Data Assimilation and Predictability: Ensemble-Based Data Assimilation

Z. Meng, F. Zhang

Research output: Chapter in Book/Report/Conference proceedingChapter


This article introduces the algorithm of ensemble-based data assimilation (EDA) and the main issues in its application to atmospheric sciences. EDA is drawing increasing attentions in data assimilation community mainly due to its flow-dependent background error covariance determined using a short-range ensemble forecast and ease of implementation. Many types of EDA have been applied with different models at different scales in both research and operational or quasi-operational communities. Various aspects involved in EDA are discussed including observations, ensemble initialization, sampling error, covariance inflation and localization, model error, verification, nonlinearity and non-Gaussian errors, intercomparison, and hybrid with variational schemes.

Original languageEnglish (US)
Title of host publicationEncyclopedia of Atmospheric Sciences
Subtitle of host publicationSecond Edition
PublisherElsevier Inc.
Number of pages7
ISBN (Electronic)9780123822260
ISBN (Print)9780123822253
StatePublished - Jan 1 2015

All Science Journal Classification (ASJC) codes

  • General Physics and Astronomy


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