2020
DOI: 10.1186/s40645-020-00358-8
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Saildrone-observed atmospheric boundary layer response to winter mesoscale warm spot along the Kuroshio south of Japan

Abstract: Using an unmanned sailing vehicle, known as a Saildrone, we observed mesoscale and smaller scale structures of oceanic and atmospheric variables across the Kuroshio south of Japan during the winter of 2018/2019. From December 28 to December 29, 2018, the Saildrone crossed just north of the center of a very warm (∼23 • C) mesoscale spot in the Kuroshio centered around 31.5 • N, 135.8 • E. The northerly winter monsoon wind was intensified by ∼2 m s −1 over the mesoscale warm spot (MWS) and accompanied by a subme… Show more

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Cited by 7 publications
(5 citation statements)
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“…Following Nagano and Ando (2020), we decomposed the anomalies of the LHF, Δ F L , from the mean values during the observation period into the components of SST, atmospheric temperature, specific humidity, and wind speed at the sea level as normalΔFL0.22em0.22em()FnormalLTnormalSnormalΔTS+()FnormalLTnormalAnormalΔTA+()FnormalLqnormalΔq+()FnormalLUnormalΔU, ${\Delta}{F}_{\mathrm{L}}\,\approx \,\left(\frac{\partial {F}_{\mathrm{L}}}{\partial {T}_{\mathrm{S}}}\right){\Delta}{T}_{\mathrm{S}}+\left(\frac{\partial {F}_{\mathrm{L}}}{\partial {T}_{\mathrm{A}}}\right){\Delta}{T}_{\mathrm{A}}+\left(\frac{\partial {F}_{\mathrm{L}}}{\partial q}\right){\Delta}q+\left(\frac{\partial {F}_{\mathrm{L}}}{\partial U}\right){\Delta}U,$ where Δ T S , Δ T A , Δ q , and Δ U are temporal variations in SST, atmospheric temperature, specific humidity, and wind speed, respectively. In this study, each term of the RHS of Equation was computed using the bulk flux algorithm (Fairall et al., 2003) and setting other variables to their mean values during the observation period.…”
Section: Resultsmentioning
confidence: 99%
See 1 more Smart Citation
“…Following Nagano and Ando (2020), we decomposed the anomalies of the LHF, Δ F L , from the mean values during the observation period into the components of SST, atmospheric temperature, specific humidity, and wind speed at the sea level as normalΔFL0.22em0.22em()FnormalLTnormalSnormalΔTS+()FnormalLTnormalAnormalΔTA+()FnormalLqnormalΔq+()FnormalLUnormalΔU, ${\Delta}{F}_{\mathrm{L}}\,\approx \,\left(\frac{\partial {F}_{\mathrm{L}}}{\partial {T}_{\mathrm{S}}}\right){\Delta}{T}_{\mathrm{S}}+\left(\frac{\partial {F}_{\mathrm{L}}}{\partial {T}_{\mathrm{A}}}\right){\Delta}{T}_{\mathrm{A}}+\left(\frac{\partial {F}_{\mathrm{L}}}{\partial q}\right){\Delta}q+\left(\frac{\partial {F}_{\mathrm{L}}}{\partial U}\right){\Delta}U,$ where Δ T S , Δ T A , Δ q , and Δ U are temporal variations in SST, atmospheric temperature, specific humidity, and wind speed, respectively. In this study, each term of the RHS of Equation was computed using the bulk flux algorithm (Fairall et al., 2003) and setting other variables to their mean values during the observation period.…”
Section: Resultsmentioning
confidence: 99%
“…Following Nagano and Ando (2020), we decomposed the anomalies of the LHF, ΔF L , from the mean values during the observation period into the components of SST, atmospheric temperature, specific humidity, and wind speed at the sea level as…”
Section: Resultsmentioning
confidence: 99%
“…A large number of applications have adopted Saildrones, such as marine mammals and fishes observation ( Mordy et al, 2017 ; Stierhoff et al, 2019 ; De Robertis et al, 2019 ; Kuhn et al, 2020 ), acidification observation ( Tilbrook et al, 2019 ), cold pools observation ( Wills et al, 2021 ), surface temperature and salinity gradients observation ( Vazquez-Cuervo et al, 2020 ; Vazquez-Cuervo et al, 2021 ), ocean CO 2 observation ( Sutton et al, 2021 ; Marouchos et al, 2018 ; Sabine et al, 2020 ), climate observation ( Meinig et al, 2019) ; Gentemann et al, 2020 ; Nagano and Ando, 2020 ), satellite ocean evaluation ( Scott et al, 2020 ; Meinig et al, 2015 ; Cokelet et al, 2015 ), gas or oil seep detection ( Scoulding et al, 2020 ; Daneshgar Asl et al, 2017 ), harsh environment exploration ( Chiodi et al, 2021 ), and ice zone observation ( Chiodi et al, 2021 ).…”
Section: Sailing Robots From Industrymentioning
confidence: 99%
“…In recent years, Saildrone (Uncrewed Surface Vehicle, Figure 1a) exhibits the potential to conduct measurements of the upper ocean and lower atmosphere layer in severe conditions (D. Zhang et al., 2019; Vazquez‐Cuervo et al., 2019; Gentemann et al., 2020; Nagano & Ando, 2020; Sutton et al., 2020; Reeves Eyre et al., 2023). These new features recorded by Saildrone can improve our understanding of fine‐scale air‐sea interaction and refine the parameterization in numerical weather models.…”
Section: Introductionmentioning
confidence: 99%