From Quantification to Mitigation of Three-Dimensional Cloud Biases in Operational Passive Imagery Products
Abstract
Since the discovery of spatial inhomogeneity artifacts in cloud and aerosol products from passive imagery, it was known that satellite-derived cloud-aerosol radiative effects are prone to substantial biases. Therefore, significant efforts were made to quantify the resulting uncertainties for various manifestations of this problem. Recently, such efforts have gradually transitioned from quantification to mitigation of some of these biases. For example, an operational 3D algorithm might be within reach for passive trace gas spectroscopy from space (OCO-2, NASA's Orbiting Carbon Observatory), where biases in the CO2 volume mixing ratio caused by clouds in the vicinity of a satellite footprint can be mitigated. We present a vision of how this could be accomplished in practice. The prospect of feasible operational mitigation strategies for spectroscopy products gave new impetus for tackling the more difficult problem of spectrometry products such as the traditional cloud retrievals from MODIS and other imagers. We used large eddy simulations in conjunction with a 3D radiative transfer model to explore semi-empirical first-order bias corrections to imagery-derived cloud radiative effects based on observed spectral radiance perturbations that are associated with 3D effects. To do that, we developed an automated simulator, the Education and Research 3D Radiative Transfer Toolbox (EaR3T), which we plan to make publicly available. Our motivation is to empower the community to run 3D codes operationally in the not too distant future.
- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2019
- Bibcode:
- 2019AGUFM.A23T2979S
- Keywords:
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- 3311 Clouds and aerosols;
- ATMOSPHERIC PROCESSES;
- 3359 Radiative processes;
- ATMOSPHERIC PROCESSES;
- 3360 Remote sensing;
- ATMOSPHERIC PROCESSES