An assessment of gridded precipitation products over High Mountain Asia

Ishrat J. Dollan; Fadji Z. Maina; Sujay V. Kumar; Efthymios I. Nikolopoulos; Viviana Maggioni

doi:10.1016/j.ejrh.2024.101675

An assessment of gridded precipitation products over High Mountain Asia

Ishrat J. Dollan, Fadji Z. Maina, Sujay V. Kumar, Efthymios I. Nikolopoulos, Viviana Maggioni

Research output: Contribution to journal › Article › peer-review

Abstract

Study region: High Mountain Asia Study focus: The study assesses five high-resolution gridded precipitation products against observations taken at twenty-seven weather stations across High Mountain Asia (HMA), one of the world's most complex and data scarce regions. Estimating spatiotemporal patterns of average and extreme precipitation in HMA is challenged by several factors, including the lack of ground observations, the diverse climate zones, and the sharp orographic gradients. Evaluating the quality and reliability of multi-source precipitation products before analyzing their trends and patterns is fundamental in such a region. A suite of high-resolution satellite-based product (the Climate Hazards Group Infrared Precipitation with Stations, CHIRPS, and the Integrated MultisatellitE Retrievals for Global Precipitation Measurement, IMERG), model reanalysis precipitation estimates (European Centre for Medium-Range Weather Forecasts, fifth generation atmospheric reanalysis product, ERA5), and their blended product (ensemble mean EM and a localized probability matched mean, LPM) are evaluated on a daily scale from 2001 to 2008. The performance of each product is analyzed in relation to elevation, seasonal accumulation, and extreme indices (e.g., frequency of wet days and consecutive wet days). New Hydrological Insights for the Region: Results show that reanalysis products present the largest overestimation, satellite retrievals the lowest, and ensemble products’ performance is encapsulated in between. LPM carries a high bias especially during winter when satellite estimates struggle to capture solid precipitation. The two ensemble products show higher correlations with the ground observations and smaller uncertainties in the error metrics than the original products.

Original language	American English
Article number	101675
Journal	Journal of Hydrology: Regional Studies
Volume	52
DOIs	https://doi.org/10.1016/j.ejrh.2024.101675
State	Published - Apr 2024
Externally published	Yes

ASJC Scopus subject areas

Water Science and Technology
Earth and Planetary Sciences (miscellaneous)

Keywords

Ensemble products
High Mountain Asia
Precipitation products
Seasons
Validation

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10.1016/j.ejrh.2024.101675

Cite this

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title = "An assessment of gridded precipitation products over High Mountain Asia",

abstract = "Study region: High Mountain Asia Study focus: The study assesses five high-resolution gridded precipitation products against observations taken at twenty-seven weather stations across High Mountain Asia (HMA), one of the world's most complex and data scarce regions. Estimating spatiotemporal patterns of average and extreme precipitation in HMA is challenged by several factors, including the lack of ground observations, the diverse climate zones, and the sharp orographic gradients. Evaluating the quality and reliability of multi-source precipitation products before analyzing their trends and patterns is fundamental in such a region. A suite of high-resolution satellite-based product (the Climate Hazards Group Infrared Precipitation with Stations, CHIRPS, and the Integrated MultisatellitE Retrievals for Global Precipitation Measurement, IMERG), model reanalysis precipitation estimates (European Centre for Medium-Range Weather Forecasts, fifth generation atmospheric reanalysis product, ERA5), and their blended product (ensemble mean EM and a localized probability matched mean, LPM) are evaluated on a daily scale from 2001 to 2008. The performance of each product is analyzed in relation to elevation, seasonal accumulation, and extreme indices (e.g., frequency of wet days and consecutive wet days). New Hydrological Insights for the Region: Results show that reanalysis products present the largest overestimation, satellite retrievals the lowest, and ensemble products{\textquoteright} performance is encapsulated in between. LPM carries a high bias especially during winter when satellite estimates struggle to capture solid precipitation. The two ensemble products show higher correlations with the ground observations and smaller uncertainties in the error metrics than the original products.",

keywords = "Ensemble products, High Mountain Asia, Precipitation products, Seasons, Validation",

author = "Dollan, {Ishrat J.} and Maina, {Fadji Z.} and Kumar, {Sujay V.} and Nikolopoulos, {Efthymios I.} and Viviana Maggioni",

note = "Publisher Copyright: {\textcopyright} 2024",

year = "2024",

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doi = "10.1016/j.ejrh.2024.101675",

language = "American English",

volume = "52",

journal = "Journal of Hydrology: Regional Studies",

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AU - Dollan, Ishrat J.

AU - Maina, Fadji Z.

AU - Kumar, Sujay V.

AU - Nikolopoulos, Efthymios I.

AU - Maggioni, Viviana

PY - 2024/4

Y1 - 2024/4

N2 - Study region: High Mountain Asia Study focus: The study assesses five high-resolution gridded precipitation products against observations taken at twenty-seven weather stations across High Mountain Asia (HMA), one of the world's most complex and data scarce regions. Estimating spatiotemporal patterns of average and extreme precipitation in HMA is challenged by several factors, including the lack of ground observations, the diverse climate zones, and the sharp orographic gradients. Evaluating the quality and reliability of multi-source precipitation products before analyzing their trends and patterns is fundamental in such a region. A suite of high-resolution satellite-based product (the Climate Hazards Group Infrared Precipitation with Stations, CHIRPS, and the Integrated MultisatellitE Retrievals for Global Precipitation Measurement, IMERG), model reanalysis precipitation estimates (European Centre for Medium-Range Weather Forecasts, fifth generation atmospheric reanalysis product, ERA5), and their blended product (ensemble mean EM and a localized probability matched mean, LPM) are evaluated on a daily scale from 2001 to 2008. The performance of each product is analyzed in relation to elevation, seasonal accumulation, and extreme indices (e.g., frequency of wet days and consecutive wet days). New Hydrological Insights for the Region: Results show that reanalysis products present the largest overestimation, satellite retrievals the lowest, and ensemble products’ performance is encapsulated in between. LPM carries a high bias especially during winter when satellite estimates struggle to capture solid precipitation. The two ensemble products show higher correlations with the ground observations and smaller uncertainties in the error metrics than the original products.

AB - Study region: High Mountain Asia Study focus: The study assesses five high-resolution gridded precipitation products against observations taken at twenty-seven weather stations across High Mountain Asia (HMA), one of the world's most complex and data scarce regions. Estimating spatiotemporal patterns of average and extreme precipitation in HMA is challenged by several factors, including the lack of ground observations, the diverse climate zones, and the sharp orographic gradients. Evaluating the quality and reliability of multi-source precipitation products before analyzing their trends and patterns is fundamental in such a region. A suite of high-resolution satellite-based product (the Climate Hazards Group Infrared Precipitation with Stations, CHIRPS, and the Integrated MultisatellitE Retrievals for Global Precipitation Measurement, IMERG), model reanalysis precipitation estimates (European Centre for Medium-Range Weather Forecasts, fifth generation atmospheric reanalysis product, ERA5), and their blended product (ensemble mean EM and a localized probability matched mean, LPM) are evaluated on a daily scale from 2001 to 2008. The performance of each product is analyzed in relation to elevation, seasonal accumulation, and extreme indices (e.g., frequency of wet days and consecutive wet days). New Hydrological Insights for the Region: Results show that reanalysis products present the largest overestimation, satellite retrievals the lowest, and ensemble products’ performance is encapsulated in between. LPM carries a high bias especially during winter when satellite estimates struggle to capture solid precipitation. The two ensemble products show higher correlations with the ground observations and smaller uncertainties in the error metrics than the original products.

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KW - Precipitation products

KW - Seasons

KW - Validation

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JF - Journal of Hydrology: Regional Studies

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ER -

An assessment of gridded precipitation products over High Mountain Asia

Abstract

ASJC Scopus subject areas

Keywords

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