Abstract
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.
Highlights
Development of data assimilation (DA) methods for global numerical weather prediction (NWP) models started with simple horizontal interpolation methods (Eliassen, 1954) which gradually developed to become three-dimensional and to include multivariate relationships (Lorenc, 1981)
DF blending is implemented in spectral space and consists of several steps: (i) first global and Limited-Area Model (LAM) spectra are truncated to a cut-off wavelength given by an empirical formula (Derkovaand Bellus, 2007); (ii) this is followed by filtering with a non-recursive Dolph–Chebyshev digital filter (Lynch et al, 1997); (iii) at the end the ALADIN background is incremented with the difference between the filtered models’ spectra interpolated back to high resolution
DA methods for convective-scale NWP at operational centres are surveyed in this article
Summary
Development of data assimilation (DA) methods for global numerical weather prediction (NWP) models started with simple horizontal interpolation methods (Eliassen, 1954) which gradually developed to become three-dimensional and to include multivariate relationships (Lorenc, 1981). It is an open question, to what extent these techniques will be completely satisfactory for convective-scale DA, or whether fully nonlinear DA techniques like particle filters (van Leeuwen, 2009) will be required, or if modifications to hybrid and ensemble methods to deal with non-Gaussianity, as suggested in Janjicet al. Only the acronym will be used in the text
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