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Advanced Data Assimilation for GeosciencesLecture Notes of the Les Houches School of Physics: Special Issue, June 2012$
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Éric Blayo, Marc Bocquet, Emmanuel Cosme, and Leticia F. Cugliandolo

Print publication date: 2014

Print ISBN-13: 9780198723844

Published to Oxford Scholarship Online: March 2015

DOI: 10.1093/acprof:oso/9780198723844.001.0001

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Data assimilation in meteorology

Data assimilation in meteorology

Chapter:
(p.433) 19 Data assimilation in meteorology
Source:
Advanced Data Assimilation for Geosciences
Author(s):

F. Rabier

M. Fisher

Publisher:
Oxford University Press
DOI:10.1093/acprof:oso/9780198723844.003.0019

This chapter discusses some of the implementation details that are necessary to apply data assimilation in the context of numerical weather prediction (NWP). It is divided into three parts. The first part addresses the processing of observations, which includes the transformation of raw data into a form that can be processed by a data assimilation system, quality control, and data thinning. The second part discusses two important aspects of data assimilation for NWP: (i) filtering of the analysis to remove spurious inertia–gravity waves and (ii) methods to handle nonlinearities and non-Gaussian error statistics. The third part discusses the development of parallel algorithms for four-dimensional variational data assimilation (4D-VAR), in order to better exploit the parallel nature of the computers on which it is run and to maintain its status as an important and viable NWP data assimilation algorithm into the foreseeable future.

Keywords:   numerical weather prediction, NWP, processing of observations, filtering of analysis, non-Gaussian data assimilation, four-dimensional variational data assimilation, 4D-VAR, parallel algorithms

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