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An Artificial Neural Network Model to Reduce False Alarms in Satellite Precipitation Products Using MODIS and CloudSat Observations

Nasrin Nasrollahi Center of Hydrometeorology and Remote Sensing, University of California, Irvine, Irvine, California

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Kuolin Hsu Center of Hydrometeorology and Remote Sensing, University of California, Irvine, Irvine, California

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Soroosh Sorooshian Center of Hydrometeorology and Remote Sensing, University of California, Irvine, Irvine, California

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Print Publication:
01 Dec 2013
DOI:
https://doi.org/10.1175/JHM-D-12-0172.1
Page(s):
1872–1883
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Abstract

The Moderate Resolution Imaging Spectroradiometer (MODIS) instrument aboard the NASA Earth Observing System (EOS) Aqua and Terra platform with 36 spectral bands provides valuable information about cloud microphysical characteristics and therefore precipitation retrievals. Additionally, CloudSat, selected as a NASA Earth Sciences Systems Pathfinder satellite mission, is equipped with a 94-GHz radar that can detect the occurrence of surface rainfall. The CloudSat radar flies in formation with Aqua with only an average of 60 s delay. The availability of surface rain presence based on CloudSat together with the multispectral capabilities of MODIS makes it possible to create a training dataset to distinguish false rain areas based on their radiances in satellite precipitation products [e.g., Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN)]. The brightness temperatures of six MODIS water vapor and infrared channels are used in this study along with surface rain information from CloudSat to train an artificial neural network model for no-rain recognition. The results suggest a significant improvement in detecting nonprecipitating regions and reducing false identification of precipitation. Also, the results of the case studies of precipitation events during the summer and winter of 2007 over the United States show an accuracy of 77% no-rain identification and 93% detection accuracy, respectively.

Corresponding author address: Nasrin Nasrollahi, Center of Hydrometeorology and Remote Sensing, Engineering Hall, Suite #5300 (Building #308) University of California, Irvine, Irvine, CA 92617. E-mail: nasrin.n@uci.edu

Abstract

The Moderate Resolution Imaging Spectroradiometer (MODIS) instrument aboard the NASA Earth Observing System (EOS) Aqua and Terra platform with 36 spectral bands provides valuable information about cloud microphysical characteristics and therefore precipitation retrievals. Additionally, CloudSat, selected as a NASA Earth Sciences Systems Pathfinder satellite mission, is equipped with a 94-GHz radar that can detect the occurrence of surface rainfall. The CloudSat radar flies in formation with Aqua with only an average of 60 s delay. The availability of surface rain presence based on CloudSat together with the multispectral capabilities of MODIS makes it possible to create a training dataset to distinguish false rain areas based on their radiances in satellite precipitation products [e.g., Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN)]. The brightness temperatures of six MODIS water vapor and infrared channels are used in this study along with surface rain information from CloudSat to train an artificial neural network model for no-rain recognition. The results suggest a significant improvement in detecting nonprecipitating regions and reducing false identification of precipitation. Also, the results of the case studies of precipitation events during the summer and winter of 2007 over the United States show an accuracy of 77% no-rain identification and 93% detection accuracy, respectively.

Corresponding author address: Nasrin Nasrollahi, Center of Hydrometeorology and Remote Sensing, Engineering Hall, Suite #5300 (Building #308) University of California, Irvine, Irvine, CA 92617. E-mail: nasrin.n@uci.edu
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  • Adler, R., Kidd C. , Petty G. , Morrissey M. , and Goodman H. , 2001: Intercomparison of global precipitation products: The third Precipitation Intercomparison Project (PIP-3). Bull. Amer. Meteor. Soc., 82, 1377–1396, doi:10.1175/1520-0477(2001)082<1377:IOGPPT>2.3.CO;2.

    • Search Google Scholar
    • Export Citation

  • AghaKouchak, A., and Nakhjiri N. , 2012: A near real-time satellite-based global drought climate data record. Environ. Res. Lett.,7, 044037, doi:10.1088/1748-9326/7/4/044037.

  • AghaKouchak, A., Bárdossy A. , and Habib E. , 2010a: Copula-based uncertainty modeling: Application to multi-sensor precipitation estimates. Hydrol. Processes, 24, 2111–2124, doi:10.1002/hyp.7632.

    • Search Google Scholar
    • Export Citation

  • AghaKouchak, A., Nasrollahi N. , Li J. , Imam B. , and Sorooshian S. , 2010b: Geometrical characterization of precipitation patterns. J. Hydrometeor., 12, 274–285, doi:10.1175/2010JHM1298.1.

    • Search Google Scholar
    • Export Citation

  • AghaKouchak, A., Behrangi A. , Sorooshian S. , Hsu K. , and Amitai E. , 2011: Evaluation of satellite-retrieved extreme precipitation rates across the central United States. J. Geophys. Res.,116, D02115, doi:10.1029/2010JD014741.

  • AghaKouchak, A., Mehran A. , Norouzi H. , and Behrangi A. , 2012: Systematic and random error components in satellite precipitation datasets. Geophys. Res. Lett.,39, L09406, doi:10.1029/2012GL051592.

  • Ajami, N., Hornberger J. , and Sunding D. , 2008: Sustainable water resource management under hydrological uncertainty. Water Resour. Res.,44, W11406, doi:10.1029/2007WR006736.

  • Amitai, E., Llort X. , and Sempere-Torres D. , 2009: Comparison of TRMM radar rainfall estimates with NOAA Next-Generation QPE. J. Meteor. Soc. Japan, 87A, 109–118, doi:10.2151/jmsj.87A.109.

    • Search Google Scholar
    • Export Citation

  • Anderson, J., Chung F. , Anderson M. , Brekke L. , Easton D. , Ejeta M. , Peterson R. , and Snyder R. , 2008: Progress on incorporating climate change into management of California's water resources. Climatic Change, 87 (Suppl.), 91–108, doi:10.1007/s10584-007-9353-1.

    • Search Google Scholar
    • Export Citation

  • Arkin, P., and Xie P. , 1994: The Global Precipitation Climatology Project: First Algorithm Intercomparison Project. Bull. Amer. Meteor. Soc., 75, 401–419, doi:10.1175/1520-0477(1994)075<0401:TGPCPF>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Aumann, H. H., DeSouza-Machado S. G. , and Behrangi A. , 2011: Deep convective clouds at the tropopause. Atmos. Chem. Phys., 11, 1167–1176, doi:10.5194/acp-11-1167-2011.

    • Search Google Scholar
    • Export Citation

  • Austin, P., 1987: Relation between measured radar reflectivity and surface rainfall. Mon. Wea. Rev., 115, 1053–1070, doi:10.1175/1520-0493(1987)115<1053:RBMRRA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Behrangi, A., Hsu K. , Imam B. , Sorooshian S. , and Kuligowski R. , 2009: Evaluating the utility of multispectral information in delineating the areal extent of precipitation. J. Hydrometeor., 10, 684–700, doi:10.1175/2009JHM1077.1.

    • Search Google Scholar
    • Export Citation

  • Behrangi, A., Hsu K. , Imam B. , and Sorooshian S. , 2010a: Daytime precipitation estimation using bispectral cloud classification system. J. Appl. Meteor Climatol., 49, 1015–1031, 10.1175/2009JAMC2291.1.

    • Search Google Scholar
    • Export Citation

  • Behrangi, A., Imam B. , Hsu K. , Sorooshian S. , Bellerby T. , and Huffman G. , 2010b: REFAME: Rain Estimation Using Forward-Adjusted Advection of Microwave Estimates. J. Hydrometeor., 11, 1305–1321, doi:10.1175/2010JHM1248.1.

    • Search Google Scholar
    • Export Citation

  • Bellerby, T., Todd M. , Kniveton D. , and Kidd C. , 2000: Rainfall estimation from a combination of TRMM precipitation radar and GOES multispectral satellite imagery through the use of an artificial neural network. J. Appl. Meteor., 39, 2115–2128, doi:10.1175/1520-0450(2001)040<2115:REFACO>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Bellerby, T., Hsu K. , and Sorooshian S. , 2009: LMODEL: A satellite precipitation methodology using cloud development modeling. Part I: Algorithm construction and calibration. J. Hydrometeor., 10, 1081–1095, doi:10.1175/2009JHM1091.1.

    • Search Google Scholar
    • Export Citation

  • Bishop, C., 1996: Neural Networks for Pattern Recognition. Oxford University Press, 482 pp.

  • Capacci, D., and Conway B. , 2005: Delineation of precipitation areas from MODIS visible and infrared imagery with artificial neural networks. Meteor. Appl., 12, 291–305, doi:10.1017/S1350482705001787.

    • Search Google Scholar
    • Export Citation

  • Dinku, T., Chidzambwa S. , Ceccato P. , Connor S. , and Ropelewski C. , 2008: Validation of high-resolution satellite rainfall products over complex Terrain. Int. J. Remote Sens., 29, 4097–4110, doi:10.1080/01431160701772526.

    • Search Google Scholar
    • Export Citation

  • Ebert, E., Manton M. , Arkin P. , Allam R. , Holpin C. , and Gruber A. , 1996: Results from the GPCP Algorithm Intercomparison Programme. Bull. Amer. Meteor. Soc., 77, 2875–2887, doi:10.1175/1520-0477(1996)077<2875:RFTGAI>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Gochis, D., Nesbitt S. , Yu W. , and Williams S. , 2009: Comparison of gauge-corrected versus non-gauge corrected satellite-based quantitative precipitation estimates during the 2004 name enhanced observing period. Atmosfera, 22, 69–98.

    • Search Google Scholar
    • Export Citation

  • Haynes, J., 2011: Level 2-C precipitation column algorithm product process description and interface control document. CloudSat Project Doc., 18 pp. [Available online at http://www.CloudSat.cira.colostate.edu/ICD/2C-PRECIP-COLUMN/2C-PRECIP-COLUMN_PDICD_P1_R04.pdf.]

  • Hong, Y., Hsu K. , Gao X. , and Sorooshian S. , 2004: Precipitation estimation from remotely sensed imagery using Artificial Neural Network Cloud Classification System. J. Appl. Meteor., 43, 1834–1853, doi:10.1175/JAM2173.1.

    • Search Google Scholar
    • Export Citation

  • Hsu, K., Gao X. , Sorooshian S. , and Gupta H. , 1997: Precipitation estimation from remotely sensed information using artificial neural networks. J. Appl. Meteor., 36, 1176–1190, doi:10.1175/1520-0450(1997)036<1176:PEFRSI>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Hsu, K., Bellerby T. , and Sorooshian S. , 2009: LMODEL: A satellite precipitation methodology using cloud development modeling. Part II: Validation. J. Hydrometeor., 10, 1096–1108, doi:10.1175/2009JHM1092.1.

    • Search Google Scholar
    • Export Citation

  • Huffman, G., Adler R. , Bolvin D. , Gu G. , Nelkin E. , Bowman K. , Stocker E. , and Wolff D. , 2007: The TRMM Multi-Satellite Precipitation Analysis: Quasi-global, multiyear, combined-sensor precipitation estimates at fine scale. J. Hydrometeor., 8, 38–55, doi:10.1175/JHM560.1.

    • Search Google Scholar
    • Export Citation

  • Inoue, T., 1987: A cloud type classification with NOAA 7 split-window measurements. J. Geophys. Res.,92, 3991–4000, doi:10.1029/JD092iD04p03991.

  • Joyce, R., Janowiak J. , Arkin P. , 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, doi:10.1175/1525-7541(2004)005<0487:CAMTPG>2.0.CO;2.

  • Kidd, C., Kniveton D. , Todd M. , and Bellerby T. , 2003: Satellite rainfall estimation using combined passive microwave and infrared algorithms. J. Hydrometeor., 4, 1088–1104, doi:10.1175/1525-7541(2003)004<1088:SREUCP>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Kuligowski, R., 2002: A self-calibrating real-time GOES rainfall algorithm for short-term rainfall estimates. J. Hydrometeor., 3, 112–130, doi:10.1175/1525-7541(2002)003<0112:ASCRTG>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Kurino, T., 1997: A satellite infrared technique for estimating deep/shallow precipitation. Adv. Space Res.,19, 511–514, doi:10.1016/S0273-1177(97)00063-X.

  • Lensky, I., and Rosenfeld D. , 2003: A night-rain delineation algorithm for infrared satellite data based on microphysical considerations. J. Appl. Meteor., 42, 1218–1226, doi:10.1175/1520-0450(2003)042<1218:ANDAFI>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Li, Z., Li J. , Menzel W. , Schmit T. , and Ackerman S. , 2007: Comparison between current and future environmental satellite imagers on cloud classification using MODIS. Remote Sens. Environ., 108, 311–326, doi:10.1016/j.rse.2006.11.023.

    • Search Google Scholar
    • Export Citation

  • 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 https://ams.confex.com/ams/Annual2005/techprogram/paper_83847.htm.]

    • Search Google Scholar
    • Export Citation

  • Liu, Z., Rui H. , Teng W. , Chiu L. , Leptoukh G. , and Kempler S. , 2009: Developing an online information system prototype for global satellite precipitation algorithm validation and intercomparison. J. Appl. Meteor. Climatol., 48, 2581–2589, doi:10.1175/2009JAMC2244.1.

    • Search Google Scholar
    • Export Citation

  • Lovejoy, S., and Mandelbrot B. , 1985: Fractal properties of rain, and a fractal model. Tellus, 37A, 209–232, doi:10.1111/j.1600-0870.1985.tb00423.x.

    • Search Google Scholar
    • Export Citation

  • Marzano, F., Palmacci M. , Cimini D. , Giuliani G. , and Turk F. , 2004: Multivariate statistical integration of satellite infrared and microwave radiometric measurements for rainfall retrieval at the geostationary scale. IEEE Trans. Geosci. Remote Sens.,42, 1018–1032, doi:10.1109/TGRS.2003.820312.

  • Roskovensky, J., and Liou K. , 2003: Detection of thin cirrus using a combination of 1.38-μm reflectance and window brightness temperature difference. J. Geophys. Res.,108, 4570, doi:10.1029/2002JD003346.

  • Sapiano, M., and Arkin P. , 2009: An intercomparison and validation of high-resolution satellite precipitation estimates with 3-hourly gauge data. J. Hydrometeor., 10, 149–166, doi:10.1175/2008JHM1052.1.

    • Search Google Scholar
    • Export Citation

  • Setvak, M., Rabin R. , Doswell C. , and Levizzani V. , 2003: Satellite observations of convective storm tops in the 1.6, 3.7 and 3.9 μm spectral bands. Atmos. Res., 67–68, 607–627, doi:10.1016/S0169-8095(03)00076-0.

    • Search Google Scholar
    • Export Citation

  • Shen, Y., Xiong A. , Wang Y. , and Xie P. , 2010: Performance of high-resolution satellite precipitation products over China. J. Geophys. Res.,115, D02114, doi:10.1029/2009JD012097.

  • Sorooshian, S., Hsu K. , Gao X. , Gupta H. , Imam B. , and Braithwaite D. , 2000: Evolution of PERSIANN system satellite–based estimates of tropical rainfall. Bull. Amer. Meteor. Soc.,81, 2035–2046, doi:10.1175/1520-0477(2000)081<2035:EOPSSE>2.3.CO;2.

  • Sorooshian, S., and Coauthors, 2011: Advanced concepts on remote sensing of precipitation at multiple scales. Bull. Amer. Meteor. Soc., 92, 1353–1357, doi:10.1175/2011BAMS3158.1.

    • Search Google Scholar
    • Export Citation

  • Stephens, G. L., and Coauthors, 2002: The CloudSat mission and the A-Train: A new dimension of space-based observations of clouds and precipitation. Bull. Amer. Meteor. Soc., 83, 1771–1790, doi:10.1175/BAMS-83-12-1771.

    • Search Google Scholar
    • Export Citation

  • Strabala, K. I., Ackerman S. A. , and Menzel W. P. , 1994: Cloud properties inferred from 812-μm data. J. Appl. Meteor.,33, 212–229, doi:10.1175/1520-0450(1994)033<0212:CPIFD>2.0.CO;2.

  • Tapiador, F., Kidd C. , Levizzani V. , and Marzano F. , 2004: A neural networks-based fusion technique to estimate half-hourly rainfall estimates at 0.1° resolution from satellite passive microwave and infrared data. J. Appl. Meteor., 43, 576–594, doi:10.1175/1520-0450(2004)043<0576:ANNFTT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Thies, B., Nauss T. , and Bendix J. , 2008: Discriminating raining from non-raining cloud area at mid-latitudes using Meteosat second generation daytime data. Atmos. Chem. Phys., 8, 2341–2349, doi:10.5194/acp-8-2341-2008.

    • Search Google Scholar
    • Export Citation

  • Tian, Y., and Coauthors, 2009: Component analysis of errors in satellite-based precipitation estimates. J. Geophys. Res., 114, D24101, doi:10.1029/2009JD011949.

    • Search Google Scholar
    • Export Citation

  • Turk, F., and Miller S. , 2005: Toward improving estimates of remotely sensed precipitation with MODIS/AMSR-E blended data techniques. IEEE Trans. Geosci. Remote Sens., 43, 1059–1069, doi:10.1109/TGRS.2004.841627.

    • Search Google Scholar
    • Export Citation

  • Wang, X., Liou K. N. , Ou S. S. C. , Mace G. G. , and Deng M. , 2009: Remote sensing of cirrus cloud vertical size profile using MODIS data. J. Geophys. Res.,114, D09205, doi:10.1029/2008JD011327.

  • Weisz, E., Li J. , Menzel W. P. , Heidinger A. , Kahn B. , and Liu C.-Y. , 2007: Comparisons of AIRS, MODIS, CloudSat and CALIPSO cloud top height retrievals. Geophys. Res. Lett., 34, L17811, doi:10.1029/2007GL030676.

    • Search Google Scholar
    • Export Citation

  • Wilks, D., 2006: Statistical Methods in the Atmospheric Sciences. 2nd ed. Academic Press, 627 pp.

  • Yilmaz, K., Hogue T. , Hsu K. , Sorooshian S. , Gupta H. , and Wagener T. , 2005: Intercomparison of rain gauge, radar and satellite-based precipitation estimates with emphasis on hydrologic forecasting. J. Hydrometeor., 6, 497–517, doi:10.1175/JHM431.1.

    • Search Google Scholar
    • Export Citation

  • Zhou, C., 2008: A two-step estimator of the extreme value index. Extremes, 11, 281–302, doi:10.1007/s10687-008-0058-2.

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