Diagnosing added value of convection-permitting regional models using precipitation event identification and tracking
Dynamical downscaling with high-resolution regional climate models may offer the possibility of realistically reproducing precipitation and weather events in climate simulations. As resolutions fall to order kilometers, the use of explicit rather than parametrized convection may offer even greater fidelity. However, these increased resolutions both allow and require increasingly complex diagnostics for evaluating model fidelity. In this study we focus on precipitation evaluation and analyze five 2-month-long dynamically downscaled model runs over the continental United States that employ different convective and microphysics parameterizations, including one high-resolution convection-permitting simulation. All model runs use the Weather Research and Forecasting Model driven by National Center for Environmental Prediction reanalysis data. We show that employing a novel rainstorm identification and tracking algorithm that allocates essentially all rainfall to individual precipitation events (Chang et al. in J Clim 29(23):8355–8376, 2016) allows new insights into model biases. Results include that, at least in these runs, model wet bias is driven by excessive areal extent of individual precipitating events, and that the effect is time-dependent, producing excessive diurnal cycle amplitude. This amplified cycle is driven not by new production of events but by excessive daytime enlargement of long-lived precipitation events. We further show that in the domain average, precipitation biases appear best represented as additive offsets. Of all model configurations evaluated, convection-permitting simulations most consistently reduced biases in precipitation event characteristics.
KeywordsPrecipitation Convection permitting simulation Parameterization Rainstorm tracking
The authors thank Bill Collins, Peter Caldwell, Matthew Huber, Robert Jacob, Andreas Prein, and the participants in the 2016 GEWEX Convection-Permitting Climate Modeling Workshop for many helpful comments and suggestions. Christopher Callahan assisted with preparation of figures and diurnal cycle analysis. This work was conducted as part of the Research Network for Statistical Methods for Atmospheric and Oceanic Sciences (STATMOS), supported by NSF awards 1106862, 1106974, and 1107046, and the Center for Robust Decision-making on Climate and Energy Policy (RDCEP), supported by the NSF Decision Making under Uncertainty program award 0951576. Additional support was provided by the University of Cincinnati TAFT Research Center and computing resources were provided by the University of Chicago Research Computing Center.
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