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Background: The Satellite's Orbit
The NASA Scatterometer (NSCAT) has provided an unprecedented wealth of wind and stress observations over the global oceans. Superb spatial and temporal resolution, as well as relative ease of use, allow the study of surface wind and stresses on remarkable scales. The base format for the observations is cells across the satellites orbital track. For NSCAT, these observational swaths are 600 km wide on each side of the satellite, with a 400 km wide gap underneath the satellite. Within the swaths, the resolution is approximately 25 km. For the study of phenomena with scales larger than the swath with, it is more convenient to work with data gridded in terms of longitude and latitude.
Swath DataStresses
The project's swath data sets are sub-sampled to remove data fields that are less likely to be used (e.g., most of the header, unselected ambiguities, and engineering data). Retained fields are wind speed, wind direction, time, latitude, and longitude. The scatterometer winds are calibrated to a height of 10 m. The data sets are also resorted from the original orbit by orbit organization, to daily observations. There is no modification of the data. The goal is to provide essential wind data in a more user-friendly format. The data set should be considered research quality.
Winds
The NSCAT backscatter observations are processed directly to determine surface stress magnitude. This process (Weissman et al. 1994) by-passed the need for a drag-coefficient based conversion from wind speed. It also has the advantage of by-passing any consideration of atmospheric stability in the drag-relation. The resulting stresses appear to include the impact of atmospheric stability. The data set includes stress magnitude, stress direction (assumed to be equal to the NSCAT wind direction), time, latitude, and longitude. The data sets are also resorted from the original orbit by orbit organization, to daily observations. The goal is to provide essential stress data in a user-friendly format. The data set should be considered research quality.
Gridded FieldsGridded fields of winds and stresses are useful for many applications including the forcing of ocean models. The tricks to making useful forcing fields from scatterometer observations is to fill the gaps between observations as accurately as possible and/or to average over sufficient time and space to make the gaps disappear. For example, see an example of daily NSCAT coverage over the Indian Ocean.
In the gap filling process, it is also important to fill the gaps as accurately as possible, and to smoothly blend the observations with the data filling the gaps. It is fairly easy to fill the gaps so that there are rarely problems in the wind field that are obvious to the eye. However, for ocean-forcing applications it is extremely important that the curl of the wind (or stress) field (essentially 'strength' of rotation) also be smooth. The curl of the wind field is much more sensitive to inadequate smoothing, making this field a far better 'test' for the quality of the gap filling and smoothing. Poor techniques have patterns that resemble the orbital pattern (sometimes called an argyle sock pattern).
Many approaches to gap filling are been pursued. The choices are somewhat dependent on the length of the averaging period. A summary of NSCAT gridded products is available. More detailed definitions for these gridded parameters (wind, pseudostress, and stress) are available.
| Gridding Technique | Field Variables | Spatial Resolution | Spatial Domain | Temporal Resolution | Temporal Domain |
| Weighted Temporal Average | uv | 1x1o | Global | Daily | Sept. 20, 1996 to June 25, 1997 |
| Direct Minimization | UW, VW | 1x1o | Indian Ocean 34.5oS to 28.5oN, 25.5oE to 124.5oE |
Daily | Oct. 1, 1996 to June 25, 1997 |
| Temporal Average |  , |
0.5x0.5o | Global | Monthly | Oct. 1996 to June 1997 |
NSCAT Monthly Gridded Stress
Monthly stress fields are produced using a monthly average of stresses determined from scatterometer swaths. These products are on a 0.5x0.5o grid. This approach bins the satellite swath observations (in 0.5x0.5o bins) and smoothed with a 1-2-1 filter (forward and backward) applied once in each spatial dimension. The tropical stresses are quite smooth without any smoothing. The smoothed stresses can be viewed.
This stress product has several advantages over other wind and stress products. It is the first gridded wind product with remotely sensed fields of stresses. Stress is normally determined as a function of wind speed and atmospheric stability, and occasionally other parameters. There is considerable uncertainty in such calculations of stress. This problem is further compounded for scatterometers, because scatterometers 'wind observations' are 'equivalent neutral winds' rather than winds. The differences between equivalent neutral winds and winds are usually small; however, they can result in considerable differences in stress products. Furthermore, these differences are spatially and temporally systematic, which is an important consideration in applications such as forcing ocean models. This relatively direct approach bypasses these problems by determining the stress directly from the observed backscatter. The data set should be considered as research quality.
A manuscript is in preparation describing this data set in review. The technique for determining stresses directly from scatterometer observations is described in:
- Weissman, D. E., K. L. Davidson, R. A. Brown, C. A. Friehe, and F. Li, The relationship between the mircrowase radar cross sectionand both wind speed and stress: Model function studies using Frontal Air-Sea interaction experiment data, J. Geophys. Res., 99, 10087-10108, 1994
Images of gridded products, as well as frames from animations have been saved for easy viewing. These figures illustrate examples of interesting wind fields. These figures are also used as examples of the content of animations. A general listing of scatterometer images is available.
AnimationsMoving pictures (animations) are extremely useful tools for examining the change or evolution of interesting features. Of course, they are also useful browsing the available data to locate such features. Many animations are stored in week-long movies to keep the size small enough that they can be downloaded (2 to 5 MB). These animations are described below.
COAPS Wind AnimationsWind Arrow Shape and Motion
The above wind fields were used to produce animations of the winds and related fields. The winds are shown with moving vectors. The motion of the vectors is Lagrangian (the arrows move with the wind), and the vector length indicates the wind speed. A length scale showing a 10 ms-1 vector is centered above the color bar. This length scale changes from region to region. It is small in regions with high wind speeds (e.g., the Southern Ocean) and long in areas of predominantly weak winds (e.g., the Northern Indian Ocean).
The vector positions are calculated by interpolating the daily wind fields to one hour time steps, and integrating with a fourth order Runge-Kutta method. The Runge-Kutta technique uses an adaptive time step, with a first guess of ten minutes. The time interval between animation frames is dependent on the highest wind speeds; however, we have found a time step of three hours to be effective. In summary, the wind arrows circulate with the surface wind, and the length of the vectors can be used to estimate the wind speed.
Arrow Color
Horizontal divergence for surface air is calculated at the grid points as a second order finite difference. The divergence field is bi-linearly interpolated to the central position of each moving vector. Divergence are shown by the color of the wind vector. Blue vectors indicate convergent flow, and red vectors indicate divergent flow. This coloring scheme helps highlight convergence zones, fronts, and structure within strong cyclones.
Background Color
The relative vorticity field is also calculated as a second order finite difference. The background colors (over water) are contours of vorticity. Vorticity indicates rotation: positive values indication clockwise (anticyclonic) rotation, and negative values indicate counter clockwise (cyclonic) rotation. In the northern hemisphere, weather systems with high surface pressure have anticyclonic rotation, and low pressure systems have cyclonic rotation. High pressure systems tend to be associated with descending air (surface divergence), and relatively clear skies. Low pressure systems are associated with ascending air (surface convergence) cloudy skies, and strong rotation often indicates a storm. The vorticity contours highlight the central position of storms, and help show the extent of flow modifications due to orographic features (e.g., land and mountains).
