Does Increasing Horizontal Resolution Produce More Skillful Forecasts?

This paper examines the impacts of increasing horizontal resolution on the performance of mesoscale numerical weather prediction models. A review of previous studies suggests that decreasing grid spacing to approximately 10 km or less generally produces more realistic mesoscale structures, with particular benefits for orographically and diurnally driven flows. There have been only a few long-term objective verification studies of high-resolution forecasts, and these studies suggest, perhaps deceptively, that there are diminishing returns as horizontal grid spacing decreases below approximately 10 km. A multiyear objective verification of the University of Washington MM5 real-time forecasting system compares the realism of predicted surface parameters at 36-, 12-, and 4-km grid spacing over western Washington state for periods up to 48 h. Traditional verification statistics (such as mean absolute, bias, and root-mean-square error) are calculated by interpolating model forecasts to the observation sites. For precipitation, it is shown that model skill over western Washington improves as grid spacing decreases from 36 to 12 km. Verification scores generally degrade as resolution is increased from 12 to 4 km as overprediction develops over the windward slopes and the crests of terrain. However, for heavy precipitation amounts on windward slopes, the transition from 12 to 4 km does appear to enhance forecast accuracy. Temperature and wind statistics indicate noticeably improved skill as horizontal resolution decreases from 36 to 12 km, while only minor increases in skill are evident as grid spacing decreases to 4 km. In contrast, verification of sea level pressure suggests little improvement as resolution is increased. The benefits of resolution are not uniform over the 4-km domain. For all parameters, the region downwind of a major barrier—the Olympic Mountains—enjoys the largest enhancement of forecast skill as resolution is increased from 36 to 12 km, with noticeable, but lesser, improvements as grid spacing decreases to 4 km. Filtering the verification times to consider only periods with relatively good synoptic-scale forecasts provides only modest forecast improvement. A case study illustrates that decreasing grid spacing to 4 km produces more realistic mesoscale structures and amplitudes, a fact that is not revealed by traditional verification approaches because a small timing error existed. The paper ends by examining the benefits of resolution, some pitfalls associated with traditional verification approaches, and suggests future directions for numerical weather prediction and mesoscale verification. It is argued that higher resolution might be more beneficial than simple skill scores and that new mesoscale verification approaches must be crafted.