Publication Date:
2018
Description:
〈b〉Retrieval of liquid water cloud properties from POLDER-3 measurements using a neural network ensemble approach〈/b〉〈br〉
Antonio Di Noia, Otto P. Hasekamp, Bastiaan van Diedenhoven, and Zhibo Zhang〈br〉
Atmos. Meas. Tech. Discuss., https//doi.org/10.5194/amt-2018-345,2018〈br〉
〈b〉Manuscript under review for AMT〈/b〉 (discussion: open, 0 comments)〈br〉
〈p〉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, on board 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 one year of POLDER-3 data. Comparisons of the retrieved cloud properties with Moderate resolution Imaging Spectroradiometer (MODIS) products show negative biases around −2 in retrieved cloud optical thicknesses (COTs) and between −1 and −2 μm in retrieved cloud effective radii. 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.〈/p〉
Print ISSN:
1867-1381
Electronic ISSN:
1867-8548
Topics:
Geosciences
