J20.3 Deeper Insights into Winter Weather via Probabilistic Snowfall Forecasts from The Weather Company

Tuesday, 14 January 2020: 2:15 PM
James I. Belanger, The Weather Company, Brookhaven, GA; and J. K. Williams, J. P. Koval, J. McDonald, P. Bayer, N. McGillis, L. Howard, and R. L. Weeks

In recent years, various segments of the Weather Enterprise have worked to expand the use of weather forecasts from single-value, deterministic predictions to probabilistic representations that expose not only the expected value but a range of reasonably-likely outcomes. At The Weather Company, an IBM Business, we view probabilistic forecasts as a way to convey weather forecast information in a richer, more complete way, enabling consumers and businesses to make better decisions. Snowfall accumulation is perhaps the forecast parameter best suited to probabilistic representations since consumers have to-date embraced deterministic snowfall forecasts that are expressed as an expected range. In this talk, we will discuss a new probabilistic snowfall capability that was introduced to consumers in Feb. 2019 via the iOS version of The Weather Channel mobile application. A number of science and engineering challenges were overcome to enable this global, on-demand solution including how to supply probabilistic information at a global scale, ensuring that the deterministic and probabilistic snowfall forecasts are consistent, and that the probabilistic snowfall forecasts are better-calibrated and more skillful than snowfall forecast distributions derived directly from the supporting multi-model ensemble model outputs. Forecasts are calibrated using a combination of logistic regression with a point-mass at 0, and construction of the continuous portion of the snowfall density using a gamma distribution via Bayesian Model Averaging.
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