CRCP: a Cloud Resolving Convection Parameterization for modeling the tropical convecting atmosphere. A new computational approach, CRCP, is proposed in which both the large-scale (LS) tropical dynamics and cloud-scale (CS) dynamics are captured explicitly. The leading idea is to represent subgrid scales of the LS model by imbedding a 2D CS model in each column of the 3D LS model – the approach tailored for distributed memory architectures. The overall philosophy underlying CRCP is the reinvestment of efforts from large-eddy simulation to elaborate yet ‘embarrassingly parallel’ turbulence models. Similar as in the traditional ‘convection parameterization’, the LS model provides ‘ambient forcings’ for the CS model imbedded inside each LS column, and the CS model feeds back a ‘convective response’ for every column of the LS model. Furthermore, availability of the cloud-scale data allows for explicit coupling of moist convection with radiative and surface processes. Following our experience with cloud-resolving modeling of the tropical convection, the CS model is oriented along the E–W direction inside each LS model column. A simple strategy for the coupling the LS and CS models derives from physical understanding of interactions between LS flow and moist tropical convection. Theoretical considerations are illustrated with an example of application to observational data from the Phase III of the Global Atmospheric Research Programme Atlantic Tropical Experiment (GATE).

References in zbMATH (referenced in 15 articles )

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  1. Lee, Yoonsang: Parameter estimation in the stochastic superparameterization of two-layer quasigeostrophic flows. Estimation of subgrid-scale modeling parameters in the stochastic superparameterization of two-layer quasigeostrophic turbulence (2020)
  2. Berry, Tyrus; Harlim, John: Semiparametric modeling: correcting low-dimensional model error in parametric models (2016)
  3. Lee, Yoonsang; Engquist, Bjorn: Multiscale numerical methods for passive advection-diffusion in incompressible turbulent flow fields (2016)
  4. Lee, Yoonsang; Majda, Andrew J.: Multiscale methods for data assimilation in turbulent systems (2015)
  5. Grooms, Ian G.; Majda, Andrew J.: Stochastic superparameterization in a one-dimensional model for wave turbulence (2014)
  6. Grooms, Ian; Lee, Yoonsang; Majda, Andrew J.: Ensemble Kalman filters for dynamical systems with unresolved turbulence (2014)
  7. Grooms, Ian; Majda, Andrew J.: Stochastic superparameterization in quasigeostrophic turbulence (2014)
  8. Majda, Andrew J.; Grooms, Ian: New perspectives on superparameterization for geophysical turbulence (2014)
  9. Malecha, Ziemowit; Chini, Greg; Julien, Keith: A multiscale algorithm for simulating spatially-extended Langmuir circulation dynamics (2014)
  10. Stechmann, Samuel N.: Multiscale eddy simulation for moist atmospheric convection: preliminary investigation (2014)
  11. Branicki, M.; Majda, A. J.: Dynamic Stochastic Superresolution of sparsely observed turbulent systems (2013)
  12. Khouider, Boualem; Majda, Andrew J.; Stechmann, Samuel N.: Climate science in the tropics: waves, vortices and PDEs (2013)
  13. Majda, Andrew J.: Challenges in climate science and contemporary applied mathematics (2012)
  14. Piotrowski, Zbigniew P.; Smolarkiewicz, Piotr K.; Malinowski, Szymon P.; Wyszogrodzki, Andrzej A.: On numerical realizability of thermal convection (2009)
  15. Grabowski, Wojciech W.; Smolarkiewicz, Piotr K.: CRCP: a cloud resolving convection parameterization for modeling the tropical convecting atmosphere (1999)