Open AccessThe Dependence of QPF on the Choice of Boundary- and Surface-Layer Parameterization for a Lake-Effect Snowstorm
Author(s) -
Robert Conrick,
Heather D. Reeves,
Shiyuan Zhong
Publication year - 2015
Publication title -
journal of applied meteorology and climatology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.079
H-Index - 134
eISSN - 1558-8432
pISSN - 1558-8424
DOI - 10.1175/jamc-d-14-0291.1
Subject(s) - precipitation , snow , environmental science , winter storm , boundary layer , flux (metallurgy) , surface layer , quantitative precipitation forecast , climatology , atmospheric sciences , meteorology , geology , layer (electronics) , physics , thermodynamics , chemistry , organic chemistry
Six forecasts of a lake-effect-snow event off Lake Erie were conducted using the Weather Research and Forecasting Model to determine how the quantitative precipitation forecast (QPF) was affected when the boundary- and surface-layer parameterization schemes were changed. These forecasts showed strong variability, with differences in liquid-equivalent precipitation maxima in excess of 20 mm over a 6-h period. The quasi-normal scale elimination (QNSE) schemes produced the highest accumulations, and the Mellor–Yamada–Nakanishi–Niino (MYNN) schemes produced the lowest. Differences in precipitation were primarily due to different sensible heat flux F H and moisture flux F Q off the lake, with lower F H and F Q in MYNN leading to comparatively weak low-level instability and, consequently, reduced ascent and production of hydrometeors. The different F H and F Q were found to have two causes. In QNSE, the higher F H and F Q were due to the decision to use a Prandtl number P R of 0.72 (all other schemes use a P R of 1). In MYNN, the lower F H and F Q were due to the manner in which the similarity stability function for heat ψ h is functionally dependent on the temperature gradient between the surface and the lowest model layer. It is not known what assumptions are more accurate for environments that are typical for lake-effect snow, but comparisons with available observations and Rapid-Update-Cycle analyses indicated that MYNN had the most accurate results.