Journal of Meteorological Research

, Volume 31, Issue 6, pp 1109–1122 | Cite as

Liquid water path retrieval using the lowest frequency channels of Fengyun-3C Microwave Radiation Imager (MWRI)

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Abstract

The Microwave Radiation Imager (MWRI) on board Chinese Fengyun-3 (FY-3) satellites provides measurements at 10.65, 18.7, 23.8, 36.5, and 89.0 GHz with both horizontal and vertical polarization channels. Brightness temperature measurements of those channels with their central frequencies higher than 19 GHz from satellite-based microwave imager radiometers had traditionally been used to retrieve cloud liquid water path (LWP) over ocean. The results show that the lowest frequency channels are the most appropriate for retrieving LWP when its values are large. Therefore, a modified LWP retrieval algorithm is developed for retrieving LWP of different magnitudes involving not only the high frequency channels but also the lowest frequency channels of FY-3 MWRI. The theoretical estimates of the LWP retrieval errors are between 0.11 and 0.06 mm for 10.65- and 18.7-GHz channels and between 0.02 and 0.04 mm for 36.5- and 89.0-GHz channels. It is also shown that the brightness temperature observations at 10.65 GHz can be utilized to better retrieve the LWP greater than 3 mm in the eyewall region of Super Typhoon Neoguri (2014). The spiral structure of clouds within and around Typhoon Neoguri can be well captured by combining the LWP retrievals from different frequency channels.

Keywords

microwave remote sensing Fengyun-3C Microwave Radiation Imager (MWRI) liquid water path (LWP) retrieval 

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Notes

Acknowledgment

The authors would like to thank the National Satellite Meteorological Center of the China Meteorological Administration for providing the FY-3C MWRI data (http://satellite.nsmc.org.cn/PortalSite/Data/Satellite.aspx). All data used in this study are available from the authors upon request (tangfei@nuist.edu.cn).

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Copyright information

© The Chinese Meteorological Society and Springer-Verlag GmbH Germany, part of Springer Nature 2017

Authors and Affiliations

  1. 1.Joint Center of Data Assimilation for Research and Application, College of Atmospheric ScienceNanjing University of Information Science & TechnologyNanjingChina
  2. 2.State Key Laboratory of Severe Weather, Chinese Academy of Meteorological SciencesChina Meteorological AdministrationBeijingChina
  3. 3.Earth System Science Interdisciplinary CenterUniversity of MarylandCollege ParkUSA

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