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Averaging scalar wind direction data yields inaccurate values due to the compass headings ranging from 0-360 degrees, so I have converted my list to u and v components from the magnitude and wind direction angles already.
In order to back out the proper wind direction, for averaging purposes, I need to develop some sort of apply, ifelse, function for the 3 following scenarios:
V > 0...((180 / pi) * atan((Ucomp/Vcomp)) + 180)
U and V < 0...((180 / pi) * atan((Ucomp/Vcomp)) + 0)
U > 0 and V < 0...((180 / pi) * atan((Ucomp/Vcomp)) + 360)
In the data set I am looking to analyze, Ucomp is greater than 0 and Vcomp is less than zero, but there will undoubtedly be scenarios where all 3 will pan out, so I need a function to parse through and calculate iteratively and applying the correct formula for each time step. I have not used lapply or functions before, so me playing around with them has not worked.
I provide a sample of data below...
DateTime Wind.Spd Wind.Direction Air.Density Temp.C GEP.GE16XLE GCF.GE16XLE Ucomp Vcomp
1 1981 7.662370 248.3395 0.9148207 11.28967 597.7513 37.35946 5.253453 -0.7404972
2 1982 8.199412 251.6763 0.9172176 10.12751 678.8595 42.42872 5.867979 -0.6191475
3 1983 8.188782 251.7889 0.9162767 10.30619 667.9461 41.74663 5.777208 -1.0473982
4 1984 7.942632 246.7908 0.9174074 10.05093 642.6374 40.16484 5.415773 -0.6796723
5 1985 8.016558 252.7305 0.9171721 10.38414 654.2588 40.89117 5.649406 -0.9999082
6 1986 7.739984 249.6431 0.9158740 10.99859 607.0542 37.94089 5.305971 -0.9118965
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Meteorological wind direction is defined as the direction from which it originates. For example, a northerly wind blows from the north to the south. Wind direction is measured in degrees clockwise from due north. Hence, a wind coming from the south has a wind direction of 180 degrees; one from the east is 90 degrees.
The U wind component is parallel to the x-axis (i.e. longitude). A positive U wind comes from the west, and a negative U wind comes from the east. The V wind component is parallel to the y- axis (i.e. latitude). A positive V wind comes from the south, and a negative V wind comes from the north.
For those of you in the future that may be wondering. To find the wind speed = apply this formula WS=SQRT((x^2)+(y^2)) If it is in Meters per Second, be sure to convert to Knots if needed. To find the wind direction = apply this formula WD=ATAN2(y,x) where Y == to North/South vector and the X == the East/West vector.
You should look at using the atan2 function, it will probably remove the need for all the if statements and extra computing.
If you are doing a lot with directions then you should also look into the circular and CircStats packages which take care of a lot of these details for you (some is similar to what you did, just more automatic).
183
First define the function to do the calculation:
windDir <- function(u, v) {
if(v > 0) ((180 / pi) * atan(u/v) + 180)
if(u < 0 & v < 0) ((180 / pi) * atan(u/v) + 0)
if(u > 0 & v < 0) ((180 / pi) * atan(u/v) + 360)
}
Then apply it to each row. Here I'm using ddply, which is a nice "apply" variety for data frames:
> library(plyr)
> ddply(data, 'DateTime', summarize, windDir=windDir(Ucomp, Vcomp))
DateTime windDir
1 1981 278.0232
2 1982 276.0232
3 1983 280.2760
4 1984 277.1531
5 1985 280.0370
6 1986 279.7517
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