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The high porosities and low densities of ceramic aerogels offer outstanding insulative performance in applications where weight is a critical factor. The high surface area-to-volume ratios and specific surface areas provide extremely low thermal conductivity, but also contribute to rapid densification of the pore structure at elevated temperatures. This densification diminishes their favorable properties and inhibits use of aerogels in high temperature applications. This work contributes to a design framework for thermally stable aerogels via the study of dopant chemistry (Y, Yb, Gd, Ca, Ce) in zirconia aerogels. The structural evolution was studied to 1200°C using nitrogen physisorption, scanning electron microscopy, and x-ray diffraction. The role of dopant identity and concentration in thermal stability was elucidated. In context of the design framework, dopant chemistry is an aggregate for many closely related material properties, each of which may contribute to aerogel structural evolution. To develop a truly predictive design framework for ceramic-based aerogels, systematic and comprehensive evaluation of thermodynamic and kinetic properties must be performed in conjunction with studies on structural evolution.

Precipitation type is a key parameter used for better retrieval of precipitation characteristics as well as to understand the cloud–convection–precipitation coupling processes. Ice crystals and water droplets inherently exhibit different characteristics in different precipitation regimes (e.g., convection, stratiform), which reflect on satellite remote sensing measurements that help us distinguish them. The Global Precipitation Measurement (GPM) Core Observatory’s microwave imager (GMI) and dual-frequency precipitation radar (DPR) together provide ample information on global precipitation characteristics. As an active sensor, the DPR provides an accurate precipitation type assignment, while passive sensors such as the GMI are traditionally only used for empirical understanding of precipitation regimes. Using collocated precipitation type flags from the DPR as the “truth”, this paper employs machine learning (ML) models to train and test the predictability and accuracy of using passive GMI-only observations together with ancillary information from a reanalysis and GMI surface emissivity retrieval products. Out of six ML models, four simple ones (support vector machine, neural network, random forest, and gradient boosting) and the 1-D convolutional neural network (CNN) model are identified to produce 90–94% prediction accuracy globally for five types of precipitation (convective, stratiform, mixture, no precipitation, and other precipitation), which is much more robust than previous similar effort. One novelty of this work is to introduce data augmentation (subsampling and bootstrapping) to handle extremely unbalanced samples in each category. A careful evaluation of the impact matrices demonstrates that the polarization difference (PD), brightness temperature (Tc) and surface emissivity at high-frequency channels dominate the decision process, which is consistent with the physical understanding of polarized microwave radiative transfer over different surface types, as well as in snow and liquid clouds with different microphysical properties. Furthermore, the view-angle dependency artifact that the DPR’s precipitation flag bears with does not propagate into the conical-viewing GMI retrievals. This work provides a new and promising way for future physics-based ML retrieval algorithm development.