Reply on AC1

Abstract

Abstract. NASA's Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission, scheduled for launch in the timeframe of 2023, will carry a hyperspectral scanning radiometer named the Ocean Color Instrument (OCI) and two multi-angle polarimeters (MAPs): 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) forward model to represent the radiative transfer simulation of coupled atmosphere and ocean systems for applications to the HARP2 instrument and its predecessors. 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 a 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. Comparing to the retrieval speed using a conventional radiative transfer forward model, the computational acceleration is 103 times faster with CPU or 104 times with GPU processors. 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.

Authors 16

1. Meng Gao corresponding

   Goddard Space Flight Center · Science Systems and Applications (United States)

   Affiliation as printed

   Ocean Ecology Laboratory – Code 616, NASA Goddard Space Flight Center, Greenbelt, Maryland 20771, USA

   Science Systems and Applications, Inc., Greenbelt, MD, USA

2. Bryan A. Franz

   Goddard Space Flight Center

   Affiliation as printed

   Ocean Ecology Laboratory – Code 616, NASA Goddard Space Flight Center, Greenbelt, Maryland 20771, USA

3. Kirk David Knobelspiesse

   Goddard Space Flight Center

   Affiliation as printed

   Ocean Ecology Laboratory – Code 616, NASA Goddard Space Flight Center, Greenbelt, Maryland 20771, USA

4. Peng‐Wang Zhai

   University of Maryland, Baltimore County

   Affiliation as printed

   JCET and Physics Department, University of Maryland, Baltimore County, Baltimore, MD 21250, USA

5. Vanderlei Martins

   University of Maryland, Baltimore County

   Affiliation as printed

   JCET and Physics Department, University of Maryland, Baltimore County, Baltimore, MD 21250, USA

6. Sharon Burton

   Langley Research Center

   Affiliation as printed

   MS 475, NASA Langley Research Center, Hampton, VA 23681-2199, USA

7. Brian Cairns

   Goddard Institute for Space Studies

   Affiliation as printed

   NASA Goddard Institute for Space Studies, New York, NY 10025, USA

8. Richard A. Ferrare

   Langley Research Center

   Affiliation as printed

   MS 475, NASA Langley Research Center, Hampton, VA 23681-2199, USA

9. Joel Gales

   Goddard Space Flight Center

   Affiliation as printed

   Ocean Ecology Laboratory – Code 616, NASA Goddard Space Flight Center, Greenbelt, Maryland 20771, USA

   Science Applications International Corp., Greenbelt, MD, USA

10. Otto Hasekamp Aachen Netherlands Institute for Space Research (SRON

    Space Research Organisation Netherlands

    Affiliation as printed

    Netherlands Institute for Space Research (SRON, NWO-I), Utrecht, the Netherlands

11. Yongxiang Hu

    Langley Research Center

    Affiliation as printed

    MS 475, NASA Langley Research Center, Hampton, VA 23681-2199, USA

12. Amir Ibrahim

    Goddard Space Flight Center · Science Systems and Applications (United States)

    Affiliation as printed

    Ocean Ecology Laboratory – Code 616, NASA Goddard Space Flight Center, Greenbelt, Maryland 20771, USA

    Science Systems and Applications, Inc., Greenbelt, MD, USA

13. Brent McBride

    Science Systems and Applications (United States) · University of Maryland, Baltimore County

    Affiliation as printed

    JCET and Physics Department, University of Maryland, Baltimore County, Baltimore, MD 21250, USA

    Science Systems and Applications, Inc., Greenbelt, MD, USA

14. Anin Puthukkudy

    University of Maryland, Baltimore County

    Affiliation as printed

    JCET and Physics Department, University of Maryland, Baltimore County, Baltimore, MD 21250, USA

15. P. Jeremy Werdell

    Goddard Space Flight Center

    Affiliation as printed

    Ocean Ecology Laboratory – Code 616, NASA Goddard Space Flight Center, Greenbelt, Maryland 20771, USA

16. Xiaoguang Xu

    University of Maryland, Baltimore County

    Affiliation as printed

    JCET and Physics Department, University of Maryland, Baltimore County, Baltimore, MD 21250, USA