Forecasting Atmospheric Turbulence Conditions from Prior Environmental Parameters Using Artificial Neural Networks PDF Download
Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Forecasting Atmospheric Turbulence Conditions from Prior Environmental Parameters Using Artificial Neural Networks PDF full book. Access full book title Forecasting Atmospheric Turbulence Conditions from Prior Environmental Parameters Using Artificial Neural Networks by Mitchell Gene Grose. Download full books in PDF and EPUB format.
Author: Mitchell Gene Grose Publisher: ISBN: Category : Languages : en Pages : 99
Book Description
Optical (atmospheric) turbulence (Cn2) is a highly stochastic process that can apply many adverse effects on imaging and laser propagation systems. Modeling atmospheric turbulence conditions has been proposed by physics-based models but they are unable to capture the many cases. Recently, machine learning surrogate models have been used to learn the relationship between local environmental (weather) and turbulence conditions. These models predict a turbulence strength at time t from weather at time t. This thesis proposes a technique to forecast four hours of future turbulence conditions at 30-minute intervals from prior environmental parameters using artificial neural networks. First, local weather and turbulence measurements are formatted to pairs of input sequence and output forecast. Next, a grid search is performed to find the best combination of model architecture and training parameters. The architectures investigated are the Multilayer Perceptron (MLP) and three variants of the Recurrent Neural Network (RNN). Finally, the selected model is applied to the test dataset and analyzed. It is shown that the model has generally learned the relationship between prior environmental and future turbulence conditions.
Author: Mitchell Gene Grose Publisher: ISBN: Category : Languages : en Pages : 99
Book Description
Optical (atmospheric) turbulence (Cn2) is a highly stochastic process that can apply many adverse effects on imaging and laser propagation systems. Modeling atmospheric turbulence conditions has been proposed by physics-based models but they are unable to capture the many cases. Recently, machine learning surrogate models have been used to learn the relationship between local environmental (weather) and turbulence conditions. These models predict a turbulence strength at time t from weather at time t. This thesis proposes a technique to forecast four hours of future turbulence conditions at 30-minute intervals from prior environmental parameters using artificial neural networks. First, local weather and turbulence measurements are formatted to pairs of input sequence and output forecast. Next, a grid search is performed to find the best combination of model architecture and training parameters. The architectures investigated are the Multilayer Perceptron (MLP) and three variants of the Recurrent Neural Network (RNN). Finally, the selected model is applied to the test dataset and analyzed. It is shown that the model has generally learned the relationship between prior environmental and future turbulence conditions.
Author: Robert Sharman Publisher: Springer ISBN: 331923630X Category : Technology & Engineering Languages : en Pages : 529
Book Description
Anyone who has experienced turbulence in flight knows that it is usually not pleasant, and may wonder why this is so difficult to avoid. The book includes papers by various aviation turbulence researchers and provides background into the nature and causes of atmospheric turbulence that affect aircraft motion, and contains surveys of the latest techniques for remote and in situ sensing and forecasting of the turbulence phenomenon. It provides updates on the state-of-the-art research since earlier studies in the 1960s on clear-air turbulence, explains recent new understanding into turbulence generation by thunderstorms, and summarizes future challenges in turbulence prediction and avoidance.
Author: Sue Ellen Haupt Publisher: Springer Science & Business Media ISBN: 1402091192 Category : Science Languages : en Pages : 418
Book Description
How can environmental scientists and engineers use the increasing amount of available data to enhance our understanding of planet Earth, its systems and processes? This book describes various potential approaches based on artificial intelligence (AI) techniques, including neural networks, decision trees, genetic algorithms and fuzzy logic. Part I contains a series of tutorials describing the methods and the important considerations in applying them. In Part II, many practical examples illustrate the power of these techniques on actual environmental problems. International experts bring to life ways to apply AI to problems in the environmental sciences. While one culture entwines ideas with a thread, another links them with a red line. Thus, a “red thread“ ties the book together, weaving a tapestry that pictures the ‘natural’ data-driven AI methods in the light of the more traditional modeling techniques, and demonstrating the power of these data-based methods.
Author: Joao Luis Garcia Rosa Publisher: BoD – Books on Demand ISBN: 9535127047 Category : Computers Languages : en Pages : 416
Book Description
The idea of simulating the brain was the goal of many pioneering works in Artificial Intelligence. The brain has been seen as a neural network, or a set of nodes, or neurons, connected by communication lines. Currently, there has been increasing interest in the use of neural network models. This book contains chapters on basic concepts of artificial neural networks, recent connectionist architectures and several successful applications in various fields of knowledge, from assisted speech therapy to remote sensing of hydrological parameters, from fabric defect classification to application in civil engineering. This is a current book on Artificial Neural Networks and Applications, bringing recent advances in the area to the reader interested in this always-evolving machine learning technique.
Author: José Eduardo Souza De Cursi Publisher: Springer Nature ISBN: 3030536696 Category : Technology & Engineering Languages : en Pages : 472
Book Description
This proceedings book discusses state-of-the-art research on uncertainty quantification in mechanical engineering, including statistical data concerning the entries and parameters of a system to produce statistical data on the outputs of the system. It is based on papers presented at Uncertainties 2020, a workshop organized on behalf of the Scientific Committee on Uncertainty in Mechanics (Mécanique et Incertain) of the AFM (French Society of Mechanical Sciences), the Scientific Committee on Stochastic Modeling and Uncertainty Quantification of the ABCM (Brazilian Society of Mechanical Sciences) and the SBMAC (Brazilian Society of Applied Mathematics).
Author: National Research Council Publisher: National Academies Press ISBN: 030915183X Category : Science Languages : en Pages : 192
Book Description
More accurate forecasts of climate conditions over time periods of weeks to a few years could help people plan agricultural activities, mitigate drought, and manage energy resources, amongst other activities; however, current forecast systems have limited ability on these time- scales. Models for such climate forecasts must take into account complex interactions among the ocean, atmosphere, and land surface. Such processes can be difficult to represent realistically. To improve the quality of forecasts, this book makes recommendations about the development of the tools used in forecasting and about specific research goals for improving understanding of sources of predictability. To improve the accessibility of these forecasts to decision-makers and researchers, this book also suggests best practices to improve how forecasts are made and disseminated.
Author: Wayan Suparta Publisher: Springer ISBN: 3319284371 Category : Science Languages : en Pages : 124
Book Description
This book investigates tropospheric delays, one of the main error sources in Global Navigation Satellite Systems (GNSS), and its impact plays a crucial role in near real-time weather forecasting. Accessibility and accurate estimation of this parameter are essential for weather and climate research. Advances in GNNS application has allowed the measurements of Zenith Tropospheric Delay (ZTD) in all weather conditions and on a global scale with fine temporal and spatial resolution. However, GPS data are not always available for a full 24-hour period. Using a soft computing technique such as Adaptive Neuro-Fuzzy Inference System (ANFIS) as a new alternative, the ZTD can be determined by using the surface meteorological data as inputs. The estimation and prediction of ZTD value are presented in this book.
Author: Christopher D. Jellen Publisher: ISBN: Category : Atmospheric turbulence Languages : en Pages : 95
Book Description
"As a beam propagates, it is subject to fluctuations in the refractive index of air. These effects can be modeled as optical turbulence. Optical turbulence limits the effectiveness of laser-based weapons and communication systems employed by the United States Navy. Models developed to predict optical turbulence through the structure constant Cn2 are sensitive to absolute air temperature. Existing models have, however, failed to accurately predict the rapid beam attenuation and corresponding high values of Cn2 observed in maritime and near-maritime environments. In response, data-driven machine learning models were developed to predict the refractive index structure parameter Cn2, and to explore the importance of various environmental factors on its prediction. The current study uses 15 months of Cn2 field measurements collected along an 890 m scintillometer link over the Severn River at the United States Naval Academy. Measures of optical turbulence are complemented by corresponding measurements of 12 environmental parameters. Fully data-driven models were trained, developed, and tested to enhance Cn2 prediction accuracy in the near-maritime environment. Analysis of these models resulted in better understanding of the relative importance of each environmental parameter in accurately predicting Cn2. To our knowledge, this is the first application of purely data-driven machine learning models for predicting Cn2 in the near-maritime environment." -- Report Documentation Page [Standard Form 298 (Rev. 8-98)].
Author: Lei-Ming Ma Publisher: BoD – Books on Demand ISBN: 1838801111 Category : Science Languages : en Pages : 170
Book Description
Although the technology of observation and prediction of atmospheric systems draws upon many common fields, until now the interrelatedness and interdisciplinary nature of these research fields have scarcely been discussed in one volume containing fundamental theories, numerical methods, and operational application results. This is a book to provide in-depth explorations of the numerical methods developed to better understand atmospheric systems, which are introduced in eight chapters. Chapter 1 presents an efficient algorithm for tropical cyclone center determination by using satellite imagery. Chapter 2 aims to identify atmospheric systems with a new polarization remote sensing method. Chapters 3-8 place emphasis on enhancing the performance of numerical models in the prediction of atmospheric systems that should be valuable for researchers and forecasters.
Author: Mitchell Gene Grose
Author: Mitchell Gene Grose
Author: Robert Sharman
Author: Sue Ellen Haupt
Author: Joao Luis Garcia Rosa
Author: Yao Wang
Author: José Eduardo Souza De Cursi
Author: National Research Council
Author: Wayan Suparta
Author: Christopher D. Jellen
Author: Lei-Ming Ma