A MULTI-DECADAL HIGH-RESOLUTION SOIL WATER CONTENT PRODUCT BASED ON THE SMAP CLIMATOLOGY
Marzo 25, 2026SNOWFALL REGIME CLASSIFICATION: APPLICATION OF MACHINE LEARNING CLASSIFIER TO PASSIVE MICROWAVE OBSERVATIONS
Marzo 25, 2026M. Klein1, M. Krause1, M. Eble1, A. Madrid1
1Boulder Environmental Sciences and Technology
Space based microwave sensors offer unique insights into climate dynamics and the water and energy cycle. The complete water cycle, i.e. precipitation, soil moisture, evaporation, and condensation can be measured by microwave sensors, active or passive. These processes are the drivers behind disruptive weather events, such as flooding, droughts, stronger hurricanes, and heat waves, among others.
The observation and measurement of precipitation (rainfall and snowfall) on a global scale is one of the most crucial variables for our understanding of the Earth’s system. Precipitation provides a direct link between the local cycle of energy and water. However, observations of precipitation are challenging. To provide observations for a wide range of precipitation events, from heavy rain and tropical cyclones, to light precipitation and snow, a wide range of microwave frequencies need to be used. Active sensors, such as the Tropical Rainfall Measuring Mission’s (TRMM) Precipitation Radar (PR) (13.6 GHz), the Dual frequency Precipitation Radar (DPR) of The Global Precipitation Measurement (GPM) mission (13.6 and 35.5 GHz), and the Cloud Profiling Radar (CPR) (94 GHz) operate between 13 and 95 GHz. Passive microwave imagers and sounders contribute to precipitation retrieval significantly and use frequencies up to 200 GHz and higher. Extending radiometers’ frequency coverage can improve precipitation retrieval, as shown by (Bauer, Moreau and Di Michele 2005), (Di Michele and Bauer 2006), and others.
To improve global precipitation mapping, improved spatial, spectral, and temporal resolution of observations are required, (Kidd, et al. 2021). The authors state, “Since the spatial variability of precipitation is on the order for a few kilometers, resolving this variability at 1 km or less would be ideal…” Improved spatial resolution has to be accompanied with a corresponding improvement in temporal resolution, i.e. the revisit time must be shorter. For example, with a 15 km spatial resolution, achieving a correlation coefficient better than 80% between precipitation observed in situ and space based passive microwave sensor retrieval would require sampling at 1.5 hours intervals. That requires ~8 sensors on orbit. To achieve this spatial resolution at 7 GHz, the antenna aperture for a conically scanning passive microwave sensor would need to be ~7 m.
Offset parabolic deployable antennas can enable the economical deployment of such passive or active sensors. The advantage of a parabolic reflector is that it can support a wide range of operational frequencies, e.g., from 1 to 120 GHz. In addition, the offset parabolic reflector is considered electrically the most efficient antenna solution.
Compact deployable reflectors are the solution that enables a new generation of microwave observing systems with improved spatial resolution, while keeping the sensors costs affordable and enabling shorter sensor revisit times.
The talk will describe the state of the art of deployable antenna technology developed by BEST and the solutions that it provides. It will describe the improved range of capabilities that this technology affords to remote sensing systems.
References
Bauer, P., E. Moreau, and S. Di Michele. 2005. “Hydrometeor Retrieval Accuracy Using Microwave Window and Sounding Channel Observations.” Journal of Applied Meteorology 1016-1032.
Di Michele, S., and P. Bauer. 2006. “Passive microwave radiometer channel selection based on cloud and precipitation information content.” Quarterly Journal of the Royal Meteorological Society 132 (617): 1299-1323.
Kidd, C., G. Huffman, V. Maggioni, P. Chambon, and R. Oki. 2021. “The Global Satellite Precipitation Constellation. Current Status and Future Requirements.” Bulletin of the American Meteorological Society. doi:10.1175/BAMS-D-20-0299.1.
