For the first time, the NOAA/Aircraft Operations Center (AOC) flew stepped frequency microwave radiometers (SFMRs) on both WP-3D research aircraft for operational hurricane surface wind speed measurement in 2005. An unprecedented number of major hurricanes provided ample data to evaluate both instrument performance and surface wind speed retrieval quality up to 70 m s−1 (Saffir–Simpson category 5). To this end, a new microwave emissivity–wind speed model function based on estimates of near-surface winds in hurricanes by global positioning system (GPS) dropwindsondes is proposed. For practical purposes, utilizing this function removes a previously documented high bias in moderate SFMR-measured wind speeds (10–50 m s−1), and additionally corrects an extreme wind speed (>60 m s−1) underestimate. The AOC operational SFMRs yield retrievals that are precise to within ∼2% at 30 m s−1, which is a factor of 2 improvement over the NOAA Hurricane Research Division’s SFMR, and comparable to the precision found here for GPS dropwindsonde near-surface wind speeds. A small (1.6 m s−1), but statistically significant, overall high bias was found for independent SFMR measurements utilizing emissivity data not used for model function development. Across the range of measured wind speeds (10–70 m s−1), SFMR 10-s averaged wind speeds are within 4 m s−1 (rms) of the dropwindsonde near-surface estimate, or 5%–25% depending on speed. However, an analysis of eyewall peak wind speeds indicates an overall 2.6 m s−1 GPS low bias relative to the peak SFMR estimate on the same flight leg, suggesting a real increase in the maximum wind speed estimate due to SFMR’s high-density sampling. Through a series of statistical tests, the SFMR is shown to reduce the overall bias in the peak surface wind speed estimate by ∼50% over the current flight-level wind reduction method and is comparable at extreme wind speeds. The updated model function is demonstrated to behave differently below and above the hurricane wind speed threshold (∼32 m s−1), which may have implications for air–sea momentum and kinetic energy exchange. The change in behavior is at least qualitatively consistent with recent laboratory and field results concerning the drag coefficient in high wind speed conditions, which show a fairly clear “leveling off” of the drag coefficient with increased wind speed above ∼30 m s−1. Finally, a composite analysis of historical data indicates that the earth-relative SFMR peak wind speed is typically located in the hurricane’s right-front quadrant, which is consistent with previous observational and theoretical studies of surface wind structure.

As part of a subcontract with the manufacturer of the Defense Meteorological Space Program (DMSP) special sensor microwave/imager (SSM/I), an operational wind speed algorithm was developed by Environmental Research and Technology, Inc. (ERT). The ERT algorithm is based on the "D-matrix" approach, which seeks a linear relationship between measured SSM/I brightness temperatures and environmental parameters. D-matrix performance was validated by comparing algorithm derived wind speeds with near-simultaneous and colocated measurements made by offshore ocean buoys maintained by the National Oceanic and Atmospheric Administration. The DMSP accuracy requirement of -+2 m/s for wind speed predictions in the range of 3 m/s to 25 m/s was not obtainable with the original version of the D-matrix, which had severe bias and scaling problems. Revisions to the algorithm made at the University of Massachusetts caused it to perform within specifications. Other topics include error budget modeling, alternate wind speed algorithms, and D-matrix performance with one or more inoperative SSM/I channels.

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New technology has been developed through a joint public‐private partnership that could greatly improve the ocean sciences community's ability to study coastal oceanography in the same way that satellitebased infrared imaging revolutionized basinscale oceanography. Recent advances in passive microwave technologies and novel means of integrating those advances haveled to the development of the Scanning Low‐Frequency Microwave Radiometer (SLFMR) for remote sensing of sea‐surface salinity. Designed and built for the National Oceanic and Atmospheric Administration (NOAA), the SLFMR—also known as the salinity mapper—was recently used by a team of scientists from government and industry to generate the first remotely sensed image of sea‐surface salinity (Figure 1). This image of salinity was obtained near the mouth of Chesapeake Bay, Virginia, during the Naval Research Laboratory's (NRL) Chesapeake Outflow Plume Experiment (COPE), elements of which were conducted in collaboration with NOAA.