Toward Improved Prediction: High-Resolution and Ensemble Modeling Systems in Operations

Abstract

A large gap in skill between forecasts of the atmospheric circulation (relatively high skill) and quantitative precipitation (low skill) has emerged over the past three decades. One common approach toward closing this gap has been to try to simulate precipitation features directly by decreasing the horizontal grid spacing of the numerical weather prediction models. Also at this time, research has begun to explore the benefits of short-range ensemble forecast methods. The authors argue that each approach has benefits: high-resolution models assist in the development of a forecaster's conceptual model of various mesoscale phenomena, whereas ensembles help quantify forecast uncertainty. A thoughtful implementation of both approaches, in which this complementary nature is recognized, will improve the forecast process, empower human forecasters, and consequently add value relative to current trends. The science and policy issues that must be addressed in order to maximize this forecast potential are discussed.