Analysis and correction of the difference between the ascending and descending orbits of the FY-3C microwave imager

Miao ZHANG; Qifeng LU; Songyan GU; Xiuqing HU; Shengli WU

doi:10.11834/jrs.20198235

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Analysis and correction of the difference between the ascending and descending orbits of the FY-3C microwave imager
Vol. 23, Issue 5, Pages: 841-849(2019)
Received：08 June 2018，

Accepted：19 October 2018，

Published：2019-09
DOI： 10.11834/jrs.20198235
稿件说明：

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张淼, 陆其峰, 谷松岩, 胡秀清, 武胜利. 2019. 风云三号C星微波成像仪升降轨偏差问题分析及订正. 遥感学报, 23(5): 841–849 DOI： 10.11834/jrs.20198235.

Zhang M, Lu Q F, Gu S Y, Hu X Q and Wu S L. 2019. Analysis and correction of the difference between the ascending and descending orbits of the FY-3C microwave imager. Journal of Remote Sensing, 23(5): 841–849 DOI： 10.11834/jrs.20198235.

摘要

风云三号(FY-3)的微波成像仪(MWRI)能够全天候获取全球大气水汽含量、云雨参数及海面温度等的空间分布

并可为数值天气预报提供初始场信息进而提高天气预报的准确性。但FY-3C MWRI O-B(O是卫星观测亮温，B是数值天气预报模式模拟亮温)偏差结果存在较大升降轨差异，严重制约了遥感信息的正确提取以及在数值天气预报模式中的业务同化应用。本文通过分析定标方程各参数：定标黑体物理温度、热反射镜背瓣亮温、热反射镜物理温度、冷空反射镜物理温度、接收通道温度、黑体观测计数值、冷空观测计数值、定标斜率、定标截距，并对定标方程各项进行敏感性分析，找出了引起MWRI升降轨偏差的主要原因是热反射镜的发射率异常增大引起的。经过不断调整MWRI的热反射镜发射率，使升轨O-B与降轨O-B的概率分布逐渐重合，初步估算了热反射镜发射率。本文的订正方法可指导未来仪器的发展，并为直接同化MWRI辐射数据提供了条件。

Abstract

The Microwave Radiometer Imager (MWRI) onboard FY-3C satellites was successfully launched on December 23

2013. MWRI observes the Earth’s atmosphere and surface at 10.65

18.7

23.8

36.5

and 89.0 GHz with dual polarization and can provide an important initial field for Numerical Weather Prediction (NWP). However

the O-B (observation minus simulation) of MWRI shows a clear bias difference between ascending and descending orbits. The magnitude of this ascending–descending bias is approximately 2 K for all channels

thereby restricting its operational application in NWP data assimilation systems. This research analyzes the causes of the bias and makes appropriate corrections. The parameters of the calibration equation were analyzed

including physical temperature of the warm load

brightness temperature of the hot reflector’s back-lobe

physical temperature of the hot reflector

physical temperature of the cold reflector

receiver channel instrument temperature

warm load radiometric counts

cold space radiometric counts

antenna brightness temperature calibration scale

antenna brightness temperature calibration offset

and a sensitivity analysis of each term of the calibration equation was conducted. Results indicated that high values of the hot load reflector are the main causes of the bias. The reflector was heated periodically by incident solar radiation and emitted a variable radiation with space and time

which caused the ascending–descending bias. Thus

the brightness temperature was simulated using the basic atmospheric parameters of ERA5 in conjunction with the radiative transfer model known as RTTOV. With the principle that the probability density difference between the O-B of ascending and descending orbits is minimum

the emissivity of the hot load reflector is estimated. Results show that before adjusting the emissivity of the hot reflector

the probability density plot of the O-B of ascending and descending orbits was separated. After correction

the bias difference between the ascending and descending orbits were clearly reduced

thereby identifying the main error source of the ascending–descending bias. Such identification can guide the development of future instruments and provide the condition for direct assimilation of MWRI radiance data. Although the accuracy of NWP fields

the radiative transfer model

calibration

and cloud detection are not the main error source of the ascending–descending bias

they may affect the estimation accuracy of the emissivity of the hot load reflector. Thus

strict quality control should be carried out in the future

and after the samples of greater uncertainty are eliminated

more accurate on-orbit emissivity of the hot load reflector can be estimated.

关键词

Keywords

references

郭杨, 卢乃锰, 谷松岩, 何杰颖, 王振占. 2014. FY-3B微波湿温探测仪辐射测量特性. 应用气象学报, 25(4): 436–444

Guo Y, Lu N M, Gu S Y, He J Y and Wang Z Z. 2014. Radiometric characteristics of FY-3B microwave humidity and temperature sounder. Journal of Applied Meteorological Science, 25(4): 436–444

黄端, 邱玉宝, 石利娟, 武胜利, 刘小生, 阮永俭. 2017. 两种星载微波辐射计被动亮温数据的交叉定标. 测绘科学, 42(1): 136–142

Huang D, Qiu Y B, Shi L J, Wu S L, Liu X S and Ruan Y J. 2017. Cross –calibration for passive microwave measurement of MWRI and AMSR-E. Science of Surveying and Mapping, 42(1): 136–142

Kunkee D B, Swadley S D, Poe G A, Hong Y and Werner M F. 2008. Special sensor microwave imager sounder (SSMIS) radiometric calibration anomalies—Part I: identification and characterization. IEEE Transactions on Geoscience and Remote Sensing, 46(4): 1017–1033

刘高峰, 武胜利, 陈卫英, 潘莉, 何嘉恺. 2014. FY-3卫星微波成像仪定标系统及实现. 微波学报, 30(S1): 576–579

Liu G F, Wu S L, Chen W Y, Pan L and He J K. 2014. Calibration system and achievement of microwave radiation imager of FY-3 satellite. Journal of Microwaves, 30(S1): 576–579

卢乃锰, 谷松岩. 2016. 气象卫星发展回顾. 遥感学报, 20(5): 832–841

Lu N M and Gu S Y. 2016. Review and prospect on the development of meteorological satellites. Journal of Remote Sensing, 20(5): 832–841

陆其峰. 2011. 风云三号A星大气探测资料数据在欧洲中期天气预报中心的初步评价与同化研究. 中国科学: 地球科学, 41(7): 890–894

Lu Q F. 2011. Initial evaluation and assimilation of FY-3A atmospheric sounding data in the ECMWF system. Science China Earth Sciences, 41(7): 890–894

Lu Q F, Bell W, Bauer P, Bormann N and Peubey C. 2011. Characterizing the FY-3A microwave temperature sounder using the ECMWF model. Journal of Atmospheric and Oceanic Technology, 28(11): 1373–1389

Lu Q F, Lawrence H, Bormann N, English S, Lean K, Atkinson N, Bell W and Carminati F. 2015. An Evaluation of FY-3C Satellite Data Quality at ECMWF and the Met Office. ECMWF Technical Memoranda 767. ECMWF

Saunders R, Matricardi M and Brunel P. 1999. An improved fast radiative transfer model for assimilation of satellite radiance observations. Quarterly Journal of the Royal Meteorological Society, 125(556): 1407–1425

Skou N. 1997. Measurement of small antenna reflector losses for radiometer calibration budget. IEEE Transactions on Geoscience and Remote Sensing, 35(4): 967–971

唐世浩, 邱红, 马刚. 2016. 风云气象卫星主要技术进展. 遥感学报, 20(5): 842–849

Tang S H, Qiu H and Ma G. 2016. Review on progress of the Fengyun meteorological satellite. Journal of Remote Sensing, 20(5): 842–849

王功雪, 蒋玲梅, 武胜利, 刘晓敬, 郝诗睿. 2017. FY-3B与FY-3C/MWRI交叉定标及雪深算法应用. 遥感技术与应用, 32(1): 49–56

Wang G X, Jiang L M, Wu S L, Liu X J and Hao S R. 2017. Intercalibrating FY-3B and FY-3C/MWRI for synergistic implementing to snow depth retrieval algorithm. Remote Sensing Technology and Application, 32(1): 49–56

Wentz F J, Ashcroft P and Gentemann C. 2001. Post-launch calibration of the TRMM microwave imager. IEEE Transactions on Geoscience and Remote Sensing, 39(2): 415–422

Wessel J, Farley R W, Fote A, Hong Y, Poe G A, Swadley S D, Thomas B and Boucher D J. 2008. Calibration and validation of DMSP SSMIS lower atmospheric sounding channels. IEEE Transactions on Geoscience and Remote Sensing, 46(4): 946–961

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

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

Yang H, Weng F Z, Lv L Q, Lu N M, Liu G F, Bai M, Qian Q Y, He J K and Xu H X. 2011. The FengYun-3 microwave radiation imager on-orbit verification. IEEE Transactions on Geoscience and Remote Sensing, 49(11): 4552–4560

张淼, 王素娟, 覃丹宇, 邱红, 唐世浩. 2018. FY-3C微波成像仪海面温度产品算法及精度检验. 遥感学报, 22(5): 713–722

Zhang M, Wang S J, Qin D Y, Qiu H and Tang S H. 2018. The inversion and quality validation of FY-3C MWRI sea surface temperature. Journal of Remote Sensing, 22(5): 713–722

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