Advancing CYGNSS-Derived Ocean Surface Heat Fluxes

Global Navigation Satellite System Reflectometry (GNSS-R) leverages GPS signals scattered from the ocean surface, offering potential utility across all weather conditions. This overview highlights recent advancements in NASA’s Cyclone Global Navigation Satellite System (CYGNSS) level-2 ocean surface turbulent heat-flux products. We adjusted the air–sea bulk formula to calculate turbulent heat-fluxes using stability-independent CYGNSS satellite winds, addressing stability-dependent biases between equivalent neutral winds and actual winds. Despite remaining errors due to uncertainties in model-derived air–sea parameters and satellite wind data, this adjustment improved the accuracy of CYGNSS-derived sensible and latent heat-flux estimates in comparison to buoy-based bulk fluxes, yielding a bias reduction of 10–20 W m−2 for latent heat-flux and 1–2 W m−2 for sensible heat-flux. Spatial analysis further indicated that the adjusted fluxes generally exhibited lower magnitudes than the unadjusted ones, with significant variations in regions prone to highly unstable atmospheric conditions, such as the Arabian Sea, the Bay of Bengal, the Kuroshio Current/Extension, and the Western Boundary Currents during winter, and near the equator in July. These developments represent a significant step in refining CYGNSS-derived surface heat flux products, offering more reliable data for studying air–sea interactions and advancing weather and climate research.

Authors

Institutions

Publication Details

Journal
Remote Sensing
Published
2026-02-26
DOI
https://doi.org/10.3390/rs18050694
Primary Topic
Soil Moisture and Remote Sensing
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Advancing CYGNSS-Derived Ocean Surface Heat Fluxes

Mark A. Bourassa, Shakeel Asharaf, Derek J. Posselt, Juan A. Crespo
Remote Sensing
Soil Moisture and Remote Sensing
article

Advancing CYGNSS-Derived Ocean Surface Heat Fluxes

Mark A. Bourassa, Shakeel Asharaf, Derek J. Posselt, Juan A. Crespo
article en

Abstract

Global Navigation Satellite System Reflectometry (GNSS-R) leverages GPS signals scattered from the ocean surface, offering potential utility across all weather conditions. This overview highlights recent advancements in NASA’s Cyclone Global Navigation Satellite System (CYGNSS) level-2 ocean surface turbulent heat-flux products. We adjusted the air–sea bulk formula to calculate turbulent heat-fluxes using stability-independent CYGNSS satellite winds, addressing stability-dependent biases between equivalent neutral winds and actual winds. Despite remaining errors due to uncertainties in model-derived air–sea parameters and satellite wind data, this adjustment improved the accuracy of CYGNSS-derived sensible and latent heat-flux estimates in comparison to buoy-based bulk fluxes, yielding a bias reduction of 10–20 W m−2 for latent heat-flux and 1–2 W m−2 for sensible heat-flux. Spatial analysis further indicated that the adjusted fluxes generally exhibited lower magnitudes than the unadjusted ones, with significant variations in regions prone to highly unstable atmospheric conditions, such as the Arabian Sea, the Bay of Bengal, the Kuroshio Current/Extension, and the Western Boundary Currents during winter, and near the equator in July. These developments represent a significant step in refining CYGNSS-derived surface heat flux products, offering more reliable data for studying air–sea interactions and advancing weather and climate research.

Remote SensingVol. 18(5)
Florida State University (US), Jet Propulsion Laboratory (US)
National Aeronautics and Space Administration, California Institute of Technology, Nuclear Safety and Security Commission, Jet Propulsion Laboratory
Climate action
Openalex Percentile: Top 50%
Soil Moisture and Remote Sensing
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.