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High-resolution gridded soil moisture and soil temperature datasets for the Indian monsoon region
High-resolution soil moisture/temperature (SM/ST) are critical components of the growing demand for fine-scale products over the Indian monsoon region (IMR) which has diverse land-surface characteristics. This demand is fueled by findings that improved representation of land-state help improve rainf...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6244185/ https://www.ncbi.nlm.nih.gov/pubmed/30457572 http://dx.doi.org/10.1038/sdata.2018.264 |
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author | Nayak, H. P. Osuri, K. K. Sinha, Palash Nadimpalli, Raghu Mohanty, U. C. Chen, Fei Rajeevan, M. Niyogi, D. |
author_facet | Nayak, H. P. Osuri, K. K. Sinha, Palash Nadimpalli, Raghu Mohanty, U. C. Chen, Fei Rajeevan, M. Niyogi, D. |
author_sort | Nayak, H. P. |
collection | PubMed |
description | High-resolution soil moisture/temperature (SM/ST) are critical components of the growing demand for fine-scale products over the Indian monsoon region (IMR) which has diverse land-surface characteristics. This demand is fueled by findings that improved representation of land-state help improve rainfall/flood prediction. Here we report on the development of a high-resolution (4 km and 3 hourly) SM/ST product for 2001–2014 during Indian monsoon seasons (June–September). First, the quality of atmospheric fields from five reanalysis sources was examined to identify realistic forcing to a land data assimilation system (LDAS). The evaluation of developed SM/ST against observations highlighted the importance of quality forcing fields. There is a significant relation between the forcing error and the errors in the SM/ST. A combination of forcing fields was used to develop 14-years of SM/ST data. This dataset captured inter-annual, intra-seasonal, and diurnal variations under different monsoon conditions. When the mesoscale model was initialized using the SM/ST data, improved simulations of heavy rain events was evident, demonstrating the value of the data over IMR. |
format | Online Article Text |
id | pubmed-6244185 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Nature Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-62441852018-11-21 High-resolution gridded soil moisture and soil temperature datasets for the Indian monsoon region Nayak, H. P. Osuri, K. K. Sinha, Palash Nadimpalli, Raghu Mohanty, U. C. Chen, Fei Rajeevan, M. Niyogi, D. Sci Data Data Descriptor High-resolution soil moisture/temperature (SM/ST) are critical components of the growing demand for fine-scale products over the Indian monsoon region (IMR) which has diverse land-surface characteristics. This demand is fueled by findings that improved representation of land-state help improve rainfall/flood prediction. Here we report on the development of a high-resolution (4 km and 3 hourly) SM/ST product for 2001–2014 during Indian monsoon seasons (June–September). First, the quality of atmospheric fields from five reanalysis sources was examined to identify realistic forcing to a land data assimilation system (LDAS). The evaluation of developed SM/ST against observations highlighted the importance of quality forcing fields. There is a significant relation between the forcing error and the errors in the SM/ST. A combination of forcing fields was used to develop 14-years of SM/ST data. This dataset captured inter-annual, intra-seasonal, and diurnal variations under different monsoon conditions. When the mesoscale model was initialized using the SM/ST data, improved simulations of heavy rain events was evident, demonstrating the value of the data over IMR. Nature Publishing Group 2018-11-20 /pmc/articles/PMC6244185/ /pubmed/30457572 http://dx.doi.org/10.1038/sdata.2018.264 Text en Copyright © 2018, The Author(s) http://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/ The Creative Commons Public Domain Dedication waiver http://creativecommons.org/publicdomain/zero/1.0/ applies to the metadata files made available in this article. |
spellingShingle | Data Descriptor Nayak, H. P. Osuri, K. K. Sinha, Palash Nadimpalli, Raghu Mohanty, U. C. Chen, Fei Rajeevan, M. Niyogi, D. High-resolution gridded soil moisture and soil temperature datasets for the Indian monsoon region |
title | High-resolution gridded soil moisture and soil temperature datasets for the Indian monsoon region |
title_full | High-resolution gridded soil moisture and soil temperature datasets for the Indian monsoon region |
title_fullStr | High-resolution gridded soil moisture and soil temperature datasets for the Indian monsoon region |
title_full_unstemmed | High-resolution gridded soil moisture and soil temperature datasets for the Indian monsoon region |
title_short | High-resolution gridded soil moisture and soil temperature datasets for the Indian monsoon region |
title_sort | high-resolution gridded soil moisture and soil temperature datasets for the indian monsoon region |
topic | Data Descriptor |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6244185/ https://www.ncbi.nlm.nih.gov/pubmed/30457572 http://dx.doi.org/10.1038/sdata.2018.264 |
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