Predicting Launch Pad Winds at the Kennedy Space Center With a Neural Network Model (Paperback) (ISBN-13: 9781249601364)

This thesis uses neural networks to forecast winds at the Kennedy Space Center and the Cape Canaveral Air Station launch pads. Variables are developed from WINDS tower observations, surface and buoy observations, and an upper-air sounding. From these variables, a smaller set of predictive inputs is chosen using a signal-to-noise variable screening method. A neural network is then trained to forecast launch pad winds from the inputs. The network forecasts are compared to persistence, and peak wind predictions are found skillful compared to persistence. An ensemble modeling technique using Toth's and Kalnay's breeding of growing modes method is explored with neural networks. The inputs are perturbed an amount representative of measurement error. Ensemble member forecasts are found to diverge, but the ensemble spread does not often encompass the resulting weather. This is due to a disproportionate amount of error originating from the model compared to error originating from measurements.

This work has been selected by scholars as being culturally important, and is part of the knowledge base of civilization as we know it. This work was reproduced from the original artifact, and remains as true to the original work as possible. Therefore, you will see the original copyright references, library stamps (as most of these works have been housed in our most important libraries around the world), and other notations in the work.

This work is in the public domain in the United States of America, and possibly other nations. Within the United States, you may freely copy and distribute this work, as no entity (individual or corporate) has a copyright on the body of the work.

As a reproduction of a historical artifact, this work may contain missing or blurred pages, poor pictures, errant marks, etc. Scholars believe, and we concur, that this work is important enough to be preserved, reproduced, and made generally available to the public. We appreciate your support of the preservation process, and thank you for being an important part of keeping this knowledge alive and relevant.