A Semi-Lagrangian NWP Model for Real-time and Research Applications: Evaluation in Single- and Multi-Processor Environments

Abstract

ABSTRACT A semi-Lagrangian numerical weather prediction (NWP) model developed for both real-time prediction and for research simulations has been evaluated. The model is second-order in time, high-order (≥ 3) in space, and employs a non-staggered grid in both the horizontal and vertical. A version of the model which is third-order in space was compared with two Eulerian models: the Australian Bureau of Meteorology's current operational regional model which is quasi-second-order in rime and space, and also a new version of this model with a third-order upwind scheme that was developed for use as the Australian Bureau of Meteorology's next operational limited-area NWP model. In a three month trial of twice-daily 48-hour forecasts it was found that both the third-order models were significantly more skillful than the current operational model, as measured by the standard performance statistics such as the S1 skill score (Teweles and Wobus, 1954), and root-mean-square (RMS) errors. The specific implications of this greater accuracy were examined in case studies of severe weather events. The semi-Lagrangian model also bas been adapted to global form and run on a daily basis out to 5 days using archived operational data over a period of almost 6 months. Finally, the semi-Lagrangian model code was parallelized on a workstation cluster and also on a scalable parallel computer, and it was found that the model was well-suited to parallelization on both computer platforms.