Development and initial assessment of a new land index for microwave humidity sounder cloud detection

Abstract

This paper describes a new quality control (QC) scheme for microwave humidity sounder (MHS) data assimilation. It consists of a cloud detection step and an O-B (i.e., differences of brightness temperatures between observations and model simulations) check. Over ocean, cloud detection can be carried out based on two MHS window channels and two Advanced Microwave Sounding Unit-A (AMSU-A) window channels, which can be used for obtaining cloud ice water path (IWP) and liquid water path (LWP), respectively. Over land, cloud detection of microwave data becomes much more challenging due to a much larger emission contribution from land surface than that from cloud. The current MHS cloud detection over land employs an O-B based method, which could fail to identify cloudy radiances when there is mismatch between actual clouds and model clouds. In this study, a new MHS observation based index is developed for identifying MHS cloudy radiances over land. The new land index for cloud detection exploits the large variability of brightness temperature observations among MHS channels over different clouds. It is shown that those MHS cloudy radiances that were otherwise missed by the current O-B based QC method can be successfully identified by the new land index. An O-B check can then be employed to the remaining data after cloud detection to remove additional outliers with model simulations deviated greatly from observations. It is shown that MHS channel correlations are significantly reduced by the newly proposed QC scheme.Citation: Qin Zhengkun and Zou Xiaolei, 2016: Development and initial assessment of a new land index for microwave humidity sounder cloud detection.

Impacts from assimilation of one data stream of AMSU‐A and MHS radiances on quantitative precipitation forecasts

“…Examples showing the effectiveness of the GSI QC in removing MHS data points that were located within clouds but failed to eliminated cloudy data points that were located at cloud edges can be found in Qin et al () and Zou et al (). The importance for improving MHS QC algorithms was demonstrated in Qin et al (), Qin and Zou () and Zou et al ().…”

“…Differences of brightness temperatures between MHS observations and CRTM simulations of the two MHS window channels 1–2 are involved in the three QC parameters in the GSI analysis system. A detailed description of MHS QC can be found in Qin and Zou (). Examples showing the effectiveness of the GSI QC in removing MHS data points that were located within clouds but failed to eliminated cloudy data points that were located at cloud edges can be found in Qin et al () and Zou et al ().…”

“…Since MHS is more sensitive to both water vapour and clouds than AMSU‐A, it is more challenging to use the MHS data in NWP systems due to the highest uncertainty in the forward operators and cloud forecasts (Bormann et al , ; Bouchard et al , ; Karbou et al , ). Recently, it was shown that impacts of MHS data assimilation on short‐term quantitative precipitation forecasts (QPFs) can be significantly improved by modifying the quality control (QC) schemes (Qin et al , ; Zou et al , ; Qin and Zou, ). The current approach of using MHS‐only data for cloud quality control could misclassify the cloud‐affected radiances as clear.…”

Impact of AMSU-A Data Assimilation over High Terrains on QPFs Downstream of the Tibetan Plateau

“…Wu et al (2002) described the theory behind and the development of the GSI system. We have applied this same GSI data assimilation and ARW modeling system in previous studies to assimilate measurements from geostationary operational environmental satellite imagers (Zou et al 2011(Zou et al , 2015Qin et al 2013Qin et al , 2017Qin and Zou 2018), the MHS (Zou et al 2013b;Qin and Zou 2016), the ATMS (Zou et al 2013a), the AMSU-A , the AMSU-A and MHS combined data stream , and the CrIS (Li and Zou 2017). Three data assimilation and forecast experiments were conducted.…”

Dynamic Channel Selection of Microwave Temperature Sounding Channels under Cloudy Conditions

“…MWHS data in the same field of view (FOV) are rejected if the TPW index is greater than 1. Qin et al [28] improved this method. They used observations from two MWHS channels and two AMSU-A channels to retrieve cloud liquid water path and cloud ice water path data over the ocean, respectively, and derived a new land index by using the mean and STDV of radiance for all five MWHS channels.…”