Effect of FY-3C snow cover products on the quality control of assimilating satellite microwave sounding data

Xinyuan MA; Gang MA; Yunfeng WANG; Yang GUO; Jing HUANG; Hua TONG; Bo ZHONG

doi:10.11834/jrs.20176268

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Effect of FY-3C snow cover products on the quality control of assimilating satellite microwave sounding data
Vol. 21, Issue 5, Pages: 679-688(2017)
Published： 2017-9 ，

Accepted： 11 May 2017
DOI： 10.11834/jrs.20176268

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马新园, 马刚, 王云峰, 郭杨, 黄静, 佟华, 钟波. 2017. FY-3C积雪产品在同化中对微波资料质量控制的影响分析. 遥感学报, 21(5): 679–688

Ma X Y, Ma G, Wang Y F, Guo Y, Huang J, Tong H and Zhong B. 2017. Effect of FY-3C snow cover products on the quality control of assimilating satellite microwave sounding data. Journal of Remote Sensing, 21(5): 679–688
马新园, 马刚, 王云峰, 郭杨, 黄静, 佟华, 钟波. 2017. FY-3C积雪产品在同化中对微波资料质量控制的影响分析. 遥感学报, 21(5): 679–688 DOI： 10.11834/jrs.20176268.

Ma X Y, Ma G, Wang Y F, Guo Y, Huang J, Tong H and Zhong B. 2017. Effect of FY-3C snow cover products on the quality control of assimilating satellite microwave sounding data. Journal of Remote Sensing, 21(5): 679–688 DOI： 10.11834/jrs.20176268.

摘要

T639-GSI全球系统同化AMSU-A资料的过程中，目前使用的月平均积雪产品并不能反映中高纬度大陆上快速地降雪/融雪过程，而FY-3C日积雪产品在时间精度上要高于GSI月平均积雪覆盖数据。由于同化系统对AMSU-A较低通道辐射率资料的质量控制需要依据更准确的地表积雪信息，所以本文结合冬春季节的FY-3C日积雪产品和NCEP再分析资料，研究了北半球中高纬度地区不同积雪覆盖率初值对分析场不同高度层温度场的影响，以及在同化过程中对预报结果的影响。结果表明，在对AMSU-A辐射率资料的质量控制中，月平均积雪数据和日积雪产品对温度场影响较大的区域与两者积雪覆盖差异区域有明显的对应；冬春季节，使用FY-3C日积雪产品代替GSI月平均积雪数据作为背景场中积雪下垫面数据，对进入同化系统的AMSU-A辐射率资料质量控制时，120 h之内1000—600 hPa的中低层温度场的预报效果得到改善。

Abstract

Inconducting direct assimilation experiments of microwave radiance data AMSU-A with the numerical weather prediction model T639-GSI 3DVAR system

the GSI monthly snow products cannot reflect the process of snow or snow melt in the middle and high latitudes of the northern hemisphere. The precision of the FY-3C snow daily products is higher than GSI snow monthly products. This study investigates the effect of different snow coverage values on the temperature field at different heights in the middle and high latitudes of the northern hemisphere. The numerical simulation results in assimilation process are based on the FY-3C SNC real-time snow daily products on January 20

2016 and March 17

2016

as well as the NCEP reanalysis data. In the assimilation experiment

we analyzed the distribution of winter snow over the northern hemisphere in January 2016 and that of spring snow over the northern hemisphere in March 2016. This study selects the largest snowpack of the FY-3C snow daily products in the two months as the test data and replaces the snow month products as the snow products of the underlying surface in the background field to reflect the process of snow or snow melt in the middle and high latitudes of the northern hemisphere. The 6-hour forecast results of the T639 model serve as the background of the assimilation experiment. Furthermore

the same assimilation data for all groups are used. The key differences are as follows. In group A

the winter snow month products are sold as the snow products of the underlying surface in the background field. In group B

the FY-3C snow daily products serve as the snow products of the underlying surface in the background field. Groups C and D are spring tests similar to groups A and B. In the assimilation experiment

we analyzed the distribution of winter snow over in the northern hemisphere in January 2016 and the distribution of spring snow over the northern hemisphere in March 2016. In order to reflect the process of snow or snow melt in the middle and high latitudes in the northern hemisphere

we selected the biggest snowpack of the FY-3C snow daily products in 2 months as the test data and replaced the snow month products as the snow products of the underlying surface in the background field. The 6 hour forecast result of the T639 model as the background of assimilation experiment. The same assimilation data were added for all groups. A key difference was as follows. In group A

the winter snow month products in business were the snow products of the underlying surface in the background field. In group B

Fy-3C snow daily products were the snow products of the underlying surface in the background field. Group C and group D are spring tests (similar to A and B). In terms of quality control

the regional differences of the temperature field using the GSI monthly products and FY-3C snow daily products evidently corresponded to the snow coverage differences between the two snow products. In winter and spring

we utilized the FY-3C snow daily products to replace the GSI snow month products as the snow products of the underlying surface in the background field. Quality control was also established in the assimilation system for the radiance data. The condition improved within 120 hours to a certain degree on the temperature field prediction at low to middle atmosphere levels from 1000 hPa to 600 hPa.

关键词

质量控制GSIAMSU-A资料同化FY-3CVIRR积雪数据

Keywords

quality controlGSIAMSU-A data assimilationFY-3CVIRR snow daily products

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