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| Titel |
Some issues in uncertainty quantification and parameter tuning: a case study of convective parameterization scheme in the WRF regional climate model |
| VerfasserIn |
B. Yang, Y. Qian, G. Lin, R. Leung, Y. Zhang |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1680-7316
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| Digitales Dokument |
URL |
| Erschienen |
In: Atmospheric Chemistry and Physics ; 12, no. 5 ; Nr. 12, no. 5 (2012-03-05), S.2409-2427 |
| Datensatznummer |
250010852
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| Publikation (Nr.) |
 copernicus.org/acp-12-2409-2012.pdf |
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| Zusammenfassung |
The current tuning process of parameters in global climate models is often
performed subjectively or treated as an optimization procedure to minimize
model biases based on observations. While the latter approach may provide
more plausible values for a set of tunable parameters to approximate the
observed climate, the system could be forced to an unrealistic physical
state or improper balance of budgets through compensating errors over
different regions of the globe. In this study, the Weather Research and
Forecasting (WRF) model was used to provide a more flexible framework to
investigate a number of issues related uncertainty quantification (UQ) and
parameter tuning. The WRF model was constrained by reanalysis of data over
the Southern Great Plains (SGP), where abundant observational data from
various sources was available for calibration of the input parameters and
validation of the model results. Focusing on five key input parameters in
the new Kain-Fritsch (KF) convective parameterization scheme used
in WRF as an example, the purpose of this study was to explore the utility
of high-resolution observations for improving simulations of regional
patterns and evaluate the transferability of UQ and parameter tuning across
physical processes, spatial scales, and climatic regimes, which have
important implications to UQ and parameter tuning in global and regional
models. A stochastic importance sampling algorithm, Multiple Very Fast
Simulated Annealing (MVFSA) was employed to efficiently sample the input
parameters in the KF scheme based on a skill score so that the algorithm
progressively moved 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 showed that the precipitation bias in the model could be significantly
reduced when five optimal parameters identified by the MVFSA algorithm were
used. The model performance was found to be sensitive to downdraft- and
entrainment-related parameters and consumption time of Convective Available
Potential Energy (CAPE). Simulated convective precipitation decreased as the
ratio of downdraft to updraft flux increased. Larger CAPE consumption time
resulted in less convective but more stratiform precipitation. The
simulation using optimal parameters obtained by constraining only
precipitation generated 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 were improved at 12-km spatial resolution. The optimal
parameters identified from the SGP region also improved the simulation of
precipitation when the model domain was moved to another region with a
different climate regime (i.e. the North America monsoon region). These
results suggest that benefits of optimal parameters determined through
vigorous mathematical procedures such as the MVFSA process are transferable
across processes, spatial scales, and climatic regimes to some extent. This
motivates future studies to further assess the strategies for UQ and
parameter optimization at both global and regional scales. |
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