Using Bayesian Model Averaging (BMA) to calibrate probabilistic surface temperature forecasts over Iran

Using Bayesian Model Averaging (BMA), an attempt was made to obtain calibrated probabilistic numerical forecasts of 2-m temperature over Iran. The ensemble employs three limited area models (WRF, MM5 and HRM), with WRF used with five different configurations. Initial and boundary conditions for MM5...

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Bibliographic Details
Published inAnnales geophysicae (1988) Vol. 29; no. 7; pp. 1295 - 1303
Main Authors SOLTANZADEH, I, AZADI, M, VAKILI, G. A
Format Journal Article
LanguageEnglish
Published Göttingen Copernicus 21.07.2011
Copernicus GmbH
Copernicus Publications
Subjects
Analysis
Earth, ocean, space
Exact sciences and technology
External geophysics
Numerical weather prediction
Physics of the ionosphere
Physics of the magnetosphere
Weather
Middle East
Iran
Asia
Europe
Ensemble simulation
Initial condition
Surface temperature
Verification
Limited area model
Meteorology and atmospheric dynamics (Mesoscale meteorology)
Atmospheric dynamics
Mesoscale
Ensemble forecast
Deterministic model
Probabilistic model
Numerical forecast
Bayes methods
boundary conditions
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Summary:Using Bayesian Model Averaging (BMA), an attempt was made to obtain calibrated probabilistic numerical forecasts of 2-m temperature over Iran. The ensemble employs three limited area models (WRF, MM5 and HRM), with WRF used with five different configurations. Initial and boundary conditions for MM5 and WRF are obtained from the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) and for HRM the initial and boundary conditions come from analysis of Global Model Europe (GME) of the German Weather Service. The resulting ensemble of seven members was run for a period of 6 months (from December 2008 to May 2009) over Iran. The 48-h raw ensemble outputs were calibrated using BMA technique for 120 days using a 40 days training sample of forecasts and relative verification data. The calibrated probabilistic forecasts were assessed using rank histogram and attribute diagrams. Results showed that application of BMA improved the reliability of the raw ensemble. Using the weighted ensemble mean forecast as a deterministic forecast it was found that the deterministic-style BMA forecasts performed usually better than the best member's deterministic forecast.
ISSN:0992-7689
1432-0576
1432-0576
DOI:10.5194/angeo-29-1295-2011