Comparison of high-level clouds represented in a global cloud system-resolving model with CALIPSO/CloudSat and geostationary satellite observations

Journal of Geophysical Research Atmospheres

Volumn 115, Issue 10, 2010, Pages

  Inoue, Toshiro ^a   Satoh, Masaki ^a,b   Hagihara, Yuichiro ^c   Miura, Hiroaki ^a,b,d   Schmetz, Johannes ^e  

^a UNIVERSITY OF TOKYO   (Japan)

^b JAPAN AGENCY FOR MARINE EARTH SCIENCE AND TECHNOLOGY   (Japan)

^c TOHOKU UNIVERSITY   (Japan)

^d Engineering Research Center and Hydraulics Laboratory   (United States)

^e EUMETSAT   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

BACKSCATTERING; CLOUDS; COMPUTER SIMULATION; EXPERIMENTS; GEOSTATIONARY SATELLITES; OCEANOGRAPHY; OPTICAL RADAR; RADAR; REFLECTION; SNOW; TEMPERATURE MEASUREMENT; TOOLS; WEATHER SATELLITES;

ATMOSPHERIC MODEL; BACKSCATTER COEFFICIENTS; CALIPSO; CLOUD LIDAR; CLOUD RADAR REFLECTIVITY; CLOUD-AEROSOL LIDAR AND INFRARED PATHFINDER SATELLITE OBSERVATIONS; CLOUDSAT; CROSS CORRELATIONS; DEEP CONVECTION; EQUITABLE THREAT SCORE; FEEDBACK MODEL; GEOSTATIONARY METEOROLOGICAL SATELLITES; GLOBAL CLOUDS; GLOBAL SIMULATION; HIGH RESOLUTION; HIGHER FREQUENCIES; HORIZONTAL DISTRIBUTION; ICE CLOUDS; INTERCOMPARISONS; LOWER FREQUENCIES; SATELLITE OBSERVATIONS; SIMULATOR PACKAGE; SPATIAL DISTRIBUTION; SPLIT WINDOWS; TEMPORAL VARIATION; VERTICAL DISTRIBUTIONS; VERTICAL STRUCTURES;

ICE;

ATMOSPHERIC MODELING; BACKSCATTER; CIRRUS; CLOUDSAT; CUMULONIMBUS; EVAPORATION; GEOSTATIONARY SATELLITE; ICE CRYSTAL; LIDAR; SIMULATION; SNOW; SPATIAL DISTRIBUTION; TEMPORAL VARIATION;

EID: 77953170577     PISSN: 01480227     EISSN: None     Source Type: Journal    
DOI: 10.1029/2009JD012371     Document Type: Article

References (44)

1. Ackerman, S. A., R. E. Holz, R. Frey, E. W. Eloranta, B. C. Maddux, and M. McGill (2008), Cloud detection with MODIS Part II: Validation, J. Atmos. Oceanic Technol., 25, 1073-1086, doi:10.1175/2007JTECHA1053.1.
2. Arakawa, A. (1975), Modeling clouds and cloud processes for use in climate models, in The Physical Basis of Climate and Climate Modeling, GARP Pub. Ser., vol. 16, pp. 183-197, World Meteorol. Org., Geneva.
3. Bodas-Salcedo, A., M. J. Webb, M. E. Brooks, M. A. Ringer, K. D. Williams, S. F. Milton, and D. R. Wilson (2008), Evaluating cloud systems in the Met Office global forecast model using simulated CloudSat radar reflectivities, J. Geophys. Res., 113, D00A13, doi:10.1029/2007JD009620 [printed 114 (D8), 2009].
4. Chepfer, H., S. Bony, D. Winker, M. Chiriaco, J. L. Dufresne, and G. Seze (2008), Use of CALIPSO lidar observations to evaluate the cloudiness simulated by a climate model, Geophys. Res. Lett., 35, L15704, doi:10.1029/2008GL034207.
5. Davies, T., M. J. P. Cullen, A. J. Malcolm, M. H. Mawson, A. Staniforth, A. A. White, and N. Wood (2005), A new dynamical core for the Met Office's global and regional modeling of the atmosphere, Q. J. R. Meteorol. Soc., 131, 1759-1782, doi:10.1256/qj.04.101. (Pubitemid 41573492)
6. Grabowski, W. W. (1998), Toward cloud resolving modeling of large-scale tropical circulation: A simple cloud microphysics parameterization, J. Atmos. Sci., 55, 3283-3298, doi:10.1175/1520-0469 (1998) 055<3283: TCRMOL>2.0. CO;2.
7. Hamada, A., N. Nishi, S. Iwasaki, Y. Ohno, H. Kumagai, and H. Okamoto (2008), Cloud type and top height estimation for tropical upper-tropospheric clouds using GMS-5 split-window measurements combined with cloud radar measurements, SOLA, 4, 57-60 doi:10.2151/sola.2008-015.
8. Haynes, J. M., R. T. Marchand, Z. Luo, A. Bodas-Salcedo, and G. L. Stephens (2007), A multi-purpose radar simulation package: QuickBeam, Bull. Am. Meteorol. Soc., 88, 1723-1727, doi:10.1175/BAMS-88-11-1723. (Pubitemid 350302548)
9. Heidinger, A. K., and M. J. Pavolonis (2009), Gazing at cirrus clouds for 25 years through a split window. Part 1: Methodology, J. Appl. Meteorol. Clim., 48, 1100-1116.
10. Horvath, A., and B. J. Soden (2008), Lagrangian diagnostics of tropical deep convection and its effect upon upper-tropospheric humidity, J. Clim., 21, 1013-1028, doi:10.1175/2007JCLI1786.1. (Pubitemid 351736226)
11. Illingworth, A. J., et al. (2007), CLOUDNET Continuous evaluation of cloud profiles in seven operational models using ground-based observations, Bull. Am. Meteorol. Soc., 88, 883-898, doi:10.1175/BAMS-88-6-883. (Pubitemid 47570137)
12. Inoue, T. (1985), On the temperature and effective emissivity determination of semi-transparent cirrus clouds by bi-spectral measurements in the 10 μm window region, J. Meteorol. Soc. Jpn., 63, 88-99.
13. Inoue, T. (1987), A cloud type classification with NOAA-7 split window measurements, J. Geophys. Res., 92, 3991-4000, doi:10.1029/JD092iD04p03991.
14. Inoue, T. (1989), Features of clouds over the tropical Pacific during northern hemisphere winter derived from split window measurements, J. Meteorol. Soc. Jpn., 67, 621-637.
15. Inoue, T. (1997), Day-to-night cloudiness change of cloud types inferred from split window aboard NOAA polar-orbiting satellite, J. Meteorol. Soc. Jpn., 75, 59-66.
16. Inoue, T., and S. Ackerman (2002), Radiative effect of various cloud types as classified by the split window technique over the eastern sub-tropical Pacific derived from collocated ERBE and AVHRR data, J. Meteorol. Soc. Jpn., 80, 1383-1394, doi:10.2151/jmsj.80.1383. (Pubitemid 36400569)
17. Inoue, T., M. Satoh, H. Miura, and B. Mapes (2008), Characteristics of cloud size of deep convection simulated by a global cloud resolving model, J. Meteorol. Soc. Jpn., 86 A, 1-12. (Available at http://www.ccsr.u-tokyo.ac.jp/ ~satoh/paper/2008. Inoue/index.html)
18. Inoue, T., D. Vila, K. Rajendran, A. Hamada, X. Wu, and L. Machado (2009), Life cycle of deep convective systems over the eastern tropical Pacific observed by TRMM and GOES-W. J, Meteorol. Soc. Jpn., 87 A, 381-391, doi:10.2151/jmsj.87A.381.
19. Kondo, Y. A. H., and K. Nakamura (2006), Small-scale cloud activity over the maritime continent and the western Pacific as revealed by satellite data, Mon. Weather Rev., 134, 1581-1599, doi:10.1175/MWR3132.1. (Pubitemid 43950928)
20. Liou, K.-N. (1986), Influence of cirrus clouds on weather and climate processes: A global perspective, Mon. Weather Rev., 114, 1167-1199, doi:10.1175/1520-0493 (1986) 114<1167:IOCCOW>2.0. CO;2.
21. Liu, Z., M. A. Vaughan, D. M. Winker, C. A. Hostetler, L. R. Poole, D. Hlavka, W. Hart, and M. McGill (2004), Use of probability distribution functions for discriminating between cloud and aerosol in lidar backscatter data, J. Geophys. Res., 109, D15202, doi:10.1029/2004JD004732. (Pubitemid 39408301)
22. Luo, Z., and W. B. Rossow (2004), Characterizing tropical cirrus life cycle, evolution, and interaction with upper-tropospheric water vapor using Lagrangian trajectory analysis of satellite observations, J. Clim., 17, 4541-4563, doi:10.1175/3222.1. (Pubitemid 40098863)
23. Mace, G. G., T. P. Ackerman, P. Minnis, and D. F. Young (1998a), Cirrus layer microphysical properties derived from surface-based millimeter radar and infrared interferometer data, J. Geophys. Res., 103, 23, 207-23, 216, doi:10.1029/98JD02117. (Pubitemid 29126427)
24. Mace, G. G., C. Jacob, and K. P. Morgan (1998b), Validation of hydrometer occurrence predicted by the ECMWF model using millimeter wave radar data, Geophys. Res. Lett., 25, 1645-1648, doi:10.1029/98GL00845.
25. Machado, L., W. Rossow, R. Guedes, and A. Walker (1998), Life cycle variations of mesoscale convective systems over the Americas, Mon. Weather Rev., 126, 1630-1654, doi:10.1175/1520-0493 (1998) 126<1630:LCVOMC>2.0. CO;2.
26. Masunaga, H., M. Satoh, and H. Miura (2008), A joint satellite and global CRM analysis of an MJO event: Model diagnosis, J. Geophys. Res., 113, D17210, doi:10.1029/2008JD009986.
27. Minnis, P., K. N. Liou, and Y. Takano (1998), Inference of cirrus cloud properties using satellite-observed visible and infrared radiances. Part I: Parameterization of radiance fields, J. Atmos. Sci., 55, 3313-3339, doi:10.1175/1520-0469 (1998) 055<3313:PORAEE>2.0. CO;2.
28. Miura, H., M. Satoh, T. Nasuno, A. T. Noda, and K. Oouchi (2007), A Madden-Julian Oscillation event simulated using a global cloudresolving model, Science, 318, 1763-1765, doi:10.1126/science.1148443. (Pubitemid 350274383)
29. Nasuno, T., H. Miura, M. Satoh, A. T. Noda, and K. Oouchi (2009), Multiscale organization of convection in a global numerical simulation of the December 2006 MJO event using explicit moist processes, J. Meteorol. Soc. Jpn., 87, 335-345.
30. Okamoto, H., S. Iwasaki, M. Yasui, H. Horie, H. Kuroiwa, and H. Kumagai (2003), An algorithm for retrieval of cloud microphysics using 95-GHz cloud radar and lidar, J. Geophys. Res., 108 (D7), 4226, doi:10.1029/2001JD001225.
31. Palmer, T. N., F. J. Doblas-Reyes, A. Weisheimer, and M. J. Rodwell (2008), Toward seamless prediction: Calibration of climate change projections using seasonal forecasts, Bull. Am. Meteorol. Soc., 89, 459-470, doi:10.1175/BAMS-89-4-459. (Pubitemid 351756658)
32. Platt, C. M. R. (1979), Remote sensing of high clouds. I: Visible and infrared optical properties from lidar and radiometer measurements, J. Appl. Meteorol., 18, 1130-1143, doi:10.1175/1520-0450 (1979) 018<1130: RSOHCI>2.0. CO;2.
33. Prabhakara, C. R., S. Fraser, G. Dalu, M. L. C. Wu, R. J. Curran, and T. Styles (1988), Thin cirrus clouds: Seasonal distribution over oceans deduced from Nimbus-4 IRIS, J. Appl. Meteorol., 27, 379-399, doi:10.1175/1520-0450 (1988) 027<0379:TCCSDO>2.0. CO;2.
34. Rossow, W. B., and R. A. Schiffer (1991), ISCCP cloud data products, Bull. Am. Meteorol. Soc., 72, 2-20, doi:10.1175/1520-0477 (1991) 072<0002:ICDP>2.0. CO;2.
35. Sassen, K., C. D. Grund, J. D. Spinhirne, J. M. Alvarez, and M. J. Hardesty (1990), The 27-28 October 1986 FIRE IFO cirrus case study: A five lidar overview of cloud structure and evolution, Mon. Weather Rev., 118, 2288-2312, doi:10.1175/1520-0493 (1990) 118<2288:TOFICC>2.0. CO;2.
36. Satoh, M., T. Matsuno, H. Tomita, H. Miura, T. Nasuno, and S. Iga (2008), Nonhydrostatic Icosahedral Atmospheric Model (NICAM) for global cloud resolving simulations, J. Comput. Phys., 227, 3486-3514, doi:10.1016/j.jcp. 2007.02.006.
37. Stephens, G. (2005), Cloud feedbacks in the climate system: A critical review, J. Clim., 18, 237-273, doi:10.1175/JCLI-3243.1. (Pubitemid 40323026)
38. Stephens, G. L., et al. (2002), The CLOUDSAT mission and the A-TRAIN, Bull. Am. Meteorol. Soc., 83, 1771-1790, doi:10.1175/BAMS-83-12-1771.
39. Stephens, G. L., et al. (2008), CloudSat mission: Performance and early science after the first year of operation, J. Geophys. Res., 113, D00A18, doi:10.1029/2008JD009982.
40. Tomita, H., and M. Satoh (2004), A new dynamical framework of nonhydrostatic global model using the icosahedral grid, Fluid Dyn. Res., 34, 357-400, doi:10.1016/j.fluiddyn. 2004.03.003.
41. Winker, D. M., W. H. Hunt, and M. J. McGill (2007), Initial performance assessment of CALIOP, Geophys. Res. Lett., 34, L19803, doi:10.1029/2007GL030135. (Pubitemid 350263036)
42. World Meteorological Organization (2006), Implementation plan for a global Space-based inter-calibration system (GSICS), version 1, report, 22 pp., Geneva.
43. Wylie, D., P. Piironen, W. Wolf, and E. Eloranta (1995), Understanding satellite cirrus cloud climatologies with calibrated lidar optical depth, J. Atmos. Sci., 52, 4327-4343, doi:10.1175/1520-0469 (1995) 052<4327: USCCCW>2.0. CO;2.
44. Yuter, S. E., and R. A. Houze Jr. (2003), Microphysical modes of precipitation growth determined by S-band vertically pointing radar in orographic precipitation during MAP, Q. J. R. Meteorol. Soc., 129, 455-476, doi:10.1256/qj.01.216. (Pubitemid 36400623)