Retrieval of Liquid Water Cloud Properties from POLDER-3 Measurements Using a Neural Network Ensemble Approach

Di Noia, Antonio ; Hasekamp, Otto P ; van Diedenhoven, Bastiaan ; Zhang, Zhibo

关键词: ALGORITHMS;    ATMOSPHERIC PHYSICS;    BIAS;    CLIMATE CHANGE;    CLOUD PHYSICS;    DROP SIZE;    IMAGING SPECTROMETERS;    INFRARED RADIATION;    METEOROLOGY;    MODIS (RADIOMETRY);    RADIATIVE TRANSFER;    REMOTE SENSING;    SPATIAL RESOLUTION;    WATER;   

RP-ID  :  GSFC-E-DAA-TN66865

学科分类：地球科学(综合)

美国|英语

来源: NASA Technical Reports Server

【 摘 要 】

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 2µm 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.