Limited-area ensemble-based data assimilation

Zhiyong Meng, Fuqing Zhang

Research output: Contribution to journalReview articlepeer-review

43 Scopus citations


Ensemble-based data assimilation is a state estimation technique that uses short-term ensemble forecasts to estimate flow-dependent background error covariance and is best known by varying forms of ensemble Kalman filters (EnKFs). The EnKF has recently emerged as one of the primary alternatives to the variational data assimilation methods widely used in both global and limited-area numerical weather prediction models. In addition to comparing the EnKF with variational methods, this article reviews recent advances and challenges in the development and applications of the EnKF, including its hybrid with variational methods, in limited-area models that resolve weather systems from convective to meso- and regional scales.

Original languageEnglish (US)
Pages (from-to)2025-2045
Number of pages21
JournalMonthly Weather Review
Issue number7
StatePublished - Jul 2011

All Science Journal Classification (ASJC) codes

  • Atmospheric Science


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