Calibration of the DSCOVR EPIC visible and NIR channels using MODIS Terra and Aqua data and EPIC lunar observations.

The unique position of the Deep Space Climate Observatory (DSCOVR) Earth Polychromatic Imaging Camera (EPIC) at the Lagrange 1 point makes an important addition to the data from currently operating low orbit Earth observing instruments. EPIC instrument does not have an onboard calibration facility. One approach to its calibration is to compare EPIC observations to the measurements from polar orbiting radiometers. Moderate Resolution Imaging Spectroradiometer (MODIS) is a natural choice for such comparison due to its well-established calibration record and wide use in remote sensing. We use MODIS Aqua and Terra L1B 1km reflectances to infer calibration coefficients for four EPIC visible and NIR channels: 443 nm, 551 nm, 680 nm and 780 nm. MODIS and EPIC measurements made between June 2015 and June 2016 are employed for comparison. We first identify favorable MODIS pixels with scattering angle matching temporarily collocated EPIC observations. Each EPIC pixel is then spatially collocated to a subset of the favorable MODIS pixels within 25 km radius. Standard deviation of the selected MODIS pixels as well as of the adjacent EPIC pixels is used to find the most homogeneous scenes. These scenes are then used to determine calibration coefficients using a linear regression between EPIC counts/sec and reflectances in the close MODIS spectral channels. We present thus inferred EPIC calibration coefficients and discuss sources of uncertainties. The Lunar EPIC observations are used to calibrate EPIC O2 absorbing channels (688 nm and 764 nm) assuming that there is a small difference between moon reflectances separated by ~10 nm in wavelength provided the calibration factors of the red (680 nm) and near-IR (780 nm) are known from comparison between EPIC and MODIS.

Satellite remote sensing reveals regional tropospheric aerosol trends.

The Global Aerosol Climatology Project data product based on analyses of channel 1 and 2 AVHRR radiances shows significant regional changes in the retrieved optical thickness of tropospheric aerosols which had occurred between the volcano-free periods 1988-91 and 2002-05. These trends appear to be generally plausible, are consistent with extensive sets of long-term ground-based observations throughout the world, and may increase the trustworthiness of the recently identified downward trend in the global tropospheric aerosol load.

New Statistical Model for Variability of Aerosol Optical Thickness: Theory and Application to MODIS Data over Ocean.

A novel model for the variability in aerosol optical thickness (AOT) is presented. This model is based on the consideration of AOT fields as realizations of a stochastic process, that is the exponent of an underlying Gaussian process with a specific autocorrelation function. In this approach AOT fields have lognormal PDFs and structure functions having the correct asymptotic behavior at large scales. The latter is an advantage compared with fractal (scale-invariant) approaches. The simple analytical form of the structure function in the proposed model facilitates its use for the parameterization of AOT statistics derived from remote sensing data. The new approach is illustrated using a year-long global MODIS AOT dataset (over ocean) with 10 km resolution. It was used to compute AOT statistics for sample cells forming a grid with 5° spacing. The observed shapes of the structure functions indicated that in a large number of cases the AOT variability is split into two regimes that exhibit different patterns of behavior: small-scale stationary processes and trends reflecting variations at larger scales. The small-scale patterns are suggested to be generated by local aerosols within the marine boundary layer, while the large-scale trends are indicative of elevated aerosols transported from remote continental sources. This assumption is evaluated by comparison of the geographical distributions of these patterns derived from MODIS data with those obtained from the GISS GCM. This study shows considerable potential to enhance comparisons between remote sensing datasets and climate models beyond regional mean AOTs.

Long-term satellite record reveals likely recent aerosol trend.

Analysis of the long-term Global Aerosol Climatology Project data set reveals a likely decrease of the global optical thickness of tropospheric aerosols by as much as 0.03 during the period from 1991 to 2005. This recent trend mirrors the concurrent global increase in solar radiation fluxes at Earth's surface and may have contributed to recent changes in surface climate.