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.Full text not available from this repository.
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 (Other)|
|Additional Information:||© 2004 The Authors|
|Library of Congress subject classification:||Q Science > QA Mathematics|
|Sets:||Departments > Statistics
Research centres and groups > Centre for the Analysis of Time Series (CATS)
|Date Deposited:||27 Feb 2014 14:11|
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