The sensitivity of a case study bow-echo simulation to eight different microphysical schemes in the Weather Research and Forecasting model was tested, with a focus on graupel parameter characteristics. The graupel parameter in one scheme was modified to have a larger mean size and faster fall speed to represent hail ("hail like"). The goal of the study was to measure the sensitivity of five parameters that are important to opera¬tional forecasters to graupel properties: timing of bowing development, system speed, wind gusts, system areal coverage, and accumulated precipitation. The time each system initiated bowing varied by as much as 105 min. Simulations containing graupel with smaller mean size and slower fall speed ("graupel like") bowed earlier due to increased microphysical cooling and stronger cold pools. These same systems had reduced precipitation efficiency, producing a peak storm-total accumulation of 36 mm, compared to a hail-like peak value of 237 mm, and observed a peak value of 53 mm. Faster-falling hail-like hydrometeors reached the surface with minimal melting, producing the largest accumulations. Graupel-like systems had 10-m wind gusts 73% stronger compared to hail-like systems, due to stronger low-level downdrafts. Systems with a smaller mean graupel size were 19% faster, also due to increased microphysical cooling. The size of the convective region varied by 150%, although this was partially due to scheme differences other than the graupel parameter. The significant differences in bow-echo characteristics produced by graupel property variations in convective-resolving models emphasize careful microphysical parameterization design. These sensitivities have forecasting implications, as graupel characteristics vary depending on the ambient environment and other factors. Detailed observations of graupel properties are recommended. © 2013 American Meteorological Society.
CITATION STYLE
Adams-Selin, R. D., Van Den Heever, S. C., & Johnson, R. H. (2013). Sensitivity of bow-echo simulation to microphysical parameterizations. Weather and Forecasting, 28(5), 1188–1209. https://doi.org/10.1175/WAF-D-12-00108.1
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