Seasonal intercomparison of observational rainfall datasets over India during the southwest monsoon season.
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| Title: | Seasonal intercomparison of observational rainfall datasets over India during the southwest monsoon season. |
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| Authors: | Prakash, Satya1, Mitra, Ashis K.1, Momin, Imran M.1, Rajagopal, E. N.1, Basu, S.1, Collins, Mat2, Turner, Andrew G.3, Achuta Rao, K.4, Ashok, K.5 |
| Source: | International Journal of Climatology. Jul2015, Vol. 35 Issue 9, p2326-2338. 13p. |
| Subject Terms: | *Rainfall, *Water security, *Evaporation (Meteorology), *Climatology, *Meteorological precipitation |
| Abstract: | ABSTRACT The Indian monsoon is an important component of Earth's climate system, accurate forecasting of its mean rainfall being essential for regional food and water security. Accurate measurement of rainfall is essential for various water-related applications, the evaluation of numerical models and detection and attribution of trends, but a variety of different gridded rainfall datasets are available for these purposes. In this study, six gridded rainfall datasets are compared against the India Meteorological Department (IMD) gridded rainfall dataset, chosen as the most representative of the observed system due to its high gauge density. The datasets comprise those based solely on rain gauge observations and those merging rain gauge data with satellite-derived products. Various skill metrics and subjective comparisons are carried out for the Indian region during the southwest monsoon season (June-September). Relative biases and skill metrics are documented at all-India and subregional scales. In the gauge-based (land-only) category, Asian Precipitation - Highly-Resolved Observational Data Integration Towards Evaluation of water resources (APHRODITE) and Global Precipitation Climatology Center (GPCC) datasets perform better relative to the others in terms of a variety of skill metrics. In the merged category, the Global Precipitation Climatology Project (GPCP) dataset is shown to perform better than the Climate Prediction Center Merged Analysis of Precipitation (CMAP) for the Indian monsoon in terms of various metrics, when compared with the IMD gridded data. Most of the datasets have difficulties in representing rainfall over orographic regions including the Western Ghats mountains, in Northeast India and the Himalayan foothills. The wide range of skill metrics seen among the datasets and even the change of sign of bias found in some years are causes of concern. This uncertainty between datasets is largest in Northeast India. These results will help those studying the Indian monsoon region to select an appropriate dataset depending on their application and focus of research. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Climatology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
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| Header | DbId: 8gh DbLabel: GreenFILE An: 108336972 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Seasonal intercomparison of observational rainfall datasets over India during the southwest monsoon season. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Prakash%2C+Satya%22">Prakash, Satya</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Mitra%2C+Ashis+K%2E%22">Mitra, Ashis K.</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Momin%2C+Imran+M%2E%22">Momin, Imran M.</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Rajagopal%2C+E%2E+N%2E%22">Rajagopal, E. N.</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Basu%2C+S%2E%22">Basu, S.</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Collins%2C+Mat%22">Collins, Mat</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Turner%2C+Andrew+G%2E%22">Turner, Andrew G.</searchLink><relatesTo>3</relatesTo><br /><searchLink fieldCode="AR" term="%22Achuta+Rao%2C+K%2E%22">Achuta Rao, K.</searchLink><relatesTo>4</relatesTo><br /><searchLink fieldCode="AR" term="%22Ashok%2C+K%2E%22">Ashok, K.</searchLink><relatesTo>5</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Climatology%22">International Journal of Climatology</searchLink>. Jul2015, Vol. 35 Issue 9, p2326-2338. 13p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Rainfall%22">Rainfall</searchLink><br />*<searchLink fieldCode="DE" term="%22Water+security%22">Water security</searchLink><br />*<searchLink fieldCode="DE" term="%22Evaporation+%28Meteorology%29%22">Evaporation (Meteorology)</searchLink><br />*<searchLink fieldCode="DE" term="%22Climatology%22">Climatology</searchLink><br />*<searchLink fieldCode="DE" term="%22Meteorological+precipitation%22">Meteorological precipitation</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: ABSTRACT The Indian monsoon is an important component of Earth's climate system, accurate forecasting of its mean rainfall being essential for regional food and water security. Accurate measurement of rainfall is essential for various water-related applications, the evaluation of numerical models and detection and attribution of trends, but a variety of different gridded rainfall datasets are available for these purposes. In this study, six gridded rainfall datasets are compared against the India Meteorological Department (IMD) gridded rainfall dataset, chosen as the most representative of the observed system due to its high gauge density. The datasets comprise those based solely on rain gauge observations and those merging rain gauge data with satellite-derived products. Various skill metrics and subjective comparisons are carried out for the Indian region during the southwest monsoon season (June-September). Relative biases and skill metrics are documented at all-India and subregional scales. In the gauge-based (land-only) category, Asian Precipitation - Highly-Resolved Observational Data Integration Towards Evaluation of water resources (APHRODITE) and Global Precipitation Climatology Center (GPCC) datasets perform better relative to the others in terms of a variety of skill metrics. In the merged category, the Global Precipitation Climatology Project (GPCP) dataset is shown to perform better than the Climate Prediction Center Merged Analysis of Precipitation (CMAP) for the Indian monsoon in terms of various metrics, when compared with the IMD gridded data. Most of the datasets have difficulties in representing rainfall over orographic regions including the Western Ghats mountains, in Northeast India and the Himalayan foothills. The wide range of skill metrics seen among the datasets and even the change of sign of bias found in some years are causes of concern. This uncertainty between datasets is largest in Northeast India. These results will help those studying the Indian monsoon region to select an appropriate dataset depending on their application and focus of research. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of International Journal of Climatology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1002/joc.4129 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 13 StartPage: 2326 Subjects: – SubjectFull: Rainfall Type: general – SubjectFull: Water security Type: general – SubjectFull: Evaporation (Meteorology) Type: general – SubjectFull: Climatology Type: general – SubjectFull: Meteorological precipitation Type: general Titles: – TitleFull: Seasonal intercomparison of observational rainfall datasets over India during the southwest monsoon season. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Prakash, Satya – PersonEntity: Name: NameFull: Mitra, Ashis K. – PersonEntity: Name: NameFull: Momin, Imran M. – PersonEntity: Name: NameFull: Rajagopal, E. N. – PersonEntity: Name: NameFull: Basu, S. – PersonEntity: Name: NameFull: Collins, Mat – PersonEntity: Name: NameFull: Turner, Andrew G. – PersonEntity: Name: NameFull: Achuta Rao, K. – PersonEntity: Name: NameFull: Ashok, K. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 07 Text: Jul2015 Type: published Y: 2015 Identifiers: – Type: issn-print Value: 08998418 Numbering: – Type: volume Value: 35 – Type: issue Value: 9 Titles: – TitleFull: International Journal of Climatology Type: main |
| ResultId | 1 |
