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The climate hazards infrared precipitation with stations—a new environmental record for monitoring extremes

The Climate Hazards group Infrared Precipitation with Stations (CHIRPS) dataset builds on previous approaches to ‘smart’ interpolation techniques and high resolution, long period of record precipitation estimates based on infrared Cold Cloud Duration (CCD) observations. The algorithm i) is built aro...

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Autores principales: Funk, Chris, Peterson, Pete, Landsfeld, Martin, Pedreros, Diego, Verdin, James, Shukla, Shraddhanand, Husak, Gregory, Rowland, James, Harrison, Laura, Hoell, Andrew, Michaelsen, Joel
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4672685/
https://www.ncbi.nlm.nih.gov/pubmed/26646728
http://dx.doi.org/10.1038/sdata.2015.66
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author Funk, Chris
Peterson, Pete
Landsfeld, Martin
Pedreros, Diego
Verdin, James
Shukla, Shraddhanand
Husak, Gregory
Rowland, James
Harrison, Laura
Hoell, Andrew
Michaelsen, Joel
author_facet Funk, Chris
Peterson, Pete
Landsfeld, Martin
Pedreros, Diego
Verdin, James
Shukla, Shraddhanand
Husak, Gregory
Rowland, James
Harrison, Laura
Hoell, Andrew
Michaelsen, Joel
author_sort Funk, Chris
collection PubMed
description The Climate Hazards group Infrared Precipitation with Stations (CHIRPS) dataset builds on previous approaches to ‘smart’ interpolation techniques and high resolution, long period of record precipitation estimates based on infrared Cold Cloud Duration (CCD) observations. The algorithm i) is built around a 0.05° climatology that incorporates satellite information to represent sparsely gauged locations, ii) incorporates daily, pentadal, and monthly 1981-present 0.05° CCD-based precipitation estimates, iii) blends station data to produce a preliminary information product with a latency of about 2 days and a final product with an average latency of about 3 weeks, and iv) uses a novel blending procedure incorporating the spatial correlation structure of CCD-estimates to assign interpolation weights. We present the CHIRPS algorithm, global and regional validation results, and show how CHIRPS can be used to quantify the hydrologic impacts of decreasing precipitation and rising air temperatures in the Greater Horn of Africa. Using the Variable Infiltration Capacity model, we show that CHIRPS can support effective hydrologic forecasts and trend analyses in southeastern Ethiopia.
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spelling pubmed-46726852015-12-10 The climate hazards infrared precipitation with stations—a new environmental record for monitoring extremes Funk, Chris Peterson, Pete Landsfeld, Martin Pedreros, Diego Verdin, James Shukla, Shraddhanand Husak, Gregory Rowland, James Harrison, Laura Hoell, Andrew Michaelsen, Joel Sci Data Data Descriptor The Climate Hazards group Infrared Precipitation with Stations (CHIRPS) dataset builds on previous approaches to ‘smart’ interpolation techniques and high resolution, long period of record precipitation estimates based on infrared Cold Cloud Duration (CCD) observations. The algorithm i) is built around a 0.05° climatology that incorporates satellite information to represent sparsely gauged locations, ii) incorporates daily, pentadal, and monthly 1981-present 0.05° CCD-based precipitation estimates, iii) blends station data to produce a preliminary information product with a latency of about 2 days and a final product with an average latency of about 3 weeks, and iv) uses a novel blending procedure incorporating the spatial correlation structure of CCD-estimates to assign interpolation weights. We present the CHIRPS algorithm, global and regional validation results, and show how CHIRPS can be used to quantify the hydrologic impacts of decreasing precipitation and rising air temperatures in the Greater Horn of Africa. Using the Variable Infiltration Capacity model, we show that CHIRPS can support effective hydrologic forecasts and trend analyses in southeastern Ethiopia. Nature Publishing Group 2015-12-08 /pmc/articles/PMC4672685/ /pubmed/26646728 http://dx.doi.org/10.1038/sdata.2015.66 Text en Copyright © 2015, Macmillan Publishers Limited http://creativecommons.org/licenses/by/4.0 This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0 Metadata associated with this Data Descriptor is available at http://www.nature.com/sdata/ and is released under the CC0 waiver to maximize reuse.
spellingShingle Data Descriptor
Funk, Chris
Peterson, Pete
Landsfeld, Martin
Pedreros, Diego
Verdin, James
Shukla, Shraddhanand
Husak, Gregory
Rowland, James
Harrison, Laura
Hoell, Andrew
Michaelsen, Joel
The climate hazards infrared precipitation with stations—a new environmental record for monitoring extremes
title The climate hazards infrared precipitation with stations—a new environmental record for monitoring extremes
title_full The climate hazards infrared precipitation with stations—a new environmental record for monitoring extremes
title_fullStr The climate hazards infrared precipitation with stations—a new environmental record for monitoring extremes
title_full_unstemmed The climate hazards infrared precipitation with stations—a new environmental record for monitoring extremes
title_short The climate hazards infrared precipitation with stations—a new environmental record for monitoring extremes
title_sort climate hazards infrared precipitation with stations—a new environmental record for monitoring extremes
topic Data Descriptor
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4672685/
https://www.ncbi.nlm.nih.gov/pubmed/26646728
http://dx.doi.org/10.1038/sdata.2015.66
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