Assessment of Saildrone Extreme Wind Measurements in Hurricane Sam Using MW Satellite Sensors

In 2021, a novel NOAA-Saildrone project deployed five uncrewed surface vehicle Saildrones (SDs) to monitor regions of the Atlantic Ocean and Caribbean Sea frequented by tropical cyclones. One of the SDs, SD-1045, crossed Hurricane Sam (Category 4) on September 30, providing the first-ever surface-oc...

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Published inRemote sensing (Basel, Switzerland) Vol. 14; no. 12; p. 2726
Main Authors Ricciardulli, Lucrezia, Foltz, Gregory R., Manaster, Andrew, Meissner, Thomas
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.06.2022
Subjects
Accuracy
AMSR2
ASCAT
Buoys
Consistency
Cyclones
Datasets
hurricane
Hurricanes
Ocean surface
ocean surface winds
Radiation
Radiometers
Remote sensing
Saildrone
Satellite observation
Satellites
Scatterometers
Sensors
SMAP
Surface vehicles
Surface wind
Systematic errors
Tropical cyclones
Uncertainty
Wind
Wind measurement
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Summary:In 2021, a novel NOAA-Saildrone project deployed five uncrewed surface vehicle Saildrones (SDs) to monitor regions of the Atlantic Ocean and Caribbean Sea frequented by tropical cyclones. One of the SDs, SD-1045, crossed Hurricane Sam (Category 4) on September 30, providing the first-ever surface-ocean videos of conditions in the core of a major hurricane and reporting near-surface winds as high as 40 m/s. Here, we present a comprehensive analysis and interpretation of the Saildrone ocean surface wind measurements in Hurricane Sam, using the following datasets for direct and indirect comparisons: an NDBC buoy in the path of the storm, radiometer tropical cyclone (TC) winds from SMAP and AMSR2, wind retrievals from the ASCAT scatterometers and SAR (RadarSat2), and HWRF model winds. The SD winds show excellent consistency with the satellite observations and a remarkable ability to detect the strength of the winds at the SD location. We use the HWRF model and satellite data to perform cross-comparisons of the SD with the buoy, which sampled different relative locations within the storm. Finally, we review the collective consistency among these measurements by describing the uncertainty of each wind dataset and discussing potential sources of systematic errors, such as the impact of extreme conditions on the SD measurements and uncertainties in the methodology.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs14122726