Given predictability challenges of heavy rainfall, winter weather, and medium range forecasts, the Center has focused on exploiting ensemble information - from the experimental NSSL storm scale NWS-e, to the NOAA High Resolution Ensemble Forecast, to the traditional NOAA Short Range and Global Ensemble Forecast Systems. These systems support a cascade of NWS products from probabilistic Outlooks, to Watches, to Warnings that gradually ramp up urgency as the confidence increases and details become clear. Further, supported by the new coupled Next Generation Global Prediction System, the Center plans to extend daily forecasts out to 10 days - providing greater lead time for decision makers. Recent focus has been on improving the calibration and sharpness of the heavy rainfall and snowfall ensemble distributions - particularly at the tails of the distribution (representing extreme events). Further, the Center has leveraged post processing to place forecasts in context - quantifying the rarity of forecast events through recurrence intervals, record alerts, and standardized anomalies. Such information aids communication in decision support.
The expansion of probabilistic forecasts, extensions out in time, and increased provision of decision support have revealed challenges. Fundamentally partner expectations often exceed current capabilities in terms of accuracy, specificity, and lead time. Further, NWP is not necessarily tuned to partner decisions. To address these challenges the Center hosts three intensive annual experiments in the Hydrometeorological Testbed - the Winter Weather Experiment, the Flash Flood and Intense Rainfall Experiment, and the Days 8, 9, and 10 Experiment. These experiments test new NWP and associated post processing among a diverse audience of operational forecasters, developers, and researchers. Experiment results drive changes in NWP development.
This talk will discuss WPC's experience in serving as a bridge between NWP and decision makers, and how this informs future modeling needs. Particular emphasis will be placed on improving accuracy, specificity, lead time, and post-processing to inform partner decisions.
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