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

  • role： First author第一作者
  • Affiliation：

    PLA University of Science and Technology, Nanjing 211101, China

  • Email：mxy1027257501@163.com
  • Introduction：马新园(1989—　)，男，硕士研究生，研究方向为数值天气预报与卫星资料同化。E-mail：mxy1027257501@163.com
MA Xinyuan1,  
  • role： Corresponding author通信作者
  • Affiliation：

    National Satellite Meteorological Center, China Meteorological Administration, Beijing 100081, China

    Key Laboratory of Radiometric Calibration and Validation for Environmental Satellites, China Meteorological Administration, Beijing 100081, China

  • Email：magang@cma.gov.cn
  • Introduction：马刚(1972—　)，男，研究员，研究方向为数值天气预报与卫星资料同化。E-mail:magang@cma.gov.cn
MA Gang23*,  
  • Affiliation：

    PLA University of Science and Technology, Nanjing 211101, China

WANG Yunfeng1,  
  • Affiliation：

    National Satellite Meteorological Center, China Meteorological Administration, Beijing 100081, China

    Key Laboratory of Radiometric Calibration and Validation for Environmental Satellites, China Meteorological Administration, Beijing 100081, China

GUO Yang23,  
  • Affiliation：

    Numerical Weather Prediction Center of China Meteorological Administration, Beijing 100081, China

HUANG Jing4,  
  • Affiliation：

    Numerical Weather Prediction Center of China Meteorological Administration, Beijing 100081, China

TONG Hua4,  
  • Affiliation：

    PLA University of Science and Technology, Nanjing 211101, China

ZHONG Bo1

resumen

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.

palabra clave

quality control;GSI;AMSU-A data assimilation;FY-3C;VIRR snow daily products

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