NEURAL NETWORK APPLICATIONS FOR CLOUD-TRACK WIND DETERMINATION

WE PROPOSE TO APPLY ARTIFICIAL NEURAL NETWORKS TO THE DERIVATION OF CLOUD MOTION VECTORS FROM SATELLITE IMAGERY. IN PHASE I, WE WILL CONCENTRATE ON USING A NEURAL NETWORK IN THE FIRST STEP IN THE DERIVATIO OF CLOUD MOTION VECTORS, THE HEIGHT ASSIGNMENT AND TARGET SELECTION. THE NEURAL NETWORK COMBINES, AT EACH IMAGE PIXEL, BRIGHTNESS IN DIFFERENT CHANNELS TO DETERMINE CLOUD TYPE AND LEVEL (I.E., LOW, MIDDLE, OR HIGH). A STANDARD CROSS-CORRELATION TECHNIQUE WILL BE USED FOR TRACKING THE SELECTED TARGET IN PHASE 1. IN PHASE 2 WE WILL ADDRESS THE AUTOMATION OF THE QUALITY CONTROL PROCEDURE, AND EXPLORE THE USE OF NEURAL NETWORK FOR TRACKING OF TARGETS. OUR PROPOSED APPROACH FOR PHASE 1 BUILDS ON PRELIMINARY WORK THAT HAS ALREADY BEEN PERFORMED AT AER: A NEURAL NETWORK TECHNIQUE FOR DETERMINING CLOUD TYPE AND LEVEL, AND A CROSS-CORRELATION TECHNIQUE FOR SHORT-TERM FORECASTS OF SATELLITE IMAGERY. THE SYSTEM DEVELOPED IN PHASE 1 WILL SERVE AS A BASELINE FOR IMPROVEMENTS TO THE TRACKING ALGORITHM TO BE DEVELOPED DURING PHASE 2.