The Education and Research 3D Radiative Transfer Toolbox (EaR3T) – towards the mitigation of 3D bias in airborne and spaceborne passive imagery cloud retrievals
Authors: Chen Hong , Schmidt K. Sebastian , Massie Steven T. , Nataraja Vikas , Norgren Matthew S. , Gristey Jake J. , Feingold Graham , Holz Robert E. , Iwabuchi Hironobu 
Published: April 2023

Abstract. We introduce the Education and Research 3D Radiative Transfer Toolbox (EaR3T, pronounced []) 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 (3D-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 Orbiting Carbon Observatory 2 (OCO-2) and Moderate Resolution Imaging Spectroradiometer (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 colocated airborne measurements. In contrast to 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 Convolutional Neural Network (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.