Advances in Science and Research

Abstract. This paper introduces the Basque Country impact weather catalogue, a versatile tool designed, developed, and maintained by the Basque Meteorology Agency (Euskalmet), with the primary objective of systematically recording detailed information regarding adverse weather events that generate some degree of impact in the Basque Autonomous Community (BAC). The catalogue serves as a repository where information is gathered for the comprehensive characterisation and evaluation of extreme weather events, focusing on both environmental conditions and their associated impacts. For this purpose, three key aspects are included: context, hazard and risk and impact. In the context section, we include information about the date, duration, spatial extent, measurement statistics data, synoptic/mesoscale aspects, and other metadata. The hazard and risk section includes information about hazard and risk typology according to Euskalmet severe weather criteria and warnings procedure. Regarding the impact, a range of metrics and indicators are defined to characterise critical aspects, encompassing the severity of impact including economic damages, human fatalities, and disruptions to normal life. The creation of the Catalogue involves utilising various data sources and repositories, not only from Euskalmet but also, particularly in terms of impact, from emergency interventions, newspapers, local media websites, social media, and data from the Spanish Insurance Consortium. While the volume of available material from each event description may vary from case to case, a standardised information structure and minimum content are imperative for all registered events. This ensures that qualitative analyses based on extensive reports, and quantitative analyses based on recorded or estimated metrics and indicators, are possible for events included. This paper outlines into the design and structuring of the catalogue, and the fundamental steps undertaken during its implementation.

Santiago Gaztelumendi

Joseba Egaña

Miriam Ruiz

Eguzkiñe Iturrioz

Advances in science and research

The Basque Impact Weather Catalogue

巴斯克地区气象影响目录

Abstract. In this study, the focus is on the characterization of floods in Basque Autonomous Community, considering damages, hydrometeorological aspects and others factors during the period 2000–2021. The ultimate goal is to contribute to understanding the processes of impact, enhancing awareness, and improving preparedness before flood events. To assess flood impact, damage data from the Spanish Insurance Compensation Consortium areexposureccccccc used as a proxy. For hydro-meteorological characterization and weather context, we use data from the Automatic Weather Station Network and various Euskalmet general and local ad-hoc weather context classifications. Different datasets are conveniently prepared to extract a range of indicators, taking into account: (1) physiographic and socio-economic factors, (2) damages and impact, (3) hydrometeorological aspects, and (4) weather context. Visual data analytics techniques are utilized for analysis, characterization, and presentation of findings. We integrate indicators with diverse temporal resolutions (daily and monthly) and spatial aggregations (primary watersheds and selected hydrological units) to facilitate discussions and derive conclusions. This work establishes a foundation for a semi-automatic procedure for data preparation, indicator extraction, analysis, and results presentation. Although it is a strategy specifically designed for its application in our context, we believe it can serve as inspiration for conducting similar free software based exercises in other parts of the world.

Kepa Otxoa de Alda

Characterization of hydrometeorological events and flood impacts in the Basque Country

巴斯克地区水文气象事件及洪水影响的特征分析

Abstract. The single-column version of the shared ALADIN-HIRLAM numerical weather prediction system, called MUSC, was developed by Météo-France in the 2000s and has a growing user-base in both HIRLAM and ALADIN countries. Tools to derive the required input, to run the experiments and to handle outputs of experiments carried out using MUSC have been developed within the HARMONIE-AROME canonical model configuration of the ALADIN-HIRLAM system are described within and constitute a large portion of this paper. The paper also illustrates the usefulness of the single-column approach for testing and developing HARMONIE-AROME physical parametrizations related to cloud microphysics and radiative transfer. Study cases concerning these physical parametrizations have been included for illustration purposes.

Emily Gleeson

Stephen Outten

Bjørg Jenny Kokkvoll Engdahl

Eoin Whelan

Ulf Andrae

Laura Rontu

HARMONIE-AROME single-column tools and experiments

HARMONIE-AROME单柱工具与实验

Abstract. Seasonal climate forecast products offer useful information for farmers supporting them in planning and making decisions in their management practices, such as crop choice, planting and harvesting time, and water management. Driven by the need of stakeholders for tailored seasonal forecast products, our goal was to assess the applicability of seasonal forecast outputs in agriculture and to develop and pilot with stakeholders a set of seasonal climate outlooks for this sector in Finland. Finnish end users were involved in both the design and testing of the outlooks during the first pilot season of 2019. The seasonal climate outlooks were developed using the SEAS5 seasonal forecast system provided by ECMWF. To improve the prediction skill of the seasonal forecast data, several bias adjustment approaches were evaluated. The tested methods increased the quality of temperature forecast, but no suitable approach was found for eliminating the biases from precipitation data. Besides the widely applied indices, such as mean temperature, growing degree days, cold spell duration, total precipitation and dry conditions, new sector-oriented indices (such as progress of growing season) have been implemented and issued for various lead times (up to 3 months). The first result of forecast evaluation, the development of seasonal forecast indices and the first pilot season of May–October 2019 are presented. We found that the temperature-based outlooks performed well, with better performance skills for short lead times, providing useful information for the farmers in activity management. Precipitation indices had poor skills for each forecasted month, and further research is needed for improving the quality of forecast for Finland. The farmers who have tested the seasonal climate outlooks considered those beneficial and valuable, helping them in planning their activities. Following the first pilot season, further research and implementation work took place to improve our understanding of the skill of seasonal forecasts and increase the quality of tailored seasonal climate services.

Andrea Vajda

Otto Hyvärinen

Development of seasonal climate outlooks for agriculture in Finland

芬兰农业季节性气候展望的发展

Abstract. This article is the result of a campaign done during the COVID-19 lockdown in Catalonia. The Television of Catalonia audience was involved in an action to inform about the weather from their own homes by posting Twitter videos. Some of the videos were shown on air in the weather segment of the television station's main news programs. We have correlated participation in the campaign with meteorological and public health data and found that weather is related to the mood of people when using social media platforms such as Twitter.

Tomàs Molina

Alex Sancliment

Jofre Janué

How weather influenced the mood of people during the COVID-19 lockdown in Catalonia: a review of Twitter posts

天气如何影响加泰罗尼亚地区COVID-19封锁期间人们的情绪：对Twitter帖子的回顾

Abstract. The dispersion of pollutants in the atmosphere, whether from industrial emissions, wildfires, or other sources, poses significant challenges to air quality management and environmental protection. Understanding the behavior of plumes is crucial for predicting their dispersion patterns and potential impacts on human health and the environment. In this work, we present a new plume rise scheme based on heat transport. The idea at the basis of the new algorithm is the same as the actual scheme embedded in the Lagrangian Particle Model SPRAY-WEB. The temperature difference between the ambient and the plume and the vertical velocity of the plume are expressed on a fixed Eulerian grid. The particles are assigned with an equivalent momentum, temperature, mass, and density, transported as scalar quantities with the particles following the Lagrangian description of the motion. This allows us to reproduce the entrainment phenomenon as a mixing of two fluids (environmental air and plume) with different temperatures: the resulting temperature is given by Richmann's law. The results obtained with the old plume rise algorithm and the new one are compared with Briggs (1975) analytical curve in the case of an idealized atmosphere with a neutral stratification and a constant horizontal wind and with experimental data. From the comparison in an ideal atmosphere, it emerged that with the new algorithm, the plume reaches a higher height than with the old one and the asymptotic trend obtained with both models follows the Briggs curve. As for the comparison with the measurements, the results obtained with the new algorithm are in better agreement with the experimental data than the old one.

Enrico Ferrero

Bianca Tenti

Stefano Alessandrini

Lagrangian model with heat-carrying particles

携带热粒子的拉格朗日模型

Abstract. We consider possible influence on severe weather occurrence in the context of solar wind coupling to the magnetosphere–ionosphere–atmosphere system, mediated by aurorally excited atmospheric gravity waves. Solar wind high-speed streams from coronal holes cause intensifications of ionospheric currents at high latitudes launching gravity waves propagating in the upper and lower atmosphere. While these gravity waves reach the troposphere with much attenuated amplitudes, they can contribute to conditional symmetric instability release and intensification of storms. Severe weather events, including winter storms and heavy rainfall causing floods and flash floods, show a tendency to follow arrivals of solar wind high-speed streams from coronal holes. The ERA5 re-analysis is used to evaluate slantwise convective available potential energy and vertically integrated extent of realizable symmetric instability to assess the likelihood of slantwise convection in frontal zones of extratropical cyclones during severe snowstorms and flash floods. The observed low-level southerly winds and high wind shears in these regions are favorable conditions for over-reflection of down-going aurorally excited gravity waves potentially contributing to conditional symmetric instability release leading to slantwise convection and high-rate precipitation.

Paul Prikryl

Mesoscale weather influenced by auroral gravity waves contributing to conditional symmetric instability release?

中尺度天气受极光重力波影响，是否有助于条件对称不稳定释放？

Abstract. This work proposes a novel approach for probabilistic end-to-end all-sky imager-based nowcasting with horizons of up to 30 min using an ImageNet pre-trained deep neural network. The method involves a two-stage approach. First, a backbone model is trained to estimate the irradiance from all-sky imager (ASI) images. The model is then extended and retrained on image and parameter sequences for forecasting. An open access data set is used for training and evaluation. We investigated the impact of simultaneously considering global horizontal (GHI), direct normal (DNI), and diffuse horizontal irradiance (DHI) on training time and forecast performance as well as the effect of adding parameters describing the irradiance variability proposed in the literature. The backbone model estimates current GHI with an RMSE and MAE of 58.06 and 29.33 W m−2, respectively. When extended for forecasting, the model achieves an overall positive skill score reaching 18.6 % compared to a smart persistence forecast. Minor modifications to the deterministic backbone and forecasting models enables the architecture to output an asymmetrical probability distribution and reduces training time while leading to similar errors for the backbone models. Investigating the impact of variability parameters shows that they reduce training time but have no significant impact on the GHI forecasting performance for both deterministic and probabilistic forecasting while simultaneously forecasting GHI, DNI, and DHI reduces the forecast performance.

Samer Chaaraoui

Sebastian Houben

Stefanie Meilinger

Probabilistic end-to-end irradiance forecasting through pre-trained deep learning models using all-sky-images

基于预训练深度学习模型利用全天图像的概率端到端辐照度预测

ISSN	1992-0628, 1992-0636
h-index	10

Publication Information	Publisher: Copernicus Publications，Publishing cycle: ，Journal Type: journal，Open Access Journals: Yes
Basic data	Year of publication: 2019，Proportion of original research papers: 0.00%，Self Citation Rate:N.A.， Gold OA Rate: 0.00%
Journal Fees	$Details
Average review cycle	网友分享经验：22 Weeks

Advances in Science and Research

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The Basque Impact Weather Catalogue

Characterization of hydrometeorological events and flood impacts in the Basque Country

HARMONIE-AROME single-column tools and experiments

Development of seasonal climate outlooks for agriculture in Finland

How weather influenced the mood of people during the COVID-19 lockdown in Catalonia: a review of Twitter posts

The Basque Impact Weather Catalogue

Characterization of hydrometeorological events and flood impacts in the Basque Country

Lagrangian model with heat-carrying particles

Mesoscale weather influenced by auroral gravity waves contributing to conditional symmetric instability release?

Probabilistic end-to-end irradiance forecasting through pre-trained deep learning models using all-sky-images