A Performance Comparison between Multiphysics and Stochastic Approaches within a North American RAP Ensemble

Abstract

A stochastic parameter perturbation (SPP) scheme consisting of spatially and temporally varying perturbations of uncertain parameters in the Grell–Freitas convective scheme and the Mellor–Yamada–Nakanishi–Niino planetary boundary scheme was developed within the Rapid Refresh ensemble system based on the Weather Research and Forecasting Model. Alone the stochastic parameter perturbations generate insufficient spread to be an alternative to the operational configuration that utilizes combinations of multiple parameterization schemes. However, when combined with other stochastic parameterization schemes, such as the stochastic kinetic energy backscatter (SKEB) scheme or the stochastic perturbation of physics tendencies (SPPT) scheme, the stochastic ensemble system has comparable forecast performance. An additional analysis quantifies the added value of combining SPP and SPPT over an ensemble that uses SPPT only, which is generally beneficial, especially for surface variables. The ensemble combining all three stochastic methods consistently produces the best spread–skill ratio and generally outperforms the multiphysics ensemble. The results of this study indicate that using a single-physics suite ensemble together with stochastic methods is an attractive alternative to multiphysics ensembles and should be considered in the design of future high-resolution regional and global ensembles.

Diagnosing spatial error structures in CO<sub>2</sub> mole fractions and XCO<sub>2</sub> column mole fractions from atmospheric transport

“…Atmospheric carbon dioxide (CO 2 ) mole fraction has been increasing steadily since the first industrial revolution, primarily due to fossil fuel emissions and land use change (IPCC, 2015). Recent estimates of sources and sinks at the global scale suggest a coincidental reinforcement of natural sinks balancing the continuously increasing anthropogenic emissions (Le Quéré et al, 2016;Keenan et al, 2016). Therefore, the fraction of fossil fuel CO 2 remaining in the atmo-12008 T. Lauvaux et al: CO 2 and XCO 2 error structures sphere 1 was kept constant at 2 ppm yr −1 , excluding shorttime anomalies such as El Niño events (Feely et al, 1999;Kim et al, 2016).…”

“…But small-size ensembles are efficient computationally and able to provide information on flow-dependent error structures compared to prescribed static error structures (Brousseau et al, 2012). With the development of new perturbation methods, the number of members may decrease significantly thanks to optimal perturbations combining physics, parameter sensitivity and energy-based perturbations (Jankov et al, 2017). In any case, small-size ensembles remain affected by sampling noise, which has to be removed before extracting spatial structures, either by modeling (Pannekoucke et al, 2008;Lauvaux et al, 2009) or by filtering unphysical structures (Hamill et al, 2001;Houtekamer and Mitchell, 2001).…”

An evaluation of the convection‐permitting ensemble COSMO‐E for three contrasting precipitation events in Switzerland

“…While in convection‐parametrizing ensembles the SKEB scheme induces larger ensemble dispersion (Berner et al ), the SPPT scheme leads to larger spread growth in convection‐permitting ensembles (Romine et al ). Ensembles combining multiple model error schemes outperform ensembles applying only a single scheme, as the particular schemes address different aspects of model error (Berner et al ; Jankov et al ). The impact of the model error schemes was found to depend on the synoptic situation.…”

Diagnosing spatial error structures in CO<sub>2</sub> mole fractions and XCO<sub>2</sub> column mole fractions from atmospheric transport

“…But small-size ensembles are efficient computationally and able to provide information on flow-dependent error structures com-5 pared to prescribed static error structures (Brousseau et al, 2012). With the development of new perturbation methods, the number of members may decrease significantly thanks to optimal perturbations combining physics, parameter sensitivity and energy-based perturbations (Jankov et al, 2017). In any case, small-size ensembles remain affected by sampling noise which has to be removed before extracting spatial structures, either by modeling (Pannekoucke et al, 2008;Lauvaux et al, 2009) or by filtering unphysical structures (Hamill et al, 2001;Houtekamer and Mitchell, 2001).…”