Publication Abstracts

Gao et al. 2022

Gao, M., K. Knobelspiesse, B. Franz, P.-W. Zhai, A. Sayer, A. Ibrahim, B. Cairns, O. Hasekamp, Y. Hu, V. Martins, J. Werdell, and X. Xu, 2022: Effective uncertainty quantification for multi-angle polarimetric aerosol remote sensing over ocean, Part 1: Performance evaluation and speed improvement. Atmos. Meas. Tech., 15, no. 16, 4859-4879, doi:10.5194/amt-15-4859-2022.

Multi-angle polarimetric (MAP) measurements can enable detailed characterization of aerosol microphysical and optical properties and improve atmospheric correction in ocean color remote sensing. Advanced retrieval algorithms have been developed to obtain multiple geophysical parameters in the atmosphere-ocean system. Theoretical pixel-wise retrieval uncertainties based on error propagation have been used to quantify retrieval performance and determine the quality of data products. However, standard error propagation techniques in high-dimensional retrievals may not always represent true retrieval errors well due to issues such as local minima and the nonlinear dependence of the forward model on the retrieved parameters near the solution. In this work, we analyze these theoretical uncertainty estimates and validate them using a flexible Monte Carlo approach. The Fast Multi-Angular Polarimetric Ocean coLor (FastMAPOL) retrieval algorithm, based on efficient neural network forward models, is used to conduct the retrievals and uncertainty quantification on both synthetic HARP2 (Hyper-Angular Rainbow Polarimeter 2) and AirHARP (airborne version of HARP2) datasets. In addition, for practical application of the uncertainty evaluation technique in operational data processing, we use the automatic differentiation method to calculate derivatives analytically based on the neural network models. Both the speed and accuracy associated with uncertainty quantification for MAP retrievals are addressed in this study. Pixel-wise retrieval uncertainties are further evaluated for the real AirHARP field campaign data. The uncertainty quantification methods and results can be used to evaluate the quality of data products, as well as guide MAP algorithm development for current and future satellite systems such as NASA's Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission.

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BibTeX Citation

@article{ga07300l,
  author={Gao, M. and Knobelspiesse, K. and Franz, B. and Zhai, P.-W. and Sayer, A. and Ibrahim, A. and Cairns, B. and Hasekamp, O. and Hu, Y. and Martins, V. and Werdell, J. and Xu, X.},
  title={Effective uncertainty quantification for multi-angle polarimetric aerosol remote sensing over ocean, Part 1: Performance evaluation and speed improvement},
  year={2022},
  journal={Atmos. Meas. Tech.},
  volume={15},
  number={16},
  pages={4859--4879},
  doi={10.5194/amt-15-4859-2022},
}

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RIS Citation

TY  - JOUR
ID  - ga07300l
AU  - Gao, M.
AU  - Knobelspiesse, K.
AU  - Franz, B.
AU  - Zhai, P.-W.
AU  - Sayer, A.
AU  - Ibrahim, A.
AU  - Cairns, B.
AU  - Hasekamp, O.
AU  - Hu, Y.
AU  - Martins, V.
AU  - Werdell, J.
AU  - Xu, X.
PY  - 2022
TI  - Effective uncertainty quantification for multi-angle polarimetric aerosol remote sensing over ocean, Part 1: Performance evaluation and speed improvement
JA  - Atmos. Meas. Tech.
VL  - 15
IS  - 16
SP  - 4859
EP  - 4879
DO  - 10.5194/amt-15-4859-2022
ER  -

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