Editorial Type:
Article
Article Type:
Research Article

On an Enhanced PERSIANN-CCS Algorithm for Precipitation Estimation

Majid Mahrooghy * Department of Electrical Engineering, Mississippi State University, Mississippi State, Mississippi
Geosystems Research Institute, Mississippi State University, Mississippi State, Mississippi

Search for other papers by Majid Mahrooghy in
Current site
Google Scholar
PubMed Close
,
Valentine G. Anantharaj National Center for Computational Sciences, Oak Ridge National Laboratory, Oak Ridge, Tennessee

Search for other papers by Valentine G. Anantharaj in
Current site
Google Scholar
PubMed Close
,
Nicolas H. Younan * Department of Electrical Engineering, Mississippi State University, Mississippi State, Mississippi
Geosystems Research Institute, Mississippi State University, Mississippi State, Mississippi

Search for other papers by Nicolas H. Younan in
Current site
Google Scholar
PubMed Close
,
James Aanstoos Geosystems Research Institute, Mississippi State University, Mississippi State, Mississippi

Search for other papers by James Aanstoos in
Current site
Google Scholar
PubMed Close
, and
Kuo-Lin Hsu Center for Hydrometeorology and Remote Sensing, Civil and Environmental Engineering, University of California, Irvine, Irvine, California

Search for other papers by Kuo-Lin Hsu in
Current site
Google Scholar
PubMed Close
Print Publication:
01 Jul 2012
DOI:
https://doi.org/10.1175/JTECH-D-11-00146.1
Page(s):
922–932
Restricted access

Abstract

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.

Corresponding author address: Majid Mahrooghy, Box 9627, Geosystems Research Institute, Mississippi State University, Mississippi State, MS 39762. E-mail: mm858@msstate.edu

Abstract

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.

Corresponding author address: Majid Mahrooghy, Box 9627, Geosystems Research Institute, Mississippi State University, Mississippi State, MS 39762. E-mail: mm858@msstate.edu
Save
Cover Journal of Atmospheric and Oceanic Technology
Journal of Atmospheric and Oceanic Technology

  • Adler, R. F., Huffman G. J. , and Keehn P. R. , 1994: Global tropical rain estimates from microwave-adjusted geosynchronous IR data. Remote Sens. Rev., 11, 125–152.

    • Search Google Scholar
    • Export Citation

  • Anagnostou, E. N., 2004: Overview of overland satellite rainfall estimation for hydro-meteorological applications. Surv. Geophys., 25, 511–537.

    • Search Google Scholar
    • Export Citation

  • Atlas, D., Rosenfeld D. , and Wolff D. B. , 1990: Climatologically tuned reflectivity-rain rate relations and links to area-time integrals. J. Appl. Meteor., 29, 1120–1135.

    • Search Google Scholar
    • Export Citation

  • Burrus, C. S., Gopinath R. A. , and Guo H. , 1997: Introduction to Wavelets and Wavelet Transforms: A Primer. Prentice Hall, 268 pp.

  • Ebert, E. E., Janowiak J. E. , and Kidd C. , 2007: Comparison of near-real-time precipitation estimates from satellite observations and numerical models. Bull. Amer. Meteor. Soc., 88, 47–64.

    • Search Google Scholar
    • Export Citation

  • Gonzalez, R. C., and Woods R. E. , 2007: Digital Image Processing. 3rd ed. Prentice Hall, 976 pp.

  • Hong, Y., Hsu K. L. , Sorooshian S. , and Gao X. G. , 2004: Precipitation estimation from remotely sensed imagery using an artificial neural network cloud classification system. J. Appl. Meteor., 43, 1834–1852.

    • Search Google Scholar
    • Export Citation

  • Hsu, K. L., Gao X. , Sorooshian S. , and Gupta H. V. , 1997: Precipitation estimation from remotely sensed information using artificial neural networks. J. Appl. Meteor., 36, 1176–1190.

    • Search Google Scholar
    • Export Citation

  • Hsu, K. L., Bellerby T. , and Sorooshian S. , 2009: LMODEL: A satellite precipitation Geostationary Operational Environmental Satellite methodology using cloud development modeling. Part II: Validation. J. Hydrometeor., 10, 1096–1108.

    • Search Google Scholar
    • Export Citation

  • Huffman, G. J., and Coauthors, 2007: The TRMM Multisatellite Precipitation Analysis (TMPA): Quasi-global, multiyear, combined-sensor precipitation estimates at fine scales. J. Hydrometeor., 8, 38–55.

    • Search Google Scholar
    • Export Citation

  • ISDR, 2008: Early warning systems can save lives when cyclones strike. Press Release UNISDR 2008/05, 1 pp. [Available online at http://www.unisdr.org/files/5434_pr200806myanmarcyclonenargis.pdf.]

  • Joyce, R. J., Janowiak J. E. , Arkin P. A. , and Xie P. , 2004: CMORPH: A method that produces global precipitation estimates from passive microwave and infrared data at high spatial and temporal resolution. J. Hydrometeor., 5, 487–503.

    • Search Google Scholar
    • Export Citation

  • Kohonen, T., 1982: Self-organized formation of topologically correct features maps. Biol. Cybern., 43, 59–69.

  • Lin, Y., and Mitchell K. E. , 2005: The NCEP Stage II/IV hourly precipitation analyses: Development and applications. Preprints, 19th Conf. on Hydrology, San Diego, CA, Amer. Meteor. Soc., 1.2. [Available online at http://ams.confex.com/ams/pdfpapers/83847.pdf.]

  • Mahrooghy, M., Anantharaj V. G. , Younan N. H. , and Anstoos J. , 2011: On the enhancement of infrared satellite precipitation estimates using genetic algorithm filter-based feature selection. Proc.34th Int. Symp. on Remote Sensing of Environment, Sydney, NSW, Australia, ISRSE.

  • Sorooshian, S., Hsu K. L. , Gao X. , Gupta H. V. , Imam B. , and Braithwaite D. , 2000: Evaluation of PERSIANN system satellite based estimates of tropical rainfall. Bull. Amer. Meteor. Soc., 81, 2035–2046.

    • Search Google Scholar
    • Export Citation

  • Torrence, C., and Compo G. P. , 1998: A practical guide to wavelet analysis. Bull. Amer. Meteor. Soc., 79, 61–78.

  • Turk, F. J., and Miller S. D. , 2005: Toward improved characterization of remotely sensed precipitation regimes with MODIS/AMSR-E blended data techniques. IEEE Trans. Geosci. Remote Sens., 43, 1059–1069.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 1185 765 29
PDF Downloads 312 95 5