The NASA Cyclone Global Navigation Satellite System (CYGNSS) Mission

The NASA Cyclone Global Navigation Satellite System (CYGNSS) is a spaceborne constellation of eight observatories with mission design objectives focused on better sampling of tropical cyclone (TC) inner core processes. CYGNSS addresses the deficiencies with current TC intensity forecasts that are a result of inadequate observations and modeling of the inner core. The inadequacy in current observations results from two causes: 1) Much of the inner core ocean surface is obscured from conventional remote sensing instruments by intense precipitation in the eye wall and inner rain bands. 2) The rapidly evolving (genesis and intensification) stages of the TC life cycle are poorly sampled in time by conventional polar-orbiting, wide-swath surface wind imagers. CYGNSS is specifically designed to address these two limitations by combining the all-weather performance of GNSS bistatic ocean surface scatterometry with the enhanced sampling properties of a constellation of satellites.

The CYGNSS observatories are currently in Phase B development. In parallel with the hardware development, a detailed end-to-end simulator has been constructed in software that is designed to reproduce the expected measurements made on orbit by CYGNSS and, in particular, when it overflies TCs. The simulator includes realistic nature run models of the TC, realistic representations of the orbital geometries and measurement configurations of the GPS transmitting satellites and the CYGNSS receiving satellites, physically based electromagnetic scattering models for the surface interaction, and accurate simulations of the on-board signal processing performed by CYGNSS to form the Level 1 science data products – the Delay Doppler Maps (DDM). Wind speed retrieval algorithms are in development to make full use of the information contained in the DDMs.

An update on the current status of the mission will be presented, with emphasis given to the wind speed retrieval algorithm development and its predicted performance, and to the expected spatial and temporal sampling properties of the retrieved winds.