Improved weather predictions through data assimilation for GFDL SHiELD

The Geophysical Fluid Dynamics Laboratory (GFDL)'s System for High-resolution prediction on Earth-to-Local Domains (SHiELD) model typically uses the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) analysis to initialize its medium-range forecasts. A data assimilation (DA) system has been implemented for the global SHiELD to demonstrate the prediction skills of the model initialized from its own analysis. The DA system leverages the advanced DA techniques used in GFS and assimilates all the observations assimilated in GFS. Compared to the forecasts initialized from GFS analysis, SHiELD forecast skills are significantly improved by using its own analysis. Remarkable improvement is found in the southern hemisphere with positive impact lasting up to 10 days. The DA system is useful in identifying and understanding model errors. The most noticeable model error detected by the DA system originates from the turbulent kinetic energy (TKE)-based moist eddy-diffusivity mass-flux vertical turbulent mixing (TKE-EDMF) scheme. The model error leads to insufficient ensemble spread. Including two versions of the TKE-EDMF scheme in the ensemble helps increase ensemble spread, further improves forecast skills and alleviate the systematic errors in marine stratocumulus regions. Applying interchannel correlated observation errors for Infrared Atmospheric Sounding Interferometer (IASI) and Cross-track Infrared Sounder (CrIS) also reduces the systematic errors and improves the forecast skill up to day 5. Further investigation of the forecast errors reveals that the ensemble spread is largely affected by the parameterization of eddy diffusivity through its impact on the gradient of the model state. The systematic forecast errors in marine stratocumulus regions are associated with the vertical location of the stratocumulus cloud, which is sensitive to model vertical resolution within the cloud layer. Enhancing eddy diffusion within the cloud or near cloud top elevates cloud top but reduces cloud amount.