• Arkin, P. A., and B. N. Meisner, 1987: The relationship between large-scale convective rainfall and cold cloud over the Western Hemisphere during 1982–84. Mon. Wea. Rev., 115, 5174, doi:10.1175/1520-0493(1987)115<0051:TRBLSC>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ba, M. B., and A. Gruber, 2001: GOES Multispectral Rainfall Algorithm (GMSRA). J. Appl. Meteor., 40, 15001514, doi:10.1175/1520-0450(2001)040<1500:GMRAG>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Behrangi, A., K.-L. Hsu, B. Imam, S. Sorooshian, G. J. Huffman, and R. J. Kuligowski, 2009a: PERSIANN-MSA: A precipitation estimation method from satellite-based multispectral analysis. J. Hydrometeor., 10, 14141429, doi:10.1175/2009JHM1139.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Behrangi, A., K.-L. Hsu, B. Imam, S. Sorooshian, and R. J. Kuligowski, 2009b: Evaluating the utility of multispectral information in delineating the areal extent of precipitation. J. Hydrometeor., 10, 684700, doi:10.1175/2009JHM1077.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Behrangi, A., K. Hsu, B. Imam, and S. Sorooshian, 2010: Daytime precipitation estimation using bispectral cloud classification system. J. Appl. Meteor. Climatol., 49, 10151031, doi:10.1175/2009JAMC2291.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bengio, Y., 2009: Learning deep architectures for AI. Found. Trends Mach. Learn., 2, 1127, doi:10.1561/2200000006.

  • Bourlard, H., and Y. Kamp, 1988: Auto-association by multilayer perceptrons and singular value decomposition. Biol. Cybern., 59, 291294, doi:10.1007/BF00332918.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Capacci, D., and B. J. Conway, 2005: Delineation of precipitation areas from MODIS visible and infrared imagery with artificial neural networks. Meteor. Appl., 12, 291305, doi:10.1017/S1350482705001787.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Glorot, X., A. Bordes, and Y. Bengio, 2011: Domain Adaptation for large-scale sentiment classification: A deep learning approach. Proceedings of the 28th International Conference on Machine Learning, L. Getoor and T. Scheffer, Eds., Omnipress, 513–520.

  • Hinton, G. E., and R. S. Zemel, 1993: Autoencoders, minimum description length and Helmholtz free energy. Advances in Neural Information Processing Systems 6, J. D. Cowan, G. Tesauro, and J. Alspector, Eds., Morgan Kaufmann, 3–10.

  • Hinton, G. E., S. Osindero, and Y. W. Teh, 2006: A fast learning algorithm for deep belief nets. Neural Comput., 18, 15271554, doi:10.1162/neco.2006.18.7.1527.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hong, Y., K. L. Hsu, S. Sorooshian, and X. G. Gao, 2004: Precipitation Estimation from Remotely Sensed Imagery using an Artificial Neural Network Cloud Classification System. J. Appl. Meteor., 43, 18341852, doi:10.1175/JAM2173.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hsu, K.-L., X. G. Gao, S. Sorooshian, and H. V. Gupta, 1997: Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks. J. Appl. Meteor., 36, 11761190, doi:10.1175/1520-0450(1997)036<1176:PEFRSI>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hsu, K.-L., H. V. Gupta, X. Gao, and S. Sorooshian, 1999: Estimation of physical variables from multichannel remotely sensed imagery using a neural network: Application to rainfall estimation. Water Resour. Res., 35, 16051618, doi:10.1029/1999WR900032.

    • Crossref
    • 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, 3855, doi:10.1175/JHM560.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Joyce, R. J., J. E. Janowiak, P. A. Arkin, and P. P. Xie, 2004: CMORPH: A method that produces global precipitation estimates from passive microwave and infrared data at high spatial and temporal resolution. J. Hydrometeor., 5, 487503, doi:10.1175/1525-7541(2004)005<0487:CAMTPG>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kidd, C., D. R. Kniveton, M. C. Todd, and T. J. Bellerby, 2003: Satellite rainfall estimation using combined passive microwave and infrared algorithms. J. Hydrometeor., 4, 10881104, doi:10.1175/1525-7541(2003)004<1088:SREUCP>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kuligowski, R. J., 2002: A self-calibrating real-time GOES rainfall algorithm for short-term rainfall estimates. J. Hydrometeor., 3, 112130, doi:10.1175/1525-7541(2002)003<0112:ASCRTG>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kummerow, C., and L. Giglio, 1995: A method for combining passive microwave and infrared rainfall observations. J. Atmos. Oceanic Technol., 12, 3345, doi:10.1175/1520-0426(1995)012<0033:AMFCPM>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kurino, T., 1997: A satellite infrared technique for estimating “deep/shallow” precipitation. Adv. Space Res., 19, 511514, doi:10.1016/S0273-1177(97)00063-X.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Martin, D. W., R. A. Kohrs, F. R. Mosher, C. M. Medaglia, and C. Adamo, 2008: Over-ocean validation of the global convective diagnostic. J. Appl. Meteor. Climatol., 47, 525543, doi:10.1175/2007JAMC1525.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Marzano, F. S., M. Palmacci, D. Cimini, G. Giuliani, and F. J. Turk, 2004: Multivariate statistical integration of satellite infrared and microwave radiometric measurements for rainfall retrieval at the geostationary scale. IEEE Trans. Geosci. Remote Sens., 42, 10181032, doi:10.1109/TGRS.2003.820312.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nasrollahi, N., K. L. Hsu, and S. Sorooshian, 2013: An artificial neural network model to reduce false alarms in satellite precipitation products using MODIS and CloudSat observations. J. Hydrometeor., 14, 18721883, doi:10.1175/JHM-D-12-0172.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Netzer, Y., T. Wang, A. Coates, A. Bissacco, B. Wu, and A. Y. Ng, 2011: Reading digits in natural images with unsupervised feature learning. 2011 NIPS Workshop on Deep Learning and Unsupervised Feature Learning, Granada, Spain, Neural Information Processing Systems Foundation, 4 pp.

  • Nguyen, P., S. Sellars, A. Thorstensen, Y. Tao, H. Ashouri, D. Braithwaite, K. Hsu, and S. Sorooshian, 2014: Satellites track precipitation of Super Typhoon Haiyan. Eos, Trans. Amer. Geophys. Union, 95, 133135, doi:10.1002/2014EO160002.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rumelhart, D. E., G. E. Hinton, and R. J. Williams, 1986: Learning representations by back-propagating errors. Nature, 323, 533536, doi:10.1038/323533a0.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sorooshian, S., and Coauthors, 2011: Advanced concepts on remote sensing of precipitation at multiple scales. Bull. Amer. Meteor. Soc., 92, 13531357, doi:10.1175/2011BAMS3158.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tao, Y., X. Gao, K. Hsu, S. Sorooshian, and A. Ihler, 2016a: A deep neural network modeling framework to reduce bias in satellite precipitation products. J. Hydrometeor., 17, 931945, doi:10.1175/JHM-D-15-0075.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tao, Y., X. Gao, A. Ihler, K. Hsu, and S. Sorooshian, 2016b: Deep neural networks for precipitation estimation from remotely sensed information. 2016 IEEE Congress on Evolution Computation, Vancouver, BC, Canada, IEEE, 1349–1355, doi:10.1109/CEC.2016.7743945.

    • Crossref
    • Export Citation
  • Tjemkes, S. A., L. van de Berg, and J. Schmetz, 1997: Warm water vapour pixels over high clouds as observed by METEOSAT. Contrib. Atmos. Phys., 70, 1522.

    • Search Google Scholar
    • Export Citation
  • Vincent, P., H. Larochelle, Y. Bengio, and P.-A. Manzagol, 2008: Extracting and composing robust features with denoising autoencoders. Proc. 25th Int. Conf. on Machine Learning, Helsinki, Finland, ACM, 1096–1103, doi:10.1145/1390156.1390294.

    • Crossref
    • Export Citation
  • Vincent, P., H. Larochelle, I. Lajoie, Y. Bengio, and P. A. Manzagol, 2010: Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion. J. Mach. Learn. Res., 11, 33713408.

    • Search Google Scholar
    • Export Citation
  • Weng, F., L. Zhao, R. R. Ferraro, G. Poe, X. Li, and N. C. Grody, 2003: Advanced microwave sounding unit cloud and precipitation algorithms. Radio Sci., 38, 8068, doi:10.1029/2002RS002679.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Xie, J., L. Xu, and E. Chen, 2012: Image denoising and inpainting with deep neural networks. Advances in Neural Information Processing Systems 25, P. L. Bartlett et al., Eds., Neural Information Processing Systems Foundation, 350–358. [Available online at https://papers.nips.cc/paper/4686-image-denoising-and-inpainting-with-deep-neural-networks.pdf.]

  • Zhang, J., and Coauthors, 2011: National Mosaic and Multi-Sensor QPE (NMQ) system: Description, results, and future plans. Bull. Amer. Meteor. Soc., 92, 1321, doi:10.1175/2011BAMS-D-11-00047.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 591 350 15
PDF Downloads 537 306 12

Precipitation Identification with Bispectral Satellite Information Using Deep Learning Approaches

View More View Less
  • 1 Center for Hydrometeorology and Remote Sensing, and Department of Civil and Environmental Engineering, University of California, Irvine, Irvine, California
  • | 2 Department of Computer Science, University of California, Irvine, Irvine, California
  • | 3 Center for Hydrometeorology and Remote Sensing, and Department of Civil and Environmental Engineering, University of California, Irvine, Irvine, California
Restricted access

Abstract

In the development of a satellite-based precipitation product, two important aspects are sufficient precipitation information in the satellite-input data and proper methodologies, which are used to extract such information and connect it to precipitation estimates. In this study, the effectiveness of the state-of-the-art deep learning (DL) approaches to extract useful features from bispectral satellite information, infrared (IR), and water vapor (WV) channels, and to produce rain/no-rain (R/NR) detection is explored. To verify the methodologies, two models are designed and evaluated: the first model, referred to as the DL-IR only method, applies deep learning approaches to the IR data only; the second model, referred to as the DL-IR+WV method, incorporates WV data to further improve the precipitation identification performance. The radar stage IV data are the reference data used as ground observation. The operational product, Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks–Cloud Classification System (PERSIANN-CCS), serves as a baseline model with which to compare the performances. The experiments show significant improvement for both models in R/NR detection. The overall performance gains in the critical success index (CSI) are 21.60% and 43.66% over the verification periods for the DL-IR only model and the DL-IR+WV model compared to PERSIANN-CCS, respectively. In particular, the performance gains in CSI are as high as 46.51% and 94.57% for the models for the winter season. Moreover, specific case studies show that the deep learning techniques and the WV channel information effectively help recover a large number of missing precipitation pixels under warm clouds while reducing false alarms under cold clouds.

© 2017 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author e-mail: Yumeng Tao, yumengt@uci.edu

Abstract

In the development of a satellite-based precipitation product, two important aspects are sufficient precipitation information in the satellite-input data and proper methodologies, which are used to extract such information and connect it to precipitation estimates. In this study, the effectiveness of the state-of-the-art deep learning (DL) approaches to extract useful features from bispectral satellite information, infrared (IR), and water vapor (WV) channels, and to produce rain/no-rain (R/NR) detection is explored. To verify the methodologies, two models are designed and evaluated: the first model, referred to as the DL-IR only method, applies deep learning approaches to the IR data only; the second model, referred to as the DL-IR+WV method, incorporates WV data to further improve the precipitation identification performance. The radar stage IV data are the reference data used as ground observation. The operational product, Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks–Cloud Classification System (PERSIANN-CCS), serves as a baseline model with which to compare the performances. The experiments show significant improvement for both models in R/NR detection. The overall performance gains in the critical success index (CSI) are 21.60% and 43.66% over the verification periods for the DL-IR only model and the DL-IR+WV model compared to PERSIANN-CCS, respectively. In particular, the performance gains in CSI are as high as 46.51% and 94.57% for the models for the winter season. Moreover, specific case studies show that the deep learning techniques and the WV channel information effectively help recover a large number of missing precipitation pixels under warm clouds while reducing false alarms under cold clouds.

© 2017 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author e-mail: Yumeng Tao, yumengt@uci.edu
Save