Articles | Volume 15, issue 23
Model description paper
12 Dec 2022
Model description paper |  | 12 Dec 2022

A local data assimilation method (Local DA v1.0) and its application in a simulated typhoon case

Shizhang Wang and Xiaoshi Qiao

Related authors

A radar reflectivity operator with ice-phase hydrometeors for variational data assimilation (version 1.0) and its evaluation with real radar data
Shizhang Wang and Zhiquan Liu
Geosci. Model Dev., 12, 4031–4051,,, 2019
Short summary

Related subject area

Atmospheric sciences
On the use of Infrared Atmospheric Sounding Interferometer (IASI) spectrally resolved radiances to test the EC-Earth climate model (v3.3.3) in clear-sky conditions
Stefano Della Fera, Federico Fabiano, Piera Raspollini, Marco Ridolfi, Ugo Cortesi, Flavio Barbara, and Jost von Hardenberg
Geosci. Model Dev., 16, 1379–1394,,, 2023
Short summary
Incorporation of aerosol into the COSPv2 satellite lidar simulator for climate model evaluation
Marine Bonazzola, Hélène Chepfer, Po-Lun Ma, Johannes Quaas, David M. Winker, Artem Feofilov, and Nick Schutgens
Geosci. Model Dev., 16, 1359–1377,,, 2023
Short summary
The impact of altering emission data precision on compression efficiency and accuracy of simulations of the community multiscale air quality model
Michael S. Walters and David C. Wong
Geosci. Model Dev., 16, 1179–1190,,, 2023
Short summary
AerSett v1.0: a simple and straightforward model for the settling speed of big spherical atmospheric aerosols
Sylvain Mailler, Laurent Menut, Arineh Cholakian, and Romain Pennel
Geosci. Model Dev., 16, 1119–1127,,, 2023
Short summary
Optimization of weather forecasting for cloud cover over the European domain using the meteorological component of the Ensemble for Stochastic Integration of Atmospheric Simulations version 1.0
Yen-Sen Lu, Garrett H. Good, and Hendrik Elbern
Geosci. Model Dev., 16, 1083–1104,,, 2023
Short summary

Cited articles

Bonavita, M., Trémolet, Y., Holm, E., Lang, S. T., Chrust, M., Janisková, M., Lopez, P., Laloyaux, P., de Rosnay, P., and Fisher, M.: A strategy for data assimilation, European Centre for Medium Range Weather Forecasts Reading, UK,, 2017. 
Branković, Č., Palmer, T., Molteni, F., Tibaldi, S., and Cubasch, U.: Extended-range predictions with ECMWF models: Time-lagged ensemble forecasting, Q. J. Roy. Meteorol. Soc., 116, 867–912, 1990. 
Brousseau, P., Berre, L., Bouttier, F., and Desroziers, G.: Background-error covariances for a convective-scale data-assimilation system: AROME–France 3D-Var, Q. J. Roy. Meteorol. Soc., 137, 409–422, 2011. 
Brousseau, P., Berre, L., Bouttier, F., and Desroziers, G.: Flow-dependent background-error covariances for a convective-scale data assimilation system, Q. J. Roy. Meteorol. Soc., 138, 310–322, 2012. 
Buehner, M.: Evaluation of a spatial/spectral covariance localization approach for atmospheric data assimilation, Mon. Weather Rev., 140, 617–636, 2012. 

The requested paper has a corresponding corrigendum published. Please read the corrigendum first before downloading the article.

Short summary
A local data assimilation scheme (Local DA v1.0) was proposed to leverage the advantage of hybrid covariance, multiscale localization, and parallel computation. The Local DA can perform covariance localization in model space, observation space, or both spaces. The Local DA that used the hybrid covariance and double-space localization produced the lowest analysis and forecast errors among all observing system simulation experiments.