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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]

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]

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]

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]

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 ms−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 linearised flow model break down, both the mean bias and spread in wind speed are worse than the mesoscale results. [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]

This is the first of two papers that documents 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. An optimal combination of model setup and parameterisations 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 largest differences in annual mean wind speed at 100 m 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. Many different parameters were varied across the simulations, for example, model version, forcing data, various physical parameterisations and the size of the model domain. These simulations showed that although virtually every parameter change affects the results in some way, significant changes on the wind climate in the boundary layer are mostly due to using different physical parameterisations, 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. The degree of similarity between winds simulated by the WRF ensemble members and the observations was assessed 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 distribution, which is important for wind resource assessment. The most realistic ensemble members were identified 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]

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]

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]

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]

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 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]

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: 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]

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]

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]

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]

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]

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]