Abstract. This paper describes the latest version of the algorithm MAIAC used for processing the MODIS Collection 6 data record. Since initial publication in 2011–2012, MAIAC has changed considerably to adapt to global processing and improve cloud/snow detection, aerosol retrievals and atmospheric correction of MODIS data. The main changes include (1) transition from a 25 to 1 km scale for retrieval of the spectral regression coefficient (SRC) which helped to remove occasional blockiness at 25 km scale in the aerosol optical depth (AOD) and in the surface reflectance, (2) continuous improvements of cloud detection, (3) introduction of smoke and dust tests to discriminate absorbing fine- and coarse-mode aerosols, (4) adding over-water processing, (5) general optimization of the LUT-based radiative transfer for the global processing, and others. MAIAC provides an interdisciplinary suite of atmospheric and land products, including cloud mask (CM), column water vapor (CWV), AOD at 0.47 and 0.55 µm, aerosol type (background, smoke or dust) and fine-mode fraction over water; spectral bidirectional reflectance factors (BRF), parameters of Ross-thick Li-sparse (RTLS) bidirectional reflectance distribution function (BRDF) model and instantaneous albedo. For snow-covered surfaces, we provide subpixel snow fraction and snow grain size. All products come in standard HDF4 format at 1 km resolution, except for BRF, which is also provided at 500 m resolution on a sinusoidal grid adopted by the MODIS Land team. All products are provided on per-observation basis in daily files except for the BRDF/Albedo product, which is reported every 8 days. Because MAIAC uses a time series approach, BRDF/Albedo is naturally gap-filled over land where missing values are filled-in with results from the previous retrieval. While the BRDF model is reported for MODIS Land bands 1–7 and ocean band 8, BRF is reported for both land and ocean bands 1–12. This paper focuses on MAIAC cloud detection, aerosol retrievals and atmospheric correction and describes MCD19 data products and quality assurance (QA) flags.

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The National Oceanic and Atmospheric Administration (NOAA) Deep Space Climate Observatory (DSCOVR) spacecraft was launched on 11 February 2015 and in June 2015 achieved its orbit at the first Lagrange point (L1), 1.5 million km from Earth toward the sun. There are two National Aeronautics and Space Administration (NASA) Earth-observing instruments on board: the Earth Polychromatic Imaging Camera (EPIC) and the National Institute of Standards and Technology Advanced Radiometer (NISTAR). The purpose of this paper is to describe various capabilities of the DSCOVR EPIC instrument. EPIC views the entire sunlit Earth from sunrise to sunset at the backscattering direction (scattering angles between 168.5° and 175.5°) with 10 narrowband filters: 317, 325, 340, 388, 443, 552, 680, 688, 764, and 779 nm. We discuss a number of preprocessing steps necessary for EPIC calibration including the geolocation algorithm and the radiometric calibration for each wavelength channel in terms of EPIC counts per second for conversion to reflectance units. The principal EPIC products are total ozone (O3) amount, scene reflectivity, erythemal irradiance, ultraviolet (UV) aerosol properties, sulfur dioxide (SO2) for volcanic eruptions, surface spectral reflectance, vegetation properties, and cloud products including cloud height. Finally, we describe the observation of horizontally oriented ice crystals in clouds and the unexpected use of the O2 B-band absorption for vegetation properties.

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[1] Accurate observations of cloud microphysical properties are needed for evaluating and improving the representation of cloud processes in climate models and better estimate of the Earth radiative budget. However, large differences are found in current cloud products retrieved from ground-based remote sensing measurements using various retrieval algorithms. Understanding the differences is an important step to address uncertainties in the cloud retrievals. In this study, an in-depth analysis of nine existing ground-based cloud retrievals using ARM remote sensing measurements is carried out. We place emphasis on boundary layer overcast clouds and high level ice clouds, which are the focus of many current retrieval development efforts due to their radiative importance and relatively simple structure. Large systematic discrepancies in cloud microphysical properties are found in these two types of clouds among the nine cloud retrieval products, particularly for the cloud liquid and ice particle effective radius. Note that the differences among some retrieval products are even larger than the prescribed uncertainties reported by the retrieval algorithm developers. It is shown that most of these large differences have their roots in the retrieval theoretical bases, assumptions, as well as input and constraint parameters. This study suggests the need to further validate current retrieval theories and assumptions and even the development of new retrieval algorithms with more observations under different cloud regimes.