By Topic

A New Technique for Temperature and Humidity Profile Retrieval From Infrared-Sounder Observations Using the Adaptive Neuro-Fuzzy Inference System

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

6 Author(s)
Kottayil S. Ajil ; Department of Space Science, Lulea Technical University, Lulea, Sweden ; Pradeep Kumar Thapliyal ; Munn V. Shukla ; Pradip K. Pal
more authors

The accuracy of the atmospheric profiles of temperature and humidity, retrieved from infrared-sounder observations using physical retrieval algorithms, depends directly on the quality of the first-guess profiles. In the past, forecasts from the numerical-weather-prediction models were extensively used as the first guess. During the past few years, the first guess for physical retrieval is being estimated using regression techniques from sounder observations. In this study, a new nonlinear technique has been described to improve the first guess using simulated infrared brightness temperatures for GOES-12 Sounder channels. The present technique uses fuzzy logic and data clustering to establish a relationship between simulated sounder observations and atmospheric profiles. This relationship is further strengthened using the Adaptive Neuro-Fuzzy Inference System (ANFIS) by fine-tuning the existing fuzzy-rule base. The results of ANFIS retrieval have been compared with those from the nonlinear (polynomial) regression retrieval. It has been found that ANFIS is more robust and reduces root mean squared error by 20% in humidity profile retrieval compared with the nonlinear regression technique. In addition, it has been shown that the ANFIS technique has an added advantage of its global application without any need for classification of the training data that is required in the regression techniques.

Published in:

IEEE Transactions on Geoscience and Remote Sensing  (Volume:48 ,  Issue: 4 )