Data Fusion of AIRS and CrIMSS Near Surface Air Temperature. Issue 10 (3rd October 2022)

• Record Type: Journal Article
• Authors: Kalmus, P.; Nguyen, H.; Roman, J.; Wang, T.; Yue, Q.; Wen, Y.; Hobbs, J.; Braverman, A.
• Is Part Of: Earth and space science. Volume 9:Issue 10(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-10-03
• Subjects: data fusion -- remote sensing -- surface temperature -- AIRS -- CrIMSS -- validation; Space sciences -- Periodicals; Geophysics -- Periodicals; 500.5
• DOI: 10.1029/2022EA002282
• Languages: English
• ISSNs: 2333-5084

Abstract

Abstract: 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, on the Aqua satellite, and the Cross‐track Infrared Microwave Sounding Suite (CrIMSS), on the Suomi National Polar‐orbiting Partnership satellite. We create the fused product using Spatial Statistical Data Fusion, a procedure for fusing multiple data sets 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 data sets. Our fused NSAT product is produced twice daily and on a 0.25° 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 data sets, 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 and 0.5 K, nighttime bias magnitude by 1.5 and 0.2 K, and overall RMSE by 35% and 15% relative to the AIRS and CrIMSS input data sets, respectively. Our method is computationally fast and generalizable, capable of data fusion from multiple data sets estimating the same quantity. Finally, because our product reduces bias, it produces long‐term data sets across multi‐instrument remote sensing records with improved bias stationarity, even as individual missions and their data records begin and end. Plain Language Summary: We have used a data fusion technique called spatial statistical data fusion (SSDF) to create an improved near surface air temperature (NSAT) data set by fusing two separate satellite data sets. NSAT is important for a variety of applications, such as drought, wildfire, and extreme heat research and prediction. The two input NSAT data sets come from the Atmospheric Infrared Sounder (AIRS) instrument on the Aqua satellite, and the Cross‐track Infrared Microwave Sounding suite on the Suomi National Polar‐orbiting Partnership satellite. Our fused NSAT product is produced twice daily and on a 0.25° latitude‐longitude grid. We also performed a detailed validation using withheld reference data (which was not included in the bias‐correction data) and comparison with ERA5‐Land reanalysis. The new fused product has no missing data; has improved accuracy and precision relative to the input satellite data sets, and comparable accuracy and precision to ERA5‐Land; and includes improved uncertainty estimates. SSDF is computationally fast and generalizable, capable of data fusion from multiple data sets so long as they estimate the same quantity. Finally, because our product reduces bias, it provides a means of creating high‐quality continuous long‐term data sets across the years, as individual satellite missions and their data records begin and end. Key Points: We demonstrate spatial statistical fusion for Level 2 remote sensing data sets which estimate the same observable We introduce a new daily and nightly fused near‐surface air temperature product from satellite hyperspectral sounders over contiguous United States The fused product decreases bias and RMSE by 1 K and 25% respectively relative to input data sets, averaged over the domain of the study