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纸质出版日期： 2017-9 ，
录用日期： 2017-5-11
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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
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。
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.
质量控制GSIAMSU-A资料同化FY-3CVIRR积雪数据
quality controlGSIAMSU-A data assimilationFY-3CVIRR snow daily products
Bennartz R, Thoss A, Dybbroe A and Michelson D B. 2002. Precipitation analysis using the advanced microwave sounding unit in support of nowcasting applications. Meteorological Applications, 9(2): 177–189
Candy B, English S, Renshaw R and Macpherson B. 2003. Use of AMSU data in the met office UK mesoscale model//Proceedings of International TOVS Study Conference-XIII. Sainte Adele, Canada: [s.n.]
Derber J C and Wu W S. 1998. The use of TOVS cloud-cleared radiances in the NCEP SSI analysis system. Monthly Weather Review, 126(8): 2287–2299
English S J, Renshaw R J, Dibben P C and Eyre J R. 1997. The AAPP module for identifying precipitation, ice cloud, liquid water, and surface type on the AMSU-A grid//Proceedings of the 9th International TOVS Study Conference. Igls, Austria: ECMWF: 119–130
Eyre J R, Kelly G A, McNally A P, Andersson E and Persson A. 1993. Assimilation of TOVS radiance information through one-dimensional variational analysis. Quarterly Journal of the Royal Meteorological Society, 119(514): 1427–1463
Geer A J, Bauer P and Lopez P. 2010. Direct 4D-Var assimilation of all-sky radiances. Part II: assessment. Quarterly Journal of the Royal Meteorological Society, 136(652): 1886–1905
管成功, 陈起英, 佟华, 王辉. 2008. T639L60全球中期预报系统预报试验和性能评估. 气象, 34(6): 11–16
Guan C G, Chen Q Y, Tong H and Wang H. 2008. Experiments and evaluations of global medium range forecast system of T639L60. Meteorological Monthly, 34(6): 11–16
李娟, 朱国富. 2008. 直接同化卫星辐射率资料在暴雨预报中的应用研究. 气象, 34(12): 36–43
Li J and Zhu G F. 2008. Application analysis of direct assimilation of satellite radiation data on heavy rain forecasting. Meteorological Monthly, 34(12): 36–43
刘玉洁, 郑照军, 王丽波. 2003. 我国西部地区冬季雪盖遥感和变化分析. 气候与环境研究, 8(1): 114–123
Liu Y J, Zheng Z J and Wang L B. 2003. Remote sensing on snow cover and variation analyzing in west of China. Climatic and Environmental Research, 8(1): 114–123
麻素红, 吴俞, 瞿安祥, 肖天贵, 李勋. 2012. T213与T639模式热带气旋预报误差对比. 应用气象学报, 23(2): 167–173
Ma S H, Wu Y, Qu A X, Xiao T G and Li X. 2012. Comparative analysis on tropical cyclone numerical forecast errors of T213 and T639 models. Journal of Applied Meteorological Science, 23(2): 167–173
McNally, Andersson A P, Kelly E and Saunders G. W R. 1999. The use of raw TOVS/ATVOS radiances in the ECMWF 4D-Var assimilation system. Ecmwf Newsletter
Purser R J, Wu W S, Parrish D F and Roberts N M. 2003a. Numerical aspects of the application of recursive filters to variational statistical analysis. Part I: spatially homogeneous and isotropic Gaussian Covariances. Monthly Weather Review, 131(8): 1524–1535
Purser R J, Wu W S, Parrish D F and Roberts N M. 2003b. Numerical aspects of the application of recursive filters to variational statistical analysis. Part II: spatially inhomogeneous and anisotropic general Covariances. Monthly Weather Review, 131(8): 1536–1548
Weng F Z and Grody N C. 1998. Physical retrieval of land surface temperature using the special sensor microwave imager. Journal of Geophysical Research: Atmospheres, 103(D8): 8839–8848
Weng F Z, Yan B H and Grody N C. 2001. A microwave land emissivity model. Journal of Geophysical Research: Atmospheres, 106(D17): 20115–20123
Weng F Z. 2007. Advances in radiative transfer modeling in support of satellite data assimilation. Journal of the Atmospheric Sciences, 64(11): 3799–3807
Wu W A, Purser J and Parrish D F. 2002. Three-dimensional variational analysis with spatially inhomogeneous covariances. Monthly Weather Review, 130(12): 2905–2916
薛纪善. 2006. 新世纪初我国数值天气预报的科技创新研究. 应用气象学报, 17(5): 602–610
Xue J S. 2006. Progress of Chinese numerical prediction in the early new century. Journal of Applied Meteorological Science, 17(5): 602–610
杨军, 董超华, 卢乃锰, 杨忠东, 施进明, 张鹏, 刘玉洁, 蔡斌. 2011. 新一代风云极轨气象卫星业务产品及应用. 北京: 科学出版社: 170–176
Yang J, Dong C H, Lu N M, Yang Z D, Shi J M, Zhang P, Liu Y J and Cai B. 2011. The New General of Fengyun Orbit Meteorological Satellite Business Applications and Data Application. Beijing: Science Publishing Press: 170–176
杨寅, 韩威, 董佩明. 2011. AMSU微波探测资料同化的质量控制方法概述. 气象, 37(11): 1395–1401
Yang Y, Han W and Dong P M. 2011. Overview on the quality control in assimilation of AMSU microwave sounding data. Meteorological Monthly, 37(11): 1395–1401
张华, 丑纪范, 邱崇践. 2004. 西北太平洋威马逊台风结构的卫星观测同化分析. 科学通报, 49(5): 493–498
Zhang H, Chou J F and Qiu C J. 2004. Assimilation analysis of Rammasun typhoon structure over Northwest Pacific using satellite data. Chinese Science Bulletin, 49(5): 493–498
朱国富, 薛纪善, 张华, 刘志权, 庄世宇, 黄丽萍, 董佩明. 2008. GRAPES变分同化系统中卫星辐射率资料的直接同化. 科学通报, 53(20): 3465–3469
Zhu G F, Xue J S, Zhang H, Liu Z Q, Zhuang S Y, Huang L P and Dong P M. 2008. Direct assimilation of satellite radiance data in GRAPES variational assimilation system. Chinese Science Bulletin, 53(20): 3465–3469
邹晓蕾, 秦正坤, 翁富忠. 2016. 晨昏轨道微波温度计资料同化对降水定量预报的影响及其对三轨卫星系统的意义. 大气科学, 40(1): 46–62
Zou X L, Qin Z K and Weng F Z. 2016. Impact of dawn-dusk satellite AMSU-a data on quantitative precipitation forecasts and the implications for three-orbit constellation. Chinese Journal of Atmospheric Sciences, 40(1): 46–62
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