Improved Himawari‐8/AHI Radiance Data Assimilation With a Double Cloud Detection Scheme

Abstract

AbstractThis study explores the possibility of improving the impact of the Advanced Himawari Imager (AHI) clear‐sky radiance data assimilation (DA), focusing on cloud detection. First, the performance of the “clear‐channel” detection scheme of the minimum residual (MR) method embedded in the Gridpoint Statistical Interpolation (GSI) DA system is compared with the performances of the CLouds from Advanced Very High Resolution Radiometer Extended (CLAVR‐x) cloud processing system and the Moderate Resolution Imaging Spectroradiometer (MODIS) cloud‐product‐generating algorithm. The MR scheme does not reliably identify optically thin clouds along cloud edges. The MR‐estimated cloud‐top pressures are often too high for upper‐level clouds, rendering some cloud‐contaminated channels falsely clear. An infrared‐only AHI cloud mask (ACM) algorithm is added to the MR scheme to perform a so‐called double cloud detection (DCD). The DCD scheme adds nine ACM tests for selecting clear pixels and two thin cloud tests for rejecting pixels affected by upper‐level clouds. For a 1‐month period, we show the positive impacts of assimilating AHI infrared channels on short‐term forecasts of temperature and humidity using the DCD scheme rather than the MR scheme. Improvements in the DCD experiment extend more vertically, horizontally, and temporally than those in the MR experiment during the 48‐hr forecasting time. In terms of daily variations in forecasting performance, the DCD experiment consistently improves while the MR experiment fluctuates between improvement and degradation. Such improvements come from an elimination of those data having negative observation‐minus‐background values of large magnitudes due to cloud contamination, which causes positive biases in humidity analyses.