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Advancing early warning capabilities with CHIRPS-compatible NCEP GEFS precipitation forecasts

CHIRPS-GEFS is an operational data set that provides daily bias-corrected forecasts for next 1-day to ~15-day precipitation totals and anomalies at a quasi-global 50-deg N to 50-deg S extent and 0.05-degree resolution. These are based on National Centers for Environmental Prediction (NCEP) Global En...

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Autores principales: Harrison, Laura, Landsfeld, Martin, Husak, Greg, Davenport, Frank, Shukla, Shraddhanand, Turner, William, Peterson, Pete, Funk, Chris
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9246965/
https://www.ncbi.nlm.nih.gov/pubmed/35773449
http://dx.doi.org/10.1038/s41597-022-01468-2
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author Harrison, Laura
Landsfeld, Martin
Husak, Greg
Davenport, Frank
Shukla, Shraddhanand
Turner, William
Peterson, Pete
Funk, Chris
author_facet Harrison, Laura
Landsfeld, Martin
Husak, Greg
Davenport, Frank
Shukla, Shraddhanand
Turner, William
Peterson, Pete
Funk, Chris
author_sort Harrison, Laura
collection PubMed
description CHIRPS-GEFS is an operational data set that provides daily bias-corrected forecasts for next 1-day to ~15-day precipitation totals and anomalies at a quasi-global 50-deg N to 50-deg S extent and 0.05-degree resolution. These are based on National Centers for Environmental Prediction (NCEP) Global Ensemble Forecast System version 12 (GEFS v12) precipitation forecasts. CHIRPS-GEFS forecasts are compatible with Climate Hazards center InfraRed Precipitation with Stations (CHIRPS) data, which is actively used for drought monitoring, early warning, and near real-time impact assessments. A rank-based quantile matching procedure is used to transform GEFS v12 “reforecast” and “real-time” forecast ensemble means to CHIRPS spatial-temporal characteristics. Matching distributions to CHIRPS makes forecasts better reflect local climatology at finer spatial resolution and reduces moderate-to-large forecast errors. As shown in this study, having a CHIRPS-compatible version of the latest generation of NCEP GEFS forecasts enables rapid assessment of current forecasts and local historical context. CHIRPS-GEFS effectively bridges the gap between observations and weather predictions, increasing the value of both by connecting monitoring resources (CHIRPS) with interoperable forecasts.
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spelling pubmed-92469652022-07-02 Advancing early warning capabilities with CHIRPS-compatible NCEP GEFS precipitation forecasts Harrison, Laura Landsfeld, Martin Husak, Greg Davenport, Frank Shukla, Shraddhanand Turner, William Peterson, Pete Funk, Chris Sci Data Data Descriptor CHIRPS-GEFS is an operational data set that provides daily bias-corrected forecasts for next 1-day to ~15-day precipitation totals and anomalies at a quasi-global 50-deg N to 50-deg S extent and 0.05-degree resolution. These are based on National Centers for Environmental Prediction (NCEP) Global Ensemble Forecast System version 12 (GEFS v12) precipitation forecasts. CHIRPS-GEFS forecasts are compatible with Climate Hazards center InfraRed Precipitation with Stations (CHIRPS) data, which is actively used for drought monitoring, early warning, and near real-time impact assessments. A rank-based quantile matching procedure is used to transform GEFS v12 “reforecast” and “real-time” forecast ensemble means to CHIRPS spatial-temporal characteristics. Matching distributions to CHIRPS makes forecasts better reflect local climatology at finer spatial resolution and reduces moderate-to-large forecast errors. As shown in this study, having a CHIRPS-compatible version of the latest generation of NCEP GEFS forecasts enables rapid assessment of current forecasts and local historical context. CHIRPS-GEFS effectively bridges the gap between observations and weather predictions, increasing the value of both by connecting monitoring resources (CHIRPS) with interoperable forecasts. Nature Publishing Group UK 2022-06-30 /pmc/articles/PMC9246965/ /pubmed/35773449 http://dx.doi.org/10.1038/s41597-022-01468-2 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Data Descriptor
Harrison, Laura
Landsfeld, Martin
Husak, Greg
Davenport, Frank
Shukla, Shraddhanand
Turner, William
Peterson, Pete
Funk, Chris
Advancing early warning capabilities with CHIRPS-compatible NCEP GEFS precipitation forecasts
title Advancing early warning capabilities with CHIRPS-compatible NCEP GEFS precipitation forecasts
title_full Advancing early warning capabilities with CHIRPS-compatible NCEP GEFS precipitation forecasts
title_fullStr Advancing early warning capabilities with CHIRPS-compatible NCEP GEFS precipitation forecasts
title_full_unstemmed Advancing early warning capabilities with CHIRPS-compatible NCEP GEFS precipitation forecasts
title_short Advancing early warning capabilities with CHIRPS-compatible NCEP GEFS precipitation forecasts
title_sort advancing early warning capabilities with chirps-compatible ncep gefs precipitation forecasts
topic Data Descriptor
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9246965/
https://www.ncbi.nlm.nih.gov/pubmed/35773449
http://dx.doi.org/10.1038/s41597-022-01468-2
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