Ardeshir Ebtehaj, Assistant Professor, St. Anthony Falls Laboratory and Department of Civil, Environmental, and Geo-Engineering
This presentation discusses complexities in land-atmosphere microwave signals and explore modern data science methodologies that can be used to improve land-atmosphere remote sensing . In particular, snow-cover and precipitation microwave signals are difficult to discern from space, as both scatter the upwelling land surface radiation in a similar way over high-frequency bands >80 GHz. We present the results using multi-satellite data from visible-to-microwave bands that enable better understanding of the distinct microwave signatures of precipitation at liquid and solid phases, especially over snow-covered surfaces. Using the data by the recently launched NASA’s Global Precipitation Measurement (GPM) satellite, we demonstrate that our new passive microwave retrieval algorithm--called shrunken locally linear embedding algorithm for retrieval of precipitation (ShARP)--promises improved snowfall detection skills without relying on any ancillary data. The results of the algorithm are compared with the standard NASA precipitation products.