Multimodel ensemble forecasts for weather and seasonal climate
In this paper the performance of a multimodel ensemble forecast analysis that shows superior forecast skills is illustrated and compared to all individual models used. The model comparisons include global weather, hurricane track and intensity forecasts, and seasonal climate simulations. The perform...
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ftciteseerx:oai:CiteSeerX.psu:10.1.1.464.6504 2023-05-15T18:18:33+02:00 Multimodel ensemble forecasts for weather and seasonal climate T. N. Krishnamurti C. M. Kishtawal Zhan Zhang Timothy Larow David Bachiochi Eric Williford The Pennsylvania State University CiteSeerX Archives 2000 application/pdf http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.464.6504 http://eprints.iisc.ernet.in/1669/1/111.pdf en eng http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.464.6504 http://eprints.iisc.ernet.in/1669/1/111.pdf Metadata may be used without restrictions as long as the oai identifier remains attached to it. http://eprints.iisc.ernet.in/1669/1/111.pdf text 2000 ftciteseerx 2016-01-08T06:50:16Z In this paper the performance of a multimodel ensemble forecast analysis that shows superior forecast skills is illustrated and compared to all individual models used. The model comparisons include global weather, hurricane track and intensity forecasts, and seasonal climate simulations. The performance improvements are completely attributed to the collective information of all models used in the statistical algorithm. The proposed concept is first illustrated for a low-order spectral model from which the multimodels and a ‘‘nature run’ ’ were constructed. Two hundred time units are divided into a training period (70 time units) and a forecast period (130 time units). The multimodel forecasts and the observed fields (the nature run) during the training period are subjected to a simple linear multiple regression to derive the statistical weights for the member models. The multimodel forecasts, generated for the next 130 forecast units, outperform all the individual models. This procedure was deployed for the multimodel forecasts of global weather, multiseasonal climate simulations, and hurricane track and intensity forecasts. For each type an improvement of the multimodel analysis is dem-onstrated and compared to the performance of the individual models. Seasonal and multiseasonal simulations demonstrate a major success of this approach for the atmospheric general circulation models where the sea surface temperatures and the sea ice are prescribed. In many instances, a major improvement in skill over the best models is noted. 1. Text Sea ice Unknown |
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description |
In this paper the performance of a multimodel ensemble forecast analysis that shows superior forecast skills is illustrated and compared to all individual models used. The model comparisons include global weather, hurricane track and intensity forecasts, and seasonal climate simulations. The performance improvements are completely attributed to the collective information of all models used in the statistical algorithm. The proposed concept is first illustrated for a low-order spectral model from which the multimodels and a ‘‘nature run’ ’ were constructed. Two hundred time units are divided into a training period (70 time units) and a forecast period (130 time units). The multimodel forecasts and the observed fields (the nature run) during the training period are subjected to a simple linear multiple regression to derive the statistical weights for the member models. The multimodel forecasts, generated for the next 130 forecast units, outperform all the individual models. This procedure was deployed for the multimodel forecasts of global weather, multiseasonal climate simulations, and hurricane track and intensity forecasts. For each type an improvement of the multimodel analysis is dem-onstrated and compared to the performance of the individual models. Seasonal and multiseasonal simulations demonstrate a major success of this approach for the atmospheric general circulation models where the sea surface temperatures and the sea ice are prescribed. In many instances, a major improvement in skill over the best models is noted. 1. |
author2 |
The Pennsylvania State University CiteSeerX Archives |
format |
Text |
author |
T. N. Krishnamurti C. M. Kishtawal Zhan Zhang Timothy Larow David Bachiochi Eric Williford |
spellingShingle |
T. N. Krishnamurti C. M. Kishtawal Zhan Zhang Timothy Larow David Bachiochi Eric Williford Multimodel ensemble forecasts for weather and seasonal climate |
author_facet |
T. N. Krishnamurti C. M. Kishtawal Zhan Zhang Timothy Larow David Bachiochi Eric Williford |
author_sort |
T. N. Krishnamurti |
title |
Multimodel ensemble forecasts for weather and seasonal climate |
title_short |
Multimodel ensemble forecasts for weather and seasonal climate |
title_full |
Multimodel ensemble forecasts for weather and seasonal climate |
title_fullStr |
Multimodel ensemble forecasts for weather and seasonal climate |
title_full_unstemmed |
Multimodel ensemble forecasts for weather and seasonal climate |
title_sort |
multimodel ensemble forecasts for weather and seasonal climate |
publishDate |
2000 |
url |
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.464.6504 http://eprints.iisc.ernet.in/1669/1/111.pdf |
genre |
Sea ice |
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Sea ice |
op_source |
http://eprints.iisc.ernet.in/1669/1/111.pdf |
op_relation |
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.464.6504 http://eprints.iisc.ernet.in/1669/1/111.pdf |
op_rights |
Metadata may be used without restrictions as long as the oai identifier remains attached to it. |
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1766195161342148608 |