Investigating hybrid three-dimensional variational and nudging data assimilation approaches for a U.S. Army meso-gamma scale Weather Running Estimate-Nowcast system

A capability to rapidly update local environmental information at a high temporal frequency is an important requirement for U.S. Army echelons operating at and below Brigade Combat Team (BCT). The U.S. Army Research Laboratory has been developing and testing a Weather Research and Forecast (WRF) model and observation nudging four-dimensional data assimilation (FDDA)-based system to provide high spatial resolution (~ 1 km) and frequently updated (~ 1 hr) short range forecasts or “nowcasts” of the battlefield environment out to the 3-6 hr time frame. This system is referred to by ARL as the Weather Running Estimate-Nowcast (WRE-N) and has the capability of assimilating asynoptic and sporadic direct observations from various sources such as local soundings, surface observations, and aircraft in-situ measurements. The WRE-N is also being developed as a system capable for future deployment at Army echelons at or below BCT, therefore with more modest nesting, data assimilation, and overall computing hardware expectations. The FDDA nudging technique used by the WRE-N has been shown in past research studies to be efficient and effective when applied at the meso-gamma and meso-beta scales; however, it lacks an inherent capability to assimilate indirect observations such as radar radial wind/reflectivity and satellite radiance data. In addition, error covariances are treated in a somewhat ad-hoc and non-statistical way as opposed to those used in variational systems derived from climatological or even flow-dependent statistics. In order to take full advantage of non-traditional meteorological airborne, in-situ and remote sensing platforms possible in the future on the Army battlefield, mixed or “hybrid” data assimilation approaches using both nudging and 3DVAR are being tested for potential WRE-N improvements using a pair of well-selected case study events. This technique uses the observation or “station” nudging for direct observations in addition to three-dimensional (3D) grid nudging. The 3D “hot-start” analyses used for grid nudging are produced by the latest variational extension of the Local Analysis and Prediction System (v-LAPS) developed by the National Oceanic and Atmospheric Administration (NOAA)'s Global System Division (GSD). As a multi-scale variational approach, an advantage of the v-LAPS is its capability to process both direct and indirect observations from all available sources including radar, satellite, lidar, wind profiler, GPS, among others. It also can be used as a “ hotstart” option in WRE-N model cycling, where all mesoscale fields (including microphysics, clouds, vertical motion) are already spun-up. Focus of the studies is on the 0-6 h forecast period, and for cases where convection and microphysics played a prominent role.