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1. Downslope Windstorm Forecasting: Easier with a Critical Level, but Still Challenging for High-Resolution Ensembles

   https://doi.org/10.1175/WAF-D-22-0135.1  

   Metz, Johnathan J.; Durran, Dale R. (August 2023, Weather and Forecasting)

   Abstract Strong downslope windstorms can cause extensive property damage and extreme wildfire spread, so their accurate prediction is important. Although some early studies suggested high predictability for downslope windstorms, more recent analyses have found limited predictability for such winds. Nevertheless, there is a theoretical basis for expecting higher downslope wind predictability in cases with a mean-state critical level, and this is supported by one previous effort to forecast actual events. To more thoroughly investigate downslope windstorm predictability, we compare archived simulations from the NCAR ensemble, a 10-member mesoscale ensemble run at 3-km horizontal grid spacing over the entire contiguous United States, to observed events at 15 stations in the western United States susceptible to strong downslope winds. We assess predictability in three contexts: the average ensemble spread, which provides an estimate of potential predictability; a forecast evaluation based upon binary-decision criteria, which is representative of operational hazard warnings; and a probabilistic forecast evaluation using the continuous ranked probability score (CRPS), which is a measure of an ensemble’s ability to generate the proper probability distribution for the events under consideration. We do find better predictive skill for the mean-state critical-level regime in comparison to other downslope windstorm–generating mechanisms. Our downslope windstorm warning performance, calculated using binary-decision criteria from the bias-corrected ensemble forecasts, performed slightly worse for no-critical-level events, and slightly better for critical-level events, than National Weather Service high-wind warnings aggregated over all types of high-wind events throughout the United States and annually averaged for each year between 2008 and 2019. 

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2. The 2018 Camp Fire: Meteorological Analysis Using In Situ Observations and Numerical Simulations

   https://doi.org/10.3390/atmos11010047  

   Brewer, Matthew J.; Clements, Craig B. (January 2020, Atmosphere)

   null (Ed.)

   The November 2018 Camp Fire quickly became the deadliest and most destructive wildfire in California history. In this case study, we investigate the contribution of meteorological conditions and, in particular, a downslope windstorm that occurred during the 2018 Camp Fire. Dry seasonal conditions prior to ignition led to 100-h fuel moisture contents in the region to reach record low levels. Meteorological observations were primarily made from a number of remote automatic weather stations and a mobile scanning Doppler lidar deployed to the fire on 8 November 2018. Additionally, gridded operational forecast models and high-resolution meteorological simulations were synthesized in the analysis to provide context for the meteorological observations and structure of the downslope windstorm. Results show that this event was associated with mid-level anti-cyclonic Rossby wave breaking likely caused by cold air advection aloft. An inverted surface trough over central California created a pressure gradient which likely enhanced the downslope winds. Sustained surface winds between 3–6 m s−1 were observed with gusts of over 25 m s−1 while winds above the surface were associated with an intermittent low-level jet. The meteorological conditions of the event were well forecasted, and the severity of the fire was not surprising given the fire danger potential for that day. However, use of surface networks alone do not provide adequate observations for understanding downslope windstorm events and their impact on fire spread. Fire management operations may benefit from the use of operational wind profilers to better understand the evolution of downslope windstorms and other fire weather phenomena that are poorly understood and observed. 

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3. The Meteorology of the August 2023 Maui Wildfire

   https://doi.org/10.1175/WAF-D-23-0210.1  

   Mass, Clifford; Ovens, David (August 2024, Weather and Forecasting)

   Abstract On 8 August 2023, a wind-driven wildfire pushed across the city of Lahaina, located in West Maui, Hawaii, resulting in at least 100 deaths and an estimated economic loss of 4–6 billion dollars. The Lahaina wildfire was associated with strong, dry downslope winds gusting to 31–41 m s⁻¹(60–80 kt; 1 kt ≈ 0.51 m s⁻¹) that initiated the fire by damaging power infrastructure. The fire spread rapidly in invasive grasses growing in abandoned agricultural land upslope from Lahaina. This paper describes the synoptic and mesoscale meteorology associated with this event, as well as its predictability. Stronger-than-normal northeast trade winds, accompanied by a stable layer near the crest level of the West Maui Mountains, resulted in a high-amplitude mountain-wave response and a strong downslope windstorm. Mesoscale model predictions were highly accurate regarding the location, strength, and timing of the strong winds. Hurricane Dora, which passed approximately 1300 km to the south of Maui, does not appear to have had a significant impact on the occurrence and intensity of the winds associated with the wildfire event. The Maui wildfire was preceded by a wetter-than-normal winter and near-normal summer conditions. Significance StatementThe 2023 Maui wildfire was one of the most damaging of the past century, with at least 100 fatalities. This paper describes the meteorological conditions associated with the event and demonstrates that excellent model forecasts made the threat foreseeable. 

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4. The Effect of Upstream Orography on the Onset of Sundowner Winds in Coastal Santa Barbara, CA

   https://doi.org/10.1029/2020JD033791  

   Duine, Gert‐Jan; Carvalho, Leila M. V.; Jones, Charles; Zigner, Katelyn (April 2021, Journal of Geophysical Research: Atmospheres)

   Abstract The impact of upstream terrain on the diurnal variability of downslope windstorms on the south‐facing slopes of the Santa Ynez Mountains (SYM) is investigated using numerical simulations. These windstorms, called Sundowners due to their typical onset around sunset, have intensified all major wildfires in the area. This study investigates the role of the orography upstream of the SYM in the diurnal behavior of Sundowners. Two types of Sundowners are examined: western sundowners (winds with dominant northwesterly direction) and eastern Sundowners (winds with dominant northeasterly direction). By using semi‐idealized simulations, in which we progressively reduce the upstream terrain, we show that the onset of the lee slope jet occurs in the late afternoon only when the flow approaches the SYM from the northeast, after interacting with a considerably higher mountain barrier. We demonstrate that during the eastern regime, the progressive reduction of the upstream terrain results in strong lee slope winds throughout the day. Conversely, the diurnal cycle of downslope winds during the western regime is less sensitive to the reduction of the upstream terrain. The Sundowner diurnal cycle during the eastern regime can be explained by boundary‐layer processes in the valley and the blocking effect of high mountains upstream of the SYM. These results contribute to a better understanding of the influence of upstream orography in the cycle and intensity of downslope windstorms in coastal mountains. 

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5. Sundowner Winds at Montecito during the Sundowner Winds Experiment

   https://doi.org/10.3390/atmos15070810  

   Fovell, Robert G; Brewer, Matthew J (July 2024, Atmosphere)

   This study investigates the predictability of downslope windstorms located in Santa Barbara County, California, locally referred to as Sundowner winds, from both observed relationships and a high-resolution, operational numerical weather prediction model. We focus on April 2022, during which the Sundowner Winds Experiment (SWEX) was conducted. We further refine our study area to the Montecito region owing to some of the highest wind measurements occurring at or near surface station MTIC1, situated on the coast-facing slope overlooking the area. Fires are not uncommon in this area, and the difficulty of egress makes the population particularly vulnerable. Area forecasters often use the sea-level pressure difference (ΔSLP) between Santa Barbara Airport (KSBA) and locations to the north such as Bakersfield (KBFL) to predict Sundowner windstorm occurrence. Our analysis indicates that ΔSLP by itself is prone to high false alarm rates and offers little information regarding downslope wind onset, duration, or magnitude. Additionally, our analysis shows that the high-resolution rapid refresh (HRRR) model has limited predictive skill overall for forecasting winds in the Montecito area. The HRRR, however, skillfully predicts KSBA-KBFL ΔSLP, as does GraphCast, a machine learning weather prediction model. Using a logistic regression model we were able to predict the occurrence of winds exceeding 9 m s−1 with a high probability of detection while minimizing false alarm rates compared to other methods analyzed. This provides a refined and easily computed algorithm for operational applications. 

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