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利用高分辨率土壤湿度和温度初始条件改进对印度季风区强雷暴的预测。

Improved prediction of severe thunderstorms over the Indian Monsoon region using high-resolution soil moisture and temperature initialization.

机构信息

Department of Earth and Atmospheric Sciences, National Institute of Technology, Rourkela, Odisha - 769 008, India.

Department of Agronomy and Department of Earth, Atmospheric, and Planetary Sciences, Purdue University, West Lafayette, IN 47907, USA.

出版信息

Sci Rep. 2017 Jan 27;7:41377. doi: 10.1038/srep41377.

DOI:10.1038/srep41377
PMID:28128293
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5269717/
Abstract

The hypothesis that realistic land conditions such as soil moisture/soil temperature (SM/ST) can significantly improve the modeling of mesoscale deep convection is tested over the Indian monsoon region (IMR). A high resolution (3 km foot print) SM/ST dataset prepared from a land data assimilation system, as part of a national monsoon mission project, showed close agreement with observations. Experiments are conducted with (LDAS) and without (CNTL) initialization of SM/ST dataset. Results highlight the significance of realistic land surface conditions on numerical prediction of initiation, movement and timing of severe thunderstorms as compared to that currently being initialized by climatological fields in CNTL run. Realistic land conditions improved mass flux, convective updrafts and diabatic heating in the boundary layer that contributed to low level positive potential vorticity. The LDAS run reproduced reflectivity echoes and associated rainfall bands more efficiently. Improper representation of surface conditions in CNTL run limit the evolution boundary layer processes and thereby failed to simulate convection at right time and place. These findings thus provide strong support to the role land conditions play in impacting the deep convection over the IMR. These findings also have direct implications for improving heavy rain forecasting over the IMR, by developing realistic land conditions.

摘要

该假设认为,现实的陆地条件,如土壤湿度/土壤温度(SM/ST),可以显著改善中尺度深对流的模拟,这一假设在印度季风区(IMR)得到了检验。一个高分辨率(3km 足迹)的 SM/ST 数据集是从一个土地数据同化系统中准备的,作为国家季风任务项目的一部分,与观测结果非常吻合。实验是在有(LDAS)和没有(CNTL)SM/ST 数据集初始化的情况下进行的。结果突出表明,与 CNTL 运行中当前由气候场初始化相比,现实的陆地表面条件对强雷暴的起始、移动和时间的数值预测具有重要意义。现实的陆地条件改善了边界层中的质量通量、对流上升气流和非绝热加热,这有助于低层正位涡。LDAS 运行更有效地再现了反射率回波和相关的雨带。CNTL 运行中对地表条件的不当表示限制了边界层过程的演化,从而未能在适当的时间和地点模拟对流。这些发现因此为陆地条件在影响 IMR 深对流方面所起的作用提供了强有力的支持。这些发现还对通过开发现实的陆地条件来改善 IMR 地区的强降雨预报具有直接意义。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/59bf/5269717/fd9a29ad3368/srep41377-f7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/59bf/5269717/55fec4865268/srep41377-f1.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/59bf/5269717/1b5d6995efd1/srep41377-f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/59bf/5269717/ee3fd675cc2d/srep41377-f4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/59bf/5269717/e66bf6d102dc/srep41377-f5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/59bf/5269717/e456ac5006c2/srep41377-f6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/59bf/5269717/fd9a29ad3368/srep41377-f7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/59bf/5269717/55fec4865268/srep41377-f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/59bf/5269717/64fc70d0e6eb/srep41377-f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/59bf/5269717/1b5d6995efd1/srep41377-f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/59bf/5269717/ee3fd675cc2d/srep41377-f4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/59bf/5269717/e66bf6d102dc/srep41377-f5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/59bf/5269717/e456ac5006c2/srep41377-f6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/59bf/5269717/fd9a29ad3368/srep41377-f7.jpg

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