Aerosol radiative forcing is a critical, though variable and uncertain, component of the global climate. Yet climate models rely on sparse information of the aerosol optical properties. In situ measurements, though important in many respects, seldom provide measurements of the undisturbed aerosol in the entire atmospheric column. Here, 8 yr of worldwide distributed data from the AERONET network of ground-based radiometers were used to remotely sense the aerosol absorption and other optical properties in several key locations. Established procedures for maintaining and calibrating the global network of radiometers, cloud screening, and inversion techniques allow for a consistent retrieval of the optical properties of aerosol in locations with varying emission sources and conditions. The multiyear, multi-instrument observations show robust differentiation in both the magnitude and spectral dependence of the absorption-a property driving aerosol climate forcing, for desert dust, biomass burning, urban-industrial, and marine aerosols. Moreover, significant variability of the absorption for the same aerosol type appearing due to different meteorological and source characteristics as well as different emission characteristics are observed. It is expected that this aerosol characterization will help refine aerosol optical models and reduce uncertainties in satellite observations of the global aerosol and in modeling aerosol impacts on climate.

Abstract. The problem of deriving a complete set of aerosol optical properties from Sun and sky radiance measurements is discussed. Algorithm development is focused on improving aerosol retrievals by means of including a detailed statistical optimization of the influence of noise in the inversion procedure. The methodological aspects of such an optimization are discussed in detail and revised according to both modern findings in inversion theory and practical experience in remote sensing. Accordingly, the proposed inversion algorithm is built on the principles of statistical estimation: the spectral radiances and various a priori constraints on aerosol characteristics are considered as multisource data that are known with predetermined accuracy. The inversion is designed as a search for the best fit of all input data by a theoretical model that takes into account the different levels of accuracy of the fitted data. The algorithm allows a choice of normal or lognormal noise assumptions. The multivariable fitting is implemented by a stable numerical procedure combining matrix inversion and univariant relaxation. The theoretical inversion scheme has been realized in the advanced algorithm retrieving aerosol size distribution together with complex refractive index from the spectral measurements of direct and diffuse radiation. The aerosol particles are modeled as homogeneous spheres. The atmospheric radiative transfer modeling is implemented with well-established publicly available radiative transfer codes. The retrieved refractive indices can be wavelength dependent; however, the extended smoothness constraints are applied to its spectral dependence (and indirectly through smoothness constraints on retrieved size distributions). The positive effects of statistical optimization on the retrieval results as well as the importance of applying a priori constraints are discussed in detail for the retrieval of both aerosol size distribution and complex refractive index. The developed algorithm is adapted for the retrieval of aerosol properties from measurements made by ground-based Sun-sky scanning radiometers used in the Aerosol Robotic Network (AERONET). The results of numerical tests together with examples of experimental data inversions are presented.

Abstract-TheIn addition to an extensive cloud mask, products include cloud-top properties (temperature, pressure, effective emissivity), cloud thermodynamic phase, cloud optical and microphysical parameters (optical thickness, effective particle radius, water path), as well as derived statistics. We will describe the various algorithms being used for the remote sensing of cloud properties from MODIS data with an emphasis on the pixel-level retrievals (referred to as Level-2 products), with 1-km or 5-km spatial resolution at nadir. An example of each Level-2 cloud product from a common data granule (5 min of data) off the coast of South America will be discussed. Future efforts will also be mentioned. Relevant points related to the global gridded statistics products (Level-3) are highlighted though additional details are given in an accompanying paper in this issue.

Abstract. Sensitivity studies are conducted regarding aerosol optical property retrieval from radiances measured by ground-based Sun-sky scanning radiometers of the Aerosol Robotic Network (AERONET). These studies focus on testing a new inversion concept for simultaneously retrieving aerosol size distribution, complex refractive index, and singlescattering albedo from spectral measurements of direct and diffuse radiation. The perturbations of the inversion resulting from random errors, instrumental offsets, and known uncertainties in the atmospheric radiation model are analyzed. Sun or sky channel miscalibration, inaccurate azimuth angle pointing during sky radiance measurements, and inaccuracy in accounting for surface reflectance are considered as error sources. The effects of these errors on the characterization of three typical and optically distinct aerosols with bimodal size distributions (weakly absorbing water-soluble aerosol, absorbing biomass-burning aerosol, and desert dust) are considered. The aerosol particles are assumed in the retrieval to be polydispersed homogeneous spheres with the same complex refractive index. Therefore we also examined how inversions with such an assumption bias the retrievals in the case of nonspherical dust aerosols and in the case of externally or internally mixed spherical particles with different refractive indices. The analysis shows successful retrieval of all aerosol characteristics (size distribution, complex refractive index, and single-scattering albedo), provided the inversion includes the data combination of spectral optical depth together with sky radiances in the full solar almucantar (with angular coverage of scattering angles up to 100 ø or more). The retrieval accuracy is acceptable for most remote sensing applications even in the presence of rather strong The difficulties in accessing the contribution of aerosols to radiative processes are caused by incomplete knowledge of aerosol macrophysical properties (sources, sinks, and loading) and of aerosol microphysical properties (composition, size distribution, chemical interaction, lifetime, and diurnal variation). The discrete spatial and temporal nature of both natural (e.g., volcanic eruption, wind-lofted (e.g., Saharan) dust, and sea spray) and anthropogenic aerosol injection (e.g., biomass burning and industrial pollution) makes the problem particularly challenging.

Abstract-Retrieving aerosol properties from satellite remote sensing over a bright surface is a challenging problem in the research of atmospheric and land applications. In this paper we propose a new approach to retrieve aerosol properties over surfaces such as arid, semiarid, and urban areas, where the surface reflectance is usually very bright in the red part of visible spectrum and in the near infrared, but is much darker in the blue spectral region (i.e., wavelength 500 nm). In order to infer atmospheric properties from these data, a global surface reflectance database of 0.1 latitude by 0.1 longitude resolution was constructed over bright surfaces for visible wavelengths using the minimum reflectivity technique (e.g., finding the clearest scene during each season for a given location). The aerosol optical thickness and aerosol type are then determined simultaneously in the algorithm using lookup tables to match the satellite observed spectral radiances. Examples of aerosol optical thickness derived using this algorithm over the Sahara Desert and Arabian Peninsula reveal various dust sources, which are important contributors to airborne dust transported over long distances.

A method is presented for determining the optical thickness and effective particle radius of stratiform cloud layers from reflected solar radiation measurements. A detailed study is presented which shows that the cloud optical thickness (7,) and effective particle radius (r,) of water clouds can be determined solely from reflection function measurements at 0.75 and 2. I6 pm, provided rc B 4 and rc Z 6 pm. For optically thin clouds the retrieval becomes ambiguous, resulting in two possible solutions for the effective radius and optical thickness. Adding a third channel near 1.65 am does not improve the situation noticeably, whereas the addition of a channel near 3.70 pm reduces the ambiguity in deriving the effective radius. The effective radius determined by the above procedure corresponds to the droplet radius at some optical depth within the cloud layer. For clouds having +< L 8, the effective radius determined using the 0.75 and 2.16 pm channels can be regarded as 85%95% of the radius at cloud top, which corresponds in turn to an optical depth 209'~40% of the total optical thickness of the cloud layer.

The MODIS Level-2 cloud product (Earth Science Data Set names MOD06 and MYD06 for Terra and Aqua MODIS, respectively) provides pixel-level retrievals of cloud-top properties (day and night pressure, temperature, and height) and cloud optical properties (optical thickness, effective particle radius, and water path for both liquid water and ice cloud thermodynamic phases-daytime only). Collection 6 (C6) reprocessing of the product was completed in May 2014 and March 2015 for MODIS Aqua and Terra, respectively. Here we provide an overview of major C6 optical property algorithm changes relative to the previous Collection 5 (C5) product. Notable C6 optical and microphysical algorithm changes include: (i) new ice cloud optical property models and a more extensive cloud radiative transfer code lookup table (LUT) approach, (ii) improvement in the skill of the shortwave-derived cloud thermodynamic phase, (iii) separate cloud effective radius retrieval datasets for each spectral combination used in previous collections, (iv) separate retrievals for partly cloudy pixels and those associated with cloud edges, (v) failure metrics that provide diagnostic information for pixels having observations that fall outside the LUT solution space, and (vi) enhanced pixel-level retrieval uncertainty calculations. The C6 algorithm changes collectively can result in significant changes relative to C5, though the magnitude depends on the dataset and the pixel's retrieval location in the cloud parameter space. Example Level-2 granule and Level-3 gridded dataset differences between the two collections are shown. While the emphasis is on the suite of cloud optical property datasets, other MODIS cloud datasets are discussed when relevant.

In this paper we describe each of these atmospheric data products, including characteristics of each of these products such as file size, spatial resolution used in producing the product, and data availability.