Global Navigation Satellite System (GNSS) radio occultations (RO) over the last 10 years have proved to be a valuable and essentially unbiased data source for operational global numerical weather prediction. However, the existing sampling coverage is too sparse in both space and time to support forecasting of severe mesoscale weather. In this study, the case study or quick observing system simulation experiment (Quick-OSSE) framework is used to quantify the impact of vastly increased numbers of GNSS RO profiles on mesoscale weather analysis and forecasting. The current study focuses on a severe convective weather event that produced both a tornado and flash flooding in Oklahoma on 31 May 2013. The WRF Model is used to compute a realistic and faithful depiction of reality. This 2-km "nature run'' (NR) serves as the "truth'' in this study. The NR is sampled by two proposed constellations of GNSS RO receivers that would produce 250 thousand and 2.5 million profiles per day globally. These data are then assimilated using WRF and a 24-member, 18-km-resolution, physics-based ensemble Kalman filter. The data assimilation is cycled hourly and makes use of a nonlocal, excess phase observation operator for RO data. The assimilation of greatly increased numbers of RO profiles produces improved analyses, particularly of the lower-tropospheric moisture fields. The forecast results suggest positive impacts on convective initiation. Additional experiments should be conducted for different weather scenarios and with improved OSSE systems.

http://dx.doi.org/10.1175/mwr-d-16-0212.1

Journal Article

NOAA Cooperative Institutes
Office of Oceanic and Atmospheric Research (OAR)