Thursday, June 1, 2023

The PO.DAAC is pleased to announce the availability of the CYGNSS Level 2 Ocean Surface Heat Flux Climate Data Record (CDR) Version 1.2. More information regarding the Cyclone Global Navigation Satellite System (CYGNSS) mission and instrumentation is available from PO.DAAC’s mission webpage.

This dataset provides the time-tagged and geolocated ocean surface heat flux parameters with 25 x 25 kilometer footprint resolution from the Delay Doppler Mapping Instrument (DDMI) aboard the CYGNSS satellite constellation. Version 1.2 represents the second release of the CDR product-line, and features improved ocean surface heat flux estimates as a bi-product of the improved CYGNSS L2 CDR V1.2 wind speed retrievals; these Level 2 input winds are used as input to compute the flux estimates and feature track de-biasing and other calibration and algorithm improvements. The auxiliary parameters are provided by the European Centre for Medium-Range Weather Forecasts Reanalysis Version 5 (ERA5) instead of MERRA-2, which has the added benefit of a 1-week latency compared to the latency of a month (or longer) with MERRA-2.

The CYGNSS satellite constellation retrieves winds that cover the global tropical oceans within a latitude band between approximately 40° S and 40° N from 1 August 2018 to present with a forward production latency of approximately one month (or better) from the last recorded measurement time. One netCDF-4 data file is produced each day (each file containing data from a combination of up to 8 unique CYGNSS spacecraft). The data, associated documentation, tools and services are described and discoverable via the PO.DAAC data portal. Users are encouraged to download the data with podaac-data-subscriber.



Download data using the PODAAC Data Subscriber: 


CYGNSS. 2023. CYGNSS Level 2 Ocean Surface Heat Flux Climate Data Record Version 1.2. Ver. 1.2. PO.DAAC, CA, USA. Dataset accessed [YYYY-MM-DD] at


Crespo, J. A., Posselt, D. J., & Asharaf, S. (2019). CYGNSS Surface Heat Flux Product Development. Remote Sens. 2019, 11, 2294. DOI: 10.3390/rs11192294.

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