云和降水影响下AMSU资料一维变分反演的评估及改进
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摘要
本文首先使用云雨检测将“凡亚比”、“巨爵”等台风个例的AMSU资料区分为晴空、云和降水三种类型,再采用一维变分方法结合考虑散射效应的观测算子CRTM对AMSU资料进行物理反演,获得了晴空、云和降水天气下的温度、湿度、水物质反演结果,并对AMSU资料的观测误差进行了重估计。对于反演结果,使用FNL资料、云卫星CloudSat资料和“追风计划”的下投式探空仪资料为验证数据进行了检验。试验结果表明：
1、将AMSU-B ch2通道的初始观测增量和降水概率组合为云雨检测方案,能够合理地将AMSU资料区分为晴空、云和降水三种类型。该云雨检测方案可以作为不同天气下的AMSU资料反演及同化的质量控制标准。
2、观测误差重估计可以得到更客观的观测误差,改进温度、湿度反演效果。
3、对于温度变量,在晴空条件下变分方法能在大气各层上产生精度高于先验信息的反演结果；在云区和雨区中,变分方法能在大气的低层和高层得到比先验信息精确度更高的反演结果,而在大气的中层(500hPa左右),变分方法反演结果的精度因云和降水的出现而低于先验信息,出现这种情况的原因是观测算子CRTM对云和降水粒子微波散射效应的模拟还不够精确。此外,在所有天气条件下,反演温度与DOTSTAR下投式探空仪资料的偏差在3k以内：在水平方向上反演温度也与FNL资料相接近。
4、对于湿度变量,变分方法能够在大气高层(约400hPa以上)产生比先验信息更精确的反演结果；但是在大气的中层和低层(400hPa以下),变分方法反演结果的精确度却低于先验信息。
5、对于固态水路径IWP、液态水路径LWP、降水率RR等水物质变量,变分方法能够产生与CloudSat卫星资料相接近的反演结果。
6、反演试验的先验信息是AMSU资料经统计回归方法反演得到的,所以本试验可以比较变分方法与统计回归方法反演效果的差异：对于温度变量,晴空条件下变分方法的反演效果优于统计回归方法,而在云区和雨区中,变分方法只有在大气的低层和高层才优于统计回归方法；对于湿度变量,只有在大气高层(约400hPa以上),变分方法的反演效果才优于统计回归方法。
In this paper, the one dimension variational method and observation operator CRTM were used in retrieving AMSU sounding data of typhoon FANAPI, KOPPU physically to get atmosphere status in weather condition of clear sky, cloudy and rainy. Firstly Cloud and rain check is used in the process of variational retrieval to separate AMSU observation into clear sky, cloudy and rainy. Besides, observation error re-estimate was preformed also in retrieval. FNL analysis data, CloudSat and DOTSRAT data are used as validation data to examine the retrieved atmospheric variables. The Variation retrieval experiment shows that:
1、The minus between observation and simulated bright temperature by background of channel 2 in AMSU-B, and the rain probability can be used as cloud and rain check and separate AMSU observation into clear sky, cloudy and rainy reasonably. Analysis to retrieved atmospheric variables under different weather condition in detail is possible by using cloud and rain check.
2、Observation error re-estimate can make better estimate to observation error and improve the retrieval result to temperature and humidity.
3、To temperature, variation retrieval could generate retrieval temperature result of much better accuracy than background temperature in all levels in clear sky, in condition of cloud and rain, variation retrieval could generate better retrieval temperature also in low and high levels, but could not generate better retrieval result in middle levels of atmosphere (about 500hP) for the CRTM can not simulate well under condition of cloud and rain. The retrieved temperature is close to verification data in horizontal and vertical dimension.
4、To humidity, in clear sky variation retrieval could generate humidity result of better accuracy than background humidity in high levels, which means that level above 400hPa, but the retrieved humidity's accuracy is worse than background data below 400hPa.
5、To water contents such as ice water path (IWP), variation retrieval could generate results of close to CloudSat's Sounding data。
6、The background data used in variation retrieval comes from statistical regression, so from the analysis to retrieved temperature and humidity result above, it is clear that variational retrieval is much useful in retrieving temperature than statistical regression in clear sky and at low and high level in cloudy and rainy area. But to the humidity, variation retrieval is better than statistical regression only in levels above 400hPa.
1、将AMSU-B ch2通道的初始观测增量和降水概率组合为云雨检测方案,能够合理地将AMSU资料区分为晴空、云和降水三种类型。该云雨检测方案可以作为不同天气下的AMSU资料反演及同化的质量控制标准。
2、观测误差重估计可以得到更客观的观测误差,改进温度、湿度反演效果。
3、对于温度变量,在晴空条件下变分方法能在大气各层上产生精度高于先验信息的反演结果；在云区和雨区中,变分方法能在大气的低层和高层得到比先验信息精确度更高的反演结果,而在大气的中层(500hPa左右),变分方法反演结果的精度因云和降水的出现而低于先验信息,出现这种情况的原因是观测算子CRTM对云和降水粒子微波散射效应的模拟还不够精确。此外,在所有天气条件下,反演温度与DOTSTAR下投式探空仪资料的偏差在3k以内：在水平方向上反演温度也与FNL资料相接近。
4、对于湿度变量,变分方法能够在大气高层(约400hPa以上)产生比先验信息更精确的反演结果；但是在大气的中层和低层(400hPa以下),变分方法反演结果的精确度却低于先验信息。
5、对于固态水路径IWP、液态水路径LWP、降水率RR等水物质变量,变分方法能够产生与CloudSat卫星资料相接近的反演结果。
6、反演试验的先验信息是AMSU资料经统计回归方法反演得到的,所以本试验可以比较变分方法与统计回归方法反演效果的差异：对于温度变量,晴空条件下变分方法的反演效果优于统计回归方法,而在云区和雨区中,变分方法只有在大气的低层和高层才优于统计回归方法；对于湿度变量,只有在大气高层(约400hPa以上),变分方法的反演效果才优于统计回归方法。
In this paper, the one dimension variational method and observation operator CRTM were used in retrieving AMSU sounding data of typhoon FANAPI, KOPPU physically to get atmosphere status in weather condition of clear sky, cloudy and rainy. Firstly Cloud and rain check is used in the process of variational retrieval to separate AMSU observation into clear sky, cloudy and rainy. Besides, observation error re-estimate was preformed also in retrieval. FNL analysis data, CloudSat and DOTSRAT data are used as validation data to examine the retrieved atmospheric variables. The Variation retrieval experiment shows that:
1、The minus between observation and simulated bright temperature by background of channel 2 in AMSU-B, and the rain probability can be used as cloud and rain check and separate AMSU observation into clear sky, cloudy and rainy reasonably. Analysis to retrieved atmospheric variables under different weather condition in detail is possible by using cloud and rain check.
2、Observation error re-estimate can make better estimate to observation error and improve the retrieval result to temperature and humidity.
3、To temperature, variation retrieval could generate retrieval temperature result of much better accuracy than background temperature in all levels in clear sky, in condition of cloud and rain, variation retrieval could generate better retrieval temperature also in low and high levels, but could not generate better retrieval result in middle levels of atmosphere (about 500hP) for the CRTM can not simulate well under condition of cloud and rain. The retrieved temperature is close to verification data in horizontal and vertical dimension.
4、To humidity, in clear sky variation retrieval could generate humidity result of better accuracy than background humidity in high levels, which means that level above 400hPa, but the retrieved humidity's accuracy is worse than background data below 400hPa.
5、To water contents such as ice water path (IWP), variation retrieval could generate results of close to CloudSat's Sounding data。
6、The background data used in variation retrieval comes from statistical regression, so from the analysis to retrieved temperature and humidity result above, it is clear that variational retrieval is much useful in retrieving temperature than statistical regression in clear sky and at low and high level in cloudy and rainy area. But to the humidity, variation retrieval is better than statistical regression only in levels above 400hPa.
引文
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[2]陈东升,沈桐立,马革兰,等.气象资料同化的研究进展[J].南京气象学院学报.2004,08,27卷第四期,550-564.
[3]董佩明,薛纪善,黄兵.等.数值天气预报中卫星资料同化应用现状和发展.[J].气象科技.2008年2月第36卷第1期.1-7.
[4]陈渭民.卫星气象学.[M].气象出版社.2003年2月.
[5]周秀骥.大气微波辐射及遥感原理.[M].科学出版社.1986年.
[6]邹晓蕾.风云FY-3A卫星微波温度湿度计探测误差分析.[C].2011年3月.
[7]Goldberg.M D. An algorithm to generate deep layer temperatures from microwave satellite observations for the purpose of monitoring climate change. [J]. Journal of Climate.1995,8.
[8]Zhu Tong. Impact of the advanced microwave sounding unit measurements on hurricane prediction. [J].Monthly Weather Review,2002,130.
[9]魏应植,许建民AMSU温度反演及其在台风研究中的应用.[J].南京气象学院院报.2005年8月第28卷第4期.522-529
[10]王曦,宋国琼,姚展予.用AMSU资料反演西北太平洋海域大气湿度廓线研究.[J].北京大学学报.2010年1月第46卷第1期.69-78.
[11]方翔,曹志强,王新.等AMSU-B资料反演对流云冰物质.[J].气候与环境研究.2004年8月.第19卷第4期.452-457.
[12]Smith.W.L. and H.M. Woolf, C.M,1984:Conference on satellite meteorology/remote sensing and application, AMS,45-48.
[13]张凤英,吴雪宝,冉茂农.卫星遥感大气中尺度热力场//张文健,许建民,方宗义,等.暴雨系统的卫星遥感理论和方法.北京.气象出版社.2004
[14]黎光清,张文建,董超华NOAA极轨气象卫星大气温湿参数的同步物理反演方法改进试验[J].中国空间科学技术.
[15]黎光清,等.东亚地区气象参数卫星遥感反演理论和方法ISPRM和SPRM.[J]气象科学,1999,第57卷第5期.594-603.
[16]Eyre JR, Kelly G A, Mcnally A P, et al. Assimilation of TOVS radiance information through one-dimensional variational analysis.[J]. Quart. J. R. Meteor. Soc.1993.119:1427-1463.
[17]John M. forsthe, Stanley Q. kidder, Andrew S. Jones. etal. Moisture profile retrievals from satellite microwave sounders for water analysis over land and ocean. [J]. Journal of Atmospheric and Oceanic Technology.2001.18(3):345-352.
[18]李俊,曾庆存.晴空时大气红外遥感及其反演问题：理论研究.[J].大气科学,1997,第21卷第一期.1-9.
[19]王寅虎,孙龙祥.应用ATOVS资料反演大气温室廓线.[J].气象科学.2001年9月第21卷第3期.348-354.
[20]Susskind J, Barnet C D, Blaisdell J M. Retrieval of atmospheric and surface parameters from AIRS/AMSU/HIRS data in the presence of clouds. IEEE Transactions on Geoscience and Remote Sensing.2003.41(2).3902409
[21]Liu quanhua, Weng Fuzhong. One dimension Varational Retrieval of Temperature and Water Vapor and Cloud Water Profile From Advanced Microwave sounding unit (AMSU) [J].IEEE.2004.4
[22]S-A. Boukabara, F. Weng, R. Ferraro. Introducing NOAA's Microwave integrated retrieval system. [C].ITSC-15.16th.Oct.2006.
[23]Karbou F, Aires F, Prigent C, et al. Potential of Advanced Microwave Sounding Unit-A (AMSU-A) and AMSU-B measurements for atmospheric temperature and humidity profiling over land. [J] Journal of Geophysical Research atmospheres.2005.110 (D7)
[24]Yao Zhigang, Chen Hongbin, Lin Longfu. Retrieving atmospheric temperature profiles from AMSU-A data with neural networks.[J].Advanced in Atmospheric Sciences.2005.22 (4):6062616
[25]Han Wei, Xue Jishan,2007:Adaptive tuning of background error and satellite radiances observatio n error for operational variational assimilation, Proc. of SPIE Vol.6790 679044-1～679044-9. doi:10. 1117/12.774771
[26]G.Desroziers, L.Berrs, B.Chapink. Diagnosis of observation, background and analysis-error statistics in observation space.[J].Q.J.R.Meteorol.Soc.2005.131,3385-3396
[27]郭锐,李泽椿,张国平ATOVS资料在淮河暴雨预报中的同化应用研究[J].气象.2010年2月第36卷第2期.1-12
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