This will involve working out the velocity of the rain band and current location to estimate the time of arrival. The loop provides 4 or more images, these can be used to estimate the direction of travel of the rain band together with wind velocities reported.
You can get individual Radar frames over Adelaide with the following format
- URL Base: http://www.bom.gov.au/radar/IDR642.T [x.T where x is the resolution level - 2,3,4]
- Date code: YYYYMMDD in UTC
- Time code: HHMM in UTC
The rainfall is colour coded so we will need to perform a look-up to convert from colour to rain density. Next a quick FFT based convolution for the 4 images to estimate x and y shifts. You can also use a shift and difference approach while minimizing the difference to establish the rain velocity. Statistically test the results to establish constant velocity . If the velocity is not fairly constant our prediction accuracy falls off. Then the time machine part, propagate the last image through the shift field and estimate the rain intensity at the location of interest for the next 30minutes to 1hour, whatever the horizon of the radar and rain velocity lets you do.
Obviously all this can be done much easier from within BOM since they have the data at hand, but it is much more fun mashing a separate application together.
2 comments:
So did you implement the predictor? Do you have any code to share?
Nope I am working on a simple feature detector using OpenCV-Scikits.image since I like numpy. Hopefully I can use the blob detection. Well all that remains to be done is to get it to build on windows. Meanwhile I am going to use standard OpenCv with adaptors.
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