Spatial Clustering of Low Probabilities Events: Possible Implications for Decision Support Services

Phil Schumacher

Cooperative Institute for Research in the Atmosphere (CIRA) - NOAA Global Systems Laboratory

Tuesday, Apr 08, 2025, 2:00 pm MT

JOIN THE WEBINAR:

Abstract

Providing weather information to decision makers in a form that is understandable and actionable is critical to preparing and keeping the public safe from hazardous weather. Decision support services for hazardous weather increasingly rely on providing decision makers probabilistic weather information. Probabilistic information can provide both details on most likely as well as less likely outcomes that may impact the public. When providing decision support briefings, forecasters must decide what information to provide to partners. For example, the National Weather Service (NWS) Probabilistic Winter Weather web page provides information on the most likely snowfall as well as the 10th and 90th percentile snowfall. Yet, if the probabilistic forecast is reliable, 20% of observations will be outside of this range.

The National Blend of Models (NBM) ingests multiple deterministic models and ensemble systems in order to create probabilistic snowfall and precipitation forecasts. The NBM is planned to be the NWS’s flagship guidance source and serve as the starting point for much of the official forecast information. Probabilistic snowfall forecasts from the NBM were examined for several storms in the winter of 2023-2024 to learn how observations outside of the 10th-90th percentile (outlier observations) were spatially distributed. The spatial distribution of outlier observations was not random or isolated across the domain. Most outlier observations clustered in areas that encompassed several thousand square kilometers. Within some outlier clusters, the observations were several centimeters above (below) the 10th (90th) percentile snowfall. In these cases, the impacts may be greater or less than forecast. Unforecast impacts can negatively affect decision makers and the public. A survey of NWS forecasters asked how they brief partners when expecting an outcome that is greater (less) than the 10th (90th) percentile. These results show that strategies will need to be developed that provide decision makers information about the low probability of different impacts due to outlier events.

Bio: I was hired by the Cooperative Institute for Research in the Atmosphere (CIRA) in 2024 to work as a Forecast Specialist at Global Systems Laboratory (GSL). Since joining CIRA and GSL, I have been evaluating probabilistic snowfall forecasts specifically looking at low probability events and how they organize and change spatially and temporally. Our goal is to develop techniques for discussing low probability events with National Weather Service partners in a way that is usable and actionable. Prior to working for CIRA, I worked for the National Weather Service (NWS) for 31 years. Most of that time was as the Science and Operations Officer in Sioux Falls, South Dakota where I trained forecasters in new technology, new scientific applications for operational forecasting, and validating probabilistic forecasts and incorporating probabilities into information shared with NWS partners and the public.