Sensitivity Study on High-Resolution Numerical Modeling of Static Topographic Data

Abstract

Both research-grade and operational numerical weather prediction models perform simulations with horizontal grid spacings as fine as 1 km, and their multi-scale terrain data have become increasingly important for high-resolution model forecasting. This study focused on the influence of multi-scale surface databases of topographical height and land use on the modeling of atmospheric circulation in a megacity. The default data were the global 30S United States Geographic Survey terrain data set and Moderate Resolution Imaging Spectroradiometer land-use data. The capacity for topographical expression under the combined scale effect was evaluated against observational data. The experiments showed that surface input data using finer resolutions for the Weather Research and Forecasting model with 1-km resolution gave better topographical expression and meteorological reproduction in a megacity and agreed with observational data in the fields of temperature and relative humidity, but precipitation values were not sensitive to the surface input data when verified against a suite of observational data including, but not limited to, ground-based instruments. The results indicated that the use of high-resolution databases improved the local atmospheric circulation in a megacity and that a fine-scale model was sensitive to the resolution of the surface input data whereas a coarse-scale model was less sensitive to it.

Sensitivity Study on High-Resolution WRF Precipitation Forecast for a Heavy Rainfall Event

“…Previous similar studies have shown similar biases in precipitation and maximum core location [16,24,25]. To improve the model performance for the precipitation amount and core location, additional methods could be applied, such as using a finer model resolution, improved physical parameterizations, and effective initial conditions via data assimilation.…”

“…The model configuration used here is the same as that described in [16] with WRF version 3.6.1 representing Seoul and its suburban cities in Korea ( Figure 1). That is, the model is configured in the Lambert conformal map projection with the central latitude of 38 • N and longitude of 126 • E. It uses a one-way nested domain configuration consisting of a 5-km resolution outer domain ( Figure 1a) and a 1-km resolution inner domain (Figure 1b), with 332 × 293 and 336 × 286 grid points, respectively.…”

“…This is partly because the initial condition is a well data-assimilated analysis field and combines with an appropriate configuration. Jee and Kim [16] also showed that topographical terrain data with a resolution higher than the model resolution results in significant improvement in the representation of topographical heights and land use, but did not produce a statistically significant improvement in model performance.…”

Evaluations of WRF Sensitivities in Surface Simulations with an Ensemble Prediction System

“…Precipitation forecasts and simulations of severe weather can be sensitive to cloud microphysics and cumulus parameterizations [15][16][17][18][19][20][21][22][23]. Surface variables (e.g., wind) can also be sensitive to the land surface model, land use and topography [24][25][26][27][28]. Data assimilation has an impact on atmospheric or oceanic models in general, and it also plays an important role in WRF model simulations [29]; post-processing can also improve the results [30].…”

Application of the WRF model to the coastal area at Ise Bay, Japan: evaluation of model output sensitivity to input data

“…Akimoto and Kusaka 2010;Carvalho, Rocha, and Gómez-Gesteira 2012;Misaki et al 2019), (b) topography and land use (e.g. Cheng et al 2013;Jee and Kim 2016;Jiménez-Esteve et al 2018;Mallard, Spero, and Taylor 2018;Kikuchi, Fukushima, and Ishihara 2020), and (c) sea surface temperature (SST) as a lower boundary condition (e.g. LaCasse et al 2008;Song et al 2009;Shimada et al 2015;Kikuchi, Fukushima, and Ishihara 2020).…”