Electrical & Computer Engineering High Resolution Satellite Precipitation Estimate Using Cluster Ensemble Cloud Classification Majid Mahrooghy, Nicolas H. Younan, Valentine G. Anantharaj, and James Aanstoos Mississippi State University Mississippi State, MS 39762 IGARSS, July 2011 Vancouver, Canada
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High Resolution Satellite Precipitation Estimate Using Cluster Ensemble Cloud Classification
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Electrical & Computer Engineering
High Resolution Satellite Precipitation
Estimate Using Cluster Ensemble
Cloud Classification
Majid Mahrooghy, Nicolas H. Younan, Valentine
G. Anantharaj, and James Aanstoos
Mississippi State University
Mississippi State, MS 39762
IGARSS, July 2011 Vancouver, Canada
Electrical & Computer Engineering
Outline
• Background
• Existing Methods
• Methodology
• Results
• Summary
Electrical & Computer Engineering
Background
• Rainfall estimation (RE) at high spatial and temporal resolutions is beneficial for research and applications
• Ground-based (RE)
– Facilitate routine monitoring of rainfall
– Coverage is not available over all regions
– Coverage is not spatially and temporally uniform
– RE over the oceans is also important for climate studies, which cannot be provided
• Satellite-based
– Monitor the earth’s environment regularly with wide coverage
– Offers a viable solution for monitoring global precipitation patterns at sufficient spatial and temporal resolutions
• This matrix represents an association degree between each sample
and each cluster of the base clustering.
3) Applying a consensus function to obtain final clustering
• In this work, the consensus function is a graph-based clustering so
the cluster-association matrix is transformed to the weighted
bipartite graph, and then spectral graph partitioning (SPEC) is
performed.
Electrical & Computer Engineering,
𝑅𝑀 𝑥𝑖 , 𝑐𝑙 = 1 𝑖𝑓 𝑐𝑙 = 𝐶∗
𝑠𝑖𝑚 𝑐𝑙, 𝐶∗ 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒
𝑠𝑖𝑚 𝐶𝑖 , 𝐶𝑗 = 𝑊𝐶𝑇𝑖𝑗
𝑊𝐶𝑇𝑚𝑎𝑥 × 𝐷𝐶
𝑊𝐶𝑇𝑖𝑗 = 𝑊𝐶𝑇𝑖𝑗𝑘𝑞
𝑘=1 ,
𝑊𝐶𝑇𝑖𝑗 𝑘 = 𝑚𝑖𝑛 𝑤𝑖𝑘 , 𝑤𝑗𝑘 ,
and 𝑤𝑖𝑗 = 𝐿𝑖∩𝐿𝑗
𝐿𝑖∪𝐿𝑗 . 𝐿𝑖 denotes the samples belonging to cluster 𝐶𝑖 , and q
represents all triples between the 𝐶𝑖 and 𝐶𝑗 . DC is also a constant delay
factor .
Link-based Cluster Ensemble
Electrical & Computer Engineering
Data
• Area of Study : United States extending between 30N to 38N
and -95E to - 85E
• Testing Time : Winter 2008 (Jan, Feb)
• Training Time : one month before the respective testing
month
• Training data : IR (GOES-12), The National Weather
Service Next Generation Weather Radar (NEXRAD) Stage
IV precipitation products
• Testing data : IR (GOES-12)
• Validation data : NEXRAD Stage IV
Electrical & Computer Engineering
Results - Segmentation
Electrical & Computer Engineering
Results: Temperature-RainRate
Electrical & Computer Engineering
Comparison Results (Hourly Estimate)
Estimated hourly rainy area ending at 1500 UTC on February 6, 2008:
(a) LCE-based; (b) SOM-based; (c) PERSIANN-CCS; and (d) NEXRAD-Stage IV
Electrical & Computer Engineering
Results: Validation
Verification result for January through March 2008:
(a) False Alarm ratio; (b) Probability of Detection; and (c) Heidke Skill Score
Electrical & Computer Engineering
Summary
A link-based cluster ensemble method is incorporated into a high resolution precipitation estimation (PERSIANN_CCS) to enhance rainfall estimation
In comparison with the SOM-based and the PERSIANN-CCS algorithms, the cluster ensemble method improves the POD and HSS at all rainfall thresholds
This improvement is about 12% for POD and 5% to 7% for HSS at medium and high level rainfall thresholds for winter 2008
Electrical & Computer Engineering
References• Y. Hong, K. L. Hsu, S. Sorooshian, and X. G. Gao, “Precipitation Estimation from Remotely Sensed Imagery using
an Artificial Neural Network Cloud Classification System,” Journal of Applied Meteorology, vol. 43, pp. 1834-1852, 2004
• R. J. Joyce, J. E. Janowiak, P. A. Arkin, and P. Xie, “ CMORPH: A method that produces global precipitation estimates from passive microwave and infrared data at high spatial and temporal resolution,” Journal of Hydrometeorology, vol. 5, pp. 487-503, 2004.
• G. J. Huffman, R. F. Adler, D. T. Bolvin, G. J. Gu, E. J. Nelkin, K. P. Bowman, Y. Hong, E. F.Stocker, and D. B. Wolff, “ The TRMM multisatellite precipitation analysis (TMPA): Quasi-global, multiyear, combined-sensor precipitation estimates at fine scales,” Journal of Hydrometeorology, vol. 8, pp. 38-55, 2007.
• F. J. Turk, and S. D. Miller, “Toward improved characterization of remotely sensed precipitation regimes with MODIS/AMSR-E blended data techniques,” IEEE Trans. Geosci. Remote Sens., vol. 43, pp. 1059–1069, 2005.
• Sorooshian, S., K. L. Hsu , X. Gao , H. V. Gupta , B.Imam and D. Braithwaite, “Evaluation of PERSIANN system satellite based estimates of tropical rainfall,” Bull. Amer. Meteorol. Soc., 81, 2035, 2000
• A. Topchy, A. K. Jain, W. Punch, "Clustering ensembles: models of consensus and weak partitions," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.27, no.12, pp.1866-1881, Dec. 2005.
• H. G. Ayad and M. S. Kamel, “On voting-based consensus of cluster ensemble,” Patter recognition J., vol 43, pp. 1943-1953, 2010.
• H. G. Ayad, M. S. Kamel, "Cumulative Voting Consensus Method for Partitions with Variable Number of Clusters," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.30, no.1, pp.160-173, Jan. 2008.
• A.L.N. Fred, A. K., Jain, "Combining multiple clustering using evidence accumulation," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.27, no.6, pp. 835- 850, Jun 2005.
• N. Iam-on, T. Boongoen, and S. Garrett “LCE: a link-based cluster ensemble method for improved gene expression data analysis,” Bioinformatics, vol.26, pp. 1513-1519, 2010.
• T. Kohonen, “Self-organized formation of topologically correct features maps,” Biol. Cybernetics, vol. 43, pp. 59–69, 1982.
• E.E. Ebert, J.E. Janowiak, and C. Kidd. “Comparison of near-real-time precipitation estimates from satellite observations and numerical models,” Bull. Amer. Meteor. Soc., pp. 47-64, 2007.