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

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00020699%3A_____%2F18%3AN0000062" target="_blank" >RIV/00020699:_____/18:N0000062 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.1002/qj.3179" target="_blank" >https://doi.org/10.1002/qj.3179</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1002/qj.3179" target="_blank" >10.1002/qj.3179</a>

Result language

angličtina

Original language name

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

Original language description

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. 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.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

10509 - Meteorology and atmospheric sciences

Project

—

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2018

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Quarterly Journal of the Royal Meteorological Society

ISSN

0035-9009

e-ISSN

1477-870X

Volume of the periodical

144

Issue of the periodical within the volume

713

Country of publishing house

GB - UNITED KINGDOM

Number of pages

39

Pages from-to

1218-1256

UT code for WoS article

000445200400017

EID of the result in the Scopus database

2-s2.0-85044433626