Data Fusion of AIRS and CrIMSS Near Surface Air Temperature

We present a near surface air temperature (NSAT) fused data product over the contiguous United States using data from the Atmospheric Infrared Sounder (AIRS), on the Aqua platform, and the Cross-track Infrared Microwave Sounding Suite (CrIMSS), on the Suomi National Polar-orbiting Partnership (NPP) platform. We create the fused product using a fast python implementation of Spatial Statistical Data Fusion (SSDF) along with weather 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 (one daytime and one nighttime estimate per day) and on a 0.25-degree latitude-longitude grid. We provide detailed validation using withheld ISD data and 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 accurate 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 any number of datasets estimating the same quantity. Finally, because our product removes bias, it produces long-term datasets across multi-instrument remote sensing records with improved stationarity for climate trend analysis, even as individual missions and their data records begin and end.