High-resolution gridded soil moisture and soil temperature datasets for the Indian monsoon region

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作者
H. P. Nayak
K. K. Osuri
Palash Sinha
Raghu Nadimpalli
U. C. Mohanty
Fei Chen
M. Rajeevan
D. Niyogi
机构
[1] School of Earth Ocean and Climate Sciences,Department of Agronomy and Department of Earth
[2] Indian Institute of Technology,Department of Earth and Atmospheric Sciences
[3] Centre for Oceans,undefined
[4] Rivers,undefined
[5] Atmosphere and Land Sciences,undefined
[6] Indian Institute of Technology,undefined
[7] Atmospheric,undefined
[8] and Planetary Sciences,undefined
[9] Purdue University,undefined
[10] National Institute of Technology,undefined
[11] National Center of Atmospheric Research,undefined
[12] State Key Laboratory of Severe Weather,undefined
[13] Chinese Academy of Meteorological Science,undefined
[14] Ministry of Earth Sciences,undefined
[15] Prithvi Bhavan,undefined
[16] Lodhi Road,undefined
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摘要
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.
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