Extended seasonal prediction of spring precipitation over the Upper Colorado River Basin
This study provides extended seasonal predictions for the Upper Colorado River Basin (UCRB) precipitation in boreal spring using an artificial neural network (ANN) model and a stepwise linear regression model, respectively. Sea surface temperature (SST) predictors are developed taking advantage of t...
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ftpubmed:oai:pubmedcentral.nih.gov:10011310 2023-05-15T17:35:04+02:00 Extended seasonal prediction of spring precipitation over the Upper Colorado River Basin Zhao, Siyu Fu, Rong Anderson, Michael L. Chakraborty, Sudip Jiang, Jonathan H. Su, Hui Gu, Yu 2022-07-21 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10011310/ https://doi.org/10.1007/s00382-022-06422-x en eng Springer Berlin Heidelberg http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10011310/ http://dx.doi.org/10.1007/s00382-022-06422-x © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . Clim Dyn Article Text 2022 ftpubmed https://doi.org/10.1007/s00382-022-06422-x 2023-03-19T01:54:23Z This study provides extended seasonal predictions for the Upper Colorado River Basin (UCRB) precipitation in boreal spring using an artificial neural network (ANN) model and a stepwise linear regression model, respectively. Sea surface temperature (SST) predictors are developed taking advantage of the correlation between the precipitation and SST over three ocean basins. The extratropical North Pacific has a higher correlation with the UCRB spring precipitation than the tropical Pacific and North Atlantic. For the ANN model, the Pearson correlation coefficient between the observed and predicted precipitation exceeds 0.45 (p-value < 0.01) for a lead time of 12 months. The mean absolute percentage error (MAPE) is below 20% and the Heidke skill score (HSS) is above 50%. Such long-lead prediction skill is probably due to the UCRB soil moisture bridging the SST and precipitation. The stepwise linear regression model shows similar prediction skills to those of ANN. Both models show prediction skills superior to those of an autoregression model (correlation < 0.10) that represents the baseline prediction skill and those of three of the North American Multi-Model Ensemble (NMME) forecast models. The three NMME models exhibit different skills in predicting the precipitation, with the best skills of the correlation ~ 0.40, MAPE < 25%, and HSS > 40% for lead times less than 8 months. This study highlights the advantage of oceanic climate signals in extended seasonal predictions for the UCRB spring precipitation and supports the improvement of the UCRB streamflow prediction and related water resource decisions. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00382-022-06422-x. Text North Atlantic PubMed Central (PMC) Pacific Climate Dynamics 60 5-6 1815 1829 |
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Article Zhao, Siyu Fu, Rong Anderson, Michael L. Chakraborty, Sudip Jiang, Jonathan H. Su, Hui Gu, Yu Extended seasonal prediction of spring precipitation over the Upper Colorado River Basin |
topic_facet |
Article |
description |
This study provides extended seasonal predictions for the Upper Colorado River Basin (UCRB) precipitation in boreal spring using an artificial neural network (ANN) model and a stepwise linear regression model, respectively. Sea surface temperature (SST) predictors are developed taking advantage of the correlation between the precipitation and SST over three ocean basins. The extratropical North Pacific has a higher correlation with the UCRB spring precipitation than the tropical Pacific and North Atlantic. For the ANN model, the Pearson correlation coefficient between the observed and predicted precipitation exceeds 0.45 (p-value < 0.01) for a lead time of 12 months. The mean absolute percentage error (MAPE) is below 20% and the Heidke skill score (HSS) is above 50%. Such long-lead prediction skill is probably due to the UCRB soil moisture bridging the SST and precipitation. The stepwise linear regression model shows similar prediction skills to those of ANN. Both models show prediction skills superior to those of an autoregression model (correlation < 0.10) that represents the baseline prediction skill and those of three of the North American Multi-Model Ensemble (NMME) forecast models. The three NMME models exhibit different skills in predicting the precipitation, with the best skills of the correlation ~ 0.40, MAPE < 25%, and HSS > 40% for lead times less than 8 months. This study highlights the advantage of oceanic climate signals in extended seasonal predictions for the UCRB spring precipitation and supports the improvement of the UCRB streamflow prediction and related water resource decisions. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00382-022-06422-x. |
format |
Text |
author |
Zhao, Siyu Fu, Rong Anderson, Michael L. Chakraborty, Sudip Jiang, Jonathan H. Su, Hui Gu, Yu |
author_facet |
Zhao, Siyu Fu, Rong Anderson, Michael L. Chakraborty, Sudip Jiang, Jonathan H. Su, Hui Gu, Yu |
author_sort |
Zhao, Siyu |
title |
Extended seasonal prediction of spring precipitation over the Upper Colorado River Basin |
title_short |
Extended seasonal prediction of spring precipitation over the Upper Colorado River Basin |
title_full |
Extended seasonal prediction of spring precipitation over the Upper Colorado River Basin |
title_fullStr |
Extended seasonal prediction of spring precipitation over the Upper Colorado River Basin |
title_full_unstemmed |
Extended seasonal prediction of spring precipitation over the Upper Colorado River Basin |
title_sort |
extended seasonal prediction of spring precipitation over the upper colorado river basin |
publisher |
Springer Berlin Heidelberg |
publishDate |
2022 |
url |
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10011310/ https://doi.org/10.1007/s00382-022-06422-x |
geographic |
Pacific |
geographic_facet |
Pacific |
genre |
North Atlantic |
genre_facet |
North Atlantic |
op_source |
Clim Dyn |
op_relation |
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10011310/ http://dx.doi.org/10.1007/s00382-022-06422-x |
op_rights |
© The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
op_doi |
https://doi.org/10.1007/s00382-022-06422-x |
container_title |
Climate Dynamics |
container_volume |
60 |
container_issue |
5-6 |
container_start_page |
1815 |
op_container_end_page |
1829 |
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1766134112069877760 |