机载微波大气温度探测仪多高度飞行观测试验结果分析
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摘要
机载微波大气温度探测仪可以机动灵活地获取大气温度廓线信息。针对一次机载微波大气温度探测仪的多高度飞行观测试验,基于逐线积分模式和大气参数廓线库,建立用于不同飞行高度的快速辐射传输模式,分析了仪器观测亮温的质量并对仪器观测进行了订正;建立了基于神经网络的微波大气温度廓线反演算式,分析了不同高度、不同通道选择对于大气温度廓线反演性能的影响。研究结果表明:(1)较低飞行高度计算得到的各地表敏感通道地表比辐射率之间具有较好的一致性;(2)采用订正算式订正后,不同飞行高度的模拟亮温与观测亮温具有较好的一致性;(3)机载微波大气温度反演最优通道组合依赖于平台飞行高度;(4)采用最优的通道组合,4 200 m、3 200 m和2 500 m高度层温度反演均方根误差范围分别为0.5～1.8 K、0.5～1.3 K和0.4～1.0 K。
Airborne microwave sounding instruments can get atmosphere temperature flexibly. Using line-by-line calculation model MPM and profile datasets, a fast radiative transfer model was applied to airborne microwave sounding instruments and an artificial neural network numerical simulation scheme was developed, which evaluated the multi-altitude flight examinations of the airborne microwave sounding instruments. A neural network was analyzed by a series of retrieval experiments. The results are shown as follows:(1) Surface emissivity of lower-altitude flight calculation has good consistency;(2) With an adjustment model, the fast radiative transfer model can preferably simulate airborne bright temperatures;(3)With the airborne height changing, the best combination of airborne microwave sounding instruments channels is different;(4) With the best combination of airborne microwave sounding instruments channels,the atmospheric temperature RMS error for 4 200 m, 3 200 m and 2 500 m is between 0.5 K and 1.8 K, 0.5 K and 1.3 K, and 0.5 K and 1.0 K, respectively.
Airborne microwave sounding instruments can get atmosphere temperature flexibly. Using line-by-line calculation model MPM and profile datasets, a fast radiative transfer model was applied to airborne microwave sounding instruments and an artificial neural network numerical simulation scheme was developed, which evaluated the multi-altitude flight examinations of the airborne microwave sounding instruments. A neural network was analyzed by a series of retrieval experiments. The results are shown as follows:(1) Surface emissivity of lower-altitude flight calculation has good consistency;(2) With an adjustment model, the fast radiative transfer model can preferably simulate airborne bright temperatures;(3)With the airborne height changing, the best combination of airborne microwave sounding instruments channels is different;(4) With the best combination of airborne microwave sounding instruments channels,the atmospheric temperature RMS error for 4 200 m, 3 200 m and 2 500 m is between 0.5 K and 1.8 K, 0.5 K and 1.3 K, and 0.5 K and 1.0 K, respectively.
引文
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[26] FLEMING H E, CROSBY D S, NEUENDORFFER A C. Correction of satellite temperature retrieval errors due to errors in atmospheric transmittances[J]. J Climate Appl Meteor, 1986, 25(6):869-882.
[27] LI J, WOLF W W, MENZEL W P, et al. Global soundings of the atmosphere from ATOVS measurements:The algorithm and validation[J]. J Appl Meteor, 2000, 39(8):1 248-1 268.
[28] CHEDIN A, SCOTT N A. Initialization of the radiative transfer equation inversion problem from a pattern recognition type approach.Application to the satellites of the TIROS-N se-ries[M]. Advances in remote sensing retrieval methods(eds. A. Deepak, HE Fleming and MT Chahine). Hampton/Virginia:Deepak Publishing, 1985:495-515.
[29] CHURNSIDE J H, STERMITZ T A, SCHROEDER J A. Temperature profiling with neural network inversion of microwave radiometer data[J]. J Atmos Ocean Tech, 1994, 11(1):105-109.
[30] MOTTELER H E, STROW L L, MCMILLIN L, et al. Comparison of neural networks and regression-based methods for temperature retrievals[J]. Applied Optics, 1995, 34(24):5 390-5 397.
[31] CHéDIN A, PéQUIGNOT E, SERRAR S, et al. Simultaneous determination of continental surface emissivity and temperature from NOAA10/HIRS observations:Analysis of their seasonal variations[J]. J Geophy Res, 2004, 109(D20):D20110.
[32] ROSENKRANZ P W. Retrieval of temperature and moisture profiles from AMSU-A and AMSU-B measurements[J]. IEEE Trans Geosci Remote Sens, 2001, 39(11):2 429-2 435.
[33] LIU Q, WENG F. One-dimensional variational retrieval algorithm of temperature, water vapor, and cloud water profiles from advanced microwave sounding unit(AMSU)[J]. IEEE Trans Geosci Remote sens, 2005, 43(5):1 087-1 095.
[34] AIRES F, PRIGENT C, ORLANDI E. Microwave hyper-spectral measurements for temperature and humidity atmospheric profiling from satellite:the clear-sky case[J]. J Geophy Res, 2015, 120(21):11 334-11 351.
[35] LI J, WOLF W W, MENZEL W P, et al. Global soundings of the atmosphere from ATOVS measurements:the algorithm and validation[J]. J Appl Meteor, 2000, 39(8):1248-1268.
[36]黄静,邱崇践,张艳武.一种利用卫星红外遥感资料反演晴空大气参数的物理统计方法[J].红外与毫米波学报, 2007, 26(2):102-106.
[37]贺秋瑞. FY_3C卫星微波湿温探测仪反演大气温湿廓线研究[D].北京:中国科学院国家空间科学中心, 2017.
[38]高大启.有教师的线性基本函数前向三层神经网络结构研究[J].计算机学报, 1998, 21(4):80-86.
[2] BLACKWELL W J. Retrieval of cloud-cleared atmospheric temperature profiles from hyperspectral infrared and microwave observations[D]. Massachusetts Institute of Technology, 2002.
[3] JONES D C. Validation of scattering microwave radiative transfer models using an aircraft radiometer and ground-based radar[D].University of Reading, 1995.
[4] ENGLISH S J, GUILLOU C, PRIGENT C, et al. Aircraft measurements of water vapour continuum absorption at millimetre wavelengths[J]. Quart J Roy Meteor Soc, 1994, 120(517):603-625.
[5] ENGLISH S J. Airborne radiometric observations of cloud liquid-water emission at 89 and 157 GHz:Application to retrieval of liquid-water path[J]. Quart J Roy Meteor Soc, 1995, 121(527):1 501-1 524.
[6] GUILLOU C, ENGLISH S J, PRIGENT C, et al. Passive microwave airborne measurements of the sea surface response at 89 and 157GHz[J]. J Geoph Res, 1996, 101(C2):3 775-3 788.
[7] HEWISON T J, ENGLISH S J. Airborne retrievals of snow and ice surface emissivity at millimeter wavelengths[J]. IEEE Trans Geosci Remote Sens, 1999, 37(4):1 871-1 879.
[8] HEWISON T J. Airborne measurements of forest and agricultural land surface emissivity at millimeter wavelengths[J]. IEEE Trans Geosci Remote Sens, 2001, 39(2):393-400.
[9] MCGRATH A, HEWISON T. Measuring the accuracy of MARSS-an airborne microwave radiometer[J]. J Atmos Ocean Technol, 2001, 18(12):2 003-2 012.
[10] LESLIE R V. Temperature profile retrievals with the NAST-M passive microwave spectrometer[D]. Massachusetts Institute of Technology,Department of Electrical Engineering and Computer Science, 2000.
[11] SPINA M S, SCHWARTZ M J, STAELIN D H, et al. Application of multilayer feedforward neural networks to precipitation cell-top altitude estimation[J]. IEEE Trans Geosci Remote Sens, 1998, 36(1):154-162.
[12]柏颖,张祖强,邵勰. MSU/AMSU微波亮温用于海洋高空大气温度气候变化研究的适用性探讨[J].热带气象学报,2017,33(2):221-230.
[13]鲍艳松,王晓丽,闵锦忠,等.风云三号卫星微波大气温度探测仪资料偏差订正方法研究[J].热带气象学报, 2014, 30(1):145-152.
[14] YAO Z G, CHEN H B, LIN L F. Retrieving atmospheric temperature profiles from AMSU-A data with neural networks[J]. Adv Atmos Sci, 2005, 22(4):606-616.
[15]谭泉,姚志刚,赵增亮,等.多波段微波辐射计反演大气温湿廓线性能分析[J].遥感技术与应用, 2015, 30(1):170-177.doi:10.11873/j.issn.1004-0323.2015.1.0170170-177.
[16]谭泉,姚志刚,赵增亮,等.机载微波辐射计大气温湿廓线反演性能分析[J].气象水文海洋仪器, 2014, 3:5-11.
[17] MAYER B, KYLLING A, EMDE C, et al. Lib Radtran User’s Guide[M]. 2014:1-3.
[18] User’s Guide for the Softwave Package SCIATRAN[Z]. Institute of Remote Sensing University of Bremen, Germany, 2007.
[19] BERK A,BERNSTEIN L S, ROBERTSON D C. MODTRAN:A moderate resolution model for LOWTRAN[Z]. Spectral Sciences Inc.,1987.
[20]武永利,栾青,田国珍,等.基于6S模型的FY-3A/MERSI可见光到近红外波段大气校正[J].应用生态学报,2011,22(6):1 537-1 542.
[21] SAUNDERS R. RTTOV-7 Users Guide[EB/OL].(2002)[2003-05].http://www.metoffice.com/research/interproj/nwpsaf/rtm/.
[22]董佩明,黄江平,刘桂青. FY-3A微波探测资料的直接同化应用及云雨条件下的亮温模拟[J].热带气象学报,2014,30(2):302-310.
[23]张培昌,王振会.大气微波遥感基础[M].北京:气象出版社,1995.
[24] SMITH W L, WOOLF H M. The use of eigenvectors of statistical covariance matrices for interpreting satellite sounding radiometer observations[J]. J Atmos Sci, 1976, 33(7):1 127-1 140.
[25] SMITH W L, WOOLF H M, HAYDEN C M, et al. The simultaneous retrieval export package[C]//Technical Proceedings of the Second International TO VS Study Conference, lgls, Austria. 1985:224-53.
[26] FLEMING H E, CROSBY D S, NEUENDORFFER A C. Correction of satellite temperature retrieval errors due to errors in atmospheric transmittances[J]. J Climate Appl Meteor, 1986, 25(6):869-882.
[27] LI J, WOLF W W, MENZEL W P, et al. Global soundings of the atmosphere from ATOVS measurements:The algorithm and validation[J]. J Appl Meteor, 2000, 39(8):1 248-1 268.
[28] CHEDIN A, SCOTT N A. Initialization of the radiative transfer equation inversion problem from a pattern recognition type approach.Application to the satellites of the TIROS-N se-ries[M]. Advances in remote sensing retrieval methods(eds. A. Deepak, HE Fleming and MT Chahine). Hampton/Virginia:Deepak Publishing, 1985:495-515.
[29] CHURNSIDE J H, STERMITZ T A, SCHROEDER J A. Temperature profiling with neural network inversion of microwave radiometer data[J]. J Atmos Ocean Tech, 1994, 11(1):105-109.
[30] MOTTELER H E, STROW L L, MCMILLIN L, et al. Comparison of neural networks and regression-based methods for temperature retrievals[J]. Applied Optics, 1995, 34(24):5 390-5 397.
[31] CHéDIN A, PéQUIGNOT E, SERRAR S, et al. Simultaneous determination of continental surface emissivity and temperature from NOAA10/HIRS observations:Analysis of their seasonal variations[J]. J Geophy Res, 2004, 109(D20):D20110.
[32] ROSENKRANZ P W. Retrieval of temperature and moisture profiles from AMSU-A and AMSU-B measurements[J]. IEEE Trans Geosci Remote Sens, 2001, 39(11):2 429-2 435.
[33] LIU Q, WENG F. One-dimensional variational retrieval algorithm of temperature, water vapor, and cloud water profiles from advanced microwave sounding unit(AMSU)[J]. IEEE Trans Geosci Remote sens, 2005, 43(5):1 087-1 095.
[34] AIRES F, PRIGENT C, ORLANDI E. Microwave hyper-spectral measurements for temperature and humidity atmospheric profiling from satellite:the clear-sky case[J]. J Geophy Res, 2015, 120(21):11 334-11 351.
[35] LI J, WOLF W W, MENZEL W P, et al. Global soundings of the atmosphere from ATOVS measurements:the algorithm and validation[J]. J Appl Meteor, 2000, 39(8):1248-1268.
[36]黄静,邱崇践,张艳武.一种利用卫星红外遥感资料反演晴空大气参数的物理统计方法[J].红外与毫米波学报, 2007, 26(2):102-106.
[37]贺秋瑞. FY_3C卫星微波湿温探测仪反演大气温湿廓线研究[D].北京:中国科学院国家空间科学中心, 2017.
[38]高大启.有教师的线性基本函数前向三层神经网络结构研究[J].计算机学报, 1998, 21(4):80-86.