Development and Testing of a Clear-Sky Data Selection Algorithm for FY-3C/D Microwave Temperature Sounder-2

Abstract

The Fengyun (FY)-3C/D microwave temperature sounder-2 (MWTS-2) is similar to the Advanced Microwave Sounding Unit-A (AMSU-A), except it lacks two window channels located at 23.8 GHz and 31.4 GHz. This makes a clear-sky data determination challenging for the MWTS-2 due to the unavailability of cloud liquid water path (LWP) retrievable from the two window channels. The purpose of this study is to develop a clear-sky data selection algorithm for the FY-3C/D MWTS-2 based on the bias-removed differences between observations and model simulations of the MWTS-2 50.3-GHz channel 1 (or equivalently AMSU-A channel 3). First, a point is defined as a temporal clear-sky (cloudy) point if the bias-removed difference between observed and simulated brightness temperatures is smaller than or equal to (greater than) 2 K. Then, a temporal clear-sky (cloudy) point is defined as a final clear-sky (cloudy) point if all points within its 60-km (100-km) radial distance are temporal clear-sky (cloudy) points. Finally, if the mean value of the bias-removed differences between observations and simulations in the 100-km circle from a temporal cloudy point are smaller than or equal to (greater than) 2 K, all temporal clear-sky points within this circle are (not) taken as the final clear-sky points. Applications of this algorithm to FY-3C MWTS-2 and MetOp-B AMSU-A lead to the following conclusions: (i) more than 70% (95%) of the clear-sky (cloudy) data points are successfully identified from both AMSU-A and MWTS-2 observations; (ii) the algorithm-selected clear-sky data points were located in clear-sky areas in the GOES-15 imager, and (iii) the bias-removed differences between observations and model simulations of MWTS-2 channel 1 well reveals the eye, the eyewall, and the spiral rainband structure of Super Typhoon Halong (2014).

Studies of FY-3 Observations over the Past 10 Years: A Review

“…fields. The value of O-B-μ(α)-μ(φ) is generally less (greater) than 2 K for AMSU-A channel 3 or MWTS channel 1 data with LWP less (greater) than 0.05 kg m -2 (see Figure 10 in Reference [41]). We can thus use…”

“…A cloud detection algorithm was developed based on the FY-3 MWTS observations [40,41]. Firstly, these studies showed that cloud regions become distinguishable in the global distribution of are not dominated by the limb effect as O-B does for both AMSU-A channel 3 [40] and MWTS channel 1 [41], where μ ϕ ( ) and μ α ( ) are calculated using data collocated with AMSU-A clear-sky data points (LWP<0.01 g kg -1 ). Many of the cloud features seen in the LWP field are revealed in the…”

“…where i r is the spatial location of the ith data point, j r is the spatial location of the jth data point, and Ni is the total number of data points within the 100 km radial distance from the ith data point. The proposed cloud detection algorithm successively identified more than 70% (95%) of the clear-sky (cloudy) data points for MWTS observations [41].…”

Estimates of Daily Evapotranspiration in the Source Region of the Yellow River Combining Visible/Near-Infrared and Microwave Remote Sensing

“…The data of the FY series satellites used in the study were all downloaded from the Fengyun Satellite Data Center (http://satellite.nsmc.org.cn/portalsite/default.aspx), including the 1-h average thermal infrared radiation brightness temperature and visible wide-band albedo, and MWRI 36.5 GHz microwave radiation brightness temperature. For specific processing steps, refer to the relevant literature [25].…”

Impact of Assimilating FY-3D MWTS-2 Upper Air Sounding Data on Forecasting Typhoon Lekima (2019)

“…However, the FY-3D MWTS-2 lacks the two window channels at 23.8 and 31.4 GHz (see Table 1), which are critical for retrieving the cloud liquid water path (CLW) [10] and for cloud detection in the Gridpoint Statistical Interpolation (GSI) data assimilation system [11]. In previous studies, the cloud detection methods for the MWTS-2 were developed mainly by setting the cloud products from other instruments [12,13]. Recently, a convenient cloud detection method was proposed by Yang and Weng (2019) [14] that uses the brightness temperatures of 23.8 and 31.4 GHz channels reconstructed from a machine learning method.…”