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| Titel |
Using the Firefly optimization method to weight an ensemble of rainfall forecasts from the Brazilian developments on the Regional Atmospheric Modeling System (BRAMS) |
| VerfasserIn |
A. F. Santos, S. R. Freitas, J. G. Z. Mattos, H. F. Campos Velho, M. A. Gan, E. F. P. Luz, G. A. Grell |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1680-7340
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| Digitales Dokument |
URL |
| Erschienen |
In: 8th EGU Alexander von Humboldt Conference "Natural Disasters, Global Change, and the Preservation of World Heritage Sites" ; Nr. 35 (2013-09-17), S.123-136 |
| Datensatznummer |
250086170
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| Publikation (Nr.) |
 copernicus.org/adgeo-35-123-2013.pdf |
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| Zusammenfassung |
| In this paper we consider an optimization problem applying the metaheuristic
Firefly algorithm (FY) to weight an ensemble of rainfall forecasts from daily
precipitation simulations with the Brazilian developments on the Regional
Atmospheric Modeling System (BRAMS) over South America during January 2006.
The method is addressed as a parameter estimation problem to weight the
ensemble of precipitation forecasts carried out using different options of
the convective parameterization scheme. Ensemble simulations were performed
using different choices of closures, representing different formulations of
dynamic control (the modulation of convection by the environment) in a deep
convection scheme. The optimization problem is solved as an inverse problem
of parameter estimation. The application and validation of the methodology is
carried out using daily precipitation fields, defined over South America and
obtained by merging remote sensing estimations with rain gauge observations.
The quadratic difference between the model and observed data was used as the
objective function to determine the best combination of the ensemble members
to reproduce the observations. To reduce the model rainfall biases, the set
of weights determined by the algorithm is used to weight members of an
ensemble of model simulations in order to compute a new precipitation field
that represents the observed precipitation as closely as possible. The
validation of the methodology is carried out using classical statistical
scores. The algorithm has produced the best combination of the weights,
resulting in a new precipitation field closest to the observations. |
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