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Methane is the second-most important greenhouse gas after carbon dioxide and accounts for around 10 % of total European Union greenhouse gas emissions. Given that the atmospheric methane budget over a region depends on its terrestrial and aquatic methane sources, inverse modelling techniques appear as powerful tools for identifying critical areas that can later be submitted to emission mitigation strategies. In this regard, an inverse modelling system of methane emissions for Europe is being implemented based on the Weather Research and Forecasting (WRF) model: the Aarhus University Methane Inversion Algorithm (AUMIA) v1.0. The forward modelling component of AUMIA consists of the WRF model coupled to a multipurpose global database of methane anthropogenic emissions. To assure transport consistency during the inversion process, the backward modelling component will be based on the WRF model coupled to a Lagrangian particle dispersion module. A description of the modelling tools, input data sets, and 1-year forward modelling evaluation from 1 April 2018 to 31 March 2019 is provided in this paper. The a posteriori methane emission estimates, including a more focused inverse modelling for Denmark, will be provided in a second paper. A good general agreement is found between the modelling results and observations based on the TROPOspheric Monitoring Instrument (TROPOMI) onboard the Sentinel-5 Precursor satellite. Model–observation discrepancies for the summer peak season are in line with previous studies conducted over urban areas in central Europe, with relative differences between simulated concentrations and observational data in this study ranging from 1 % to 2 %. Domain-wide correlation coefficients and root-mean-square errors for summer months ranged from 0.4 to 0.5 and from 27 to 30 ppb, respectively. On the other hand, model–observation discrepancies for winter months show a significant overestimation of anthropogenic emissions over the study region, with relative differences ranging from 2 % to 3 %. Domain-wide correlation coefficients and root-mean-square errors in this case ranged from 0.1 to 0.4 and from 33 to 50 ppb, respectively, indicating that a more refined inverse analysis assessment will be required for this season. According to modelling results, the methane enhancement above the background concentrations came almost entirely from anthropogenic sources; however, these sources contributed with only up to 2 % to the methane total-column concentration. Contributions from natural sources (wetlands and termites) and biomass burning were not relevant during the study period. The results found in this study contribute with a new model evaluation of methane concentrations over Europe and demonstrate a huge potential for methane inverse modelling using improved TROPOMI products in large-scale applications. [ABSTRACT FROM AUTHOR]

The article discusses a study conducted to update ECOCLIMAP-I in Europe. It is evident that ECOCLIMAP-I is a database designed to cater needs of meteorological community to examine natural and managed ecosystems in association with weather forecasting and climate change modelling. It discusses methods, datasets and characteristics of producing ECOCLIMAPII/Europe.

In this study we present a series of sensitivity experiments conducted for the Last Glacial Maximum (LGM, ∼21 ka) over Europe using the regional climate Weather Research and Forecasting model (WRF). Using a four-step two-way nesting approach, we are able to reach a convection-permitting horizontal resolution over the inner part of the study area, covering central Europe and the Alpine region. The main objective of the paper is to evaluate a model version including a series of new developments better suitable for the simulation of paleo-glacial time slices with respect to the ones employed in former studies. The evaluation of the model is conducted against newly available pollen-based reconstructions of the LGM European climate and takes into account the effect of two main sources of model uncertainty: a different height of continental glaciers at higher latitudes of the Northern Hemisphere and different land cover. Model results are in good agreement with evidence from the proxies, in particular for temperatures. Importantly, the consideration of different ensemble members for characterizing model uncertainty allows for increasing the agreement of the model against the proxy reconstructions that would be obtained when considering a single model realization. The spread of the produced ensemble is relatively small for temperature, besides areas surrounding glaciers in summer. On the other hand, differences between the different ensemble members are very pronounced for precipitation, in particular in winter over areas highly affected by moisture advection from the Atlantic. This highlights the importance of the considered sources of uncertainty for the study of European LGM climate and allows for determining where the results of a regional climate model (RCM) are more likely to be uncertain for the considered case study. Finally, the results are also used to assess the effect of convection-permitting resolutions, at both local and regional scales, under glacial conditions. [ABSTRACT FROM AUTHOR]

The article presents a study which evaluates the ability of COSMO-ART, which is an online-coupled, regional-scale chemistry transport model, to measure air pollutants in Europe. It relates that the chemistry transport model is based on the combination of numerical weather prediction model of the Consortium for Small Scale Modeling (COSMO) with an extension for Aerosols and Reactive Trace (ART) gases. It mentions that the combination allows a consistent treatment of all air pollutants.

In this study we present a series of sensitivity experiments conducted for the Last Glacial Maximum (LGM, ~21000 years ago) over Europe using the regional climate Weather Research and Forecasting model (WRF). Using a 4 step 2-way nesting approach, we are able to reach a convection-permitting horizontal resolution over the inner part of the study area, covering Central Europe and the Alpine region. The main objective of the paper is to evaluate a model version including a series of new developments better suitable for the simulation of paleo-glacial time slices with respect to the ones employed in former studies. The evaluation of the model is conducted against newly available pollen-based reconstructions of the LGM European climate and takes into account the effect of two main sources of model uncertainty: a different height of continental glaciers at higher latitudes of the northern hemisphere and different land cover. Model results are in good agreement with evidence from the proxies, in particular for temperatures. Importantly, the consideration of different ensemble members for characterizing model uncertainty allows to increase the agreement of the model against the proxy reconstructions that would be obtained when considering a single model realization. The spread of the produced ensemble is relatively small for temperature, beside areas surrounding glaciers in summer. On the other hand, differences between the different ensemble members are very pronounced for precipitation, in particular in winter over areas highly affected by moisture advection from the Atlantic. This highlights the importance of the considered sources of uncertainty for the study of European LGM climate and allows to determine where the results of an RCM are more likely to be uncertain for the considered case study. Finally, the results are also used to demonstrate the added value of convection-permitting resolutions, at both local and regional scales, under glacial conditions. [ABSTRACT FROM AUTHOR]

Methane is the second most important greenhouse gas after carbon dioxide, and accounts for around 10 % of total European Union greenhouse gases emissions. Given that the atmospheric methane budget over a region depends on its terrestrial and aquatic methane sources, inverse modeling techniques appear as a powerful tools for identifying critical areas that can later be submitted to emission mitigation strategies. In this regard, an inverse modeling system of methane emissions for Europe is being implemented based on the Weather Research and Forecasting (WRF) model: the Aarhus University Methane Inversion Algorithm (AUMIA) v1.0. The forward modeling component of AUMIA consists of the WRF model coupled to a multipurpose global database of methane anthropogenic emissions. To assure transport consistency during the inversion process, the backward modeling component will be based on the WRF model coupled to a lagrangian particle dispersion module. A description of the modeling tools, input data sets and one-year forward modeling evaluation from April 01, 2018 to March 31, 2019 is provided in this paper. The a posteriori methane emission estimates, including a more focused inverse modeling for Denmark, will be provided in a second paper. A good general agreement is found between the modeling results and observations based on the TROPOspheric Monitoring Instrument (TROPOMI) onboard the Sentinel-5 Precursor satellite. Model-observation discrepancies for summer peak season are in line with previous studies conducted over urban areas in central Europe, with relative differences between simulated concentrations and observational data in this study ranging from 1 to 2%. Domain-wide correlation coefficients and root-mean-square-errors for summer months ranged from 0.4 to 0.5 and from 27 to 30 ppb, respectively. For winter months, otherwise, model-observation discrepancies show a significant overestimation of anthropogenic emissions over the study region, with relative differences ranging from 2 to 3%. Domain-wide correlation coefficients and root-mean-square-errors in this case ranged from 0.1 to 0.4 and from 33 to 50 ppb, respectively, indicating that a more refined inverse analysis assessment will be required for this season. According to modeling results, the methane enhancement above the background concentrations came almost entirely from anthropogenicsources; however, these sources contributed with only up to 2 % to the methane total column concentration. Contributions from natural sources (wetlands and termites) and biomass burning were not relevant during the study period. The results found in this study contribute with a new model evaluation of methane concentrations over Europe, and demonstrate a huge and under explored potential for methane inverse modeling using improved TROPOMI products in large-scale applications. [ABSTRACT FROM AUTHOR]

In this study, we assess the skill of a stochastic weather generator (SWG) to forecast precipitation in several cities in western Europe. The SWG is based on a random sampling of analogs of the geopotential height at 500 hPa (Z500). The SWG is evaluated for two reanalyses (NCEP and ERA5). We simulate 100-member ensemble forecasts on a daily time increment. We evaluate the performance of SWG with forecast skill scores and we compare it to ECMWF forecasts. Results show significant positive skill score (continuous rank probability skill score and correlation) compared with persistence and climatology forecasts for lead times of 5 and 10 d for different areas in Europe. We find that the low predictability episodes of our model are related to specific weather regimes, depending on the European region. Comparing the SWG forecasts to ECMWF forecasts, we find that the SWG shows a good performance for 5 d. This performance varies from one region to another. This paper is a proof of concept for a stochastic regional ensemble precipitation forecast. Its parameters (e.g., region for analogs) must be tuned for each region in order to optimize its performance. [ABSTRACT FROM AUTHOR]

The article reports on the development of the Met Office Unified Model's (MetUM) Global Atmosphere 3.0 (GA3.0) and Global Land 3.0 (GL3.0) configurations in Europe. It notes that GA3.0 and GL3.0 is used for climate research and weather predictions. It also tackles the Global Atmosphere development process and the absorption of the shortwave (SW) radiation from the Sun.

This is the second of two papers that document the creation of the New European Wind Atlas (NEWA). In Part 1, we described the sensitivity experiments and accompanying evaluation done to arrive at the final mesoscale model setup used to produce the mesoscale wind atlas. In this paper, Part 2, we document how we made the final wind atlas product, covering both the production of the mesoscale climatology generated with the Weather Research and Forecasting (WRF) model and the microscale climatology generated with the Wind Atlas Analysis and Applications Program (WAsP). The paper includes a detailed description of the technical and practical aspects that went into running the mesoscale simulations and the downscaling using WAsP. We show the main results from the final wind atlas and present a comprehensive evaluation of each component of the NEWA model chain using observations from a large set of tall masts located all over Europe. The added value of the WRF and WAsP downscaling of wind climatologies is evaluated relative to the performance of the driving ERA5 reanalysis and shows that the WRF downscaling reduces the mean wind speed bias and spread relative to that of ERA5 from -1.50±1.30 to 0.02±0.78 m s -1. The WAsP downscaling has an added positive impact relative to that of the WRF model in simple terrain. In complex terrain, where the assumptions of the linearized flow model break down, both the mean bias and spread in wind speed are worse than those from the raw mesoscale results. [ABSTRACT FROM AUTHOR]

Agriculture is the one of the most important priorities and economic activities in developing countries. Climate change, which is already affecting the society at a global level, has an impact on the increase in magnitude and frequency of severe weather events. As such, both agriculture and industry are affected by climate change and their revenues decrease, thus affecting the entire economic cycle. The aim of this paper is to present theoretical considerations regarding climate change induced severe weather events and to highlight some practical techniques used in operational meteorology in order to improve the forecast of severe weather events. By analysing the morphology and dynamics of thunderstorms, by applying the correct parametrization in weather forecast models, nowcasting and by issuing on time weather alerts, financial losses may be limited. Throughout this paper, the parametrization of convective cells will be described, operational forecasting techniques will be presented and the application of ingredient-based forecasting methodology will be discussed. Severe weather associated hazards like hail, heavy rainfall, damaging winds and even frost will be analysed in order to establish a local climatology of these phenomena. With the aid of Weather Surveillance Doppler Radar (WSDR) and by associating some instability convection indices with synoptic charts, the agriculture-related severe weather events can be identified and their formation pattern can be established. Further on, by establishing an analysis methodology and by compelling all available data, weather-related hazard maps are to be compelled and are to be used by agrometeorology forecasters in order to seek viable solutions. The Western part of Romania is characterized as having a moderatecontinental climate, with hot influences from the Southern part of Europe and with humid advections from the South-Western part of Europe. In combination with cold advections from the Northern Europe, both summer as well as winter specific agriculture-related severe weather events are present [ABSTRACT FROM AUTHOR]

Coupled atmosphere-ocean general circulation models (GCMs) simulate different realizations of possible future climates at global scale under contrasting scenarios of land-use and greenhouse gas emissions. Such data require several additional processing steps before it can be used to drive impact models. Spatial downscaling, typically by regional climate models (RCM), and bias-correction are two such steps that have already been addressed for Europe. Yet, the errors in resulting daily meteorological variables may be too large for specific model applications. Crop simulation models are particularly sensitive to these inconsistencies and thus require further processing of GCM-RCM outputs. Moreover, crop models are often run in a stochastic manner by using various plausible weather time series (often generated using stochastic weather generators) to represent climate time scale for a period of interest (e.g. 2000 ± 15 years), while GCM simulations typically provide a single time series for a given emission scenario. To inform agricultural policy-making, data on near- and medium-term decadal time scale is mostly requested, e.g. 2020 or 2030. Taking a sample of multiple years from these unique time series to represent time horizons in the near future is particularly problematic because selecting overlapping years may lead to spurious trends, creating artefacts in the results of the impact model simulations. This paper presents a database of consolidated and coherent future daily weather data for Europe that addresses these problems. Input data consist of daily temperature and precipitation from three dynamically downscaled and bias-corrected regional climate simulations of the IPCC A1B emission scenario created within the ENSEMBLES project. Solar radiation is estimated from temperature based on an auto-calibration procedure. Wind speed and relative air humidity are collected from historical series. From these variables, reference evapotranspiration and vapour pressure deficit are estimated ensuring consistency within daily records. The weather generator ClimGen is then used to create 30 synthetic years of all variables to characterize the time horizons of 2000, 2020 and 2030, which can readily be used for crop modelling studies. [ABSTRACT FROM AUTHOR]

The extreme dependency score EDS has been recently proposed as a nonvanishing measure of skill to verify predictions of rare events. An idealized example is employed to investigate the properties of the EDS when the probability of the event happening is small, as is the case for rare events. The paper warns about the nondependency of EDS on the false alarms and the correct rejections when the sample size is fixed, which encourages hedging. Thus, the EDS should not be the sole score used to assess a models performance on forecasting rare events. The dependency of the EDS on the event probability base rate is also analysed. It is shown that the EDS can be written as a function of the base rate and the hit rate, and that only in a very particular condition the score is independent of the event probability. Therefore, when comparing forecasts skills in terms of the EDS, special attention has to be paid to separate the variations in skill from the variations of the probability that the event happens over time.The score is used to assess the performance of the ECMWF deterministic precipitation forecast over Europe for different forecast ranges and precipitation thresholds. Verification of precipitation forecasts in terms of EDS, combined with an assessment of the false alarm rate, shows a clear improvement of the ECMWF deterministic forecast for precipitation of rare events i.e. large rainfall amounts. Copyright © 2009 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

This is the first of two papers that document the creation of the New European Wind Atlas (NEWA). It describes the sensitivity analysis and evaluation procedures that formed the basis for choosing the final setup of the mesoscale model simulations of the wind atlas. The suitable combination of model setup and parameterizations, bound by practical constraints, was found for simulating the climatology of the wind field at turbine-relevant heights with the Weather Research and Forecasting (WRF) model. Initial WRF model sensitivity experiments compared the wind climate generated by using two commonly used planetary boundary layer schemes and were carried out over several regions in Europe. They confirmed that the most significant differences in annual mean wind speed at 100 m a.g.l. (above ground level) mostly coincide with areas of high surface roughness length and not with the location of the domains or maximum wind speed. Then an ensemble of more than 50 simulations with different setups for a single year was carried out for one domain covering northern Europe for which tall mast observations were available. We varied many different parameters across the simulations, e.g. model version, forcing data, various physical parameterizations, and the size of the model domain. These simulations showed that although virtually every parameter change affects the results in some way, significant changes in the wind climate in the boundary layer are mostly due to using different physical parameterizations, especially the planetary boundary layer scheme, the representation of the land surface, and the prescribed surface roughness length. Also, the setup of the simulations, such as the integration length and the domain size, can considerably influence the results. We assessed the degree of similarity between winds simulated by the WRF ensemble members and the observations using a suite of metrics, including the Earth Mover's Distance (EMD), a statistic that measures the distance between two probability distributions. The EMD was used to diagnose the performance of each ensemble member using the full wind speed and direction distribution, which is essential for wind resource assessment. We identified the most realistic ensemble members to determine the most suitable configuration to be used in the final production run, which is fully described and evaluated in the second part of this study. [ABSTRACT FROM AUTHOR]

Purpose The purpose of this paper is to ascertain whether weather variables can explain the stock return reaction on the Dow Jones Sustainability Europe Index by employing a number of macroeconomic indicators as control variables.Design/methodology/approach The authors incorporate the generalized autogressive conditional heteroskeasticity model in methodology for the period August 26, 2009 to May 30, 2014 using daily data.Findings The empirical results indicate that not only do changes in humidity and wind levels seem to affect positively the European stock market but changes in returns oil and gold prices as well. However, the results show that the volatility of the US dollar/Yen exchange rate and ten-year bond value exerts significant negative impact on companies’ stock returns.Originality/value This study adds to the international literature by documenting the impact of weather variables on socially responsible companies. [ABSTRACT FROM AUTHOR]

From Global Navigation Satellite Systems (GNSS) signals, accurate and high-frequency atmospheric parameters can be determined in all-weather conditions. GNSS tomography is a technique that takes advantage of these parameters, especially of slant troposphere observations between GNSS receivers and satellites, traces these signals through a 3-D grid of voxels, and estimates by an inversion process the refractivity of the water vapour content within each voxel. In the last years, the GNSS tomography development focused on numerical methods to stabilize the solution, which has been achieved to a great extent. Currently, we are facing new challenges and possibilities in the application of GNSS tomography in numerical weather forecasting, the main research objective of this paper. In the first instance, refractivity fields were estimated using two different GNSS tomography models (TUW, WUELS), which cover the area of central Europe during the period of 29 May–14 June 2013, when heavy-precipitation events were observed. For both models, slant wet delays (SWDs) were calculated based on estimates of zenith total delay (ZTD) and horizontal gradients, provided for 88 GNSS sites by Geodetic Observatory Pecny (GOP). In total, three sets of SWD observations were tested (set0 without compensation for hydrostatic anisotropic effects, set1 with compensation of this effect, set2 cleaned by wet delays outside the inner voxel model), in order to assess the impact of different factors on the tomographic solution. The GNSS tomography outputs have been assimilated into the nested (12 and 36 km horizontal resolution) Weather Research and Forecasting (WRF) model, using its three-dimensional variational data assimilation (WRFDA 3D-Var) system, in particular, its radio occultation observation operator (GPSREF). As only total refractivity is assimilated in GPSREF, it was calculated as the sum of the hydrostatic part derived from the ALADIN-CZ model and the wet part from the GNSS tomography. We compared the results of the GNSS tomography data assimilation to the radiosonde (RS) observations. The validation shows the improvement in the weather forecasting of relative humidity (bias, standard deviation) and temperature (standard deviation) during heavy-precipitation events. Future improvements to the assimilation method are also discussed. [ABSTRACT FROM AUTHOR]

Timely forecasts of the onset or possible evolution of droughts are an important contribution to mitigate their manifold negative effects. In this paper we therefore analyse and compare the performance of the first month of the probabilistic extended range forecast and of the seasonal forecast from the European Centre for Medium-range Weather Forecasts (ECMWF) in predicting droughts over the European continent. The Standardized Precipitation Index (SPI- 1) is used to quantify the onset or likely evolution of ongoing droughts for the next month. It can be shown that on average the extended range forecast has greater skill than the seasonal forecast, whilst both outperform climatology. No significant spatial or temporal patterns can be observed, but the scores are improved when focussing on large-scale droughts. In a second step we then analyse several different methods to convert the probabilistic forecasts of SPI into a Boolean drought warning. It can be demonstrated that methodologies which convert low percentiles of the forecasted SPI cumulative distribution function into warnings are superior in comparison with alternatives such as the mean or the median of the ensemble. The paper demonstrates that up to 40% of droughts are correctly forecasted one month in advance. Nevertheless, during false alarms or misses, we did not find significant differences in the distribution of the ensemble members that would allow for a quantitative assessment of the uncertainty. [ABSTRACT FROM AUTHOR]

This paper presents a validation and intercomparison of the non-linear Partition Rescaling and Shift Algorithm (PARSA) for deriving full two-dimensional ocean wave spectra from ENIVSAT Advanced Synthetic Aperture Radar (ASAR) Wave Mode (WM) data. ASAR WM data acquired globally are used for the validation exercise by comparing the retrieved significant wave height (SWH), zero up-crossing wave period (Tm02), swell SWH (H12, for waves with a period longer than 12 s), mean wave frequency and mean wave direction to in situ buoy measurements and results from the European Centre for Medium-Range Weather Forecast (ECMWF) reanalysis wave model and the Deutscher Wetterdienst (DWD) forecast wave model. An intercomparison of results from the PARSA algorithm with those from the quasi-linear retrieval algorithm adopted by the European Space Agency (ESA) to generate the ASAR WM level-2 product (ASA_WVW_2P, termed WVW hereafter) is also presented in this paper. In addition to the intercomparison with the existing ASAR WM WVW product, sea state parameters (SWH and Tm02), integrated from the PARSA spectra are also compared to the results derived using the empirical algorithm CWAVE_ENV. Validation results indicate that the PARSA inversion can yield full two-dimensional ocean wave spectra. The retrieved SWH corresponds well to buoy measurements with a scatter index of 21%, as demonstrated by 1247 collocated data pairs. By comparing SWH to the ECMWF reanalysis wave model and the DWD forecast wave model, better agreement is achieved, with scatter indices of 9% and 16%, respectively. In addition to comparing conventional integral wave parameters normally used to assess the quality of inverted spectra, a comparison of individual PARSA spectra chosen in different sea state with the nearest numerical wave model spectra and the WVW spectra is performed to illustrate two-dimensional spectral differences. [ABSTRACT FROM AUTHOR]

This paper presents the development of the European Flood Alert System (EFAS), which aims at increasing preparedness for floods in trans-national European river basins by providing local water authorities with mediumrange and probabilistic flood forecasting information 3 to 10 days in advance. The EFAS research project started in 2003 with the development of a prototype at the European Commission Joint Research Centre (JRC), in close collaboration with the national hydrological and meteorological services. The prototype covers the whole of Europe on a 5 km grid. In parallel, different high-resolution data sets have been collected for the Elbe and Danube river basins, allowing the potential of the system under optimum conditions and on a higher resolution to be assessed. Flood warning lead-times of 3-10 days are achieved through the incorporation of medium-range weather forecasts from the German Weather Service (DWD) and the European Centre for Medium-Range Weather Forecasts (ECMWF), comprising a full set of 51 probabilistic forecasts from the Ensemble Prediction System (EPS) provided by ECMWF. The ensemble of different hydrographs is analysed and combined to produce early flood warning information, which is disseminated to the hydrological services that have agreed to participate in the development of the system. In Part 1 of this paper, the scientific approach adopted in the development of the system is presented. The rational of the project, the systems set-up, its underlying components, basic principles and products are described. In Part 2, results of a detailed statistical analysis of the performance of the system are shown, with regard to both probabilistic and deterministic forecasts. [ABSTRACT FROM AUTHOR]

The political pressure on the scientific community to provide medium to long term flood forecasts has increased in the light of recent flooding events in Europe. Such demands can be met by a system consisting of three different model components (weather forecast, rainfall-runoff forecast and flood inundation forecast) which are all liable to considerable uncertainty in the input, output and model parameters. Thus, an understanding of cascaded uncertainties is a necessary requirement to provide robust predictions. In this paper, 10-day ahead rainfall forecasts, consisting of one deterministic, one control and 50 ensemble forecasts, are fed into a rainfall-runoff model (LisFlood) for which parameter uncertainty is represented by six different parameter sets identified through a Generalised Likelihood Uncertainty Estimation (GLUE) analysis and functional hydrograph classification. The runoff of these 52 * 6 realisations form the input to a flood inundation model (LisFlood-FP) which acknowledges uncertainty by utilising ten different sets of roughness coefficients identified using the same GLUE methodology. Likelihood measures for each parameter set computed on historical data are used to give uncertain predictions of flow hydrographs as well as spatial inundation extent. This analysis demonstrates that a full uncertainty analysis of such an integrated system is limited mainly by computer power as well as by how well the rainfall predictions represent potential future conditions. However, these restrictions may be overcome or lessened in the future and this paper establishes a computationally feasible methodological approach to the uncertainty cascade problem. [ABSTRACT FROM AUTHOR]

A regional reanalysis covering the years 1989-2010 has been produced with the HIgh Resolution Limited-Area Model (HIRLAM) forecast model and data assimilation system. Surface and upper-air variables were analysed at 0000, 0600, 1200 and 1800 UTC on a three-dimensional grid-mesh with 22 km spacing covering Europe using conventional insitu observations. Information from the global reanalysis ERA-Interim has been used as a large-scale constraint in the data assimilation and as lateral boundaries in the forecast model integrations. Comparison to the global forcing reanalysis shows good agreement in the large-scale structures, as expected given the forcing from the boundaries and in the analysis. Comparison to the observed climatological distribution and a skill score evaluation showed that the HIRLAM reanalysis is better than ERA-Interim at describing extreme values of 2 m temperature and 24 h accumulated precipitation. However, no added value in the HIRLAM reanalysis could be quantified for the wind speed at 10 m over land. The first production run covered the years 1989-2010 and the statistics presented in this paper are based on that dataset. This reanalysis has also been used as input to a two-dimensional surface analysis using the MESoscale ANalysis (MESAN) system which is presented in Part 2. Since then both the two- and three dimensional reanalyses have been extended to cover the years 1979-2014. [ABSTRACT FROM AUTHOR]

This paper presents an approach to postprocess ensemble forecasts for the discrete and bounded weather variable of total cloud cover. Two methods for discrete statistical postprocessing of ensemble predictions are tested: the first approach is based on multinomial logistic regression and the second involves a proportional odds logistic regression model. Applying them to total cloud cover raw ensemble forecasts from the European Centre for Medium-Range Weather Forecasts improves forecast skill significantly. Based on stationwise postprocessing of raw ensemble total cloud cover forecasts for a global set of 3330 stations over the period from 2007 to early 2014, the more parsimonious proportional odds logistic regression model proved to slightly outperform the multinomial logistic regression model. [ABSTRACT FROM AUTHOR]

The paper explores exceptional thermal conditions, an area of research that has increased in significance in the context of the changes that are being observed in climate. Specifically, the study addressed the frequency, long-term change and spatial coverage of exceptionally hot summers, and exceptionally cool summers (EHS and ECS) in Europe. The statistical criterion of plus/minus two standard deviations from the long-term average was used to identify EHS and ECS at 60 weather stations over the period 1951-2010. The study has demonstrated that EHS are characterised by greater temperature anomalies than ECS and are approximately twice as frequent. They occurred virtually everywhere within the study area, whilst ECS are concentrated in its northern part. Five EHS (1972, 2002, 2003, 2007, and 2010) and three ECS (1956, 1962, and 1976) were large enough to be recorded by at least 10% of the stations [ABSTRACT FROM AUTHOR]

The prediction of climate on time scales of years to decades is attracting the interest of both climate researchers and stakeholders. The German Ministry for Education and Research (BMBF) has launched a major research programme on decadal climate prediction called MiKlip (Mittelfristige Klimaprognosen, Decadal Climate Prediction) in order to investigate the prediction potential of global and regional climate models (RCMs). In this paper we describe a regional predictive hindcast ensemble, its validation, and the added value of regional downscaling. Global predictions are obtained from an ensemble of simulations by the MPI-ESM-LR model (baseline 0 runs), which were downscaled for Europe using the COSMO-CLM regional model. Decadal hindcasts were produced for the 5 decades starting in 1961 until 2001. Observations were taken from the E-OBS data set. To identify decadal variability and predictability, we removed the long-term mean, as well as the long-term linear trend from the data. We split the resulting anomaly time series into two parts, the first including lead times of 1-5 years, reflecting the skill which originates mainly from the initialisation, and the second including lead times from 6-10 years, which are more related to the representation of low frequency climate variability and the effects of external forcing. We investigated temperature averages and precipitation sums for the summer and winter half-year. Skill assessment was based on correlation coefficient and reliability. We found that regional downscaling preserves, but mostly does not improve the skill and the reliability of the global predictions for summer half-year temperature anomalies. In contrast, regionalisation improves global decadal predictions of half-year precipitation sums in most parts of Europe. The added value results from an increased predictive skill on grid-point basis together with an improvement of the ensemble spread, i.e. the reliability. [ABSTRACT FROM AUTHOR]

Abstract: This paper describes different numerical models of ocean circulation the output of which can be applied to study patterns and pathways of drifting marine debris. The paper focuses on model output that is readily available rather than on numerical models that could be configured and run locally. These include operational models from the US Navy (the Navy Layered Ocean Model (NLOM), Coastal Ocean Model (NCOM), and Hybrid Coordinate Ocean Model (HYCOM)), data assimilating reanalysis models (the Simple Ocean Data Assimilation (SODA), the Global Ocean Data Assimilation Experiment (GODAE) models), and the European Center for Medium-Range Weather Forecasts (ECMWF) ocean reanalysis (Ocean Reanalysis System, ECMWF/ORA-S3). The paper describes the underlying physics in each model system, limitations, and where to obtain the model output. [Copyright &y& Elsevier]

From Global Navigation Satellite Systems (GNSS) signals, accurate and high-frequency atmospheric parameters can be determined in all-weather conditions. GNSS tomography is a novel technique that takes advantage of these parameters, especially of slant troposphere observations between GNSS receivers and satellites, traces these signals through a 3D grid of voxels and estimates by an inversion process the refractivity of the water vapour content within each voxel. In the last years, the GNSS tomography development focused on numerical methods to stabilize the solution, which has been achieved to a great extent. Currently, we are facing new challenges and possibilities in the application of GNSS tomography in numerical weather forecasting - the main research objective of this paper. In the first instance, refractivity fields were estimated using two different GNSS tomography models (TUW, WUELS), which cover the area of Central Europe during the period of 29 May-14 June 2013, when heavy precipitation events were observed. For both models, Slant Wet Delays (SWD) were calculated based on estimates of Zenith Total Delay (ZTD) and horizontal gradients, provided for 72 GNSS sites by Geodetic Observatory Pecny (GOP). In total, three sets of SWD observations were tested (set0 without compensation for hydrostatic anisotropic effects, set1 with compensation of this effect, set2 cleaned by wet delays outside the inner voxel model). The GNSS tomography outputs have been assimilated into the nested (12- and 36-km horizontal resolution) Weather Research and Forecasting (WRF) model, using its three-dimensional variational data assimilation (WRFDA 3DVar) system, in particular its radio occultation observations operator (GPSREF). As only total refractivity is assimilated in GPSREF, it was calculated as the sum of the hydrostatic part derived from the ALADIN-CZ model and the wet part from the GNSS tomography. We compared the results of the GNSS tomography data assimilation to the radiosonde (RS) observations. The validation shows the improvement in the weather forecasting of relative humidity (bias, standard deviation) and temperature (standard deviation) during heavy precipitation events. Future improvements to the assimilation method are also discussed. [ABSTRACT FROM AUTHOR]

In North America and Europe, most fatalities due to snow avalanches occur in the backcountry during recreational pursuits. Of these, more than 90% of the fatal avalanches are triggered by the victims themselves. This pattern suggests that the primary cause of avalanche fatalities for human-triggered avalanches is a failure in human perception. For the latter, people thought that the state of stability or instability of the snow cover was different than it actually was. In this paper, the strength and weight of evidence used to make decisions in backcountry travel are discussed from: (a) the perspective of the favored hypothesis to proceed for good recreational enjoyment based on stability evaluation and (b) the null hypothesis based on an assessment of instability. Based on the facts about snow slab avalanche release, it is argued that instability analysis is the best framework for avalanche forecasting, whereas human action is most closely related to the favored hypothesis (stability evaluation). Using scaling laws derived from: (a) fracture mechanics about the size of imperfections causing avalanches and (b) avalanche dimensions, it is suggested that a snow slab could show stability over more than 99% of the total area. From the concepts of Bayesian probability, it is shown that overconfidence about stability can arise when the weight of the likelihood is high and the weight of prior is low. Similarly, underconfidence (excessive conservatism) often results when the weight of the prior is high with little regard for the likelihood, which may be low. Overconfidence about stability is considered to be a prime source of accidents. [ABSTRACT FROM AUTHOR]

This paper presents the Integrated Nowcasting through Comprehensive Analysis (INCA) system, which has been developed for use in mountainous terrain. Analysis and nowcasting fields include temperature, humidity, wind, precipitation amount, precipitation type, cloudiness, and global radiation. The analysis part of the system combines surface station data with remote sensing data in such a way that the observations at the station locations are reproduced, whereas the remote sensing data provide the spatial structure for the interpolation. The nowcasting part employs classical correlation-based motion vectors derived from previous consecutive analyses. In the case of precipitation the nowcast includes an intensity-dependent elevation effect. After 2--6 h of forecast time the nowcast is merged into an NWP forecast provided by a limited-area model, using a predefined temporal weighting function. Cross validation of the analysis and verification of the nowcast are performed. Analysis quality is high for temperature, but comparatively low for wind and precipitation, because of the limited representativeness of station data in mountainous terrain, which can be only partially compensated by the analysis algorithm. Significant added value of the system compared to the NWP forecast is found in the first few hours of the nowcast. At longer lead times the effects of the latest observations becomes small, but in the case of temperature the downscaling of the NWP forecast within the INCA system continues to provide some improvement compared to the direct NWP output. [ABSTRACT FROM AUTHOR]

Following trends in operational weather forecasting, where ensemble prediction systems (EPS) are now increasingly the norm, flood forecasters are beginning to experiment with using similar ensemble methods. Most of the effort to date has focused on the substantial technical challenges of developing coupled rainfall-runoff systems to represent the full cascade of uncertainties involved in predicting future flooding. As a consequence much less attention has been given to the communication and eventual use of EPS flood forecasts. Drawing on interviews and other research with operational flood forecasters from across Europe, this paper highlights a number of challenges to communicating and using ensemble flood forecasts operationally. It is shown that operational flood forecasters understand the skill, operational limitations, and informational value of EPS products in a variety of different and sometimes contradictory ways. Despite the efforts of forecasting agencies to design effective ways to communicate EPS forecasts to non-experts, operational flood forecasters were often skeptical about the ability of forecast recipients to understand or use them appropriately. It is argued that better training and closer contacts between operational flood forecasters and EPS system designers can help ensure the uncertainty represented by EPS forecasts is represented in ways that are most appropriate and meaningful for their intended consumers, but some fundamental political and institutional challenges to using ensembles, such as differing attitudes to false alarms and to responsibility for management of blame in the event of poor or mistaken forecasts are also highlighted. Copyright © 2010 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

In this paper the skill of seasonal prediction of river discharge and how this skill varies between the branches of European rivers across Europe is assessed. A prediction system of seasonal (winter and summer) discharge is evaluated using 1) predictions of the average North Atlantic Oscillation (NAO) index for the coming winter based on May SST anomalies of the North Atlantic; 2) a global-scale hydrological model; and 3) 40-yr European Centre for Medium-Range Weather Forecasts Re-Analysis (ERA-40) data. The skill of seasonal discharge predictions is investigated with a numerical experiment. Also Europe-wide patterns of predictive skill are related to the use of NAO-based seasonal weather prediction, the hydrological properties of the river basin, and a correct assessment of initial hydrological states. These patterns, which are also corroborated by observations, show that in many parts of Europe the skill of predicting winter discharge can, in theory, be quite large. However, this achieved skill mainly comes from knowing the correct initial conditions of the hydrological system (i.e., groundwater, surface water, soil water storage of the basin) rather than from the use of NAO-based seasonal weather prediction. These factors are equally important for predicting subsequent summer discharge. [ABSTRACT FROM AUTHOR]

At the end of December 1999, two extremely severe storms only one day apart affected western Europe and caused considerable damage. A variable derived from satellite observations, the so-called temperature of the lower stratosphere (TLS), is used in this study for detecting and tracking the upper-level components of these storms. TLS is computed from a regression over five Television and Infrared Observation Satellite (TIROS-N) Operational Vertical Sounder (TOVS, aboard NOAA satellites) channels, with coefficients calculated from a climatological dataset [thermodynamical initial-guess retrieval (TIGR)], and provides information on the temperature near the tropopause. The objective of this paper is to assess the ability of TLS, in situations such as these two exceptional storms, to track and depict upper-tropospheric precursors of surface lows. After a brief synoptic description of the meteorological situation, TLS fields as well as the Action de Recherche Petite Échelle Grand Échelle (ARPEGE) model fields (mean sea level pressure, temperature, wind velocity, and geopotential height of the dynamical tropopause) are discussed concurrently for the period 23-27 December. Although the upper-level thermal fields are consistent overall, differences appear, especially during the incipient stage of the second storm. The forecast, which was poor in the operational context, is modified when a configuration close to the TLS one is adopted. Qualitative comparisons of TLS with Microwave Sounding Unit (MSU) channel-3 limb-corrected brightness temperatures and with the water vapor imagery are also shown. One advantage of TLS over these two other fields is the earlier detection of the upper-level precursor of the second storm. Because TLS computation is easy and fast, the suitability of TLS as a possible forecasting aid over midoceanic regions is promoted. [ABSTRACT FROM AUTHOR]

ABSTRACT The European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis (ERA) results for July 1987 are compared with stratocumulus observations for the same period made during the First International Satellite Cloud Climatology Project (ISCCP) Regional Experiment (FIRE). The FIRE I experiment, conducted off the coast of California, provided a comprehensive observational set of data on marine stratocumulus during July 1987. The observations reported in this paper and used as a basis for comparison with the ERA results include the mean diurnal variation of cloud cover, liquid water path, cloud-base and cloud-top height, incoming short-wave and long-wave radiation at the surface, and monthly mean vertical profiles of (potential) temperature and specific humidity. It is concluded that the ERA thermal structure in the boundary layer is quite realistic and the ERA data reveal some diurnal variability. However, in ERA the cloud thickness is overestimated and cloud cover and liquid water path are strongly underestimated as compared with the FIRE I observations. As a result the ERA downwelling short-wave radiation at the surface is much larger than observed in FIRE I. It is argued that the large difference between the ERA downwelling shortwave radiation at the surface and that observed in FIRE I can lead to large biases in the sea surface temperature in a coupled ocean-atmosphere model. [ABSTRACT FROM AUTHOR]

Results are presented from the project Clear-sky Longwave from ERA (CLERA), in which simulations were performed of clear-sky longwave fluxes and heating rates for the period 1979-93, based on data from the European Centre for Medium-Range Weather Forecasts Re-Analysis project (ERA). This paper presents selected results from CLERA and compares the clear-sky outgoing longwave radiation (OLR) with data from the Earth Radiation Budget Experiment (ERBE). Over much of the globe, especially over the oceans, the clear-sky OLR from CLERA is within the expected uncertainty in the ERBE data of 65 W m 22 . Elsewhere, there are larger differences and to study these the ERA data are compared with independent sources of information: surface synoptic observations of screen-level temperatures and retrievals of the total column moisture and upper-tro-pospheric humidity from satellite data. Over land, the largest clear-sky OLR differences occur at high latitudes in winter and these can be explained by the fact that the ERA surface temperatures are too low in these regions. However, for many other regions over land there was no obvious explanation for the clear-sky OLR differences. Over the oceans, the clear-sky OLR differences in the Tropics are consistent with known systematic biases in ERBE, the most important consequence of which is that the ERBE clear-sky OLR is too high in convective regions such as the ITCZ. [ABSTRACT FROM AUTHOR]

The increasing impact of severe weather over Europe on lives and weather‐sensitive economies can be mitigated by accurate 0–6 h forecasts (nowcasts), supporting a vital 'last line of defence' for civil protection and many other applications. Recognizing lack of skill in some complex situations, often at convective and local sub‐kilometre scales and associated with rare events, we identify seven recommendations with the aim to improve nowcasting in Europe by the national meteorological and hydrological services (NMHSs) by 2030. These recommendations are based on a review of user needs, the state of the observing system, techniques based on observations and high‐resolution numerical weather models, as well as tools, data and infrastructure supporting the nowcasting community in Europe. Denser and more accurate observations are necessary particularly in the boundary layer to better characterize the ingredients of severe storms. A key driver for improvement is next‐generation European satellite data becoming available as of 2023. Seamless ensemble prediction methods to produce enhanced weather forecasts with 0–24 h lead times and probabilistic products require further development. Such products need to be understood and interpreted by skilled forecasters operating in an evolving forecasting context. We argue that stronger co‐development and collaboration between providers and users of nowcasting‐relevant data and information are key ingredients for progress. We recommend establishing pan‐European nowcasting consortia, better exchange of data, common development platforms and common verification approaches as key elements for progressing nowcasting in Europe in this decade. [ABSTRACT FROM AUTHOR]

Wind-blown dust (WBD) emitted by the Earth's surface due to sandblasting can potentially have important effects on both climate and human health via interaction with solar and thermal radiation, reducing air quality. Apart from the main dust "centres" around the world, like deserts, dust can be emitted from partly vegetated mid- and high-latitude areas like Europe if certain conditions are suitable (strong winds, bare soil, reduced soil moisture, etc.). Using a wind-blown dust model (WBDUST) along with a chemical transport model (Comprehensive Air-quality model with Extensions, CAMx) coupled to a regional climate model (Weather Research and Forecasting, WRF), this study is one of the first to provide a model-based estimate of such emissions over Europe as well as the long-term impact of WBD emissions on the total particulate matter (PM) concentrations for the 2007–2016 period. We estimated average WBD emissions of about 0.5 and 1.5 Mgyr-1km-2 in fine and coarse modes. Maximum emissions occur over Germany, where the average seasonal fine- and coarse-mode emission flux can reach 0.5 and 1 gs-1km-2 , respectively. Large variability is seen in the averaged daily emissions with values of up to 2 gs-1km-2 for the coarse-mode aerosol on selected days. The WBD emissions increased the modelled winter PM 2.5 and PM 10 concentrations by up to 10 and 20 µgm-3 , respectively, especially over Germany, where the highest emissions occur. The impact on other seasons is lower. Much higher impacts are modelled, however, on selected days when occasionally the urban PM 2.5 and PM 10 concentrations are increased by more than 50 and 100 µgm-3. The comparison with measurements revealed that if WBD is considered, the summer biases are reduced; however, the winter PM is overestimated even more greatly (so the bias increases). We identified a strong overestimation of the modelled wind speed (the maximum daily wind is almost 2 times higher in WRF than the measured ones) suggesting that WBD emissions are also overestimated – hence the enhanced winter PM biases. Moreover, we investigated the secondary impacts of the crustal composition of fine WBD particles on secondary inorganic aerosol (SIA): sulfates (PSO 4), nitrates (PNO 3) and ammonium (PNH 4). Because the water pH value, and thus the uptake of the gaseous precursors of SIA, is perturbed and because the increased aerosol surface serves as an oxidation site, we modelled seasonal PSO 4 and PNO 3 concentrations increased by up to 0.1 µgm-3 and PNH 4 ones decreased by up to -0.05 µgm-3 , especially during winter. In terms of average daily impact, these numbers can, however, reach much larger values of up to 1–2 µgm-3 for sulfates and nitrates, while the decrease in ammonium due to WBD can reach -1 µgm-3 on selected days. The sensitivity test on the choice of the inorganic equilibrium model (ISORROPIA vs. EQuilibrium Simplified Aerosol Model V4, EQSAM) showed that if EQSAM is used, the impact on SIA is slightly stronger (by a few 10 %) due to larger number of cations considered for water pH in EQSAM. Our results have to be regarded as a first estimate of the long-term WBD emissions and the related effects on PM over Europe. Due to the strong positive wind bias and hence strong WBD emissions, we should consider these results as an upper bound. More sensitivity studies involving the impact of the driving meteorological fields, WBD model choice and the input data used to describe the land surface need to be carried out in future to better constrain these emissions. [ABSTRACT FROM AUTHOR]

Biogenic volatile organic compounds (BVOCs) emitted from the natural ecosystem are highly reactive and can thus impact air quality and aerosol radiative forcing. BVOC emission models (e.g., Model of Emissions of Gases and Aerosols from Nature – MEGAN) in global and regional chemical transport models still have large uncertainties in estimating biogenic trace gases because of uncertainties in emission activity factors, specification of vegetation type, and plant emission factors. This study evaluates a set of updates made to MEGAN v2.04 in the Weather Research and Forecasting model coupled with chemistry (WRF-Chem version 3.9). Our study considers four simulations for each update made to MEGAN v2.04: (i) a control run with no changes to MEGAN, (ii) a simulation with the emission activity factors modified following MEGAN v2.10, (iii) a simulation considering the changes to the plant functional type (PFT) emission factor, and (iv) a simulation with the isoprene emission factor calculated within the MEGAN module instead of being prescribed by the input database. We evaluate two regions, Europe and the southeastern United States, by comparing WRF-Chem results to ground-based monitoring observations in Europe (i.e., AirBase database) and aircraft observations obtained during the NOMADSS field campaign. We find that the updates to MEGAN v2.04 in WRF-Chem caused overpredictions in ground-based ozone concentrations in Europe and in isoprene mixing ratios compared to aircraft observations in the southeastern US. The update in emission activity factors caused the largest biases. These results suggest that further experimental and modeling studies should be conducted to address potential shortcomings in BVOC emission models. [ABSTRACT FROM AUTHOR]

This paper analyses the dependency of regional air pollution in Europe on the atmospheric circulation as represented by a set of 8 weather regimes (WRs). For this purpose, daily average PM 2.5 concentrations and daily maximum concentrations of O 3 , NO 2 and CO obtained from an atmospheric composition reanalysis have been used. The results indicate that the pollutant concentrations change substantially with the occurrence of each WR according to the associated circulation patterns. In the extended winter, the responses of PM 2.5 , NO 2 and CO are quite similar, with zonal circulation regimes leading to better air quality than anticyclonic regimes linked to steady flows over Europe. On the other hand, the spatial patterns of O 3 anomalies under most WRs resemble those of temperature in the extended summer as temperature enhancements favour the photochemical production of O 3. The WR characterized by anticyclonic anomalies over Scandinavia leads to the highest concentrations at continental scale and is concurrent with an increased probability of exceeding the poor air quality limits of winter PM 2.5 and summer O 3 in most European countries. It is also found that a multiple linear regression model on the monthly frequency of WRs can explain a considerable fraction of the intermonthly variability of concentration anomalies over large parts of Europe regardless of the pollutant, with R2 values around 0.6–0.8 over large regions. Overall, the application of these models to sub-seasonal weather forecasts does not seem to bring major improvements to the prediction of month-ahead concentration anomalies as compared to the climatology. The poor skill of sub-seasonal forecasts in predicting the WRs for lead times beyond 15 days limits the forecast capability. However, these results open the possibility of extending current air quality forecast to scales of 5–10 days at a low cost in a near future. [Display omitted] • European-wide study of the modulation of air quality by weather regimes (WRs). • WRs explain 60–80% of the monthly variability in air pollution over large regions. • The poorest air quality in most of Europe is associated with Scandinavian blocking. • Evaluation of WRs as a tool to predict month-ahead air pollution anomalies. [ABSTRACT FROM AUTHOR]

Approximately 1–3 violent tornadoes hit Europe each decade. We use the ERA5 reanalysis and WRF model to reconstruct environments for 12 cases between 1957 and 2021. Violent tornadoes in Europe occur in a variety of synoptic and mesoscale patterns, but they share environmental similarities with significant tornadoes from the United States. Downscaling simulations to 3 km grid‐spacing showed the added value of improved resolution in representing local convective environments. In 8 out of 12 simulations, the model indicated updraft helicity (UH) tracks in a favorable convective environment in spatial (+/−50 km) and temporal (+/−3 hr) proximity to the tornado report. Tornadoes were accompanied by a mean 0–6 km wind shear of 24.3 m s−1 and mean convective available potential energy of 1678 J kg−1. The combination of UH tracks with convective environments offers promising results for operational forecasting in Europe, and should be explored in future studies. Plain Language Summary: Violent tornadoes produce large societal impacts and considerable damage. Approximately 1–3 such events hit Europe each decade. In this study, we reconstruct the environmental conditions using the Weather Research and Forecasting model for 12 violent tornadoes in Europe from 1957 to 2021. We use metrics providing information about atmospheric instability and wind variability to analyze conditions under which these tornadoes formed. This helps to study events that occurred before the radar and satellite era when only limited information is available about them. In 8 out of 12 cases, the model successfully simulated storm tracks in a favorable tornado environment close to the location of the actual tornado report. Our results indicate the importance of performing high‐resolution simulations in reconstructing tornado events, and they offer promising results in improving operational forecasting of tornadoes across Europe. Key Points: Tornadoes were accompanied by a mean 0–6 km wind shear of 24.3 m s−1 and mean convective available potential energy of 1678 J kg−1Dynamical downscaling with WRF model indicated more favorable near‐ground wind profile compared to initial ERA5 conditionsIn 8 out of 12 violent tornado events, the WRF model simulated updraft helicity tracks in the area of a favorable tornado environment [ABSTRACT FROM AUTHOR]

Abstract: Forecasting hailstorms is an important challenge. Improving these forecasts is of vital importance to reduce the risks that this phenomenon causes to the environment and the population. This paper presents a methodology for the characterization of hailstorms in different scales by means of: the definition of the synoptic environment favorable to convection, the establishment of mesoscale configurations on hailstorm days, and an analysis of prediction parameters for the presence of hail. Simulations with the WRF model were done for 100 study days in which hailstorms were produced in the Middle Ebro Valley. The synoptic environment was defined from the average geopotential and temperature fields at 500hPa. Using multivariate techniques of the Principal Component Analysis in T-Mode and a Cluster Analysis, four mesoscale configurations were defined for the fields of convective instability, water vapor flux divergence and wind flow and humidity at low layers. Finally, the study included several hail forecast parameters: CAPE, Storm Relative Helicity between 0 and 3km (SRH0–3), Energy–Helicity Index (EHI) and Showalter Index (SI); making a comparison between hail/hail-free grid points, which allowed us to determine their values and thresholds in order to discern the presence of hail within convective precipitation areas. The analysis, along with these three basic ingredients, provides a very useful tool that allows for a good approximation for predicting storms with hail in the study area. [Copyright &y& Elsevier]

Abstract: A European chemical weather forecasting portal is presented in this paper that has been developed within the COST (European Cooperation in Science and Technology) ES0602 action, “Towards a European Network on Chemical Weather Forecasting and Information Systems”. The portal includes an access to a substantial number (currently 21) of available chemical weather forecasting systems and their numerical forecasts; these cover in total more than 30 regions in Europe. This portal can be used, e.g., to find out, which services are available for a specific domain, for specific source categories or for specific pollutants. The portal currently expands its functionalities to allow for a harmonized presentation and inter-comparison of the various available forecasts, as well as for the computation of model ensemble predictions. It provides functions for obtaining relevant supplementary information, e.g., using the Model Documentation System of the European Environmental Agency. The new portal is an open access system, through which chemical weather forecasts can be added to the system, and the predictions can be accessed, analysed and inter-compared. Such a single point of reference for the European chemical weather forecasting information has previously not been in operation. We present the characteristics of the new portal, and discuss how this activity complements the GEMS and PROMOTE air quality forecasting portals. [Copyright &y& Elsevier]

Changes in large-scale circulation patterns over the North-Atlantic-European region are presented and analyzed for the 20th century. First, changes in decadal frequency of Hess-Brezowsky macrocirculation patterns (MCP) are evaluated for the period between 1881 and 2000. Frequency of several MCP types increased or decreased considerably during these 120 years, which may be explained by large-scale changes in circulation characteristics, e.g. by changes in cyclone activity in the different regions. Therefore, cyclone center identification and cyclone tracks and intensity analysis have been accomplished on the basis of the European Centre for Medium-range Weather Forecast (ECMWF) reanalysis data sets (ERA-40) on a 2.5° horizontal resolution grid for the period between 1957 and 2002. Results suggest that both the number of midlatitude cyclones and the cyclone activity increased considerably in the North-Atlantic-European region, especially in the northwestern part of the domain. Copyright © 2006 Royal Meteorological Society. [ABSTRACT FROM AUTHOR]

Urban areas are hot spots of intense emissions, and they influence air quality not only locally but on a regional or even global scale. The impact of urban emissions over different scales depends on the dilution and chemical transformation of the urban plumes which are governed by the local- and regional-scale meteorological conditions. These are influenced by the presence of urbanized land surface via the so-called urban canopy meteorological forcing (UCMF). In this study, we investigate for selected central European cities (Berlin, Budapest, Munich, Prague, Vienna and Warsaw) how the urban emission impact (UEI) is modulated by the UCMF for present-day climate conditions (2015–2016) using two regional climate models, the regional climate models RegCM and Weather Research and Forecasting model coupled with Chemistry (WRF-Chem; its meteorological part), and two chemistry transport models, Comprehensive Air Quality Model with Extensions (CAMx) coupled to either RegCM and WRF and the "chemical" component of WRF-Chem. The UCMF was calculated by replacing the urbanized surface by a rural one, while the UEI was estimated by removing all anthropogenic emissions from the selected cities. We analyzed the urban-emission-induced changes in near-surface concentrations of NO2 , O3 and PM 2.5. We found increases in NO2 and PM 2.5 concentrations over cities by 4–6 ppbv and 4–6 µgm-3 , respectively, meaning that about 40 %–60 % and 20 %–40 % of urban concentrations of NO2 and PM 2.5 are caused by local emissions, and the rest is the result of emissions from the surrounding rural areas. We showed that if UCMF is included, the UEI of these pollutants is about 40 %–60 % smaller, or in other words, the urban emission impact is overestimated if urban canopy effects are not taken into account. In case of ozone, models due to UEI usually predict decreases of around -2 to -4 ppbv (about 10 %–20 %), which is again smaller if UCMF is considered (by about 60 %). We further showed that the impact on extreme (95th percentile) air pollution is much stronger, and the modulation of UEI is also larger for such situations. Finally, we evaluated the contribution of the urbanization-induced modifications of vertical eddy diffusion to the modulation of UEI and found that it alone is able to explain the modeled decrease in the urban emission impact if the effects of UCMF are considered. In summary, our results showed that the meteorological changes resulting from urbanization have to be included in regional model studies if they intend to quantify the regional footprint of urban emissions. Ignoring these meteorological changes can lead to the strong overestimation of UEI. [ABSTRACT FROM AUTHOR]

The number of large-scale solar thermal installations has increased rapidly in Europe in recent years, with 70 % of these systems operating with flat-plate solar collectors. Since these systems cannot be easily switched on and off but directly depend on the solar radiation, they have to be combined with other technologies or integrated in large energy systems. In order to most efficiently integrate and operate solar systems, it is of great importance to consider their expected energy yield to better schedule heat production, storage and distribution. To do so the availability of accurate forecasting methods for the future solar energy yield are essential. Currently available forecasting methods do not meet three important practical requirements: simple implementation, automatic adaption to seasonal changes and wide applicability. For these reasons, a simple and adaptive forecasting method is presented in this paper, which allows to accurately forecast the solar heat production of flat-plate collector systems considering weather forecasts. The method is based on a modified collector efficiency model where the parameters are continuously redetermined to specifically consider the influence of the time of the day. In order to show the wide applicability the method is extensively tested with measurement data of various flat-plate collector systems covering different applications (below 200 ° Celsius), sizes and orientations. The results show that the method can forecast the solar yield very accurately with a Mean Absolute Range Normalized Error (MARNE) of about 5 % using real weather forecasts as inputs and outperforms common forecasting methods by being nearly twice as accurate. • Self-learning approach with only very few parameters to be determined. • Automatically considers seasonal changes and pollution of collector fields. • Simple regarding its mathematical structure and straightforward to implement. • Experimental long-term validation with data from various collector systems. • Superior to state-of-the-art forecasting methods regarding accuracy. [ABSTRACT FROM AUTHOR]

Estimating the likelihood of compound climate extremes such as concurrent drought and heatwaves or compound precipitation and wind speed extremes is important for assessing climate risks. Typically, simulations from climate models are used to assess future risks, but it is largely unknown how well the current generation of models represents compound extremes. Here, we introduce a new metric that measures whether the tails of bivariate distributions show a similar dependence structure across different datasets. We analyse compound precipitation and wind extremes in reanalysis data and different high-resolution simulations for central Europe. A state-of-the-art reanalysis dataset (ERA5) is compared to simulations with a weather model (Weather Research and Forecasting – WRF) either driven by observation-based boundary conditions or a global circulation model (Community Earth System Model – CESM) under present-day and future conditions with strong greenhouse gas forcing (Representative Concentration Pathway 8.5 – RCP8.5). Over the historical period, the high-resolution WRF simulations capture precipitation and wind extremes as well as their response to orographic effects more realistically than ERA5. Thus, WRF simulations driven by observation-based boundary conditions are used as a benchmark for evaluating the dependence structure of wind and precipitation extremes. Overall, boundary conditions in WRF appear to be the key factor in explaining differences in the dependence behaviour between strong wind and heavy precipitation between simulations. In comparison, external forcings (RCP8.5) are of second order. Our approach offers new methodological tools to evaluate climate model simulations with respect to compound extremes. [ABSTRACT FROM AUTHOR]

The size distribution of atmospheric aerosols plays a key role for understanding and quantifying the uncertainties related to aerosol–radiation and aerosol–cloud interactions. These interactions ultimately depend on the size distribution through optical properties (such as aerosol optical depth, AOD) or cloud microphysical properties. Hence, the main objective of this contribution is to disentangle the impact of the representation of aerosol size distribution on aerosol optical properties over central Europe, particularly over the Mediterranean Basin, during a summertime aerosol episode. To fulfill this objective, a sensitivity test has been conducted using the coupled chemistry–meteorology model WRF-Chem (Weather Research Forecast model coupled with Chemistry). The test modified the parameters defining a lognormal size distribution (geometric diameter and standard deviation) by 10 %, 20 %, and 50 %. Results reveal that the reduction in the standard deviation of the accumulation mode leads to the largest impacts on AOD due to a transfer of particles from the accumulation mode to the coarse mode. A reduction in the geometric diameter of the accumulation mode also has an influence on AOD representation since particles in this mode are assumed to be smaller. In addition, an increase in the geometric diameter of the coarse mode produces a redistribution through the total size distribution by relocating particles from the finer modes to the coarse. [ABSTRACT FROM AUTHOR]

Cloud-affected radiances from geostationary satellite sensors provide the first area-wide observable signal of convection with high spatial resolution in the range of kilometers and high temporal resolution in the range of minutes. However, these observations are not yet assimilated in operational convection-resolving weather prediction models as the rapid, nonlinear evolution of clouds makes the assimilation of related observations very challenging. To address these challenges, we investigate the assimilation of satellite radiances from visible and infrared channels in idealized observing system simulation experiments (OSSEs) for a day with summertime deep convection in central Europe. This constitutes the first study assimilating a combination of all-sky observations from infrared and visible satellite channels, and the experiments provide the opportunity to test various assimilation settings in an environment where the observation forward operator and the numerical model exhibit no systematic errors. The experiments provide insights into appropriate settings for the assimilation of cloud-affected satellite radiances in an ensemble data assimilation system and demonstrate the potential of these observations for convective-scale weather prediction. Both infrared and visible radiances individually lead to an overall forecast improvement, but best results are achieved with a combination of both observation types that provide complementary information on atmospheric clouds. This combination strongly improves the forecast of precipitation and other quantities throughout the whole range of 8-h lead time. [ABSTRACT FROM AUTHOR]

In this work we present downscaling experiments with the Weather Research and Forecasting model (WRF) to test the sensitivity to resolving aerosol–radiation and aerosol–cloud interactions on simulated regional climate for the EURO-CORDEX domain. The sensitivities mainly focus on the aerosol–radiation interactions (direct and semi-direct effects) with four different aerosol optical depth datasets (Tegen, MAC-v1, MACC, GOCART) being used and changes to the aerosol absorptivity (single scattering albedo) being examined. Moreover, part of the sensitivities also investigates aerosol–cloud interactions (indirect effect). Simulations have a resolution of 0.44 ∘ and are forced by the ERA-Interim reanalysis. A basic evaluation is performed in the context of seasonal-mean comparisons to ground-based (E-OBS) and satellite-based (CM SAF SARAH, CLARA) benchmark observational datasets. The impact of aerosols is calculated by comparing it against a simulation that has no aerosol effects. The implementation of aerosol–radiation interactions reduces the direct component of the incoming surface solar radiation by 20 %–30 % in all seasons, due to enhanced aerosol scattering and absorption. Moreover the aerosol–radiation interactions increase the diffuse component of surface solar radiation in both summer (30 %–40 %) and winter (5 %–8 %), whereas the overall downward solar radiation at the surface is attenuated by 3 %–8 %. The resulting aerosol radiative effect is negative and is comprised of the net effect from the combination of the highly negative direct aerosol effect (-17 to -5 W m -2) and the small positive changes in the cloud radiative effect (+5 W m -2), attributed to the semi-direct effect. The aerosol radiative effect is also stronger in summer (-12 W m -2) than in winter (-2 W m -2). We also show that modelling aerosol–radiation and aerosol–cloud interactions can lead to small changes in cloudiness, mainly regarding low-level clouds, and circulation anomalies in the lower and mid-troposphere, which in some cases, mainly close to the Black Sea in autumn, can be of statistical significance. Precipitation is not affected in a consistent pattern throughout the year by the aerosol implementation, and changes do not exceed ± 5 % except for the case of unrealistically absorbing aerosol. Temperature, on the other hand, systematically decreases by -0.1 to -0.5 ∘ C due to aerosol–radiation interactions with regional changes that can be up to -1.5 ∘ C. [ABSTRACT FROM AUTHOR]

The application of regional-scale air quality models is an important tool in air quality assessment and management. For this reason, the understanding of model abilities and performances is mandatory. The main objective of this research was to investigate the spatial and temporal variability of background particulate matter (PM) concentrations, to evaluate the regional air quality modelling performance in simulating PM concentrations during statically stable conditions and to investigate processes that contribute to regionally increased PM concentrations with a focus on eastern and central Europe. The temporal and spatial variability of observed PM was analysed at 310 rural background stations in Europe during 2011. Two different regional air quality modelling systems (offline coupled European Monitoring and Evaluation Programme, EMEP, and online coupled Weather Research and Forecasting with Chemistry) were applied to simulate the transport of pollutants and to further investigate the processes that contributed to increased concentrations during high-pollution episodes. Background PM measurements from rural background stations, wind speed, surface pressure and ambient temperature data from 920 meteorological stations across Europe, classified according to the elevation, were used for the evaluation of individual model performance. Among the sea-level stations (up to 200 m), the best modelling performance, in terms of meteorology and chemistry, was found for both models. The underestimated modelled PM concentrations in some cases indicated the importance of the accurate assessment of regional air pollution transport under statically stable atmospheric conditions and the necessity of further model improvements. [ABSTRACT FROM AUTHOR]

Clouds and aerosols contribute the largest uncertainty to current estimates and interpretations of the Earth's changing energy budget. Here we use a new-generation large-domain large-eddy model, ICON-LEM (ICOsahedral Non-hydrostatic Large Eddy Model), to simulate the response of clouds to realistic anthropogenic perturbations in aerosols serving as cloud condensation nuclei (CCN). The novelty compared to previous studies is that (i) the LEM is run in weather prediction mode and with fully interactive land surface over a large domain and (ii) a large range of data from various sources are used for the detection and attribution. The aerosol perturbation was chosen as peak-aerosol conditions over Europe in 1985, with more than fivefold more sulfate than in 2013. Observational data from various satellite and ground-based remote sensing instruments are used, aiming at the detection and attribution of this response. The simulation was run for a selected day (2 May 2013) in which a large variety of cloud regimes was present over the selected domain of central Europe. It is first demonstrated that the aerosol fields used in the model are consistent with corresponding satellite aerosol optical depth retrievals for both 1985 (perturbed) and 2013 (reference) conditions. In comparison to retrievals from ground-based lidar for 2013, CCN profiles for the reference conditions were consistent with the observations, while the ones for the 1985 conditions were not. Similarly, the detection and attribution process was successful for droplet number concentrations: the ones simulated for the 2013 conditions were consistent with satellite as well as new ground-based lidar retrievals, while the ones for the 1985 conditions were outside the observational range. For other cloud quantities, including cloud fraction, liquid water path, cloud base altitude and cloud lifetime, the aerosol response was small compared to their natural variability. Also, large uncertainties in satellite and ground-based observations make the detection and attribution difficult for these quantities. An exception to this is the fact that at a large liquid water path value (LWP >200 g m -2), the control simulation matches the observations, while the perturbed one shows an LWP which is too large. The model simulations allowed for quantifying the radiative forcing due to aerosol–cloud interactions, as well as the adjustments to this forcing. The latter were small compared to the variability and showed overall a small positive radiative effect. The overall effective radiative forcing (ERF) due to aerosol–cloud interactions (ERFaci) in the simulation was dominated thus by the Twomey effect and yielded for this day, region and aerosol perturbation -2.6 W m -2. Using general circulation models to scale this to a global-mean present-day vs. pre-industrial ERFaci yields a global ERFaci of -0.8 W m -2. [ABSTRACT FROM AUTHOR]

The teleconnection from the Madden‐Julian Oscillation (MJO) provides a source of subseasonal variability and predictability to the North Atlantic‐European (NAE) region. The El Niño‐Southern Oscillation (ENSO) modulates the seasonal mean state, through which the MJO and its teleconnection pattern propagates; however, its impact on this teleconnection to the NAE region has not been investigated. Here we find a robust dependence of the teleconnections from the MJO to NAE weather regimes on the phase of ENSO. We show that the MJO to NAO+ regime tropospheric teleconnection is strongly enhanced during El Niño years, via enhanced Rossby wave activity, and suppressed during La Niña. Conversely, the MJO to NAO− regime stratospheric teleconnection is enhanced during La Niña years and suppressed during El Niño. This dependence on the background state has strong implications for subseasonal predictability, including interannual variations in subseasonal predictive skill. Plain Language Summary: The Madden‐Julian Oscillation (MJO) is the dominant source of differing weather conditions in the tropics on timescales within a season. The remote linkage (teleconnection) from the MJO to the North Atlantic‐European (NAE) region provides a source to modify or persist weather conditions and add predictive power to weather forecasts on 10‐ to 30‐day timescales. The El Niño‐Southern Oscillation (ENSO) has an influence on the seasonal climate state, through which the waves and linkages from the MJO to the NAE region travel. Here we find a robust dependence of these teleconnections from the MJO to NAE weather regime patterns on the ENSO state, such that under certain states of the MJO, certain regimes occur more than twice as often. The different sources and pathways also become clearer, with the teleconnections travelling via the troposphere and the stratosphere. This dependence on ENSO state has significant implications for predictions on 10‐ to 30‐day timescales. Key Points: There is a robust dependence of the teleconnections from the MJO to North Atlantic‐European weather regimes on the phase of ENSOThe MJO to NAO+ regime teleconnection via the troposphere is strongly enhanced during El Niño years, and suppressed during La NiñaThe MJO to NAO− regime teleconnection via the stratosphere is enhanced during La Niña years, and suppressed during El Niño [ABSTRACT FROM AUTHOR]