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Dynamically stratified Monte Carlo forecastingA new method for performing Monte Carlo forecasts is introduced. The method, called dynamic stratification, selects initial perturbations based on a stratification of the error distribution. A simple implementation is presented in which the error distribution used for the stratification is estimated from a linear model derived from a large ensemble of 12-h forecasts with the full dynamic model. The stratification thus obtained is used to choose a small subsample of initial states with which to perform the dynamical Monte Carlo forecasts. Several test cases are studied using a simple two-level general circulation model with uncertain initial conditions. It is found that the method provides substantial reductions in the sampling error of the forecast mean and variance when compared to the more traditional approach of choosing the initial perturbations at random. The degree of improvement, however, is sensitive to the nature of the initial error distribution and to the base state. In practice the method may be viable only if the computational burden involved in obtaining an adequate estimate of the error distribution is shared with the data-assimilation procedure.
Document ID
19920058751
Acquisition Source
Legacy CDMS
Document Type
Reprint (Version printed in journal)
Authors
Schubert, Siegfried
(NASA Goddard Space Flight Center Greenbelt, MD, United States)
Suarez, Max
(NASA Goddard Space Flight Center Greenbelt, MD, United States)
Schemm, Jae-Kyung
(General Sciences Corp.; NASA, Goddard Space Flight Center Greenbelt, MD, United States)
Epstein, Edward
(NOAA, Climate Analysis Center Camp Springs, MD, United States)
Date Acquired
August 15, 2013
Publication Date
June 1, 1992
Publication Information
Publication: Monthly Weather Review
Volume: 120
Issue: 6, Ju
ISSN: 0027-0644
Subject Category
Meteorology And Climatology
Accession Number
92A41375
Distribution Limits
Public
Copyright
Other

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