#### Title

A Predictor Analysis Framework for Surface Radiation Budget Reprocessing Using Design of Experiments

#### Date of Award

Spring 2017

#### Document Type

Dissertation

#### Degree Name

Doctor of Philosophy (PhD)

#### Department

Engineering Management

#### Committee Director

Resit Unal

#### Committee Member

Steven T. Cotter

#### Committee Member

C. B. Daniels

#### Committee Member

Paul W. Stackhouse, Jr.

#### Abstract

Earth’s Radiation Budget (ERB) is an accounting of all incoming energy from the sun and outgoing energy reflected and radiated to space by earth’s surface and atmosphere. The National Aeronautics and Space Administration (NASA)/Global Energy and Water Cycle Experiment (GEWEX) Surface Radiation Budget (SRB) project produces and archives long-term datasets representative of this energy exchange system on a global scale. The data are comprised of the longwave and shortwave radiative components of the system and is algorithmically derived from satellite and atmospheric assimilation products, and acquired atmospheric data. It is stored as 3-hourly, daily, monthly/3-hourly, and monthly averages of 1°x1° grid cells.

Input parameters used by the algorithms are a key source of variability in the resulting output data sets. Sensitivity studies have been conducted to estimate the effects this variability has on the output data sets using linear techniques. This entails varying one input parameter at a time while keeping all others constant or by increasing all input parameters by equal random percentages, in effect changing input values for every cell for every three hour period and for every day in each month. This equates to almost 11 million independent changes without ever taking into consideration the interactions or dependencies among the input parameters. A more comprehensive method is proposed here for the evaluating the shortwave algorithm to identify both the input parameters and parameter interactions that most significantly affect the output data. This research utilized designed experiments that systematically and simultaneously varied all of the input parameters of the shortwave algorithm. A D-Optimal design of experiments (DOE) was chosen to accommodate the 14 types of atmospheric properties computed by the algorithm and to reduce the number of trials required by a full factorial study from millions to 128.

A modified version of the algorithm was made available for testing such that global calculations of the algorithm were tuned to accept information for a single temporal and spatial point and for one month of averaged data. The points were from each of four atmospherically distinct regions to include the Amazon Rainforest, Sahara Desert, Indian Ocean and Mt. Everest. The same design was used for all of the regions. Least squares multiple regression analysis of the results of the modified algorithm identified those parameters and parameter interactions that most significantly affected the output products.

It was found that Cosine solar zenith angle was the strongest influence on the output data in all four regions. The interaction of Cosine Solar Zenith Angle and Cloud Fraction had the strongest influence on the output data in the Amazon, Sahara Desert and Mt. Everest Regions, while the interaction of Cloud Fraction and Cloudy Shortwave Radiance most significantly affected output data in the Indian Ocean region.

Second order response models were built using the resulting regression coefficients. A Monte Carlo simulation of each model extended the probability distribution beyond the initial design trials to quantify variability in the modeled output data.

#### ISBN

9780355082395

#### Recommended Citation

Quigley, Patricia Allison, "A Predictor Analysis Framework for Surface Radiation Budget Reprocessing Using Design of Experiments" (2017). *Engineering Management & Systems Engineering Theses & Dissertations*. 13.

http://digitalcommons.odu.edu/emse_etds/13

#### ORCID

0000-0002-5480-2071

#### Included in

Atmospheric Sciences Commons, Systems Engineering Commons, Theory and Algorithms Commons