Diagnosing added value of convection-permitting regional models using precipitation event identification and tracking

Chang, Won; Wang, Jiali; Marohnic, Julian; Kotamarthi, V. Rao; Moyer, Elisabeth J.

doi:10.1007/s00382-018-4294-0

Title: Diagnosing added value of convection-permitting regional models using precipitation event identification and tracking

Journal Article · Wed Jul 11 00:00:00 EDT 2018 · Climate Dynamics

DOI:https://doi.org/10.1007/s00382-018-4294-0· OSTI ID:1544253

Chang, Won ^[1]; Wang, Jiali ^[2]; Marohnic, Julian ^[3]; Kotamarthi, V. Rao ^[2]; Moyer, Elisabeth J. ^[3]

Univ. of Cincinnati, Cincinnati, OH (United States)
Argonne National Lab. (ANL), Lemont, IL (United States)
Univ. of Chicago, Chicago, IL (United States)

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. Here 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 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. Here, 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.

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Research Organization:: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States). National Energy Research Scientific Computing Center (NERSC); Argonne National Laboratory (ANL), Argonne, IL (United States)

Sponsoring Organization:: USDOE Office of Science (SC); National Science Foundation (NSF)

Grant/Contract Number:: 1106862; 1106974; 1107046; 0951576; AC02-06CH11357

OSTI ID:: 1544253

Alternate ID(s):: OSTI ID: 1569848

Journal Information:: Climate Dynamics, Vol. 55, Issue 1-2; ISSN 0930-7575

Publisher:: Springer-VerlagCopyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 12 works

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Title: Diagnosing added value of convection-permitting regional models using precipitation event identification and tracking

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