All-sky ATMS radiance data assimilation with MPAS-JEDI

Abstract. This study extends the all-sky radiance data assimilation capability in MPAS-JEDI (data assimilation system for the Model for Prediction Across Scales-Atmosphere based upon the Joint Effort for Data assimilation Integration), previously implemented for the Advanced Microwave Sounding Unit-A (AMSU-A), to the Advanced Technology Microwave Sounder (ATMS). Compared with AMSU-A, ATMS covers a broad frequency range, including high-frequency humidity-sounding channels, in addition to the temperature-sounding and low-frequency imager channels. In contrast to the previous AMSU-A implementation, which assimilated only imaging channels under all-sky conditions, this work assimilates all ATMS channels using the all-sky approach. A situation-dependent observation error model is employed, with distinct cloud predictors over land and ocean surfaces for both temperature- and humidity-sounding channels. The analysis variables, radiance observation operator, and bias correction method are inherited from the AMSU-A all-sky assimilation. The impact of assimilating all-sky ATMS radiances is evaluated with three month-long global hybrid three-dimensional ensemble-variational (hybrid-3DEnVar) experiments: a benchmark experiment without ATMS data, an experiment assimilating only ATMS temperature-sounding channels, and an experiment assimilating all ATMS channels. The 6-hour background forecasts during the assimilation cycling and extended 5-day forecasts are verified against conventional observations, satellite radiances, and Global Forecast System (GFS) analysis. The results show that the background fits to radiosonde observations, satellite radiances, and GFS analyses have improved. Forecast verifications against GFS analyses and independent radiance observations demonstrate statistically significant improvements relative to the benchmark for up to 3 days in both ATMS experiments, across dynamic, thermodynamic, moisture, and cloud fields.