If you are using ArcGIS you would find these tools in the Spatial Analyst toolbar > Interpolation. You will input your featureclass containing the wind data, your Z value will be your wind speed, and then choose your output FC which is where you will save it.
Some choices have to be made as to your interpolation type. IDW is best for a dense sample set, SPLINE is best for smooth data. Quoted below is what ESRI has to say on the matter.
IDW (Inverse Distance Weighted) tool uses a method of interpolation that estimates cell values by averaging the values of sample data points in the neighborhood of each processing cell. The closer a point is to the center of the cell being estimated, the more influence, or weight, it has in the averaging process.
Kriging is an advanced geostatistical procedure that generates an estimated surface from a scattered set of points with z-values. More so than other interpolation methods supported by ArcGIS Spatial Analyst, a thorough investigation of the spatial behavior of the phenomenon represented by the z-values should be done before you select the best estimation method for generating the output surface.
Natural Neighbor interpolation finds the closest subset of input samples to a query point and applies weights to them based on proportionate areas to interpolate a value (Sibson, 1981). It is also known as Sibson or "area-stealing" interpolation.
The Spline tool uses an interpolation method that estimates values using a mathematical function that minimizes overall surface curvature, resulting in a smooth surface that passes exactly through the input points.
Spline with Barriers The Spline with Barriers tool uses a method similar to the technique used in the Spline tool, with the major difference being that this tool honors discontinuities encoded in both the input barriers and the input point data.
The Topo to Raster and Topo to Raster by File tools use an interpolation technique specifically designed to create a surface that more closely represents a natural drainage surface and better preserves both ridgelines and stream networks from input contour data.
The algorithm used is based on that of ANUDEM, developed by Hutchinson et al at the Australian National University.
Trend is a global polynomial interpolation that fits a smooth surface defined by a mathematical function (a polynomial) to the input sample points. The trend surface changes gradually and captures coarse-scale patterns in the data.
Here is a previous article on the matter: Deciding what interpolation method to use for resampling raster data?
Here is a good paper on choosing the correct method: http://webapps.fundp.ac.be/geotp/SIG/interpolating.pdf