We present a near surface air temperature (NSAT) fused data product over the contiguous United States using Level 2 data from the Atmospheric Infrared Sounder (AIRS), on the Aqua satellite, and the Cross-track Infrared Microwave Sounding Suite (CrIMSS), on the Suomi National Polar-orbiting Partnership (SNPP) satellite. We create the fused product using Spatial Statistical Data Fusion (SSDF), a procedure for fusing multiple datasets by modeling spatial dependence in the data, along with ground station data from NOAA’s Integrated Surface Database (ISD) which is used to estimate bias and variance in the input satellite datasets. Our fused NSAT product is produced twice daily and on a 0.25-degree latitude-longitude grid. We provide detailed validation using withheld ISD data and comparison with ERA5-Land reanalysis. The fused gridded product has no missing data; has improved accuracy and precision relative to the input satellite datasets, and comparable accuracy and precision to ERA5-Land; and includes improved uncertainty estimates. Over the domain of our study, the fused product decreases daytime bias magnitude by 1.7 K and 0.5 K, nighttime bias magnitude by 1.5 K and 0.2 K, and overall RMSE by 35% and 15% relative to the AIRS and CrIMSS input datasets, respectively. Our method is computationally fast and generalizable, capable of data fusion from multiple datasets estimating the same quantity. Finally, because our product reduces bias, it produces long-term datasets across multi-instrument remote sensing records with improved bias stationarity, even as individual missions and their data records begin and end.

Ka-band (32 GHz) communications links utilized by the National Aeronautics and Space Administration (NASA) flight missions for science downlink are susceptible to degradation due to weather. In this study, a customized real-time forecast system has been developed to predict zenith atmospheric noise temperature (Tatm) at the Deep Space Network (DSN) tracking sites using machine learning (ML). A random forest model is trained with the Global Forecast System (GFS) forecast and analysis datasets in addition to the Tatm measurements derived from on-site advanced water vapor radiometers (AWVR). The real-time forecast uncertainty is quantified for different error regimes using the Self-Organizing Map method. The results show that the Root Mean Square Error (RMSE) of the 24-hour Tatm prediction at Goldstone, CA increases with the increase of Tatm. Ninety percent of the forecasts have RMSE (bias) of less than 3.50 K (0.22 K) for fair-weather conditions with Tatm < 17 K. In comparison to the current approach in designing Ka-band communications links, application of weather forecasts can increase data return to the downlink for 80% of the time. A downlink gain of up to 1.61 dB (45% more data) can be realized at 20 elevation angle when Tatm = 9 K.