bluuebunny/crossref_metadata_2025 huggingface dataset:
Evaluation and machine-learning-based downscaling of ERA5 snow depth in Central Europe
Authors: Stachura Gabriel
Published: July 2024

Reanalyses are currently a major source of information about the state of the troposphere in many meteorological and climatological investigations. However, since they are a product of numerical modeling, they contain some systematic errors, which occur mainly due to the discretisation of a domain, parametrisation of physical processes and numerical approximations. The errors are particularly apparent for meteorological variables which cumulate over time and have great spatial heterogeneity. One example of such a variable is snow depth. In this work, snow depth from ERA5 and ERA5-Land reanalyses was evaluated against observations from over 300 stations in Poland, Czech Republic and Slovakia in winter seasons from 2001 to 2021. Additionally, an attempt was made to reduce existing biases and produce the field in finer resolution based on terrain characteristics derived from a digital elevation model. Verification results show that the difference between modelled and real elevation, together with station rank, are major factors contributing to systematic errors. In complex terrain, bias occurs nearly at every station, while in the lowlands, it is roughly neutral at synoptic stations as their data are assimilated during the production of the reanalyses. For lower-ranked stations, errors are apparently higher. On average, ERA5-Land has lower Root Mean Square Error (RMSE) than ERA5, however, there are stations with greater positive and negative bias. Despite higher resolution, ERA5-Land performs worse than ERA5 for around 30% of stations. Machine learning methods are capable of reducing systematic errors existing in the reanalyses. The greatest improvement occurs especially for sites in complex terrain. Statistical downscaling provides some useful insight in spatial distribution of snow depth, however, its physical consistency needs to be enhanced, e.g., by using physically-constrained machine learning.
Subtropical air intrusions in the Western Mediterranean: their impacts and associated atmospheric circulation
Authors: Cos Pep , Marcos-Matamoros Raül , Olmo Matías , G. Muñoz Ángel , Palma García Lluís , Campos Diego , Doblas-Reyes Francisco
Published: July 2024

One of the critical mechanisms influencing extreme temperatures on a daily basis in the Mediterranean region, particularly during the summer months, involves the intrusion of Saharan or subtropical continental air masses. These warm and stable air masses, which move northward, significantly impact temperatures in the Mediterranean region and have been associated with severe heat waves. This phenomenon has not received extensive attention in the literature despite its impacts. Therefore, understanding these intrusions and the underlying mechanisms driving them is crucial, especially given the observed increase in the frequency of such events in recent decades (1991-2020) compared to the climatology of the latter part of the previous century (1961-1990). In this study, we analyze air masses originating from low-latitude subtropical African desert areas during the historical period using data from the ERA5 observational dataset. We identify intrusion events in the western Mediterranean region through a study of air mass stability (geopotential thickness and vertical mean potential temperature), and we examine their frequency, spatial distribution, persistence, and trends over time. The impacts of these events extend beyond the region of the intrusion, leading to temperature anomalies across the Mediterranean basin and central Europe, indicating the non-local nature of the involved physical mechanisms. Finally, we employ clustering techniques to gain insights into the large-scale weather types responsible for these intrusions and their geographical origins. Specifically, we utilise various applications of the k-means method, using diverse variables and sets of data. The results offer novel insight into the conditions that favour intrusions in different locations of the western Mediterranean, their temporal evolution and their relationship with well-studied teleconnection patterns. It also offers an unprecedented observational climatology to assess the ability of climate models to represent this phenomenon appropriately.
Development of METAL-WRF model for the description of mineral dust processes in the atmosphere based on NASA's EMIT satellite retrievals
Authors: Solomos Stavros , Spyrou Christos , Bartsotas Nikolaos S. , Kalogeri Christina , Fountoulakis Ilias , Zerefos Christos S. , Aslanoğlu Yeşer , Chadoulis Rizos-Theodoros , Charalampous Georgia , Herrero-Anta Sara , Herrero del Barrio Celia , Kouklaki Dimitra , Moustaka Anna , Mytilinaios Michail , Papetta Alkistis
Published: July 2024

Mineral dust stands out as a pivotal climate modulator due to its substantial mass, optical depth, and long-term atmospheric life cycle. It impacts the atmospheric radiative balance, influences cloud dynamics and precipitation patterns, and exerts notable effects on terrestrial and aquatic ecosystems as well as human health. The extent and intensity of these impacts are governed by the mineral composition of dust particles sourced from diverse regions worldwide. METAL-WRF is an advanced numerical framework extending the GOCART-AFWA dust scheme in WRF-Chem 4.4.1 and it is designed to simulate the atmospheric dynamics of dust mineral components, including emission, transport, dry deposition due to gravitational settling, and wet deposition due to the scavenging of dust particles by the hydrometeors. The model accounts for ten mineral types: illite, kaolinite, smectite, calcite, quartz, feldspar, hematite, gypsum, phosphorus, and iron. In the previous version of METAL-WRF the mineralogical composition of dust is derived from the global geological datasets GMINER30 and FERRUM30. A significant improvement in the mapping of dust composition comes from NASA's EMIT sensor that has been operational aboard the International Space Station (ISS) since July 2022. EMIT utilizes advanced imaging spectroscopy to capture light across visible and infrared wavelengths, identifying distinct spectral signatures indicative of surface mineral composition. In this study, we introduce the integration of the first comprehensive EMIT mineralogical dataset in METAL-WRF. The model is used for the simulation of atmospheric dust in HARMONIA 2024 Berlin school. We discuss the comparative distribution of various mineral dust types in the Mediterranean between METAL-WRF simulations incorporating EMIT mineralogy data and earlier versions relying on GMINER30 and FERRUM30 geological databases. The effects of the different mineral types in radiative transfer and ice nuclei activation are examined for specific case studies in comparison with ground based and spaceborne observations.Acknowledgments. The authors acknowledge financial support from the Hellenic Foundation for Research and Innovation project “Mineralogy of Dust Emissions and Impacts on Environment and Health (MegDeth - HFRI no. 703)” and from the COST Action CA21119, “HARMONIA: International network for harmonization of atmospheric aerosol retrievals from ground-based photometers”.
Decadal Changes in Surface Radiative Fluxes 
Authors: Wild Martin
Published: July 2024

The aim of this presentation is to give an overview on the state of our knowledge on decadal changes in surface radiative fluxes, with a focus on both shortwave and longwave changes. With respect to changes in the shortwave, the focus will be on recent studies on the solar dimming and brightening phenomenon. This phenomenon refers to the increasing evidence that solar radiation at the Earth’s surface is not stable over time, but undergoes substantial multidecadal variations (i.e., dimming and brightening). An inadequate representation of these effects in climate models can for example lead to an inaccurate reproduction of the observed decadal warming rates, as evidenced in different studies.  A growing number of studies assess the magnitude of dimming and brightening also in remote areas. Artificial intelligence methods have recently been applied in attempts to expand station data information over larger areas. Studies using methods to filter out clouds in the observational data records allow for more insight into the relative contribution of direct aerosol and cloud effects to the dimming and brightening trends. Modelling studies help to quantify the contribution of unforced climate system-inherent variability to this phenomenon, and provide projections of future availability of the solar resource e.g. for energy applications.   In the longwave, climate models simulate an increase in downward longwave radiation at the surface at a rate of  approx. 2 Wm-2 / decade under current forcings globally, due to the enhanced greenhouse effect. Current model projections suggest that this rate will increase or decrease over the coming decades depending on future emission scenarios.  Reanalyses suggest an increase of around 1.5 Wm-2 over recent decades. This is supported by station observations from the Baseline Surface Radiation Network (BSRN), which overall also indicate an increase in downward longwave radiation over recent decades. 
Observing the influence of low-level moisture on rainfall behavior using vehicle-based remote sensing
Authors: Kim Yoo-Jun , Lim Byunghwan
Published: July 2024

In this study, we examined the influence of low-level moisture on rainfall behavior in the southern Korean Peninsula using remote sensing of the mobile vehicle platform. An intensive dataset, with high temporal resolution rawinsonde soundings and global navigation satellite system (GNSS) observations in target area, was used. We analyzed the low-level thermodynamic structure in terms of the relationship between precipitable water vapor (PWV) and heavy rainfall. Results demonstrated that an increase in precipitation on the leeward side of the mountainous region coincided with the prevailing winds on the southeasterly flank of the windward site. The probability of heavy rainfall increased in the highest PWV bin (> 60 mm). Interestingly, the vertical structures of the horizontal wind speed indicate a low-level jet (LLJ) with a maximum of ~40 m s-1, ~3–4 km where low-level moisture tends to be concentrated. In addition, the vertical variability of the horizontal wind speed in the highest PWV bins corresponds to considerable changes in upslope flows for all precipitation bins, indicating that the LLJ along with strong low-level moisture of ~3–4 km is an important indicator of rain occurrence in the downstream mountains. The observational evidence base obtained from mobile vehicle platform equipped with GNSS and rawinsonde sensor can provide some insights to improve the predictability of heavy rainfall in the southern Korean Peninsula. In future, combined analysis using numerical modeling, radar observations, satellites (e.g., SSM/I, GK-2A) and other upper-air observations (e.g., wind profiler and Doppler wind lidar) will be conducted.           
Investigating emissions of methane (CH4) to the atmosphere in Cluj-Napoca urban area 
Authors: Hmoudah Mustafa , Baciu Calin
Published: July 2024

Methane is a primary greenhouse gas with a relatively short lifetime in the atmosphere (~ 12 y), making it a candidate for mitigation efforts aimed at improving the climate in the near future.Urban areas account for ~2% of our planet’s surface, but they host more than half of the world’s population and a series of potential CH4 source systems (e.g., natural gas distribution, landfills, sewages, and in some cases, natural or artificial wetlands). The role of urban areas in the global atmospheric CH4 budget is, however, uncertain.Our understanding of the exact CH4 emission systems and related emission factors in urban areas is still limited (and unknown in Romania).In Cluj-Napoca, the second-biggest city in Romania, our study aims at identifying potential urban sources for CH4emissions via direct detection of CH4 at potential emission sites (opposed to atmospheric monitoring, as generally done).Our study aims to investigate potential sources in three major urban systems (natural gas distribution networks, sewage system, and aquatic systems).We used a highly sensitive laser CH4 sensor based on Tunable Diode Laser Absorption Spectrometry (TDLAS) with a resolution of ± 0.1 ppmv.Preliminary data show that 86% of the natural gas end-use points (74 measured) release CH4 into the atmosphere, 51% of the SEWAGE manholes (126 measured) are net sources of methane, and the aquatic systems (river and ponds, 50 sampling sites) are all oversaturated with dissolved CH4 and represent potential sources.Due to both natural and anthropogenic CH4 sources, Cluj-Napoca can be considered a hybrid methane source. This approach helps in reducing the ambiguity in the attribution of CH4 sources when only atmospheric CH4 monitoring is performed.This study can advance our understanding of CH4 release in urban areas and serve as the first national systematic-based approach that can be replicated to effectively take advantage of all available resources. It will also serve as a reference for future research on CH4 in the urban area.
Impact of Climate Change Scenarios on Indoor Comfort and Cooling Energy Consumption in Urban Heat Island Residential Areas
Authors: Cheng Ching-Yin , Lin Tzu-Ping
Published: July 2024

In Taiwan, the birth rate is declining annually, and by 2024, a quarter of the cities will be super-aged societies. With the highly dense development of urban areas, many cities have experienced the urban heat island phenomenon. In addition, climate change has led to rising temperatures in various regions in recent years. However, there has been a lack of research exploring the potential hazards and impacts on the health and well-being of the elderly resulting from these two phenomena. Due to the limited number of monitoring stations established by the Central Weather Administration, and the fact that some of these stations are located in higher altitude areas, the measured data are unable to clearly depict the extent and intensity of urban heat island phenomena. Additionally, future climate data will need to be simulated using global atmospheric models and scenario of global warming. This study analyzed the differences in indoor thermal comfort and annual cooling energy consumption by using high-resolution simulated climate data to assess the effects of the urban heat island phenomenon, and evaluated the impact of global warming scenario.The National Science and Technology Center for Disaster Reduction (NCDR) adopted the high-resolution global atmospheric models developed by the Geophysical Fluid Dynamics Laboratory (GFDL) at Princeton University to conduct global climate estimation simulations under the RCP8.5 warming scenario. Subsequently, the data was downscaled to a 5-kilometer resolution for the Taiwan region using the Weather Research and Forecasting (WRF) model. The simulation periods included the baseline period (1995-2014) and a 2°C temperature increase scenario under RCP8.5 (2034-2053). This study focused on the densely developed built-up areas of Taipei City and New Taipei City as the research area, comprising a total of 38 delineated zones. EnergyPlus was employed to simulate indoor temperatures and cooling energy consumption for aggregated residential households across these different zones. The results showed that during the baseline period, the urban heat island center areas had an average indoor temperature 0.4 degrees Celsius higher and an average annual cooling energy consumption 320 kWh/year higher compared to surrounding areas in summer. Furthermore, under the global warming scenario, the indoor temperatures became more severe. The urban heat island phenomenon not only leads to increased indoor temperatures in residential buildings but also exacerbates health risks and economic burdens for the elderly due to climate change. The results can serve as a reference for local governments in developing climate adaptation strategies.Keywords: Global Warming Scenario, Urban Heat Island, Health Risk for the Elderly, Indoor Comfort, Cooling Energy Consumption
Evaluation of Mesoscale Eddy-Ice interaction in the Southern Ocean using High-Resolution models
Authors: Libera Stephy , Goosse Hugues , Chen Ting-Chen , Putrasahan Dian
Published: July 2024

Antarctic sea ice plays an important role in the global climate through its influence on local and global oceanic and atmospheric circulations, planetary radiative balance, and the crucial support it provides for Southern Ocean ecosystem. Understanding the physical processes influencing Antarctic sea ice, and the drivers of its change are therefore of broad interest. The sea ice–covered the Southern Ocean, has relatively weak stratification in the upper ocean, where a relatively thin halocline separates the cold winter mixed layer from significantly warmer ocean interior. When warmer waters from the ocean interior enter the mixed layer, it can melt sea ice at its base. Features in the upper ocean, like mesoscale eddies can impact the thermohaline structure and stratification in this region and can then influence the heat delivered to the surface. However, the mesoscale dynamics in the polar regions, especially under sea ice cover, is little known due to the limited observations and the inability of many numerical models to resolve mesoscale processes in the high latitudes.   This study aims to understand better the interaction between ocean mesoscale eddies and sea ice using high-resolution European Eddy RIch Earth System Models (EERIE) models. We investigate the effect of mesoscale eddies locally, and the integrated effect of eddy-sea ice interaction in the circumpolar Southern Ocean. Previous studies have identified eddy ice interactions to vary within regions of varying sea ice concentrations, such as in the high concentration pack ice and low-concentration marginal ice zones. The variations in the eddy-sea ice interaction in the Southern Ocean, within the open ocean, pack ice, and marginal ice zones are further investigated in this study.  
Analysis of the role of Saharan Dust Intrusions in PM10 precipitation scavenging in the NW Mediterranean Region
Authors: Reñé Josep Maria , Udina Mireia , Bech Joan
Published: July 2024

Nowadays, atmospheric pollution is one of the most relevant environmental issues. Some pollutants such as fine particulate matter with a diameter of 10 μm or less (PM10) have a considerable impact on human health. In Catalonia (NE Spain), Saharan Dust Intrusions are a major source of PM10. In recent decades, a positive trend of these intrusion episodes has been detected in the NW Mediterranean basin. Moreover, precipitation plays a role in pollutant scavenging processes.In our work, we make an analysis of the changes in PM10 concentrations with the precipitation episodes. Moreover, we put a special emphasis in the precipitation episodes which happen simultaneously with a Saharan Dust Intrusion. In consequence, we have analysed PM10 concentration data from the Catalan Network for Pollution Control and Prevention and precipitation data from Automatic Weather Stations Network of the Meteorological Service of Catalonia. We use data from four measurement points in Catalonia (Montsec Observatory, Fabra Observatory, Vic and Sort) which are the unique points with precipitation and PM10 measurement instruments at the same location. Dataset contains 4-year data from 2019 to 2022. Specifically, we evaluate how daily mean PM10 concentration values for all the days in the dataset change in comparison to the values of the same variable for the previous day. Moreover, we do a separate analysis for days with observed precipitation (wet days) and days without precipitation (dry days). Also, we perform the analysis for days with Saharan Dust Intrusion and days without Saharan Dust Intrusion. Furthermore, we filter daily PM10 concentration changes for different absolute values of this daily variation to see the differences between great and small changes of daily PM10 concentration. To our knowledge, this is the first study of these characteristics in this region of study.In general, we observe a decrease of daily PM10 mean concentration levels with precipitation in approximately 60% of the days. This percentage increases to 80% for daily changes of PM10 concentration higher than 10 µg m-3. In wet days with Saharan Dust Intrusion, daily PM10 mean concentration decreases only in 50% of the cases independently of the absolute value of PM10 concentration variation. However, in wet days without Saharan Dust Intrusion, daily PM10 mean concentration decreases approximately in 60% of the cases. This percentage grows up to 90% if we only consider changes of PM10 concentration higher than 10 µg m-3. In consequence, Saharan Dust Intrusions clearly interfere with the usual pollutant precipitation scavenging processes. In addition, we find that scavenging processes are more effective above a certain PM10 concentration variation threshold. This study was performed in the framework of the project "Towards a climate resilient cross-border mountain community in the Pyrenees (LIFE22-IPC-ES-LIFE PYRENEES4CLIMA)".
A Monthly Precipitation Ensemble Prediction Model Based on LSTM and Shapley Values
Authors: Huang Ying , Huang Xiao Yan , Zhao Hua Sheng , Wu Yu Shuang
Published: July 2024

 In the current short-term climate prediction of monthly precipitation, there is a lack of nonlinear data mining techniques and objective ensemble forecasting methods of machine learning. A new nonlinear deep learning ensemble objective forecasting model has been established by generating multiple long short-term memory neural networks (LSTMs) with the same expected output as the individual forecasters, and using the cooperative game Shapley value method to determine the weight coefficients of each forecaster in the ensemble forecasting. The forecasting modeling of the monthly precipitation forecasting model has been studied based on the July precipitation samples of 81 meteorological stations in Guangxi from 1960 to 2023, and using height fields, temperature fields, and sea surface temperature field as the basic forecasting factors for monthly precipitation. The experimental results show that under the same forecast modeling samples and forecast factor conditions, the newly established prediction model has higher predictive ability than linear stepwise regression prediction methods and a single LSTM model, demonstrating its applicability to nonlinear monthly precipitation prediction problems. Further analysis reveals that the introduction of storage unit states and gate structures in the hidden layer of the LSTM model enables the network to retain long-term states, making it more suitable for handling and predicting important problems with relatively long intervals and delays in time series. And the Shapley value method can improve the predictive ability of ensemble individuals and enhance the population diversity of ensemble individuals, thereby improving the predictive accuracy of ensemble forecasting models. Therefore, the generalization ability of this deep learning ensemble forecasting model is significantly improved, and the improvement of its forecasting ability has a reasonable analytical basis. There is no overfitting phenomenon in the practical short-term climate prediction business application of general neural network methods, and it has good practical application value.
Investigating the Diurnal Cycle of Summer Precipitation over Mainland Southeast Asia: Insights from Dynamic downscaling Simulations
Authors: Lai Hui-Wen , Ou Tinghai
Published: July 2024

The precipitation changes over mainland Southeast Asia (SEA) are important to water resources in many countries in this region. The diurnal cycle of precipitation is a crucial aspect of the local climate. The sub-daily convective activities and precipitation variability are strongly modulated by the surrounding complex topography However, the intricate interplay between topographical features and local atmospheric processes, and how they shape the spatial distribution of diurnal precipitation variability across SEA, remains unclear. This study investigates diurnal precipitation patterns and associated physical processes, particularly focusing on modeling the diurnal cycles in this region using convection-permitting models (CPMs). To investigate the effect of model resolution on diurnal precipitation and associated processes, we conducted two high-resolution simulations using the Weather Research and Forecasting (WRF) model driven by ERA5 at spatial resolutions of 9 and 3 km, focusing on summertime (June-August) during 2002-2005. We compared the output from the two WRF experiments to ERA5 and observation-based datasets, including in situ observations (GHCN-D), and gridded observations (APHRODITE, IMERG). The diurnal patterns in space were clustered into 5 distinct groups based on K-means classification. Compared with the ERA5 reanalysis, the two high-resolution WRF simulations show a reduced wet bias relative to IMERGE and better captured intense precipitation events found in the in situ measurements, while the precipitation in ERA5 is more similar to APHRODITE. Furthermore, the results show that the WRF simulations outperform ERA5 in capturing the spatial patterns of precipitation intensities and peak time, especially in mountainous and coastline regions, using IMERG as the reference. These differences can be explained by differences in convective available potential energy (CAPE) between the WRF simulations and ERA5, as well as near-surface winds. Between the two WRF simulations, the 3-km WRF simulation displays weaker precipitation intensities compared to the 9-km WRF simulations, which better match the hourly IMERG data, while the 9-km WRF simulations perform better in peak time of diurnal precipitation. Assessing the benefits of higher-resolution modeling is challenging because the benefits vary between variables and regions. In conclusion, the work highlights the importance of applying CPMs to capture diurnal cycles of precipitation, local convective activities (CAPE), and near-surface winds over complex topography.
Synoptically-forced downslope wind induced by low mountain over lake surface
Authors: Kurcsics Máté , Geresdi István , Horváth Ákos
Published: July 2024

Downslope windstorm is a synoptically forced wind forming on the lee side of mountains. In Hungary this phenomenon is frequently observed at Lake Balaton, where the Hungarian Meteorological Service operates a lake storm warning system. North to Lake Balaton lies the Bakony Mountains with highest peak of around 700 m. The axis of this hilly area is perpendicular to the most frequent, northwest wind direction. This pattern allows the frequent formation of downslope windstorms. Although these downslope winds are significantly weaker than that can form on the lee side of the higher mountains, accurate forecast of them is crucial for the operation of the lake warning system. Our experience suggests that the lake due to its lower surface roughness and its effect on surface temperature can significantly impact the large scale wind locally. In this study the WRF (Weather Research and Forecasting) numerical model was used, and the model results were validated by data provided by dense wind measurements network. Our results reveal: (i) even the relatively low Bakony Mountains can generate downslope windstorms and the wind speed is amplified by the lake effects over Lake Balaton; (ii)  the backside of a cyclon with a north, northwest, nearly geostrophic wind in the Bakony-Balaton region is the most preferable synoptic condition for the formation of the severe downslope wind;  (iii)  the presence of lee trough, stable layer over the mountain, self-induced critical layer in the wind cross sections can also promote the formation the severe downslope wind; (iv) in the case of weaker downslope wind events the turbulence has considerable role in the formation of strong wind gusts; (v) the wind field observed by the surface network and the cloud structure can be also used to justify impact of the mountain waves on the formation of downslope wind. The comparison of the observation data and the model results of NWP (WRF) model shows that the forecast of strong downslope wind events is reliable.  However, in the case of weaker downslope winds the WRF frequently overestimates the peak of the gusts. The results of this study can be taken into consideration for the improvement of the reliability of the lake storm warning system.
Automatic quality control for a nation-wide high-resolution network
Authors: Uboldi Francesco , Oberto Elena , Pellegrini Umberto , Lagasio Martina , Milelli Massimo
Published: July 2024

Observations from local networks of surface stations, managed by local (regional) weather services, are collected in a nation-wide network, providing hourly observations in near real time, accessible through the MyDewetra Database, for the Italian national Civil Protection Department. Such national network provides detailed information on meteorological fields near the surface; it is inhomogeneous though, and affected by Gross Errors and Large Representativeness Errors of various origin. CIMA has undertaken an effort to operationally and automatically check such information for quality, in order to provide a dataset suitable for various uses, such as model verification, data assimilation, meteorological analysis of past events and climatological characterization of recent years and present time.An important part of the work is devoted to ensure quality of basic station metadata, such as geographical coordinates and orographic elevation above mean sea level. A station archive for each variable is created and monthly checked and updated.The main checks on observed meteorological variables are based on the idea of spatial consistency. The Spatial Consistency Test (SCT) represents a fine check based on Optimal Interpolation and leave-one-out Cross Validation. This and other tests have been recently included in the open-source library Titan (https://github.com/metno/titanlib).The Titan library test functions are routinely used at CIMA to provide quality control of precipitation raingauge observations. Such quality control checks for precipitation are presently supervised by a meteorologist, aware of the current weather state.For temperature data the SCT has actually been rewritten to ensure a detailed analysis of check behaviour on the Italian dataset and is monthly used to provide reliable data for model forecast verification.Plans are in place to:
extend the quality control to other meteorological variables, such as relative humidity;

test the inclusion of existing high resolution observational network managed by private amateur citizens;

test the impact of such quality-controlled local observations in data assimilation experiments for short-range forecasts;

compare SCT results with those obtained by Titan library functions, also with the possible scope of contributing to that open-source software test and development.

 
Research on machine learning-based modeling method for forecasting Maximum wind speed of typhoon in Guangxi 
Authors: Wu Yu-shuang , Huang Xiao-yan , Zhao Hua-sheng
Published: July 2024

Aiming at the lack of nonlinear intelligent computational modelling methods for the fixed-point and quantitative forecasting of typhoon gales in the current numerical forecast products, the paper takes the daily extreme winds at five typical representative meteorological stations (Guilin, Wuzhou, Longzhou, Nanning, Yulin) as the forecast object, and carries out the construction of a daily extreme wind forecast model based on multivariate linear regression (MR), support vector machine (SVM), fuzzy neural network (FNN), and the ground-based observation and reanalysis of the data of the typhoon in the past 40 years during the typhoon impact in Guangxi. The construction of the daily maximum wind prediction model based on multiple linear regression (MR), support vector machine (SVM) and fuzzy neural network (FNN) is carried out. The test results of the independent samples show that the FNN model has the smallest mean absolute error for the four stations of Guilin, Wuzhou, Longzhou, and Yulin in terms of the mean absolute error of the full-sample wind speed forecast, and the best overall forecast accuracy, while the MR forecast model has a better forecast capability for Nanning station, and the SVM model has an overall bias in the forecast effect, in which the FNN forecast model has a 1% to 1% reduction in mean absolute error compared with that of MR. The mean absolute errors of the FNN forecast model are reduced by 1%-29% (except for Nanning station); the mean absolute errors of the FNN forecast model are reduced by 6%-29% compared with the SVM forecast model. the mean absolute errors of the MR forecast model are reduced by 5%-13% compared with the SVM forecast model (except for Guilin station). The statistical results of the four evaluation indexes, including TS score, hit rate, null rate and forecast bias, for winds of magnitude 6 or above show that the FNN model has the highest and relatively stable prediction accuracy, followed by the MR scheme, and the SVM has the worst prediction effect among the three schemes. The fuzzy neural network has certain applicability to the prediction of very high wind speed, which can be a good reference for the prediction of daily very high wind speed on the ground during typhoons in Guangxi, and can provide theoretical references and empirical evidence basis for the later development of the research on the prediction of high wind disasters in Guangxi.
Use of climate change scenarios for pest algorithms in Austria
Authors: Thaler Sabina , Kolkmann Kerstin , Blümel Sylvia , Eitzinger Josef
Published: July 2024

Global warming will modify the dynamics of thermophilic pest insect populations and their spread, which could raise the risk of crop damage and plant production. Long-term insect pest monitoring can provide important data for developing pest models, which can help to predict future pest risk trends in the face of climate change. Within the ACRP-project RIMPEST1 ("The effect of changing climate on potential risks from important insect pests on plant production in Austria and related adaptation options") pest trends under crop land-use and climate scenarios by applying pest models are therefore investigated. Corresponding databases from monitoring programs for various insect pests of major crops are used for pest model development and testing for Austrian case study regions. To estimate the range of potential pest risks in the various Austrian crop growth regions in the future (2021-2050; 2071-2100), an ensemble of downscaled Austrian climate scenarios (ÖKS15) of two emission scenarios, RCP 4.5 and RCP 8.5, is used. Using validated pest algorithms on a site-specific and grid-based application reveals variable-sized shifts in pest phenology, depending on the climate region and the specified future time periods.First results for the American grapevine leafhopper (Scaphoideus titanus), European grapevine/berry moths (Lobesia botrana and Eupoecilia ambiguella), and plum moth (Grapholita funebrana) indicate a significant response to climate change toward earlier first occurrence dates of relevant development stages, which partially coincides with shifted growing areas of the host plants. While hardly any changes are likely in the near future (2021-2050) compared to current conditions, a significantly earlier occurrence of the pests can be expected at the end of the century, which varies regionally and depending on the projection. On average, for example, European grapevine moths can be expected to appear around 4 days earlier (RCP 4.5) / 7 days earlier (RCP 8.5) than today.The findings of the project should help practitioners and policymakers to develop future strategies for optimised cultivation and pest control options. 1ACRP-13th Call Project RIMPEST (KR20AC0K17957) ("The effect of changing climate on potential risks from important insect pests on plant production in Austria and related adaptation options"). https://www.klimafonds.gv.at/report/acrp-13th-call-2020/; https://rimpest.boku.ac.at
New night heat warnings
Authors: Segalà Gallofré Santi , Aran Roura Montse , Brucet Vinyals Clara , Gallego Santiago Sergio , Serra Uró Aleix
Published: July 2024

In the recent decades, extreme hot days and heat waves have been one of the main meteorological dangers that have affected mediterranean regions. In 2009, the Meteorological Service of Catalonia (SMC) implemented heat warnings using a specific threshold for every municipality. These thresholds were calculated from a study that was conducted in coordination with the Public Health Agency (ASPCAT) and the Directorate-General for Civil Protection and Emergencies (DGPCE), based on finding the correlation between high temperatures and mortality. A turning point in mortality appeared around the 98th percentile of the maximum daily temperature during the summer period, so that was the threshold adopted. That is, only 2% of the days during that period exceed the considered value. Heat warnings have been operational since 2009, but due to the increase in the number of tropical nights (minimum temperature above 20 ºC) and torrid nights (temperature above 25 ºC) and knowing the relationship between high night-time temperatures and the aggravation of heat-sensitive health effects during the night, SMC and DGPCE decided to establish new warning thresholds for Meteorological Situation of Danger (SMP) due to night heat. The new thresholds that have been set are as follows: - Low Level Warning, intense nocturnal heat, defined when the minimum temperature exceeds the 98th percentile of the night-time minimum temperature from June to August. - High Level Warning, very intense nocturnal heat, defined if the minimum temperature is higher than the 98th percentile of the night-time minimum temperature from June to August plus two degrees Celsius. Like other operational warnings in Catalonia, the new system consists of six degrees of danger. These warnings are issued for each county that constituted this region, with a time interval of 6 hours and are updated twice a day. These warnings are sent to Civil Protection who are responsible for managing and coordinating actions aimed at protecting people, property, and the environment. Simultaneously, these warnings are made available for everyone on the website and social media platforms. In 2023, this new night heat warning in Catalonia operated as a pilot project and it belongs to the meteorological emergency system from now on.
Ensemble weather forecasting scenarios tailored to users' needs
Authors: Roubelat Flore , Joly Bruno , Mounier Arnaud , Raynaud Laure
Published: July 2024

Ensemble forecasting has become the standard approach to represent uncertainties and produce multiple scenarios. Ensembles therefore provide rich information, but it often remains challenging to identify the useful signal for decision-making.Ensemble information can be synthesized using clustering algorithms, which gather similar members in a limited number of meaningful scenarios. The current work builds on the strategy proposed by Mounier et al. (from EMS Annual Meeting 2023) for clustering convective-scale precipitation forecasts of the French Arome-EPS. We demonstrate that the approach can be applied to other meteorological fields, and we propose a way to adapt the design of the scenarios to users' needs.In our methodology, scenarios are obtained by assigning ensemble members to climatological classes defined after applying a classification method to a meteorological database. Members assigned to the same class are grouped together to form a scenario. To reduce the dimensionality of the problem, the first step of the methodology performs a dimension reduction of input meteorological fields, using a convolutional autoencoder. The clustering algorithm is then applied to the meteorological training database projected in the autoencoder's latent space in order to issue climatological clusters.To obtain scenarios that best fit the users' needs, the baseline autoencoder has been modified to account for users' variables.This is achieved by adding a dense neural network in the decoder part of the architecture, that aims at predicting the user variable. We show how we can control the latent representation learned by the autoencoder by adding this supervised learning term to the reconstruction.This user-oriented clustering approach will be illustrated for the case study of wind energy production. Thanks to a collaboration with the Compagnie Nationale du Rhône, the method has been trained considering a 5-year dataset of 100m wind analysis from the Arome model as meteorological inputs, and wind energy production values in north-western France as the user variable.
Machine Learning methodologies for identifying atmospheric deep convective events in ERA5
Authors: Sanchez-Marroquin Alberto , Barcons Roca Jordi , Dutta Omjyoti , Guerrero Calzas Iciar , Rossetto Lorenzo , Rodriguez Pinilla Mirta , Cucchietti Fernando
Published: July 2024

Atmospheric deep convection can occur when the warming of the Earth’s surfaceby solar radiation leads to buoyant plumes that break through the mixed layer andproduce vertical clouds reaching the tropopause. This phenomenon is associated withthunderstorms, heavy precipitation, hail, strong winds and other events that cause se-vere damage to life and property. However, representing deep convection and its as-sociated events in models is challenging as they depend on many high-resolution sub-grid processes which are difficult and expensive to simulate. As a consequence, someapproaches based on artificial intelligence and specially Machine Learning (ML) haverecently emerged to bypass some of these limitations of physical models. Here we discusssome of the ML methodologies implemented in the Convective Day Detector (CDD), astatistical model designed to identify hazardous convective events at ground level basedon ERA5 reanalysis data.First, we will describe the CDD, which is a ML classifier based on meteorologicalvariables from ERA5 reanalysis which are associated with deep convection, such asconvective available potential energy, vertical wind velocity or specific humidity. TheCDD is trained to find the relationship between these variables and the occurrence ofsevere weather events such as hailstorms and severe wind from observation-based reportsdatabases. The trained CDD is subsequently employed to infer the probability of theoccurrence of these convective events beyond the the training region, where observationsare more limited or inconsistent, if available at all.However, this modelling approach presents many challenges that need to be over-come. To start with, hazardous convective events are rare and difficult to measure in aconsistent manner. This leads to a very unbalanced training dataset, with many posi-tive unlabelled data. Therefore, we will discuss some ways to address these problems,such as under sampling, artificially filtering the storm database or positive unlabelledlearning methodologies. Additionally, the meteorological conditions that lead to thedevelopment of convective events are different depending on the location. As a conse-quence, we will also discuss transfer learning methodologies to apply a classifier trainedin North America to different regions of the world such as Europe, and how to validatethe results with very scarce and inconsistent observations.
Enhancing climate resilience in the AGORA project: A digital academy for improved access and use of climate, risk and adaptation information
Authors: Hoy Andreas , Adinolfi Marianna , Biondi Riccardo , Milelli Massimo
Published: July 2024

The AGORA project supports climate-resilient communities in Europe by engaging various stakeholders, including citizens, policymakers, experts, and marginalized groups. It aims to accelerate societal transformation by sharing innovative climate adaptation solutions, recognizing the need for diverse approaches. Through pilot regions in Italy, Sweden, Germany, and Spain, AGORA facilitates co-production activities to design and implement context-specific adaptation strategies. Other regions can participate as followers, learning from and replicating AGORA's actions. The project aims to empower citizens and promote proactive decision-making. A key component is the Digital AGORA, an online platform facilitating citizen engagement and decision-making processes. It includes digital academies focusing on climate data access, climate risk monitoring, and combating climate disinformation, with the goal of enhancing adaptation efforts through education and peer networking. Capacity-building events ensure user-centred design and address existing barriers to climate data utilization, as demonstrated through collaborative efforts showcased at the 2023 ECCA and SISC conferences.This contribution presents three comprehensive inventories that highlight a variety of international (European-focused) and national tools encompassing climate, climate risk, and climate adaptation themes. These inventories compile web-based resources such as tools, atlases, portals, and platforms, which have seen a notable increase in publication in recent years. They serve as valuable repositories capable of disseminating climate data, derived climate insights, and tailored climate services to a diverse audience. The selection process involved thorough screenings of scientific literature, web resources, and expert consultations. The tools are systematically evaluated and compared through a detailed spreadsheet analysis, which examines their content, temporal and spatial relevance, language, style, functionalities, and included variables. Additionally, the spreadsheet provides direct links to the tools, tutorials, data sources, metadata, methodologies, and associated scientific publications, ensuring accessibility and transparency for users.A digital academy guides users by leveraging the developed inventories to provide accessible climate resources. This fosters transparency and empowers a diverse set of stakeholders. The academy features eight modular learning pathways tailored to basic, intermediate, and advanced skill levels. Each module integrates general topical knowledge, links to relevant tools, and utilizes engaging elements like quizzes, questionnaires, videos, and tutorials. This innovative digital academy seamlessly integrates with the broader Digital AGORA platform.