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The NASA Quick Scatterometer (QuikSCAT ) satellite carries the SeaWinds instrument, the first satellite-borne scanning radar scatterometer. QuikSCAT, which was launched on 19 June 1999, is designed to provide accurate ocean surface winds in all conditions except for moderate to heavy rain (i.e., except for vertically integrated rain rate Ͼ2.0 km mm h Ϫ1 , the value used to tune the SeaWinds rain flag). QuikSCAT data are invaluable in providing high-quality, high-resolution winds to detect and locate precisely significant meteorological features and to produce accurate ocean surface wind analyses. QuikSCAT has an 1800-kmwide swath. A representative swath of data in the North Atlantic at 2200 UTC 28 September 2000, which contains several interesting features, reveals some of the capabilities of QuikSCAT. Careful quality control is vital for flagging data that are affected by rain and for flagging errors during ambiguity removal. In addition, an understanding of the instrument and algorithm characteristics provides insights into the factors controlling data quality for QuikSCAT. For example data quality is reduced for low wind speeds, and for locations either close to nadir or to the swath edges. The special data characteristics of the QuikSCAT scatterometer are revealed by examining the likelihood or objective function. The objective function is equal to the sum of squared scaled differences between observed and simulated normalized reflected radar power. The authors present typical examples and discuss the associated data quality concerns for different parts of the swath, for different wind speeds, and for rain versus no rain.

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Satellite scatterometer observations of the ocean surface wind speed and direction improve the depiction of storms at sea. Over the ocean, scatterometer surface winds are deduced from multiple measurements of reflected radar power made from several directions. In the nominal situation, the scattering mechanism is Bragg scattering from centimeter-scale waves, which are in equilibrium with the local wind. These data are especially valuable where observations are otherwise sparse-mostly in the Southern Hemisphere extratropics and Tropics, but also on occasion in the North Atlantic and North Pacific. The history of scatterometer winds research and its application to weather analysis and forecasting is reviewed here. Two types of data impact studies have been conducted to evaluate the effect of satellite data, including satellite scatterometer data, for NWP. These are simulation experiments (or observing system simulation experiments or OSSEs) designed primarily to assess the potential impact of planned satellite observing systems, and real data impact experiments (or observing system experiments or OSEs) to evaluate the actual impact of available space-based data. Both types of experiments have been applied to the series of satellite scatterometers carried on the Seasat, European Remote Sensing-1 and-2, and the Advanced Earth Observing System-1 satellites, and the NASA Quick Scatterometer. Several trends are evident: The amount of scatterometer data has been increasing. The ability of data assimilation systems and marine forecasters to use the data has improved substantially. The ability of simulation experiments to predict the utility of new sensors has also improved significantly.

The Cross‐Calibrated Multiplatform (CCMP) ocean surface wind data set was originally developed by Atlas and coworkers to blend cross‐calibrated satellite winds, in situ data, and wind analyses from numerical weather prediction. CCMP uses a variational analysis method to smoothly blend these data sources into a gap‐free gridded wind estimate every 6 hr. CCMP version 2.0 is currently produced by Remote Sensing Systems using consistently cross‐calibrated satellite winds, in situ data from moored buoys, and background winds from the ERA‐Interim reanalysis. The reanalysis fields are only available after a delay of several months, making it impossible to produce CCMP 2.0 in near real time. Measurements from in situ sources such as moored buoys are also often delayed. To overcome these obstacles and produce a near‐real‐time (NRT) version of CCMP (CCMP‐NRT), two changes are made to the input data sets: The background winds are now the operational 0.25‐degree NCEP analysis winds, and no in situ data are used. This allows CCMP‐NRT to be routinely processed with a latency of less than 48 hr. An intercomparison of the CCMP‐NRT results with CCMP 2.0, and independent measurements from moored buoys shows that CCMP‐NRT provides a modest improvement over the background wind from NCEP in regions where satellite data are available. Analysis shows that the inclusion of in situ measurement in CCMP improves the agreement with these measurements, artificially reducing estimates of the error.