使用物理方法由TRMM/TMI亮温资料反演中国陆地降水
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摘要
研究使用MM5中尺度模式模拟了中国陆地的11个中尺度降水个例以获得降水廓线,由Monte-Carlo三维辐射传输模式计算这些降水廓线的上行辐射亮温,构建出初始的云-辐射数据库。采用对照表和综合判据方法控制初始数据库并从中获得Database_m11、Database_m12、Database_m21、Database_m22共四个数据集,作为GPROF反演算法的先验数据库。对2007年7月间的三次强降水过程的地面雨强进行反演,与TMI 2A12产品及NESDIS、GSCAT、PCT-SI三种经验算法反演的雨强相比较,发现Database_m12参与反演的地面雨强与TMI 2A12的结果较接近,明显优于使用数据集Database_m11获得的反演结果,甚至优于三种经验算法的反演结果;使用数据库Database_m22获得的结果比Database_m21更接近TMI 2A12的结果。使用PR 2A25产品的雨强对上述各算法反演的雨强做综合检验,发现总体上TMI 2A12产品提供的雨强效果最好。各数据库中,Database_m12的反演效果较好。对照表方法控制的数据库Database_m11和Database_m12反演的雨强的效果优于综合判据方法控制的数据库Database_m21和Database_m22的反演效果。且使用数据库Database_m21和Database_m22反演的地面雨强通常表现偏大。使用地面S波段雷达反演的雨强检验各算法的效果,发现物理算法的结果与地面雷达结果的相关性明显高于经验算法,平均相对偏差也较小,反演的效果较为稳定,其中Database_m12的反演效果甚至优于GPROF 6.0算法的反演效果。这是因为虽然Database_m12以GPROF 6.0算法的数据库为对照表,但其中包含的廓线是具有中国陆地、特别是该地区的梅雨锋的降水特征的降水廓线。使用雨量计观测到的逐时降水量考察各算法反演效果,显示数据库Database_m21和Database_m22的反演结果相对较好。经验算法中PCT-SI综合指数法对这三个降水个例的反演效果较稳定,优于另外两种经验算法。数据库Database_m12参与反演的可降水(pw)在垂直分布和含量上与TMI 2A12的结果以及地面雷达反演的液态水结果非常一致,而云水(cw)、云冰(ci)和可降冰(pi)与TMI的反演结果在垂直高度分布上分别较接近,各水凝物的含量最大值所在高度也分别一致,含量上都高于TMI的结果。使用另外3个数据集反演的4种水凝物垂直结构与使用Database_m12的结果在垂直分布结构基本一致,而可降水(pw)的含量偏低一些。综合判据法控制的数据库对可降水的位置的反演与地面雷达反演的液态水位置最接近。研究还发现将数据库Database_m21和Database_m22中雨强小于0.01mm/hr的降水廓线过滤后,对于地面雨强的反演结果没有影响,而且检验发现过滤后的数据库反演效果略好。对于水凝物垂直廓线的反演也可以收到更好的效果,并将运算时间缩短了1/4。综上,研究所确立的物理算法是成功的,可作为发展适用于我国的由微波亮温反演地面雨强和水凝物廓线的物理方法一次有益尝试。
In order to construct the initial cloud-radiation database, 11 cloud-precipitating systems over land surface in China were simulated by MM5 to obtain the rainfall profiles whose upwelling radiative brightness temperature were computed by Monte-Carlo radiative transfer model. Then as the priori databases, 4 databases named Database_m11, Database_m12, Database_m21 and Database_m22 were extracted from the initial cloud-radiation database by using check list and compositve criteria method. The rain rates of three cloud-precipitation systems during July 2007 were retrieved by importing those 4 databases to the GPROF algorithm respectively. Comparing with the results from TMI 2A12 product and three statistic algorithms containing NESDIS, GSCAT and PCT-SI, the rain rate from Database_m12 was close to TMI’s and better than that from Database_m11 significently, even better than those from statistical algorithms sometimes. The rain rate of Database_m22 was closer to TMI than that of Database_m21. The rainfall intensities of PR were used to inspect the results calculated by the above algorithms. It is found that TMI 2A12 product shows the best results. Among the four databases, Database_m12 has better impression in general. Database_m11 and Database_m12 controlled by check list display better behavior than Database_m21 or Database_m22 obtained by compositive criteria method. And the databases controlled with compositive criteria method would cause rain rate more intensive. In the same way, the rainfall intensities calculated from the Doppler Radars on land surface were also utilized to compare the results from the above databases. The rain rates from physical methods are more compatible with those observed by the Doppler Radars than those from the statistical methods. Especially the results retrieved by Database_m12 are even better than those from TMI 2A12 product. The reason is although the Database_m12 is based upon the database of GPROF 6.0, it contains the cloud-precipitation profiles which represent the rainfall character of Meiyu front on that area in China. The better expression of Database_m21 and Database_m22 can be found when one-hour accumulated precipitation observed by rain gauges is supposed to test the above various algorithms. Generally PCT-SI has the best behavior than the other two statistical methods. Among the four kinds of hydrometeors retrieved by Database_m12, the precipitable water (pw) displays consistent with that from TMI 2A12 product or the cloud liquid water retrieved by Radar on land. The vertical distributions of other three hydrometeors including cloud water (cw), cloud ice (ci) and precipitable ice (pi) are close to those of TMI. However the contents of those three kinds of hydrometeors are sometimes higher than those from TMI respectively. The hydrometeor structures obtained from other three databases are similar to those from Database_m12 except that the contents of those hydrometeors are lower generally. The distribution place of pw retrieved by the databases controlled with compositive criteria is consistent with cloud liquid water computed from Doppler Radar. When the databases neglected the less than 0.01mm/hr in precipitating profiles, there is almost no negative effect on the retrieved rainfall intensity, but the better hydrometors vertical distribution would be produced. Also the computing time can be reduced by 1/4. Thus, the algorithm constructed in this study has a positive effect on retrieving rainfall intensity and the profiles of hydrometeors. And it can be regarded as a helpful attempt to improve the physical algorithm applied in China with which the rainfall intensity and hydrometeor structures can be retrieved from brightness temperature.
In order to construct the initial cloud-radiation database, 11 cloud-precipitating systems over land surface in China were simulated by MM5 to obtain the rainfall profiles whose upwelling radiative brightness temperature were computed by Monte-Carlo radiative transfer model. Then as the priori databases, 4 databases named Database_m11, Database_m12, Database_m21 and Database_m22 were extracted from the initial cloud-radiation database by using check list and compositve criteria method. The rain rates of three cloud-precipitation systems during July 2007 were retrieved by importing those 4 databases to the GPROF algorithm respectively. Comparing with the results from TMI 2A12 product and three statistic algorithms containing NESDIS, GSCAT and PCT-SI, the rain rate from Database_m12 was close to TMI’s and better than that from Database_m11 significently, even better than those from statistical algorithms sometimes. The rain rate of Database_m22 was closer to TMI than that of Database_m21. The rainfall intensities of PR were used to inspect the results calculated by the above algorithms. It is found that TMI 2A12 product shows the best results. Among the four databases, Database_m12 has better impression in general. Database_m11 and Database_m12 controlled by check list display better behavior than Database_m21 or Database_m22 obtained by compositive criteria method. And the databases controlled with compositive criteria method would cause rain rate more intensive. In the same way, the rainfall intensities calculated from the Doppler Radars on land surface were also utilized to compare the results from the above databases. The rain rates from physical methods are more compatible with those observed by the Doppler Radars than those from the statistical methods. Especially the results retrieved by Database_m12 are even better than those from TMI 2A12 product. The reason is although the Database_m12 is based upon the database of GPROF 6.0, it contains the cloud-precipitation profiles which represent the rainfall character of Meiyu front on that area in China. The better expression of Database_m21 and Database_m22 can be found when one-hour accumulated precipitation observed by rain gauges is supposed to test the above various algorithms. Generally PCT-SI has the best behavior than the other two statistical methods. Among the four kinds of hydrometeors retrieved by Database_m12, the precipitable water (pw) displays consistent with that from TMI 2A12 product or the cloud liquid water retrieved by Radar on land. The vertical distributions of other three hydrometeors including cloud water (cw), cloud ice (ci) and precipitable ice (pi) are close to those of TMI. However the contents of those three kinds of hydrometeors are sometimes higher than those from TMI respectively. The hydrometeor structures obtained from other three databases are similar to those from Database_m12 except that the contents of those hydrometeors are lower generally. The distribution place of pw retrieved by the databases controlled with compositive criteria is consistent with cloud liquid water computed from Doppler Radar. When the databases neglected the less than 0.01mm/hr in precipitating profiles, there is almost no negative effect on the retrieved rainfall intensity, but the better hydrometors vertical distribution would be produced. Also the computing time can be reduced by 1/4. Thus, the algorithm constructed in this study has a positive effect on retrieving rainfall intensity and the profiles of hydrometeors. And it can be regarded as a helpful attempt to improve the physical algorithm applied in China with which the rainfall intensity and hydrometeor structures can be retrieved from brightness temperature.
引文
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[8] Shin Dong-Bin, Kummerow C.D. Parametric Rainfall Retrieval Algorithms for Passive Microwave Radiometers. J. Appl. Meteor., 2003, 42:1480-1496.
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[13]Reisner J, Rasmussen R M, Bruintjes R T. Explecit Forecasting of Supercooled Liquid Water in Winter Storms Using the MM5 Mesoscale Model. Q. J. R. Meteor. Soc., 1998, 124:1071-1107.
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[17]Adler R F, Haw-Young M Yeh, N Prasad, Wei-Kuo and Joanne Simpson. Microwave Simulation of a Tropical Rainfall System with a Three-dimensional Cloud Model, J. Appl. Meteor., 1991, 30:924-953.
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[18]Liu Q, C Simmer and E Ruprecht. Three-dimensional Radiative Transfer Effects of Clouds in the Microwave Spectral Range, Journal of Geophysical Research, 1996, 101: 4289-4298
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[20]Wu Rongzhang, J A Weinman. Microwave radiances from precipitating clouds containing aspherical ice, combined phase, and liquid hydrometeors, J. Geo. Res., 1984, 89: 7170-7178
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[1] Kummerow C, William S Olson and L Giglio. A Simplified Scheme for Obtaining Precipitation and Vertical Hydrometeor Profiles from Passive Microwave Sensors. IEEE Trans. Geosci. Remote Sens., 1996, 34:1213-1232.
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[9] Olson W S, Hong Y, C D Kummerow,Joseph Turk. A Texture-Polarization Method for Estimating Convective-Stratiform Precipitation Area Coverage from Passive Microwave Radiometer Data. Journal of Applied Meteorology, 2001b, 40(9),1577-1591.
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[43] Kummerow C, Y Hong, W S Olson, S Yang, R F Adler, J Mccollum, R Ferraro, G Petty, D-B Shin and T T Wilheit. The Evolution of the Goddard PROFiling Algorithm(GPROF)for Rainfall Estimation from Passive Microwave Sensors. J. A. M., 2001, 40(11):1801-1820.
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[45] Smith E A, X Xiang, A Mugnai, R E Hood and R W Spencer. Behavior of an Inversion-Based Precipitation Retrieval Algorithm with High-Resolution AMPR Measurement Including a Low-Frequency 10.7-GHz Channel. Journal of Atmospheric and Oceanic Technology, 1994, 11:858-873.
[46] Pierdicca N, Marzano F S, G D Auria, P Basili, P Ciotti and A Mugnai. Precipitation Retrieval from Spaceborne Microwave Radiometers Based on Maximum a Posteriori Probability Estimation. IEEE Trans. Geosci.Remote Sens., 1996, 34:831-847.
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[54]李小青.利用TRMM资料反演降水强度和水凝物垂直结构.硕士论文,中国气象科学研究院,2003.
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