Preprints
https://doi.org/10.5194/amt-2022-112
https://doi.org/10.5194/amt-2022-112
 
05 May 2022
05 May 2022
Status: this preprint is currently under review for the journal AMT.

Effective uncertainty quantification for multi-angle polarimetric aerosol remote sensing over ocean, Part 1: performance evaluation and speed improvement

Meng Gao1,2, Kirk Knobelspiesse1, Bryan Franz1, Peng-Wang Zhai3, Andrew Sayer1,4, Amir Ibrahim1, Brian Cairns6, Otto Hasekamp7, Yongxiang Hu5, Vanderlei Martins3,4, Jeremy Werdell1, and Xiaoguang Xu3,4 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
  • 4Goddard Earth Sciences Technology and Research (GESTAR) II, University of Maryland, Baltimore County, Baltimore, MD 21250, USA
  • 5MS 475 NASA Langley Research Center, Hampton, VA 23681-2199, USA
  • 6NASA Goddard Institute for Space Studies, New York, NY 10025, USA
  • 7Netherlands Institute for Space Research (SRON, NWO-I), Utrecht, The Netherlands

Abstract. 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 nonlinearity of radiative transfer 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 several 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 technique to uncertainty evaluation 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, and guide MAP algorithm development for current and future satellite systems such as NASA’s Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission.

Meng Gao et al.

Status: open (until 10 Jun 2022)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse

Meng Gao et al.

Meng Gao et al.

Viewed

Total article views: 154 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
113 37 4 154 2 3
  • HTML: 113
  • PDF: 37
  • XML: 4
  • Total: 154
  • BibTeX: 2
  • EndNote: 3
Views and downloads (calculated since 05 May 2022)
Cumulative views and downloads (calculated since 05 May 2022)

Viewed (geographical distribution)

Total article views: 221 (including HTML, PDF, and XML) Thereof 221 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 25 May 2022
Download
Short summary
In this work, we assessed the pixel-wise retrieval uncertainties derived from multi-angle polarimetric measurements. Standard error propagation methods are used to compute the uncertainties. A flexible framework is proposed to evaluate how representative of these uncertainties comparing with real retrieval errors. Meanwhile, to assist operational data processing, we optimized the computational speed to evaluate the retrieval uncertainties based on neural network.