Received 5 January 2007; accepted 12 October 2007Available online 4 November 2007
demonstrated the damaging consequences that can occur. Whilevarying degrees of engine, windshield and fuselage damage
The location of ash clouds can be determined using satellitedata, although these data are not always available when needed,and only areas with relatively high concentrations are detected.The detection limit depends on several things involving the
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Journal of Volcanology and Geothermal Roccurred in each case, fortunately no catastrophic lose of life1. Introduction
Volcanic clouds pose a significant hazard to aircraft (Millerand Casadevall, 2000). Volcanic ash clouds can be composed ofsub-millimeter sized particles of rock (tephra), water vapor, andother gases such as sulfur dioxide (SO2). Many instances ofaircraft flying into ash clouds over the past three decades have
resulted. The cost of repairing the damaged aircraft can be large.Windscreens and fuselage may need to be replaced, and enginesdismantled, cleaned and rebuilt. The single August 2000 erup-tion of Miyake-jima was reported to cause over 12 million USdollars of damage to aircraft (Tupper et al., 2004). There isobviously both a public safety and financial incentive for air-craft to avoid volcanic clouds.A technique has been developed that uses Puff, a volcanic ash transport and dispersion (VATD) model, to forecast the relative exposure ofaircraft and ground facilities to ash from a volcanic eruption. VATD models couple numerical weather prediction (NWP) data with physicaldescriptions of the initial eruptive plume, atmospheric dispersion, and settling of ash particles. Three distinct examples of variations on thetechnique are given using ERA-40 archived reanalysis NWP data. The Feb. 2000 NASA DC-8 event involving an eruption of Hekla volcano,Iceland is first used for analyzing a single flight. Results corroborate previous analyses that conclude the aircraft did encounter a diffuse cloud ofvolcanic origin, and indicate exposure within a factor of 10 compared to measurements made on the flight. The sensitivity of the technique todispersion physics is demonstrated. The Feb. 2001 eruption of Mt. Cleveland, Alaska is used as a second example to demonstrate how thistechnique can be utilized to quickly assess the potential exposure of a multitude of aircraft during and soon after an event. Using flight trackingdata from over 40,000 routes over three days, several flights that may have encountered low concentrations of ash were identified, and theexposure calculated. Relative changes in the quantity of exposure when the eruption duration is varied are discussed, and no clear trend is evidentas the exposure increased for some flights and decreased for others. A third application of this technique is demonstrated by forecasting the near-surface airborne concentrations of ash that the cities of Yakima Washington, Boise Idaho, and Kelowna British Columbia might have experiencedfrom an eruption of Mt. St. Helens anytime during the year 2000. Results indicate that proximity to the source does not accurately determine thepotential hazard. Although an eruption did not occur during this time, the results serve as a demonstration of how existing cities or potentiallocations of research facilities or military bases can be assessed for susceptibility to hazardous and unhealthy concentrations of ash and othervolcanic gases. 2007 Elsevier B.V. All rights reserved.
Keywords: exposure; ash; aircraft; model; healthAbstractForecasting exposure to volcanic as
Rorik A. Petersona Mechanical Engineering, University of Alaska, Fb Geophysical Institute, University of Alaska, Fai Corresponding author.E-mail addresses: email@example.com (R.A. Peterson), firstname.lastname@example.org
0377-0273/$ - see front matter 2007 Elsevier B.V. All rights reserved.doi:10.1016/j.jvolgeores.2007.10.003based on ash dispersion modeling
, Ken G. Dean b
anks, PO Box 755905, Fairbanks AK 99775 USAnks, PO Box 757320, Fairbanks AK 99775 USA
esearch 170 (2008) 230246www.elsevier.com/locate/jvolgeoresorbital and environmental conditions, and there is currently nodeterministic value. Despite these limitations, satellite detectionin conjunction with VATD models compose the common
ogymethod for identifying ash-contaminated air space (Dean et al.,2002, 2004).
There is an obvious need for information about the potentialdamage volcanic ash can cause to jet engines. There have beenlimited experimental results published in the open literature.Experiments in the late 1980s measured the performance of aturbofan and a turbojet engine when subjected to variousconcentrations of sand, clay and ash collected from Mt. St.Helens (Batcho et al., 1987; Dunn et al., 1987). Theseexperiments concluded that the four major sources of enginedamage are compressor blade erosion, deposition of glassifiedmaterial, blockage of fuel nozzles, and blockage of coolingducts. Based on these results, more comprehensive experimentswere conducted in the 1990s using a hot section test system(HSTS) that comprised the hot sections from two differentrepresentative combustor engines (Kim et al., 1993; Dunn et al.,1996), reducing the cost of obtaining the complete engineassemblies. Again using a range of particulates that includedvolcanic ash, concentrations from 100 to 500 mg/m3 and expo-sure times from 7 to 14 min resulted in damage that includedabrasion and glassified deposition. Another notable conclusionis that higher turbine inlet temperatures (TIT) result in greaterdegrees of deposition. Accordingly, the International CivilAviation Organization (ICAO) recommends that when an air-craft does inadvertently encounter an ash cloud, the crew shouldimmediately reduce thrust to idle (ICAO, 2001), therebyreducing the turbine inlet temperature and any further deposi-tion of fused material.
Aircraft engines continue to evolve with advances in hightemperature materials and changes in design. The changes inaircraft engines will probably always out pace the under-standing of how volcanic ash may affect them during operation.The current view of the aviation industry is to avoid any areawhere discernible ash can be detected either visually or bysatellite (Guffanti et al., 2005); an effective zero-tolerancepolicy. However, some recent events have indicated that somevolcanic ash and/or gases may be present in non-trivial con-centrations long after falling below the detection limit ofsatellites. A well-known example of this occurred after theFebruary 2000 eruption of Hekla, when a research aircraftequipped with scientific instruments made several measure-ments of particles and trace gases of volcanic origin (Rose et al.,2006). This encounter is used as the first example of theexposure calculation technique in this paper.
2. VATD modeling
VATD models are useful tools for forecasting the movementof volcanic ash clouds. As a complement to satellite data andvisual observations from both the ground and aircraft, VATDmodels provide another source of information about manyaspects of airborne volcanic ash clouds such as cloud height,relative concentration of ash and predicted cloud movement.VATD models have their own limitations which include uncer-
R.A. Peterson, K.G. Dean / Journal of Volcanoltainty in the volcanic ash source and meteorology (Servranckxand Chen, 2004). Additionally, some complex physicalphenomena such as turbulent diffusion are modeled usingempirical parameters because solution of the turbulent fluiddynamics equations is quite formidable and time consuming.Despite this, VATD models have proven to be important forreanalyzing data after a volcanic eruption in an attempt to betterunderstand the event, and determine methods for improving thecapabilities of the model for future use.
When VATD models are used to help reanalyze a past event,model data are combined with satellite observations and infor-mation from the air (pilot reports or PIREPS) and ground. It isduring these post-event analyses that techniques and methodsfor improving the forecast capability of VATD models areidentified and eventually implemented. Numerous comparisonsof VATD simulations to satellite observations have shown themto be reasonably accurate in most cases (e.g. Dean et al., 2004).However, VATD models will likely never forecast the exacttrajectory and composition details of an evolving ash cloud, andthe forecasts may range from exceptionally good to quiteinaccurate, which is why satellite observations are often used tovalidate models. When VATD models appear to agree well withthis observational data, some insight into the physical processesthat occurred during the event is achievable. It is possible toassume that these accurate model forecasts indicate that themodeled physical processes correspond to the actual physicalprocesses that occurred, although that is not guaranteed.Proceeding with this assumption, the model can then providefurther information about other processes that occurred duringthe event, which are more difficult to observe otherwise, such asatmospheric reaction or wet removal (particle scavenging).
There is a wide array of VATD models used throughout theworld. Use of a particular model is often associated with ageographical region or institution, although some of the morepopular models are used world-wide. An operational model,sometimes called a runtime model, must be capable ofproducing trajectory forecasts fast enough to be used for hazardmitigation. In the case of preventing encounters with aircraftthat can travel 500 mph, this correlates to minutes. In order toprovide ample time for ash avoidance, model results still mustbe available in less than an hour. Many runtime VATD modelsuse a Lagrangian framework to describe turbulent dispersion.Tracer particles that represent and behave as individual ashparticles are tracked as they advect, diffuse and fall due togravity. Diffusion is simulated by the random-directional walkof each particle, with a spatial step size determined by a localdispersion coefficient. This formulation is often much fastercomputationally than the conventional Eulerian diffusiontechnique, and the results are comparable when the number oftracer particles is large. Obtaining a relative value of con-centration is straight-forward by counting the number of tracerparticles in a given volume at any time, with each tracer particlerepresenting some small fraction of the total eruption mass.Because each particle can have a range of characteristics such assize, shape, density and chemical identity, spatial gradients ofeach characteristic within the cloud can also be determined.
The Puff model uses a Lagrangian framework with an
231and Geothermal Research 170 (2008) 230246adjustable number of tracer particles. The dispersion coeffi-cients can be calculated locally based on velocity deformation(Smagorinsky, 1963; Draxler and Hess, 1998), or a constant
and true aircraft trajectories that ascend, descend and changebearing with time can be utilized.
The methodology for calculating ash-cloud exposure for amoving object (e.g. airplane) is illustrated in Fig. 1. The totalexposure, E, is calculated by integrating the concentration ofash, c, along the trajectory, s, of the object. The trajectory canbe decomposed into the product of the velocity, V, and the traveltime, t.
E R c ds R cV dt 1
The SI units of E are g/m2. Both cloud concentration andvelocity may be functions of time. Numerically, the integral is
gy and Geothermal Research 170 (2008) 230246value can be specified for the entire domain. The latter optioneliminates the need to continually calculate the local dis-persion coefficient, and eliminates the tendency of large grid-size NWP data to yield unrealistically large local diffusioncoefficients.
All VATD models rely on meteorological data from a NWPmodel for wind velocity, temperature, and pressure information.There are several different NWP models used throughout theworld covering either a global or regional domain. Regionalmodels typically have a higher resolution, which can potentiallyprovide more detailed information near the ground surfacewhere topography has a greater influence. The primary draw-back of using regional NWP data with a VATDmodel is in long-term forecasting when the ash cloud may leave the modeldomain. Another drawback is that data from many regionalmodels is only available over regions of high population such asthe contiguous United States, while many volcanoes are locatedin remote regions.
A particular challenge can arise when using different NWPmodels covering the same domain because they can providedifferent forecasts. The variability stems from differences intheir underlying modeling techniques and also assimilation ofdifferent initialization data. Under those conditions, a VATDmodel will also produce different forecasts when using differentNWP data sets. Because this variability is beyond the control ofa VATD model, only the ERA-40 global NWP will be usedthroughout this paper. This choice was made to keep the focuson the utility and limitations of the exposure technique only.The importance of forecast variability due to different NWPdata sets should not be downplayed, however, and acomprehensive study of NWP effects on VATD forecasts willbe the subject of a future manuscript.
The ERA-40 model (Uppala et al., 2005) is a product of theEuropean Centre for Medium-Range Weather Forecasts(ECMWF). The model assimilates a range of meteorologicaldata from satellite, radiosonde, aircraft, and ships among othersources. The project then produces a reanalysis of theatmospheric conditions using a global NWP model. Theresolution of the data used here is 2.52.5 with 23 verticallevels from 1000 to 1 mbar, or about ground level to 50 km.
3. Exposure calculation
The calculation of potential exposure to volcanic ash by anaircraft was first proposed by Armienti et al. (1988). They useda Eulerian-based VATDmodel with the Stokes Settling Law andconstant, parameterized diffusion coefficients. They createdhypothetical, linear aircraft trajectories during the May 1980Mt. St. Helens eruption event and calculated the total quantity ofairborne ash that each trajectory would encounter. The windfield was based on a linear interpolation of three points ofmeteorological soundings. Advances since then in computa-tional speed and methodology permit an improvement on thistechnique. High resolution wind fields from NWP models can
232 R.A. Peterson, K.G. Dean / Journal of Volcanolobe used with VATD models that incorporate more detailedtransport and settling physics. Exposure to different sizes,shapes and identities of particulates can be rapidly quantified,approximated by summation at discrete time steps. The VATDmodel provides concentration as a function of time and space.The object trajectory is a list of three-dimensional locations as afunction of time. The numerical approximation of exposure istherefore