Douglas Vandemark

Extensive, coherent circulations in the marine atmospheric boundary layer (MABL) are detected and classified using an automated image processing routine applied to synthetic aperture radar (SAR) images from Sentinel‐1 over subtropical oceans at sunrise and sunset. Circulations are inferred from patterns of capillary waves detected by the SAR. Classifications include wind streaks (WS, the surface imprint of roll structures), microscale convective patches (MC), combined WS/MC, cold pools, and rain columns. Using 12‐hr Lagrangian trajectories, classified MABL states are linked to warm rain rates estimated from the Advanced Microwave Scanning Radiometer (AMSR/2) sensor and to cloud variables observed by the Moderate Resolution Imaging Spectroradiometer (MODIS) in NASA's A‐Train, to assess whether MABL states correspond to variations in rain rates, and other cloud variables, including cloud drop concentration, water content, and cloud top height. Results indicate that rain rates and cloud water path observed along trajectories that contain cold pools and rain columns, as seen by SAR, are greater than in other states. Wind streaks are associated with higher rain rates than MC and WS/MC, even after controlling for a selection of confounding variables, while cloud top heights are higher in MC environments. A sensitivity test shows that rain rates are significantly more sensitive to a selection of predictors when WS are present compared to MC.

The Gulf of Maine (GoM) hosts a variety of fish and sea mammals, beaches, and active commercial fishery. Understanding, monitoring, and predicting the status of and future changes in its food web and water quality are key goals of an ocean observing system that utilizes in situ buoys, gliders, shipboard surveys, satellite remote sensing, and numerical modeling. This study defines and explores the utility of a new Gulf‐specific water mass exchange predictor designed to capture changes in winter inflow of fresh and cold waters from the upstream Scotian Shelf using soil moisture active passive satellite sea surface salinity (SSS) in the eastern GoM. A data assimilative ocean circulation model is used to characterize and assess results. GoM food web dynamics and water quality both depend on lower trophic productivity associated with Gulf‐wide phytoplankton and zooplankton communities, and these are fundamentally controlled by water temperature and inorganic nutrients that often change due to varied exchange with the adjoining offshore North Atlantic and upstream Nova Scotian Shelf Water (SSW) that flows around southwestern Nova Scotia. Once in the Gulf, most of the SSW inflow to the eastern GoM is advected along southwestern Nova Scotia. This eastern GoM area (termed eGoM) is a useful gauge area where SSS reflects variations in SSW inflow. Results indicate that the SMAP‐derived winter eGoM salinity index can help explain interannual variability in GoM conditions during the ensuing spring to summer, the seasons influenced by several advective pathways, as discussed in the study.

A dataset of multi‐tagged sea surface roughness synthetic aperture radar (SAR) satellite images was established near Barbados from January to June 2016 to 2019. It is an advancement of the Sentinel‐1 Wave Mode TenGeoP‐SARwv (a labelled SAR imagery dataset of 10 geophysical phenomena from Sentinel‐1 wave mode) dataset that targets SAR marine atmospheric boundary layer (MABL) coherent structures. Twelve tags define roll vortices, convective cells, mixed rolls and convective cells, fronts, rain cells, cold pools and low winds. Examples are provided for each signature. The final dataset is comprised of 2100 Sentinel‐1 wave mode SAR images acquired at 36 incidence angle over an 8° × 8°region centered at 51° W, 15° N. Each image is tagged with one or multiple phenomena by five experts. This strategy extends the TenGeoP‐SARwv by identifying coexisting phenomena within a single SAR image and by the addition of mixed roll/cell states and cold pools. The dataset includes PNG‐formatted SAR image files along with two text files containing the file name, the central latitude/longitude, expert tags for each image, and all dataset metadata. There is a high degree of consensus among expert tags. The dataset complements existing hand‐labelled ocean SAR image datasets and offers the potential for new deep‐learning SAR image classification model developments. Future use is also expected to yield new insights into the tradewind MABL processes such as structure transitions and their relation to the stratification.

The 1-day fast-sampling orbit phase of the Surface Water Ocean Topography (SWOT) satellite mission provides a unique opportunity to analyze high-frequency sea-state variability and its implications for altimeter sea state bias (SSB) model development. Time series with 1-day repeat sampling of sea-level anomaly (SLA) and SSB input parameters—comprising the significant wave height (SWH), wind speed (WS), and mean wave period (MWP)—are constructed using SWOT’s nadir altimeter data. The analyses corroborate the following key SSB modelling assumption central to empirical developments: the SLA noise due to all factors, aside from sea state change, is zero-mean. Global variance reduction tests on the SSB model’s performance using corrected SLA differences show that correction skill estimation using a specific (1D, 2D, or 3D) SSB model is unstable when using short time difference intervals ranging from 1 to 5 days, reaching a stable asymptotic limit after 5 days. It is proposed that this result is related to the temporal auto- and cross-correlations associated with the SSB model’s input parameters; the present study shows that SSB wind-wave input measurements take time (typically 1–4 days) to decorrelate in any given region. The latter finding, obtained using unprecedented high-frequency satellite data from multiple ocean basins, is shown to be consistent with estimates from an ocean wave model. The results also imply that optimal time-differencing (i.e., >4 days) should be considered when building SSB model data training sets. The SWOT altimeter data analysis of the temporal cross-correlations also permits an evaluation of the relationships between the SSB input parameters (SWH, WS, and MWP), where distinct behaviors are found in the swell- and wind-sea-dominated areas, and associated time scales are less than or on the order of 1 day. Finally, it is demonstrated that computing cross-correlations between the SLA (with and without SSB correction) and the SSB input parameters offers an additional tool for evaluating the relevance of candidate SSB input parameters, as well as for assessing the performance of SSB correction models, which, so far, mainly rely on the reduction in the variance of the differences in the SLA at crossover points.

Ocean waves are essential elements across the air‐sea interface, regulating momentum and energy transfer. The mixture of wind sea and ocean swell coupled with surface winds results in diverse sea state conditions that modify the local air‐sea interaction. Previous classifications of wind waves and swells are mostly binary that are insufficient to represent the complexity of sea states. In this study, we utilize wind and wave measurements from the China‐France Oceanography Satellite (CFOSAT) to construct an observational wind‐wave ensemble. Four key parameters: wind speed, significant wave height, inverse wave age, and spectral width are selected out of six variables based on their correlations. Employing the unsupervised learning of k‐means clustering, global sea states are categorized into six distinct classes. These classes, characterized by unique centroids and separated in the feature space, represent specific wind regimes and degrees of wave development. Global occurrence highlights that each sea state is region‐specific, bridging the spatial gap of swell and wind sea dominated areas, respectively. This new grouping scheme complements the traditional wind sea and/or swell classification by resolving the diversity of wave regimes. The six‐class classification enables us to identify transitional states and hybrid conditions that may have been overlooked in the binary classification scheme, which shall help investigate the impact of ocean waves on the air‐sea interaction under varying sea states.

The Chukchi Sea is an open estuary in the southwestern Arctic. Its near-surface salinities are higher than those of the surrounding open Arctic waters due to the key inflow of saltier and warmer Pacific waters through the Bering Strait. This salinity distribution may suggest that interannual changes in the Bering Strait mass transport are the sole and dominant factor shaping the salinity distribution in the downstream Chukchi Sea. Using satellite sea surface salinity (SSS) retrievals and altimetry-based estimates of the Bering Strait transport, the relationship between the Strait transport and Chukchi Sea SSS distributions is analyzed from 2010 onward, focusing on the ice-free summer to fall period. A comparison of five different satellite SSS products shows that anomalous SSS spatially averaged over the Chukchi Sea during the ice-free period is consistent among them. Observed interannual temporal change in satellite SSS is confirmed by comparison with collocated ship-based thermosalinograph transect datasets. Bering Strait transport variability is known to be driven by the local meridional wind stress and by the Pacific-to-Arctic sea level gradient (pressure head). This pressure head, in turn, is related to an Arctic Oscillation-like atmospheric mean sea level pattern over the high-latitude Arctic, which governs anomalous zonal winds over the Chukchi Sea and affects its sea level through Ekman dynamics. Satellite SSS anomalies averaged over the Chukchi Sea show a positive correlation with preceding months’ Strait transport anomalies. This correlation is confirmed using two longer (>40-year), separate ocean data assimilation models, with either higher- (0.1°) or lower-resolution (0.25°) spatial resolution. The relationship between the Strait transport and Chukchi Sea SSS anomalies is generally stronger in the low-resolution model. The area of SSS response correlated with the Strait transport is located along the northern coast of the Chukotka Peninsula in the Siberian Coastal Current and adjacent zones. The correlation between wind patterns governing Bering Strait variability and Siberian Coastal Current variability is driven by coastal sea level adjustments to changing winds, in turn driving the Strait transport. Due to the Chukotka coastline configuration, both zonal and meridional wind components contribute.

The Poseidon-4 radar altimeter on board Sentinel-6 “Michael Freilich” (S6-MF) offers unique opportunities to assess the impact of ocean surface motion on Delay-Doppler altimetry. In this paper, earlier “frozen-sea” studies of the instrument response to an isolated sea surface facet are extended to include the effect of surface motions in its Delay-Doppler Map signature. Integrating this elementary signature over the instrument field of view, an analytical stacked echo waveform model, the IASCO waveform model, is then derived. This waveform is validated against the well-established SAMOSA waveform model for the special case of a frozen sea. Model sensitivity to changes in surface significant wave height, vertical velocity standard deviation, and the “Geophysical Doppler” vector U _GD projection along the satellite ground-track velocity are discussed. These developments provide theoretical and analytical means to jointly exploit the S6-MF conventional and Delay-Doppler radar waveforms to improve estimates of Essential Climate Variables (Sea Level, Significant Wave Height), and to retrieve and map two new observables, the along-track projection of the “Geophysical Doppler” vector and the ocean waves vertical velocity standard deviation. These new variables, being sensitive to higher-order spectral moments of the wave directional spectrum, may help to mitigate sea state range bias impacts on altimeter sea level measurements.

The monsoon freshwater and wind forcing drive high Sea Surface Salinity (SSS) contrasts and variability (up to 10 pss range) in the Bay of Bengal (BoB), with important consequences for upper ocean mixing and air-sea interactions. Synoptic SSS maps did only become available with the advent of L-band radiometers in 2010, due to insufficient prior in situ data coverage. Here, we build tools aiming at reconstructing the monthly BoB SSS at ¼° resolution since 2002 from AMSR-E radiometer data. The C-band low sensitivity to SSS requires a very careful processing. Taking the X- minus C- bands signals reduce the impact of Sea Surface Temperature (SST) and wind on brightness temperatures. It was however further necessary to train the algorithm with SSS data from L-Band radiometers to remove residual surface winds, SST, and atmospheric water contents signals. We also found that a separate treatment of the ascending and descending passes was necessary, as well as a proper data screening to minimize contamination by land signals. The resulting SSS product reproduces the broad BoB climato-logical SSS, and has a 0.66 correlation, 1.08 pss rms-difference to co-located in situ surface salinity from Array for Real-time Geostrophic Oceanography (ARGO) floats. Comparisons with ocean re-analyses in two SSS interannual variability hotspots indicate poor performance in the Northern BoB, but some skill along the East coast of India. Our results provide a proof of concept for reconstructing the BoB SSS from AMSR-E data, and we discuss possible future improvements of the data processing to further reduce the impact of spurious signals.

One long-standing technical problem affecting the accuracy of eddy correlation air-sea CO₂ flux estimates has been motion contamination of the CO₂ mixing ratio measurement. This sensor-related problem is well known but its source remains unresolved. This report details an attempt to identify and reduce motion-induced error and to improve the infrared gas analyzer (IRGA) design. The key finding is that a large fraction of the motion sensitivity is associated with the detection approach common to most closed- and open-path IRGA employed today for CO₂ and H₂O measurements. A new prototype sensor was developed to both investigate and remedy the issue. Results in laboratory and deep water tank tests show marked improvement. The prototype shows a factor of 4-10 reduction in CO₂ error under typical at-sea buoy pitch and roll tilts in comparison to an off-the-shelf IRGA system. A similar noise reduction factor of 2-8 is observed in water vapor measurements. The range of platform tilt motion testing also helps to document motion-induced error characteristics of standard analyzers. Study implications are discussed including findings relevant to past field measurements and the promise for improved future flux measurements using similarly modified IRGA on moving ocean observing and aircraft platforms. One long-standing technical problem affecting the accuracy of eddy correlation air-sea CO₂ flux estimates has been motion contamination of the CO₂ mixing ratio measurement. This sensor-related problem is well known but its source remains unresolved. This report details an attempt to identify and reduce motion-induced error and to improve the infrared gas analyzer (IRGA) design. The key finding is that a large fraction of the motion sensitivity is associated with the detection approach common to most closed- and open-path IRGA employed today for CO₂ and H₂O measurements. A new prototype sensor was developed to both investigate and remedy the issue. Results in laboratory and deep water tank tests show marked improvement. The prototype shows a factor of 4-10 reduction in CO₂ error under typical at-sea buoy pitch and roll tilts in comparison to an off-the-shelf IRGA system. A similar noise reduction factor of 2-8 is observed in water vapor measurements. The range of platform tilt motion testing also helps to document motion-induced error characteristics of standard analyzers. Study implications are discussed including findings relevant to past field measurements and the promise for improved future flux measurements using similarly modified IRGA on moving ocean observing and aircraft platforms.