Top-of-atmosphere flux retrievals from CERES using artificial neural networks

Abstract

The Clouds and the Earth's Radiant Energy System (CERES) instruments on the Terra spacecraft provide accurate shortwave (SW), longwave (LW) and window (WN) region top-of-atmosphere (TOA) radiance measurements from which TOA radiative flux values are obtained by applying Angular Distribution Models (ADMs). These models are developed empirically as functions of the surface and cloud properties provided by coincident high-resolution imager measurements over CERES field-of-view. However, approximately 5.6% of the CERES/ Terra footprints lack sufficient imager information for a reliable scene identification. To avoid any systematic biases in regional mean radiative fluxes, it is important to provide TOA fluxes for these footprints. For this purpose, we apply a feedforward error-backpropagation Artificial Neural Network (ANN) technique to reproduce CERES/ Terra ADMs relying only on CERES measurements. All-sky ANN-based angular distribution models are developed for 10 surface types separately for shortwave, longwave and window TOA flux retrievals. To optimize the ANN performance, we use a partially connected first hidden neuron layer and compact training sets with reduced data noise. We demonstrate the performance of the ANN-based ADMs by comparing TOA fluxes inferred from ANN and CERES anisotropic factors. The global annual average bias in ANN-derived fluxes relative to CERES is less than 0.5% for all ANN scene types. The maximum bias occurs over sea ice and permanent snow surfaces. For all surface types, instantaneous ANN-derived TOA fluxes are self-consistent in viewing zenith angle to within 9% for shortwave, 3.5% and 3% longwave daytime and nighttime, respectively.