Survey of data assimilation methods for convective-scale numerical weather prediction at operational centres
Gustafsson, N. ; Janjic, T. ; Schraff, C. ; Leuenberger, D. ; Weissman, M. ; Reich, H. ; Brousseau, Pierre ; Montmerle, Thibaut ; Wattrelot, Eric ; Bucánek, A. ; Mile, M. ; Hamdi, R. ; Lindskog, M. ; Barkmeijer, J. ; Dahlbom, M. ; Macpherson, B. ; Ballard, S. ; Inverarity, G. ; Carley, J. ; Alexander, C. ; Dowell, D. ; Liu, S. ; Ikuta, Y. ; Fujita, T.
Année de publication
2018
<font face="Calibri, sans-serif"><font style="font-size: 12pt" size="3">Abstract </font></font></h2><h2 class="western" style="margin-top: 0cm; margin-bottom: 0cm; font-weight: normal" lang="en-US" align="justify"> <font face="Calibri, sans-serif"><font style="font-size: 12pt" size="3">Data assimilation methods for convective-scale numerical weather prediction at operational centres are surveyed in this paper. 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 of the 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 data assimilation is significantly better than the quality of forecasts from simple downscaling of larger-scale initial data. The duration of positive impact depends however on the weather situation, the size of the computational domain and the data that are assimilated. It is furthermore shown that more-advanced methods applied at convective scales provide improvements compared to simpler methods. This motivates continued research and development in convective-scale data assimilation. </font></font></h2><h2 class="western" style="margin-top: 0cm; margin-bottom: 0cm; font-weight: normal" lang="en-US" align="justify"> <font face="Calibri, sans-serif"><font style="font-size: 12pt" size="3">Challenges in research and development for improvements of convective-scale data assimilation are also reviewed and discussed in this paper. 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, for example weather radar data and satellite image data, it is necessary to provide improved statistical descriptions of the observation errors associated with these observations.</font></font></h2>
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