A predictor analysis framework for surface radiation budget reprocessing using satellite data

Abstract

Equipped with various types of imagers, lasers and radars, dozens of satellites orbit the earth every day collecting and relaying data for weather and atmospheric analysis, communication and navigation applications and planetary studies. Earth orbiting satellites are part of the critical space infrastructures. NASA's Global Energy and Water Cycle (GEWEX) surface radiation budget (SRB) shortwave algorithm derives long-term datasets from satellite data of the distribution of the sun's energy to the surface and back to space. This paper presents an analysis framework to describe propagation of input parameter variability to output data results in algorithmic computations, and then quantify the variability in the solution sets. The SRB shortwave algorithm and design of experiments (DOE) methods are utilised to determine significant input parameters and interactions. A sensitivity analysis is also conducted to determine the variability in the output data for each dependent variable varying within their range using Monte Carlo simulation.