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

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