Highlights

• First deep learning model to classify ten geophysical phenomena from S-1 WV SAR data.
• Model performance is evaluated using an independent eye-selected dataset.
• Classified rain cells and sea ice are compared with other satellite measurements.
• The global S-1 SAR data show great potential for sea surface processes studies.

A method for capturing the different dynamical components of the Madden–Julian oscillation (MJO) is presented. The tropical wind field is partitioned into three components using free-space Green's functions: 1) a nondivergent component, 2) an irrotational component, and 3) a background or environmental flow that is interpreted as the influence on the tropical flow due to vorticity and divergence elements outside of the tropical region. The analyses performed in this study show that this background flow is partly determined by a train of extratropical waves. Space–time power spectra for each flow component are calculated. The strongest signal in the nondivergent wind spectrum corresponds to equatorial Rossby, mixed Rossby–gravity, and easterly waves. The strongest signal in the irrotational winds corresponds to Kelvin and inertia–gravity modes. The strongest signal in the power spectrum of the background flow corresponds to the wave band of extratropical Rossby waves. Furthermore, a coherence analysis reveals that the background flow has the highest coherence with geopotential height variations in the latitude bands from 30° to 45° in both the Northern and Southern Hemispheres.

The flow partitions are further studied through a composite analysis based on the Wheeler–Hendon MJO index. Anomalies in the background flow are strongest in the western and central Pacific, possess an equivalent barotropic structure, and show an eastward propagation. By contrast, the irrotational and nondivergent winds possess a first-mode baroclinic structure. An oscillation in the zonally averaged background flow with the MJO phases is observed but contributes little to tropical angular momentum when compared to the nondivergent flow.

A combined analysis of ocean surface wind vector measurements by the European Advanced Scatterometer (ASCAT) and the National Aeronautics and Space Administration QuikSCAT (QS) scatterometer using buoy measurements, numerical weather prediction model analyses, and spectral decomposition reveals significant statistical differences between the two data sets. While QS wind speeds agree better with buoy wind speeds than ASCAT above 15 m s^-1, ASCAT wind directions agree better with buoy directions overall than QS. In contrast, it is shown that sea-level pressure (SLP) fields derived from ASCAT and QS measurements compare better with each other than the winds in a statistical sense, even though ASCAT bulk pressure gradients (BPGs) are slightly weaker than buoy pressure gradients and have slightly lower spectral energy than QS. Weaker BPGs in ASCAT are consistent with the low bias in ASCAT wind speeds. Thus, it is proposed that scatterometer-derived SLP fields can be used as a filter to improve the wind directions. This improves the QS wind directions but has less effect on the more accurate ASCAT wind directions. The unfiltered ASCAT wind vector statistics compare well with the statistics of the direction-filtered QS winds. It is suggested that this methodology might provide a basis for minimizing the discrepancies between various satellite wind measurement data sets in view of producing a long-term record of satellite-derived SLP fields and ocean surface wind vectors.

A method for improving scatterometer wind retrievals based on the mesoscale and synoptic-scale structure of the flow field is presented and evaluated. The large-scale structure of the flow is inferred from the sea-level pressure (SLP) field derived from the scatterometer winds using a planetary boundary layer model. The wind vectors derived from this SLP field can be used to either (1) inform the ambiguity selection, (2) correct the direction of the scatterometer wind vectors, in all cases or above a certain threshold, or (3) replace the scatterometer winds. The methodology is demonstrated with QuikSCAT (QS) scatterometer wind vectors.

The new wind vector set is evaluated statistically by comparison with buoy measurements, with numerical weather prediction model analyses, and by spectral analysis. It is found that the new wind vectors are particularly valuable at nadir and in rain-contaminated areas, and that their spectral behavior is closer to a power law than are the uncorrected QS winds.

We present similarity solutions for the mean boundary-layer profiles under an axisymmetric vortex that is in gradient wind balance; the similarity model includes the nonlinear momentum advection and curvature terms. These solutions are a generalization of the Ekman layer mean flow, which is the canonical boundary-layer basic state under a uniform, geostrophically-balanced flow. Near-surface properties such as inflow angle, surface wind factor, diffusive transport of kinetic energy into the surface layer and dissipational heating are calculated and shown to be sensitive to the choice of turbulence parameterization.

During ONR's Shoaling Waves Experiment (SHOWEX) off the coast of North Carolina in November and December 1999, measurements of wind speed and direction as well as air and water temperatures were made using a variety of techniques. This paper shows a comparison of the measurements taken on December 3, 1999.