An artificial neural network model to reduce false alarms in satellite precipitation products using MODIS and cloudsat observations

Journal of Hydrometeorology

Volumn 14, Issue 6, 2013, Pages 1872-1883

  Nasrollahi, Nasrin ^a   Hsu, Kuolin ^a   Sorooshian, Soroosh ^a  

^a University of California at Irvine   (United States)

Author keywords

Hydrology; Precipitation

Indexed keywords

ARTIFICIAL NEURAL NETWORK; CLOUDSAT; EOS; HYDROLOGY; MODIS; PRECIPITATION ASSESSMENT; RAINFALL; SATELLITE DATA; SATELLITE IMAGERY;

UNITED STATES;

EID: 84888864484     PISSN: 1525755X     EISSN: 15257541     Source Type: Journal    
DOI: 10.1175/JHM-D-12-0172.1     Document Type: Article

References (55)

1. Adler, R., C. Kidd, G. Petty, M. Morrissey, and H. Goodman, 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.
2. AghaKouchak, A., and N. Nakhjiri, 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.
3. AghaKouchak, A., and N. Nakhjiri, A. Bardossy, and E. Habib, 2010a: Copula-based uncertainty modeling: Application to multi-sensor precipitation estimates. Hydrol. Processes, 24, 2111-2124, doi:10.1002/hyp.7632.
4. AghaKouchak, A., and N. Nakhjiri, A. Bardossy, and E. Habib, N. Nasrollahi, J. Li, B. Imam, and S. Sorooshian, 2010b: Geometrical characterization of precipitation patterns. J. Hydrometeor., 12, 274-285, doi:10.1175/2010JHM1298.1.
5. AghaKouchak, A., and N. Nakhjiri, A. Bardossy, and E. Habib, N. Nasrollahi, J. Li, B. Imam, and S. Sorooshian, A. Behrangi, S. Sorooshian, K. Hsu, and E. Amitai, 2011: Evaluation of satellite-retrieved extreme precipitation rates across the central United States. J. Geophys. Res., 116, D02115, doi:10.1029/2010JD014741.
6. AghaKouchak, A., and N. Nakhjiri, A. Bardossy, and E. Habib, N. Nasrollahi, J. Li, B. Imam, and S. Sorooshian, A. Behrangi, S. Sorooshian, K. Hsu, and E. Amitai, A. Mehran, H. Norouzi, and A. Behrangi, 2012: Systematic and random error components in satellite precipitation datasets. Geophys. Res. Lett., 39, L09406, doi:10.1029/2012GL051592.
7. Ajami, N., J. Hornberger, and D. Sunding, 2008: Sustainable water resource management under hydrological uncertainty. Water Resour. Res., 44, W11406, doi:10.1029/2007WR006736.
8. Amitai, E., X. Llort, and D. Sempere-Torres, 2009: Comparison of TRMMradar rainfall estimates withNOAANext-Generation QPE. J. Meteor. Soc. Japan, 87A, 109-118, doi:10.2151/jmsj.87A.109.
9. Anderson, J., F. Chung, M. Anderson, L. Brekke, D. Easton, M. Ejeta, R. Peterson, and R. Snyder, 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.
10. Arkin, P., and P. Xie, 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.
11. Aumann, H. H., S. G. DeSouza-Machado, and A. Behrangi, 2011: Deep convective clouds at the tropopause. Atmos. Chem. Phys., 11, 1167-1176, doi:10.5194/acp-11-1167-2011.
12. 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.
13. Behrangi, A., K. Hsu, B. Imam, S. Sorooshian, and R. Kuligowski, 2009: Evaluating the utility of multispectral information in delineating the areal extent of precipitation. J. Hydrometeor., 10, 684-700, doi:10.1175/2009JHM1077.1.
14. Behrangi, A., K. Hsu, B. Imam, S. Sorooshian, and R. Kuligowski, 2010a: Daytime precipitation estimation using bispectral cloud classification system. J. Appl. Meteor Climatol., 49, 1015-1031, 10.1175/2009JAMC2291.1.
15. Behrangi, A., K. Hsu, B. Imam, S. Sorooshian, and R. Kuligowski, B. Imam, K. Hsu, S. Sorooshian, T. Bellerby, andG. Huffman, 2010b: REFAME: Rain Estimation Using Forward-Adjusted Advection ofMicrowave Estimates. J. Hydrometeor., 11, 1305-1321, doi:10.1175/2010JHM1248.1.
16. Bellerby, T., M. Todd, D. Kniveton, and C. Kidd, 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.
17. Bellerby, T., M. Todd, D. Kniveton, and C. Kidd, K. Hsu, and S. Sorooshian, 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.
18. Bishop, C., 1996: Neural Networks for Pattern Recognition. Oxford University Press, 482 pp.
19. Capacci, D., and B. Conway, 2005: Delineation of precipitation areas from MODIS visible and infrared imagery with artificial neural networks. Meteor. Appl., 12, 291-305, doi:10.1017/S1350482705001787.
20. Dinku, T., S. Chidzambwa, P. Ceccato, S. Connor, andC. Ropelewski, 2008: Validation of high-resolution satellite rainfall products over complex Terrain. Int. J. Remote Sens., 29, 4097-4110, doi:10.1080/01431160701772526.
21. Ebert, E., M. Manton, P. Arkin, R. Allam, C. Holpin, and A. Gruber, 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.
22. Gochis, D., S. Nesbitt, W. Yu, and S. Williams, 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.
23. 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-PRECIPCOLUMN_ PDICD_P1_R04.pdf.]
24. Hong, Y., K. Hsu, X. Gao, and S. Sorooshian, 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.
25. Hsu, K., X. Gao, S. Sorooshian, and H. Gupta, 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.
26. Hsu, K., X. Gao, S. Sorooshian, and H. Gupta, T. Bellerby, and S. Sorooshian, 2009: LMODEL: A satellite precipitation methodology using cloud development modeling. Part II: Validation. J. Hydrometeor., 10, 1096-1108, doi:10.1175/2009JHM1092.1.
27. Huffman, G., R. Adler, D. Bolvin, G. Gu, E. Nelkin, K. Bowman, E. Stocker, and D. Wolff, 2007: The TRMM Multi-Satellite Precipitation Analysis: Quasi-global, multiyear, combinedsensor precipitation estimates at fine scale. J. Hydrometeor., 8, 38-55, doi:10.1175/JHM560.1.
28. Inoue, T., 1987: A cloud type classification with NOAA 7 splitwindow measurements. J. Geophys. Res., 92, 3991-4000, doi:10.1029/JD092iD04p03991.
29. Joyce, R., J. Janowiak, P. Arkin, and 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, 487-503, doi:10.1175/1525-7541(2004)005,0487:CAMTPG.2.0.CO;2.
30. Kidd, C., D. Kniveton, M. Todd, and T. Bellerby, 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.
31. 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.
32. Kurino, T., 1997:Asatellite infrared technique for estimating deep/shallowprecipitation. Adv. SpaceRes., 19, 511-514, doi:10.1016/S0273-1177(97)00063-X.
33. Lensky, I., and D. Rosenfeld, 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.
34. Li, Z., J. Li, W. Menzel, T. Schmit, and S. Ackerman, 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.
35. Lin, Y., and K. E. Mitchell, 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.]
36. Liu, Z., H. Rui, W. Teng, L. Chiu, G. Leptoukh, and S. Kempler, 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.
37. Lovejoy, S., and B. Mandelbrot, 1985: Fractal properties of rain, and a fractal model. Tellus, 37A, 209-232, doi:10.1111/j.1600-0870.1985.tb00423.x.
38. Marzano, F., M. Palmacci, D. Cimini, G. Giuliani, and F. 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, 1018-1032, doi:10.1109/TGRS.2003.820312.
39. Roskovensky, J., and K. Liou, 2003: Detection of thin cirrus using a combination of 1.38-mm reflectance and window brightness temperature difference. J. Geophys. Res., 108, 4570, doi:10.1029/2002JD003346.
40. Sapiano, M., and P. Arkin, 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.
41. Setvak, M., R. Rabin, C. Doswell, and V. Levizzani, 2003: Satellite observations of convective storm tops in the 1.6, 3.7 and 3.9mm spectral bands. Atmos. Res., 67-68, 607-627, doi:10.1016/S0169-8095(03)00076-0.
42. Shen, Y., A. Xiong, Y. Wang, and P. Xie, 2010: Performance of high-resolution satellite precipitation products over China. J. Geophys. Res., 115, D02114, doi:10.1029/2009JD012097.
43. Sorooshian, S., K. Hsu, X. Gao, H. Gupta, B. Imam, and D. Braithwaite, 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.
44. Sorooshian, S., K. Hsu, X. Gao, H. Gupta, B. Imam, and D. Braithwaite, 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.
45. 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.
46. Strabala, K. I., S. A. Ackerman, and W. P. Menzel, 1994: Cloud properties inferred from812-mmdata. J. Appl. Meteor., 33, 212-229, doi:10.1175/1520-0450(1994)033,0212:CPIFD.2.0.CO;2.
47. Tapiador, F., C. Kidd, V. Levizzani, and F. Marzano, 2004: A neural networks-based fusion technique to estimate halfhourly rainfall estimates at 0.18 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.
48. Thies, B., T. Nauss, and J. Bendix, 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.
49. Tian, Y., and Coauthors, 2009: Component analysis of errors in satellite-based precipitation estimates. J. Geophys. Res., 114, D24101, doi:10.1029/2009JD011949.
50. Turk, F., and S. Miller, 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.
51. Wang, X., K. N. Liou, S. S. C. Ou, G.G. Mace, andM. Deng, 2009: Remote sensing of cirrus cloud vertical size profile using MODIS data. J. Geophys. Res., 114, D09205, doi:10.1029/2008JD011327.
52. Weisz, E., J. Li, W. P. Menzel, A. Heidinger, B. Kahn, and C.-Y. Liu, 2007: Comparisons of AIRS, MODIS, CloudSat and CALIPSO cloud top height retrievals. Geophys. Res. Lett., 34, L17811, doi:10.1029/2007GL030676.
53. Wilks, D., 2006: Statistical Methods in the Atmospheric Sciences. 2nd ed. Academic Press, 627 pp.
54. Yilmaz, K., T. Hogue, K. Hsu, S. Sorooshian, H. Gupta, and T. Wagener, 2005: Intercomparison of rain gauge, radar and satellite-based precipitation estimateswith emphasis on hydrologic forecasting. J. Hydrometeor., 6, 497-517, doi:10.1175/JHM431.1.
55. Zhou, C., 2008: A two-step estimator of the extreme value index. Extremes, 11, 281-302, doi:10.1007/s10687-008-0058-2.