On an Enhanced PERSIANN-CCS Algorithm for Precipitation Estimation

By employing wavelet and selected features (WSF), median merging (MM), and selected curve-fitting (SCF) techniques, the Precipitation Estimation from Remotely Sensed Imagery using an Artificial Neural Networks Cloud Classification System (PERSIANN-CCS) has been improved. The PERSIANN-CCS methodology...

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Bibliographic Details
Published in:Journal of atmospheric and oceanic technology 2012-07, Vol.29 (7), p.922-932
Main Authors: Mahrooghy, Majid, Anantharaj, Valentine G., Younan, Nicolas H., Aanstoos, James, Hsu, Kuo-Lin
Format: Article
Language:English
Subjects:
Algorithms
Approximation
Artificial neural networks
Atmosphere
Bias
Classification
Cloud classification
Clouds
Curve fitting
Daily
Decomposition
Estimates
Feature extraction
Floods
Hourly rainfall
Image processing
Image segmentation
Marine
Methods
Neural networks
Precipitation
Precipitation estimation
Rain
Rainfall
Remote sensing
Satellite imagery
Satellites
Sensors
Standard deviation
Summer
Temperature
Thresholds
Time series
Wavelet transforms
Winter
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Summary:By employing wavelet and selected features (WSF), median merging (MM), and selected curve-fitting (SCF) techniques, the Precipitation Estimation from Remotely Sensed Imagery using an Artificial Neural Networks Cloud Classification System (PERSIANN-CCS) has been improved. The PERSIANN-CCS methodology includes the following four main steps: 1) segmentation of satellite cloud images into cloud patches, 2) feature extraction, 3) classification of cloud patches, and 4) derivation of the temperature–rain-rate (T–R) relationship for every cluster. The enhancements help improve step 2 by employing WSF, and step 4 by employing MM and SCF. For the study area herein, the results show that the enhanced methodology improves the equitable threat score (ETS) of the daily and hourly rainfall estimates mostly in the winter and fall. The ETS percentage improvement is about 20% for the daily (10% for hourly) estimates in the winter, 10% for the daily (8% for hourly) estimates in the fall, and at most 5% for the daily estimates in the summer at some rainfall thresholds. In the winter and fall, the area bias is improved almost at all rainfall thresholds for daily and hourly estimates. However, no significant improvement is obtained in the spring, and the area bias in the summer is also greater than that of the implemented PERSIANN-CCS algorithm.
ISSN:0739-0572
1520-0426
DOI:10.1175/JTECH-D-11-00146.1