Title: Diagnosing added value of convection-permitting regional models using precipitation event identification and tracking,
Abstract: 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 model resolutions both allow and require increasingly
complex diagnostics for evaluating model fidelity. In this study we use a suite
of dynamically downscaled simulations of the summertime U.S. (WRF driven by
NCEP) with systematic variations in parameters and treatment of convection as a
test case for evaluation of model precipitation. In particular, we use a novel
rainstorm identification and tracking algorithm that allocates essentially all
rainfall to individual precipitation events (Chang et al. 2016). This approach
allows multiple insights, including that, at least in these runs, model wet
bias is driven by excessive areal extent of precipitating events. Biases are
time-dependent, producing excessive diurnal cycle amplitude. We show that this
effect is produced not by new production of events but by excessive enlargement
of long-lived precipitation events during daytime, and 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.