Retrieval ocean surface wind speed of Triton meteorological satellite mission

Read the full article See related articles

Discuss this preprint

Start a discussion What are Sciety discussions?

Listed in

This article is not in any list yet, why not save it to one of your lists.
Log in to save this article

Abstract

Global Navigation Satellite Systems-Reflectometry (GNSS-R) technique is used to explore the Earth environment by using the Earth surface reflected GNSS signal. The Earth surface reflected GNSS signal can be used to retrieve the Earth surface parameters. The space based GNSS-R, which set receiver on the satellite in space to receive the Earth reflected GNSS signal, is developed from the early 21st century. Triton, a Taiwan designed and manufactured experimental micro-satellite, is one of the satellites for GNSS-R mission and was launched in October 9th, 2023. The mission payload of Triton is Taiwan Space Agency (TASA) self-developed GNSS-R receiver and used to process ocean surface reflected Global Positioning System (GPS) signal. The product of mission payload is delay-Doppler map (DDM) for ocean surface wind speed retrieving. The GNSS-R retrieval system of Triton is developed in Taiwan R/RO process system (TROPS) to retrieve ocean surface wind speed by using DDM. In the retrieval process, the first step is DDM calibration, which is used to remove the influence of payload hardware in the signal strength. Then the calibrated DDM is used to calculate supporting data, such like normalized bistatic radar cross section (NBRCS). After that, the calibrated DDM and supporting data can be used to retrieve ocean surface wind speed. Before retrieving ocean surface wind speed by using GNSS-R function in TROPS, the geophysical model function (GMF) needs to be developed. The ocean surface wind speed product of Triton has been released freely in Taiwan Analysis Center for COSMIC (TACC). In this paper, the detail of retrieval process is introduced. The retrieval ocean surface wind speed is compared with those obtained from European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (EAR5) for validation. Furthermore, some supporting data is also be demonstrated.

Article activity feed