PNNL

Abstract

CloudSat’s Cloud Profiling Radar is a valuable tool for remotely monitoring high-latitude snowfall, but its ability to observe hydrometeor activity near the Earth’s surface is limited by a radar blind zone caused by ground-clutter contamination. This study presents the development of a deeply supervised U-Net-style convolutional neural network to predict cold season reflectivity profiles within the radar blind zone using 6.5 years of CloudSat-calibrated Ka-band ARM Zenith radar data from two Arctic locations. The network learns to predict the presence and intensity of near-surface hydrometeors by coupling latent features encoded in blind zone-aloft clouds with additional context from collocated atmospheric state variables (i.e. temperature, specific humidity and wind speed). Results show that the U-Net predictions outperform traditional linear interpolation methods, with low mean absolute error (MAE), a 38% higher Sørensen–Dice coefficient, and vertical reflectivity distributions 60% closer to observed values. Moreover, the U-Net displays additional MAE reductions of 6% when trained using the reanalysis-derived atmospheric covariates in addition to reflectivity, compared to when the model is trained using reflectivity as the sole predictor. The U-Net is also able to detect the presence of near surface cloud with a critical success index (CSI) of 72%, and cases of shallow cumuliform snowfall and virga with 18% higher CSI values compared to linear methods. An Explainable Artificial Intelligence analysis highlights the fact that a combination of both the reflectivity information throughout the scene (most notably across cloud edges and at the 1.2 km blind zone threshold), along with atmospheric state variables near the tropopause, are the most significant contributors to model skill. This surface-trained generative inpainting technique has the potential to enhance current and future remote sensing precipitation missions by providing a better understanding of the nonlinear relationship between blind zone reflectivity values and the surrounding atmospheric state.