NASA CYGNSS Science Team Project

Funding by: National Aeronautics and Space Administration (NASA), US

Project Coordinator: Ohio State University (OSU), US

Funding period: 2018-2020

The Cyclone Global Navigation Satellite System (CYGNSS) has the primary objectives to quantify wind fields with improved temporal and spatial sampling during the evolution of Tropical Cyclones (TC) and measure all-weather wind speed within the eyes of the cyclones, to enhance the understanding of physics of cyclones towards improving cyclone forecasting. The proposed CYGNSS Science Team member project focuses on the CYGNSS AO Research Area for Physical Oceanography via Altimetry.

We propose to address the following scientific objectives including the study of feasibility to use CYGNSS GNSS-R altimetry measurements for oceanographic studies:

1. We will study and develop CYGNSS Level 1 DDM data product based GNSS-R altimetry retrieval algorithms, including the Full DDM Maximum Likelihood Estimator (MLE), Leading-Edge Method, and the novel Multi-Doppler Waveform Retracking, and process CYGNSS data to generate CYGNSS altimetry geophysical data records (GDR);
2. we will study and develop methodologies of applying precision orbit determination techniques on the 8 CYGNSS LEOs, by using the Level 1 zenith code phase data (with only 4 GPS tracking), to convert the data into conventional C/A bias range, to mitigate ionosphere delays by using GNSS Ionosphere Maps (GIM), towards improved orbits as compared to the onboard navigation solution;
3. we will develop that Geophysical Data Record (GDR) algorithms for CYGNSS GNSS-R ocean altimetry, the resulting Algorithm Theoretical Basis Document (ATBDs), assessing their uncertainties and error budget, including GIM based ionosphere correction, geophysical corrections (solid Earth, ocean and pole tides, dynamic atmosphere, L-band electromagnetic bias), media corrections (dry and wet troposphere, GIM based 1- or multi-layer regional or global ionosphere modeling), and mean sea surface/gradient corrections;
4. we will study the DDM retrieval algorithms by using the potentially available raw CYGNSS IF data which are acquired using both antennae during hurricanes, with the benefit of more complete DDM generation and potentially higher data sampling using OSU’s end-to-end simulator (E2ES), and IEEC/ICE’s wavpy software system, to potentially generate improved sea surface height/gradient measurements during cyclones; and
5. we will generate and validate the largescale sea surface height and gradient methods using available and incidental multi-mission satellite radar altimetry data during evolutions of cyclones, as well as using cyclone model outputs.

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