Sensitivity Analysis of Fourier Transformation Spectrometer: FTS Against Observation Noise on Retrievals of Carbon Dioxide and Methane
Journal Title: International Journal of Advanced Computer Science & Applications - Year 2012, Vol 3, Issue 11
Abstract
Sensitivity analysis of Fourier Transformation Spectrometer: FTS against observation noise on retrievals of carbon dioxide and methane is conducted. Through experiments with real observed data and additive noise, it is found that the allowable noise on FTS observation data is less than 2.1x10-5 if estimation accuracy of total column carbon dioxide and methane is better than 1(%).
Authors and Affiliations
Kohei Arai , Hiroshi Okumura , Takuya Fukamachi , Shuji Kawakami , Hirofumi Ohyama
Keywords
Related Articles
- EP ID EP92945
- DOI 10.14569/IJACSA.2012.031110
- Views 218
- Downloads 0
SentiNeural: A Depression Clustering Technique for Egyptian Women Sentiments
Online Sentiments Analysis is a trending research domain of study which is based on natural language processing, artificial intelligence, and computational linguistics. Negation sentiments usually are not included in se...
Innovative Automatic Discrimination Multimedia Documents for Indexing using Hybrid GMM-SVM Method
In this paper, a new parameterization method sound discrimination of multimedia documents based on entropy phase is presented to facilitate indexing audio documents and speed up their searches in digital libraries or the...
Circular Calibration of Depth Extraction in Stereo Configuration
Lens distortion is defined as departure from rectilinear projection of an imaging system which affects the accuracy of almost all vision applications. This work addresses the problem of distortion with investigating the...
Solving Nonlinear Eigenvalue Problems using an Improved Newton Method
Finding approximations to the eigenvalues of non-linear eigenvalue problems is a common problem which arises from many complex applications. In this paper, iterative algo-rithms for finding approximations to the eigenval...
Fast Efficient Clustering Algorithm for Balanced Data
The Cluster analysis is a major technique for statistical analysis, machine learning, pattern recognition, data mining, image analysis and bioinformatics. K-means algorithm is one of the most important clustering algorit...