Improving cloud forecasts with assimilation of cloud-/precipitation-affected microwave and infrared radiances using MPAS-JEDI
- United States of America (liuz@ucar.edu)
MPAS-JEDI, a relatively-new data assimilation (DA) system for the Model for Prediction Across Scales – Atmosphere (MPAS-A) based upon the Joint Effort for Data assimilation Integration (JEDI), allows to assimilate cloud-/precipitation-affected satellite microwave and infrared radiance data to analysis microphysical parameters, e.g., mixing ratios of hydrometeors. Global cycling DA experiments were conducted in the context of MPAS-JEDI’s hybrid-3DEnVar configured at 30km resolution with 80-member ensemble input at 60km that is produced using MPAS-JEDI's ensemble of 3DEnVar. The benchmark experiment assimilates conventional observations plus clear-sky radiances from AMSU-A and MHS. All-sky experiments add the assimilation of all-sky microwave (MW) radiances from AMSU-A’s and/or ATMS’s window channels over water as well as infrared (IR) channels of two geostationary sensors GOES-ABI and Himawari-AHI. In addition to the impact assessment on dynamic and thermodynamic variables, we investigated more the impact on cloud forecasts in terms of fitting to ABI/AHI radiance data at different wavelengths. The community radiative transfer model (CRTM) is used as the observation operator in both all-sky radiance DA and evaluation. The substantial positive impact on cloud forecasts was obtained with all-sky microwave DA (individually or collectively from AMSU-A and ATMS) in terms of a better forecast fitting to observed ABI/AHI channel 13's radiances up to 7 days, especially over tropical regions, where the day-1 forecast root-mean-square error is reduced up to 10%. Cloud forecast impact from assimilating all-sky ABI/AHI 3 water vapor channels' radiances is more limited although a clear benefit is seen for middle/upper troposphere moisture field, which is consistent with ABI/AHI water vapor channels' sensitivity height. Future research direction for all-sky MW and IR radiance DA with MPAS-JEDI will also be discussed.
How to cite: Liu, Z., Ban, J., and Banos, I.: Improving cloud forecasts with assimilation of cloud-/precipitation-affected microwave and infrared radiances using MPAS-JEDI, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7021, https://doi.org/10.5194/egusphere-egu24-7021, 2024.