Optical Turbulence Model Evaluation
Download Optical Turbulence Model Evaluation full books in PDF, epub, and Kindle. Read online free Optical Turbulence Model Evaluation ebook anywhere anytime directly on your device. Fast Download speed and no annoying ads. We cannot guarantee that every ebooks is available!
Optical Turbulence Model for Laser Propagation and Imaging Applications
Author | : |
Publisher | : |
Total Pages | : 9 |
Release | : 2004 |
Genre | : |
ISBN | : |
We evaluate a simple model for predicting and understanding the structural behavior of Cn 2 for a specific location, date, time, and given environmental parameters. This model is compared with Cn 2 data taken at the Chesapeake Bay Detachment of the Naval Research Laboratory in Chesapeake Beach, Maryland. This simplified model predicts and explains the fluctuation in Cn 2 reasonably well, and also shows that Cn 2 is a strong function of solar irradiation.
Modeling Optical Turbulence with COAMPS During Two Observation Periods at Vandenberg AFB
Author | : Jimmy D. Horne, Jr. |
Publisher | : |
Total Pages | : 163 |
Release | : 2004-03-01 |
Genre | : |
ISBN | : 9781423514985 |
The objective of this thesis is to investigate the forecastability of optical turbulence using the U.S. Navy's Coupled Ocean Atmosphere Mesoscale Prediction System (COAMPS). First, a detailed synoptic study was performed over the Eastern Pacific region for observation periods in October 2001 and March 2002 to focus on mesoscale features affecting Vandenberg AFB. Second, a modified version of COAMPS version 2.0.16 model output was evaluated to ensure reasonable modeling of the mesoscale. Next, temperature and dewpoint temperature vertical profiles of COAMPS, modified with the Turbulent Kinetic Energy (TKE) Method, were compared with balloon-launched rawinsondes, initially, then with higher resolution thermosondes. Optical turbulence parameters were then calculated from the data and a comparison between synthetic profiles and thermosonde-derived profiles were qualitatively and quantitatively studied. Then the vertical resolution of the model was increased for selected forecasts to determine the potential for forecast improvement.
Assessment of Optical Turbulence Profiles Derived from Probabilistic Climatology
Author | : Brett W. Wisdom |
Publisher | : |
Total Pages | : 204 |
Release | : 2007 |
Genre | : Atmospheric turbulence |
ISBN | : |
Optical Turbulence
Author | : Elena Masciadri |
Publisher | : Imperial College Press |
Total Pages | : 415 |
Release | : 2009 |
Genre | : Science |
ISBN | : 1848164866 |
This book collects most of the talks and poster presentations presented at the Optical Turbulence OCo Astronomy meets Meteorology international conference held on 15OCo18 September, 2008 at Nymphes Bay, Alghero, Sardinia, Italy. The meeting aimed to deal with one of the major causes of wavefront perturbations limiting the astronomical high-angular-resolution observations from the ground. The uniqueness of this meeting has been the effort to attack this topic in a synergic and multidisciplinary approach promoting constructive discussions between the actors of this science OCo the astronomers, meteorologists, physicists of the atmosphere and the experts in adaptive optics and interferometry techniques whose main goal is to correct, in real-time, the wavefront perturbations induced by atmospheric turbulence to restore at the telescope foci the best available image quality. Sample Chapter(s). Chapter 1: Optical Turbulence in High Angular Resolution Techniques in Astronomy (494 KB). Contents: Optical Turbulence in High Angular Resolution Techniques in Astronomy (J M Beckers); Optical Turbulence Profiles at CTIO from a 12-Element Lunar Scintillometer (P Hickson et al.); High Resolution SLODAR Measurements on Mauna Kea (T Butterley et al.); How We Can Understand the Antarctic Atmospheric? (J W V Storey et al.); The Paranal Surface Layar (J Melnick et al.); Introduction to Data Assimilation in Meteorology (P Brousseau OC L Auger); The Mauna Kea Weather Center: A Case for Custom Seeing Forecasts (T Cherubini et al.); Dealing with Turbulence: MCAO Experience and Beyond (R Ragazzoni et al.); Future-Look Science Operations for the LBT (R F Green); Surface Layer SLODAR (J Osborn et al.); and other papers. Readership: Advanced undergraduates and graduate students, and physicists working in the field of astronomy.
The Refractive Index Structure Parameter/Atmospheric Optical Turbulence Model: CN2
Author | : Arnold Tunick |
Publisher | : |
Total Pages | : 30 |
Release | : 1998 |
Genre | : |
ISBN | : |
The CN2 model is a semi-empirical algorithm that makes a quantitative assessment of atmospheric optical turbulence. The algorithm uses surface layer gradient assumptions applied to two levels of discrete vertical profile data to calculate the refractive index structure parameter. Model results can be obtained for unstable, stable, and near-neutral atmospheric conditions. The CN2 model has been benchmarked on data from the REBAL'92 field study. The model will shortly be added to the Electro- Optics Atmospheric Effects Library (EOSAEL). This report gives technical and user's guide information on the CN2 model.
A Critical Evaluation of Various Turbulence Models as Applied to Internal Fluid Flows
Author | : M. Nallasamy |
Publisher | : |
Total Pages | : 80 |
Release | : 1985 |
Genre | : Computational fluid dynamics |
ISBN | : |
Modeling Optical Turbulence with Coamps During Two Observation Periods at Vandenberg AFB
Author | : |
Publisher | : |
Total Pages | : 143 |
Release | : 2004 |
Genre | : Atmospheric models |
ISBN | : |
The objective of this thesis is to investigate the forecastability of optical turbulence using the U.S. Navy's Coupled Ocean Atmosphere Mesoscale prediction System (COAMPS). First, a detailed synoptic study was performed over the Eastern Pacific region for observation periods in October 2001 and March 2002 to focus on mesoscale features affecting Vandenberg AFB. Second, a modified version of COAMPS version 2.0.16 model output was evaluated to ensure reasonable modeling of the mesoscale. Next, temperature and dewpoint temperature vertical profiles of COAMPS, modified with the Turbulent Kinetic Energy (TKE) Method, were compared with balloon-launched rawinsondes, initially, then with higher resolution thermosondes. Optical turbulence parameters were then calculated from the data and a comparison between synthetic profiles and thermosonde-derived profiles were qualitatively and quantitatively studied. Then the vertical resolution of the model was increased for selected forecasts to determine the potential for forecast improvement.
Machine Learning Enabled Prediction of Atmospheric Optical Turbulence from No-reference Imaging
Author | : Skyler P. Schork |
Publisher | : |
Total Pages | : 0 |
Release | : 2022 |
Genre | : Atmospheric turbulence |
ISBN | : |
Laser based communication and weapons systems are integral to maintaining the operational readiness and dominance of our Navy. Perhaps one of the most intransigent obstacles for such systems is the atmosphere. This is particularly true in the near-maritime environment. Atmospheric turbulence perturbs the propagation of laser beams as they are subject to fluctuations in the refractive index of air. As the beams travel through the atmosphere there is loss of irradiance on target, beam spread, beam wander, and intensity fluctuations of the propagating laser beam. The refractive index structure parameter, C2n, is a measure of the intensity of the optical turbulence along a path. If C2n can be easily and efficiently determined in an operating environment, the prediction of laser performance will be greatly enhanced. The goal of this research is to use image quality features in combination with machine learning techniques to accurately predict the refractive index structure parameter, C2n. In order to construct a machine learning model for the refractive index structure parameter, a series of image quality features were evaluated. Seven image quality features were selected, and have been applied to an image dataset of 34,000 individual exposures. This dataset, along with independently measured C2n values from a scintillometer as the supervised variable, were then used to train a variety of machine learning models. The models of particular interest to this research are the Generalized Linear Model, the Bagged Decision Tree, the Boosted Decision Tree, as well as the Random Forest Model. While the quantity of available training data had a significant impact on model performance, the findings indicate that image quality can be used to assist in the prediction of C2n, and that the machine learning models outperform the linear model.