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Advances in Atmospheric Sciences

, Volume 36, Issue 7, pp 711–720 | Cite as

Striping Noise Analysis and Mitigation for Microwave Temperature Sounder-2 Observations

  • Xiaolei ZouEmail author
  • Xiaoxu Tian
Original Paper
  • 9 Downloads

Abstract

The Microwave Temperature Sounder (MWTS)-2 has a total of 13 temperature-sounding channels with the capability of observing radiance emissions from near the surface to the stratosphere. Similar to the Advanced Technology Microwave Sounder (ATMS), striping pattern noise, primarily in the cross-track direction, exists in MWTS-2 radiance observations. In this study, an algorithm based on principal component analysis (PCA) combined with ensemble empirical mode decomposition (EEMD) is described and applied to MWTS-2 brightness temperature observations. It is arguably necessary to smooth the first three principal component (PC) coefficients by removing the first four intrinsic mode functions (IMFs) using the EEMD method (denoted as PC3/IMF4). After the PC3/IMF4 noise mitigation, the striping pattern noise is effectively removed from the brightness temperature observations. The noise level in MWTS-2 observations is significantly higher than that detected in ATMS observations. In May 2014, the scanning profile of MWTS-2 was adjusted from varying-speed scanning to constantspeed scanning. The impact on striping noise levels brought on by this scan profile change is also analyzed here. The striping noise in brightness temperature observations worsened after the profile change. Regardless of the scan profile change, the striping noise mitigation method reported in this study can more or less suppress the noise levels in MWTS-2 observations.

Key words

MWTS-2 striping noise temperature sounding microwave radiometry 

摘要

第二代微波温度探测 (MWTS-2) 仪共有13个温度探测通道, 这些通道具有观测从地面到平流层大气放射的辐射量的能力. 然后, 与ATMS仪器类似, MWTS-2 观测中被发现有主要分布在垂直于扫描线方向的带状噪音. 此研究提出一个以结合经验证交分解(PCA) 与集合经验模态分解 (EEMD)为基础的算法并应用于MWTS-2观测中. 研究中论证了PCA中前三个分量有必要以EEMD算法去掉前四个模态以达到平滑的作用(文中表示为PC3/IMF4). 经过PC3/IMF4 降噪以后, 亮温观测中的带状噪音可以被有效的去除. MWTS-2观测中探测到的带状噪音显著高于ATMS仪器中的噪音. MWTS-2的扫描设置在2014年5月从变速扫描被调整到了恒定速度扫描. 本次研究对此调整对带状噪音的影响也作出了分析. 所得结果显示扫描设置调整以后带状噪音变得相较调整前更大. 不论观测来自扫描设置调整前或者调整后, 本文中描述的去除带状噪音的算法均可适用.

关键词

第二代微波探测仪 带状噪 温度探测 微波辐射计 

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Notes

Acknowledgements

This research was supported by the National Key R&D Program (Grant No. 2018YFC1506702). The authors thank the FengYun Satellite Data Center (http://satellite.nsmc.org.cn) for providing the MWTS-2 observations. The authors also thank Dr. Yuan MA for conducting some of this work during her Ph.D. studies and the anonymous reviewers for their time and contributions to this study. The software developed to perform the calculations in this study is available by contacting the corresponding author at xzou1@umd.edu.

References

  1. Andersson, E., J. Pailleux, J.-N. Thépaut, J. R. Eyre, A. P. Mc-Nally, G. A. Kelly, and P. Courtier, 1994: Use of cloud-cleared radiances in three/four-dimensional variational data assimilation. Quart. J. Roy. Meteor. Soc., 120, 627–653,  https://doi.org/10.1002/qj.49712051707.CrossRefGoogle Scholar
  2. Bormann, N., A. Fouilloux, and W. Bell, 2013: Evaluation and assimilation of ATMS data in the ECMWF system. J. Geophys. Res., 118, 12 970–12 980,  https://doi.org/10.1002/2013JD020325.Google Scholar
  3. Derber, J. C., and W.-S. Wu, 1998: The use of TOVS cloudcleared radiances in the NCEP SSI analysis system. Mon. Wea. Rev., 126, 2287–2299,  https://doi.org/10.1175/1520-0493(1998)126<2287:TUOTCC>2.0.CO;2.Google Scholar
  4. Eyre, J. R., G. A. Kelly, A. P. McNally, E. Andersson, and A. Persson, 1993: Assimilation of TOVS radiance information through one-dimensional variational analysis. Quart. J. Roy. Meteor. Soc., 119, 1427–1463,  https://doi.org/10.1002/qj.49711951411.CrossRefGoogle Scholar
  5. Li, J., and X. L. Zou, 2015: Assimilation of FY-3 MWTS radiance data into Chinese NWP systems. World Climate Research Programme, section 1, pp 11–15.Google Scholar
  6. Li, J., and G. Q. Liu, 2016: Assimilation of Chinese Fengyun-3B Microwave Temperature Sounder radiances into the global GRAPES system with an improved cloud detection threshold. Frontiers of Earth Science, 10, 145–158,  https://doi.org/10.1007/s11707-015-0499-2.CrossRefGoogle Scholar
  7. Li, J., G. Q. Liu, J. Li, and G. Q. Liu, 2016: Direct assimilation of Chinese FY-3C Microwave Temperature Sounder-2 radiances in the global GRAPES system. Atmospheric Measurement Techniques, 9, 3095–3113,  https://doi.org/10.5194/amt-9-3095-2016.CrossRefGoogle Scholar
  8. Qin, Z. K., X. L. Zou, and F. Z. Weng, 2013: Analysis of ATMS striping noise from its Earth scene observations. J. Geophys. Res., 118, 13 214–13 229,  https://doi.org/10.1002/2013JD020399.CrossRefGoogle Scholar
  9. Tian, X. X., and X. L. Zou, 2016: ATMS- and AMSU-A-derived hurricane warm core structures using a modified retrieval algorithm. J. Geophys. Res., 121, 12 630–12 646,  https://doi.org/10.1002/2016JD025042.Google Scholar
  10. Tian, X. X., and X. L. Zou, 2018: Capturing size and intensity changes of hurricanes Irma and Maria (2017) from polarorbiting satellite microwave radiometers. J. Atmos. Sci., 75, 2509–2522,  https://doi.org/10.1175/JAS-D-17-0315.1.CrossRefGoogle Scholar
  11. Tian, X. X., X. L. Zou, and S. P. Yang, 2018: A limb correction method for the Microwave Temperature Sounder 2 and its applications. Adv. Atmos. Sci., 35, 1547–1552,  https://doi.org/10.1007/s00376-018-8092-8.CrossRefGoogle Scholar
  12. Wu, Z. H., and N. E. Huang, 2009: Ensemble empirical mode decomposition: A noise-assisted data analysis method. Advances in Adaptive Data Analysis, 1, 1–41,  https://doi.org/10.1142/S1793536909000047.CrossRefGoogle Scholar
  13. Zou, X., F. Weng, B. Zhang, L. Lin, Z. Qin, and V. Tallapragada, 2013: Impacts of assimilation of ATMS data in HWRF on track and intensity forecasts of 2012 four landfall hurricanes. J. Geophys. Res., 118, 11 558–11 576,  https://doi.org/10.1002/2013JD020405.Google Scholar
  14. Zou, X., H. Dong, and Z. Qin, 2017: Striping noise reduction for ATMS window channels using a modified destriping algorithm. Quart. J. Roy. Meteor. Soc., 143, 2567–2577,  https://doi.org/10.1002/qj.3107.CrossRefGoogle Scholar
  15. Zou, X. L., and X. X. Tian, 2018: Hurricane warm-core retrievals from AMSU-A and remapped ATMS measurements with rain contamination eliminated. J. Geophys. Res., 123, 10 815–10 829,  https://doi.org/10.1029/2018JD028934.Google Scholar
  16. Zou, X. L., and X. X. Tian, 2019: Comparison of ATMS striping noise between NOAA-20 and S-NPP and noise impact on warm core retrieval of typhoon jelawat (2018). IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 1–9,  https://doi.org/10.1109/JSTARS.2019.2891683. (in press)Google Scholar
  17. Zou, X. L., X. X. Tian, and F. Z. Weng, 2014: Detection of television frequency interference with satellite microwave imager observations over oceans. J. Atmos. Oceanic Technol., 31, 2759–2776,  https://doi.org/10.1175/JTECH-D-14-00086.1.CrossRefGoogle Scholar

Copyright information

© Institute of Atmospheric Physics/Chinese Academy of Sciences, and Science Press and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Earth Science System Interdisciplinary CenterUniversity of MarylandCollege ParkUSA

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