  The objective of this research is to develop techniques for assimilating
GOES-R Series observations in precipitating scenes for the purpose of improving
short-term convective-scale forecasts of high impact weather hazards. Whereas
one approach is radiance assimilation, the information content of GOES-R
radiances from its Advanced Baseline Imager (ABI) saturates in precipitating
scenes, and radiance assimilation does not make use of lightning observations
from the GOES Lightning Mapper (GLM). Here, a convolutional neural network
(CNN) is developed to transform GOES-R radiances and lightning into synthetic
radar reflectivity fields to make use of existing radar assimilation
techniques. We find that the ability of CNNs to utilize spatial context is
essential for this application and offers breakthrough improvement in skill
compared to traditional pixel-by-pixel based approaches. To understand the
improved performance, we use a novel analysis methodology that combines several
techniques, each providing different insights into the network's reasoning.
Channel withholding experiments and spatial information withholding experiments
are used to show that the CNN achieves skill at high reflectivity values from
the information content in radiance gradients and the presence of lightning.
The attribution method, layer-wise relevance propagation, demonstrates that the
CNN uses radiance and lightning information synergistically, where lightning
helps 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 predicted
reflectivity. Finally, geostationary lightning observations are found to be
uniquely valuable for their ability to pinpoint locations of strong radar
echoes.
