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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ftunivsthongkong:oai:repository.hkust.edu.hk:1783.1-120239 2024-05-19T07:45:20+00:00 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 https://repository.hkust.edu.hk/ir/Record/1783.1-120239 https://doi.org/10.1007/s00382-022-06422-x http://lbdiscover.ust.hk/uresolver?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rfr_id=info:sid/HKUST:SPI&rft.genre=article&rft.issn=0930-7575&rft.volume=&rft.issue=&rft.date=2022&rft.spage=&rft.aulast=Zhao&rft.aufirst=&rft.atitle=Extended+seasonal+prediction+of+spring+precipitation+over+the+Upper+Colorado+River+Basin&rft.title=Climate+Dynamics http://www.scopus.com/record/display.url?eid=2-s2.0-85134585327&origin=inward http://gateway.isiknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=LinksAMR&SrcApp=PARTNER_APP&DestLinkType=FullRecord&DestApp=WOS&KeyUT=000828443400001 English eng Springer Science and Business Media Deutschland GmbH https://repository.hkust.edu.hk/ir/Record/1783.1-120239 Climate Dynamics, v. 60, 21 July 2022, p. 1815-1829 0930-7575 1432-0894 https://doi.org/10.1007/s00382-022-06422-x http://lbdiscover.ust.hk/uresolver?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rfr_id=info:sid/HKUST:SPI&rft.genre=article&rft.issn=0930-7575&rft.volume=&rft.issue=&rft.date=2022&rft.spage=&rft.aulast=Zhao&rft.aufirst=&rft.atitle=Extended+seasonal+prediction+of+spring+precipitation+over+the+Upper+Colorado+River+Basin&rft.title=Climate+Dynamics http://www.scopus.com/record/display.url?eid=2-s2.0-85134585327&origin=inward http://gateway.isiknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=LinksAMR&SrcApp=PARTNER_APP&DestLinkType=FullRecord&DestApp=WOS&KeyUT=000828443400001 Artificial neural network Extended seasonal prediction North American Multi-Model Ensemble Sea surface temperature Statistical forecast Upper Colorado River Basin precipitation Article 2022 ftunivsthongkong https://doi.org/10.1007/s00382-022-06422-x 2024-04-30T23:35:43Z 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. © 2022, The Author(s). Article in Journal/Newspaper North Atlantic The Hong Kong University of Science and Technology: HKUST Institutional Repository Climate Dynamics |
institution |
Open Polar |
collection |
The Hong Kong University of Science and Technology: HKUST Institutional Repository |
op_collection_id |
ftunivsthongkong |
language |
English |
topic |
Artificial neural network Extended seasonal prediction North American Multi-Model Ensemble Sea surface temperature Statistical forecast Upper Colorado River Basin precipitation |
spellingShingle |
Artificial neural network Extended seasonal prediction North American Multi-Model Ensemble Sea surface temperature Statistical forecast Upper Colorado River Basin precipitation 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 |
Artificial neural network Extended seasonal prediction North American Multi-Model Ensemble Sea surface temperature Statistical forecast Upper Colorado River Basin precipitation |
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. © 2022, The Author(s). |
format |
Article in Journal/Newspaper |
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 Science and Business Media Deutschland GmbH |
publishDate |
2022 |
url |
https://repository.hkust.edu.hk/ir/Record/1783.1-120239 https://doi.org/10.1007/s00382-022-06422-x http://lbdiscover.ust.hk/uresolver?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rfr_id=info:sid/HKUST:SPI&rft.genre=article&rft.issn=0930-7575&rft.volume=&rft.issue=&rft.date=2022&rft.spage=&rft.aulast=Zhao&rft.aufirst=&rft.atitle=Extended+seasonal+prediction+of+spring+precipitation+over+the+Upper+Colorado+River+Basin&rft.title=Climate+Dynamics http://www.scopus.com/record/display.url?eid=2-s2.0-85134585327&origin=inward http://gateway.isiknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=LinksAMR&SrcApp=PARTNER_APP&DestLinkType=FullRecord&DestApp=WOS&KeyUT=000828443400001 |
genre |
North Atlantic |
genre_facet |
North Atlantic |
op_relation |
https://repository.hkust.edu.hk/ir/Record/1783.1-120239 Climate Dynamics, v. 60, 21 July 2022, p. 1815-1829 0930-7575 1432-0894 https://doi.org/10.1007/s00382-022-06422-x http://lbdiscover.ust.hk/uresolver?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rfr_id=info:sid/HKUST:SPI&rft.genre=article&rft.issn=0930-7575&rft.volume=&rft.issue=&rft.date=2022&rft.spage=&rft.aulast=Zhao&rft.aufirst=&rft.atitle=Extended+seasonal+prediction+of+spring+precipitation+over+the+Upper+Colorado+River+Basin&rft.title=Climate+Dynamics http://www.scopus.com/record/display.url?eid=2-s2.0-85134585327&origin=inward http://gateway.isiknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=LinksAMR&SrcApp=PARTNER_APP&DestLinkType=FullRecord&DestApp=WOS&KeyUT=000828443400001 |
op_doi |
https://doi.org/10.1007/s00382-022-06422-x |
container_title |
Climate Dynamics |
_version_ |
1799485359132770304 |