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

  • role： First author第一作者
  • Affiliation：

    Key Laboratory of Radiometric Calibration and Validation for Environmental Satellites, China Meteorological Administration (LRCVES/CMA) and National Satellite Meteorological Center, Beijing 100081, China

  • Email：zhangmiao@cma.gov.cn
  • Introduction：张淼，女，1985年生，助理研究员，研究方向为微波遥感及应用。E-mail：zhangmiao@cma.gov.cn
ZHANG Miao,  
  • role： Corresponding author通信作者
  • Affiliation：

    Key Laboratory of Radiometric Calibration and Validation for Environmental Satellites, China Meteorological Administration (LRCVES/CMA) and National Satellite Meteorological Center, Beijing 100081, China

  • Email：luqf@cma.gov.cn
  • Introduction：陆其峰，男，1975年生，研究员，研究方向为气象卫星资料同化、卫星探测资料观测系统偏差诊断、订正及卫星观测系统性能优化。E-mail：luqf@cma.gov.cn
LU Qifeng*,  
  • Affiliation：

    Key Laboratory of Radiometric Calibration and Validation for Environmental Satellites, China Meteorological Administration (LRCVES/CMA) and National Satellite Meteorological Center, Beijing 100081, China

GU Songyan,  
  • Affiliation：

    Key Laboratory of Radiometric Calibration and Validation for Environmental Satellites, China Meteorological Administration (LRCVES/CMA) and National Satellite Meteorological Center, Beijing 100081, China

HU Xiuqing,  
  • Affiliation：

    Key Laboratory of Radiometric Calibration and Validation for Environmental Satellites, China Meteorological Administration (LRCVES/CMA) and National Satellite Meteorological Center, Beijing 100081, China

WU Shengli

Resümee

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.

Schlüsselwort

remote sensing;FY-3C;microwave imager;calibration;difference between the ascending and descending orbits

References

  1. 1.
    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
  2. 2.
    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
  3. 3.
    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
  4. 4.
    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
  5. 5.
    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
  6. 6.
    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
  7. 7.
    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
  8. 8.
    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
  9. 9.
    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
  10. 10.
    Skou N. 1997. Measurement of small antenna reflector losses for radiometer calibration budget. IEEE Transactions on Geoscience and Remote Sensing, 35(4): 967–971
  11. 11.
    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
  12. 12.
    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
  13. 13.
    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
  14. 14.
    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
  15. 15.
    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
  16. 16.
    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
  17. 17.
    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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