Research in remote sensing at STORM Lab focuses on the retrieval and characterization of precipitation and atmospheric structure using satellite and ground-based observations. A major emphasis of the lab has been on integrating space-borne radiometers and radars with ground-based radar observations to improve rainfall estimation and understanding of precipitation systems.

The lab has contributed extensively to the development of rainfall retrieval methodologies using microwave radiances, including neural-network-based and physically informed approaches to estimate vertical rainfall structure from satellite observations such as TRMM, GPM, and the MADRAS imager . Early work includes the development of PCA–ANN and radiative transfer-based models for simulating and retrieving atmospheric parameters from radiometric measurements.

A key contribution of the lab is the formulation of alignment-based frameworks for intercomparison of ground radar and space-borne radar observations, enabling consistent evaluation of reflectivity and rainfall products across platforms . These methodologies address resolution mismatches and observational inconsistencies, improving the reliability of multi-sensor precipitation datasets.

The lab has also explored information-theoretic approaches to quantify the relationship between radar and radiometer observations, providing insights into the information content of different sensing systems and enabling cross-retrieval between measurement platforms.

Overall, the remote sensing research integrates observations, physical modeling, and data-driven techniques to enhance rainfall estimation, validate satellite products, and improve understanding of precipitation processes across spatial and temporal scales.