Data Assimilation and Predictability: Ensemble-Based Data Assimilation

Z. Meng, F. Zhang

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

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.
Pages241-247
Number of pages7
ISBN (Electronic)9780123822260
ISBN (Print)9780123822253
DOIs
StatePublished - Jan 1 2015

All Science Journal Classification (ASJC) codes

  • Physics and Astronomy(all)

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