Persistent upwelling in the Mid-Atlantic Bight detected using gap-filled, high-resolution satellite SST

Murphy, Sarah; Nazzaro, Laura; Simkins, James; Oliver, Matthew J.; Kohut, Josh; Crowley, Michael F.; Miles, Travis

doi:10.1016/j.rse.2021.112487

Cited by 19 publications

(11 citation statements)

References 17 publications

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“…However, due to the greater number of missing pixels that required gapfilling in July as compared with June (36.86 % more over the course of each month, resulting from the increased cloud cover -and the possible stronger coastal cold pool), this indication of product superiority may be questionable. The addition of more refined post-processing to account for incorrectly cloud-filtered pixels, as shown in Murphy et al (2021), has been demonstrated to improve the accuracy of GOES-16 SSTs. A comparison of simulations using OSTIA against those using the sophisticated GOES-16 product should be conducted in the future to evaluate the level of improvement S. Redfern et al: Offshore wind energy forecasting sensitivity to sea surface temperature input in the Mid-Atlantic delivered using the latter's more computationally expensive data set.…”

Section: Discussionmentioning

confidence: 99%

“…Although GOES-16 SSTs validate worse against buoy observations than OSTIA SSTs, on both monthly and event timescales, the wind characterization produced using GOES-16 inputs validates as well as, or in some cases better than, the OSTIA characterization, indicating that finer temporal and spatial SST resolution may contribute to improved wind forecasts in this region. To further explore this, a higher-quality postprocessed GOES-16 data set assimilated with in situ observations, such as that created by Murphy et al (2021), could be evaluated in future studies; generating a comparable data set was beyond the scope of this study.…”

Section: Discussionmentioning

confidence: 99%

“…Thus, the cold pool generally persists from the spring through the fall. Southerly winds that drive surface currents offshore will result in coastal upwelling of this colder water, and, at times, strong winds associated with storm development can mix the cold pool upward, cooling the surface and influencing near-surface temperatures and winds (Colle and Novak, 2010;Chen et al, 2018;Murphy et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

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Offshore wind energy forecasting sensitivity to sea surface temperature input in the Mid-Atlantic

Redfern

Optis²,

Xia

et al. 2023

Wind Energ. Sci.

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Abstract. As offshore wind farm development expands, accurate wind resource forecasting over the ocean is needed. One important yet relatively unexplored aspect of offshore wind resource assessment is the role of sea surface temperature (SST). Models are generally forced with reanalysis data sets, which employ daily SST products. Compared with observations, significant variations in SSTs that occur on finer timescales are often not captured. Consequently, shorter-lived events such as sea breezes and low-level jets (among others), which are influenced by SSTs, may not be correctly represented in model results. The use of hourly SST products may improve the forecasting of these events. In this study, we examine the sensitivity of model output from the Weather Research and Forecasting model (WRF) 4.2.1 to different SST products. We first evaluate three different data sets: the Multiscale Ultrahigh Resolution (MUR25) SST analysis, a daily, 0.25∘ × 0.25∘ resolution product; the Operational Sea Surface Temperature and Ice Analysis (OSTIA), a daily, 0.054∘ × 0.054∘ resolution product; and SSTs from the Geostationary Operational Environmental Satellite 16 (GOES-16), an hourly, 0.02∘ × 0.02∘ resolution product. GOES-16 is not processed at the same level as OSTIA and MUR25; therefore, the product requires gap-filling using an interpolation method to create a complete map with no missing data points. OSTIA and GOES-16 SSTs validate markedly better against buoy observations than MUR25, so these two products are selected for use with model simulations, while MUR25 is at this point removed from consideration. We run the model for June and July of 2020 and find that for this time period, in the Mid-Atlantic, although OSTIA SSTs overall validate better against in situ observations taken via a buoy array in the area, the two products result in comparable hub-height (140 m) wind characterization performance on monthly timescales. Additionally, during hours-long flagged events (< 30 h each) that show statistically significant wind speed deviations between the two simulations, both simulations once again demonstrate similar validation performance (differences in bias, earth mover's distance, correlation, and root mean square error on the order of 10−1 or less), with GOES-16 winds validating nominally better than OSTIA winds. With a more refined GOES-16 product, which has been not only gap-filled but also assimilated with in situ SST measurements in the region, it is likely that hub-height winds characterized by GOES-16-informed simulations would definitively validate better than those informed by OSTIA SSTs.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Offshore wind energy forecasting sensitivity to sea surface temperature input in the Mid-Atlantic

Redfern

Optis²,

Xia

et al. 2023

Wind Energ. Sci.

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“…There are three transects: i) Off Cape Hatteras, samples M35 (590 mwd), M54 (1742 mwd), and M4 (3865 mwd). This is the only transect directly affected by both Gulf Stream an Deep Western Boundary Current and are influenced by the Mid-Atlantic Bight upwelling (Murphy et al, 2021).…”

Section: Study Sites and Samplingmentioning

confidence: 99%

“…LCD distributions in bulk sediment samples are generally similar to those reported in previous studies. For example, large proportions of 1,14-diols (Rampen et al, 2014a;De Bar et al, 2020;Erdem et al, 2021;Gal et al, 2021;Versteegh et al, 2022) are observed in strong upwelling sites such as the Benguela upwelling (NAM = 78% of all LCDs), Peruvian upwelling system (PER = 33% of all LCDs), and the Mid-Atlantic Bight upwelling (M4 = 26%, M54 = 31%, and M35 = 26%, Murphy et al, 2021). In addition, BER also presents a relatively higher amount of 1,14-diols (39% of all LCDs) compared to the other non-upwelling sites, which may be linked to lateral export of diatomaceous particles from the Mid-Atlantic Bight upwelling (Murphy et al, 2021).…”

Section: Lcd Concentrationmentioning

confidence: 99%

Grain size controls on long-chain diol distributions and proxy signals in marine sediments

et al. 2022

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Long chain alkyl diols (LCDs) are lipid biomarkers that occur ubiquitously in sediments. Their abundance and distributions are increasingly used as the basis of molecular proxies for environmental parameters such as sea surface temperature (SST) via the Long chain Diol Index (LDI), and upwelling intensity and nutrient conditions (parametrized as diol indices, DI-2, and Nutrient Diol Index, NDI, respectively). Their marine producers remain the subject of debate, but in cultures, they can be found within the outer wall (algaenan) of eustigmatophytes or in Proboscia diatoms. LCDs appear to be well preserved in sediments, potentially as a result of their association with algaenan and/or minerals, but little is known of their pre-depositional histories, in particular transport dynamics. Here, 15 surface continental margin sediments as well as one high-deposition-rate sediment core (50 cm, spanning the last ~30 years) were analyzed in order to evaluate the impact of organo-mineral associations, lateral transport, and hydrodynamic sorting on sedimentary LCD signals. The abundance and distribution of LCDs in bulk sediments and corresponding grain-size fractions was determined. The highest proportion of all LCD isomers is found in the fine fraction (2 – 10 µm), which also holds the highest proportion of organic matter in relation to the other grain-size fractions. However, LCDs are also found in the other fractions (sand, coarse silt, and clay), and their concentrations are not correlated with bulk organic carbon content, indicating different preservation or transport mechanisms. LDI-SST in the bulk sediment is comparable to the mean annual SST at all sites except those influenced by upwelling and characterized by strong seasonal SST gradients. To the contrary of other biomarker-related proxies (e.g., alkenones), lateral transport does not appear to strongly affect LDI-SST in size fractions, suggesting that the intimate relationship of LCD with the algaenan may counteract the influence of hydrodynamic mineral sorting processes on related proxy signals. The difference between the fraction-weighted LCD concentration and bulk sedimentary LCD concentration indicates potential release of LCD during laboratory fractionation, suggesting degradation of algaenan or dissolution of opal frustules.

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Population structure of the Atlantic angel shark (Squatina dumeril) in United States waters of the western North Atlantic Ocean

Fields,

Driggers,

Jones

et al. 2024

Environ Biol Fish

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Persistent upwelling in the Mid-Atlantic Bight detected using gap-filled, high-resolution satellite SST

Cited by 19 publications

References 17 publications

Offshore wind energy forecasting sensitivity to sea surface temperature input in the Mid-Atlantic

Offshore wind energy forecasting sensitivity to sea surface temperature input in the Mid-Atlantic

Grain size controls on long-chain diol distributions and proxy signals in marine sediments

Population structure of the Atlantic angel shark (Squatina dumeril) in United States waters of the western North Atlantic Ocean

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