Abstract

Abstract. The Multi-sensor Cloud and Aerosol Retrieval Simulator (MCARS) presently produces synthetic radiance data from Goddard Earth Observing System version 5 (GEOS-5) model output as if the Moderate Resolution Imaging Spectroradiometer (MODIS) were viewing a combination of atmospheric column inclusive of clouds, aerosols, and a variety of gases and land–ocean surface at a specific location. In this paper we use MCARS to study the MODIS Above-Cloud AEROsol retrieval algorithm (MOD06ACAERO). MOD06ACAERO is presently a regional research algorithm able to retrieve aerosol optical thickness over clouds, in particular absorbing biomass-burning aerosols overlying marine boundary layer clouds in the southeastern Atlantic Ocean. The algorithm's ability to provide aerosol information in cloudy conditions makes it a valuable source of information for modeling and climate studies in an area where current clear-sky-only operational MODIS aerosol retrievals effectively have a data gap between the months of June and October. We use MCARS for a verification and closure study of the MOD06ACAERO algorithm. The purpose of this study is to develop a set of constraints a model developer might use during assimilation of MOD06ACAERO data. Our simulations indicate that the MOD06ACAERO algorithm performs well for marine boundary layer clouds in the SE Atlantic provided some specific screening rules are observed. For the present study, a combination of five simulated MODIS data granules were used for a dataset of 13.5 million samples with known input conditions. When pixel retrieval uncertainty was less than 30 %, optical thickness of the underlying cloud layer was greater than 4, and scattering angle range within the cloud bow was excluded, MOD06ACAERO retrievals agreed with the underlying ground truth (GEOS-5 cloud and aerosol profiles used to generate the synthetic radiances) with a slope of 0.913, offset of 0.06, and RMSE=0.107. When only near-nadir pixels were considered (view zenith angle within ±20∘) the agreement with source data further improved (0.977, 0.051, and 0.096 respectively). Algorithm closure was examined using a single case out of the five used for verification. For closure, the MOD06ACAERO code was modified to use GEOS-5 temperature and moisture profiles as an ancillary. Agreement of MOD06ACAERO retrievals with source data for the closure study had a slope of 0.996 with an offset of −0.007 and RMSE of 0.097 at a pixel uncertainty level of less than 40 %, illustrating the benefits of high-quality ancillary atmospheric data for such retrievals.

Highlights

  • The MODerate resolution Imaging Spectroradiometer (MODIS) (Barnes et al, 1998) has proven to be an important sensor for aerosol data assimilation purposes for models such as the Goddard Earth Observing System Model, Version 5 (GEOS-5; Rienecker et al, 2008; Molod et al, 2012)

  • When pixel retrieval uncertainty was less than 30 %, optical thickness of the underlying cloud layer was greater than 4, and scattering angle range within the cloud bow was excluded, MOD06ACAERO retrievals agreed with the underlying ground truth (GEOS-5 cloud and aerosol profiles used to generate the synthetic radiances) with a slope of 0.913, offset of 0.06, and RMSE = 0.107

  • The Multi-sensor Cloud and Aerosol Retrieval Simulator (MCARS) is a tool that combines model output with a radiative transfer code in order to simulate radiances that may be measured by a remote-sensing instrument if it were passing over the model fields (Wind et al, 2013, 2016)

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Summary

Introduction

The MODerate resolution Imaging Spectroradiometer (MODIS) (Barnes et al, 1998) has proven to be an important sensor for aerosol data assimilation purposes for models such as the Goddard Earth Observing System Model, Version 5 (GEOS-5; Rienecker et al, 2008; Molod et al, 2012). G. Wind et al.: Analysis of the MODIS above-cloud aerosol retrieval algorithm using MCARS product files use a designation of MOD for Terra MODIS and MYD for Aqua MODIS. Due to aforementioned limitations of the standard Dark Target MODIS aerosol algorithm, a model that assimilates aerosol data from SEAO would have very few aerosol retrievals over the ocean available to it. The MOD06ACAERO algorithm (Meyer et al, 2015) fills in the aerosol data gap in SEAO as it is able to perform retrievals of aerosol properties above MBL clouds. The Multi-sensor Cloud and Aerosol Retrieval Simulator (MCARS) is a tool that combines model output with a radiative transfer code in order to simulate radiances that may be measured by a remote-sensing instrument if it were passing over the model fields (Wind et al, 2013, 2016).

MCARS description
MODIS above-cloud aerosol property product
Analysis
Findings
Conclusions and future directions
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