 2022, the MAE of NEMS4 was reduced from 1.66 m/s to 1.13 m/s and for NEMSGLOBAL it improved by almost 33%. The MBE was reduced to near 0 in all cases. Furthermore, spatial evaluations show that local wind speed effects often falling below the grid size in NWP models, such as hilltop speed-up or sheltering valleys, are resolved by the downscaling. The study suggests that localized wind speeds at wind turbine sites improve the accuracy of wind power output predictions. However, several limitations are identified, including challenges in applying corrections during specific weather conditions. Additionally, the modelled wind power output could not be validated at single turbine sites, limiting the validity of estimates for the entire country. The study demonstrates the potential of WiCoMo-enhanced NWP models in improving wind speed forecasting capabilities. The findings have important implications for various applications, including renewable energy planning and risk assessment. * Christopher Jung and Dirk Schindler. Introducing a new wind speed complementarity model. Energy, 265:126284, 2023. ISSN 0360-5442. doi: https://doi.org/10.1016/j.energy.2022.126284.
Data rescue of national and international meteorological observations at Deutscher Wetterdienst
Authors: Möller Thomas , Leiding Tina , Andersson Axel , michaelis Janosch , Hansen Akio , Imbery Florian , Junghänel Thomas
Published: July 2024

Historic observational data records are an important contribution to climate reconstructions and the analysis of past weather events. Particularly in remote and data sparse regions, such as the open ocean, newly rescued data can significantly improve the knowledge of weather and climatic conditions in earlier decades and centuries.Deutscher Wetterdienst (DWD) has several collections of original historical weather records from land stations and ships worldwide. They comprise not only observations from Germany, but also from the world’s oceans and land stations in many parts of the world.All German state-owned meteorological observations since the founding of the Prussian Meteorological Institute in 1848 are collected in DWD’s main archive in Offenbach.DWD’s office in Hamburg holds the marine archive, starting with the collections of the German Naval Observatory, 'Deutsche Seewarte', which existed from 1868 to 1945. It includes marine data records from ships and land stations in many parts of the world (e.g. from former German colonies), as well as signal stations situated at the coasts of the North and Baltic Sea.With increasing computing resources, high temporal resolution data has increasingly become the focus of climate research in recent years. Thus, the processing of such unique historical datasets creates considerable added value. The digitization of recording strips from pluviographs, for example, is currently one focal point of the data rescue activities at DWD.The documentation, digitization and quality check of the enormous amount of handwritten journals of the data archives is still ongoing. The digitized data are freely accessible to the scientific community and are also continuously submitted to international data archives, such as ICOADS and ISPD. These datasets also make the data an important input for regional and global reanalyses.The presentation will give an overview of the historical archives of Deutscher Wetterdienst. The challenges are discussed the latest progress of the digitization efforts and ongoing analysis of the data shown.
Assessing climate change risks and exploring feasible adaptation measures for the tourism sector in Slovenian Istria 
Authors: Pogačar Tjaša , Kokot Katja , Turnšek Maja
Published: July 2024

Climate change poses a threat to the tourism industry, potentially shifting travel patterns and destination preferences. The aim of the research was to assess the risks in Slovenian Istria, propose appropriate measures and discuss their feasibility in stakeholders’ workshops. The analysis of past climate data from the archive of Slovenian Environment Agency showed that since 1950, the number of hot days (with maximal daily temperature above 30°C) has increased by about 4.5 days per decade, and tropical nights (with minimum daily temperature above 20°C) by 2–5 days per decade. Precipitation patterns are changing. Climate projections show the trend is expected to continue with an increase in hot days by an average of 10 (RCP4.5) to 19 (RCP8.5) in the first period (2011–2040), and 19 (RCP4.5) to even 30 (RCP8.5) in the second period (2041–2070). Tropical nights are also projected to become more common. Climate index for beach tourism (CIT:3S) shows an increase in ideal days during summer months since 1971–2000. While the overall share of ideal and marginal days for beach tourism is projected to further increase from May to September across all scenarios, the number of ideal days in July and August is expected to decline, particularly under RCP8.5 by the end of the century. The season is likely to extend primarily into September. An index for urban tourism suitability (HCI) shows over ¾ of days from May to September are ideal, with a slight decrease in July recently. The percentage of ideal days is rising significantly in March, April, and October. All scenarios predict a year-round extension of the season.Global warming threatens to make Slovenian Istria a less comfortable destination, especially during peak season. More frequent and severe heat waves and floods threaten tourist infrastructure and safety. Frequent droughts could exacerbate water shortages, impacting both residents and tourists. Despite the challenges, there are also potential opportunities for extending the tourist season into spring and autumn. Slovenian Istria can adapt with investments in water-saving measures, building seawalls, and thermal insulation of the buildings. Promoting Istria as a year-round destination with events and activities in spring and autumn can reduce reliance on summer tourism. Additionally, implementing early warning systems, providing natural and artificial shade, drinking fountains, staff training on self-protective measures, and adaptations like scheduling activities outside peak heat hours can ensure tourists' safety and comfort.By implementing an appropriate adaptation strategy, the region can remain a competitive tourist destination. However, financial and logistical limitations may exist. We will also present the results of workshops where tourism stakeholders in four tourist destinations discussed the feasibility of adaptation measures.
Towards operational subseasonal heatwave prediction in Switzerland
Authors: Büeler Dominik , Pyrina Maria , Imamovic Adel , Moret Lionel , Spirig Christoph , Lehning Michael , Domeisen Daniela I. V.
Published: July 2024

The subseasonal predictability of heatwaves in Europe is relatively well understood regarding prediction skill horizon and physical drivers of predictability. Despite this progress, few studies have translated subseasonal model output into skillful operational heatwave forecast products and end-user-tailored impact forecasts. These are substantial challenges, given the relatively high uncertainties and the flow-dependent skill inherent in subseasonal prediction. In this project, we aim to translate subseasonal model output from the European Centre for Medium-Range Weather Forecasts (ECMWF) into end-user-tailored heatwave forecast products for Switzerland. We first perform a detailed verification of average subseasonal (hindcast) prediction skill for temperature and heatwaves in Switzerland on different spatial and temporal aggregation scales. This analysis demonstrates a significant increase in subseasonal forecast skill with increasing temporal aggregation scales. We then analyze to what extent previously-studied local and remote drivers (such as dry soils, lower-frequency atmospheric modes, or sea surface temperature anomalies) manifest as “windows of forecast opportunity” for heatwave prediction in Switzerland and its subregions. These steps are performed with two sets of hindcasts – one with the native grid resolution and one that has been downscaled (and bias-corrected) to a higher resolution using quantile mapping. This postprocessing helps to quantify the added value of downscaling at subseasonal lead times. Finally, we present some ideas on how the gained knowledge on spatio-temporal and flow-dependent characteristics of skill could be translated into an operational subseasonal heatwave prediction system for Switzerland – a step that is closely linked to the challenging question of how much skill is enough skill for specific end-user applications.
Evaluation of numerical model simulations of precipitation events over Korean Peninsula
Authors: Cho Sujeong , Seol Kyung-Hee , Lee Eun-Hee
Published: July 2024

The NWP model-named the Korean Integrated Model (KIM), which was developed through the first-phase of Korean Institute of Atmospheric Prediction Systems (KIAPS) was adopted for the operational forecast at the Korea Meteorological Administration (KMA) in April 2020. KIM is a non-hydrostatic spectral-element model based on a cubed-sphere grid with the physics package for the medium-range prediction, and a hybrid four-dimensional ensemble-variation data assimilation (DA) system. The second-phase KIAPS has been developing both model physics, dynamics and DA to improve prediction performance. There have been two recent updates, one being a DA update including extending the usage of several satellite observations, which is mainly done by the KMA, and the other being a model physics done by the KIAPS. In this study, we evaluate the impact of model physics update, especially focusing on the high-impact weather events over the the Korean Peninsula. In a previous study, we found that when the forecast lead time is longer, KIM simulates mostly rainfall in the West Sea without heavy rainfall inland. In addition, most of precipitation is produced by the cumulus parameterization scheme (CPS). We confirmed that suppression of deep convection by adjusting the trigger condition in the CPS can improve precipitation distribution and intensity for a heavy rainfall event on the Korean Peninsula. Therefore, including the modification of the CPS process, modified physical processes are updated. In this study, we performe a full-cycle experiment for 10-day forecasts over each 2-month summer and winter, and evaluate the overall performance according to the update. By analyzing precipitation events, we will discover the key factors affecting performance of precipitation forecast. These results are expected to show improved forecasting performance compared to the previous version.
The Impact of Meteorological Forcings on Agri-Photovoltaic Systems: Advances in Greenhouse Energy Modeling
Authors: Petitta Marcello , Seri Emiliano , Bovesecchi Gianluigi , Cornaro Cristina
Published: July 2024

Agri-photovoltaics (Agri-PV) represents a new strategy for maximising land use by integrating solar energy production into the agricultural operation. Greenhouses, which are essential to modern agriculture, provide a controlled environment that promotes plant growth, extends the growing season and protects crops from extreme weather conditions. The integration of photovoltaic (PV) systems into these greenhouse structures is a forward-thinking move toward sustainable and energy-efficient agricultural practises. The REGACE project, funded by Horizon Europe, aims to develop and validate a new technology, i.e. a responsive tracking system placed inside the greenhouse driven by a PLC controller that changes the angle of the tracking system according to the needs of the plants, while enabling the continuous production of renewable energy in greenhouses throughout the year, supporting uninterrupted food production without the constraints of energy availability.In this progressive and dynamic context, we are advancing the development and testing of a Digital Twin (DT) ecosystem. This DT ecosystem simulates the energy production of PV greenhouse systems under specific internal conditions and external meteorological forcings. This paper presents our results from the use of Deep and Shallow Neural Networks (DSNN) to model the environmental conditions in these greenhouses. These networks improve our understanding of microclimatic variables and can lead to more general control strategies that optimise both crop yield and energy consumption.Preliminary results indicate that Deep and Shallow Neural Networks (DSNN), like traditional complex physical models, can effectively model the internal conditions of greenhouses with relatively low error and strong correlationpredictive capability, but with less computational effort. These results were derived from data collected during the summer at one of the pilot sites in Greece. The DSNN skilfully captures the variability of internal thermodynamic parameters, which is crucial for the management of external environmental influences and internal systems such as cooling and shading.The next steps aim to integrate the modelling of plant, water and energy balances within the greenhouse environment. This extended model will be further validated in the first pilot plant in Greece as well as at five other greenhouse sites involved in the project. This step is crucial for the development of integrated and adaptive control strategies that can improve both agricultural productivity and energy efficiency under different climatic conditions.
Extrapolation of wind speed for wind energy - how to (?) for high nacelle heights 
Authors: Schicker Irene , Lexer Annemarie , Tilg Anna-Maria , Andre Konrad
Published: July 2024

The past years have shown a rapid increase in the installation of new wind farms, despite the usual regulatory slow downs and social acceptance issues. Furthermore, existing wind farms have undergone repowering processes with some being repowered to newer, more efficient, and higher wind turbines. This repowering process included sometimes even the deinstallation of a number of wind turbines within a wind farm to avoid unwanted side effects such as wake inferences.  This, however, poses some issues for the extrapolation of wind speed to hub heights higher than approximately 130m agl and adjustments to the power law and log law approaches may be necessary.In Austria, a new wind atlas based on a concise set of 10 m meteorological observations of wind speed and direction, for wind energy applications and climate scenario downscaling, is currently being generated. With the idea of providing wind speed at different nacelles heights and including the requests of the wind industry, wind speed analyses at heights of up to 300m agl need to be provided. This requires thorough testing and adjusting of the classical methods as well as looking into machine learning methods for extrapolation (trained on e.g. the NEWA data).In this work, a comparison of methods, including adjusting classical methods as well as testing ML methods for Austria, is shown. The methods are evaluated against both the NEWA data sets, the DANRA reanalysis, AROME operational, and AROME RUC data, was well as the ERA5 data. Furthermore, testing against open tall tower data such as the Cabauw tower or the FINO platforms is carried out.
Using CALIPSO to characterise the Cloud-Aerosol Transition Zone
Authors: Ruiz de Morales Jaume , Andersen Hendrik , Calbó Josep , Cermak Jan , Fuchs Julia , González Josep Abel , Sola Yolanda
Published: July 2024

The Cloud-Aerosol Transition Zone (TZ) corresponds to particle aggregates showing properties between that of cloud and clear air (though containing some aerosol load). Its potential impact on Earth's radiation budget is becoming increasingly recognized. Furthermore, its characterisation is also crucial to better understand aerosol-cloud interactions and improve satellite retrieval of aerosol properties. This study aims to evaluate the extent and vertical distribution of TZ aggregates for different cloud regimes using the cloud-aerosol discrimination (CAD) score of the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument on the CALIPSO satellite. The CAD score classifies clouds and aerosols by the probability density functions of attenuated backscatter (AB), total colour ratio (TCR), volume depolarization ratio (VDR), altitude and latitude. The TZ has been assessed similarly to Fuchs and Cermak (2015), by quantifying the detected layers within the no-confidence range (NCR; CAD values between ─20 and 20) and applying several filters, such as analysing only tropospheric features and applying an IAB QA factor threshold (integrated attenuated backscatter quality-assured above a feature). As shown in previous literature, the AB-TCR and AB-VDR distributions of the detected features present an overlap between the characteristics of clouds and aerosols, and the CAD algorithm tends to classify uncertain features as clouds to avoid cloud contamination in aerosol profiles. This methodology has enabled us to identify the heights and regions where the TZ is most prevalent, as well as the vertical distribution of clouds and aerosols. For instance, we have observed that most NCR values are located near cloud edges, smoke plumes and dense dust. This result is based on the analysis of several retrievals within the same ground track across the southeastern Atlantic Ocean coast (the region with the highest NCR values) where stratocumulus, deep convective clouds, and high aerosol loads from biomass burning are prevalent. The vertical distribution analysis has revealed that there are more NCR detections at low altitudes (below 3 km) than over 6 km. On the other hand, in the horizontal direction most TZ aggregates are found within the first 15 km from the nearest high confidence cloud (CAD score over 70), with higher frequencies closer to the cloud.AcknowledgementsThis study has been funded through project NUBESOL-2 (PID2019-105901RB-I00) of the Spanish Ministry of Science and Innovation (MICINN). Jaume Ruiz de Morales is supported by an IFUdG-AE 2022 fellowship.ReferencesFuchs, J., & Cermak, J. (2015). Where Aerosols become clouds - Potential for global analysis based on CALIPSO data. Rem. Sens., 7(4).
Gulf Stream Ocean Conditions Influence on Atmospheric Rivers
Authors: Lopez-Marti Ferran , Czaja Arnaud , Messori Gabriele , Rutgersson Anna
Published: July 2024

Extreme precipitation and wind events in Western Europe are driven by Atmospheric Rivers (ARs) developing over the North Atlantic Ocean. While extensive research has been conducted on the atmospheric dynamics of ARs in this region and their connection with the North Atlantic Storm Track, gaps persist in understanding how oceanic variability influences AR activity, particularly in the eddy-rich environment of the Gulf Stream extension. The enhanced ocean heat supply and high mesoscale eddy activity over these western oceanic currents increase the surface latent heat flux in the area, thereby increasing moisture availability in the lower atmosphere and potentially facilitating AR genesis.This study focuses on evaluating the status of mesoscale eddies and oceanic heat flux within the Gulf Stream extension and their downstream impact on AR activity. To achieve this, we employ a high-pass Fourier Filter Transformation to isolate and then quantify the mesoscale eddy activity (smaller than ~500 km) of the Gulf Stream extension region in two high-resolution (0.25º) observational products for sea surface height and temperature. Additionally, we utilize heat transport data from the RAPID dataset to quantify the oceanic heat supply to the Gulf Stream extension region. Finally, we identify and track Atmospheric Rivers in the ECMWF reanalysis ERA5 dataset and calculate their intensity over the North Atlantic, including their landfall on the European West Coast.Our analysis provides a spatial and temporal cross-correlation analysis between the Gulf Stream state (high and low pass eddy variance) and the AR activity downstream. Furthermore, we investigate temporal lags between various oceanic conditions and their impact on ARs, thereby identifying oceanic precursors for AR genesis. Consequently, our study establishes a novel statistical relationship between Gulf Stream state and AR activity, with a particular emphasis on the role of mesoscale features. This includes a comprehensive characterization of mesoscale eddy activity within the region, contributing to a deeper understanding of the mechanisms driving AR formation and propagation in Western Europe.
Modeling Indoor Thermal Environments in Detached Wooden Homes Using Linear Regression and Neural Networks
Authors: Li Yiming , Hashimoto Yuki , Tsunematsu Nobumitsu , Ihara Tomohiko
Published: July 2024

Heat-related fatalities, predominantly from heatstroke, commonly occur indoors within residential settings. Consequently, accurate prediction of indoor temperatures is crucial for preventing such fatalities. However, few studies have measured the indoor thermal environment of general existing residential buildings rather than newly built residential buildings, and few studies that have predicted the indoor thermal environment based on the measurement.This study measured the indoor thermal environment of 17 detached wooden homes in two towns in Ota Ward, Tokyo Metropolis, Japan from July 25 to September 20, 2018, spanning a total of 58 days. Indoor measurements included room temperature and relative humidity, globe temperature, atmospheric pressure, window open/closed status, and air conditioner operating status. Outdoor measurements included outdoor temperature and relative humidity. In addition, the wind speed, precipitation, and cloud cover observed by the Japan Meteorological Agency were obtained. Based on these data, we developed models to predict indoor temperatures and wet-bulb globe temperature (WBGT). The prediction models were constructed using linear regression and a three-layer neural network. The input variables include measured or observed values and characteristics of the residential buildings, such as the age of the buildings, the number of floors, the floor on which the room is located, and the number of occupants.The model predicted room temperature and room WBGT with mean absolute errors (MAE) of 1.0°C and 0.8°C for non-air-conditioned rooms. The accuracy of room temperature and WBGT prediction improved by 0.1°C when the model was trained using data for the predicted room collected during the previous period. The linear regression model was as accurate as the neural network. The factors that are important for predicting the indoor thermal environment were identified using linear regression.This research enables the development of government-led alert systems to mitigate heat-related risks in residential areas.
Individualized weather warnings in the DWD warning portal prototype
Authors: Riedl Kira , Vogel Christian , Reetz Björn , Schaab Reik , Noel Linda , Niebuhr Heiko , Feige Kathrin
Published: July 2024

Weather warnings are often based on a catalog of hazard-related meteorological thresholds. However, there are weather-dependent applications that need warnings beyond these fixed criteria. In the course of the program RainBoW ("Risk-based, Application-oriented and INdividualizaBle delivery of Optimized Weather warnings") by the German Meteorological Service (DWD), a key field of action is to provide individualized weather warnings to account for specialized application-dependent needs. This will be realized through a warning portal ("DWD-Warnportal"), in which users with specific requirements may configure profiles with individual warning criteria matching their particular use case in terms of warning thresholds and considered areas.A current light-weight prototype version of the DWD warning portal allows the automated evaluation of probabilistic warning information for a set of preconfigured profiles containing warning criteria that partly differ from the standard warnings of the DWD for the general public. To obtain a final product tailored to the needs of specialized users, access to the prototype was granted to a group of test users from various areas of expertise. This group includes, among others, experts from the field of disaster control, energy network operation, forestry offices, media and academic research. Their feedback through regular user workshops and questionnaires is then used as a guidance to prioritize which additional features should be implemented and in which order. As requested by the test users, the first individual aspect implemented in the prototype is the warning location, which may be defined by an address, geographical coordinates or a GeoJSON polygon. For future releases, it is planned to provide persistent user-configurable profiles, in which the warning thresholds may be adjusted to individual use cases.
RODEO WP5: Towards unified and uniform access to European climate data using the OGC EDR API 
Authors: van Schayck J. Paul , van der Schee Marlies , Phaf Lukas , Kruithof Gijsbert
Published: July 2024

Providing access to observational climate data and its metadata has been important for climatology. Historically, climate data has been made available by some national data platforms or through the European Climate Assessment and Database (ECA&D). Users often had to pay for data access and output was provided in different formats. The newly in force EU open Data Directive 2019/1024 requires National Meteorological and Hydrological Services (NMHSs) to make their High Value Datasets, including meteorological datasets, available through machine readable application programming interfaces (APIs). RODEO work package 5 (WP5) aims to unite ten NMHSs that are developing APIs for their climate databases. Afterwards, an API aggregator service unifies the individual APIs, so that the clients can use all APIs with only one request.  In this presentation we will report on progress made in RODEO WP5.  

WP5 worked on uniforming climate datasets, including its metadata. A group of experts decided on the choice of variable to include, granularity of daily and hourly observations, and preferred controlled vocabularies. Metadata guidance from WMO, Copernicus and ESIP are incorporated to adhere to the FAIR principles. 

The API recommendations for NMHS data providers will follow the Environmental Data Retrieval (EDR) API specification. Using an EDR API it is possible to perform geospatial and temporal filterable queries of climate data. The EDR specification, published by Open Geospatial Consortium (OGC) in 2016, has, over recent years, gained traction as an ideal method to distribute meteorological data over the internet.  

The OGC has adopted CoverageJSON as data format standard alongside the EDR API. This format will be the preferred output format for an NMHS climate data API.  The EDR aggregator service will facilitate the unification of individual NMHS APIs to one central EDR API. The aggregator service can add caching, output format conversion from CoverageJSON to NetCDF or CSV and support queries across collections. 

To aid in the development of NMHS climate data EDR APIs, a workshop “Build an EDR API in a day” is being prepared to run in June 2024. A spin-off of this event is being planned in Spring 2025 for non-RODEO partners. 
Climatological variability of air-sea CO2 fluxes induced by iron flux in the North Pacific
Authors: Kim Daehyuk , Jung Hyun-Cha , Moon Jae-Hong
Published: July 2024

It is known that the ocean absorbs approximately on third of anthropogenic CO2 in the atmosphere through air-sea gas exchanges. The ocean’s uptake of anthropogenic CO2 plays a crucial role in mitigating the net source of CO2 in the atmosphere. The ocean biogeochemical processes are one of the most important fields for understanding Earth’s carbon cycle to understand the global climate system. The ocean’s capacity to absorb anthropogenic CO2 in the atmosphere is controlled by two processes: the biological carbon pump and solubility carbon pump. The biological carbon pump involves CO2 uptake through photosynthesis, and the solubility carbon pump is influenced by water temperature. Therefore, the spatial and temporal variability of carbon sink/source in the ocean is influenced significantly by the solubility and biological carbon pumps. Especially, phytoplankton growth can induce a strong biological pump, which can have a significant impact on regional carbon cycle. The North Pacific is known as a key region where the biological carbon pump occurs effectively. In this region, seasonal variability in chlorophyll concentration peaks occurs in spring and autumn. The peaks are influenced by factors such as water temperature, vertical mixing, and atmospheric deposition. Iron supply, among various factors, can lead to spatial and temporal variations in chlorophyll concentration, thereby potentially impacting the biological carbon pump. In this study, a coupled ocean physical-biogeochemistry model was employed to investigate the climatological variability in biogeochemical environment and CO2 flux (carbon cycle) resulting from atmospheric iron supply. The increase in chlorophyll concentration due to iron into the ocean can potentially trigger CO2 absorption through photosynthesis.
Moisture sources of summertime intense extratropical cyclones in the North-Atlantic
Authors: Stoffels Rikke , Weijenborg Chris , Benedict Imme
Published: July 2024

Extratropical cyclones are essential for redistributing energy, moisture, and momentum from lower latitudes to higher latitude regions. Although extratropical cyclones during the winter season are relatively well studied, less is known about summer cyclones and their moisture sources. Therefore, this research focuses to enhance our understanding of how summertime extratropical cyclones in the Northern Hemisphere shape the characteristics of the global water cycle. More specifically, the study focused on determining the moisture sources of these storms and analyses how precipitating air parcels are transported to the cyclone center. For this purpose, 8-day backward trajectories were calculated for a subset of the 20 % most intense summertime cyclones over the North-Atlantic and for all air parcels within the vicinity of cyclone center, using the Lagrangian Analysis Tool LAGRANTO. Subsequently, moisture uptakes along the trajectories of precipitating air parcels were identified using the moisture source diagnostic WaterSip. Using this approach, we find that the bulk of the precipitation associated with summertime cyclones falls close to the cyclone center within the WCB, mainly during the intensification phase. The origins of this moisture correspond to areas of high evaporation, with significant hotspots over the Gulf Stream region and its northeastern extension, and continental sources for cyclones in the Labrador Sea. During the early stages of cyclone development, moisture is delivered to the cyclone center, and this changes to more remote sources when intensity is increasing and when precipitation is at its peak. Local evaporation becomes again more dominant as cyclones generate less precipitation and start to decay. Therefore, the source distance is largest during the intensification phase and decreases thereafter. We lastly discuss differences between extratropical cyclones that undergo an extratropical transition. 