Assessment of the Forecast Skill of Multiphysics and Multistochastic Methods within the GRAPES Regional Ensemble Prediction System in the East Asian Monsoon Region

Abstract

To more comprehensively and accurately address model uncertainties in the East Asia monsoon region, a single-physics suite, where each ensemble member uses the same set of physics parameterizations as the control member in combination with multiple stochastic schemes, is developed to investigate if the multistochastic schemes that combine different stochastic schemes together can be an alternative to a multiphysics suite, where each ensemble member uses a different set of physics parameterizations (e.g., cumulus convection, boundary layer, surface layer, microphysics, and shortwave and longwave radiation). For this purpose, two experiments are performed for a summer monsoon month over China: one with a multiphysics suite and the other with a single-physics suite combined with multistochastic schemes. Three stochastic schemes are applied: the stochastically perturbed parameterizations (SPP) scheme, consisting of temporally and spatially varying perturbations of 18 parameters in the microphysics, convection, boundary layer, and surface layer parameterization schemes; the stochastically perturbed parameterization tendencies (SPPT) scheme; and the stochastic kinetic energy backscatter (SKEB) scheme. The combination of the three stochastic schemes is compared with the multiphysics suite in the Global and Regional Assimilation and Prediction Enhanced System–Regional Ensemble Prediction System with a horizontal grid spacing of 15 km. Verification results show that, overall, a single-physics suite that combines SPP, SPPT, and SKEB outperforms the multiphysics suite in precipitation verification and verification for upper-air weather variables, 10-m zonal wind, and 2-m temperature in the East Asian monsoon region. The indication is that a single-physics suite combining SPP, SPPT, and SKEB may be an appropriate alternative to a multiphysics suite. This finding lays a foundation for the development and design of future regional and global ensembles.

Time Evolution and Diurnal Variability of the Parametric Sensitivity of Turbine‐Height Winds in the MYNN‐EDMF Parameterization

“…Di et al (2015) found that, in general, the parameters that explain the variability of convective storms were independent of forecast lead time. In contrast, Xu et al (2020) found some sensitivity to lead time for variables above 850 mb, but there was less sensitivity close to the surface.…”

“…In contrast, Xu et al. (2020) found some sensitivity to lead time for variables above 850 mb, but there was less sensitivity close to the surface.…”

The roles of small‐scale topographic perturbations in precipitation forecasting using a convection‐permitting ensemble prediction system over southern China

“…(2008) and Xu et al . (2020), and the following description of the random fields is derived from their's with slight modifications and simplification and displayed here only to better understand the change. The random fields defined in past studies have a horizontal distribution related to time and space, and defined as: $$$$ \varphi \left(\lambda, \phi, t,\mathrm{mem}\right)=\mu +\sum \limits_{l=1}^L\sum \limits_{m=-l}^l{\alpha}_{l.m}\left(t,\mathrm{mem}\right){Y}_{l,m}\left(\lambda, \phi \right) $$$$ where $$$ \lambda $$$,$$$ \phi $$$, t and mem are the same as the variables defined above; $$$ \mu $$$ is the average of the random fields; L is the maximum truncation wavenumber of the random fields with a value of 400 in our study, and corresponds to the minimum truncation wavelength; $$$ {Y}_{l,m}\left(\lambda, \phi \right) $$$ are the spherical harmonics; $$$ {\alpha}_{l,m}\left(t,\mathrm{mem}\right) $$$ are the spectral coefficients.…”

“…The random fields are the same as those employed by Li et al (2008) and Xu et al (2020), and the following description of the random fields is derived from their's with slight modifications and simplification and displayed here only to better understand the change. The random fields defined in past studies have a horizontal distribution related to time and space, and defined as:…”

Multi-Model Multi-Physics Ensemble: A Futuristic Way to Extended Range Prediction System

“…Further, it is proposed that the multi-physics ensemble scheme can be an alternative to account for these model-errors (Richardson, 1997;Harrison et al, 1999;Orrell et al, 2001). The intra-model diversification introduced by using more than one physical parameterization showed significant improvement over single physics predictions (Stensrud and Fritsch, 1994;Berner et al, 2011;Tapiador et al, 2012;Greybush et al, 2017;Xu et al, 2020).…”