Seasonal Rainfall Prediction for the Southeast U.S. Using Sea Surface Temperature Information
Proceedings of the 2011 Georgia Water Resources Conference, April 11, 12, and 13, 2011, Athens, Georgia. This article presents a novel method able to identify and use the most relevant sea surface temperature (SST) information for seasonal rainfall prediction in the Southeast United States (SE US)....
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ftgeorgiatech:oai:repository.gatech.edu:1853/46241 2024-06-02T08:11:43+00:00 Seasonal Rainfall Prediction for the Southeast U.S. Using Sea Surface Temperature Information Chen, Chia-Jeng Georgakakos, Aristidis P. Georgia Institute of Technology Carroll, G. Denise 2011-04 application/pdf http://hdl.handle.net/1853/46241 en_US eng Georgia Institute of Technology Warnell School of Forestry and Natural Resources, The University of Georgia GWRI2011. Environmental Protection 0-9794100-24 http://hdl.handle.net/1853/46241 Water resources management Seasonal rainfall prediction Southeast United States Sea surface temperatures Text Proceedings 2011 ftgeorgiatech 2024-05-06T11:43:25Z Proceedings of the 2011 Georgia Water Resources Conference, April 11, 12, and 13, 2011, Athens, Georgia. This article presents a novel method able to identify and use the most relevant sea surface temperature (SST) information for seasonal rainfall prediction in the Southeast United States (SE US). The method searches for oceanic dipole areas with strong teleconnection relationships with rainfall, and generates seasonal forecasts based on a Bayesian forecasting scheme. The dipoles comprise oceanic areas of various sizes and geographic location, with the difference of the average SST over the poles being the predictor information. Dipole generation is based on teleconnection strength evaluation by the Gerrity Skill Score (GSS). In this application, seasonal rainfall series in the SE US is adopted as the predictand variable. Results show that the strongest predictability exists in winter (December – February). Even at lead times of 3 – 6 months, ensemble forecasts explain more than 50% of the observed rainfall variation. Zonal dipoles in North Atlantic near the Tropic of Cancer, dipoles between North Pacific and Northeast Atlantic, and ENSO-like dipoles are the most statistically significant patterns influencing winter SE US rainfall. Temporal and spatial persistence of SST are identified as oceanic patterns driving corresponding atmospheric circulation modes and affecting rainfall. Skill in other seasons are moderate compared to winter, however useful predictabilities appear in different lead times. On-going work focuses on assessing the value of the forecasts for agriculture and water resources management. Three rainfall stations at Buford dam, West Point Dam, and Montezuma as shown in Figure 1 are used for generating seasonal rainfall series. The monthly average rainfall climatology is also shown indicating that winter rainfall is significant for the ACF water resources. Figure 2 presents ensemble forecast results of winter rainfall with 6 months lead time compared with observations. In the model calibration ... Conference Object North Atlantic Northeast Atlantic Georgia Institute of Technology: SMARTech - Scholarly Materials and Research at Georgia Tech Pacific |
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Georgia Institute of Technology: SMARTech - Scholarly Materials and Research at Georgia Tech |
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ftgeorgiatech |
language |
English |
topic |
Water resources management Seasonal rainfall prediction Southeast United States Sea surface temperatures |
spellingShingle |
Water resources management Seasonal rainfall prediction Southeast United States Sea surface temperatures Chen, Chia-Jeng Georgakakos, Aristidis P. Seasonal Rainfall Prediction for the Southeast U.S. Using Sea Surface Temperature Information |
topic_facet |
Water resources management Seasonal rainfall prediction Southeast United States Sea surface temperatures |
description |
Proceedings of the 2011 Georgia Water Resources Conference, April 11, 12, and 13, 2011, Athens, Georgia. This article presents a novel method able to identify and use the most relevant sea surface temperature (SST) information for seasonal rainfall prediction in the Southeast United States (SE US). The method searches for oceanic dipole areas with strong teleconnection relationships with rainfall, and generates seasonal forecasts based on a Bayesian forecasting scheme. The dipoles comprise oceanic areas of various sizes and geographic location, with the difference of the average SST over the poles being the predictor information. Dipole generation is based on teleconnection strength evaluation by the Gerrity Skill Score (GSS). In this application, seasonal rainfall series in the SE US is adopted as the predictand variable. Results show that the strongest predictability exists in winter (December – February). Even at lead times of 3 – 6 months, ensemble forecasts explain more than 50% of the observed rainfall variation. Zonal dipoles in North Atlantic near the Tropic of Cancer, dipoles between North Pacific and Northeast Atlantic, and ENSO-like dipoles are the most statistically significant patterns influencing winter SE US rainfall. Temporal and spatial persistence of SST are identified as oceanic patterns driving corresponding atmospheric circulation modes and affecting rainfall. Skill in other seasons are moderate compared to winter, however useful predictabilities appear in different lead times. On-going work focuses on assessing the value of the forecasts for agriculture and water resources management. Three rainfall stations at Buford dam, West Point Dam, and Montezuma as shown in Figure 1 are used for generating seasonal rainfall series. The monthly average rainfall climatology is also shown indicating that winter rainfall is significant for the ACF water resources. Figure 2 presents ensemble forecast results of winter rainfall with 6 months lead time compared with observations. In the model calibration ... |
author2 |
Georgia Institute of Technology Carroll, G. Denise |
format |
Conference Object |
author |
Chen, Chia-Jeng Georgakakos, Aristidis P. |
author_facet |
Chen, Chia-Jeng Georgakakos, Aristidis P. |
author_sort |
Chen, Chia-Jeng |
title |
Seasonal Rainfall Prediction for the Southeast U.S. Using Sea Surface Temperature Information |
title_short |
Seasonal Rainfall Prediction for the Southeast U.S. Using Sea Surface Temperature Information |
title_full |
Seasonal Rainfall Prediction for the Southeast U.S. Using Sea Surface Temperature Information |
title_fullStr |
Seasonal Rainfall Prediction for the Southeast U.S. Using Sea Surface Temperature Information |
title_full_unstemmed |
Seasonal Rainfall Prediction for the Southeast U.S. Using Sea Surface Temperature Information |
title_sort |
seasonal rainfall prediction for the southeast u.s. using sea surface temperature information |
publisher |
Georgia Institute of Technology |
publishDate |
2011 |
url |
http://hdl.handle.net/1853/46241 |
geographic |
Pacific |
geographic_facet |
Pacific |
genre |
North Atlantic Northeast Atlantic |
genre_facet |
North Atlantic Northeast Atlantic |
op_relation |
GWRI2011. Environmental Protection 0-9794100-24 http://hdl.handle.net/1853/46241 |
_version_ |
1800757963958255616 |