The Northern California Wildfires of 8–9 October 2017: The Role of a Major Downslope Wind Event

Abstract

A series of large wildfires began over the terrain north of San Francisco, California, during the evening of 8 October 2017 and spread across nearly 250,000 acres, including areas near the towns of Santa Rosa and Napa. These “Wine Country” wildfires were the most destructive in California history, with 44 deaths; the loss of 9,000 buildings; damage to approximately 21,000 structures; $10 billion of insured losses; and substantially greater total economic loss. This paper describes the synoptic and mesoscale conditions that were associated with the wildfires, with strong, easterly “Diablo” winds playing a central role in both initiating and supporting the fires. The climatological conditions preceding the fires are reviewed, including near-normal precipitation and above-normal temperatures during the summer, as well as much above-normal precipitation the previous winter, which led to abundant dry grass that provided fuel for the wind-driven fires. High-resolution meteorological modeling realistically simulated the strong winds associated with this event. Importantly, operational mesoscale forecast models provided excellent forecasts of the high winds several days in advance. It appears that a vulnerable power system, urbanization of fire-prone areas, flammable invasive species, and poor communication of dangerous conditions contributed to this catastrophic event. The potential for mitigating or preventing such destructive wildfires using skillful weather prediction is examined, as well as the possible role of global warming.

Results of Three Years of Ambient Air Monitoring Near a Petroleum Refinery in Richmond, California, USA

“…For PM2.5, there is no chronic or acute REL value set by the California OEHHA, but PM2.5 is a criteria air pollutant and is therefore regulated by the USA EPA through implementation of NAAQS. As shown in Figure 5, 24-h average PM2.5 concentrations were generally below the 24-h NAAQS threshold of 35 µ g/m 3 , except for a period in October 2017 that was coincident with the Northern California wildfire [46]. A similar peak was observed for BC concentrations during that period (as well as for benzene, as discussed in Section 3.1.3).…”

“…The 8-h average concentrations for benzene exceeded the REL in a few instances over the 3-year study period as shown in Figure S2. One of such periods (October 2017) was coincident with the Northern California wildfire [46], which resulted in significant deterioration of air quality across the region. Similarly, the 1-h average concentrations of benzene, which were predominantly below the corresponding REL at all community locations ( Figure 2), exhibited two exceedances at the PR site during this period (9 October 2017 and 10 October 2017).…”

Santa Ana Winds of Southern California Impact PM2.5With and Without Smoke From Wildfires

“…Simple statistical approaches quantifying relationships between wind and PM 2.5 in the presence and absence of pollution sources (e.g., wildfire), could be particularly useful to help in forecasting and identifying wildfire‐generated PM 2.5 . The same approach used here could be extended to other downslope wind systems in California and elsewhere, which also spread wildfires, for example, Diablo winds of Northern California (Mass & Ovens, ; Smith et al, ). Specific wind systems, and wind data in general, could also serve as the link between wildfire smoke PM 2.5 and wildfire risk in climate change scenarios (Westerling, ).…”

Dynamic Wildfire Navigation System

“…This study has three aims: (1) it identifies limitations in the development of fire prediction models using cellular automata; (2) it suggests a solution to these limitations and implements an example prototype as a preliminary approach, concentrating on the functionality of fire propagation prediction in detail using geographical information system (GIS) software; and (3) it proposes an efficient strategy of evacuation guidance from wildfires. In particular, the last aim is important because there is a trade-off between quality of prediction and the performance speed of fire calculation.…”

“…Globally, wildfires have devastated lives and properties. For instance, a state-wide, fire cost the lives of 44 people and devastated about 100,000 hectares in California, USA in 2017 [1]. In Portugal in the same year, there were more than 62 casualties in the forest fire of Pedrógão Grande area [2].…”