Evaluation of precipitation datasets available on Google earth engine over India
Monthly mean precipitation estimates of seven products (TerraClimate, TRMM, CHIRPS, PERSIANN‐CDR, GPM‐IMERG, ERA5 and CFSR) available on Google earth engine (GEE) are evaluated against gridded gauge‐based precipitation product available from Indian Meteorological Department (IMD) for their skills an...
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Veröffentlicht in: | International journal of climatology 2021-08, Vol.41 (10), p.4844-4863 |
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description | Monthly mean precipitation estimates of seven products (TerraClimate, TRMM, CHIRPS, PERSIANN‐CDR, GPM‐IMERG, ERA5 and CFSR) available on Google earth engine (GEE) are evaluated against gridded gauge‐based precipitation product available from Indian Meteorological Department (IMD) for their skills and presence of systematic biases (during 2001–2018). All these products represent the climatological features reasonably well. Presence of systematic biases in these products is also observed from their evaluation. Biases across the periphery of the country are relatively on the higher side in comparison to the central regions. The magnitude of spatial variability is represented better for winter precipitation in comparison to summer precipitation. During both winter and summer, ensemble mean of various products outperforms individual products in terms of both RMSE and correlation. Performance of these products is also assessed across various Indian states, elevation bands and climate zones. The ability of these products to represent the seasonality was observed to be highest for the states with mid‐ranged peaks (10–20 mm·day−1) which tend to decrease with both increasing and decreasing peaks. Ability of the precipitation products to resemble the annual cycle does not vary with the amount of precipitation, although individual disparity among the products exists. Additionally, an alternative approach for data evaluation using Multiple Triple Collocation (MTC) was performed for the period 2001–2015 using an additional dataset obtained from soil‐moisture‐based rainfall estimates (SM2RAIN). Results from MTC convey that ERA5 performs relatively poor in comparison to the other products for central India followed by CFSR. In brief, the comprehensive evaluation of precipitation products reported herein will act a valuable reference for the researchers as well as decision makers to select the optimal product for their intended application and will inform the users about the various uncertainties in the foundations and specification of these products.
Annual mean precipitation over land surface of India for the period (2001–2018) from IMD data (scale on the top left corner in mm·day−1). Biases in annual mean precipitation with respect to IMD for each individual precipitation product (b–h) and (i) represent multiproduct ensemble of all these datasets (scale on the right represent the bias values in mm·day−1). |
doi_str_mv | 10.1002/joc.7102 |
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Annual mean precipitation over land surface of India for the period (2001–2018) from IMD data (scale on the top left corner in mm·day−1). Biases in annual mean precipitation with respect to IMD for each individual precipitation product (b–h) and (i) represent multiproduct ensemble of all these datasets (scale on the right represent the bias values in mm·day−1).</description><identifier>ISSN: 0899-8418</identifier><identifier>EISSN: 1097-0088</identifier><identifier>DOI: 10.1002/joc.7102</identifier><language>eng</language><publisher>Chichester, UK: John Wiley & Sons, Ltd</publisher><subject>Annual precipitation ; Annual variations ; Atmospheric precipitations ; Datasets ; Estimates ; Evaluation ; Google earth engine ; India ; Köppen–Geiger climate classification ; Mean precipitation ; Precipitation ; Precipitation estimation ; Products ; Rain ; Rainfall ; Seasonal variations ; Seasonality ; Soil moisture ; Spatial variability ; Spatial variations ; Summer ; Summer precipitation ; Tropical Rainfall Measuring Mission (TRMM) ; Winter ; Winter precipitation</subject><ispartof>International journal of climatology, 2021-08, Vol.41 (10), p.4844-4863</ispartof><rights>2021 Royal Meteorological Society</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c2932-f4130e06118c71231e5a078a494fd667b8d3048f5c1513e256b181061e9b9ca43</citedby><cites>FETCH-LOGICAL-c2932-f4130e06118c71231e5a078a494fd667b8d3048f5c1513e256b181061e9b9ca43</cites><orcidid>0000-0001-9619-4185 ; 0000-0001-9777-6128</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1002%2Fjoc.7102$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1002%2Fjoc.7102$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>314,780,784,1416,27915,27916,45565,45566</link.rule.ids></links><search><creatorcontrib>Dubey, Saket</creatorcontrib><creatorcontrib>Gupta, Harshit</creatorcontrib><creatorcontrib>Goyal, Manish Kumar</creatorcontrib><creatorcontrib>Joshi, Nitin</creatorcontrib><title>Evaluation of precipitation datasets available on Google earth engine over India</title><title>International journal of climatology</title><description>Monthly mean precipitation estimates of seven products (TerraClimate, TRMM, CHIRPS, PERSIANN‐CDR, GPM‐IMERG, ERA5 and CFSR) available on Google earth engine (GEE) are evaluated against gridded gauge‐based precipitation product available from Indian Meteorological Department (IMD) for their skills and presence of systematic biases (during 2001–2018). All these products represent the climatological features reasonably well. Presence of systematic biases in these products is also observed from their evaluation. Biases across the periphery of the country are relatively on the higher side in comparison to the central regions. The magnitude of spatial variability is represented better for winter precipitation in comparison to summer precipitation. During both winter and summer, ensemble mean of various products outperforms individual products in terms of both RMSE and correlation. Performance of these products is also assessed across various Indian states, elevation bands and climate zones. The ability of these products to represent the seasonality was observed to be highest for the states with mid‐ranged peaks (10–20 mm·day−1) which tend to decrease with both increasing and decreasing peaks. Ability of the precipitation products to resemble the annual cycle does not vary with the amount of precipitation, although individual disparity among the products exists. Additionally, an alternative approach for data evaluation using Multiple Triple Collocation (MTC) was performed for the period 2001–2015 using an additional dataset obtained from soil‐moisture‐based rainfall estimates (SM2RAIN). Results from MTC convey that ERA5 performs relatively poor in comparison to the other products for central India followed by CFSR. In brief, the comprehensive evaluation of precipitation products reported herein will act a valuable reference for the researchers as well as decision makers to select the optimal product for their intended application and will inform the users about the various uncertainties in the foundations and specification of these products.
Annual mean precipitation over land surface of India for the period (2001–2018) from IMD data (scale on the top left corner in mm·day−1). Biases in annual mean precipitation with respect to IMD for each individual precipitation product (b–h) and (i) represent multiproduct ensemble of all these datasets (scale on the right represent the bias values in mm·day−1).</description><subject>Annual precipitation</subject><subject>Annual variations</subject><subject>Atmospheric precipitations</subject><subject>Datasets</subject><subject>Estimates</subject><subject>Evaluation</subject><subject>Google earth engine</subject><subject>India</subject><subject>Köppen–Geiger climate classification</subject><subject>Mean precipitation</subject><subject>Precipitation</subject><subject>Precipitation estimation</subject><subject>Products</subject><subject>Rain</subject><subject>Rainfall</subject><subject>Seasonal variations</subject><subject>Seasonality</subject><subject>Soil moisture</subject><subject>Spatial variability</subject><subject>Spatial variations</subject><subject>Summer</subject><subject>Summer precipitation</subject><subject>Tropical Rainfall Measuring Mission (TRMM)</subject><subject>Winter</subject><subject>Winter precipitation</subject><issn>0899-8418</issn><issn>1097-0088</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><recordid>eNp10FFLwzAQB_AgCs4p-BEKvvjSeZe2afIoY87JYD7oc0jTdGbUpibdZN_ezPrq0x1_fncHR8gtwgwB6MPO6VmJQM_IBEGUKQDn52QCXIiU58gvyVUIOwAQAtmEvC4Oqt2rwboucU3Se6Ntb4cxqNWgghlCog7KtqpqTRLTpXPb2Bnlh4_EdFvbxfhgfLLqaquuyUWj2mBu_uqUvD8t3ubP6XqzXM0f16mmIqNpk2MGBhgi1yXSDE2hoOQqF3lTM1ZWvM4g502hscDM0IJVyDF6IyqhVZ5Nyd24t_fua2_CIHdu77t4UtKiKBmjjPOo7kelvQvBm0b23n4qf5QI8vSvOKXl6V-RpiP9tq05_uvky2b-638AIABqWw</recordid><startdate>202108</startdate><enddate>202108</enddate><creator>Dubey, Saket</creator><creator>Gupta, Harshit</creator><creator>Goyal, Manish Kumar</creator><creator>Joshi, Nitin</creator><general>John Wiley & Sons, Ltd</general><general>Wiley Subscription Services, Inc</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7TG</scope><scope>7TN</scope><scope>F1W</scope><scope>H96</scope><scope>KL.</scope><scope>L.G</scope><orcidid>https://orcid.org/0000-0001-9619-4185</orcidid><orcidid>https://orcid.org/0000-0001-9777-6128</orcidid></search><sort><creationdate>202108</creationdate><title>Evaluation of precipitation datasets available on Google earth engine over India</title><author>Dubey, Saket ; Gupta, Harshit ; Goyal, Manish Kumar ; Joshi, Nitin</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c2932-f4130e06118c71231e5a078a494fd667b8d3048f5c1513e256b181061e9b9ca43</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Annual precipitation</topic><topic>Annual variations</topic><topic>Atmospheric precipitations</topic><topic>Datasets</topic><topic>Estimates</topic><topic>Evaluation</topic><topic>Google earth engine</topic><topic>India</topic><topic>Köppen–Geiger climate classification</topic><topic>Mean precipitation</topic><topic>Precipitation</topic><topic>Precipitation estimation</topic><topic>Products</topic><topic>Rain</topic><topic>Rainfall</topic><topic>Seasonal variations</topic><topic>Seasonality</topic><topic>Soil moisture</topic><topic>Spatial variability</topic><topic>Spatial variations</topic><topic>Summer</topic><topic>Summer precipitation</topic><topic>Tropical Rainfall Measuring Mission (TRMM)</topic><topic>Winter</topic><topic>Winter precipitation</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Dubey, Saket</creatorcontrib><creatorcontrib>Gupta, Harshit</creatorcontrib><creatorcontrib>Goyal, Manish Kumar</creatorcontrib><creatorcontrib>Joshi, Nitin</creatorcontrib><collection>CrossRef</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Oceanic Abstracts</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources</collection><collection>Meteorological & Geoastrophysical Abstracts - Academic</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><jtitle>International journal of climatology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Dubey, Saket</au><au>Gupta, Harshit</au><au>Goyal, Manish Kumar</au><au>Joshi, Nitin</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Evaluation of precipitation datasets available on Google earth engine over India</atitle><jtitle>International journal of climatology</jtitle><date>2021-08</date><risdate>2021</risdate><volume>41</volume><issue>10</issue><spage>4844</spage><epage>4863</epage><pages>4844-4863</pages><issn>0899-8418</issn><eissn>1097-0088</eissn><abstract>Monthly mean precipitation estimates of seven products (TerraClimate, TRMM, CHIRPS, PERSIANN‐CDR, GPM‐IMERG, ERA5 and CFSR) available on Google earth engine (GEE) are evaluated against gridded gauge‐based precipitation product available from Indian Meteorological Department (IMD) for their skills and presence of systematic biases (during 2001–2018). All these products represent the climatological features reasonably well. Presence of systematic biases in these products is also observed from their evaluation. Biases across the periphery of the country are relatively on the higher side in comparison to the central regions. The magnitude of spatial variability is represented better for winter precipitation in comparison to summer precipitation. During both winter and summer, ensemble mean of various products outperforms individual products in terms of both RMSE and correlation. Performance of these products is also assessed across various Indian states, elevation bands and climate zones. The ability of these products to represent the seasonality was observed to be highest for the states with mid‐ranged peaks (10–20 mm·day−1) which tend to decrease with both increasing and decreasing peaks. Ability of the precipitation products to resemble the annual cycle does not vary with the amount of precipitation, although individual disparity among the products exists. Additionally, an alternative approach for data evaluation using Multiple Triple Collocation (MTC) was performed for the period 2001–2015 using an additional dataset obtained from soil‐moisture‐based rainfall estimates (SM2RAIN). Results from MTC convey that ERA5 performs relatively poor in comparison to the other products for central India followed by CFSR. In brief, the comprehensive evaluation of precipitation products reported herein will act a valuable reference for the researchers as well as decision makers to select the optimal product for their intended application and will inform the users about the various uncertainties in the foundations and specification of these products.
Annual mean precipitation over land surface of India for the period (2001–2018) from IMD data (scale on the top left corner in mm·day−1). Biases in annual mean precipitation with respect to IMD for each individual precipitation product (b–h) and (i) represent multiproduct ensemble of all these datasets (scale on the right represent the bias values in mm·day−1).</abstract><cop>Chichester, UK</cop><pub>John Wiley & Sons, Ltd</pub><doi>10.1002/joc.7102</doi><tpages>20</tpages><orcidid>https://orcid.org/0000-0001-9619-4185</orcidid><orcidid>https://orcid.org/0000-0001-9777-6128</orcidid></addata></record> |
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subjects | Annual precipitation Annual variations Atmospheric precipitations Datasets Estimates Evaluation Google earth engine India Köppen–Geiger climate classification Mean precipitation Precipitation Precipitation estimation Products Rain Rainfall Seasonal variations Seasonality Soil moisture Spatial variability Spatial variations Summer Summer precipitation Tropical Rainfall Measuring Mission (TRMM) Winter Winter precipitation |
title | Evaluation of precipitation datasets available on Google earth engine over India |
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