Publication Abstracts

Di Noia et al. 2019

Di Noia, A., O.P. Hasekamp, B. van Diedenhoven, and Z. Zhang, 2019: Retrieval of liquid water cloud properties from POLDER-3 measurements using a neural network ensemble approach. Atmos. Meas. Tech., 12, 1697-1716, doi:10.5194/amt-12-1697-2019.

This paper describes a neural network algorithm for the estimation of liquid water cloud optical properties from the Polarization and Directionality of Earth's Reflectances-3 (POLDER-3) instrument aboard the Polarization & Anisotropy of Reflectances for Atmospheric Sciences coupled with Observations from a Lidar (PARASOL) satellite. The algorithm has been trained on synthetic multi-angle, multi-wavelength measurements of reflectance and polarization and has been applied to the processing of 1 year of POLDER-3 data. Comparisons of the retrieved cloud properties with Moderate Resolution Imaging Spectroradiometer (MODIS) products show that the neural network algorithm has a low bias of around 2 in cloud optical thickness (COT) and between 1 and 2 µm in the cloud effective radius. Comparisons with existing POLDER-3 datasets suggest that the proposed scheme may have enhanced capabilities for cloud effective radius retrieval, at least over land. An additional feature of the presented algorithm is that it provides COT and effective radius retrievals at the native POLDER-3 Level 1B pixel level.

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BibTeX Citation

@article{di00200u,
  author={Di Noia, A. and Hasekamp, O. P. and van Diedenhoven, B. and Zhang, Z.},
  title={Retrieval of liquid water cloud properties from POLDER-3 measurements using a neural network ensemble approach},
  year={2019},
  journal={Atmos. Meas. Tech.},
  volume={12},
  pages={1697--1716},
  doi={10.5194/amt-12-1697-2019},
}

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RIS Citation

TY  - JOUR
ID  - di00200u
AU  - Di Noia, A.
AU  - Hasekamp, O. P.
AU  - van Diedenhoven, B.
AU  - Zhang, Z.
PY  - 2019
TI  - Retrieval of liquid water cloud properties from POLDER-3 measurements using a neural network ensemble approach
JA  - Atmos. Meas. Tech.
VL  - 12
SP  - 1697
EP  - 1716
DO  - 10.5194/amt-12-1697-2019
ER  -

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