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| Titel |
Using Bayesian Model Averaging (BMA) to calibrate probabilistic surface temperature forecasts over Iran |
| VerfasserIn |
I. Soltanzadeh, M. Azadi, G. A. Vakili |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
0992-7689
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| Digitales Dokument |
URL |
| Erschienen |
In: Annales Geophysicae ; 29, no. 7 ; Nr. 29, no. 7 (2011-07-21), S.1295-1303 |
| Datensatznummer |
250017064
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| Publikation (Nr.) |
 copernicus.org/angeo-29-1295-2011.pdf |
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| Zusammenfassung |
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. |
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