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From MOS to eMOS: generalising model output statistics for full ensemble forecasts

Kilminster, Devin, Clark, Liam, Roulston, Mark, Ziehmann, Christine, Bröcker, Jochen and Smith, Leonard (2004) From MOS to eMOS: generalising model output statistics for full ensemble forecasts. Centre for the Analysis of Time Series. London School of Economics and Political Science, London, UK.

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It has long been known that the information content of weather forecasts extends beyond the model-variablesthat share the same name as the forecast target-variables of interest. Traditional model output statistics(MOS) algorithms extract information from any model-variable deemed relevant to estimating a given target-variable, especially when the “corresponding” model-variable, taken at face value, forecasts “poorly”.Mathematically, this form of MOS can be seen as adopting a “projection” operator between model-state spaceand observations that is more complex than the identity operator. Ensemble forecasts allow the introduction of a new twist. Typically, one treats each individual ensemble member as a viable scenario, projecting it intoobservation space as a (dressed) forecast, and then combining all ensemble members; an alternativeapproach is to condition the probability forecast of the target value upon properties of the joint distribution of allthe ensemble members (in a potentially multi-model ensemble). Thus eMOS goes beyond MOS in that it notonly aims to locate information in each individual model run, but also considers the ensemble as a whole, notmerely as a collection of scenarios. The approach is illustrated in precipitation forecasts, and more generalinterpretations relevant to THORPEX's core aims are noted.

Item Type: Monograph (Report)
Official URL:
Additional Information: © 2004 The Authors
Divisions: Statistics
Centre for Analysis of Time Series
Subjects: Q Science > QA Mathematics
Date Deposited: 27 Feb 2014 14:11
Last Modified: 16 May 2024 13:09

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