Remote sensing of water cloud parameters using neural networks

Journal of Atmospheric and Oceanic Technology

Volumn 24, Issue 1, 2007, Pages 52-63

  Cerdeña, Abidán ^a   González, Albano ^a   Pérez, Juan C ^a  

^a UNIVERSIDAD DE LA LAGUNA   (Spain)

Author keywords

[No Author keywords available]

Indexed keywords

BACKPROPAGATION; GENETIC ALGORITHMS; NEURAL NETWORKS; PHYSICAL PROPERTIES; REMOTE SENSING; TEMPERATURE; WATER;

RADIATIVE TRANSFER; STRATOCUMULUS CLOUD; WATER CLOUD;

CLOUDS;

BACKPROPAGATION; CLOUDS; GENETIC ALGORITHMS; NEURAL NETWORKS; PHYSICAL PROPERTIES; REMOTE SENSING; TEMPERATURE; WATER;

ARTIFICIAL NEURAL NETWORK; CLOUD MICROPHYSICS; DATA INVERSION; GENETIC ALGORITHM; IMAGE ANALYSIS; NOAA SATELLITE; RADIATIVE TRANSFER; REMOTE SENSING; STRATOCUMULUS;

EID: 33846811601     PISSN: 07390572     EISSN: None     Source Type: Journal    
DOI: 10.1175/JTECH1943.1     Document Type: Article

References (46)

1. Anderson, G. P., S. A. Clough, F. X. Kneizys, J. H. Chetwynd, and E. P. Shettle, 1986: AFGL atmospheric constituent profiles (0-120 km). Hanscom Air Force Base, AFGL Tech. Rep. A-FGL-TR-86-0100, 46 pp.
2. Baum, B. A., R. F. Arduini, B. A. Wielicki, P. Minnis, and S. Tsay, 1994: Multilevel cloud retrieval using multispectral HIRS and AVHRR data: Nighttime oceanic analysis. J. Geophys. Res., 99, 5499-5514.
3. Baum, B. A., R. Frey, G. G. Mace, M. K. Harkey, and P. Yang, 2003: Nighttime multilayered cloud detection using MODIS and ARM data. J. Appl. Meteor., 42, 905-919.
4. Borys, R. D., D. H. Lowenthal, M. A. Wetzel, F. Herrera, A. Gonzalez, and J. Harris, 1998: Chemical and microphysical properties of marine stratus clouds in the North Atlantic. J. Geophys. Res., 103, 22 073-22 085.
5. Braun, H., and T. Ragg, 1996: ENZO: Evolution of neural networks. User manual and implementation guide, University of Karlsruhe, 62 pp.
6. Brenguier, J. L., H. Pawlowska, L. Schiffler, R. Preusker, J. Fischer, and Y. Fouquart, 2000: Radiative properties of boundary layer cloud: Droplet effective radius versus number concentration. J. Atmos. Sci., 57, 803-821.
7. Chester, D. L., 1990: Why two hidden layers are better than one. Proc. Int. J. Conf. Neural Network, 1, 265-268.
8. Coakley, J. A., and F. P. Bretherton, 1982: Cloud cover from high resolution scanner data: Detecting and allowing for partially filled fields of views. J. Geophys. Res., 87, 4917-4932.
9. Cox, C., and W. Munk, 1954: Measurement of the roughness of the sea surface from photographs of the sun's glitter. J. Opt. Soc. Amer., 44, 838-850.
10. Deneke, H., A. Feijt, A. van Lammeren, and C. Simmer, 2005: Validation of a physical retrieval scheme of solar surface irradiances from narrow-band satellite radiances. J. Appl. Meteor., 44, 1453-1466.
11. Gonzalez, A., J. C. Perez, F. Herrera, F. Rosa, M. A. Wetzel, R. D. Borys, and D. H. Lowenthal, 2002: Stratocumulus properties retrieval method from NOAA-AVHRR data based on the discretization of cloud parameters. Int. J. Remote Sens., 23, 627-645.
12. Han, Q., W. B. Rossow, and A. A. Lacis, 1994: Near-global survey of effective droplet radii in liquid water clouds using ISCCP data. J. Climate, 7, 465-497.
13. Han, W., K. Stamnes, and D. Lubin, 1999: Remote sensing of surfaces and clouds properties in the arctic from AVHRR measurements. J. Appl. Meteor., 38, 989-1012.
14. Higgins, G. J., and A. George, cited 2000: Cloud top parameters. Visible/ Infrared Image/Radiometer Suite (VIIRS). Raytheon Systems Company, Algorithm Theoretical Basis Document SBRS Doc. Y2399. [Available online at http://npoesslib.ipo.noaa.gov.]
15. Hristev, R. M., 2000: Matrix techniques in artificial neural networks. M.S. thesis, Department of Mathematics and Statistics, University of Canterbury, 174 pp.
16. Kawamoto, K., T. Nakajima, and T. Y. Nakajima, 2001: A global determination of cloud microphysics with AVHRR remote sensing. J. Climate, 14, 2054-2068.
17. King, M. D., S.-C. Tsay, S. E. Platnick, M. Wang, and K.-N. Liou, 1997: Cloud retrieval algorithms for MODIS: Optical thickness, effective particle radius, and thermodynamic phase (MOD06). NASA Goddard Space Flight Center Rep. ATBD-MOD-05, 79 pp.
18. Kokhanovsky, A. A., V. V. Rozanov, P. E. Zege, H. Bovensmann, and J. P. Burrows, 2003: A semi-analytical cloud retrieval algorithm using backscattered radiation in 0.4-2.4 μm spectral region. J. Geophys. Res., 108, 4008, doi:10.1029/2001JD001543.
19. Kröse, B., and P. V. D. Smagt, 1996: An Introduction to Neural Networks. University of Amsterdam, 135 pp.
20. Lawrence, S., C. L. Giles, and A. C. Tsoi, 1996: What size neural network gives optimal generalization? Convergence properties of backpropagation. Institute for Advanced Computer Studies, University of Maryland Rep. UMIACS-TR-96-22 and CS-TR-3617, 35 pp.
21. Mandischer, M., 1993: Representation and evolution of neural networks. Artificial Neural Nets and Genetic Algorithms Proceedings of the International Conference at Innsbruck, Austria, C. R. R. F. Albrecht, and N. Steele, Eds., Springer, 643-649.
22. Mayer, T., and A. Killing, 2005: Technical note: The libradtran software package for radiative transfer calculations: Description and examples of use. Atmos. Chem. Phys., 5, 1855-1877.
23. Miles, N. L., J. Verlinde, and E. E. Clothiaux, 2000: Cloud droplet size distribution in low-level stratiform clouds. J. Atmos. Sci., 57, 295-311.
24. Minnis, P., K. N. Liou, and Y. Takano, 1993: Inference of cirrus cloud properties using satellite-observed visible and infrared radiances. Part I: Parameterization of radiance fields. J. Atmos. Sci., 50, 1279-1304.
25. Nakajima, T., and M. D. King, 1990: Determination of the optical thickness and effective particle radius of clouds from reflected solar radiation measurements. Part I: Theory. J. Atmos. Sci., 47, 1878-1893.
26. Nakajima, T., M. D. King, J. D. Spinhirne, and L. F. Radke, 1991: Determination of the optical thickness and effective particle radius of clouds from reflected solar radiation measurements. Part II: Marine stratocumulus observations. J. Atmos. Sci., 48, 728-750.
27. Nakajima, T. Y., and T. Nakajima, 1995: Wide-area determination of cloud microphysical properties from NOAA AVHRR measurements for FIRE and ASTEX regions. J. Atmos. Sci., 52, 4043-4059.
28. Obradovic, Z., and R. Srikumar, 2000: Constructive neural networks design using genetic optimization. Ser. Math. Inf., 15, 133-146.
29. Ou, S. C., K. N. Liou, W. M. Gooch, and Y. Takano, 1993: Remote sensing of cirrus cloud parameters using Advanced Very-High-Resolution Radiometer 3.7 and 10.9 μm channels. Appl. Opt., 32, 2171-2180.
30. Ou, S. C., K. N. Liou, Y. Takano, G. J. Higgins, A. George, and R. Slonaker, cited 2002: Cloud effective particle size and cloud optical thickness. Visible/Infrared Image/Radiometer Suite (VIIRS). Raytheon Systems Company Advanced Theoretical Basis Document SBRS Doc. Y2395. [Available online at http://npoesslib.ipo.noaa.gov.]
31. Perez, J. C., F. Herrera, F. Rosa, A. Gonzalez, M. A. Wetzel, R. D. Borys, and D. H. Lowenthal, 2000: Retrieval of marine stratus cloud droplet size from NOAA-AVHRR nighttime imagery. Remote Sens. Environ., 73, 31-45.
32. Perez, J. C., P. H. Austin, and A. González, 2002: Retrieval of boundary layer cloud properties using infrared satellite data during the DYCOMS-II field experiment. Proc. 15th Symp. of Boundary Layer and Turbulence, Wageningen, Netherlands, Amer. Meteor. Soc., CD-ROM, 5.3.
33. Platnick, S., and S. Twomey, 1994: Determining the susceptibility of cloud albedo to changes in droplet concentration with Advanced Very High Resolution Radiometer. J. Appl. Meteor., 33, 334-347.
34. Platnick, S., and F. P. J. Valero, 1995: A validation of satellite cloud retrieval during ASTEX J. Atmos. Sci., 52, 2985-3001.
35. Platnick, S., and Coauthors, 2000: The role of background cloud microphysics in the radiative formation of ship tracks. J. Atmos. Sci., 57, 2607-2624.
36. Platnick, S., J. Y. Li, M. D. King, H. Gerber, and P. V. Hobbs, 2001: A solar reflectance method for retrieving cloud optical thickness and droplet size over snow and ice surfaces. J. Geophys. Res., 106, 15 185-15 199.
37. Platnick, S., M. D. King, S. A. Ackerman, W. P. Menzel, B. A. Baum, J. C. Riedi, and R. A. Frey, 2003: The MODIS cloud products: Algorithms and examples from Terra. IEEE Trans. Geosci. Remote Sens., 41, 459-473.
38. Ramanathan, V., 1987: The role of earth radiation budget studies in climate and general circulation research. J. Geophys. Res., 92, 4075-4095.
39. Rao, C. R. N., and J. Chen, 1999: Revised post-launch calibration of the visible and near-infrared channels of the Advanced Very High Resolution Radiometer on the NOAA-14 spacecraft. Int. J. Remote Sens., 20, 3485-3491.
40. Seiffert, U., 2001: Multiple layer perceptron training using genetic algorithms. Proc. European Symp. on Artificial Neural Networks ESANN, Bruges, Belgium, ESANN, 159-164.
41. Stamnes, K., S. Tsay, W. Wiscombe, and K. Jayaweera, 1988: Numerically stable algorithm for discrete-ordinate-method radiative transfer in multiple scattering and emitting layered media. Appl. Opt., 27, 2502-2509.
42. Szczodrack, M., P. H. Austin, and P. B. Ktummel, 2001: Variability of optical depth and effective radius in marine stratocumulus clouds. J. Atmos. Sci., 58, 2912-2926.
43. Thim, G., and E. Fiesler, 1997: Optimal setting of weights, learning rate and gain. IDIAP Research Rep. 97-04, 15 pp.
44. Trishchenko, A. P., G. Fedosejevs, Z. Li, and J. Cihlar, 2002: Trends and uncertainties in thermal calibration of AVHRR radiometers onboard NOAA-9 to NOAA-16. J. Geophys. Res., 107, 4778, doi:10.1029/2002JD002353.
45. Zell, A., and Coauthors, 1995: SNNS: Stuttgart Neural Network Simulator. User's manual, version 4.1, University of Stuttgart Rep. 6/95, 350 pp. [Available online at http://www-ra.informatik.uni-tuebingen.de/SNNS/.]
46. Zhang, G., B. E. Fiesler, and M.-Y. Hu, 1998: Forecasting with artificial neural networks: The state of the art. Int. J. Forecasting, 14, 35-62.