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


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.


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.


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© 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

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