ALBERT

Description: Based on the numerical weather prediction model COSMO of Germany's national meteorological service (Deutscher Wetterdienst, DWD), regional reanalysis datasets have been developed with grid spacing of up to 2 km. This development started as a fundamental research activity within the Hans-Ertel-Centre for Weather Research (HErZ) at the University of Bonn and the University of Cologne. Today, COSMO reanalyses are an established product of the DWD and have been widely used in applications on European and national German level. Successful applications of COSMO reanalyses include renewable energy assessments as well as meteorological risk estimates. The COSMO reanalysis datasets are now publicly available and provide spatio-temporal consistent data of atmospheric parameters covering both near-surface conditions and vertical profiles. This article reviews the status of the COSMO reanalyses, including evaluation results and applications. In many studies, evaluation of the COSMO reanalyses point to an overall good quality and often an added value compared to different contemporary global reanalysis datasets. We further outline current plans for the further development and application of regional reanalyses in the HErZ research group Cologne/Bonn in collaboration with the DWD.

Description: In this work, some preliminary numerical simulations with the COSMO model including TERRA_URB parameterization have been performed. In particular, this work concerns simulations over a small domain located in southern Italy, in order to test the capabilities of the model in reproducing the main climate features of Urban Heat Islands over this area. Model evaluation has been performed in terms of 2 m temperature in an urban area and in a rural one, in order to highlight the behaviour of the parameterization in different contexts. Numerical results encourage further investigation and development of urban parameterization in very high-resolution configuration of limited area models and specifically of COSMO, to improve the representation of the maximum daily values of temperature and diurnal cycle especially in urban, but also in rural areas. Furthermore a better parameter tuning is still required for this specific test case.

Description: The goal of an interdisciplinary team of scientists at the Climate Service Center Germany (GERICS) was to make the findings of the special report IPCC SR1.5 more accessible to the citizens of Hamburg. Therefore, a flyer was created that is understandable to non-climate scientists, visually attractive and generates interest. It contains up-to-date climate information, readily understandable texts and several graphical visualisations. The team has been working intensively on analysing and processing further the consequences of a 1.5 ∘C global warming for the Hamburg metropolitan region. While the team's natural scientists elaborated the impacts on specific climate indices, other team members focused on the visualisation and communication of the results.

Description: Seasonal forecasts for forestry have been developed in the Finnish Meteorological Institute in cooperation with Finnish end-users. Such forecasts could help forest companies in preparing for adverse conditions of timber harvesting operations. Bearing capacity for harvesting operations is dependent on soil moisture, and skillful forecasts have potentially large economic value. Using the ECMWF seasonal forecasts, we evaluated the monthly mean soil moisture forecasts for four different start months, with lead times from 0 to 2 months. Forecasts were bias adjusted, and showed skill in the first month for all four months. After the first lead month, winter months fared a bit better than summer months.

Description: In science, poor quality input data will invariably lead to faulty conclusions, as in the spirit of the saying “garbage in, garbage out”. Atmospheric sciences make no exception and correct data is crucial to obtain a useful representation of the real world in meteorological, climatological and hydrological applications. Titan is a computer program for the automatic quality control of meteorological data that has been designed to serve real-time operational applications that process massive amounts of observations measured by networks of automatic weather stations. The need to quality control third-party data, such as citizen observations, within a station network that is constantly changing was an important motivation that led to the development of Titan. The quality control strategy adopted is a sequence of tests, where several of them utilize the expected spatial consistency between nearby observations. The spatial continuity can also be evaluated against independent data sources, such as numerical model output and remote sensing measurements. Examples of applications of Titan for the quality control of near-surface hourly temperature and precipitation over Scandinavia are presented. In the case of temperature, this specific application has been integrated into the operational production chain of automatic weather forecasts at the Norwegian Meteorological Institute (MET Norway). Titan is an open source project and it is made freely available for public download. One of the objectives of the Titan project is to establish a community working on common tools for automatic quality control, and the Titan program represents a first step in that direction for MET Norway. Further developments are necessary to achieve a solution that satisfies more users, for this reason we are currently working on transforming Titan into a more flexible library of functions.

Description: Having a common framework for early action to cope with complex disasters can make it easier for authorities and other stakeholders, including populations at risk, to understand the full spectrum of secondary and tertiary effects and thus where to focus preparedness efforts, and how best to provide more targeted warnings and response services. Meteorological and hydrological services world-wide have developed and implemented Multi-Hazard Early Warning Systems (MHEWS) for weather and climate related hazards that are now being expanded and transitioned towards Multi-Hazard Impact-based Early Warning Systems (MHIEWS). While it is still early days it is becoming clear that there are useful lessons from this approach in the COVID-19 global pandemic, and some valuable insight to be gained in risk communication, risk analysis and monitoring methodologies and approaches. The ability to understand and respond effectively to warnings through appropriate behaviours and actions is central to resilient societies and communities. By avoiding physical, societal and economic harm to the greatest extent possible, recovery from a hazard is likely to be faster, less costly and more complete. MHIEWS can be a common approach for all hazards and therefore more likely to become a trusted tool that everyone can understand and use as a basic element of their national disaster risk management system. The interconnectedness of hazards and their impacts is a strong motivator for a common approach. One of the lessons from the COVID-19 pandemic and extreme weather events is the need to understand the vulnerability of individuals, communities and societies so as to provide reliable, targeted guidance and warnings and the willingness and capacity to prepare for a reasonable worst-case scenario based on informed long-term planning. Meteorology and hydrology are making good progress in this direction and the process can be readily applied to health and other sectors.

Description: The present work deals with the spatial consistency of two well-known databases of solar radiation received at ground level: the CAMS Radiation Service database version 3.2, abbreviated as CAMS-Rad and the HelioClim-3 database version 5, abbreviated as HC3v5. Both databases are derived from satellite images. They are validated against 10 min means of irradiance for the period 2010–2018 recorded in a network of 26 ground stations in Germany operated by the Deutscher Wetterdienst (DWD). For the CAMS-Rad database, the correlation coefficient between ground measurements and estimates ranges between 0.83 and 0.92 for all sky conditions. The bias ranges from −41 and 32 W m−2 (−11 % and 10 % of the mean irradiance). The standard deviation ranges between 89 and 129 W m−2 (25 % and 39 %). For the HC3v5 database, the correlation coefficient ranges between 0.90 and 0.95. The bias and the standard deviation are comprised between −22 and 16 W m−2 (−6 % and 5 %), and between respectively 70 and 104 W m−2 (20 % and 31 %). For the CAMS Rad database, overestimation is observed in the South, and underestimation in the North with a faint tendency of the bias to increase from East to West. For the HC3v5 database, the bias is fairly homogeneous across Germany. For both databases, there is no noticeable spatial trend in the standard deviation.

Description: European NMHSs are progressing from warnings based on fixed thresholds or climatology-based thresholds to impact-oriented and impact-based warnings. This publication gives an overview of warning implementation as surveyed at 32 of the 37 NMHSs participating in the EUMETNET Meteoalarm project. The report addresses these topics: warning format, legislation and production process of warnings, dissemination and verification of warnings, impact databases, warning strategy and cooperation, legal obstacles and cross-border collaboration. Potential obstacles are identified and possible trends are discussed.

Description: As variable renewable energies are developing, their impacts on the electric system are growing. To anticipate these impacts, prospective studies may use wind power production simulations in the form of 1 h or 30 min time series that are often based on reanalysis wind-speed data. The purpose of this study is to assess how several wind-speed datasets are performing when used to simulate wind-power production at the local scale, when no observation is available to use bias correction methods. The study evaluates two global reanalysis (MERRA-2 from NASA and ERA5 from ECMWF), two high-resolution models (COSMO-REA6 reanalysis from DWD, AROME NWP model from Météo-France) and the New European Wind Atlas mesoscale data. The study is conducted over continental France. In a first part, wind-speed measurements (between 55 and 100 m above ground) at eight locations are directly compared to modelled wind speeds. In a second part, 30 min wind-power productions are simulated for every wind farm in France and compared to two open datasets of observed production published by the distribution and transmission system operators, either at the local scale in terms of annual bias, or aggregated at the regional scale, in terms of bias, correlations and diurnal cycles. ERA5 is very skilled, despite its low resolution compared to the regional models, but it underestimates wind speeds, especially in mountainous areas. AROME and COSMO-REA6 have better skills in complex areas and have generally low biases. MERRA-2 and NEWA have large biases and overestimate wind speeds especially at night. Several problems affecting diurnal cycles are detected in ERA5 and COSMO-REA6.

Description: The electric power that can be transmitted via high-voltage transmission lines is limited by the Joule heating of the conductors. In the case of coastal wind farms, the wind that produces power simultaneously contributes to the cooling of high-voltage overhead conductors. Ideally this would allow for increased power transmission or decreased dimensions and cost of the conductor wires. In this study we investigate how well the wind speed in coastal wind farms is correlated with wind along a 75 km long 330 kW power line towards inland. It is found that correlations between wind speed in coastal wind farms at turbine height and conductor-level (10 m) are remarkably lower (R=0.39–0.64) than between wind farms at distances up to 100 km from each other (R=0.76–0.97). Dense mixed forest surrounding the power line reduces both local wind speed and the correlations with coastal higher-level wind, thus making the cooling effect less reliable.

Description: Forty percent of the world's population live within 100 km of the coastal line. Coastal zones are changing because of the interaction between the oceans and the land as well as human activities. The processes in coastal Atmospheric Boundary Layer (ABL) have being continuously studied as they are important for the ecosystems, recreational activities, fishery, economics and renewable energy resources. Remote sensing (RS) measurements of the ABL have being performed with sodar at Ahtopol synoptic station (Southeast Bulgaria) since 2008. The Intergovernmental Panel on Climate Change (IPCC) criteria for “rare” events have been applied to the period August 2008–October 2016 of the RS measurements and a reference extreme wind speed profile has been obtained. In this study, we test the ability of the Weather Research and Forecasting (WRF) model with Mellor-Yamada-Janjic ABL scheme to simulate extreme wind events, followed by sea breeze. As a measure of the ability of the used model configuration to reproduce the spatial and temporal structure of these complex weather situations some basic statistical metrics has been used. The results have shown relatively good agreement between measured and modeled wind speed but the sea breeze development has not been reproduced by the model.

Description: Parameterizing the effect of vertically-distributed vegetation through an effective roughness (z0,eff) – whereby momentum loss through a three-dimensional foliage volume is represented as momentum loss over an area at one vertical level – can facilitate the use of forest data in flow models, to any level of detail, and simultaneously reduce computational cost. Results of numerical experiments and comparison with observations show that a modelling approach based on z0,eff can estimate wind speed and turbulence levels over forested areas, at heights of interest for wind energy applications (∼60 m and higher), but only above flat terrain. Caution must be exercised in the application of such a model to zones of forest edges. Advanced flow models capable of incorporating local (distributed) drag forces are recommended for complex terrain covered by forest.

Description: Ensemble forecasting has gained popularity in the field of numerical medium-range weather prediction as a means of handling the limitations inherent to predicting the behaviour of high dimensional, nonlinear systems, that have high sensitivity to initial conditions. Through small strategical perturbations of the initial conditions, and in some cases, stochastic parameterization schemes of the atmosphere-ocean dynamical equations, ensemble forecasting allows one to sample possible future scenarii in a Monte-Carlo like approximation. Results are generally interpreted in a probabilistic way by building a predictive density function from the ensemble of weather forecasts. However, such a probabilistic interpretation is regularly criticized for not being reliable, because of the chaotic nature of the dynamics of the atmospheric system as well as the fact that the ensembles of forecasts are not, in reality, produced in a probabilistic manner. To address these limitations, we propose a novel approach: a possibilistic interpretation of ensemble predictions, taking inspiration from fuzzy and possibility theories. Our approach is tested on an imperfect version of the Lorenz 96 model and results are compared against those given by a standard probabilistic ensemble dressing. The possibilistic framework reproduces (ROC curve, resolution) or improves (ignorance, sharpness, reliability) the performance metrics of a standard univariate probabilistic framework. This work provides a first step to answer the question whether probability distributions are the right tool to interpret ensembles predictions.

Description: In developing countries, and particularly in West Africa, the role of Climate Services (CS) for sustainable development is growing thanks to wide spreading collaboration among European institutions, including National Meteorological and Hydrological Services (NMHS) research centers, universities, and homologue local institutions. Operationally, the implementation of CSs in developing countries is mainly pivoted on NMHS, which, according to the World Meteorological Organization (WMO), are dramatically affected by unmet learning demand. The global scale of learning needs for co-development of CSs calls for innovative solutions and a range of flexible modalities to reach learners in a variety of ways, and for sharing resources and successful strategies within the global education and training community. In order to harmonize expected learning outcomes, WMO defined a competency framework (CF) for CSs to be used in the implementation of training initiatives and knowledge sharing tools. This paper presents the strategic and methodological approach adopted in the implementation of the TOPaCS, a new knowledge-based distance learning initiative, aiming to provide a flexible learning environment within the CSs CF of WMO ensuring coherence with other WMO education initiatives (Global Campus, other RTCs, etc.). The methodological approach adopted is based on the competency-based approach to training, where competencies are composed by elements of knowledge and skill. TOPaCS integrates the WMO CF for CSs into a taxonomy co-designed with stakeholders at different levels, and allows the definition of learning paths, which are a further interactive opportunity for co-development of CSs within the TOPaCS learning ecosystem. Indeed, the approach aims also to guide further instructional strategies and assessments and becomes a starting point to build a common language enabling a better cooperation and exchange between the different CSs training initiatives.

Description: High-resolution precipitation observation based on signal attenuation in a Commercial Microwave Link (CML) network is an emerging technique that is becoming more and more used. Commonly, the raw data – line measurements from successive time steps – are mapped onto a grid to estimate precipitation fields with a full spatio-temporal coverage. Assuming the CML-estimated precipitation to be accurate, the attainable resolutions in time and space are primarily dependent on two factors: (i) the spatial distribution of the link network and (ii) the spatial correlation properties of the precipitation. Here we outline a pragmatic method for estimating the optimal resolution based on variogram analysis. The method is demonstrated using a CML network and a representative variogram in Stockholm, Sweden. Conceivable applications include preliminary investigations in cities considering starting CML-based precipitation observations.

Description: The formation of convective sea-effect snowfall (i.e., snow bands) is triggered by cold air outbreaks over a relatively warm and open sea. Snow bands can produce intense snowfall which can last for several days over the sea and potentially move towards the coast depending on wind direction. We defined the meteorological conditions which statistically favor the formation of snow bands over the north-eastern Baltic Sea of the Finnish coastline and investigated the spatio-temporal characteristics of these snow bands. A set of criteria, which have been previously shown to be able to detect the days favoring sea-effect snowfall for Swedish coastal area, were refined for Finland based on four case study simulations, utilizing a convection-permitting numerical weather prediction (NWP) model (HARMONIE-AROME). The main modification of the detection criteria concerned the threshold for 10 m wind speed: the generally assumed threshold value of 10 m s−1 was decreased to 7 m s−1. The refined criteria were then applied to regional climate model (RCA4) data, for an 11-year time period (2000–2010). When only considering cases in Finland with onshore wind direction, we found on average 3 d yr−1 with favorable conditions for coastal sea-effect snowfall. The heaviest convective snowfall events were detected most frequently over the southern coastline. Statistics of the favorable days indicated that the lower 10 m wind speed threshold improved the representation of the frequency of snow bands. For most of the favorable snow band days, the location and order of magnitude of precipitation were closely captured, when compared to gridded observational data for land areas and weather radar reflectivity images. Lightning were observed during one third of the favorable days over the Baltic Sea area.

Description: The Pyrenees, located in the transition zone of Atlantic and Mediterranean climates, constitute a paradigmatic example of mountains undergoing rapid changes in environmental conditions, with potential impact on the availability of water resources, mainly for downstream populations. High-resolution probabilistic climate change projections for precipitation and temperature are a crucial element for stakeholders to make well-informed decisions on adaptation to new climate conditions. In this line, we have generated high–resolution climate projections for 21st century by applying two statistical downscaling methods (regression for max and min temperatures, and analogue for precipitation) over the Pyrenees region in the frame of the CLIMPY project over a new high-resolution (5 km × 5 km) observational grid using 24 climate models from CMIP5. The application of statistical downscaling to such a high resolution observational grid instead of station data partially circumvent the problems associated to the non-uniform distribution of observational in situ data. This new high resolution projections database based on statistical algorithms complements the widely used EUROCORDEX data based on dynamical downscaling and allows to identify features that are dependent on the particular downscaling method. In our analysis, we not only focus on maximum and minimum temperatures and precipitation changes but also on changes in some relevant extreme indexes, being 1986–2005 the reference period. Although climate models predict a general increase in temperature extremes for the end of the 21st century, the exact spatial distribution of changes in temperature and much more in precipitation remains uncertain as they are strongly model dependent. Besides, for precipitation, the uncertainty associated to models can mask – depending on the zones- the signal of change. However, the large number of downscaled models and the high resolution of the used grid allow us to provide differential information at least at massif level. The impact of the RCP becomes significant for the second half of the 21st century, with changes – differentiated by massifs – of extreme temperatures and analysed associated extreme indexes for RCP8.5 at the end of the century.

Description: Wind-induced waves play a key role in air–sea momentum and heat exchange. Fetch-limited shallow lakes differ significantly from open ocean circumstances since the wave field is characterized by young and growing waves that (i) are steeper and can collapse by white-capping at lower wind speeds, and (ii) travel with lower phase velocity. Consequently, momentum (and heat) flux estimation methods arising from oceanographic observations cannot be directly applied; however, few attempts have been made to describe air–water turbulent exchange in case of large, but still fetch-limited shallow lakes. Within a Croatian-Hungarian measurement campaign, turbulent flux measurements were performed in Lake Balaton. Momentum and heat fluxes were measured with eddy-covariance technique at an offshore station, while waves were simultaneously recorded with underwater acoustic surface tracking. Momentum fluxes were also recorded at two further stations closer to the shore. In this study, we analyze the measured wind stress and surface waves to reveal surface drag in case of highly fetch-limited conditions. We compare our results with relevant model formulations that attempt to estimate momentum flux using different wave state parameterizations (i.e. wave age and wave slope modified Charnock formulations) and show that derived drag and roughness length parameterizations differ significantly from oceanographic formulas.

Description: The temperature of photovoltaic modules is modelled as a dynamic function of ambient temperature, shortwave and longwave irradiance and wind speed, in order to allow for a more accurate characterisation of their efficiency. A simple dynamic thermal model is developed by extending an existing parametric steady-state model using an exponential smoothing kernel to include the effect of the heat capacity of the system. The four parameters of the model are fitted to measured data from three photovoltaic systems in the Allgäu region in Germany using non-linear optimisation. The dynamic model reduces the root-mean-square error between measured and modelled module temperature to 1.58 K on average, compared to 3.03 K for the steady-state model, whereas the maximum instantaneous error is reduced from 20.02 to 6.58 K.

Description: The satellite-based cloud climate data record CLARA-A2 has been used to analyse regional average time-series and regional maps of trends in the Baltic Sea region, 1982–2015. The investigated cloud parameters were total fractional cloud cover and cloud top height. Cloud observations from the Tartu-Tõravere meteorological station were used as reference data for the same period. Fractional cloud cover from CLARA-A2 was in a good agreement with in situ data regarding the maxima and minima years and a downward trend in March over the 1982–2015 period. In June the fractional cloud cover interannual variability was very high and no clear trend was seen. For cloud top heights summer and spring regional averages showed opposite signs of the trend: for June positive and for March negative. Winter and autumn seasons have been left out of analysis due to too large uncertainties in cloud products over latitudes higher than 60∘.

Description: Kilometric-resolution Ensemble Prediction Systems (EPSs) will be the new state-of-the-art forecasting tools for short-range prediction in the following decade. Their value will be even greater in Antarctica due to the increasingly demanding weather forecasts for logistic services. During the 2018–2019 austral summer (1 December–31 March), coinciding with the Southern Hemisphere Special Observation Period of the Year of Polar Prediction (YOPP), the 2.5 km AEMET-γSREPS was operationally integrated over the Antarctic Peninsula. In particular, the Antarctic version of γSREPS comes up with crossing four non-hydrostatic convection-permitting NWP models at 2.5 km with three global NWP driving models as boundary conditions. The γSREPS forecasting system has been validated in comparison with ECMWF EPS. It is concluded that γSREPS has an added value to ECMWF EPS due to both its higher resolution and its multi-boundary conditions and multi-NWP model approach. γSREPS performance has a positive impact on logistic activities at research stations and its design may contribute to polar prediction research.

Description: Precipitation in high-mountain regions is characterized by a strong heterogeneity due to complex interaction between atmospheric circulation and steep topography, however, extremely rare network of high elevation stations hampers the adequate high resolution regional climate modeling. In this study we present new data of precipitation directly measured in high-mountain catchment, on the continental glacier (East Sayan Range, south of East Siberia) during the summer periods of 2015–2017 using automatic weather station. The precipitation record was compared with near located weather stations and ERA Interim and NCEP/NCAR reanalysis data. Precipitation mode similar to the glacier site was found at the stations located west and northwest, while ERA Interim and NCEP/NCAR reanalysis data underestimated the precipitation by 40 % and 70 %, respectively. Atmospheric circulation patterns in days with precipitation were analyzed by using mean sea level pressure, geopotential height at 700 and 500 hPa and classification of macro scale atmospheric processes of the Northern Hemisphere by Dzerdzeevskii. Summer precipitation was mostly associated with meridional southern group of large scale circulation the Northern Hemisphere, while at synoptic scale it basically fell in cyclonic (49 % of precipitation) and low-gradient cyclonic (30 %) baric fields. Six typical atmospheric circulation patterns over the East Sayan were identified for days with precipitation. The sources and atmospheric moisture transfer to the glacier was defined by using the HYSPLIT trajectory model. The most of summer precipitation (70 %) was related with western cyclones, while about 25 % of rainfalls (mainly of moderate to strong intensity) was originated from the south-east (Pacific monsoon influence).

Description: The Pannonian Basin Experiment (PannEx) is a Regional Hydroclimate Project (RHP) of the World Climate Research Programme (WCRP) Global Energy and Water Exchanges (GEWEX) Project. A gridded meteorological dataset is available for the PannEx region as part of the CarpatClim database, which consists of homogenized and harmonized daily meteorological observations for several climate parameters with 0.1∘ spatial resolution in the period of 1961–2010. The estimation of the Penman–Monteith reference evapotranspiration (ET0) on the daily scale was performed for the CarpatClim grid as one of the first results in the PannEx initiative. This study compares the already accessible Thornthwaite estimates of potential evapotranspiration (PET_Th) on the monthly scale to the newly derived Penman–Monteith estimates. The comparison is made on an annual and seasonal basis for the 50-year period. The distribution of both estimates is influenced by geographical location and orographic features. The annual time series are similar but the regional-average annual values of ET0 are ∼80 mm greater than the Thornthwaite estimate in the whole CarpatClim region. The relative bias and root mean square error was computed as well. The classical Thornthwaite method underestimates the ET0 by more than 20 % over extensive regions for selected grid points at elevations lower than 200 m in the Pannonian Basin. The slope of the fitted linear trend indicate increasing reference evapotranspiration in the Pannonian/Carpathian Basin due to climate change.

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

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

Description: Agricultural production is largely determined by weather conditions during the crop growing season. An important aspect of crop yield estimation concerns crop growth development. The occurrence of meteorological events such as frosts, droughts or heat stress during the crop life cycle or during certain phenological stages helps explain yield fluctuations of common arable crops. We developed a methodology and visualisation tool for risk assessment, and tested the workflow for drought and frost risk for winter wheat, winter barley and grain maize in Belgium. The methodology has the potential to be extended to other extreme weather events and their impacts on crop growth in different regions of the world.

Description: Accurate and reliable solid precipitation estimates for high mountain regions are crucial for many research applications. Yet, measuring snowfall at high elevation remains a major challenge. In consequence, observational coverage is typically sparse, and the validation of spatially distributed precipitation products is complicated. This study presents a novel approach using reliable daily snow water equivalent (SWE) estimates by a cosmic ray sensor on two Swiss glacier sites to assess the performance of various gridded precipitation products. The ground observations are available during two and four winter seasons. The performance of three readily-available precipitation data products based on different data sources (gauge-based, remotely-sensed, and re-analysed) is assessed in terms of their accuracy compared to the ground reference. Furthermore, we include a data set, which corresponds to the remotely-sensed product with a local adjustment to independent SWE measurements. We find a large bias of all precipitation products at a monthly and seasonal resolution, which also shows a seasonal trend. Moreover, the performance of the precipitation products largely depends on in situ wind direction during snowfall events. The varying performance of the three precipitation products can be partly explained with their compilation background and underlying data basis.

Description: We assess the sustainability of groundwater irrigation in the Euro-Mediterranean region. After analysing the available data on groundwater irrigation, we identify areas where irrigation causes groundwater depletion. To prevent the latter, we experiment with guidelines to restrict groundwater irrigation to sustainable levels, simulating beneficial and detrimental impacts in terms of improved environmental flow conditions and crop yield losses. To carry out these analyses, we apply the integrated model of water resources, irrigation and crop production LISFLOOD-EPIC. Crop growth is simulated accounting for atmospheric conditions and abiotic stress factors, including transpiration deficit. Four irrigation methods are modelled: drip, sprinkler, and intermittent and permanent flooding. Hydrologic and agricultural modules are dynamically coupled at the daily time scale through soil moisture, plant water uptake, and irrigation water abstraction and application. Water abstractions of other sectors are simulated based on requirement data. Water may be withdrawn from groundwater, rivers, lakes and reservoirs. As groundwater is abstracted to buffer the effects of drought, we use groundwater depletion to detect unsustainable water exploitation. We characterise reported data of annual groundwater abstractions for irrigation available at country and sub-national levels. Country data are the most complete, but their spatial resolution is often coarse. While the resolution of sub-national data is finer, their coverage is heterogeneous. Simulated and reported irrigation groundwater abstractions compare well in several areas, particularly in France, while some structural discrepancies emerge: simulated values tend to be larger than those reported, especially in southern Spain; and simulated inter-annual variability is significantly smaller than reported in some areas, most remarkably in Turkey. Potential causes of these discrepancies are simplified model assumptions influencing irrigation frequency and amounts; lack of high temporal and spatial resolution data on irrigated areas, and irrigation technologies and distribution; and possible unreported abstractions in areas where groundwater irrigation is significant. We identify areas undergoing groundwater depletion from model simulations. In the southern Iberian Peninsula, Greece, Middle East and northern Africa, most simulated depletion is caused by irrigation. In other Mediterranean areas, depletion is caused by all sectors combined. From well measurements of groundwater table depth in Spain, we find statistically significant decline rates affecting large areas of the south, thus in agreement with the model, but also areas in the north-eastern and central parts where model estimates detect no depletion. The comparison of model- and well-based depletion rates is limited by spatial scale differences and groundwater model assumptions, for which we suggest potential research directions. We design rules restricting irrigation groundwater abstraction to prevent groundwater depletion and minimise severe irrigation shortages. We optimise them and simulate their effects in the southern Iberian Peninsula. Irrigation restrictions cause crop yield reductions in groundwater-dependent irrigated areas, particularly in the Algarve and Segura river basin districts. At the same time, they positively impact environmental flows. This study shows the potential of integrated agro-hydrologic modelling for detecting water resources over-exploitation and exploring trade-offs between crop production, sustainable irrigation and ecosystem support.

Description: Laboratory measurements of drop fall speeds by Gunn–Kinzer under still air conditions with pressure corrections of Beard are accepted as the “gold standard”. We present measured fall speeds of 2 and 3 mm raindrops falling in turbulent flow with 2D-video disdrometer (2DVD) and simultaneous measurements of wind velocity fluctuations using a 3D-sonic anemometer. The findings based on six rain events are, (i) the mean fall speed decreases (from the Gunn–Kinzer terminal velocity) with increasing turbulent intensity, and (ii) the standard deviation increases with increase in the rms of the air velocity fluctuations. These findings are compared with other observations reported in the literature.

Description: The Urban Heat Island (UHI) describes the increase of near surface temperatures within an urban area compared to its rural surrounding. While the concept of the UHI is in itself quite simple, it is more complex to apply it to gridded datasets. The main complication lies in the rural baseline definition. Therefore, we propose three approaches to calculate the spatial UHI representation for gridded datasets from (i) a single point baseline, (ii) an area averaged baseline, and (iii) a nearest neighbor-based baseline field. Based on these approaches, seven methods are tested as an example for a case study utilizing model simulations for three metropolitan areas in Central and Western Europe (Berlin, Paris and Rhine-Ruhr Metropolitan Area). The results show that all methods perform reasonable in absence of complex terrain, biases and large scale temperature gradients. However, with at least one of these features present, the UHI visualization is less prominent or nonexistent, except for the nearest-neighbor approach which consistently shows reasonable spatial characteristics of the UHI across all scenarios.

Description: Climate change ought to be a natural part of the weather conversation on TV, radio and social media. Inspired by similar projects in other countries, the Norwegian Meteorological institute established a project in 2019 to develop their TV meteorologists as climate change communicators. The main objective in the project was to integrate research-based, localized climate content in the weather presentation, as to inform and engage the Norwegian public about climate change. Over a period of almost two years, the project has produced several climate stories on the national TV-news. The majority of the stories have also been shared through social media and through press releases to reach a wide range of audiences. In this paper, experiences from the project at the Norwegian Meteorological institute are shared along with recommendations for climate communication. We claim that TV meteorologists can have an important role in climate change communication, with a potential that is often not yet fully realized, and give our thoughts on how to further develop their role.

Description: Convection-permitting weather forecasting models allow for prediction of rainfall events with increasing levels of detail. However, the high resolutions used can create problems and introduce the so-called “double penalty” problem when attempting to verify the forecast accuracy. Post-processing within an ensemble prediction system can help to overcome these issues. In this paper, two new up-scaling algorithms based on Machine Learning and Statistical approaches are proposed and tested. The aim of these tools is to enhance the skill and value of the forecasts and to provide a better tool for forecasters to predict severe weather.

Description: In the framework of the KlimAdat national project, the Hungarian Meteorological Service (OMSZ) is aiming to perform 10 km horizontal resolution simulations with the 2015 version of the REMO regional climate model over Central and Eastern Europe. The long-term simulations were preceded by a 10-year long sensitivity study on domain size, which is summarised in this paper. We selected three different domains embedded in each other, which contain the whole area of the Danube and Tisza river catchments. Lateral boundary conditions were obtained from the 50 km resolution REMO driven by the MPI-ESM-LR global climate model. Simulations were performed for the period of 1970–1980 including 1-year spin-up. Monthly and seasonal means of daily 2 m temperature, precipitation sum and several precipitation indices were evaluated. Reference datasets were E-OBS 19.0 and CarpatClim-HU. We can conclude, that the selection of domain size has a larger impact on the simulation of precipitation, and in the case of the seasonal mean of the precipitation indices, the differences amongst the results obtained on each model domain exceed 10 %. In general, the smallest biases occurred on the largest domain, therefore further long-term simulations are being produced on this domain.