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Estimating and Forecasting Optical T...

North Carolina State University.

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Estimating and Forecasting Optical Turbulence in Atmosphere Using an Artificial Neural Network Approach.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Estimating and Forecasting Optical Turbulence in Atmosphere Using an Artificial Neural Network Approach./
作者:	Wang, Yao.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2016,
面頁冊數:	165 p.
附註:	Source: Dissertation Abstracts International, Volume: 78-08(E), Section: B.
Contained By:	Dissertation Abstracts International78-08B(E).
標題:	Atmospheric sciences. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10583589
ISBN:	9781369622683

Estimating and Forecasting Optical Turbulence in Atmosphere Using an Artificial Neural Network Approach.
Wang, Yao.

Estimating and Forecasting Optical Turbulence in Atmosphere Using an Artificial Neural Network Approach. - Ann Arbor : ProQuest Dissertations & Theses, 2016 - 165 p.

Source: Dissertation Abstracts International, Volume: 78-08(E), Section: B.

Thesis (Ph.D.)--North Carolina State University, 2016.

Predicting the atmospheric structure parameter of refractive index, C 2n has always been a challenging task. The C2 n parameter has become significant in a number of applications, ranging from civil to military. In this study, an artificial neural network (ANN) approach is proposed for the prediction of C2n profiles, and tested at multiple locations. Mean values of meteorological variables are used as the inputs. Observed C2n values from a field campaign over Mauna Kea, Hawaii and a Master database (measured profiles from all over the world) are utilized for validation tests. This ANN approach is found to outperform an existing statistical-based formulation.

ISBN: 9781369622683Subjects--Topical Terms:

3168354
Atmospheric sciences.

Estimating and Forecasting Optical Turbulence in Atmosphere Using an Artificial Neural Network Approach.
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300 $a 165 p.
500 $a Source: Dissertation Abstracts International, Volume: 78-08(E), Section: B.
500 $a Adviser: Sukanta Basu.
502 $a Thesis (Ph.D.)--North Carolina State University, 2016.
520 $a Predicting the atmospheric structure parameter of refractive index, C 2n has always been a challenging task. The C2 n parameter has become significant in a number of applications, ranging from civil to military. In this study, an artificial neural network (ANN) approach is proposed for the prediction of C2n profiles, and tested at multiple locations. Mean values of meteorological variables are used as the inputs. Observed C2n values from a field campaign over Mauna Kea, Hawaii and a Master database (measured profiles from all over the world) are utilized for validation tests. This ANN approach is found to outperform an existing statistical-based formulation.
520 $a During the development of the ANN approach, the limitations of several conventional metrics for validating C2n profile predictions have been documented using both idealized and realistic case studies. Based on those studies, the (normalized) Kantorovich metric is introduced and found to be a powerful alternative. It has been used to demonstrated the advantage of using ANN approach.
520 $a The ANN approach also provides the prediction of C2 n time series in the atmospheric surface layer. Five routinely available meteorological variables are used as the inputs. One of the case studies uses observed C2n data near the Mauna Loa observatory, Hawaii and another utilizes a dataset from Lubbock, TX for validation. A study that ranks the ANN sensitivity for the several measured parameters is based on the TX dataset. Interestingly, this ANN approach is found to outperform a widely used similarity theory-based formulation for a range of atmospheric conditions (including strongly stratified conditions).
520 $a The ANN model also uses the outputs from the Weather Research Forecasting (WRF) model as inputs to forecast temporal evolution of C2 n within the atmospheric surface layer. The WRF model is used to generate the meteorological inputs, including mean values of the parameters and the flux variables, under different boundary layer conditions of Hawaii case study using different boundary layer schemes. Based on the improved results, we suggest that this WRF-ANN framework should be considered for the future applications.
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