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The objective of this research is to develop techniques for assimilatingGOES-R Series observations in precipitating scenes for the purpose of improvingshort-term convective-scale forecasts of high impact weather hazards. Whereasone approach is radiance assimilation, the information content of GOES-Rradiances from its Advanced Baseline Imager (ABI) saturates in precipitatingscenes, and radiance assimilation does not make use of lightning observationsfrom the GOES Lightning Mapper (GLM). Here, a convolutional neural network(CNN) is developed to transform GOES-R radiances and lightning into syntheticradar reflectivity fields to make use of existing radar assimilationtechniques. We find that the ability of CNNs to utilize spatial context isessential for this application and offers breakthrough improvement in skillcompared to traditional pixel-by-pixel based approaches. To understand theimproved performance, we use a novel analysis methodology that combines severaltechniques, each providing different insights into the network's reasoning.Channel withholding experiments and spatial information withholding experimentsare used to show that the CNN achieves skill at high reflectivity values fromthe information content in radiance gradients and the presence of lightning.The attribution method, layer-wise relevance propagation, demonstrates that theCNN uses radiance and lightning information synergistically, where lightninghelps the CNN focus on which neighboring locations are most important.Synthetic inputs are used to quantify the sensitivity to radiance gradients,showing that sharper gradients produce a stronger response in predictedreflectivity. Finally, geostationary lightning observations are found to beuniquely valuable for their ability to pinpoint locations of strong radarechoes.

Development and Interpretation of a Neural Network-Based Synthetic Radar Reflectivity Estimator Using GOES-R Satellite Observations