The Improved NRL Tropical Cyclone Monitoring System with a Unified Microwave Brightness Temperature Calibration Scheme

The near real-time NRL global tropical cyclone (TC) monitoring system based on multiple satellite passive microwave (PMW) sensors is improved with a new inter-sensor calibration scheme to correct the biases caused by differences in these sensor's high frequency channels. Since the PMW sensor 89 GHz channel is used in multiple current and near future operational and research satellites, a unified scheme to calibrate all satellite PMW sensor's ice scattering channels to a common 89 GHz is created so that their brightness temperatures (TBs) will be consistent and permit more accurate manual and automated analyses. In order to develop a physically consistent calibration scheme, cloud resolving model simulations of a squall line system over the west Pacific coast and hurricane Bonnie in the Atlantic Ocean are applied to simulate the views from different PMW sensors. To clarify the complicated TB biases due to the competing nature of scattering and emission effects, a four-cloud based calibration scheme is developed (rain, non-rain, light rain, and cloudy). This new physically consistent inter-sensor calibration scheme is then evaluated with the synthetic TBs of hurricane Bonnie and a squall line as well as observed TCs. Results demonstrate the large TB biases up to 13 K for heavy rain situations before calibration between TMI and AMSR-E are reduced to less than 3 K after calibration. The comparison stats show that the overall bias and RMSE are reduced by 74% and 66% for hurricane Bonnie, and 98% and 85% for squall lines, respectively. For the observed hurricane Igor, the bias and RMSE decrease 41% and 25% respectively. This study demonstrates the importance of TB calibrations between PMW sensors in order to systematically monitor the global TC life cycles in terms of intensity, inner core structure and convective organization. A physics-based calibration scheme on TC's TB corrections developed in this study is able to significantly reduce the biases between different PMW sensors.