Removing Numerical Pathologies in a Turbulence Parameterization Through Convergence Testing
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- Pacific Northwest National Laboratory (PNNL), Richland, WA (United States). Atmospheric Science and Global Change Div. (ASGC)
- Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
- Pacific Northwest National Laboratory (PNNL), Richland, WA (United States). Atmospheric Science and Global Change Div. (ASGC); University of Wisconsin, Madison, WI (United States)
- Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States); Blue River Technology, Sunnyvale, CA (United States)
Discretized numerical models of the atmosphere are usually intended to faithfully represent an underlying set of continuous equations, but this necessary condition is violated sometimes by subtle pathologies that have crept into the discretized equations. Such pathologies can introduce undesirable artifacts, such as sawtooth noise, into the model solutions. The presence of these pathologies can be detected by numerical convergence testing. This study employs convergence testing to verify the discretization of the Cloud Layers Unified By Binormals (CLUBB) model of clouds and turbulence. That convergence testing identifies two aspects of CLUBB's equation set that contribute to undesirable noise in the solutions. First, numerical limiters (i.e., clipping) used by CLUBB introduce discontinuities or slope discontinuities in model fields. Second, nonlinear artificial diffusion employed for improving numerical stability can introduce unintended small-scale features into the solution of the model equations. Smoothing the limiters and using linear artificial diffusion reduces the noise and restores the expected first-order convergence in CLUBB's solutions. These model reformulations enhance our confidence in the trustworthiness of solutions from CLUBB by eliminating the unphysical oscillations in high-resolution simulations. The improvements in the results at coarser, near-operational grid spacing and timestep are also seen in cumulus cloud and dry turbulence tests. In addition, convergence testing is proven to be a valuable tool for detecting pathologies, including unintended discontinuities and grid dependence, in the model equation set.
- Research Organization:
- Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States); Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
- Sponsoring Organization:
- USDOE National Nuclear Security Administration (NNSA); USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR); USDOE Office of Science (SC), Biological and Environmental Research (BER)
- Grant/Contract Number:
- AC52-07NA27344; 70276; AC05-76RL01830
- OSTI ID:
- 1973558
- Alternate ID(s):
- OSTI ID: 1973560; OSTI ID: 1985224; OSTI ID: 1985897
- Report Number(s):
- LLNL-JRNL-843646; PNNL-SA-171844; 1066343
- Journal Information:
- Journal of Advances in Modeling Earth Systems, Vol. 15, Issue 5; ISSN 1942-2466
- Publisher:
- American Geophysical Union (AGU)Copyright Statement
- Country of Publication:
- United States
- Language:
- English
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