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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10509 - Meteorology and atmospheric sciences

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2018

Kód důvěrnosti údajů

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

Název periodika

Quarterly Journal of the Royal Meteorological Society

ISSN

0035-9009

e-ISSN

1477-870X

Svazek periodika

144

Číslo periodika v rámci svazku

713

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

39

Strana od-do

1218-1256

Kód UT WoS článku

000445200400017

EID výsledku v databázi Scopus

2-s2.0-85044433626