You can view the full text of this article for free using the link below.

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

Authors: Patricia A. Quigley; Resit Unal; Paul W. Stackhouse Junior; Stephen J. Cox

Addresses: Science Systems and Applications, Inc. (SSAI), Hampton, VA 23666, USA ' Old Dominion University (ODU), Norfolk, VA 23529, USA ' National Aeronautics and Space Administration (NASA), Hampton, VA 23666, USA ' Science Systems and Applications, Inc. (SSAI), Hampton, VA 23666, USA

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.

Keywords: surface radiation budget; SRB; variability; design of experiments; DOE; augmented minimum point designs; GEWEX SRB.

DOI: 10.1504/IJCIS.2021.10031156

International Journal of Critical Infrastructures, 2021 Vol.17 No.1, pp.71 - 85

Received: 16 Jan 2020
Accepted: 15 Feb 2020

Published online: 19 Apr 2021 *

Full-text access for editors Access for subscribers Free access Comment on this article