Design validation of an eye-safe scanning aerosol lidar with the Center for Lidar and Atmospheric Sciences Students (CLASS) at Hampton University

2002 ◽  
Author(s):  
Dale A. Richter ◽  
N. S. Higdon ◽  
Patrick L. Ponsardin ◽  
David Sanchez ◽  
Thomas H. Chyba ◽  
...  
Keyword(s):  
Design Validation ◽  
Atmospheric Sciences ◽  
Hampton University

The Hampton University Center for Lidar and Atmospheric Sciences Students

2001 ◽  
Author(s):  
Thomas Chyba ◽  
Doyle A. Temple ◽  
Scott Bailey ◽  
Arthur Bowman ◽  
Wei Gong ◽  
...  
Keyword(s):  
Atmospheric Sciences ◽  
University Center ◽  
Hampton University

Career plans of atmospheric sciences graduate students: Does gender matter?

2011 ◽  
Author(s):  
Carlie D. Trott ◽  
Silvia Sara Canetto ◽  
Jenifer Thomas ◽  
Cheryl A. Wynstra ◽  
Tess Stoops
Keyword(s):  
Graduate Students ◽  
Atmospheric Sciences ◽  
Career Plans

Development of a Hampton University Program for Novel Breast Cancer Imaging and Therapy Research

2013 ◽  
Author(s):  
Nicholas J. Kenney ◽  
Drew Weisenberger ◽  
Richard Brittan ◽  
Benjamin Welch
Keyword(s):  
Breast Cancer ◽  
Cancer Imaging ◽  
Breast Cancer Imaging ◽  
Hampton University

scholarly journals A Sequential Autoencoder for Teleconnection Analysis

2020 ◽  
Vol 12 (5) ◽  
pp. 851 ◽  
Author(s):  
Jiena He ◽  
J. Ronald Eastman
Keyword(s):  
Time Series ◽  
Neural Representation ◽  
Strongly Correlated ◽  
Atmospheric Sciences ◽  
Teleconnection Patterns ◽  
The Earth ◽  
Unsupervised Neural Network ◽  
Residual Series ◽  
Components Analysis ◽  
Computationally Intensive

Many aspects of the earth system are known to have preferred patterns of variability, variously known in the atmospheric sciences as modes or teleconnections. Approaches to discovering these patterns have included principal components analysis and empirical orthogonal teleconnection (EOT) analysis. The latter is very effective but is computationally intensive. Here, we present a sequential autoencoder for teleconnection analysis (SATA). Like EOT, it discovers teleconnections sequentially, with subsequent analyses being based on residual series. However, unlike EOT, SATA uses a basic linear autoencoder as the primary tool for analysis. An autoencoder is an unsupervised neural network that learns an efficient neural representation of input data. With SATA, the input is an image time series and the neural representation is a unidimensional time series. SATA then locates the 0.5% of locations with the strongest correlation with the neural representation and averages their temporal vectors to characterize the teleconnection. Evaluation of the procedure showed that it is several orders of magnitude faster than other approaches to EOT, produces teleconnection patterns that are more strongly correlated to well-known teleconnections, and is particularly effective in finding teleconnections with multiple centers of action (such as dipoles).

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