A Decadal Gridded Hyperspectral Infrared Record for Climate. Sep 1st 2002 - Aug 31st 2012

We present a gridded Fundamental Decadal Data Record (FDDR) of Brightness Temperatures (BT) from the NASA Atmospheric Infrared Sounder (AIRS) from ten years of hyperspectral Infrared Radiances onboard the NASA EOS Aqua satellite. Although global surface temperature data records are available for over 130 years, it was not until 1978 when the Microwave Sounding Unit (MSU) was the first instrument series to reliably monitor long-term trends of the upper atmosphere. AIRS, operational on September 1, 2002 is the first successful hyperspectral satellite weather instrument of more than 1 year, and provides a 10 year global hyperspectral IR radiance data record. Our contribution was to prepare a gridded decadal data record of climate resolution from the AIRS Outgoing Longwave Spectrum (OLS). In order to do this, we developed a robust software infrastructure "Gridderama" using large multivariate array storage to facilitate this multi-terabyte parallel data processing task while ensuring integrity, tracking provenance, logging errors, and providing extensive visualization. All of our data, code, logs and visualizations are freely available online and browsable via a real-time "Data Catalog" interface. We show that these global all-sky trends are consistent with the expected radiative forcings from an increase in greenhouse gasses. We have also measured high global correlations with the GISS global surface air temperatures as well as high regional anticorrelations with the NOAA ONI index of El Niño phase. In addition, we have performed inter-annual inter-comparisons with the Moderate Resolution Spectro-radiometer (MODIS) on the same Aqua satellite to examine the relative consistency of their calibrations. The comparisons of the two instruments for the 4µ spectral channels (between 3.9µ and 4.1µ) indicate an inter-annual warming of 0.13K per decade of AIRS more than MODIS. This decadal relative drift is small compared to inter-annual variability but on the order of historic surface temperature trends. In the 12µ window channels (between 11.5µ and 12.5µ), AIRS - MODIS exhibits a standard deviation of 0.01K over a decade suggesting that the AIRS longwave has remained extremely well calibrated. This relative calibration result is to first order consistent with a recent radiometric comparison by H. Aumann et al. (2012) against pacific sea surface temperatures [19]. It is convenient to observe the climate variability by using monthly average lat-lon grid projections, but gridding is a lossy process that invariably introduces aliasing artifacts and noise. We observed an exponential decay between the number of days averaged and the expected noise due to gridding. We have extended the Observation Coverage (Obscov) gridding algorithm developed for the MODIS instrument that incorporates the Point Spread Function (PSF), and we show that the Obscov gridding algorithm reduces the aliasing noise from AIRS grids by nearly 40% by comparing the spatial correlation of gridded MODIS IR data. We also show that the use of a circular approximate PSF is a sufficient representation to obtain the noise reduction of Obscov at the climate resolution 0.5x1 degree monthly average grids. We extended these spatial sampling methods to the AIRS Level 3 retrieval records for which quality filtering due to opaque clouds is an additional spatial sampling challenge, and corrected an observed dry sampling bias in the AIRS v5 and v6 Level 3 monthly average gridded moisture retrieval records by means of spatial interpolation with the Nearest Neighbor (NN) and Ordinary Kriging (OK) strategies