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Accession Number ADA602312
Title Verification-Based Model Tuning.
Publication Date Jan 2014
Media Count 9p
Personal Author C. Marzban D. W. Jones S. A. Sandgathe
Abstract All numerical models (e.g., Numerical Weather Prediction models) have certain parameters within model algorithms which effect forecasts to a different degree, depending on the forecast quantity. The specific values of these model parameters are determined either theoretically using fundamental physics laws but incorporating necessary approximations to reduce computational cost, or empirically using observations from field experiments where observational error introduces uncertainty. In either case, the exact value of the parameter is often unknown a priori, and so their values are usually set to improve forecast quality through some form of forecast verification. Such an approach to model tuning, however, requires knowledge of the observations to which the forecasts must be compared, and therefore, a multitude of highly detailed experimental cases in order to fully resolve parameter values, a data set very difficult to obtain. A knowledge of the relationship between model parameters and forecast quantities, without reference to observations, can not only aid in such an observation-based approach to model tuning, it can also aid in tuning the model parameters according to other criteria that may not be based on observations directly, e.g., a desire to affect the forecasts according to some long-term experience of a forecaster. The main goal of our work has been to develop a framework for representing the complex relationship between model parameters and forecast quantities, without any reference to observations.
Keywords Algorithms
Experimental design
Lorenz model
Mathematical models
Mathematical prediction
Model tuning
Numerical analysis
Numerical models
Numerical weather prediction models
Sensitivity
Sensitivity analysis
Tuning
Weather forecasting


 
Source Agency Non Paid ADAS
NTIS Subject Category 55C - Meteorological Data Collection, Analysis, & Weather Forecast
72B - Algebra, Analysis, Geometry, & Mathematical Logic
72F - Statistical Analysis
Corporate Author Washington Univ., Seattle. Applied Physics Lab.
Document Type Technical report
Title Note Final rept. 1 Jan 2012-30 Sep 2013.
NTIS Issue Number 1423
Contract Number N00014-12-1-0276

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