A Deep Learning−Based Correction to Radar Beam Blockage

Radar beam blockage due to terrain or other sources is a major source of degradation in weather radar measurements in several areas of the country, particularly mountainous regions in the Western US. This degradation affects several widely used radar products, including Vertically Integrated Liquid (VIL), Composite Reflectivity, Echo tops, Quantitative Precipitation Estimates (QPE) and others. Beam blockage introduces biases into these products and can limit their applicability in many safety critical applications, such as Air Traffic Control.

In this work, convolutional neural networks (CNNs) are applied to correct beam blockage errors in VIL mosaics created from Weather Surveillance Radar-1988 Doppler (WSR-88D) radars. The CNNs trained in this work combine multiple sources of data to compute this correction, including degraded VIL mosaics, visible and infrared satellite imagery, lightning flash detections, reflectivity profiles in unblocked regions, and radar beam height. The target variable used to train the CNN is created by simulating beam blockage in unblocked regions and then artificially degrading VIL mosaics in those regions. CNNs were found to be an effective model for this application because of their ability to leverage multiple forms of gridded data, as well as their ability to learn non-trivial features from the input data sources. Results will be validated against WSR-88D data in unblocked regions, as well as against data from the Global Precipitation Measurement Missions space borne Dual Frequency Precipitation radar in blocked regions.

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This material is based upon work supported by the Federal Aviation Administration under Air Force Contract No.
FA8702‐15‐D‐0001. Any opinions, findings, conclusions or recommendations expressed in this material are those of
the author(s) and do not necessarily reflect the views of the Federal Aviation Administration.