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Open AccessArticle

Evaluation of MWHS-2 Using a Co-located Ground-Based Radar Network for Improved Model Assimilation

Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration, Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science & Technology, Nanjing 210044, Jiangsu, China
Nanjing Xinda Institute of Meteorological Science and Technology Co., Ltd., Nanjing 210044, Jiangsu, China
Key Laboratory of Radiometric Calibration and Validation for Environmental Satellite, China Meteorological Administration; National Satellite Meteorological Center, Beijing 100081, China
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(20), 2338;
Received: 23 August 2019 / Revised: 6 October 2019 / Accepted: 7 October 2019 / Published: 9 October 2019
(This article belongs to the Special Issue Weather Forecasting and Modeling Using Satellite Data)
Accurate precipitation detection is one of the most important factors in satellite data assimilation, due to the large uncertainties associated with precipitation properties in radiative transfer models and numerical weather prediction (NWP) models. In this paper, a method to achieve remote sensing of precipitation and classify its intensity over land using a co-located ground-based radar network is described. This method is intended to characterize the O−B biases for the microwave humidity sounder -2 (MWHS-2) under four categories of precipitation: precipitation-free (0–5 dBZ), light precipitation (5–20 dBZ), moderate precipitation (20–35 dBZ), and intense precipitation (>35 dBZ). Additionally, O represents the observed brightness temperature (TB) of the satellite and B is the simulated TB from the model background field using the radiative transfer model. Thresholds for the brightness temperature differences between channels, as well as the order relation between the differences, exhibited a good estimation of precipitation. It is demonstrated that differences between observations and simulations were predominantly due to the cases in which radar reflectivity was above 15 dBZ. For most channels, the biases and standard deviations of O−B increased with precipitation intensity. Specifically, it is noted that for channel 11 (183.31 ± 1 GHz), the standard deviations of O−B under moderate and intense precipitation were even smaller than those under light precipitation and precipitation-free conditions. Likewise, abnormal results can also be seen for channel 4 (118.75 ± 0.3 GHz).
Keywords: satellite data assimilation; bias characterization; precipitation; remote sensing satellite data assimilation; bias characterization; precipitation; remote sensing
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MDPI and ACS Style

Liu, S.; Chu, Z.; Yin, Y.; Liu, R. Evaluation of MWHS-2 Using a Co-located Ground-Based Radar Network for Improved Model Assimilation. Remote Sens. 2019, 11, 2338.

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