Advertisement

Precipitation Statistics Comparison Between Global Cloud Resolving Simulation with NICAM and TRMM PR Data

  • M. Satoh
  • T. Nasuno
  • H. Miura
  • H. Tomita
  • S. Iga
  • Y. Takayabu

Summary

A “global cloud resolving simulation” with horizontal grid interval of 3.5 km is conducted using a nonhydrostatic icosahedral atmospheric model (NICAM). NICAM is a cloud resolving model in the sense that updraft cores of deep cumulus that have a few km in horizontal size are marginally represented using explicit microphysical schemes. Results from the aqua-planet experiment of NICAM are compared with the TRMM PR (Tropical Rainfall Measurement Mission, Precipitation Radar) data that have a horizontal resolution close to the grid interval of NICAM.

Precipitation statistics of NICAM are compared to those of the TRMM PR data over oceans. Probability distribution functions of rainfall rate show that relative occurrence of rainfall rate of NICAM is similar to that of the TRMM PR data for strong rains. Spectral representation of rainfall rate shows that the result of NICAM has generally higher rain-top height than that of TRMM PR. NICAM produces stronger rainfall especially for deep convective rains. Both NICAM and TRMM PR have two peaks of rainfall rate in shallow and high rain-top heights for stratiform rains.

One of the advantages of using the global cloud resolving model is that the model results provide plenty of information concerning physical quantities, which are not easily retrieved from the observation, such as vertical velocity and ice phase concentrations. In particular, since the precipitation intensity of the TRMM PR data does not take account of updraft velocities of the air, the rainfall speed might be upward relative to the ground in very strong cloud cores of deep cumulus, even when the positive rainfall rate is analyzed by the TRMM PR algorithm.

Keywords

Tropical Rainfall Measure Mission Rainfall Rate Precipitation Radar Grid Interval Convective Rain 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

 

  1. Bryan, G. H., J. C. Wyngaard, and J. M. Fritsch, 2003: Resolution requirements for the simulation of deep moist convection. Mon. Wea. Rev., 131, 2394-2416.CrossRefGoogle Scholar
  2. Grabowski, W. W., 1998: Toward cloud resolving modeling of large-scale tropical circulation: A simple cloud microphysics parameterization. J. Atmos. Sci., 55, 3283-3298.CrossRefGoogle Scholar
  3. Grabowski, W.W., 2003: Impact of ice microphysics on multiscale organization of tropical convection in two-dimensional cloud-resolving simulations. Q. J. R. Meteorol. Soc., 129, 67-81.CrossRefGoogle Scholar
  4. Jorgensen, D.P., M.A. LeMone and S. Trier, 1997: Structure and evolution of the 22 February 1993 TOGA COARE squall line: Aircraft observations of precipitation, circulation, and surface energy fluxes. J. Atmos. Sci., 54, 1961-1985.CrossRefGoogle Scholar
  5. Lang, S., W.-K. Tao, J. Simpson, and B. Ferrier, 2003: Modeling of convective-stratiform precipitation processes: Sensitivity to partitioning methods. J. Appl. Meteor., 42, 505-527.CrossRefGoogle Scholar
  6. Louis, J. F., M. Tiedke, and J.-F. Geleyn, 1982: A short history of the PBL parameterization at ECMWF. Workshop on Planetary Boundary layer Parameterization, ECMWF, Reading, U.K. 59-80.Google Scholar
  7. Miura, H., H. Tomita, T. Nasuno, S. Iga, M. Satoh, T. Matsuno, 2005: A climate sensitivity test using a global cloud resolving model under an aqua planet condition, Geophys. Res. Lett., 32, L19717, doi:10.1029/2005GL023672.CrossRefGoogle Scholar
  8. Montmerle, T., J.-p. Lafore and J.-L. Redelsperger, 2000: A tropical squall line observed during TOGA COARE: Extended comparisons between simulations and Doppler radar data and the role of midlevel wind shear. Mon. Weath. Rev., 128, 3709-3730.CrossRefGoogle Scholar
  9. Nakajima, T., M. Tsukamoto, Y. Tsushima, A. Numaguti, and T. Kimura, 2000: Modeling of the radiative process in an atmospheric general circulation model. Appl. Opt., 39, 4869-4878.CrossRefGoogle Scholar
  10. Nakazawa, T., 1988: Tropical super clusters within intraseasonal variations over the western Pacific. J. Meteor. Soc. Japan, 66, 823-839.Google Scholar
  11. Nasuno, T., H. Tomita, S. Iga, H. Miura, M. Satoh, Multi-scale organization of convection simulated with explicit cloud processes on an aqua planet. J. Atmos. Sci. 64, 1902-1921.Google Scholar
  12. Neale, R.B., and B.J. Hoskins, 2000: A standard test for AGCMs including their physical parameterizations: the proposal. Atmos. Sci. Lett., 1, doi: 10.1006/asle.2000.0019.Google Scholar
  13. Randall, D. A., M. Khairoutdinov, A. Arakawa, W. Grabowski, 2003: Breaking the cloudparameterization deadlock. Bull. Amer. Meteor. Soc., 84, 1547-1564.CrossRefGoogle Scholar
  14. Satoh, M., H. Tomita, H. Miura, S. Iga, and T. Nasuno, 2005: Development of a global cloud resolving model - A multi-scale structure of tropical convections. J. Earth Simulator, 3, 11-19.Google Scholar
  15. Satoh, M., T. Matsuno, H. Tomita, H. Miura, T. Nasuno, and S. Iga, 2007: Nonhydrostatic Icosahedral Atmospheric Model (NICAM) for global cloud resolving simulations. J. Comp. Phys., doi: 10.1016/j.jcp.2007.02.006.Google Scholar
  16. Schumacher, C. and R. A. Houze Jr., 2003: The TRMM precipitation radar’s view of shallow, isolated rain. J. Appl. Meteor., 42, 1519-1524.CrossRefGoogle Scholar
  17. Shige, S., Y. Takayabu, W.-K. Tao, and D. E. Johnson, 2004: Spectral retrieval of latent heating profiles from TRMM PR Data. Part I: Development of a model-based algorithm. J. Appl. Meteor., 43,1095-1113.CrossRefGoogle Scholar
  18. Skamarock, W. C., 2004: Evaluating mesoscale NWP models using kinetic energy spectra. Mon. Wea. Rev., 132, 3019-3032. doi: 10.1175/MWR2830.1.CrossRefGoogle Scholar
  19. Takayabu, Y. N., 2002: Spectral representation of rain profiles and diurnal variations observed with TRMM PR over the equatorial area. Geophys. Res. Let., 29, doi:10.1029/2001GL014113.Google Scholar
  20. Takayabu, Y. N., T. Iguchi, M. Kachi, A. Shibata, and H. Kanzawa, 1999: Abrupt termination of the 1997-98 El Nino in response to a Madden-Julian oscillation. Nature, 402, 279-282.CrossRefGoogle Scholar
  21. Tomita, H. M. Tsugawa, M. Satoh, and K. Goto, 2001: Shallow water model on a modified icosahedral geodesic grid by using spring dynamics. J. Comput. Phys., 174, 579-613.CrossRefGoogle Scholar
  22. Tomita, H. M. Satoh, and K. Goto, 2002: An optimization of the icosahedral grid modified by the spring dynamics. J. Comput. Phys., 183, 307-331.CrossRefGoogle Scholar
  23. Tomita, H. and M. Satoh, 2004: A new dynamical framework of nonhydrostatic global model using the icosahedral grid. Fluid Dyn. Res., 34, 357-400.Google Scholar
  24. Tomita, H., H. Miura, S. Iga, T. Nasuno, and M. Satoh, 2005: A global cloud-resolving simulation : Preliminary results from an aqua planet experiment. Geophys. Res. Lett., 32, L08805, doi:10.1029/2005GL022459.CrossRefGoogle Scholar
  25. TRMM Precipitation Radar Team, 2005: Tropical rainfall measuring mission (TRMM) precipitation radar algorithm instruction manual for version 6, Japan Aerospace Exploration Agency (JAXA) and National Aeronautics and Space Administration (NASA), 175 pp., available at http://www.eorc.nasda.go.jp/TRMM/documment/text/TRMM V6.pdf.

Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  • M. Satoh
  • T. Nasuno
  • H. Miura
  • H. Tomita
  • S. Iga
  • Y. Takayabu

There are no affiliations available

Personalised recommendations