浏览全部资源
扫码关注微信
Published： 07 June 2020 ，
扫 描 看 全 文
窦芳丽,商建,吴琼,谷松岩.2020.风云三号卫星被动微波反演海洋上空云液态水含量.遥感学报,24(6): 766-775
DOU Fangli,SHANG Jian,WU Qiong,GU Songyan. 2020. Retrieval of cloud liquid water content over global oceans using FY-3C/3D microwave imager. Journal of Remote Sensing(Chinese)，24(6): 766-775
云液态水含量是气候和水循环研究的重要云微物理参数，也是目前气候变化研究中的最不确定因素之一。通过极轨气象卫星被动微波观测的光谱和极化特征能够实现对云液态水含量的直接测量，本文介绍了一种基于风云三号卫星微波成像仪（MWRI）观测亮温的全天候云液态水含量反演算法，利用快速辐射传输模式、云模型和大气廓线库建立MWRI模拟亮温库并训练反演系数的宽气候态物理算法可以保证算法系数在不同季节和不同地区的适应性。同时提出了一种基于观测增量（O-B）筛选晴空像元并对算法系数及比例因子进行订正的方法。利用统计直方图方法和卫星间交叉比对方法对反演产品精度进行了检验，统计直方图方法检验结果表明，FY-3C云水反演误差为0.028 mm，FY-3D为0.025 mm，与国外同类产品的精度相当；与低轨卫星微波辐射计GMI云水产品的交叉比对结果表明，两者具有较高一致性，均方根误差为0.0325 mm。FY-3C/3D CLW产品目前已经投入业务应用，上下午星组网能够一天内基本覆盖全球，实现全球云水分布监测。
Cloud Liquid Water Content (CLW) links the hydrological and radiative components of the climate system and is an important parameter for research in climate and cloud microphysics. Cloud liquid water is a highly variable target and depends on cloud type. The different cloud types exist on different levels and vary in the satellite sensor view.
Cloud liquid water is one the most uncertain factors in climate change research. CLW can be directly measured from the passive microwave measurements on the basis of its spectral and polarization signatures. The all-sky CLW retrieval algorithms for FY-3C and FY-3D microwave imagers (MWRI) are presented in this study. The CRTM rapid radiative transfer and various cloud models
as well as ECMWF short-range forecast profile datasets are utilized for training the retrieval coefficients. Hence
the physical-based algorithm could ensure the adaptability of the CLW products for different seasons and regions. To prevent matching errors between visible and microwave pixels
a novel clear-sky detection method based on O-B (observations minus backgrounds) errors of FY-3 MWRI brightness temperatures is given and proved effective to adjust the coefficients and the scale factor of the retrieval equation. Then
the retrievals under rainy conditions based on the climate statistical features are added in the FY-3 CLW all-sky algorithms.
As the validation of satellite derived CLWs is difficult to carry out
the statistical histogram method raised by Remote Sensing System (RSS) is used to estimate the accuracy of FY-3 CLW daily products and DMSP-F16 SSM/I CLW products. Results showed that the RMSE of FY-3C CLWs is 0.028 mm and FY-3D is 0.025 mm. As the RMSE of SSM/I CLWs is 0.025 mm according to the same method
the accuracy of FY-3 CLWs is comparable to that of the RSS operational products. We selected 15 days of FY-3 CLW orbital product data in March 2015 to compare with the GPM GMI orbital products from RSS under strict matching constraint. The comparison result shows that the two kinds of products are of good consistency
and the correlation coefficient reached 0.9061. The mean deviation and RMSE are 0.0075±0.0325 mm.
The global distribution of FY-3 daily CLW was analyzed and compared with the distributions of clouds observed by SSM/I and FY-3D MERSI. According to the analysis
the cloud distribution observed by FY-3 is consistent with SSM/I and MERSI observations. FY-3 CLWs were more sensitive to thin clouds with smaller particles and less water content. The CLW values of thick clouds were slightly underestimated than SSM/I. FY-3 CLWs could depict the detailed structure of typhoons
including eye area
inner cloud wall
and outer spiral rain belt. At present
FY-3C/D CLW products are used in operations. The networking of morning and afternoon orbit satellites could achieve global coverage in one day.
遥感FY-3 MWRI云中液态水辐射传输云模型统计直方图真实性检验
remote sensingFY-3 MWRIcloud liquid waterradiative transfercloud modelstatistical histogramvalidation
Alishouse J C, Snider J B, Westwater E R, Swift C T, Ruf C S, Snyder S A, Vongsathorn J and Ferraro R R. 1990. Determination of cloud liquid water content using the SSM/I. IEEE Transactions on Geoscience and Remote Sensing, 28(5): 817-822 [DOI: 10.1109/36.58968http://dx.doi.org/10.1109/36.58968]
Barrett A H and Chung V K. 1962. A method for the determination of high-altitude water-vapor abundance from ground-based microwave observations. Journal of Geophysical Research, 67(11): 4259-4266 [DOI: 10.1029/jz067i011p04259http://dx.doi.org/10.1029/jz067i011p04259]
Bauer P and Schluessel P. 1993. Rainfall, total water, ice water, and water vapor over sea from polarized microwave simulations and special sensor microwave/imager data. Journal of Geophysical Research, 98(D11): 20737-20759 [DOI: 10.1029/93jd01577http://dx.doi.org/10.1029/93jd01577]
Baum B A, Yang P, Heymsfield A J, Platnick S, King M D, Hu Y X and Bedka S T. 2005. Bulk scattering properties for the remote sensing of ice clouds. Part II: narrowband models. Journal of Applied Meteorology, 44(12): 1896-1911 [DOI: 10.1175/JAM2309.1http://dx.doi.org/10.1175/JAM2309.1]
Bennartz R. 2007. Global assessment of marine boundary layer cloud droplet number concentration from satellite. Journal of Geophysical Research, 112(D2): D16302 [DOI: 10.1029/2006JD007547http://dx.doi.org/10.1029/2006JD007547]
Bormann N, Geer A and English S. 2012. Evaluation and comparisons of FASTEM versions 2 to 5[2018-4-1]. http://cimss.ssec.wisc.edu/itwg/itsc/itsc18/program/files/links/8.07_Bormann_po.pdhttp://cimss.ssec.wisc.edu/itwg/itsc/itsc18/program/files/links/8.07_Bormann_po.pd
Chen H B. 2000. A retrieval algorithm for deriving liquid water path from space-borne microwave radiometric measurements. Journal of Remote Sensing, 4(3): 165-171
陈洪滨. 2000. 星载微波辐射计遥感反演云水量的一个算式. 遥感学报, 4(3): 165-171 [DOI: 10.11834/jrs.20000301http://dx.doi.org/10.11834/jrs.20000301]
Davis M A. 2011. Cloud-Radiative Feedback and Ocean-Atmosphere Feedback in the Southeast Pacific Ocean Simulated by IPCC AR4 GCMs. Columbus: Ohio State University: 34-35
Eresmaa R and McNally A P. 2014. Diverse Profile Datasets from the ECMWF 137-level Short-range Forecasts. NWPSAF-EC-TR-017. ECMWF [DOI: 10.13140/2.1.4476.8963http://dx.doi.org/10.13140/2.1.4476.8963]
Falcone Jr V J, Abreu L W and Shettle E P. 1979. Atmospheric attenuation of millimeter and submillimeter waves//Proceedings of EASCON 79, Electronics and Aerospace Systems Conference. Arlington: Institute of Electrical and Electronics Engineers: 36-41
Fowler L D, Randall D A and Rutledge S A. 1996. Liquid and ice cloud microphysics in the CSU general circulation model. Part 1: model description and simulated microphysical processes. Journal of Climate, 9(3): 489–529 [DOI: 10.1175/1520-0442(1996)009<0489:laicmi>2.0.co;2http://dx.doi.org/10.1175/1520-0442(1996)009<0489:laicmi>2.0.co;2]
Greenwald T J, Combs C L, Jones A S, Randel D L and Haar T H V. 1997. Further developments in estimating cloud liquid water over land using microwave and infrared satellite measurements. Journal of Applied Meteorology, 36(4): 389-405 [DOI: 10.1175/1520-0450(1997)036<0389:fdiecl>2.0.co;2http://dx.doi.org/10.1175/1520-0450(1997)036<0389:fdiecl>2.0.co;2]
Grody N. 1976. Remote sensing of atmospheric water content from satellites using microwave radiometry. IEEE Transactions on Antennas and Propagation, 24(2): 155-162 [DOI: 10.1109/tap.1976.1141324http://dx.doi.org/10.1109/tap.1976.1141324]
Han Q, Welch R, Chou J, Rossow W and White A. 1995. Validation of satellite retrievals of cloud microphysics and liquid water path using observations from FIRE. Journal of the Atmospheric Sciences, 52(23): 4183-4195 [DOI: 10.1175/1520-0469(1995)052<4183:vosroc>2.0.co;2http://dx.doi.org/10.1175/1520-0469(1995)052<4183:vosroc>2.0.co;2]
Han Y, van Delst P, Liu Q H, Weng F Z, Yan B H, Treadon R and Derber J. 2006. JCSDA community radiative transfer model (CRTM)—version 1. NOAA Technical Report NESDIS 122. NOAA
Jones A S and Haar T H V. 1990. Passive microwave remote sensing of cloud liquid water over land regions. Journal of Geophysical Research, 95(D10): 16673-16683 [DOI: 10.1029/jd095id10p16673http://dx.doi.org/10.1029/jd095id10p16673]
Liau G N. 2002. An Introduction to Atmospheric Radiation. Guo C L and Zhou S J, trans. 2nd ed. Beijing: Academic Press: 425-430
廖国男. 2002. 大气辐射导论. 郭彩丽, 周诗健, 译. 2版. 北京: 气象出版社: 425-430
Liu G S and Curry J A. 1993. Determination of characteristic features of cloud liquid water from satellite microwave measurements. Journal of Geophysical Research, 98(D3): 5069-5092 [DOI: 10.1029/92jd02888http://dx.doi.org/10.1029/92jd02888]
Liu Z W and Guan L. 2013. Assessing the ability for using O-B of FY-3A microwave humidity sounders to identify clouds. Journal of the Meteorological Sciences, 33(5): 536-542
刘兆祎, 官莉. 2013. 评估FY-3A微波湿度计O-B对云的识别能力. 气象科学, 33(5): 536-542 [DOI: 10.3969/2012jms.0186http://dx.doi.org/10.3969/2012jms.0186]
Lu Q F, Bell W, Bauer P, Bormann N and Peubey C. 2011. An evaluation of FY-3A satellite data for numerical weather prediction. Quarterly Journal of the Royal Meteorological Society, 137(658): 1298-1311 [DOI: 10.1002/qj.834http://dx.doi.org/10.1002/qj.834]
O'Dell C W, Wentz F J and Bennartz R. 2008. Cloud liquid water path from satellite-based passive microwave observations: a new climatology over the global oceans. Journal of Climate, 21(8): 1721-1739 [DOI: 10.1175/2007JCLI1958.1http://dx.doi.org/10.1175/2007JCLI1958.1]
Okamoto K and Derber J C. 2006. Assimilation of SSM/I radiances in the NCEP global data assimilation system. Monthly Weather Review, 134(9): 2612-2631 [DOI: 10.1175/mwr3205.1http://dx.doi.org/10.1175/mwr3205.1]
Petty G W. 1990. On the Response of the Special Sensor Microwave/Imager to the Marine Environment: Implications for Atmospheric Parameter Retrievals. Washington: University of Washington: 118
Staelin D H, Kunzi K F, Pettyjohn R L, Poon R K L, Wilcox R W and Waters J W. 1976. Remote sensing of atmospheric water vapor and liquid water with the Nimbus 5 microwave spectrometer. Journal of Applied Meteorology, 15(11): 1204-1214 [DOI: 10.1175/1520-0450(1976)015<1204:rsoawv>2.0.co;2http://dx.doi.org/10.1175/1520-0450(1976)015<1204:rsoawv>2.0.co;2]
Tang F and Zou X L. 2017. Liquid water path retrieval using the lowest frequency channels of Fengyun-3C Microwave Radiation Imager (MWRI). Journal of Meteorological Research, 31(6): 1109-1122 [DOI: 10.1007/s13351-017-7012-7http://dx.doi.org/10.1007/s13351-017-7012-7]
Waller J A, Dance S L and Nichols N K. 2016. Theoretical insight into diagnosing observation error correlations using observation-minus-background and observation-minus-analysis statistics. Quarterly Journal of the Royal Meteorological Society, 142(694): 418-431 [DOI: 10.1002/qj.2661http://dx.doi.org/10.1002/qj.2661]
Weng F, Zou X, Wang X, Yang S and Goldberg M D. 2012. Introduction to Suomi national polar-orbiting partnership advanced technology microwave sounder for numerical weather prediction and tropical cyclone applications. Journal of Geophysical Research, 117(D19): D19112 [DOI: 10.1029/2012JD018144http://dx.doi.org/10.1029/2012JD018144]
Weng F Z and Grody N C. 1994. Retrieval of cloud liquid water using the Special Sensor Microwave Imager (SSM/I). Journal of Geophysical Research, 99(D12): 25535-25551 [DOI: 10.1029/94jd02304http://dx.doi.org/10.1029/94jd02304]
Weng F Z, Grody N C, Ferraro R, Basist A and Forsyth D. 1997. Cloud liquid water climatology from the special sensor microwave/imager. Journal of Climate, 10(5): 1086-1098 [DOI: 10.1175/1520-0442(1997)010<1086:clwcft>2.0.co;2http://dx.doi.org/10.1175/1520-0442(1997)010<1086:clwcft>2.0.co;2]
Weng F Z, Zhao L M, Ferraro R R, Poe G, Li X F and Grody N C. 2003. Advanced microwave sounding unit cloud and precipitation algorithms. Radio Science, 38(4): 33-1-33-13 [DOI: 10.1029/2002rs002679http://dx.doi.org/10.1029/2002rs002679]
Wentz F J. 1997. A well-calibrated ocean algorithm for special sensor microwave/imager. Journal of Geophysical Research, 102(C4): 8703-8718 [DOI: 10.1029/96JC01751http://dx.doi.org/10.1029/96JC01751]
Wentz F J and Spencer R W. 1998. SSM/I rain retrievals within a unified all-weather ocean algorithm. Journal of the Atmospheric Sciences, 55(9): 1613-1627 [DOI: 10.1175/1520-0469(1998)055<1613:sirrwa>2.0.co;2http://dx.doi.org/10.1175/1520-0469(1998)055<1613:sirrwa>2.0.co;2]
Wu S L and Chen J. 2016. Instrument performance and cross calibration of FY-3C MWRI//Proceedings of 2016 IEEE International Geoscience and Remote Sensing Symposium. Beijing: IEEE: 388-391 [DOI: 10.1109/igarss.2016.7729095http://dx.doi.org/10.1109/igarss.2016.7729095]
Yang H, Shang J, Lv L Q, He J K and Xu H X. 2012. Study of channel resolution matching of spaceborne microwave radiometer and its application in MWRI of FY-3 satellite. Aerospace Shanghai, 29(1): 23-28, 51
杨虎, 商建, 吕利清, 何嘉恺, 徐红新. 2012. 星载微波辐射计通道分辨率匹配技术及其在FY-3卫星MWRI中的应用. 上海航天, 29(1): 23-28, 51 [DOI: 10.3969/j.issn.1006-1630.2012.01.005http://dx.doi.org/10.3969/j.issn.1006-1630.2012.01.005]
Yang H, Li X Q, You R and Wu S L. 2013. Environmental data records from Fengyun-3B microwave radiation imager. Advances in Meteorological Science and Technology, 3(4): 136-143
杨虎, 李小青, 游然, 武胜利. 2013. 风云三号微波成像仪定标精度评价及业务产品介绍. 气象科技进展, 3(4): 136-143 [DOI: 10.3969/j.issn.2095-1973.2013.04.014http://dx.doi.org/10.3969/j.issn.2095-1973.2013.04.014]
Yao Z Y and Peng L. 2009. Advances in satellite passive microwave remote sensing of cloud liquid water. Acta Meteorologica Sinica, 67(2): 331-341
姚展予, 彭亮. 2009. 卫星微波被动遥感云中液态水的研究进展. 气象学报, 67(2): 331-341 [DOI: 10.3321/j.issn:0577-6619.2009.02.016http://dx.doi.org/10.3321/j.issn:0577-6619.2009.02.016]
相关文章
相关作者
相关机构
京公网安备11010802024621