Abstract

Abstract. Aerosols play a critical role in radiative transfer within the atmosphere, and they have a significant impact on climate change. In this paper, we propose and implement a framework for developing an aerosol model using their microphysical properties. Such microphysical properties as the size distribution, the complex refractive index, and the percentage of sphericity are derived from the global AERosol RObotic NETwork (AERONET). These measurements, however, are typically retrieved when almucantar measurement procedures are performed (i.e., early mornings and late afternoons with clear sky) and might not have a temporal correspondence to a satellite overpass time, so a valid validation of satellite-derived products cannot be carried out. To address this problem of temporal inconsistency of satellite and ground-based measurements, we developed an approach to retrieve these microphysical properties (and the corresponding aerosol model) using the optical thickness at 440 nm, τ440, and the Ångström coefficient between 440 and 870 nm, α440–870. Such aerosol models were developed for 851 AERONET sites within the last 28 years. Obtained results suggest that empirically microphysical properties can be retrieved with uncertainties of up to 23 %. An exception is the imaginary part of the refractive index ni, for which the derived uncertainties reach up to 38 %. These specific parametric models of aerosol can be used for the studies when retrieval of microphysical properties is required as well as validation of satellite-derived products over land. Specifically, we demonstrate the usefulness of the aerosol models to validate surface reflectance records over land derived from optical remote sensing sensors. We then quantify the propagation of uncertainties in the surface reflectance due to uncertainties with the aerosol model retrieval that is used as a reference from radiative transfer simulations. Results indicate that individual aerosol microphysical properties can impact uncertainties in surface reflectance retrievals between 3.5 × 10−5 to 1 × 10−3 (in reflectance units). The overall impact of microphysical properties combined yields an overall uncertainty in surface reflectance < 0.004 (in reflectance units). That corresponds, for example, to 1 to 3 % of the retrieved surface reflectance in the red spectral band (620–670 nm) by the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument. These uncertainty values are well below the specification (0.005 + 0.05ρ; ρ is the retrieved surface reflectance) used for the MODIS atmospheric correction.

Highlights

  • AERosol RObotic NETwork (AERONET) estimates several microphysical properties of aerosols. These parameters are derived during the almucantar measurement procedures, which are typically carried out early morning and late afternoon under clear-sky conditions

  • We propose a method to retrieve microphysical properties using a parametric model with two variables: the optical thickness at 440 nm, τ440, and the Ångström coefficient between 440 and 870 nm, α440–870

  • In the last part of this paper, we evaluate the uncertainties in our aerosol microphysical properties according to the definition of the surface reflectance in the Moderate Resolution Imaging Spectroradiometer (MODIS) red band

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Summary

Introduction

Aerosols play a key role in the atmosphere as an important climate forcing in climate assessment (IPCC, 2018, 2019), and their better characterization would improve our knowledge of their properties for a better assessment of their impacts We use an indirect approach for the validation of satellite products from MODIS (Moderate Resolution Imaging Spectroradiometer) and VIIRS (Visible Infrared Imager Radiometer Suite; Vermote et al, 2002, 2014), for the NASA Harmonized Landsat 8 Sentinel-2 project (Vermote et al, 2016; Claverie et al, 2018), or for the CEOS ACIX working group for atmospheric correction intercomparison (Doxani et al, 2018) In the former, we compare a surface reflectance retrieved from satellite data to a surface reflectance reference determined from the top-of-atmosphere (TOA) reflectance corrected using the accurate radiative transfer 6SV code (Vermote et al, 1997; Kotchenova et al, 2006, 2008; Kotchenova and Vermote, 2007) and detailed measurements of the atmosphere. In the last part of this paper, we evaluate the uncertainties in our aerosol microphysical properties according to the definition of the surface reflectance (to be used as reference) in the MODIS red band

Aerosol microphysical description
Description of the dataset
Metrics used
Parameterization of the aerosol microphysical properties
Retrieved microphysical properties from the whole dataset
Retrieved microphysical properties considering each AERONET site
Impact of the uncertainties on the surface reflectance product over land
Findings
Conclusion
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