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.
Best Answer
For such a small area the differences between the observation-points and the points where you need a measurement are likely very small and the error between a measuring station and any other point within this parameter is small, too.
Maybe the easiest solution for you would be to use the Kriging-tools from SAGA. These are integrated in QGIS and can be found in the QGIS processing/toolbox/SAGA/Raster Creation. For example the
Ordinary krigingor theSimple kriging.If you want to go more advanced there's also an experimental Plug-In
Smart-Mapwhich is explained here a bit: https://gis.stackexchange.com/a/402087/176374