Mapping Atmospheric Water Vapor Content Using Grazing Angle GNSS-R Over Oceans and Sea Ice

This presentation will focus on estimation and mapping of atmospheric water vapor (AWV) content over the calm tropical ocean waters and polar sea ice using grazing angle GNSS reflectometry (GNSS-R) measurements obtained by low-cost CubeSats. Observations of AWV in the polar regions and equatorial oceans are sparse due to challenges in deploying ground and airborne sensors in challenging environment and the cost associated with dedicated spaceborne systems for AWV sensing. Spaceborne grazing angle GNSS reflectometry (GNSS-R) utilizes low elevation GNSS signals reflected off Earth surface and received by low Earth orbiting (LEO) satellites with side- or downward-looking antennas. GNSS-R has the potential to fill the data gap in these regions with high accuracy and high spatial and temporal resolution at a low cost by utilizing the high precision carrier phase measurements of coherent GNSS signals reflected from ice-covered surface or calm waters. The dominant error source in grazing angle GNSS-R range measurements is the signal propagation delay in the troposphere. By leveraging known sea ice and calm ocean surface height variations through well-established models or through satellite altimetry measurements, we can infer the troposphere delay using GNSS-R measurements. AWV content can be inferred from the troposphere delay. This presentation will discuss the method and algorithms to retrieve tropospheric delay and AWV from Spire Global grazing angle GNSS-R data. We will highlight the results based on processing of over 3000 tracks of Spire GNSS-R data collected over Indonesia Archipelago and the Arctic Ocean. The GNSS-R based retrieval results are compared with co-located observations obtained from optical instrument and microwave radiometry onboard Sentinel-3 as well as the total content of water vapor from ERA5 and derived from VMF3. Case studies and statistical distributions of the comparison results will be presented to demonstrate that the GNSS-R data and the retrieval technique can indeed provide reliable observations of AWV in the critically important regions.