Radar Data Quality Control and Assimilation at the National Weather Radar Testbed (NWRT)

Abstract

Study and develop advanced approaches for radar data quality control (QC) and assimilation that will not only optimally utilize Doppler wind information from WSR-88D and Terminal Doppler Weather Radar (TDWR) but also take full advantage of rapid and flexible agile-beam scans from the phased array radar (PAR) at NWRT. OBJECTIVES Develop new variational methods to improve the existing radar wind analysis system so it can be applied to any radar scans to produce real-time vector wind displays and monitor data quality. Study radar data quality problems and develop statistically reliable quality control (QC) techniques. Explore new data assimilation techniques to optimally utilize the PAR scan capabilities. APPROACH Continue monitoring the real-time performances of the radar data QC packages (delivered to NRL and NCEP) with operational raw level-II data collected in different regions under various weather conditions (especially high-impact weather conditions). Develop reflectivity-velocity combined radar data QC and assimilation capabilities that expand and upgrade the existing radar data QC and assimilation packages. Use the modern information theory to study how to measure information content from observations for data assimilation and design data compression strategies for the existing and currently developed assimilation capabilities based on the information theory. Develop a new prototype ensemble hybrid filter to combine the merits of the ensemble-based filters (such as the ensemble Kalman filter) and variational data assimilation (such as the 3.5dVar delivered to NRL) for flow-dependent covariance estimation and high-resolution radar data assimilation. Toward this goal, the first step is to explore new ideas and sampling techniques to improve the covariance estimation and computational efficiency of the existing ensemble-based filters.