Retrieving 3D Cloud Radar Reflectivity from MODIS Cloud Products Based on CGAN

3D cloud structures are highly desirable for severe weather forecast and warning, solar energy assessment and forecast, aviation safety and control, weather modification, climate modeling, and so on. In this study, the conditional adversarial neural network (CGAN) developed by Leinonen et al. (2019) for retrieving the equivalent cloud radar reflectivity at 94 GHz of the Cloud Profile Radar (CPR) onboard CloudSat is extended and evaluated comprehensively with respect to different cloud types and geographically regions. An algorithm is developed to construct seamless 3D cloud radar reflectivity using the slices of the CGAN-retrieved vertical profiles of the cloud radar reflectivity factors. 3D structures of several Typhoons, squall lines, and other convective systems are retrieved and verifies against the ground-based radar observations.

The retrieval based the test dataset that contains 24427 cloud samples was statistically analyzed to assess the performance of the cloud retrieving model, for eight cloud types and three latitude zones, respectively. The results show that the CGAN model possesses good ability for retrieving clouds with cloud radar reflectivity factors > -25 dBZ. The model performed the best for deep convective systems, followed by nimbostratus, altostratus, and cumulus, but presented a very limited ability for stratus, cirrus, and altocumulus. The model performs better in the low and middle latitudes than in the high latitudes. In general, the model can be used to retrieve vertical structures of deep convective clouds and nimbostratus with a good confidence in the mid- and lower latitude region. By verifying the 3D cloud radar reflectivity factors retrievals of Typhoons, squall lines, and other convective systems with the ground-based radar observations, we feel confident that the model can be applied for real-time applications, including the cloud analysis and data assimilation for cloud-resolvable numerical weather prediction.