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A neural network is presented for estimating cloud water and ice paths, effective scattering heights of cloud water and ice, and column water vapor. The cloud water and ice are then used to classify scenes as either clear or cloudy using a simple threshold test of 2 gm −2 for water and 10 gm −2 for ice. Training of the neural networks was performed using high resolution spectra in micro-windows of O 2 and CO 2 near infrared absorption bands generated from an ensemble of analyzed meteorological fields from ECMWF and surface properties from MODIS. An independent test data set was generated using the same radiative transfermodel, but coupled with atmospheric profiles derived from CloudSat and Calipso data. Analysis indicates that the algorithmprovides approximately 75-90% accuracy with a 95-99% confidence level for classifying scenes as either cloudy or clear over land surfaces in nadir viewing geometry. These estimates are shown to be robust, in the sense that they are insensitive to realistic instrumental errors, errors in the meteorological analyses and surface properties, and errors in the simulations used for training.

journal of applied remote sensing

Neural network cloud screening algorithm, Part I: A synthetic case over land using micro-windows in O&lt;sub&gt;2&lt;/sub&gt;&amp;gt; and CO&lt;sub&gt;2&lt;/sub&gt;&amp;gt; near infrared absorption bands with nadir viewing