Survey of data assimilation methods for convective-scale numerical weather prediction at operational centres
- Author(s)
- Nils Gustafsson, Tijana Janjic-Pfander, Christoph Schraff, Daniel Leuenberger, Martin Weissmann, Martin Weissmann, Hendrik Reich, Pierre Brousseau, Thibaut Montmerle, Eric Wattrelot, Antonín Bučánek, Máté Mile, Rafiq Hamdi, Magnus Lindskog, Jan Barkmeijer, Mats Dahlbom, Bruce Macpherson, Sue Ballard, Gordon Inverarity, Jacob Carley, Curtis Alexander, David Dowell, Shun Liu, Yasutaka Ikuta, Tadashi Fujita
- 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.- Organisation(s)
- Department of Meteorology and Geophysics
- External organisation(s)
- Swedish Meteorological and Hydrological Institute, Hans-Ertel-Zentrum für Wetterforschung, MeteoSwiss, Ludwig-Maximilians-Universität München, Centre national de recherches météorologiques (CNRM), Czech Hydrometeorological Institute, Hungarian Meteorological Service, Budapest, Hungary, Royal Meteorological Institute of Belgium, Royal Netherlands Meteorological Institute, Danish Meteorological Institute (DMI), Met Office, National Oceanic and Atmospheric Administration Earth Systems Research Laboratory (NOAA/ESRL), James J. Howard Marine Sciences Laboratory, Meteorological Research Institute - Japan Meteorological Agency, Meteorological Service of Germany
- Journal
- Quarterly Journal of the Royal Meteorological Society
- Volume
- 144
- Pages
- 1218-1256
- No. of pages
- 38
- ISSN
- 0035-9009
- DOI
- https://doi.org/10.1002/qj.3179
- Publication date
- 2018
- Peer reviewed
- Yes
- Austrian Fields of Science 2012
- 105206 Meteorology
- Portal url
- https://ucrisportal.univie.ac.at/en/publications/survey-of-data-assimilation-methods-for-convectivescale-numerical-weather-prediction-at-operational-centres(105b9eeb-d7f8-4e07-aeec-87abcb67cd57).html