Dynamic Channel Selection of Microwave Temperature Sounding Channels under Cloudy Conditions

• Main Authors: Luyao Qin, Yaodeng Chen, Tianlei Yu, Gang Ma, Yang Guo, Peng Zhang
• Format: Article
• Language: English
• Published: MDPI AG 2020-01-01
• Series: Remote Sensing
• Subjects: data assimilation; microwave temperature sounding; dynamic channel selection; cloud parameter remap
• Online Access: https://www.mdpi.com/2072-4292/12/3/403

To make better use of microwave radiance observations for data assimilation, removal of radiances contaminated by hydrometeor particles is one of the most important steps. Generally, all observations below the middle troposphere are eliminated before the analysis when precipitation is present. However, the altitude of the cloud top varies; when the weighting function peak height of a channel is higher than the altitude of the cloud top, observations are not affected by the absorption or scattering of cloud particles. Thus, the radiative transfer calculation can be performed under a clear sky scenario. In this paper, a dynamic channel selection (DCS) method was developed to determine the radiance observations unaffected by clouds under cloudy conditions in assimilation. First, the sensitivity of cloud liquid water (CLW) profiles to radiance from the microwave temperature sounding frequencies was analyzed. It was found that the impact of CLW on transmittance can be neglected where the cloud top height is below the weighting function peak height. Second, three lookup tables were devised through analysis of the impact of cloud fraction and cloud top height on radiance, which is the basis of the DCS method. The unified cloud top height of the Microwave Temperature Sounder (MWTS)-2 fields of view (FOVs) can be calculated by remapping the cloud mask and cloud top height data from the Medium Resolution Spectral Imager-2 (MERSI-2). Observations from various channels may be removed or retained based on real-time dynamic unified cloud top height data. Twelve-hour and long-term time-series brightness temperature simulation experiments both showed that an increase in the amount of observations used for data assimilation of more than 300% can be achieved by application of DCS, but this had no effect on the amount of error. Through DCS, areas of strong precipitation can be accurately identified and removed, and more observations above cloud top height can be included in the data assimilation. The application of DCS to data assimilation will greatly improve the data utilization rate, and therefore allow for more accurate characterization of upper atmospheric circulation.