Developing a WRF-based mixed variational and nudging data assimilation scheme for the US Army convection-scale nowcasting system

The US Army Research Laboratory (ARL) has been developing and testing a WRF-based observation nudging FDDA (four-dimensional data assimilation) 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 FDDA nudging technique used by the WRE-N has been proven in numerous past research studies to be both efficient and effective at such convection-resolving scales; however, it lacks the capability to assimilate indirect observations such as radar radial wind/reflectivity and satellite radiance data. In order to take advantage of indirect local observations from various remote sensing instruments (often of resolution critical to convection-resolving scales), a mixed or “hybrid” data assimilation system is in the process of being developed and tested for the WRE-N model. This technique uses the observation or “station” nudging for direct observations in addition to three-dimensional (3D) grid nudging using analyses created by a different assimilation technique in order to combine the advantages of both nudging and 3DVAR. The 3D analyses used for the grid nudging will be produced by the Variational LAPS (Local Analysis and Prediction System) developed by National Oceanic and Atmospheric Administration (NOAA)'s Global System Division (GSD). An added advantage of the Variational LAPS is its capability to process both direct and indirect observations from many sources such as radar, satellite, lidar, wind profiler, GPS, among others. The Variational LAPS created multi-scale variational analysis could also be used as the WRE-N initial conditions in order to “hotstart” (more rapidly spin up) WRE-N model cycling.

The effectiveness of utilizing the Variational LAPS analysis for analysis nudging in combination with standard observation nudging will be demonstrated initially through well-selected case studies. It is expected that the WRE-N model utilizing both observation nudging and analysis nudging using the Variational LAPS analysis (assimilating radar and satellite data) will improve forecasting skill and predictability, especially in the 0-3 hr time frame.