Multi‐model fusion and error parameter estimation

A robust and practical methodology for multi‐model ocean forecast fusion has been sought. Present regional ocean forecasting systems adapt and evolve in response to modelled processes. This makes it imperative that a forecast combination methodology be adaptive and capable to operate with a small sa...

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Vydáno v:	Quarterly Journal of the Royal Meteorological Society Ročník 131; číslo 613; s. 3397 - 3408
Hlavní autoři:	Logutov, O. G., Robinson, A. R.
Médium:	Journal Article Konferenční příspěvek
Jazyk:	angličtina
Vydáno:	Chichester, UK John Wiley & Sons, Ltd 01.10.2005 Wiley
Témata:	Adaptive methods Data assimilation Earth, ocean, space Exact sciences and technology External geophysics Geophysics. Techniques, methods, instrumentation and models Ocean and atmospheric forecasting Other topics Physics of the oceans United States California Monterey Bay INE, USA, California, Monterey Bay ocean currents Multimodel Parameter estimation Uncertainty Error estimation Adaptive system Regional scope Forecast model Parameterization North America Real time system Ocean model Ocean and atmospheric forecasting Adaptive methods maximum likelihood Data assimilation
ISSN:	0035-9009, 1477-870X
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Abstract	A robust and practical methodology for multi‐model ocean forecast fusion has been sought. Present regional ocean forecasting systems adapt and evolve in response to modelled processes. This makes it imperative that a forecast combination methodology be adaptive and capable to operate with a small sample of past validating events. To this end, we consider an extension of maximum‐likelihood error parameter estimation to multi‐model predictive systems, and utilize the resulting methodology for adaptive Bayesian model fusion. The methodology consists of the following three general steps: (a) parametrization of forecast uncertainties through either a suitable parametric family (e.g. covariance models) or through a low‐rank approximation (e.g. flow‐dependent error subspaces); (b) update of uncertainty parameters via maximum likelihood; and (c) combining model forecasts based on their uncertainty parameters via maximum likelihood. In order to implement step (b), we have extended the maximum‐likelihood error parameter estimation methodology to multi‐model forecasting systems using the expectation‐maximization technique, with the true state‐space vector at observation locations treated as missing data. With only one forecasting model, the procedure reduces to the standard maximum‐likelihood error parameter estimation. The proposed multi‐model fusion methodology neglects cross‐model error correlations in order to gain the capability to work with a small sample of past events. We illustrate the methodology with a two‐model forecasting example (HOPS, ROMS) within the framework of the real‐time forecasting experiment held in Monterey Bay during 2003. Copyright © 2005 Royal Meteorological Society
AbstractList	A robust and practical methodology for multi‐model ocean forecast fusion has been sought. Present regional ocean forecasting systems adapt and evolve in response to modelled processes. This makes it imperative that a forecast combination methodology be adaptive and capable to operate with a small sample of past validating events. To this end, we consider an extension of maximum‐likelihood error parameter estimation to multi‐model predictive systems, and utilize the resulting methodology for adaptive Bayesian model fusion. The methodology consists of the following three general steps: (a) parametrization of forecast uncertainties through either a suitable parametric family (e.g. covariance models) or through a low‐rank approximation (e.g. flow‐dependent error subspaces); (b) update of uncertainty parameters via maximum likelihood; and (c) combining model forecasts based on their uncertainty parameters via maximum likelihood. In order to implement step (b), we have extended the maximum‐likelihood error parameter estimation methodology to multi‐model forecasting systems using the expectation‐maximization technique, with the true state‐space vector at observation locations treated as missing data. With only one forecasting model, the procedure reduces to the standard maximum‐likelihood error parameter estimation. The proposed multi‐model fusion methodology neglects cross‐model error correlations in order to gain the capability to work with a small sample of past events. We illustrate the methodology with a two‐model forecasting example (HOPS, ROMS) within the framework of the real‐time forecasting experiment held in Monterey Bay during 2003. Copyright © 2005 Royal Meteorological Society A robust and practical methodology for multi-model ocean forecast fusion has been sought. Present regional ocean forecasting systems adapt and evolve in response to modelled processes. This makes it imperative that a forecast combination methodology be adaptive and capable to operate with a small sample of past validating events. To this end, we consider an extension of maximum-likelihood error parameter estimation to multi-model predictive systems, and utilize the resulting methodology for adaptive Bayesian model fusion. The methodology consists of the following three general steps: (a) parametrization of forecast uncertainties through either a suitable parametric family (e.g. covariance models) or through a low-rank approximation (e.g. flow-dependent error subspaces); (b) update of uncertainty parameters via maximum likelihood; and (c) combining model forecasts based on their uncertainty parameters via maximum likelihood. In order to implement step (b), we have extended the maximum-likelihood error parameter estimation methodology to multi-model forecasting systems using the expectation-maximization technique, with the true state-space vector at observation locations treated as missing data. With only one forecasting model, the procedure reduces to the standard maximum-likelihood error parameter estimation. The proposed multi-model fusion methodology neglects cross-model error correlations in order to gain the capability to work with a small sample of past events. We illustrate the methodology with a two-model forecasting example (HOPS, ROMS) within the framework of the real-time forecasting experiment held in Monterey Bay during 2003.
Author	Robinson, A. R. Logutov, O. G.
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Title	Multi‐model fusion and error parameter estimation
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