Environmental modelling of remote areas requires dynamical downscaling of meteorological data to obtain precipitation values that could substitute for sparse in-situ observations. This study examined numerical simulations of precipitation over the Terrace-Kitimat Valley, an industrializing corridor in the Coast Mountains of northern British Columbia, Canada. Modelling uncertainty was explored for 1 year of output from the Weather Research and Forecasting model at 1-km grid spacing for three atmospheric forcing datasets and two planetary boundary layer (PBL) schemes. The observed total precipitation ranged from 1170 to 2380 mm and was often underestimated by more than 40% when using the North American Regional Reanalysis as atmospheric forcing data or the Mellor-Yamada-Nakanishi-Niino level 3 (MYNN3) parameterization as PBL scheme. Persistent low bias from model configurations using these configurations suggested that merely selecting an alternative atmospheric forcing dataset does not ameliorate systematic error occasioned by a poorly performing PBL parameterization. Hence, the choice of the PBL scheme and the meteorological dataset is important for spatial estimation of precipitation using WRF. Model output best corresponded with annual gauge measurements when simulations with the Mellor-Yamada-Janji c (MYJ) PBL scheme were forced with ERA5. The North American Mesoscale Analyses (NAM-ANL) however demonstrated better performance for monthly variation and high-intensity precipitation than ERA5. Using both datasets therefore may be valuable for calculations related to environmental change. With either NAM-ANL or ERA5 as atmospheric forcing data and MYJ as the PBL scheme, the uncertainty in annual simulated precipitation amount ranged between 38% overestimation and 21% underestimation of observational data.
Evaluation of downscaled meteorological information is crucial to identifying model behaviors that may propagate to end applications such as the simulation of local air quality. This study conducted and assessed yearlong simulations of hourly meteorological conditions over the Terrace–Kitimat Valley of northwestern British Columbia, Canada, at 1-km horizontal gridding for six PBL schemes in the Weather and Forecasting (WRF) Model, version 4.0. In terms of key surface meteorological variables that affect air quality, simulations over land demonstrated better skill for specific humidity and wind direction than for air temperature and wind speed. Spatial differences in modeled atmospheric properties and vertical profiles, especially for moisture content, were used to diagnose the relative capacity of each PBL scheme to represent pollutant dispersion and dilution. Stable conditions at night increased suppression of boundary layer mixing by the nonlocal Yonsei University (YSU) scheme when compared with suppression by the local eddy-diffusion component of the Asymmetric Convective Model, version 2 (ACM2), scheme, resulting in decreased wind speed and ambient temperature but moister air with the YSU scheme. The weakening of mixing by the Mellor–Yamada–Nakanishi–Niino (MYNN3) scheme with inland distance suggested that higher-order, nonlocal transport is sensitive to increasing topographic steepness toward the northern part of the valley. Disparities in mixing strengths among PBL schemes were greater in the summer when conditions were generally less stable with moist, warm air blowing inland than in winter when the valley channels cold, stable air from the interior. Increased convection in daytime led to greater entrainment of air from aloft and a thicker PBL with the YSU scheme than with the ACM2 scheme in summer while increasing countergradient transport in the MYNN3 scheme that reduces dilution.
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