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

dc.contributor.authorGustafsson, Nils
dc.contributor.authorJanjić, Tijana
dc.contributor.authorSchraff, Christoph
dc.contributor.authorLeuenberger, Daniel
dc.contributor.authorWeissman, Martin
dc.contributor.authorReich, Hendrik
dc.contributor.authorBrousseau, Pierre
dc.contributor.authorMontmerle, Thibaut
dc.contributor.authorBučánek, Antonín
dc.contributor.authorMile, Máté
dc.contributor.authorHamdi, Rafiq
dc.contributor.authorLindskog, Magnus
dc.contributor.authorBarkmeijer, Jan
dc.contributor.authorDahlbom, Mats
dc.contributor.authorMacpherson, Bruce
dc.contributor.authorBallard, Sue
dc.contributor.authorInverarity, Gordon
dc.contributor.authorCarley, Jacob
dc.contributor.authorAlexander, Curtis
dc.contributor.authorDowell, David
dc.contributor.authorLiu, Shun
dc.contributor.authorIkuta, Yasukata
dc.contributor.authorFujita, Tadashi
dc.date2018-04-20
dc.date.accessioned2019-05-16T14:45:35Z
dc.date.available2019-05-16T14:45:35Z
dc.identifier.citationGustafsson, N, Janjić, T, Schraff, C, et al. Survey of data assimilation methods for convective‐scale numerical weather prediction at operational centres. Q J R Meteorol Soc. 2018; 144: 1218– 1256. https://doi.org/10.1002/qj.3179en_US
dc.identifier.urihttps://orfeo.belnet.be/handle/internal/7267
dc.descriptionData 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.en_US
dc.languageengen_US
dc.publisherWileyen_US
dc.titleSurvey of data assimilation methods for convective-scale numerical weather prediction at operational centresen_US
dc.typeArticleen_US
dc.subject.frascatiEarth and related Environmental sciencesen_US
dc.audienceScientificen_US
dc.source.titleQuarterly Journal of the Royal Meteorological Societyen_US
dc.source.volume144en_US
dc.source.issue713en_US
dc.source.page1218-1256en_US
Orfeo.peerreviewedYesen_US
dc.identifier.doihttp://dx.doi.org/10.1002/qj.3179


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