Some Issues in Uncertainty Quantification and Parameter Tuning: A Case Study of Convective Parameterization Scheme in the WRF Regional Climate Model
The current tuning process of parameters in global climate models is often performed subjectively, or treated as an optimization procedure to minimize the difference between model fields and observations. The later approach may be generating a set of tunable parameters that approximate the observed climate but via an unrealistic balance of physical processes and/or compensating errors over different regions in the globe. In this study, we run the Weather Research and Forecasting (WRF) regional model constrained by the reanalysis data over the Southern Great Plains (SGP) where abundant observational data from various resources are available for calibration of the input parameters and validation of the model results. Our goal is to quantify the uncertainty ranges and identify the optimal values of five key input parameters in a new Kain-Frisch (KF) convective parameterization scheme incorporated in the WRF model. A stochastic sampling algorithm, Multiple Very Fast Simulated Annealing (MVFSA), is employed to efficiently sample the input parameters in KF scheme based on the skill score so that the algorithm progressively moves toward regions of the parameter space that minimize model errors. The results based on the WRF simulations with 25-km grid spacing over the SGP show that the model bias for precipitation can be significantly reduced by using five optimal parameters identified by the MVFSA algorithm. The model performance is very sensitive to downdraft and entrainment related parameters and consumption time of Convective Available Potential Energy (CAPE). Simulated convective precipitation decreases as the ratio of downdraft to updraft flux increases. Larger CAPE consumption time results in less convective but more stratiform precipitation. The simulation using optimal parameters obtained by only constraining precipitation generates positive impact on the other output variables, such as temperature and wind. By using the optimal parameters obtained at 25 km simulation, both the magnitude and spatial pattern of simulated precipitation are improved at 12-km spatial resolution. The optimal parameters identified from the SGP region have also improved the simulation of precipitation when moving model domain to another region with a different climate regime (i.e. North America monsoon region). These results suggest the improvement of precipitation simulation by using the optimal parameters remains when the model domain or spatial resolution is changed.
- Research Organization:
- Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States). Oak Ridge Leadership Computing Facility (OLCF); Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
- Sponsoring Organization:
- USDOE Office of Science (SC)
- DOE Contract Number:
- AC05-76RL01830
- OSTI ID:
- 1036416
- Report Number(s):
- PNNL-SA-83188; KJ0401000; TRN: US201206%%290
- Journal Information:
- Atmospheric Chemistry and Physics, Vol. 12, Issue 5; ISSN 1680-7316
- Country of Publication:
- United States
- Language:
- English
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Related Subjects
ALGORITHMS
ANNEALING
CALIBRATION
CLIMATE MODELS
CLIMATES
ENTRAINMENT
FORECASTING
MONSOONS
OPTIMIZATION
PERFORMANCE
POTENTIAL ENERGY
PRECIPITATION
SAMPLING
SIMULATION
SPATIAL RESOLUTION
TUNING
VALIDATION
WEATHER
Quantification
Optimization
Kain-Frisch
Convective Parameterization Scheme
WRF
regional climate model