Aspects of Effective Mesoscale, Short-Range Ensemble Forecasting

Abstract

This study developed and evaluated a short-range ensemble forecasting (SREF) system with the goal of producing useful, mesoscale forecast probability (FP). Real-time, 0–48-h SREF predictions were produced and analyzed for 129 cases over the Pacific Northwest. Eight analyses from different operational forecast centers were used as initial conditions for running the fifth-generation Pennsylvania State University–National Center for Atmospheric Research (PSU–NCAR) Mesoscale Model (MM5). Model error is a large source of forecast uncertainty and must be accounted for to maximize SREF utility, particularly for mesoscale, sensible weather phenomena. Although inclusion of model diversity improved FP skill (both reliability and resolution) and increased dispersion toward statistical consistency, dispersion remained inadequate. Conversely, systematic model errors (i.e., biases) must be removed from an SREF since they contribute to forecast error but not to forecast uncertainty. A grid-based, 2-week, running-mean bias correction was shown to improve FP skill through 1) better reliability by adjusting the ensemble mean toward the mean of the verifying analysis, and 2) better resolution by removing unrepresentative ensemble variance. Comparison of the multimodel (each member uses a unique model) and varied-model (each member uses a unique version of MM5) approaches indicated that the multimodel SREF exhibited greater dispersion and superior performance. It was also found that an ensemble of unequally likely members can be skillful as long as each member occasionally performs well. Finally, smaller grid spacing led to greater ensemble spread as smaller scales of motion were modeled. This study indicates substantial utility in current SREF systems and suggests several avenues for further improvement.

Exchange Processes in the Atmospheric Boundary Layer Over Mountainous Terrain

“…In addition, parameterization schemes that are appropriate at coarse resolutions may be inadequate for km-scale or sub-km-scale simulations, in particular for ABL mixing [232]. These considerations point to the need of complementing high-resolution forecasts with an evaluation of their uncertainty and explain the increasing efforts devoted to the development of limited-area ensemble prediction systems (EPSs) [233][234][235][236][237].…”

The Central European limited‐area ensemble forecasting system: ALADIN‐LAEF

“…Some research studies suggest that the quality of shortrange probabilistic forecasts, in particular for high-impact weather, can be improved by introducing LAM native smallscale perturbations (Tracton et al, 1998), by perturbing surface initial conditions (Sutton et al, 2006), and by accounting for model physics errors (Du et al, 2003;Eckel and Mass, 2005).…”

The Effect of Error Growth and Propagation on the Predictability of Quantitative Precipitation in a Cloud-Resolving Model

“…The perturbations used here are all "small errors" compared to the realistic uncertainties in the initial conditions in numerical weather prediction (NWP) systems, which might be crudely represented by the different analyses from different operational centers (e.g., Eckel and Mass, 2005). If realistic initial errors are used, the predictability could possibly be reduced.…”