Enhancement of satellite precipitation estimation via unsupervised dimensionality reduction

Journal Article · Sun Jan 01 00:00:00 EST 2012 · IEEE Tranactions on Geoscience and Remote Sensing

DOI:https://doi.org/10.1109/TGRS.2012.2189406· OSTI ID:1055000

Mahrooghy, Majid ^[1]; Younan, Nicolas H. ^[1]; Anantharaj, Valentine G ^[2]; Aanstoos, James ^[1]

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1. Mississippi State University (MSU)
2. ORNL

A methodology to enhance Satellite Precipitation Estimation (SPE) using unsupervised dimensionality reduction (UDR) techniques is developed. This enhanced technique is an extension to the Precipitation Estimation from Remotely Sensed Imagery using an Artificial Neural Network (PERSIANN) and Cloud Classification System (CCS) method (PERSIANN-CCS) enriched using wavelet features combined with dimensionality reduction. Cloud-top brightness temperature measurements from Geostationary Operational Environmental Satellite (GOES-12) are used for precipitation estimation at 4 km 4 km spatial resolutions every 30 min. The study area in the continental United States covers parts of Louisiana, Arkansas, Kansas, Tennessee, Mississippi, and Alabama. Based on quantitative measures, root mean square error (RMSE) and Heidke skill score (HSS), the results show that the UDR techniques can improve the precipitation estimation accuracy. In addition, ICA is shown to have better performance than other UDR techniques; and in some cases, it achieves 10% improvement in the HSS.

• Research Organization: Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Oak Ridge Leadership Computing Facility (OLCF); Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). National Center for Computational Sciences (NCCS)
• Sponsoring Organization: USDOE Office of Science (SC)
• DOE Contract Number: DE-AC05-00OR22725
• OSTI ID: 1055000
• Journal Information: IEEE Tranactions on Geoscience and Remote Sensing, Vol. 50, Issue 10; ISSN 0196--2892
• Country of Publication: United States
• Language: English