Survey of data assimilation methods for convective-scale numerical weather prediction at operational centres

Data assimilation (DA) methods for convective-scale numerical weather predictionat operational centres are surveyed. The operational methods include variationalmethods (3D-Var and 4D-Var), ensemble methods (LETKF) and hybrids betweenvariational and ensemble methods (3DEnVar and 4DEnVar). At several operationalcentres, other assimilation algorithms, like latent heat nudging, are additionallyapplied to improve the model initial state, with emphasis on convective scales. It isdemonstrated that the quality of forecasts based on initial data from convective-scaleDA is significantly better than the quality of forecasts from simple downscalingof larger-scale initial data. However, the duration of positive impact depends onthe weather situation, the size of the computational domain and the data that areassimilated. Furthermore it is shown that more advanced methods applied at convec-tive scales provide improvements over simpler methods. This motivates continuedresearch and development in convective-scale DA.Challenges in research and development for improvements of convective-scale DAare also reviewed and discussed. The difficulty of handling the wide range of spa-tial and temporal scales makes development of multi-scale assimilation methodsand space–time covariance localization techniques important. Improved utilizationof observations is also important. In order to extract more information from exist-ing observing systems of convective-scale phenomena (e.g. weather radar data andsatellite image data), it is necessary to provide improved statistical descriptions ofthe observation errors associated with these observations