10.2 Fire Weather Forecasting in the Pacific Northwest using 500mb Map Types

Thursday, 4 May 2023: 11:00 AM
Scandinavian Ballroom Salon 4 (Royal Sonesta Minneapolis Downtown )
Reed Humphrey, Univ. of Washington, Seattle, WA; and J. Saltenberger, J. T. Abatzoglou, and A. Cullen

Increasing levels of simultaneous wildfire over recent years have further complicated the task of coordinating wildfire suppression efforts and resources, both within and between regions. Near-term fire weather forecasts are a key source of information used by fire managers in decisions relating to the allocation and pre-positioning of wildfire suppression resources. In this research, we assess the value of incorporating data on upper-air circulation patterns in predictions of wildfire activity in the Pacific Northwest. Specifically, we test a number of subregional models that predict daily ignitions and the probability of large wildfire, incorporating a set of thirteen 500mb map types previously linked by the Northwest Coordination Center (NWCC) to differential lightning activity in the region.

We find that certain 500mb map types are strongly correlated with the number of fires that start on a given day in the Pacific Northwest, and that these data capture additional variation in wildfire risk that is not reflected in the fire weather indices of the National Fire Danger Rating System alone. The effect of different 500mb map types also varies spatially within the region, providing fire managers with insights on where fire risk may be elevated under different upper-air conditions. Additionally, we find that transitions between 500mb map types can moderate wildfire outcomes.

Fire managers rely on a variety of decision-support tools when allocating and pre-positioning wildfire suppression resources. This research contributes to the set of fire weather forecasting tools available to fire managers, providing additional information that may contribute to better decisionmaking. Further, leveraging data that are already collected by the NWCC provides an opportunity to improve fire weather forecasting without creating significant additional data processing burdens.

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