COWVR BRIGHTNESS TEMPERATURE CALIBRATION AND TWO-LOOK WIND DIRECTION PERFORMANCE
Marzo 25, 2026FILLING THE OBSERVING GAP OVER COMPLEX TERRAIN: CHARACTERIZE WATER VAPOR AND CLOUDS OVER SARNTAL ALPS USING GROUND-BASED REMOTE SENSING OBSERVATIONS AT DIFFERENT ALTITUDES
Marzo 25, 2026B. Beechler1, K. Schaefer1,2
1Weather Stream, 2University of Colorado, Boulder
The NASA Sounder for Microwave Based Applications (SMBA) instruments will provide the next-generation microwave sounding capabilities on the Near-Earth Orbit Network (NEON) program. Goals of the SMBA missions include providing ATMS continuity while enabling next-generation hyperspectral capabilities across several passive microwave bands. Hyperspectral Digital Radiometry (HDR) has challenges associated with instrument downlink bandwidth, correlation of observations across channels, and degraded noise characteristics at small bandwidths. However, HDR improves the vertical resolution of estimated atmospheric profiles in the boundary layer, providing opportunities for improved high-resolution Numerical Weather Prediction, in addition to flexibility in on-orbit calibration.
We investigated the impact of hyperspectral channel sets on retrieval performance for temperature and humidity for a range of atmospheric profiles. We used ERA5 reanalysis to generate synthetic, full-frequency-resolution HDR data with the MonoRTM model. We compressed HDR channels into pseudo-channels using constant optical depth spacing for the 24, 50, 118, and 183 GHz absorption bands. We developed a new Machine Learning (ML) retrieval in addition to a 1D-Var physical retrieval to 1) compare temperature and humidity retrieval performance and 2) identify the most important channels for retrieval performance. We identified channel importance using orthogonality metrics for the 1D-Var retrieval and feature importance for the ML retrieval. Comparing the 1D-Var and ML retrievals demonstrated (1) similar choices for the most important channels for retrieval performance, (2) improved retrieval performance compared to using the current ATMS channel set, and (3) a limit to retrieval performance as a function of the number of pseudo-channels. These results support the design and deployment of on-board and ground data processing for Weather Stream’s next-generation HDR instrument.
