Preprints
https://doi.org/10.5194/amt-2020-507
https://doi.org/10.5194/amt-2020-507

  09 Feb 2021

09 Feb 2021

Review status: this preprint is currently under review for the journal AMT.

Efficient multi-angle polarimetric inversion of aerosols and ocean color powered by a deep neural network forward model

Meng Gao1,2, Bryan A. Franz1, Kirk Knobelspiesse1, Peng-Wang Zhai3, Vanderlei Martins3, Sharon Burton4, Brian Cairns5, Richard Ferrare4, Joel Gales1,6, Otto Hasekamp7, Yongxiang Hu4, Amir Ibrahim1,2, Brent McBride3, Anin Puthukkudy3, P. Jeremy Werdell1, and Xiaoguang Xu3 Meng Gao et al.
  • 1NASA Goddard Space Flight Center, Code 616, Greenbelt, Maryland 20771, USA
  • 2Science Systems and Applications, Inc., Greenbelt, MD, USA
  • 3JCET/Physics Department, University of Maryland, Baltimore County, Baltimore, MD 21250, USA
  • 4MS 475 NASA Langley Research Center, Hampton, VA 23681-2199, USA
  • 5NASA Goddard Institute for Space Studies, New York, NY 10025, USA
  • 6Science Applications International Corp., Greenbelt, MD, USA
  • 7Netherlands Institute for Space Research (SRON, NWO-I), Utrecht, The Netherlands

Abstract. NASA's Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission, scheduled for launch in the timeframe of 2023, will carry a hyperspectral Ocean Color Instrument (OCI) and two Multi-Angle Polarimeters (MAP): the UMBC Hyper-Angular Rainbow Polarimeter (HARP2) and the SRON Spectro-Polarimeter for Planetary EXploration one (SPEXone). The MAP measurements contain rich information on the microphysical properties of aerosols and hydrosols, and therefore can be used to retrieve accurate aerosol properties for complex atmosphere and ocean systems. Most polarimetric aerosol retrieval algorithms utilize vector radiative transfer models iteratively in an optimization approach, which leads to high computational costs that limit their usage in the operational processing of large data volumes acquired by the MAP imagers. In this work, we propose a deep neural network (NN) model to represent the radiative transfer simulation of coupled atmosphere and ocean systems, for applications to the HARP instrument. Through the evaluation of synthetic datasets for AirHARP (airborne version of HARP2), the NN model achieves a numerical accuracy smaller than the instrument uncertainties, with a running time of 0.01 s in a single CPU core or 1 ms in GPU. Using the NN as a forward model, we built an efficient joint aerosol and ocean color retrieval algorithm called FastMAPOL, evolved from the well-validated Multi-Angular Polarimetric Ocean coLor (MAPOL) algorithm. Retrievals of aerosol properties and water leaving signals were conducted on both the synthetic data and the AirHARP field measurements from the Aerosol Characterization from Polarimeter and Lidar (ACEPOL) campaign in 2017. From the validation with the synthetic data and the collocated High Spectral Resolution Lidar (HSRL) aerosol products, we demonstrated that the aerosol microphysical properties and water leaving signals can be retrieved efficiently and within acceptable error. The FastMAPOL algorithm can be used to operationally process the large volume of polarimetric data acquired by PACE and other future Earth observing satellite missions with similar capabilities.

Meng Gao et al.

Status: open (until 06 Apr 2021)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on amt-2020-507', Yingxi Shi, 23 Feb 2021 reply
    • AC1: 'Reply on RC1', Meng Gao, 23 Feb 2021 reply
      • RC2: 'Reply on AC1', Yingxi Shi, 23 Feb 2021 reply

Meng Gao et al.

Meng Gao et al.

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