Improved CYGNSS Wind Speed Retrieval Using Significant Wave Height Correction

Daniel Pascual,Maria Paola Clarizia,Christopher S Ruf

doi:10.3390/rs13214313

Abstract

This article presents the methodology for an improved estimation of the sea surface wind speed measured by the cyclone global navigation satellite system (CYGNSS) constellation of satellites using significant wave height (SWH) information as external reference data. The methodology consists of a correcting 2D look-up table (LUT) with inputs: (1) the CYGNSS wind speed given by the geophysical model function (GMF); and (2) the collocated reference SWH given by the WW3 model, which is forced by winds from the European Centre for Medium-Range Weather Forecasts (ECMWF) organization. In particular, the analyzed CYGNSS wind speeds are the fully developed seas (FDS) obtained with the GMF 3.0, and the forcing winds are the ECMWF forecast winds. Results show an increase in sensitivity to large winds speeds and an overall reduction in the root mean square difference (RMSD) with respect to the ECMWF winds from 2.05 m/s to 1.74 m/s. The possible influence of the ECWMF winds on the corrected winds (due to their use in the WW3 model) is analyzed by considering the correlation between: (1) the difference between the ECMWF winds and those from another reference; and (2) the difference between the corrected CYGNSS winds and those from the same reference. Results using ASCAT, WindSat, Jason, and AltiKa as references show no significant influence.

Highlights

Munoz-Martin, NereidaThe cyclone global navigation satellite system (CYGNSS) is a constellation of 8 small satellites launched in December 2016 and operated by the National Aeronautics and Space Administration (NASA) [1].The primary objective of the mission is to estimate sea surface scalar wind speed (WS)in the tropics from the power dispersion in the delay and Doppler domains of the forward reflected L1 C/A signals transmitted by global positioning system (GPS) satellites.In particular, CYGNSS measures the power dispersion by cross-correlating said signals with a locally generated clean replica of the pseudo random noise (PRN) C/A code transmitted by GPS
The method consists of a correcting 2D look-up-table (LUT) with inputs: (1) the CYGNSS wind speed given by the geophysical model function (GMF) 3.0; and (2) the collocated reference significant wave height (SWH) given by the WAVE-height, water depth, and current hindcasting version 3 (WaveWatch III©or in-short WW3) model, which is forced by winds from the European Centre for Medium-Range Weather Forecasts (ECMWF) organization
The method utilizes a 2D look-up table (LUT) correction with inputs given by the CYGNSS wind speed estimates and collocated WW3 SWH data generated with ECMWF forecast winds, followed by matching the cumulative density function (CDF) of the final retrieved wind to that of the reference winds

Summary

Introduction

CYGNSS measures the power dispersion by cross-correlating said signals with a locally generated clean replica of the pseudo random noise (PRN) C/A code transmitted by GPS. This technique is called conventional GNSS reflectometry (cGNSS-R) to encompass the GPS constellation but any other GNSS system [2]. The first issue is the uncertainty and the variability of the transmitted power by the GNSS satellites This has been observed to lead to biased estimates depending on which FMs and PRNs have been used [3,4]. These effects (among others) are being mitigated in successive L1 product versions (e.g., [4])

Methods

Results

Discussion

Conclusion