High-resolution oceanic precipitation estimates are needed to increase our understanding of and ability to monitor ocean–atmosphere coupled processes. Satellite multisensor precipitation products such as IMERG provide global precipitation estimates at relatively high resolution (0.1°, 30 min), but the resolution at which IMERG precipitation estimates are considered reliable is coarser than the nominal resolution of the product itself. In this study, we examine the ability of the Rainfall Autoregressive Model (RainFARM) statistical downscaling technique to produce ensembles of precipitation fields at relatively high spatial and temporal resolution when applied to spatially and temporally coarsened precipitation fields from IMERG. The downscaled precipitation ensembles are evaluated against in situ oceanic rain-rate observations collected by passive aquatic listeners (PALs) in 11 different ocean domains. We also evaluate IMERG coarsened to the same resolution as the downscaled fields to determine whether the process of coarsening then downscaling improves precipitation estimates more than averaging IMERG to coarser resolution only. Evaluations were performed on individual months, seasons, by ENSO phase, and based on precipitation characteristics. Results were inconsistent, with downscaling improving precipitation estimates in some domains and time periods and producing worse performance in others. While the results imply that the performance of the downscaled precipitation estimates is related to precipitation characteristics, it is still unclear what characteristics or combinations thereof lead to the most improvement or consistent improvement when applying RainFARM to IMERG.

Drifting buoy observations of ocean surface waves in hurricanes are combined with modeled surface wind speeds. The observations include targeted aerial deployments into Hurricane Ian (2022) and opportunistic measurements from the Sofar Ocean Spotter global network in Hurricane Fiona (2022). Analysis focuses on the slope of the waves, as quantified by the spectral mean square slope. At low-to-moderate wind speeds (<15 m^s-1), slopes increase linearly with wind speed. At higher winds (>15 m^s-1), slopes continue to increase, but at a reduced rate. At extreme winds (>30 m^s-1), slopes asymptote. The mean square slopes are directly related to the wave spectral shapes, which over the resolved frequency range (0.03–0.5 Hz) are characterized by an equilibrium tail (f^-4) at moderate winds and a saturation tail (f^-5) at higher winds. The asymptotic behavior of wave slope as a function of wind speed could contribute to the reduction of surface drag at high wind speeds.

Observations from two autonomous Wave Gliders and six Lagrangian Surface Wave Instrument Float with Tracking drifters in the northwestern tropical Atlantic during the January–February 2020 NOAA Atlantic Tradewind Ocean-atmosphere Mesoscale Interaction Campaign (ATOMIC) are used to evaluate the spatial variability of bulk air-sea heat, moisture, and buoyancy fluxes. Sea surface temperature (SST) gradients up to 0.7°C across 10–100 km frequently persisted for several days. SST gradients were a leading cause of systematic spatial air-sea sensible heat flux gradients, as variations over 5 Wm^-2 across under 20 km were observed. Wind speed gradients played no significant role and air temperature adjustments to SST gradients sometimes acted to reduce spatial flux gradients. Wind speed, air temperature, and air humidity caused high-frequency spatial and temporal flux variations on both sides of SST gradients. A synthesis of observations demonstrated that fluxes were usually enhanced on the warm SST side of gradients compared to the cold SST side, with variations up to 10 Wm^-2 in sensible heat and upward buoyancy fluxes and 50 Wm^-2 in latent heat flux. Persistent SST gradients and high-frequency air temperature variations each contributed up to 5 Wm^-2 variability in sensible heat flux. Latent heat flux was instead mostly driven by air humidity variability. Atmospheric gradients may result from convective structures or high-frequency turbulent fluctuations. Comparisons with 0.05°-resolution daily satellite SST observations demonstrate that remote sensing observations or lower-resolution models may not capture the small-scale spatial ocean variability present in the Atlantic trade wind region.

Observations from six Lagrangian Surface Wave Instrument Float with Tracking drifters in January–February 2020 in the northwestern tropical Atlantic during the Atlantic Tradewind Ocean–atmosphere Mesoscale Interaction Campaign are used to evaluate the influence of wave–current interactions on wave slope and momentum flux. At observed wind speeds of 4––12 ms^-1, wave mean square slopes are positively correlated with wind speed. Wave-relative surface currents varied significantly, from opposing the wave direction at 0.24 ms^-1 to following the waves at 0.47 ms^-1. Wave slopes are 5%–10% higher when surface currents oppose the waves compared to when currents strongly follow the waves, consistent with a conservation of wave energy flux across gradients in currents. Assuming an equilibrium frequency range in the wave spectrum, wave slope is proportional to wind friction velocity and momentum flux. The observed variation in wave slope equates to a 10%–20% variation in momentum flux over the range of observed wind speeds (4–12 ms^-1), with larger variations at higher winds. At wind speeds over 8 ms^-1, momentum flux varies by at least 6% more than the variation expected from current-relative winds alone, and suggests that wave-current interactions can generate significant spatial and temporal variability in momentum fluxes in this region of prevailing trade winds. Results and data from this study motivate the continued development of fully coupled atmosphere-ocean-wave models.