Sensitivity of a weather research and forecasting model to downscaling schemes in ensemble rainfall estimation

Abstract

Rainfall estimation using the weather research and forecasting (WRF) model is sensitive to physical parameterizations and downscaling configurations. Concerned with the correlations between physical parameterizations and dynamical downscaling, these significant issues were considered simultaneously in this study, and WRF‐based ensembles were integrated and used to estimate eight representative rainfall events. The results revealed that both rainfall estimates and intensities were sensitive to downscaling configurations. Specifically, light rainfall events that were homogeneous over space and time were estimated well using a 5 km horizontal resolution and were overestimated with a 1 km resolution where the planetary boundary layer (PBL) schemes were probably the source of positive errors in light rain conditions. However, when the rainfall was intense and displayed spatiotemporal heterogeneity, the rainfall peaks and volume were only well estimated with a 1 km resolution where cumulus (CU) schemes were the dominant schemes, demonstrating that higher resolutions could better reproduce rainfall patterns for wetter cases. At a 10 km horizontal resolution, the simulations did not display much accuracy, and different physics schemes did not make a substantial difference. Therefore, the rainfall characteristics cannot be ignored. More importantly, the contributions of microphysics, CU and PBL ensembles were quantified, and the sensitivities of the rainfall estimates to three downscaling configurations were studied.

WRF Rainfall Modeling Post-Processing by Adaptive Parameterization of Raindrop Size Distribution: A Case Study on the United Kingdom

“…The Morrison scheme can predict the number concentrations of ice, snow, rain, and graupel particles; the WDM6 scheme can predict the number concentrations of cloud droplets, rain, and cloud condensation nuclei [15,39]; and the Thompson aerosol-aware scheme can predict the number concentrations of cloud droplets and ice, rain, cloud condensation nuclei, and ice nuclei [40]. For the cumulus scheme, the Kain-Fritsch scheme [41] is used, whereas the cumulus scheme is not used in the inner domain because convective rainfall generation is definitely resolved when the model grid spacing is ≤5 km [37,42]. Other physical parameterizations include the Mellor-Yamada-Janjić planetary boundary layer scheme [43], RRTM longwave radiation scheme [44], Dudhia shortwave radiation scheme [45], and Noah land-surface model [46].…”

Intercomparison of atmospheric forcing datasets and twoPBLschemes for precipitation modelling over a coastal valley in northern British Columbia, Canada

“…Hu et al (2018) reached similar conclusions for the Great Plains region of the US where differences were more profound with change in cumulus schemes than other physics schemes. Sensitivity studies in England by Cai et al (2020) found light rainfall was estimated credibly using a 5-km grid spacing while intense, spatiotemporally heterogeneous rainfall was only well estimated with a 1-km grid. For snowfall over the northwestern Iberian Peninsula, Fern andez-Gonz alez et al ( 2015) inferred better choices of microphysics and PBL schemes.…”

Non-Parametric and Robust Sensitivity Analysis of the Weather Research and Forecast (WRF) Model in the Tropical Andes Region

“…As highlighted by [43,44], a sensitivity analysis of a model can be developed using an ensemble (multi-realization of the model), where random perturbation of IC, using a probability density function (PDF), is used. However, most sensitivity analysis research in the WRF model, focuses on the same aspects: modifying the parametrizations of IC/BC [10,14,15,18,32,45,46], or other parameters.…”