Cloud Detection from Multi-Angular Polarimetric Satellite Measurements using a Neural Network Ensemble Approach

Abstract. This paper describes a neural network cloud masking scheme from PARASOL (Polarisation and Anisotropy of Reflectances for Atmospheric Science coupled with Observations from a Lidar) Multi-Angle Polarimetric measurements. The algorithm has been trained on synthetic measurements and has been applied to the processing of one year of PARASOL data. Comparisons of the retrieved cloud fraction with MODIS (Moderate Resolution Imaging Spectroradiometer) products show overall agreement in spatial and temporal patterns but the PARASOL-NN retrieves lower cloud fractions. Comparisons with a goodness-of-fit mask from aerosol retrievals suggest that the NN cloud mask flags less clear pixels as cloudy than MODIS (∼3 % of the clear pixels, versus ∼15 % by MODIS). On the other hand the NN classifies more pixels incorrectly as clear than MODIS (∼19 % by NN, versus ∼15 % by MODIS). Additionally, the NN and MODIS cloud mask have been applied to the aerosol retrievals from PARASOL using the Remote Sensing of Trace Gas and Aerosol Products (RemoTAP) algorithm. Validation with AERONET shows that the NN cloud mask performs comparably with MODIS in screening residual cloud contamination in retrieved aerosol properties. Our study demonstrates that cloud masking from MAP aerosol retrievals can be performed based on the MAP measurements themselves, making the retrievals independent of the availability of a cloud imager.