Preprints
https://doi.org/10.5194/amt-2022-143
https://doi.org/10.5194/amt-2022-143
 
29 Jun 2022
29 Jun 2022
Status: this preprint is currently under review for the journal AMT.

The Education and Research 3D Radiative Transfer Toolbox (EaR3T) – Towards the Mitigation of 3D Bias in Airborne and Spaceborne Passive Imagery Cloud Retrievals

Hong Chen1,2, Sebastian Schmidt1,2, Steven T. Massie2, Vikas Nataraja2, Matthew S. Norgren2, Jake J. Gristey3,4, Graham Feingold4, Robert E. Holz5, and Hironobu Iwabuchi6 Hong Chen et al.
  • 1Department of Atmospheric and Oceanic Sciences, University of Colorado, Boulder, CO, USA
  • 2Laboratory for Atmospheric and Space Physics, University of Colorado, Boulder, CO, USA
  • 3Cooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder, CO, USA
  • 4NOAA Chemical Sciences Laboratory, Boulder, CO, USA
  • 5Space Science and Engineering Center, University of Wisconsin–Madison, Madison, WI, USA
  • 6Center for Atmospheric and Oceanic Studies, Tohoku University, Sendai, Miyagi, Japan

Abstract. We introduce the Education and Research 3D Radiative Transfer Toolbox (EaR3T) for quantifying and mitigating artifacts in atmospheric radiation science algorithms due to spatially inhomogeneous clouds and surfaces, and show the benefits of automated, realistic radiance and irradiance generation along extended satellite orbits, flight tracks from entire aircraft field missions, and synthetic data generation from model data. EaR3T is a modularized Python package that provides high-level interfaces to automate the process of 3D radiative transfer (RT) calculations. After introducing the package, we present initial findings from four applications, which are intended as blueprints to future in-depth scientific studies. The first two applications use EaR3T as a satellite radiance simulator for the NASA OCO-2 and MODIS missions, which generate synthetic satellite observations with 3D-RT on the basis of cloud field properties from imagery-based retrievals and other input data. In the case of inhomogeneous cloud fields, we show that the synthetic radiances are often inconsistent with the original radiance measurements. This lack of radiance consistency points to biases in heritage imagery cloud retrievals due to sub-pixel resolution clouds and 3D-RT effects. They come to light because the simulator’s 3D-RT engine replicates processes in nature that conventional 1D-RT retrievals do not capture. We argue that 3D radiance consistency (closure) can serve as a metric for assessing the performance of a cloud retrieval in presence of spatial cloud inhomogeneity even with limited independent validation data. The other two applications show how airborne measured irradiance data can be used to independently validate imagery-derived cloud products via radiative closure in irradiance. This is accomplished by simulating downwelling irradiance from geostationary cloud retrievals of Advanced Himawari Imager (AHI) along all the below-cloud aircraft flight tracks of the Cloud, Aerosol and Monsoon Processes Philippines Experiment (CAMP2Ex, NASA 2019), and comparing the irradiances with the collocated airborne measurements. In contrast to isolated case studies in the past, EaR3T facilitates the use of observations from entire field campaigns for the statistical validation of satellite-derived irradiance. From the CAMP2Ex mission, we find a low bias of 10 % in the satellite-derived cloud transmittance, which we are able to attribute to a combination of the coarse resolution of the geostationary imager and 3D-RT biases. Finally, we apply a recently developed context-aware CNN cloud retrieval framework to high-resolution airborne imagery from CAMP2Ex and show that the retrieved cloud optical thickness fields lead to better 3D radiance consistency than the heritage independent pixel algorithm, opening the door to future mitigation of 3D-RT cloud retrieval biases.

Hong Chen et al.

Status: open (until 09 Aug 2022)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on amt-2022-143', Hartwig Deneke, 20 Jul 2022 reply

Hong Chen et al.

Hong Chen et al.

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Short summary
We introduce the Education and Research 3D Radiative Transfer Toolbox (EaR3T) for quantifying and mitigating artifacts in atmospheric radiation science algorithms due to spatially inhomogeneous clouds and surfaces, and show the benefits of automated, realistic radiance and irradiance generation along extended satellite orbits, flight tracks from entire aircraft field missions, and synthetic data generation from model data.