408 WEATHER AND FORECASTING VOLUME 28 A Comparison of Wind Speed and Forest Damage Associated with Tornadoes in Northern Arizona DAVID O. BLANCHARD NOAA/National Weather Service, Flagstaff, Arizona (Manuscript received 26 April 2012, in final form 23 September 2012) Downloaded from http://journals.ametsoc.org/waf/article-pdf/28/2/408/4650674/waf-d-12-00046_1.pdf by guest on 26 September 2020 ABSTRACT Damage surveys in the aftermath of tornadoes occurring in the forested regions of the Mogollon Rim in northern Arizona have been assessed using the enhanced Fujita scale (EF scale) damage indicator (DI) and degree of damage (DOD) tables. These surveys often revealed different DODs within close proximity as well as different spatial patterns and areal extent of tree damage exhibiting the same DOD, making the de- termination of wind speed and EF-scale ratings challenging. A localized tornado outbreak occurred across northern Arizona on 6 October 2010, producing at least 11 tornadoes and substantial areas of forest damage. Remarkably, one of these tornadoes passed over a three-dimensional sonic anemometer. Wind data from this sensor are compared with tree damage in the adjacent forest to assess the performance of the EF-scale metrics for damage to trees. 1. Introduction of vortex winds (radial Vr and tangential Vt velocities) and forward speed (translational velocity V ). The In recent years, there has been an enhanced effort to trans pioneering results of Letzmann have been discussed at conduct detailed damage surveys in the aftermath of length by Peterson (1992), Holland et al. (2006), Dotzek tornadoes in northern Arizona (Blanchard 2006). Most et al. (2008), and Beck and Dotzek (2010). of these surveys have taken place in the forested regions Other studies examining the effects of tornadoes in of the Mogollon Rim (Fig. 1), an escarpment defining trees can be found in Hall and Brewer (1959), who in- the southwestern edge of the Colorado Plateau. The rim vestigated a cluster of tornadoes that struck forested ranges in elevation from approximately 2000 to 2500 m regions in west-central Wisconsin. They described a va- (;6500 to 8000 ft) with a few mountainous locations riety of damage patterns and compared them with three rising above 3500 m (;11 500 ft). Conifers are the idealized flow models, which enabled them to account dominant type of vegetation, but stands of deciduous for the different patterns exhibited in the damage paths. trees can also be found. Later, Budney (1965) used aerial photographs to pro- Although detailed tornado damage surveys in forests duce drawings representing the types of treefall damage are a recent development in northern Arizona, damage patterns from a tornado in Pennsylvania. Fujita (1989) surveys and investigations in forests elsewhere have presented an exceptionally detailed analysis of the ex- a much longer history. Some of the earliest formal tensive high-elevation forest damage produced by a se- studies were conducted in Europe by Wegener (1917) vere thunderstorm in Wyoming. He concluded that the and later by Letzmann (1923), who examined different damage was caused both by tornadic vortices and mi- combinations of radial, tangential, and forward speeds croburst outflows based on patterns of treefall and other to develop schematic illustrations of several funda- damage indicators. mental wind-field patterns. In this early work, Letzmann Peltola et al. (1997, 1999) developed mechanistic tree suggested that when a tornado moved through a forest, models to predict the thresholds necessary to break the it produced a damage pattern that was related to the sum stems of trees. This required different tree models for different species. They found, for example, as crown size Corresponding author address: David O. Blanchard, NOAA/ grows with greater tree spacing, the wind force on the National Weather Service, P.O. Box 16057, Bellemont, AZ 86001. tree increases and the critical wind speed for damage E-mail: [email protected] decreases. DOI: 10.1175/WAF-D-12-00046.1 APRIL 2013 B L A N C H A R D 409 Downloaded from http://journals.ametsoc.org/waf/article-pdf/28/2/408/4650674/waf-d-12-00046_1.pdf by guest on 26 September 2020 FIG. 1. Map showing major geographic features over the state of AZ. The Mogollon Rim is noted. Holland et al. (2006) presented a quantitative physical a (the angle between the radial component vector and model to describe patterns of downed trees produced by total wind vector). Their methodology helped to confirm tornadic winds. Their model was based on a simple the tornadic character of the damage, while discarding Rankine vortex and a modified tree model designed by microbursts as a cause, and gave a reasonable estimate Peltola et al. (1999). The resulting treefall patterns were of vortex strength. shown to depend on translational velocity, radial velocity, Beck and Dotzek (2010) also used a Letzmann-type and tree resistance. Dotzek et al. (2008) noted that the model for treefall patterns and concluded that the model computer results produced by Holland et al. (2006) sup- allows for ‘‘a more accurate classification of tornado ported the analytical hand-drawn analysis produced by intensity in comparison with the classification based on Letzmann (1923), as the underlying model was the same. pure damage analysis...’’ They further noted that an Bech et al. (2009) compared tree fall in a forest with advantage of the Letzmann-type model is its indepen- the results from a combined Rankine vortex model that dence of tree species and other tree parameters such varied the rotational (i.e., both the tangential and radial) as those described by Peltola et al. (1997, 1999) and and the translational components of the wind, the Peterson (2003). Most importantly, they stated that the ratio Gmax between the rotational and translational damage pattern completely determines the wind field components of the wind vector, and the deflection angle and its intensity in the Letzmann model if the tornado 410 WEATHER AND FORECASTING VOLUME 28 translation speed is known and an average critical ve- Equally challenging was that DOD3 and DOD4 were locity for stem breakage is used. They noted, however, often present in the same area and even side by side (i.e., that additional factors including slope and terrain might within a few meters or less of each other), resulting in influence the treefall pattern. some uncertainty in determining the appropriate DOD The inherent difficulties of performing a damage and wind speed causing the damage. Both DOD3 and survey were discussed by Doswell and Burgess (1988), DOD4, however, have expected wind speeds (EXP) that who pointed out that tornadoes that occur in open are in the same EF rating (EF1); thus, this may have country (or forests) often do not damage structures, little practical implication. hence making a Fujita scale (Fujita and Pearson 1973) Consequently, in natural forests, we are likely limited rating more difficult. Similarly, Bech et al. (2009) noted to DOD1–DOD4 representing a range of EXP from 2 2 that while the newer enhanced Fujita scale (EF scale; 60 to 104 mi h 1 (;27–46.5 m s 1) and which corre- WSEC 2006) describes the effects on trees and vegeta- sponds to sub-EF0 to upper-end EF1 storms. The EF Downloaded from http://journals.ametsoc.org/waf/article-pdf/28/2/408/4650674/waf-d-12-00046_1.pdf by guest on 26 September 2020 tion in more detail than the original Fujita scale, it does scale includes, in addition to the EXP, a lower bound so much less precisely than for artificial structures. wind speed (LB) and upper bound wind speed (UB). It is evident that the research and observations of Including the full range from LB to UB for DOD1– damage to forests and trees by tornadoes has an exten- DOD4 corresponds to sub-EF0 to midrange EF2 events 2 2 sive history. Yet, the recently adopted EF scale has [48–128 mi h 1 (;21.5–57 m s 1)]. a limited selection of damage indicators (DIs) and de- As currently implemented, there are no DODs in the grees of damage (DODs) from which to assess tornadic EF scale available for assessing EF4 or EF5 damage in tree damage and assign an EF-scale rating. Nor does it trees and EF3 is only available if debarking of trees consider treefall pattern even though it has played an (DOD5) is present. Beck and Dotzek (2010) noted that essential part of the analyses by Fujita (1989) and others. while damaged objects provide an estimate of the lower The intent of this presentation is to examine and dis- limit of wind speeds, inference of an upper limit of wind cuss the EF-scale DIs and DODs for assessing tree dam- speeds requires objects strong enough to remain un- age in a forest during recent tornado events in northern damaged by the storm. Peterson (2003) had previously Arizona. As part of the discussion, damage surveys and suggested that ‘‘the existing tree damage metrics are meteorological data from a localized tornado outbreak overly simplistic, and that their application is likely to be that occurred across northern Arizona on 6 October 2010 vague, and perhaps even misleading.’’ and produced at least 11 tornadoes are examined. One Figure 2 shows a damage swath through a ponderosa of these tornadoes—a long-track event that was 42 km pine forest. This damage path, which occurred during in pathlength—passed over a three-dimensional sonic the morning hours of 14 October 2006, was ;15 km in anemometer in its later stages. Wind data from this length, but rarely more than 25–50 m wide. In the sensor are compared with nearby tree damage to assess damage path are numerous trees with snapped trunks the EF scale for damage to trees and whether modifi- and crowns (DOD4) at a variety of heights as well as cations might be considered for forest damage.