Evaluation of heat wave forecasts seamlessly across subseasonal timescales. Issue 1 (December 2018) - Ford, Trent; Dirmeyer, Paul; Benson, David
Abstract We develop an extreme heat validation approach for medium-range forecast models and apply it to the NCEP coupled forecast model, for which we also attempt to diagnose sources of poor forecast skill. A weighting strategy based on the Poisson function is developed to provide a seamless transition from short-term day-by-day weather forecasts to expanding time means across subseasonal timescales. The skill of heat wave forecasts over the conterminous United States is found to be rather insensitive to the choice of skill metric; however, forecast skill does display spatial patterns that vary depending on whether daily mean, minimum, or maximum temperatures are the basis of the heat wave metric. The NCEP model fails to persist heat waves as readily as is observed. This inconsistency worsens with longer forecast lead times. Land–atmosphere feedbacks appear to be a stronger factor for heat wave maintenance at southern latitudes, but the NCEP model seems to misrepresent those feedbacks, particularly over the Southwest United States, leading to poor skill in that region. The NCEP model also has unrealistically weak coupling over agricultural areas of the northern United States, but this does not seem to degrade model skill there. Overall, we find that the Poisson weighting strategy combined with a variety of deterministic and probabilistic skill metrics provides a versatile framework for validation of dynamical model heat wave forecasts at subseasonal timescales. Climate science: evaluating forecasts of extreme heat across subseasonal timescales A validation window that broadens with time is developed to provide seamless verification of extreme heat forecasts from days to weeks. Trent Ford (Southern Illinois University), Paul Dirmeyer (COLA-George Mason University), and David Benson (George Mason University) applied several skill metrics with a Poisson function weighting strategy to verify NOAA coupled forecast system model extreme heat forecasts over the United States. The model fails to persist heat waves as readily as is observed, and this inconsistency worsens with longer forecast lead times. Land surface–atmosphere interactions appear to influence heat wave persistence, but the model misrepresents these interactions, leading to poor skill in the Southwest and Midwest regions. The Poisson weighting strategy provides a versatile framework for verifying forecasts across subseasonal timescales. Continued verification and improvement of model forecasts contributes to reducing the risk of extreme climate events.