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

Abstract

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).

CDL: A Cloud Detection Algorithm Over Land for MWHS-2 Based on the Gradient Boosting Decision Tree

“…Then the value of composite reflectivity whose measure time is closest to the time when MWHS-2 passes the East China and measure range is in the area of MWHS-2's filed-of-view (FOV), is selected to calculate the average value in each FOV. Radar reflectivity factor is often used for cloud detection [21]- [25]. In this study, the scenes are flagged as cloudy when the radar reflectivity exceeds 5 dBZ.…”

Dynamic Channel Selection of Microwave Temperature Sounding Channels under Cloudy Conditions

“…To make better use of microwave radiance observations for data assimilation, the removal of data contaminated by hydrometeor particles is important. The most common cloud detection method allows for the detection of precipitation based on the deviation between observation and simulation brightness temperature (O-B) of satellite channels [22]. A scattering index [linear regression model of channel 15 and channels 1-3 of AMSU-A, used by the Advanced TIROS Operational Vertical Sounder (ATOVS) and Advanced Very High Resolution Radiometer (AVHRR) Pre-processing Package (AAPP)] has also been employed by English et al [23].…”