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Classifying Meteorological Echoes in Weather Radar Images with Transfer Learning

One of the limiting factors in any research into meteorological phenomena is in locating data where the phenomena of interest is present. In the case of weather radar data, it is often required to aggregate notes, storm reports, and historical references to cross reference the vast repositories of image data available to the researcher, allowing the researcher to then manually examine the appropriate scans. In this work we present a method for classifying meteorological regimes by applying computer vision techniques, including a convolutional neural network, using weather radar imagery from the CASA DFW X-band radar network in Dallas-Fort Worth, TX. This work demonstrates the utility and validity of using Transfer Learning to apply image insights gained from careful and exhaustive training on photographic images to an unrelated data domain and achieve valuable results. This strategy is expected to assist scientists in reducing time and effort in data discovery and reducing time to science.