Use of randomization to diagnose the impact of observations on analyses and forecasts | Zendy

Desroziers G'erald | Zendy; Brousseau Pierre | Zendy; Chapnik Bernard | Zendy

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Use of randomization to diagnose the impact of observations on analyses and forecasts

Author(s) -

Desroziers G'erald,

Brousseau Pierre,

Chapnik Bernard

Publication year - 2005

Publication title -

quarterly journal of the royal meteorological society

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 1.744

H-Index - 143

eISSN - 1477-870X

pISSN - 0035-9009

DOI - 10.1256/qj.04.151

Subject(s) - data assimilation , randomization , variance reduction , computer science , forecast error , nonlinear system , error analysis , reduction (mathematics) , set (abstract data type) , econometrics , statistics , mathematics , meteorology , geography , randomized controlled trial , medicine , programming language , physics , geometry , surgery , quantum mechanics , monte carlo method

A method is proposed to diagnose the impact of a complete set, or subsets, of observations on the reduction of error variance in an analysis and in the subsequent forecasts run from this analysis. A practical method to estimate the error reduction, based on a randomization procedure, is also introduced and investigated in a simple framework given by the analysis and forecast of wind on a circular domain using the nonlinear Burger's equation. The randomization procedure is also applied and tested in the French ARPEGE 4D‐Var assimilation. The first results in a real‐size data assimilation system are realistic and provide useful information on the use of observations in an operational analysis. Copyright © 2005 Royal Meteorological Society

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