Search Results
waves (e.g., Lueck 2016 ). Recent examples of surface-following platforms include a sailboard adapted to measure salinity profiles in the top meter of the ocean ( Asher et al. 2014 ), a trimaran adapted to measure atmospheric turbulence just above the sea surface ( Bourras et al. 2014 ), and “SWIFT” drifters to measure near-surface turbulence and shear ( Thomson 2012 ; Thomson et al. 2019 ). Like Asher et al. (2014) , our platform is towed so as to sample undisturbed water outside the ship’s wake
waves (e.g., Lueck 2016 ). Recent examples of surface-following platforms include a sailboard adapted to measure salinity profiles in the top meter of the ocean ( Asher et al. 2014 ), a trimaran adapted to measure atmospheric turbulence just above the sea surface ( Bourras et al. 2014 ), and “SWIFT” drifters to measure near-surface turbulence and shear ( Thomson 2012 ; Thomson et al. 2019 ). Like Asher et al. (2014) , our platform is towed so as to sample undisturbed water outside the ship’s wake
.488 Callies , J. , and R. Ferrari , 2013 : Interpreting energy and tracer spectra of upper-ocean turbulence in the submesoscale range (1–200 km) . J. Phys. Oceanogr. , 43 , 2456 – 2474 , https://doi.org/10.1175/JPO-D-13-063.1 . 10.1175/JPO-D-13-063.1 Callies , J. , R. Ferrari , and O. Bühler , 2014 : Transition from geostrophic turbulence to inertia–gravity waves in the atmospheric energy spectrum . Proc. Natl. Acad. Sci. USA , 111 , 17 033 – 17 038 , https://doi.org/10.1073/pnas
.488 Callies , J. , and R. Ferrari , 2013 : Interpreting energy and tracer spectra of upper-ocean turbulence in the submesoscale range (1–200 km) . J. Phys. Oceanogr. , 43 , 2456 – 2474 , https://doi.org/10.1175/JPO-D-13-063.1 . 10.1175/JPO-D-13-063.1 Callies , J. , R. Ferrari , and O. Bühler , 2014 : Transition from geostrophic turbulence to inertia–gravity waves in the atmospheric energy spectrum . Proc. Natl. Acad. Sci. USA , 111 , 17 033 – 17 038 , https://doi.org/10.1073/pnas
Fig. 9 . The red star in (b) is the launch location of the atmospheric soundings discussed in section 2d . These offshore rainfall maxima have previously been examined in modeling studies. Ogura and Yoshizaki (1988) utilized a two-dimensional cloud model to examine the Western Ghats precipitation maximum. They found that both vertical wind shear and ocean surface fluxes contributed to the offshore precipitation maximum. When either of these factors were suppressed in the model simulations, the
Fig. 9 . The red star in (b) is the launch location of the atmospheric soundings discussed in section 2d . These offshore rainfall maxima have previously been examined in modeling studies. Ogura and Yoshizaki (1988) utilized a two-dimensional cloud model to examine the Western Ghats precipitation maximum. They found that both vertical wind shear and ocean surface fluxes contributed to the offshore precipitation maximum. When either of these factors were suppressed in the model simulations, the
Indian Ocean [see Fig. 1 of Rydbeck et al. (2017 )]. Horizontal advection by downwelling equatorial Rossby waves accounts for a majority of the intraseasonal SST warming (+0.15° to +0.3°C) ( Rydbeck et al. 2017 ). The sea surface warming associated with downwelling equatorial Rossby waves is remarkably unyielding, even when cooling by processes related to atmospheric convection, such as reduced incoming shortwave radiation and increased latent heat fluxes, are active. The persistent SST pattern
Indian Ocean [see Fig. 1 of Rydbeck et al. (2017 )]. Horizontal advection by downwelling equatorial Rossby waves accounts for a majority of the intraseasonal SST warming (+0.15° to +0.3°C) ( Rydbeck et al. 2017 ). The sea surface warming associated with downwelling equatorial Rossby waves is remarkably unyielding, even when cooling by processes related to atmospheric convection, such as reduced incoming shortwave radiation and increased latent heat fluxes, are active. The persistent SST pattern
-real-time observations. Climatology data are used to produce anomalies on interannual time scales and to filter out tropical waves. Near-real-time data are processed for monitoring weather events. a. Observation and reanalysis datasets For the climatological outgoing longwave radiation (OLR) field, we use interpolated data obtained from National Oceanic and Atmospheric Administration (NOAA) polar-orbiting satellite ( Liebmann and Smith 1996 ). It has daily 2.5° × 2.5° resolution and the period between January 1979
-real-time observations. Climatology data are used to produce anomalies on interannual time scales and to filter out tropical waves. Near-real-time data are processed for monitoring weather events. a. Observation and reanalysis datasets For the climatological outgoing longwave radiation (OLR) field, we use interpolated data obtained from National Oceanic and Atmospheric Administration (NOAA) polar-orbiting satellite ( Liebmann and Smith 1996 ). It has daily 2.5° × 2.5° resolution and the period between January 1979
1. Introduction Deep convective structures populate the tropics, provide the energetics that drive the large-scale tropical circulation, and interact with superimposed atmospheric waves ( Riehl and Malkus 1957 ; Lorenz 1969 ; Hendon and Liebmann 1991 ; Kiladis and Weickmann 1992 ; Chang 1995 ; Lane et al. 2001 ; Fierro et al. 2009 ). The Madden–Julian oscillation (MJO; Madden and Julian 1971 , 1972 , 1994 ; Zhang 2005 ) is one such disturbance, and while the MJO is commonly defined
1. Introduction Deep convective structures populate the tropics, provide the energetics that drive the large-scale tropical circulation, and interact with superimposed atmospheric waves ( Riehl and Malkus 1957 ; Lorenz 1969 ; Hendon and Liebmann 1991 ; Kiladis and Weickmann 1992 ; Chang 1995 ; Lane et al. 2001 ; Fierro et al. 2009 ). The Madden–Julian oscillation (MJO; Madden and Julian 1971 , 1972 , 1994 ; Zhang 2005 ) is one such disturbance, and while the MJO is commonly defined
–Julian oscillation (MJO), the quasi-biweekly 10–20-day signal, and the synoptic 3–7-day mode associated with the oscillations in the monsoon trough ( Subrahmanyam et al. 2018 ). The MJO is characterized by eastward propagating (~5 m s −1 ) equatorial atmospheric waves, which modulate the intensity of tropical cyclogenesis and monsoonal intensity ( Madden and Julian 1971 ; Madden and Julian 1972 ; Zhang 2005 ). The MJO operates with a periodicity on the order of 30–90 days ( Zhang 2005 ). The MJO is a large
–Julian oscillation (MJO), the quasi-biweekly 10–20-day signal, and the synoptic 3–7-day mode associated with the oscillations in the monsoon trough ( Subrahmanyam et al. 2018 ). The MJO is characterized by eastward propagating (~5 m s −1 ) equatorial atmospheric waves, which modulate the intensity of tropical cyclogenesis and monsoonal intensity ( Madden and Julian 1971 ; Madden and Julian 1972 ; Zhang 2005 ). The MJO operates with a periodicity on the order of 30–90 days ( Zhang 2005 ). The MJO is a large
surface water was typically advected 3 km farther per day than water at 30 m. The shear that occurs between the diurnal jet and the mixed layer (0.03 s −1 ; Sutherland et al. 2016 ; Bogdanoff 2017 ) is comparable to that found in estuarine flows (0.05 s −1 ; Stacey and Pond 1997 ), at the base of internal solitary waves (0.05 s −1 ; Moum et al. 2003 ), and in the sheared layer above the equatorial undercurrent (0.02 s −1 ; Smyth et al. 2013 ). Under weak forcing (wind < 2 m s −1 ), clear sky, and
surface water was typically advected 3 km farther per day than water at 30 m. The shear that occurs between the diurnal jet and the mixed layer (0.03 s −1 ; Sutherland et al. 2016 ; Bogdanoff 2017 ) is comparable to that found in estuarine flows (0.05 s −1 ; Stacey and Pond 1997 ), at the base of internal solitary waves (0.05 s −1 ; Moum et al. 2003 ), and in the sheared layer above the equatorial undercurrent (0.02 s −1 ; Smyth et al. 2013 ). Under weak forcing (wind < 2 m s −1 ), clear sky, and
source of convective heating for the global atmospheric circulation ( Ramage 1968 ; Yamanaka et al. 2018 ). However, a high-resolution cloud-resolving model is often required to accurately capture the detailed features of the precipitation distribution ( Sato et al. 2009 ; Birch et al. 2015 ), and errors in global climate models in this region cascade into substantial simulation errors from pole to pole ( Neale and Slingo 2003 ; Inness and Slingo 2006 ). A greater understanding of the diurnal
source of convective heating for the global atmospheric circulation ( Ramage 1968 ; Yamanaka et al. 2018 ). However, a high-resolution cloud-resolving model is often required to accurately capture the detailed features of the precipitation distribution ( Sato et al. 2009 ; Birch et al. 2015 ), and errors in global climate models in this region cascade into substantial simulation errors from pole to pole ( Neale and Slingo 2003 ; Inness and Slingo 2006 ). A greater understanding of the diurnal
and surface waves on the drifter positions is small since their drogues are at 15-m depth and the Stokes’ drift is a second-order effect on the drifter displacement ( Niiler et al. 1995 ). The Lagrangian frequency spectra of the observed drifters show high energy density at the inertial frequency and the M 2 tidal frequency ( Fig. 2 ; cf. Hormann et al. 2016 ), however, substantially less than at subinertial frequencies. For part of the subsequent analysis, we low-pass filter the position data
and surface waves on the drifter positions is small since their drogues are at 15-m depth and the Stokes’ drift is a second-order effect on the drifter displacement ( Niiler et al. 1995 ). The Lagrangian frequency spectra of the observed drifters show high energy density at the inertial frequency and the M 2 tidal frequency ( Fig. 2 ; cf. Hormann et al. 2016 ), however, substantially less than at subinertial frequencies. For part of the subsequent analysis, we low-pass filter the position data
