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therefore unreliable in the presence of high winds and heavy rainfall associated with tropical cyclones ( Reul et al. 2012 ). Therefore, we do not use satellite SSS data during the period when the cyclone is active, but only examine the difference between prestorm and poststorm SSS. b. One-dimensional ocean model We use the one-dimensional Price–Weller–Pinkel (PWP) ocean model ( Price et al. 1986 ) to simulate the ocean response to Cyclone Phailin. Model control runs at the location of mooring BD09 (200
therefore unreliable in the presence of high winds and heavy rainfall associated with tropical cyclones ( Reul et al. 2012 ). Therefore, we do not use satellite SSS data during the period when the cyclone is active, but only examine the difference between prestorm and poststorm SSS. b. One-dimensional ocean model We use the one-dimensional Price–Weller–Pinkel (PWP) ocean model ( Price et al. 1986 ) to simulate the ocean response to Cyclone Phailin. Model control runs at the location of mooring BD09 (200
min ( Hormann et al. 2016 ) giving a particularly high temporal resolution. After that, drifters reported every 30 min. A conservative estimate of the position error is 50 m, which is likely a function of the region, sea state, and GPS coverage. The position data were quality controlled to remove erroneous GPS fixes and median filtered with a 1-h window to remove spurious events of acceleration. The velocity data of each drifter were then calculated by centered differencing. We bin the data to a
min ( Hormann et al. 2016 ) giving a particularly high temporal resolution. After that, drifters reported every 30 min. A conservative estimate of the position error is 50 m, which is likely a function of the region, sea state, and GPS coverage. The position data were quality controlled to remove erroneous GPS fixes and median filtered with a 1-h window to remove spurious events of acceleration. The velocity data of each drifter were then calculated by centered differencing. We bin the data to a
-Seasonal Tropical Oscillations) project. SurfOtter was specifically constructed by the Ocean Mixing Group for this project thanks to Craig Van Appledorn, Kerry Latham, and Pavan Vutukur. They and Josh Logan provided at-sea engineering support for SurfOtter. Sally Warner processed the Chameleon dataset and helped organize a successful cruise. We acknowledge the quality-controlled meteorological dataset provided by NOAA’s Earth System Research Laboratory (C. Fairall, B. Blomquist, L. Bariteau, S. DeSzeoke, and E
-Seasonal Tropical Oscillations) project. SurfOtter was specifically constructed by the Ocean Mixing Group for this project thanks to Craig Van Appledorn, Kerry Latham, and Pavan Vutukur. They and Josh Logan provided at-sea engineering support for SurfOtter. Sally Warner processed the Chameleon dataset and helped organize a successful cruise. We acknowledge the quality-controlled meteorological dataset provided by NOAA’s Earth System Research Laboratory (C. Fairall, B. Blomquist, L. Bariteau, S. DeSzeoke, and E
active phase has been shown in many successive studies for Sumatra, Borneo, and New Guinea ( Ichikawa and Yasunari 2006 ; Fujita et al. 2011 ; Rauniyar and Walsh 2011 ; Oh et al. 2012 ; Peatman et al. 2014 ; Birch et al. 2016 ; Vincent and Lane 2016 ; Sakaeda et al. 2017 ; Vincent and Lane 2017 ). Recent field data from Sumatra has shown a robust diurnal cycle in the suppressed and transition MJO phases, until the onset of low-level westerly winds in the active phase dramatically alters the
active phase has been shown in many successive studies for Sumatra, Borneo, and New Guinea ( Ichikawa and Yasunari 2006 ; Fujita et al. 2011 ; Rauniyar and Walsh 2011 ; Oh et al. 2012 ; Peatman et al. 2014 ; Birch et al. 2016 ; Vincent and Lane 2016 ; Sakaeda et al. 2017 ; Vincent and Lane 2017 ). Recent field data from Sumatra has shown a robust diurnal cycle in the suppressed and transition MJO phases, until the onset of low-level westerly winds in the active phase dramatically alters the
