Accurately predicting weather down to the convective storm scales requires setting initial model conditions that accurately represent the atmospheric state at all scales (from the planetary through synoptic large-scale, mesoscale, to convective). The importance of these various interactions can not be overlooked. A well-performing data assimilation (DA) system must accurately analyze flow features at all scales. For this visitor project, a multi-scale DA capability within the GSI-based Ensemble Kalman Filter System (EnKF) system was proposed for the FV3 limited-area model (LAM) that can assimilate both dense convective-scale data, such as those of radar and high-resolution GOES-R observations, as well as all other coarser-resolution data. The operational GSI hybrid Ensemble Variational (EnVar) system was recently selected to work with the FV3 LAM system that runs at NCEP which does not yet have a self-consistent multi-scale EnKF system. The EnKF is a prerequisite for an optimal multi-scale hybrid EnVar system because EnKF is essential to provide ensemble perturbations for reliable flow-dependent covariance estimation.
For the planned operational use of the FV3 LAM for convection-allowing model (CAM) forecasts over CONUS or larger domains, the multi-scale DA issue must be properly addressed. Two main goals proposed for this visitor project were to:
- develop a GSI-based multiscale EnKF DA system capable of effectively assimilating all observations sampling synoptic through convective scales for balanced NWP initial conditions on a 3-km continent-sized CAM-resolution grid, and
- test the multiscale DA system coupled with FV3 LAM using retrospective cases, tune and optimize the system configurations, including the filter separation length scale, localization radii, covariance inflation, etc.
The proposed multiscale DA (MDA) method uses filtered background covariances with long localization lengths for assimilating conventional observations that sample synoptic to meso-scale perturbations. Sensitivity experiments were performed to determine ideal filtering-length scales sufficient to diminish unfavorable noise in analyses. In addition, the height-dependent filtering length was proposed and its impact was examined with one-time upper-air data assimilation; the benefit was evident for up to 24 hours in subsequent forecasts, particularly for prediction of humidity. The post inflation in the GSI, relaxation to prior spread (RTPS), was optimized accordingly for MDA to restore only the large-scale background perturbations, which prevents reintroducing small-scale noise in analyses. The MDA was examined with a hourly cycled update configuration for 12 hours for real cases and its impact was evaluated. In terms of the deterministic forecasts from the final ensemble mean analysis, consistent improvement of MDA was found in prediction of most variables for up to 48 hours when only assimilating conventional data; when including radar DA, the benefit of MDA was relatively limited on the storm prediction and humidity forecast, for a shorter lead time. The figure below gives an example of the advantageous performance of MDA on the prediction of storm systems, mostly in reducing overforecast in both coverage and intensity, over the regular single-scale EnKF experiment. The positive impact of the MDA was found to be even more significant in the performance of individual ensemble members as well as the ensemble average. Our ongoing work will apply the MDA method to more cases to support a statistically robust conclusion.
It has been a precious experience to work under the DTC Visitor Program, especially during the critical pandemic period. During the one-month on-site visit period, I was able to collect all the data necessary for the planned retrospective experiments with assistance from the DTC Data Assimilation team. Valuable input toward the work was provided by regular weekly meetings with DTC members Drs. Ming Hu and Guoqing Ge, the program host Mr. Will Mayfield, and Ivette Hernandez (also a visitor, but on another project) throughout the entire one-year Visitor Program period.