Impact of NOAA's CyGNSS Surface Windspeed Dataset on Forecasts from NCEP's Global Forecast System

Global Navigation Satellite System reflectometry (GNSS-R) is a method of using reflected GNSS radio signals to remotely sense a variety of attributes of the reflecting surface. In the case of reflections off the sea surface, reflections can be used to retrieve wind speeds from measurements of surface roughness. The normalized bistatic radar cross section (NBRCS) is the key observable in the wind retrieval process and is a measure of the intensity of the return signal. Combined with nearby wind observations and/or model analysis values, the NBRCS can be empirically linked to near-surface wind speed. Sea surface winds can be inferred from NBRCS measurements with the use of a semi-empirical geophysical model function (GMF). In fact, NOAA has developed its own GMF for the Cyclone Global Navigation Satellite System (CyGNSS) mission. With the use of such a GMF, NOAA is now producing 25 km CyGNSS winds using its own wind retrieval algorithm.

In this work, and in preparation for the evaluation of GNSS-R retrieved winds provided by the NOAA Commercial Weather Data Program (CWDP), we are evaluating the global weather forecast impact of NOAA ocean surface winds from the CyGNSS mission. Specifically, we aim to evaluate and quantify the impact of NOAA-derived CyGNSS winds on the NCEP’s Global Forecast System. This is important work for two reasons: First, as an evaluation of the value of existing NOAA derived GNSS-R winds for weather forecast skill and second, to optimize the use of wind retrievals purchased through the CWDP to improve global numerical weather prediction forecasts.