Human endeavours are becoming ever more sensitive to the weather in a changing climate. Accurate and timely forecasts and warnings of weather and its impacts provide valuable intelligence to take actions and to avoid harm. Furthermore, weather intelligence increases economic productivity enabling businesses and other enterprises make more efficient decisions. The quality and richness of weather intelligence is increasing substantially because of scientific and technological advances in areas such as remote sensing, modelling and simulation, data science, behavioural science and artificial intelligence. These advances are happening alongside the digital transformation of economies and societies, presenting a unique opportunity to integrate weather intelligence with the digital transformation. Realising the full value of weather intelligence in this context needs diverse data resources to be brought together and it requires collaboration among private, public and academic sector actors in the global weather enterprise including National Meteorological and Hydrological Services (NMHSs). Here we examine how the opportunities arising from innovative science and technology and the digital transformation of economies can be exploited to enhance decision-making for societal benefit. Whilst these general considerations apply to many countries and regions, here we discuss their application within South Asia.

The exchange of adaptation-relevant climate information between scientists, stakeholders and the general public is marked by a gap between user needs and provided information. This multidimensional gap can be described in terms of temporal and spatial scales, variable selection, specificity of needs, and consideration of uncertainty. To bridge this gap, we argue for a multi-way format of co-creating (a) a viable form of information exchange and (b) the relevant information itself, while recognising the needs of users and capabilities of providers. This is to ensure that relevant information can be provided to users who are motivated to apply them. We here describe the offer-need gap in the Main River catchment (central Germany), which is increasingly characterized by climate change and user-induced water scarcity, and present a framework for bridging the gap in stakeholder dialogues.

The coupling between the atmosphere and the ocean at the oceanic mesoscale (∼100–1000 km) plays a significant role in shaping the energy exchanges between the two fluids. We investigate how such coupling is represented in a state-of-the-art high resolution ocean-atmosphere coupled numerical simulation. In particular, we look into the thermodynamic adjustment of the marine atmospheric boundary layer (MABL) to sea surface temperature (SST) spatial anomalies. Mesoscale SST impacts the lower-tropospheric static stability by modifying the surface turbulent fluxes; these changes can be traced up to the top of MABL as a consequence of the modified air column buoyancy, with a subsequent impact on MABL top entrainment fluxes. Alongside, MABL temperature is found to partially adjust to SST, whereas MABL humidity does not, as surface evaporation and the entrainment of dry air mass at top-of-MABL have opposing effects which partially balance out: this results in a high sensitivity (∼25 % K^−1) of the anomalous surface latent heat fluxes to mesoscale SST anomalies. Our findings, thus, indicate that small scale SST variability can have upscaling effects on the surface energy exchanges via non-linear MABL responses.

Accurate and reliable long time series of precipitation data are essential for a variety of applications and for accurate quantification of climate change.Such time series should be as homogeneous as possible, but certain changes in measurement conditions cannot be avoided. Parallel measurements are carried out at the German climate reference stations to analyse the effects of changes in the measurement systems, e.g. changing one measuring instrument to another. The aim of these parallel measurements is to identify measurement uncertainties and to analyse the comparability of the measurement systems in order to investigate homogeneity. In this study, the influence of funnel heating on precipitation measurement was analysed using the rain[e] device. General analyses show that the rain[e] sensor measures more precipitation than the PLUVIO sensor, for liquid precipitation regardless of the amount of precipitation. But for solid precipitation PLUVIO measures more. Differences in the equipment of the devices, such as heaters, could cause the measurement differences.An investigation during winter of 2023/2024 showed that changing the funnel heater temperature setting in the rain[e] influences the amount of precipitation measured. A change in the funnel temperature by 2 °C changed the amount of precipitation by approx. 1.5 %.

The value of a weather forecast and a warning is the benefit realized by decision makers through the use of the product. It is important to know what determines how valuable a warning is from a user's point of view. Further, it is unclear whether users and providers have the same understanding of the value of a warning. To address the question of how to best evaluate and communicate the value of weather warnings a survey was designed, conducted and analysed. The survey includes questions with respect to the relevance of various aspects of a warning, the potential benefit of providing information on warning quality, the type of potentially useful information, and suitable ways of providing such information. 66 experts in the field of weather warnings and/or their communication responded to the survey in the frame of scientific conference in 2024. Additionally, a small sample of the general public responded to a selected excerpt of the original survey during the German national weather service's open house. Apart from the core information of warnings, i.e. time, place and severity of the hazard, impact information, and behavioural advice are seen to add value to a warning. Being able to understand the information in the warning is seen as important for the majority of the respondents in the study. Information on the accuracy in timing, location and severity of an event are rated as useful, either to increase trust in warnings or for decision making. Dependent on which user groups are asked, different formats are most suited for warning quality communication. The experts stated that they have more practical experience regarding the content of warning quality information and its usefulness compared to the suitability of different formats. This suggest that there is still a need for testing various formats for best communicating warning quality information to different user groups.

The quality of wind speed from different reanalyses (ERA5, COSMO-REA6 and CERRA) is assessed along the different coasts of mainland France. SYNOP (surface synoptic observations) wind measurements from Météo-France at 10 m height and floating LiDAR (Light Detection and Ranging) measurements from DGEC (Direction Générale de l'Énergie et du Climat) at 100 m height are used as reference. The inter-annual variability, distribution of wind, seasonal cycle, diurnal cycle and extremes are evaluated using several metrics (bias, correlation, normalized root mean square error). Results show that the shape of the 10 m wind seasonal cycle is well represented by all reanalyses. However, along the Mediterranean coast, wind speed is underestimated by ERA5, and overestimated by COSMO-REA6 during winter. COSMO-REA6 does not reproduce well the diurnal cycle along the Mediterranean coast, nor does ERA5 for the Atlantic coast. Overall, CERRA has better skills in representing surface wind speed on the three French seafronts, as well as for offshore wind speed at 100 m. The present study provides insights on the use of CERRA as a reference for offshore wind studies over the French maritime zone.

In order to forecast the impact of meteorological events, such as large wind storms, on the Belgian offshore wind energy production and mitigate its impact on the high-voltage electricity grid, the Royal Meteorological Institute of Belgium (RMI) has in the past developed a dedicated storm forecast tool for Elia, the Belgian transmission system operator (TSO). The storm forecast tool, which has been operational since November 2018, provides 15 min wind speed and wind power forecasts for each wind farm in the Belgian offshore wind energy zone (BOZ), together with cut-out probabilities and uncertainty quantification, by combining the RMI high-resolution (4 km) ALARO model with the ENS ensemble forecasts of the European Centre for Medium Range Weather Forecasting (ECMWF).Since the completion of the first Belgian offshore wind energy zone in 2020, for an installed capacity of 2.26 GW, a significant amount of wind energy is now available in the Belgian part of the North Sea. There are considerable wake losses in the BOZ, as all wind farms lie close together in a narrow band, and each wind farm has a high density, in terms of number of turbines, and/or installed power per area. Moreover, the adjacent Dutch Borssele Wind Farm Zone, completed in 2021, can also significantly influence the BOZ (and vice versa).We report on two approaches to improve RMI's offshore wind power forecasts, and in particular to take into account wake losses. First the Fitch et al. wind farm parameterization (WFP) was implemented in the ALARO model, based on an earlier implementation by KNMI into HARMONIE-AROME. Both these models are being developed in the ACCORD consortium, and use the same dynamical core to some extent, with IFS/ARPEGE global codes as basis, but differ greatly in the different physics parameterizations used, and the physics-dynamics coupling (tendencies vs fluxes). Secondly, we investigated using an artificial neural network trained on Elia wind power production data and NWP forecasts. Verification of the improved wind and power forecasts is based on lidar data at an anonymous wind farm, and power data from Elia. Each method is found to improve forecast accuracy and able to capture certain wake effects in the BOZ. A combination of both methods gives the best results on average, and leads to competitive forecast scores.

Renewable energy sources (RES), such as wind and solar photovoltaic (PV), account for a significant share of today's power systems. This share is set to grow significantly in the near future, due to ambitious emission reduction targets in many countries. A significant proportion of energy generation in the future, therefore, will be dependent on weather conditions, which can potentially change significantly over short time horizons. These changes in renewable generation will need to be managed by power system operators, who will need to ensure sufficient ramping capacity to maintain grid stability, particularly if an increase/decrease in renewable generation is coincident with a decrease/increase in electrical demand.RES ramps are defined as changes in generation, taking place over a number of hours, that exceed a given threshold. RES-induced ramps are generally caused by changes in weather, which can result in fairly rapid and large changes in electricity generation, particularly as weather fronts sweep across a country with the associated winds and cloud coverage. Other events linked to large ramps are solar eclipses for PV (which are rare and predictable) and high-wind periods that can lead to wind farm shutdowns. In this work, ramping events over Ireland are explored at national scale, with insights at the farm scale for onshore wind.First, models driven by ERA5 reanalysis data are used to generate hourly, farm-level wind and PV data from 1940 to 2023, and their error when representing national ramps in recent years is quantified. This model is then used for temporal analysis of national RES ramping events, allowing us to capture a broader range of extremes and return periods, and better understand the seasonality and temporal cycles linked to ramps. This is done under three different energy scenarios representative of the Irish reality at different stages of its energy transition: an onshore wind-only scenario, a scenario with some PV added to the onshore wind, and a scenario with a more balanced proportion of onshore wind and PV with the addition of offshore wind farms. Finally, we extend our analysis to wind farm ramps, quantifying the error of our model at reproducing farm ramps and exploring the spatial patterns that govern RES ramping in Ireland.

Ensemble forecasting is widely recognised as a more powerful and informative approach than deterministic forecasting, especially for anticipating high-impact events and supporting decisions under uncertain weather evolution. However, many end-users still struggle to interpret probabilistic forecasts and apply them in operational contexts. Misunderstandings, improper usage, miscommunication, and/or inappropriate design can lead to suboptimal or delayed decisions. In response, Météo-France has developed tools that aim to better adapt ensemble forecast information to the needs of different users. This article presents two prototype applications designed in collaboration with stakeholders. The first supports heat stress events management during the Paris 2024 Olympic and Paralympic Games, based on the WBGT (Wet Bulb Globe Temperature) index. The second focuses on late frost affecting vineyards during spring, with regard to mitigation measures and financial cost/loss optimisation. In both cases, ensemble forecasts were used to generate impact-oriented products to enhance operational decision-making procedures. The tools were evaluated through retrospective testing and end-user feedback. The results suggest that probabilistic forecasts are helpful when adapted to specific contexts. They can provide earlier and more confident decisions, even for users without meteorological expertise. These tools are not intended to replace expert assessment, but rather to clarify when action may be needed. Generalising such approaches could help extend the benefits of ensemble forecasting for sectors in which its potential remains underused.

The occurrence of tornado outbreaks in the United States is investigated in the context of solar wind coupling to the magnetosphere-ionosphere-atmosphere system. The superposed epoch analysis of daily occurrence of tornadoes reveals a peak in the cumulative number of tornadoes near the interplanetary magnetic field sector boundary crossings. Most of the large tornado outbreaks are associated with high-density plasma adjacent to the heliospheric current sheet and with co-rotating interaction regions at the leading edge of high-speed streams. Large tornado outbreaks also followed impacts of interplanetary coronal mass ejections or occurred in the declining phase of major high-speed streams. We consider the role of aurorally generated atmospheric gravity waves in severe weather development leading to tornado outbreaks. While these gravity waves reach the troposphere with attenuated amplitudes, they can contribute to conditional symmetric instability release in frontal zones of extratropical cyclones leading to synoptic-scale weather conditions favouring formation of supercells in a strong wind shear environment and high tornado occurrence.

Using the novel PBL evolution model EDMF-AERO and the dataset collected during a measurement campaign at the Swider Geophysical Observatory in 2014, we studied morning PBL dynamics under high pollution. We tuned the model to the data and reached good accuracy in PBLH estimation (3 % deviation) and sufficient accuracy in the average potential temperature of PBL $\stackrel{\mathrm{\&\#8254;}}{{\mathit{\&\#952;}}_{\mathrm{PBL}}}$RMSE=2.7 K). The study focused on the Aerosol-PBL Interactions (API). In particular, we examined the influence of absorbing aerosol on the morning dynamics of PBL growth. Although no significant change in the height of the developed PBL was found, a nonzero convection onset delay was detected alongside the rapid formation of thermals. We also evaluated which one of the API component effects (“Surface cooling” or “Aerosol heating”) is dominant in terms of influence on $\stackrel{\mathrm{\&\#8254;}}{{\mathit{\&\#952;}}_{\mathrm{PBL}}}$$\stackrel{\mathrm{\&\#8254;}}{{\mathit{\&\#952;}}_{\mathrm{PBL}}}$ Copernicus Electronic Production Support Office 2025-07-17T18:06:37+02:00 2025-07-17T18:06:37+02:00 https://doi.org/10.5194/asr-22-9-2025

Measurements are essential to provide information on the actual state of the atmosphere in order to improve our understanding of atmospheric processes and their role in water cycle and the climate system. In this paper we focus on measurements from optical disdrometers which seek to improve our understanding of complexity of precipitations processes at surface. In this work, we present a study focused on analyzing the key characteristics of precipitation episodes in the Basque Country. For this purpose 1 min data from disdrometers are aggregated into precipitation episodes. This analysis involves incorporating information derived from various aggregated statistics applied to various episodes variables, including duration, number of particles, rain intensity and total rainfall segmented by precipitation type, total rain amount, season and others. Finally, some comparison in between disdrometer precipitation episodes and tipping-bucket rain-gauge episodes has been done.

UrbanKlima2050 is up to the date the most ambitious initiative led by the Basque Country to ensure the resilience of the territory through a multi-level governance and climate action on the ground. Within the UrbanKlima2050 project, and in order to assess the evolution of climate-related variables, future projections for wind, relative humidity and solar radiation were performed for the Basque Country. Starting from the scenario projections (RCP4.5 and RCP8.5) developed by the EURO-CORDEX community, bias adjusted data are obtained. Results show a downward trend for the wind under the RCP8.5 scenario and in the late future (2071–2100) a decrease of about 4 % is projected with respect to the historical period (1980–2009). In the RCP4.5 scenario, in general, a smoother decrease appears but is still significant. The relative humidity shows a decrease overall, with the largest values in the south, but for the late future an increase is detected in the coast, especially for the RCP4.5 scenario. The solar radiation displays a generalized increase for future projections, with an increase close to 4 % for the RCP8.5 scenario, yet under the RCP4.5 scenario the tendency does not show a significant signal. These results are of interest to perform the full implementation of the Basque Climate Change Strategy 2050.

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.

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.

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 Briggs1975) Copernicus Electronic Production Support Office 2024-08-02T18:06:37+02:00 2024-08-02T18:06:37+02:00 https://doi.org/10.5194/asr-21-1-2024

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.

In order to facilitate offshore wind farm tenders, Deutscher Wetterdienst (DWD, Germany's national meteorological service) provides reanalysis data and quality assessments to Bundesamt für Seeschifffahrt und Hydrographie (BSH, Federal Maritime and Hydrographic Agency). The regional reanalysis COSMO-REA6 is used besides the global reanalysis ERA5. New reanalyses and derived products getting available are (i) the regional reanalysis CERRA (C3S), (ii) COSMO-R6G2, a successor of COSMO-REA6 which is currently produced by DWD and (iii) HoKliSim-De, a convection-permitting climate simulation for Germany with COSMO-CLM as a regional downscaling of ERA5. In the present study, the quality of the different data sets for offshore wind energy application is compared using in-situ measurements of the wind speed and wind direction from the top anemometer and vane of the FINO1 research platform and satellite-based data of the near-surface wind speed from the Copernicus Marine Environment Monitoring Service (CMEMS) and the EUMETSAT Satellite Application Facility on Climate Monitoring (CM SAF).Evaluation at FINO1 focuses on the time period prior to the installation of nearby wind farms to avoid wake effects. COSMO-REA6, CERRA and HoKliSim-De show only small biases and resemble the observed distribution of the wind speed at FINO1 whereas ERA5 shows slightly lower values of the wind speed at 100 m. All model-based products tend to slightly underestimate the occurrence of south-westerly wind directions and overestimate wind directions from West to Northwest. Smallest directional biases are analysed for COSMO-REA6. Analysis of the windstorm CHRISTIAN suggests that ensemble information is required for the representation of individual extreme events.Evaluation of the near-surface wind speed using satellite-based data is performed for an area around the German Exclusive Economic Zone (EEZ) of the North Sea. The median bias of ERA5 and COSMO-REA6 is close to zero. CERRA shows a systematic overestimation of the near-surface wind speed compared to the satellite-based reference datasets. By contrast, a slight underestimation is analysed for HoKliSim-De. The bias distribution analysed for a first simulation stream of COSMO-R6G2 is similar to COSMO-REA6 which provides initial indication for the applicability of the new product.