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The Global Atlas for Siting Parameters project compiles a suite of models into a complex modeling system, uses up-to-date global datasets, and creates global atlases of siting parameters at a spatial resolution of 275 m. These parameters include the 50-year wind, turbulence, and turbine class recommendations based on relevant generic turbines. The suite of models includes the microscale Linear Computational Model (LINCOM), a statistical, spectral correction method here revised for strong convective areas and tropical cyclone affected areas, two turbulence models with four setups, and load models. To this complexity, an uncertainty model was developed to classify the various sources of uncertainties for both the extreme wind and the turbulence calculations, and accordingly, atlases of uncertainty classification were created. Preliminary validation of the global calculations of the 50-year wind and turbulence is done through comparisons with measurements, and the results are promising. This is the first time the siting parameters are obtained with such a high spatial resolution and shared on open data portal. It is expected to benefit the global wind energy planning and development. [ABSTRACT FROM AUTHOR]

ABSTRACT A new approach for assimilation of 2D precipitation in numerical weather prediction models is presented and tested in a case with convective, heavy precipitation. In the scheme a nudging term is added to the horizontal velocity divergence tendency equation. In case of underproduction of precipitation, the strength of the nudging is proportional to the offset between observed and modelled precipitation, leading to increased moisture convergence. If the model over-predicts precipitation, the low level moisture source is reduced, and in-cloud moisture is nudged towards environmental values. The method was implemented in the Danish Meteorological Institute numerical weather prediction (DMI NWP) nowcasting system, running with hourly cycles, performing a surface analysis and 3D variational analysis for upper air assimilation at each cycle restart, followed by nudging assimilation of precipitation and then a free forecast. The precipitation fields are based on a 2D composite CAPPI (constant altitude plan position indicator) field made from observations with the DMI weather radars, and have a 10 min time resolution. The results obtained in this study indicate that the new method implies fast adjustment of the dynamical state of the model to facilitate precipitation release when the model precipitation intensity is too low. Removal of precipitation is shown to be of importance and the position of the model precipitation cells becomes skilful even at the smallest scales (∼3 km). Bias is reduced for low and extreme precipitation rates. In this meteorological case, the usage of the nudging procedure has been shown to improve the prediction of heavy precipitation substantially. [ABSTRACT FROM AUTHOR]