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This study investigates an exceptional Gulf of Mexico dry air outbreak triggered by Hurricane Ian and fueled by dry air originating from drought‐stricken mid‐latitudes under a high‐pressure system. The convergence of meteorological forces, combining cooler, dry air with a warmer, humid sea surface and strong winds, intensified latent and sensible heat exchanges, resulting in significant oceanic heat loss. Data from the 2022 Atlantic hurricane Saildrone mission and satellite flux analysis revealed that the outbreak's total turbulent heat fluxes peaked above 850 Wm−2, comparable to or even surpassing the hurricane’s impact. Argo float measurements recorded a 40‐m deepening of the mixed layer and a 1.4°C temperature decrease. In the tropical Atlantic, wind effects outweighed humidity in driving flux variability. Saildrone’s high‐frequency linewise measurements, distinct from satellite’s footprint averages, provide unique insights into wind variability under high wind conditions. Plain Language Summary Dry air outbreaks in the Gulf of Mexico are meteorological events marked by the influx of drier and often cooler air masses into the typically warm and humid Gulf region. These events occur mostly during the fall and winter months and are associated with atmospheric circulation patterns, particularly the transit of high‐pressure systems from the North American continent. This study highlights an exceptional dry air outbreak in late September 2022, triggered by Hurricane Ian and intensified by dry air originating from drought‐stricken mid‐latitudes, a condition sustained by a persistent high‐pressure system. The interaction between cold, dry air and warm, humid sea surface, coupled with strong winds, intensified the turbulent transfer of heat from the ocean to the atmosphere, resulting in significant ocean heat loss. Data from the 2022 Atlantic hurricane Saildrone mission and satellite flux analysis revealed that the outbreak’s total turbulent heat fluxes peaked above 850 Wm−2, comparable to or even surpassing the hurricane’s impact. Concurrently, the ocean’s surface layer deepened by about 40 m, and the temperature dropped by around 1.4°C. These findings hold substantial implications for understanding the Gulf's weather patterns and their impact on tropical storms, with the potential to influence both their intensity and trajectories. Key Points Hurricane Ian triggered a dry air outbreak, causing substantial turbulent heat loss (>850 Wm−2) and Gulf of Mexico surface cooling (∼1.4°C) Winds, not air‐sea humidity, are a dominant contributor to turbulent heat flux in the tropical Atlantic warm water pool Saildrone’s high‐frequency linewise data, differing from satellite’s footprint averages, offer unique insight into high wind variability

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.

The infrared (IR) satellite remote sensing of sea surface skin temperature (SSTskin) is challenging in the northern high-latitude region, especially in the Arctic because of its extreme environmental conditions, and thus the accuracy of SSTskin retrievals is questionable. Several Saildrone uncrewed surface vehicles were deployed at the Pacific side of the Arctic in 2019, and two of them, SD-1036 and SD-1037, were equipped with a pair of IR pyrometers on the deck, whose measurements have been shown to be useful in the derivation of SSTskin with sufficient accuracy for scientific applications, providing an opportunity to validate satellite SSTskin retrievals. This study aims to assess the accuracy of MODIS-retrieved SSTskin from both Aqua and Terra satellites by comparisons with collocated Saildrone-derived SSTskin data. The mean difference in SSTskin from the SD-1036 and SD-1037 measurements is ~0.4 K, largely resulting from differences in the atmospheric conditions experienced by the two Saildrones. The performance of MODIS on Aqua and Terra in retrieving SSTskin is comparable. Negative brightness temperature (BT) differences between 11 μm and 12 μm channels are identified as being physically based, but are removed from the analyses as they present anomalous conditions for which the atmospheric correction algorithm is not suited. Overall, the MODIS SSTskin retrievals show negative mean biases, −0.234 K for Aqua and −0.295 K for Terra. The variations in the retrieval inaccuracies show an association with diurnal warming events in the upper ocean from long periods of sunlight in the Arctic. Also contributing to inaccuracies in the retrieval is the surface emissivity effect in BT differences characterized by the Emissivity-introduced BT difference (EΔBT) index. This study demonstrates the characteristics of MODIS-retrieved SSTskin in the Arctic, at least at the Pacific side, and underscores that more in situ SSTskin data at high latitudes are needed for further error identification and algorithm development of IR SSTskin.

Characterizing temperature and salinity (T-S) conditions is a standard framework in oceanography to identify and describe deep water masses and their dynamics. At the surface, this practice is hindered by multiple air–sea–land processes impacting T-S properties at shorter time scales than can easily be monitored. Now, however, the unsurpassed spatial and temporal coverage and resolution achieved with satellite sea surface temperature (SST) and salinity (SSS) allow us to use these variables to investigate the variability of surface processes at climate-relevant scales. In this work, we use SSS and SST data, aggregated into domains using a cluster algorithm over a T-S diagram, to describe the surface characteristics of the California Current System (CCS), validating them with in situ data from uncrewed Saildrone vessels. Despite biases and uncertainties in SSS and SST values in highly dynamic coastal areas, this T-S framework has proven useful in describing CCS regional surface properties and their variability in the past and in real time, at novel scales. This analysis also shows the capacity of remote sensing data for investigating variability in land–air–sea interactions not previously possible due to limited in situ data.

Validation of satellite-based retrieval of ocean parameters like Sea Surface Temperature (SST) and Sea Surface Salinity (SSS) is commonly done via statistical comparison with in situ measurements. Because in situ observations derived from coastal/tropical moored buoys and Argo floats are only representatives of one specific geographical point, they cannot be used to measure spatial gradients of ocean parameters (i.e., two-dimensional vectors). In this study, we exploit the high temporal sampling of the unmanned surface vehicle (USV) Saildrone (i.e., one measurement per minute) and describe a methodology to compare the magnitude of SST and SSS gradients derived from satellite-based products with those captured by Saildrone. Using two Saildrone campaigns conducted in the California/Baja region in 2018 and in the North Atlantic Gulf Stream in 2019, we compare the magnitude of gradients derived from six different GHRSST Level 4 SST (MUR, OSTIA, CMC, K10, REMSS, and DMI) and two SSS (JPLSMAP, RSS40km) datasets. While results indicate strong consistency between Saildrone- and satellite-based observations of SST and SSS, this is not the case for derived gradients with correlations lower than 0.4 for SST and 0.1 for SSS products.