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Survey of Data Assimilation Methods For Convective-scale Numerical Weather Prediction at Operational Centres


Data assimilation (DA) methods for convective-scale numerical weather prediction at operational centres are surveyed. The operational methods include variational methods (3D-Var and 4D-Var), ensemble methods (LETKF) and hybrids between variational and ensemble methods (3DEnVar and 4DEnVar). At several operational centres, other assimilation algorithms, like latent heat nudging, are additionally applied to improve the model initial state, with emphasis on convective scales. It is demonstrated that the quality of forecasts based on initial data from convective-scale DA is significantly better than the quality of forecasts from simple downscaling of larger-scale initial data. However, the duration of positive impact depends on the weather situation, the size of the computational domain and the data that are assimilated. Furthermore it is shown that more advanced methods applied at convective scales provide improvements over simpler methods. This motivates continued research and development in convective-scale DA. Challenges in research and development for improvements of convective-scale DA are also reviewed and discussed. The difficulty of handling the wide range of spatial and temporal scales makes development of multi-scale assimilation methods and space–time covariance localization techniques important. Improved utilization of observations is also important. In order to extract more information from existing observing systems of convective-scale phenomena (e.g. weather radar data and satellite image data), it is necessary to provide improved statistical descriptions of the observation errors associated with these observations.

Article / Publication Data
Available Metadata
Accepted On
October 09, 2017
Fiscal Year
Peer Reviewed
Publication Name
Quarterly Journal of The Royal Meteorological Society
Published On
March 01, 2018
Publisher Name
Royal Meteorological Society
Print Volume
Print Number
Page Range
Submitted On
March 31, 2017


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