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Ensemble Mean (ensemble + mean)

Distribution by Scientific Domains
Earth and Environmental Science75%

Selected Abstracts

Unbiased ensemble square root filters

PROCEEDINGS IN APPLIED MATHEMATICS & MECHANICS, Issue 1 2007
S. L. Dance
Ensemble square root filters are a method of data assimilation, where model forecasts are combined with observations to produce an improved state estimate, or analysis. There are a number of different algorithms in the literature and it is not clear which of these is the best for any given application. This work shows that in some implementations there can be a systematic bias in the analysis ensemble mean and consequently an accompanying shortfall in the spread of the analysis ensemble as expressed by the ensemble covariance matrix. We have established a set of necessary and sufficient conditions for the scheme to be unbiased. While these conditions are not a cure-all and cannot deal with independent sources of bias such as model and observation errors, they should be useful to designers of ensemble square root filters in the future. (© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim) [source]

The local ETKF and SKEB: Upgrades to the MOGREPS short-range ensemble prediction system

THE QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY, Issue 640 2009
Neill E. Bowler
Abstract The Met Office has been routinely running a short-range global and regional ensemble prediction system (EPS) since the summer of 2005. This article describes a major upgrade to the global ensemble, which affected both the initial condition and model uncertainty perturbations applied in that ensemble. The change to the initial condition perturbations is to allow localization within the ensemble transform Kalman filter (ETKF). This enables better specification of the ensemble spread as a function of location around the globe. The change to the model uncertainty perturbations is the addition of a stochastic kinetic energy backscatter scheme (SKEB). This adds vorticity perturbations to the forecast in order to counteract the damping of small-scale features introduced by the semi-Lagrangian advection scheme. Verification of ensemble forecasts is presented for the global ensemble system. It is shown that the localization of the ETKF gives a distribution of the spread as a function of latitude that better matches the forecast error of the ensemble mean. The SKEB scheme has a substantial effect on the power spectrum of the kinetic energy, and with the scheme a shallowing of the spectral slope is seen in the tail. A k,5/3 slope is seen at wavelengths shorter than 1000 km and this better agrees with the observed spectrum. The local ETKF significantly improves forecasts at all lead times over a number of variables. The SKEB scheme increases the rate of growth of ensemble spread in some variables, and improves forecast skill at short lead times. ©Crown Copyright 2009. Reproduced with the permission of HMSO. Published by John Wiley & Sons Ltd. [source]

Using ensemble forecasts to predict the size of forecast changes, with application to weather swap value at risk

ATMOSPHERIC SCIENCE LETTERS, Issue 1-4 2003
Stephen Jewson
We show that the standard deviation of the distribution from which changes in the ensemble mean are drawn can be predicted using the ensemble spread. Such a forecast has direct application for companies that trade weather swaps and need to evaluate their risk. Copyright © 2003 Royal Meteorological Society. Published by Elsevier Ltd. All rights reserved. [source]

Effects of stochastic parametrizations in the Lorenz '96 system

THE QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY, Issue 606 2005
Daniel S. Wilks
Abstract Stochastic parametrization of the effects of unresolved variables is studied in the context of the Lorenz '96 system. These parametrizations are found to produce clear improvements in correspondence between the model and ,true' climatologies; they similarly provide clear improvements in all ensemble forecast verification measures investigated, including accuracy of ensemble means and ensemble probability estimation, and including measures operating on both scalar (each resolved forecast variable evaluated individually) and vector (all forecast variables evaluated simultaneously) predictands. Scalar accuracy measures for non-ensemble (i.e. single integration) forecasts are, however, degraded. The results depend very strongly on both the amplitude (standard deviation) and time-scale of the stochastic forcing, but only weakly on its spatial scale. In general there seems not to be a single clear optimum combination of time-scale and amplitude, but rather there exists a range of combinations producing similar results. Copyright © 2005 Royal Meteorological Society. [source]