European Weather Research Articles

Predictability of European winter 2022/23

AbstractThe boreal winter of 2022/23 was notable as a third consecutive winter in which La Niña had an influence on the European weather. The GloSea6 seasonal forecast system predicted a blocked circulation pattern in the North Atlantic in early winter (December), and then a transition through mid‐winter (January) into a more zonal pattern in late winter (February), consistent with the canonical La Niña teleconnection pattern seen previously. The seasonal forecast for the UK was an increased likelihood of near average temperatures, and drier‐ and calmer‐than‐average conditions. Both the predicted broad‐scale circulation patterns and UK winter mean weather conditions verified well against observations, and we show that seasonal forecasts of the North Atlantic Oscillation (NAO) over the last 10 winters show similar skill to previously reported hindcasts. Throughout the winter, the Madden–Julian Oscillation (MJO) was particularly active. On three occasions, it exhibited strong phases 6 and 7. There was also a sudden stratospheric warming (SSW) that occurred on 16th February. This was followed by colder conditions and associated impacts similar to the canonical negative NAO response over the UK, although the main impact fell in March and so did not affect the winter (December–January–February) mean conditions.

On atmospheric pressure and temperature correlation across various terrain types

Temperature (T), pressure (p), and density (ρ) are fundamental variables describing the atmospheric behavior. This study investigates the interdependence of these variables near Earth's surface in real-world conditions, by evaluating data from different European weather stations. It has been found that the correlation between pressure and temperature is strongly related to the dynamics of the PBL that facilitates a two-group classification. The first group includes all the stations in the plain or in the valley-floor and exhibits a weak correlation, R(p,T). 2D density plots representing hourly pressure against temperature have a distinctive triangular shape at these stations. Regardless of location, the upper boundary of this triangle consistently fits a linear equation with a constant slope and an intercept that scales with the average pressure of the station. This finding holds promising implications for enhancing the quality check of pressure and temperature data, enabling the identification of implausible measurements using a unified equation. In contrast, the second group includes stations with a strongly positive correlation R(p,T) and a more linear density plot; it includes all stations near a mountain-top. Their correlations exhibit identical features when compared to radiosounding data extracted at corresponding heights. The study concludes that: i) the first group of stations is significantly influenced by non-hydrostatic processes such as turbulence, friction and surface radiative heating/cooling in the PBL, resulting in weakly negative R(p,T) for shorter timescales that become null over longer durations; ii) the second group of stations has R(p,T) characteristics similar to the free atmosphere, predominantly regulated by hydrostatic balance and the advection of sensible heat.

Understanding the Intermittency of the Wintertime North Atlantic Oscillation and East Atlantic Pattern Seasonal Forecast Skill in the Copernicus C3S Multi‐Model Ensemble

AbstractThe wintertime North Atlantic Oscillation (NAO) and East Atlantic Pattern (EA) are the two leading modes of North Atlantic pressure variability and have a substantial impact on winter weather in Europe. The year‐to‐year contributions to multi‐model seasonal forecast skill in the Copernicus C3S ensemble of seven prediction systems are assessed for the wintertime NAO and EA, and well‐forecast and poorly‐forecast years are identified. Years with high NAO predictability are associated with substantial tropical forcing, generally from the El Niño Southern Oscillation (ENSO), while poor forecasts of the NAO occur when ENSO forcing is weak. Well‐forecast EA winters also generally occurred when there was substantial tropical forcing, although the relationship was less robust than for the NAO. These results support previous findings of the impacts of tropical forcing on the North Atlantic and show this is important from a multi‐model seasonal forecasting perspective.

The fire weather in Europe: large-scale trends towards higher danger

Abstract The climate over Europe has been recorded to be hotter, drier, and more fire-prone over the last decade than ever before, leading to concerns about how climate change will alter fire weather in the future. A typical measure to estimate fire weather severity based on climate is the Canadian fire weather index (FWI). In this study, we used high-resolution, bias-corrected climate model output (∼9 km) from six CMIP6 climate models and four shared socio-economic pathway projections (SSPs) to calculate consistent and comparable daily FWI datasets for Europe from 1950 to 2080. Our study aims to identify regional and large-scale shifts in fire weather severity and its predictability over time to support adaptive planning. We show that irrespective of the future SSP, fire weather will become more severe, but the increase is much stronger under high greenhouse gas emissions. This leads to new areas being exposed to severe fire weather, such as central Europe and rapidly warming mountainous areas. Already fire-prone regions in southern Europe will experience more extreme conditions. We conclude that only the low-emission SSP1-2.6 pathway can prevent strong increases in fire weather beyond the 2050s. Fire surveillance and management will become more important, even in areas and in seasons where they have not been in the focus so far.

Investigating the impact of coupling HARMONIE-WINS50 (cy43) meteorology to LOTOS-EUROS (v2.2.002) on a simulation of NO2 concentrations over the Netherlands

Abstract. Meteorological fields calculated by numerical weather prediction (NWP) models drive offline chemical transport models (CTMs) to solve the transport, chemical reactions, and atmospheric interaction over the geographical domain of interest. HARMONIE (HIRLAM ALADIN Research on Mesoscale Operational NWP in Euromed) is a state-of-the-art non-hydrostatic NWP community model used at several European weather agencies to forecast weather at the local and/or regional scale. In this work, the HARMONIE WINS50 (cycle 43 cy43) reanalysis dataset at a resolution of 0.025° × 0.025° covering an area surrounding the North Sea for the years 2019–2021 was coupled offline to the LOTOS-EUROS (LOng-Term Ozone Simulation-EURopean Operational Smog model, v2.2.002) CTM. The impact of using either meteorological fields from HARMONIE or from ECMWF on LOTOS-EUROS simulations of NO2 has been evaluated against ground-level observations and TROPOMI tropospheric NO2 vertical columns. Furthermore, the difference between crucial meteorological input parameters such as the boundary layer height and the vertical diffusion coefficient between the hydrostatic ECMWF and non-hydrostatic HARMONIE data has been studied, and the vertical profiles of temperature, humidity, and wind are evaluated against meteorological observations at Cabauw in The Netherlands. The results of these first evaluations of the LOTOS-EUROS model performance in both configurations are used to investigate current uncertainties in air quality forecasting in relation to driving meteorological parameters and to assess the potential for improvements in forecasting pollution episodes at high resolutions based on the HARMONIE NWP model.

Melting Ice in the Polar North Drives Weather in Europe

Influxes of meltwater into the North Atlantic eventually lead to warmer and drier conditions over Europe.

Inorganic–organic modular silicon and dye-sensitized solar cells and predicted role of artificial intelligence towards efficient and stable solar chargers based on supercapacitors

Appropriate and rational management of the energy produced by renewable energy sources is one of the most urgent challenges for the global energy sector. This paper is devoted to the systematic experimental and theoretical studies of a modular solar charger based on silicon and dye-sensitized solar cells as an energy source, and supercapacitor as an energy bank. Using the MathCAD program, I–V characteristics were plotted for both a single cell and a photovoltaic module based on various series-to-parallel connections. To assess the surface quality of the modules, additional tests using a thermal imaging camera were carried out as well. The charging characteristics of the supercapacitor (two series-connected cells with a capacity of 300 F), were determined depending on the parameters of the photovoltaic module as well as considering the influence of the voltage balancing system and control system. The charge, discharge, and recharge characteristics were carefully analyzed to optimize the operating conditions, i.e. the number of photovoltaic cells. To evaluate the stability of parameters with operation time, and their temperature dependence (17–65 °C), solar modules were tested for ten days under Central European weather conditions. Importantly, a comparative analysis of solar chargers based on different configurations of photovoltaic cells showed an increase in electrical parameters for the proposed modular inorganic–organic concept compared to dye-sensitized solar cells produced alone on a rigid substrate. Finally, preliminary assumptions (requirements) were developed regarding the electrical and optical parameters for new dye-sensitized solar cells that could be used in the innovative solar charger instead of silicon cells along with a predicted role of artificial intelligence (AI) in these devices.

The linkage between autumn Barents-Kara sea ice and European cold winter extremes

While the Arctic's accelerated warming and sea ice decline have been associated with Eurasian cooling, debates persist between those attributing this to sea ice retreat and those to internal variability. Our study examines the association between autumn sea ice variability over the Barents-Kara Seas and extreme cold winters in Europe. Using the observational data and composite analysis, we explore the interannual variability and the potential linkage between sea ice and atmospheric circulation patterns. It reveals a correlation with shifts toward a negative phase of North Atlantic Oscillation and more frequent episodes of the atmospheric blocking over Greenland and the North Atlantic. Furthermore, the negative phase of the North Atlantic Oscillation and enhanced blocking are closely related and mutually reinforcing, shaping the spatial distribution of cold anomalies over much of the European continent. Our results suggest a link between the unusual decrease in Barents-Kara Sea ice during autumn and the occurrence of intense European weather extremes in subsequent winter months, emphasizing the need for delving deeper into this relationship on monthly time scales to enhance our predictive capabilities for midlatitude extreme events.

Climate variability on Fit for 55 European power systems.

The use of variable renewable energy sources to generate electricity introduces a dependency on meteorological factors into power systems. With the renewables share growing globally, often driven by political pressures, the reliability and efficiency of power systems are increasingly affected by this dependency. In this paper, we investigate the impact of the natural variability of meteorological parameters on the European power system in 2030. We specifically focus on (1) analysing the main European weather patterns affecting renewable energy production and (2) understanding the co-variability of this production among European countries. The identification of a set of patterns in the behaviour of key power system operation indicators allows us to analyse the relationship between large-scale weather regimes and daily power system operations in a 2030 European energy context. Regarding renewable generation, analysis of the co-variability shows that European power systems tend to form two clusters, in each of which all the regions tend to show a positive correlation among themselves and a negative correlation with the other cluster. Our analysis of the most important large-scale weather regimes shows that during cyclonic patterns, the carbon intensity of all the European power systems is lower than normal, while the opposite happens during blocking regimes.

Revisiting Masselot et al. (2023): assessing the share of excess mortality linked to cold and hot weather in Europe.

While a considerable focus has been placed on excess fatalities during hot weather, a reanalysis of European data reveals that excess mortality attributed to cold weather is significantly more pronounced, surpassing that linked to hot weather by an order of magnitude. These ratios are noteworthy: 56.32 for the United Kingdom, 43.56 for Northern Europe, 8.49 for Western Europe, 12.41 for Eastern Europe, 5.50 for Southern Europe, and an overall ratio of 10.09 for Europe as a whole. These ratios of cold to hot excess deaths indicate a significant disparity in the number of excess deaths caused by cold weather compared to those caused by hot weather. This significant difference underscores the greater health risks and vulnerabilities associated with cold weather.

Jet Configurations Leading to Extreme Winter Temperatures Over Europe

AbstractThe North Atlantic eddy‐driven jet (EDJ) is the main driver of winter weather in Europe and has often been described by its latitude or strength. Here, we show that the influence of the EDJ on European winter temperature extremes can be better characterized by a multiparametric perspective that accounts for additional aspects of the EDJ structure (tilt, zonal elongation, etc.). We identify four regions where extreme temperatures are distinctly associated with the EDJ: Scandinavia, Central Europe, Eastern Europe, and Western Mediterranean (WMED). Overall, the anomalous horizontal advection induced by blockings during cold spells and enhanced westerlies during warm events is the main mechanism leading to extreme event occurrence. However, diabatic processes play an important role in WMED region. Both processes generate asymmetric effects in minimum and maximum temperatures contributing to higher intensities of cold than warm events. These extreme events are associated with different EDJ configurations, which typically involve perturbed EDJs during cold spells and strong tilted EDJs during warm events, but with important variations depending on the region. In almost every region, the combined effects of more than two EDJ parameters yield significant increases in the probability of cold and warm events, suggesting an oversimplification of traditional approaches based on a single EDJ parameter. We show, using logistic regression models, that, although important, latitude and intensity are often unable to discriminate unequivocally the region of extreme event occurrence, and in some regions, they do not drive the largest changes in the odds of extremes.

Assessment of Typical Meteorological Year Data in Photovoltaic Geographical Information System 5.2, Based on Reanalysis and Ground Station Data from 147 European Weather Stations

The Photovoltaic Geographical Information System (PVGIS) is a web application that provides free access to solar radiation and temperature data, typical meteorological year (TMY) data, and to photovoltaic performance assessment tools for any place in most parts of the world. The PVGIS was originally developed over 20 years ago, and since then, it has been under continuous development. At present, there are two versions of the PVGIS online—the older version 5.1 and the newest version 5.2. PVGIS 5.2 includes substantial improvements compared to the previous version, e.g., the update of the underlying data sets both in terms of quality, resolution, and geographical coverage and the extension of the time period used. This paper focuses on comparing the TMYs (and more specifically the TMY time series of air temperature), coming from both PVGIS 5.1 and 5.2, with the TMY produced on the basis of ground station meteorological data and with the ground station data itself. The results show that whereas overall the errors and biases for most locations are within the expected range (mean stationRMSE 4.27), these differences increase in places with complicated topography, e.g., in the Alps (maximum stationRMSE 9.50).

Optimal Use of Radar Radial Winds in the HARMONIE Numerical Weather Prediction System

Abstract The use of radial velocity information from the European weather radar network is a challenging task, because of a heterogeneous radar network and the different ways of providing the Doppler velocity information. Preprocessing is therefore needed to harmonize the data. Radar observations consist of a very high resolution dataset, which means that it is both demanding to process as well as that the inherent resolution is much higher than the model resolution. One way of reducing the number of data is to create “super observations” (SO) by averaging observations in a predefined area. This paper describes the preprocessing necessary to use radar radial velocities in the data assimilation where the SO construction is included. Our main focus is to optimize the use of radial velocities in the HARMONIE–AROME numerical weather model. Several experiments were run to find the best settings for first-guess check limits as well as a tuning of the observation error value. The optimal size of the SO and the corresponding thinning distance for radar radial velocities was also studied. It was found that the radial velocity information and the reflectivity from weather radars can be treated differently when it comes to the size of the SO and the thinning. A positive impact was found when adding the velocities together with the reflectivity using the same SO size and thinning distance, but the best results were found when the SO and thinning distance for the radial velocities are smaller than the corresponding values for reflectivity.

Modulation of North Atlantic extratropical cyclones and extreme weather in Europe during North American cold spells

Recent research has established a statistical link between North American cold spells (CS) and concurrent wet or windy extremes in Europe. Here, we investigate whether such a link can be related to changes in the characteristics of North Atlantic extratropical cyclones (hereafter cyclones). Despite large regions of anomalous baroclinicity during periods of North American CS, the number of cyclones across the North Atlantic as a whole is not significantly above climatology. However, we observe rates of explosive cyclogenesis significantly above climatology. We further find that CS over different North American regions are associated with large-scale atmospheric configurations displaying a strenghtened jet stream in the vicinity of the CS and different upper-level wind anomalies in the East Atlantic. These, in turn, modulate the regional distributions and characteristics of cyclones in the North Atlantic and Europe. We compute a fixed-radius cyclone footprint which we use to associate extreme precipitation and wind occurrences to individual cyclones. For eastern Canada CS, the North Atlantic jet extends over Northern Europe, resulting in a heightened numbers of cyclones for the British Isles and Scandinavia, while France, the British Isles, Northern Europe and Scandinavia all experience more intense cyclones. The British Isles consistently experience wind extremes associated with cyclones during east Canada CS. For central Canada CS, the jet is displaced poleward and only partly extends over Europe, resulting in an above average cyclone density in the eastern Atlantic and a higher number of cyclones affecting Iberia only. Iberia consistently experiences precipitation extremes associated with the cyclones during central Canada CS. For eastern United States CS, the jet is displaced equatorward and extends over Southern Europe, with a significantly heightened number of cyclones affecting Iberia and France. Iberia experiences wind extremes associated with the cyclones during eastern United States CS. Our results provide a dynamical explanation for previous statistical findings on the concurrence of North American CS and wet or windy extremes over Europe.

Quantifying uncertainty in probabilistic volcanic ash hazard forecasts, with an application to weather pattern based wind field sampling

Probabilistic forecasting of volcanic ash dispersion involves simulating an ensemble of realistic event scenarios to estimate the probability of a particular hazard threshold being exceeded. Although the number of samples that make up the ensemble, how they are chosen, and the desired threshold all set the uncertainty of (or confidence in) the estimated exceedance probability, current practice does not quantify and communicate the uncertainty in ensemble predictions. In this study, we use standard statistical methods to estimate the variance in probabilistic ensembles and use this measure of uncertainty to assess different sampling strategies for the wind field, using the example of volcanic ash transport from a representative explosive eruption in Iceland. For stochastic (random) sampling of the wind field, we show how the variance is reduced with increasing ensemble size and how the variance depends on the desired hazard threshold and the proximity of a target site to the volcanic source. We demonstrate how estimated variances can be used to compare different ensemble designs, by comparing stochastic forecasts with forecasts obtained from a stratified sampling approach using a set of 29 Northern European weather regimes, known as Grosswetterlagen (GWL). Sampling wind fields from within the GWL regimes reduces the number of samples needed to achieve the same variance as compared to conventional stochastic sampling. Our results show that uncertainty in volcanic ash dispersion forecasts can be straightforwardly calculated and communicated, and highlight the need for the volcanic ash forecasting community and operational end-users to jointly choose acceptable levels of variance for ash forecasts in the future.

A Joint Perspective on North American and Euro‐Atlantic Weather Regimes

AbstractWeather regimes are recurrent and quasi‐stationary large‐scale atmospheric circulation patterns, typically linking to surface weather. Two commonly used sets of weather regimes are wintertime North American and Euro‐Atlantic regimes. Notwithstanding recent evidence pointing to a connection between winter weather in North America and Europe, there is little knowledge on the possible relation between North American and Euro‐Atlantic regimes. Here, we find that specific pairs of North American and Euro‐Atlantic regimes show a close visual and statistical correspondence. Moreover, the joint analysis of the two sets of regimes can provide medium‐range statistical predictability for anomalies in their occurrence frequencies. Conditioning on North American weather regimes also results in anomalies in both the large‐scale circulation during specific Euro‐Atlantic regimes, and the associated European surface weather. We conclude that there is a benefit in conducting joint analyses of North American and European weather regimes, as opposed to considering the two in isolation.

Exploring the Use of European Weather Regimes for Improving User-Relevant Hydrological Forecasts at the Subseasonal Scale in Switzerland

Abstract Across the globe, there has been an increasing interest in improving the predictability of subseasonal hydrometeorological forecasts, as they play a valuable role in medium- to long-term planning in many sectors, such as agriculture, navigation, hydropower, and emergency management. However, these forecasts still have very limited skill at the monthly time scale; hence, this study explores the possibilities for improving forecasts through different pre- and postprocessing techniques at the interface with a Precipitationn–Runoff–Evapotranspiration Hydrological Response Unit Model (PREVAH). Specifically, this research aims to assess the benefit of European weather regime (WR) data within a hybrid forecasting setup, a combination of a traditional hydrological model and a machine learning (ML) algorithm, to improve the performance of subseasonal hydrometeorological forecasts in Switzerland. The WR data contain information about the large-scale atmospheric circulation in the North Atlantic–European region, and thus allow the hydrological model to exploit potential flow-dependent predictability. Four hydrological variables are investigated: total runoff, baseflow, soil moisture, and snowmelt. The improvements in the forecasts achieved with the pre- and postprocessing techniques vary with catchments, lead times, and variables. Adding WR data has clear benefits, but these benefits are not consistent across the study area or among the variables. The usefulness of WR data is generally observed for longer lead times, e.g., beyond the third week. Furthermore, a multimodel approach is applied to determine the “best practice” for each catchment and improve forecast skill over the entire study area. This study highlights the potential and limitations of using WR information to improve subseasonal hydrometeorological forecasts in a hybrid forecasting system in an operational mode.

Multi‐model assessment of sub‐seasonal predictive skill for year‐round Atlantic–European weather regimes

AbstractThe prediction skill of sub‐seasonal forecast models is evaluated for seven year‐round weather regimes in the Atlantic–European region. Reforecasts based on models from three prediction centers are considered and verified against weather regimes obtained from ERA‐Interim reanalysis. Results show that predicting weather regimes as a proxy for the large‐scale circulation outperforms the prediction of raw geopotential height. Greenland blocking tends to have the longest year‐round skill horizon for all three models, especially in winter. On the other hand, the skill is lowest for the European blocking regime for all three models, followed by the Scandinavian blocking regime. Furthermore, all models struggle to forecast flow situations that cannot be assigned to a weather regime (so‐called no regime), in comparison with weather regimes. Related to this, variability in the occurrence of no regime, which is most frequent in the transition seasons, partly explains the predictability gap between transition seasons and winter and summer. We also show that models have difficulties in discriminating between related regimes. This can lead to misassignments in the predicted regime during flow situations in which related regimes manifest. Finally, we document the changes in skill between model versions, showing important improvements for the ECMWF and NCEP models. This study is the first multi‐model assessment of year‐round weather regimes in the Atlantic–European domain. It advances our understanding of the predictive skill for weather regimes, reveals strengths and weaknesses of each model, and thus increases our confidence in the forecasts and their usefulness for decision‐making.

Stress factor identification and Risk Probabilistic Number (RPN) analysis of Li-ion batteries based on worldwide electric vehicle usage

Having clear insights of the stress factors that the electric vehicle (EV) batteries encounter during their service lifetime is crucial for more reliable ageing testing and modelling. Since the first deployment of Li-ion battery based EV, numerous driving campaigns with field data were published. The goal of this article is to gather, assess and analyse them in order to quantify the stress factors depending on the EV type. The targeted stress factors are the temperature of the cells, the discharging and charging rates, as well as the SOC ranges. 228 million km of driving and 7.8 million trips worth of data for over 37,000 EV were investigated. Along with this literature enquiry, data from an EV in which cells’ temperature was monitored for driving, charging and parking conditions, complemented the analysis. For each stress factor, results were collected, homogenised and compared with each other in order to draw conclusions. Finally, a Risk Probabilistic Number (RPN) was used to evaluate the stress factors with respect to their impact on the ageing of Li-ion batteries, considering a central European weather. The most critical stress factors for BEV cells are cycling at high mid-SOC regions and high SOC idle times. Concerning HEV cells, high power cycling at mid-SOC regions is the most critical stress, and no stresses were identified during idle times. PHEV cells’ most critical stress factors are large DOD cycling and high charge/discharge power. Mild and low temperatures are found to be the most common in such weathers. The RPN analysis serves as a guide for parametrizing and designing reliable accelerated ageing testing on Li-ion batteries depending on their application.

Combining multiple markers significantly increases the sensitivity and precision of eDNA‐based single‐species analyses

AbstractThe analysis of environmental DNA (eDNA) is becoming integrated as an established biomonitoring tool, often characterized by detection limits exceeding those of conventional counterparts. However, further improving the sensitivity of these methods may be invaluable for the early detection of invasive species, or for locating remnant populations of endangered species, and the adequate quantification of their abundances. In this study, we provide empirical evidence showing that the implementation of multiple genetic markers targeting different loci is a surprisingly overlooked strategy to increase the sensitivity of single‐species detections and quantification of their abundance, particularly at the lower end of the species abundance range. We analyzed 45 natural eDNA samples obtained from a wide range of water bodies in Belgium, in which either the invasive American bullfrog (Lithobates catesbeianus) or the rare European weather loach (Misgurnus fossilis) occurred under variable abundances, and compared detection success and precision of quantification of simplex (single locus) versus multiplex (multilocus) droplet digital PCR (ddPCR) analyses. Multiplexing different primer/probe assays targeting independent loci resulted in a significantly enhanced detection probability compared to the simplex analyses, gaining a twofold reduction in the limit of detection (LOD). Also the precision of eDNA quantification significantly improved in multiplex reactions, especially in low concentration samples. This was reflected in a significant reduction in the coefficient of variation (CV) among technical replicates, resulting in an associated decrease of the limit of quantification (LOQ). We conclude that the use of multiple markers can significantly improve the analytical sensitivity of eDNA‐based single‐species detections and precision of absolute abundance quantifications.