A Surface Observation Based Climatology of Diablo-Like Winds in California's Wine Country and Western Sierra Nevada

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Short Note A Surface Observation Based Climatology of Diablo-Like Winds in California’s Wine Country and Western Sierra Nevada

Craig Smith 1,2,*, Benjamin J. Hatchett 1 ID and Michael Kaplan 1

1 Division of Atmospheric Sciences, Desert Research Institute, 2215 Raggio Parkway, Reno, NV 89512, USA; [email protected] (B.J.H.); [email protected] (M.K.) 2 Cumulus Weather Solutions, LLC, Reno, NV 89511, USA * Correspondence: [email protected]; Tel.: +1-541-231-4802

Received: 23 May 2018; Accepted: 20 July 2018; Published: 23 July 2018

Abstract: Diablo winds are dry and gusty north-northeasterly downslope windstorms that affect the San Francisco Bay Area in Northern California. On the evening of 8 October 2017, Diablo winds contributed to the ignitions and rapid spread of the “Northern California ﬁrestorm”, including the Tubbs Fire, which burned 2800 homes in Santa Rosa, resulting in 22 fatalities and $1.2 B USD in damages. We analyzed 18 years of data from a network of surface meteorological stations and showed that Diablo winds tend to occur overnight through early morning in fall, winter and spring. In addition to the area north of the San Francisco Bay Area, conditions similar to Diablo winds commonly occur in the western Sierra Nevada. Both of these areas are characterized by high wind speeds and low relative humidity, but they neither tend to be warmer than climatology nor have a higher gust factor, or ratio of wind gusts to mean wind speeds, than climatology.

Keywords: Diablo winds; downslope windstorms; Northern California; wildﬁre meteorology

1. Introduction The Northern California firestorm of October 2017 consisted of over 250 separate wildfires that burned over 245,000 acres (99,000 ha) in Napa, Lake, Sonoma, Mendocino, Butte and Solano counties during dry and windy conditions that followed an anomalously hot and dry summer. In total, these fires caused over $9 billion in damages, left 350,000 people without power, destroyed 8900 buildings and resulted in 44 fatalities. Downslope winds, locally known as “Diablo winds,” promoted the rapid spread of many wildfires on the evening of 8 October and the morning of 9 October. At the Hawkeye remote automated weather station (RAWS), wind speeds of 22 ms−1 (50 mph) coincided with relative humidity below 15% around midnight. The Tubbs fire, which burned over 2800 homes and ultimately became the most destructive wildfire in California history, was ignited on the evening of 8 October, and the fire front travelled rapidly, covering over 12 miles (19 km) in its first three hours. It burned over 25,000 acres (10,111 ha) in its first day and ultimately burned over 5600 structures in the city of Santa Rosa, California. Despite the massive destruction wrought by the Diablo winds, associated with the Northern California firestorm of October 2017 and the Oakland Hills firestorm of 1991, very little is known quantitatively about them, especially relative to the Southern California downslope winds that favor wildfires, such as Santa Ana [1–10] and Sundowner [11–15] winds. A prescient preliminary analysis of ‘Santa Ana-like’ winds in the Oakland Hills was performed in 1973 [16], some 18 years prior to the Oakland Hills fire of 1991, and analyses [17–19] subsequent to that fire confirmed their localization at the eastern San Francisco Bay Area. Some time thereafter, colloquial usage of the term “Diablo winds”

Fire 2018, 1, 25; doi:10.3390/fire1020025 www.mdpi.com/journal/fire Fire 2018, 1, 25 2 of 9 by the National Weather Service expanded to include the Bay area in general (Jan Null, personal communication), in agreement with news stories on the October 2017 Northern California firestorm. These analyses found that Diablo winds originate from the high deserts of Nevada [20] and the Great Basin, andFire 2018 that, 1,they x FOR arePEER dry REVIEW and warm due to adiabatic compression through descent over the2 of 9western slopes of the Sierra Nevada [16] and “blow through the mountain passes and spill over the coastal hills winds” by the National Weather Service expanded to include the Bay area in general (Jan Null, toward the Pacific Ocean“ [21]. Taken together, the historical literature on Diablo winds in the eastern personal communication), in agreement with news stories on the October 2017 Northern California San Franciscofirestorm. Bay These Area analyses suggests found that a possible Diablo winds meso- originate to synoptic-scale from the high deser linkagets of Nevada to similar [20] and conditions along thethewestern Great Basin, slopes and that of the they Sierra are dry Nevada. and warm Notwithstanding due to adiabatic compression semantics through around descent colloquial over usage, there doesthe western not exist slopes any of peer-reviewedthe Sierra Nevada literature [16] and “blow that through objectively the mountain supports passes or refutes and spill that over linkage, circumscribesthe coastal the hills occurrence toward the of DiabloPacific Ocean“ winds to[21]. one Ta areaken together, of the San the Franciscohistorical literature Bay Area on versus Diablo another or the occurrencewinds in the oreastern absence San Francisco of Diablo-like Bay Area wind sugges conditionsts a possible along meso- theto synoptic-scale western slope linkage of theto Sierra similar conditions along the western slopes of the Sierra Nevada. Notwithstanding semantics around Nevada mountains. colloquial usage, there does not exist any peer-reviewed literature that objectively supports or refutes Here,that welinkage, provide circumscribes an analysis the occurrence of the RAWS of Diablo station winds network to one inarea Northern of the San California, Francisco Bay which Area did not exist priorversus to another the Oakland or the occurrence Hills fire or of absence 1991, and of Di inablo-like previous wind publications conditions along on the Diablo western winds slope in order to betterof understand,the Sierra Nevada and mountains. provide some basic information about, Diablo winds. We evaluate where and when theyHere, tend we provide to occur an and analysis how of warm, the RAWS dry andstation windy network they in tendNorthern to be. California, We also which explore did whether not exist prior to the Oakland Hills fire of 1991, and in previous publications on Diablo winds in order conditions similar to Diablo winds also occur along the western slopes of the Sierra Nevada. to better understand, and provide some basic information about, Diablo winds. We evaluate where and when they tend to occur and how warm, dry and windy they tend to be. We also explore whether 2. Materials and Methods conditions similar to Diablo winds also occur along the western slopes of the Sierra Nevada. We used available surface meteorological data from the RAWS network, which was acquired from 2. Materials and Methods MesoWest for the period spanning 1 January 1999 through 1 January 2018. We divided our area of interest intoWe two used sections available of surface interest: meteorological One centered data onfrom the the area RAWS north network, of the which San Franciscowas acquired Bay Area (NoBA),from including MesoWest the for Northern the period spanning Coast Range 1 January and 1999 another through centered 1 January on 2018. the We western divided Sierraour area Nevada of interest into two sections of interest: One centered on the area north of the San Francisco Bay Area (SN; Figure1). We included stations in those areas with a period of record greater than 15 years, (NoBA), including the Northern Coast Range and another centered on the western Sierra Nevada with the(SN; exception Figure 1). ofWe the included following stations stations, in those areas which with exhibited a period of very record low greater Diablo-like than 15 years, wind with condition occurrence:the exception NoBA RAWS of the stations following of Santastations, Rosa, which Hopland exhibited and very Highglade, low Diablo-like SN RAWS wind stations condition of Mount Elizabeth,occurrence: Bald Mountain, NoBA RAWS and stations Banner of Road,Santa Rosa, and Hopland all of the and Automated Highglade, SurfaceSN RAWS Observing stations of System (ASOS)Mount network. Elizabeth, Jarbo Bald Gap Mountain, was excluded and Banner due to Road channeling, and all of of the flow Automated down the Surface Feather Observing River canyon. The stationsSystem considered (ASOS) network. in our Jarbo analysis Gap was are excluded shown indue Figure to channeling1 and detailed of flow down in Table the Feather1. River canyon. The stations considered in our analysis are shown in Figure 1 and detailed in Table 1.

Figure 1. Study area map, including the stations considered. Stations are colored according to their Figure 1. Study area map, including the stations considered. Stations are colored according to their frequency of Diablo-like event days per year. Station name colors correspond to the northern San frequencyFrancisco of Diablo-like Bay Area (NoBA) event (red) days and per Sierra year. Nevada Station (blue) name area. colors correspond to the northern San Francisco Bay Area (NoBA) (red) and Sierra Nevada (blue) area. Fire 2018, 1, 25 3 of 9

Table 1. Station metadata, including name, period of record (POR), longitude, latitude, elevation and corresponding area.

Station Name POR (Years) Longitude (◦W) Latitude (◦N) Elevation (Meters) Area Duncan 16.5 −120.509 39.144 2164 SN Cottage 15.3 −120.230 38.346 1848 SN Mendocino Pass 16.8 −122.945 39.807 1640 NoBA Saddleback 16.8 −120.865 39.638 2033 SN Knoxville Creek 18.4 −122.417 38.862 671 NoBA Hell Hole 18.4 −120.420 39.070 1597 SN Hawkeye 18.4 −122.837 38.735 617 NoBA Lyons Valley 18.4 −123.073 39.126 1023 NoBA County Line 18.4 −122.412 39.019 636 NoBA Eagle Peak 17.1 −122.642 39.927 1132 NoBA Pike County Lookout 18.4 −121.202 39.475 1128 SN

RAWS stations report variables, such as wind speed, as 10 min averages at hourly intervals near their specified Geostationary Operational Environmental Satellite (GOES) transmission time and report wind speed gust as the maximum wind speed identified in the previous hour, such that the wind speed gust may be drawn from a sequence of observations that are not included in the reported wind speed values. Most RAWS station observation samples are taken at 5 s intervals (Greg McCurdy, personal communication). The observations for each station were mapped to their nearest hour, such that our results have a temporal fidelity of not less than 29 min in either direction. From this, we computed hourly averages of wind speed (ws), wind speed gust (wsg), wind direction (wd), relative humidity (RH) and temperature (T) for each hour across all stations for each area of interest (NoBA and SN). The example time series of the station average wind speeds and relative humidity of two representative Diablo events are shown for the 22 November 2013 event and the 9 October 2017 event (Figure2). While both of these events show a short period of large wind speeds, with rapid onset and decay, many other events lasted multiple days, often with a decay in the magnitude of wind speeds during the day and increases in wind speeds overnight. The 22 November 2013 event exhibited a massive multi-day secular decrease in relative humidity. This pattern was seen in multiple events, sometimes superimposed on moderate relative humidity recoveries, according to a typical diurnal pattern. Both of these events show a relatively uniform degree of wind speed and relative humidity characteristics that help justify our usage of an area-wide average of stations. However, in other events, a significant amount of inter-station variability is present. Therefore, we do not attempt to address this in our analysis; instead, we focus on the variability between the North of the Bay and the Sierra Nevada areas. Our criteria for identifying Diablo wind events on an hourly per station basis consisted of the following requirements:

• wind speeds greater than 11.17 ms−1 (25 mph); • a wind direction between 315◦ and 135◦; • a relative humidity below 30%; • the above conditions satisﬁed for three or more consecutive hours.

Conditions meeting the above criteria at any station were considered Diablo-like events on an hourly per station basis. Diablo-like events on an hourly per area basis were defined as any single station in an area that meets the criteria. Diablo-like days on a per station basis were defined as any Diablo-like event on an hourly per station basis at any hour of a given day. Diablo-like days on a per area basis were defined as any single station in an area that meets the criteria at any hour of a given day. Fire 2018, 1, 25 4 of 9 Fire 2018, 1, x FOR PEER REVIEW 4 of 9

FigureFigure 2. 2.Station-wide Station-wide average (thick (thick lin lines)es) and and individual individual stations stations (thin (thinlines): lines): Wind speed Wind and speed wind and windspeed speed gust gust (a,b) ( aand,b) relative and relative humidity humidity (c,d) for (c ,thed) fornorth the of north Bay (NoBA) of Bay and (NoBA) western and Sierra western Nevada Sierra Nevada(SN) areas (SN) areasduring during the 22 theNovember 22 November 2013 (a 2013,c) and (a, c9) October and 9 October 2017 (b 2017,d) Diablo-like (b,d) Diablo-like wind events. wind events.The Thehorizontal horizontal dark dark gray gray lines lines corresp correspondond to tothe the minimum minimum wind wind speed speed (a (,ba,)b and) and maximum maximum relative relative humidityhumidity (c, d(c),d criteria) criteria for for Diablo-like Diablo-like events. events.

Several select Diablo-like events, ranked by wind speed and wind speed gust magnitude, at Several select Diablo-like events, ranked by wind speed and wind speed gust magnitude, Duncan and Knoxville Creek are presented in Table 2. We verified that our findings are robust with at Duncanrespect to and different Knoxville values Creek of minimum are presented wind in speed, Table 2relative. We verified humidity, that consecutive our findings hours are robust and withnumber respect of tostations, different simultaneously values of minimum meeting the wind crit speed,eria. Example relative plots, humidity, showing consecutive all available hours hourly and numberwind speed of stations, and wind simultaneously direction observations meeting the for criteria. the Duncan Example and Knoxville plots, showing Creek RAWS, all available along hourlywith windour speed wind speed and wind and wind direction direction observations criteria, are for shown the Duncan in Figure and 3. KnoxvilleWe computed Creek the RAWS,mean gust along factor with our(gf wind), defined speed as and wind wind speed direction gust divided criteria, by are wind shown speed, in Figure as a function3. We computed of wind speed, the mean for gustall factorobservations. (gf ), defined In order as wind to characterize speed gust how divided hot, bydry, wind and speed,windy asDiablo-like a function days of windare compared speed, for to all observations.climatology, Inwe order also computed to characterize anomalies how for hot, each dry, Diablo-like and windy day. Diablo-like In this calculation, days are the compared minimum to climatology,and maximum we also daily computed wind speed, anomalies temperature, for each and Diablo-like relative humidity day. In for this each calculation, Diablo-like the event minimum day andwas maximum compared daily to a wind climatology speed, temperature,calculated from and the relative corresponding humidity Julian for each day Diablo-like long-term eventaverage day wasminimum compared and to maximum a climatology values calculatedon a per station from basis. the corresponding Julian day long-term average minimum and maximum values on a per station basis.

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Fire 2018, 1, x FOR PEER REVIEW 5 of 9 Table 2. Select list of the strongest events by wind speed and wind speed gust magnitudes at Duncan andTable Knoxville 2. Select Creek, list ofsatisfying the strongest the events Diablo-like by wind event speed criteria. and wind speed gust magnitudes at Duncan and Knoxville Creek, satisfying the Diablo-like event criteria. −1 −1 Date Date DuncanDuncanws wsmaxmax/wsgmaxmax (ms(ms−1) )Knoxville Knoxville Creek ws Creekmax/wsgwsmaxmax (ms/wsg−1) max (ms ) 2002-02-282002-02-28 9.8/15.6 9.8/15.6 17.9/27.3 17.9/27.3 2002-03-012002-03-01 13.4/25.0 13.4/25.0 14.3/29.5 14.3/29.5 2004-10-112004-10-11 20.1/26.8 20.1/26.8 13.4/22.8 13.4/22.8 2006-12-282006-12-28 15.2/19.7 15.2/19.7 17.0/26.4 17.0/26.4 2009-01-092009-01-09 -/- -/- 18.3/27.7 18.3/27.7 2011-12-012011-12-01 18.8/36.7 18.8/36.7 12.5/20.1 12.5/20.1 2011-12-162011-12-16 24.6/35.3 24.6/35.3 12.5/20.1 12.5/20.1 2013-11-222013-11-22 25.9/40.7 25.9/40.7 15.6/26.4 15.6/26.4 2017-10-092017-10-09 18.8/27.3 18.8/27.3 16.1/28.2 16.1/28.2

FigureFigure 3. 3.Scatter Scatter plot plot of of wind wind speedspeed versusversus wind direction for for all all available available hourly hourly observations observations at at DuncanDuncan Canyon Canyon (a ()a and) and Knoxville Knoxville CreekCreek ((bb).). Conditions satisfying satisfying the the Diablo-like Diablo-like criteria criteria of ofwind wind speed,speed, wind wind direction direction and and relative relative humidity humidity are are shown shown in magenta.in magenta. Cyan Cyan lines lines correspond correspond to theto the wind directionwind direction and wind and speed wind criteriaspeed criteria for Diablo-like for Diablo-like events. events.

3.3. Results Results and and Discussion Discussion TheThe average average number number of of daily daily Diablo-like Diablo-like events per per st stationation per per year year that that meet meet the the criteria criteria is shown, is shown, as a function of station elevation, in Figure 4a, and as colored dots, in Figure 1. The relationships shown as a function of station elevation, in Figure4a, and as colored dots, in Figure1. The relationships shown indicate that interstation variability and micro-siting is likely an important component of observed indicate that interstation variability and micro-siting is likely an important component of observed Diablo-like wind conditions in the RAWS network. The large difference in event occurrence at the Diablo-like wind conditions in the RAWS network. The large difference in event occurrence at the somewhat closely located stations Mendocino Pass and Eagle Peak suggests that station micro-siting somewhat closely located stations Mendocino Pass and Eagle Peak suggests that station micro-siting issues cannot be fully excluded from our analysis. Since Eagle Peak experiences a high event count due issues cannot be fully excluded from our analysis. Since Eagle Peak experiences a high event count due to north-northeasterly events, we verified that our results are similar if we excluded Eagle Peak from tothe north-northeasterly analysis. Hourly Diablo-like events, we events veriﬁed per that area, our averag resultsed as are a function similar ifof we the excludedhour of day, Eagle is shown Peak fromin theFigure analysis. 4b. Daily Hourly Diablo-like Diablo-like events events per area, per area,averaged averaged as a function as a function of the month of the of hour the ofyear, day, is isshown shown inin Figure Figure4b. 4c.Daily Diablo-like Diablo-like wind events events tend per to area, occur averaged overnight, as through a function early ofmorning the month most frequently of the year, is shown(more than in Figure once 4perc. Diablo-like month) during wind late events fall and tend early to occur spring overnight, (October–March). through earlyMid-to-late morning spring most frequently(April–June) (more and than early once fall per (September) month) during events late fallare andless earlyfrequent, spring occurring (October–March). at a frequency Mid-to-late of springapproximately (April–June) one andevent early per two fall months. (September) They eventsare quite are uncommon less frequent, during occurring summerat (July–August). a frequency of approximatelyThese results oneare consistent event per with two months.a previous They analysis are quite [16]. uncommonThe results indicate during a summer very similar (July–August). diurnal

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TheseFire results 2018, 1, arex FOR consistent PEER REVIEW with a previous analysis [16]. The results indicate a very similar6 diurnalof 9 pattern for the SN stations to the NoBA stations (Figure4b). Monthly frequencies are also similar, althoughpattern NoBA for the stations SN stations appear to tothe have NoBA slightly stations more (Figure frequent 4b). Mont eventshly duringfrequencies December are also andsimilar, January (Figurealthough4c). The NoBA SN stations appear indicate to have a modest slightly relationship more frequent between events during Diablo-like December events and andJanuary station elevation,(Figure while 4c). The the NoBASN stations stations indicate do nota modest (Figure re4lationshipa). between Diablo-like events and station Theelevation, departures while the from NoBA the stations Julian do day not climatology, (Figure 4a). on a per station basis, show that Diablo-like The departures from the Julian day climatology, on a per station basis, show that Diablo-like events tend to be very dry (Figure5a) relative to climatology, with depressed daily minimum and events tend to be very dry (Figure 5a) relative to climatology, with depressed daily minimum and maximum relative humidity on the order of 30%. This is consistent with the Southern California maximum relative humidity on the order of 30%. This is consistent with the Southern California downslopedownslope wind wind regimes regimes [1 ,13[1,13]]. However,. However, minimumminimum and and maximum maximum daily daily temperatures temperatures are not are not elevatedelevated relative relative to climatology to climatology (Figure (Figure5b), indicating 5b), indicating that Diablo-like that Diablo-like wind events wind areevents not anomalouslyare not warm.anomalously Daily maximum warm. Daily wind maximum speeds and wind wind speeds speed and gustswind arespeed elevated gusts are relative elevated to climatologicalrelative to averagesclimatological (Figure5c), averages and both (Figure the wind 5c), speedand both and the wind wind speed speed gust and are wind highly speed correlated gust are to eachhighly other (Figurecorrelated5d), which to each is in other agreement (Figure with5d), which previous is in workagreement on Santa with previous Ana winds work ([3 on], cf.Santa Figure Ana 13a).winds The([3], gustcf. Figure factor 13a). analysis (Figure4d), in which the mean gust factor is computed per wind speed bin for Diablo-likeThe gust factor event analysis days on (Figure a per area4d), basis,in which and the compared mean gust to factor all observations, is computed per shows wind that speed the gust bin for Diablo-like event days on a per area basis, and compared to all observations, shows that the gust factor during Diablo-like event days is not elevated relative to non-Diablo-like event days. The wind factor during Diablo-like event days is not elevated relative to non-Diablo-like event days. The wind speeds and wind speed gusts are both elevated during Diablo-like events, although the gust factor is speeds and wind speed gusts are both elevated during Diablo-like events, although the gust factor is not, indicatingnot, indicating no supportno support for for Diablo-like Diablo-like winds winds beingbeing gustier gustier than than other other high high wind wind events events that thataffect affect thesethese stations stations under under other other atmospheric atmospheric conditions. conditions. Th Thee identified identiﬁed relationship relationship of gust of gust factor factor versus versus sustainedsustained wind wind speed speed appears appears to to be be similar similar to th thatat of of Santa Santa Ana’s Ana’s ([3], ([ cf.3], their cf. their Figure Figure 11c). 11c).

FigureFigure 4. Average 4. Average number number of of daily daily Diablo-like Diablo-like eventsevents per station station per per year year versus versus station station elevation elevation (a), (a), averageaverage hourly hourly Diablo-like Diablo-like events events per pe area,r area, as function as function of theof the hour, hour, (b), ( averageb), average daily daily Diablo-like Diablo-like events per area,events as per a function area, as ofa function the month of the of month the year, of the (c) theyear, area (c) the mean area gust mean factor, gust factor, as a function as a function of the of wind speedthe bin wind for Diablo-likespeed bin for days Diablo-like per area, days and per ( darea,) all and observations (d) all observations not meeting not meeting the criteria. the criteria.

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FigureFigure 5. 5.Daily Daily Diablo-likeDiablo-like event event departur departureses from from Julian Julian day climatology day climatology on a per on station a per basis station for the basis fornorth the north of Bay of Area Bay Area(NoBA; (NoBA; red) and red) western and western Sierra SierraNevada Nevada (SN; blue) (SN; area blue) observations area observations of: Daily of: Dailyminimum minimum and andmaximum maximum relative relative humidity humidity (a), daily (a), minimum daily minimum and maximum and maximum temperature temperature (b), daily (b ), dailymaximum maximum wind wind speed speed and wind and speed wind gust speed (c), gust and daily (c), and maximum daily maximum wind speed wind versus speed daily versusmaximum daily maximumwind speed wind gust speed (d). The gust diagonal (d). The black diagonal line in black (d) corresponds line in (d) corresponds to a gust factor to aslope gust of factor 1.7. slope of 1.7.

4. Summary4. Summary and and Conclusions Conclusions DuringDuring the the October October 2017 2017 Northern Northern California California Firestorm, Firestorm, Diablo Diablo winds winds were thrustwere thrust to the forefrontto the forefront of the public consciousness and the broader wildfire meteorology community as a of the public consciousness and the broader wildﬁre meteorology community as a meteorological meteorological phenomenon, about which little is known. Our analysis showed that conditions phenomenon, about which little is known. Our analysis showed that conditions similar to Diablo similar to Diablo winds tend to occur at night or in the early morning and are most common during winds tend to occur at night or in the early morning and are most common during the cool season the cool season (late fall through spring). However, our analysis is somewhat constrained by the (late fall through spring). However, our analysis is somewhat constrained by the binary nature binary nature of our criteria, and we did not consider the eastern San Francisco Bay Area nor the San of ourFrancisco criteria, Peninsula. and we Further did not research consider is required the eastern to address San Francisco many issues Bay posed Area norby the the sparse San Francisco RAWS Peninsula.network Furtherused in this research study, is including required tothe address production many and issues analysis posed of bya long-term, the sparse high RAWS resolution network useddownscaled in this study, numerical including climatology. the production and analysis of a long-term, high resolution downscaled numericalOf the climatology. total number of Diablo-like wind conditions, identified on a daily basis per area, 35% occurredOf the north total of number the San ofFrancisco Diablo-like Bay area wind only, conditions, 41% occurred identiﬁed in the western on a dailySierra basisNevada per only, area, 35%and occurred 23% occurred north in of both the Sanareas. Francisco Thus, conditions Bay area similar only, to 41% Diablo occurred winds in appear the western to be just Sierra as common Nevada only, and 23% occurred in both areas. Thus, conditions similar to Diablo winds appear to be just as

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common in the Sierra Nevada as they are in the area north of the San Francisco Bay Area. This implies that they are a regional wind system of Northern California and not a local phenomenon of the San Francisco Bay Area. This indicates that wildfire fighting resources may be required throughout Northern California, including the western Sierra Nevada, when conditions similar to Diablo winds are forecast. Since historical usage of the term Diablo winds was initially confined to the Oakland Hills, it might be more appropriate to refer to Diablo-like wind conditions in the Sierra Nevada as “Diablos del Sierra” or “Bruja” winds. Associated numerical weather simulations (not shown) strongly suggest the downstream linking of mountain wave breaking over the Sierra Nevada to Diablo wind conditions north of the San Francisco Bay Area. We hypothesize that this mechanism drives Diablo-like wind conditions in both areas and that vertical profiles of wind speed and stability, east of the Sierra Nevada, play a primary role in determining the altitude at which the Sierra downslope jet is lofted off the surface and control of the characteristics of Diablo winds in the San Francisco Bay Area.

Author Contributions: C.S. conceptualized and executed the analysis. B.J.H. and M.K. contributed to improving the analysis and writing. C.S. and M.K. contributed to funding Acquisition. Funding: This research was funded by National Science Foundation Grant AGS-1419267. Conﬂicts of Interest: The authors declare no conﬂict of interest.

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