WMO Aeronautical Meteorology Scientific Conference 2017

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1 Session 1 Science underpinning meteorological observations, forecasts, advisories and warnings 1.5 Atmospheric aerosols, volcanic ash research Development of an ensemble-based volcanic ash dispersion model for operations at the Darwin VAAC Rodney Potts, Bureau of Meteorology rodney.potts@bom.gov.au co-authors: Chris Lucas, Richard Dare, Mey Manickam, Alan Wain, Meelis Zidikheri and Adele Bear-Crozier speaker: Rodney Potts Introduction Airborne volcanic ash presents a significant safety risk to aviation. International arrangements have been developed to mitigate this risk through the activities of the ICAO International Airways Volcano Watch program and operations at associated Volcanic Ash Advisory Centres (VAAC). Warnings for volcanic ash clouds are based on reports of an eruption, observations of the ash cloud, including satellite observations, and forecasts of the movement of the ash based on dispersion model guidance. Following the Eyjafjallajökull eruption in 2010 there has been a need for more information on the spatial variation in ash concentration and the associated uncertainties to enable airlines to better manage operational risk. There are significant challenges with this objective. The Australian Bureau of Meteorology operates the Darwin VAAC with an area of responsibility that includes the volcanically active region of Indonesia and Papua New Guinea. There has been ongoing development to improve available guidance for the preparation of warnings. The improved spatial, temporal and spectral resolution of the new Japanese Himawari-8 satellite data (Bessho, et al 2016) has greatly improved the detection and tracking of volcanic ash. Moreover these data are processed with the Spectrally Enhanced Cloud Objects (SECO) algorithm developed by NOAA NESDIS to provide quantitative estimates of cloud parameters including cloud top and the mass load (Pavolonis et al 2015a; Pavolonis et al 2015b). Forecast guidance is provided using the HYSPLIT dispersion model (Draxler and Hess, 1995; Stein et al, 2015) which is coupled with the Australian Community Climate and Earth System Simulator (ACCESS) model suite (Puri et al, 2013, Dare et al, 2016b). For operations this is generally the deterministic regional ACCESS-R model. In HYSPLIT there has been work to better represent the particle size distribution of ash and improve the parameterisation for the particle fall speed (Dare, 2015). It has also been shown that wet deposition processes can have a very significant impact on the dispersion of ash in the Maritime Continent region which is both volcanically and convectively active (Dare et al, 2016a).

2 There are significant uncertainties in forecasting the dispersion of volcanic ash associated with uncertainties in the NWP model forecast fields and the source term for the volcanic ash. The use of dispersion model guidance based on an ensemble NWP model can help to quantify the uncertainties associated with the meteorology as demonstrated by Dare et al (2016b). Following from this work the Bureau has recently developed a Dispersion Ensemble Prediction System (DEPS) for volcanic ash which allows for running the HYSPLIT dispersion model coupled to the Bureau s 24 member global ensemble model ACCESS-GE and other deterministic NWP models. Work to implement this for operational use in the VAAC is currently in progress. In this paper we briefly describe the DEPS system and present preliminary results using the 13 Feb 2014 Kelut eruption as a case study. Kelut volcano eruption of 13 Feb 2014 The Kelut volcano (7.930 S, E), is located in Java, Indonesia. approximately 4 hours from UTC, 13 February It erupted for Available observations suggest most of the ash was ejected in a spreading plume or umbrella with a top near the tropopause around 19 km together with an overshooting core extending as high as 26 km (Kristiansen, et al 2015; Lucas and Majewski, 2015). The local wind field at low altitudes (0-5 km) was a light south-westerly. This shifted to a relatively light southeast to easterly flow at 5-10 km with a stronger easterly flow above 10 km. There was significant disruption to aircraft operations in the area and one reported case of a commercial jet aircraft accidentally encountering ash from the volcano (Kristiansen, et al, 2015). Figure 1. MTSAT2 satellite IR images for 0130 UTC, 14 Feb 2014 with overlay of (a) ash probability and (b) estimate of mass load (g/m2).

3 Conditions at the time of the eruption were relatively clear and most of the ash plume was clearly evident in satellite imagery as a cold and optically thick cloud for a number of hours as it moved to the west. Figure 1 shows the 11 µm IR image from the Japanese geostationary satellite, MTSAT2, for 0130 UTC 14 Feb 2014, around 9 hr after the start of the eruption. This is overlain with the ash probability (Figure 1a) and the estimated mass load with a maximum around 50 g/m 2 (Figure 1b) derived from the SECO algorithm (Pavolonis et al 2015a,b). The Dispersion Ensemble Prediction System (DEPS) The DEPS has been developed to enable forecasters to initialize an ensemble run based on output from the Bureau s 24 member global ensemble model (ACCESS-GE) together with the Bureau s deterministic models ACCESS-G (global) and ACCESS-R (regional) along with the NOAA s GFS model. The system is run with defined eruption source parameters, including the height, duration of the eruption, mass emission rate and particle size distribution. The source can be defined as a single vertically uniform column or a simple umbrella shaped plume. The total ash emission rate (M in kg/s) is derived, following Mastin et al (2009) and Webster et al (2012), from the relation M = H 4.15, where H is the plume rise height above the vent (km). To account for the fact that a large fraction of the ash will fall out in the near field a factor ε is applied such that M = ε.m, where ε represents the fine ash fraction. The system provides output guidance on the mass load from each of the ensemble members at defined forecast time steps and the probability of exceeding defined thresholds for the mass load. Default values of the ash emission rate and fine ash fractions (Mastin et al, 2009) are provided but these can be changed manually based on other guidance that may be available. It is anticipated the output will enable a better assessment of the uncertainties associated with the meteorology and facilitate the provision of improved guidance to airlines for the safety risk assessment. For the purposes of this study we present the model output valid at 0100 UTC, 14 Feb 2014 assuming a uniform column source and a simple umbrella source as presented in Table 1. For the column source there is a uniform fine ash emission rate of 7.88 x10 6 kg.s -1 and for the umbrella source the total fine ash emission rate is also 7.88 x10 6 kg.s -1 but 60% of the mass is emitted in the layer km. Figures 2a and 2b show the dispersion model output valid for 0100 UTC 14 Feb 2014 based on the deterministic ACCESS-R NWP model with a uniform column source and the simple umbrella source respectively. For the column source the mass load is concentrated relatively close to the volcano with a maximum around 3.3 kg.m -2 while for the umbrella source the mass load is distributed more widely in space with a maximum around 2.2 kg.m -2. Both are around 2 orders of magnitude greater than the estimated maximum in Figure 1b. Figures 2c and 2d show the same output for one member of the ensemble ACCESS-GE. For the column source the result is similar to that for ACCESS-R (Fig 2a) but for the umbrella source (Figure 2d) the maximum in the mass load is well to the west of the volcano and is more consistent with the spatial distribution in the satellite image (Figure 1b). As with Figures 2a and 2b the maximum in the mass load is around 2 orders of magnitude greater than observed.

4 Table 1. Source term parameters for initiation of HYSPLIT dispersion model for Kelut eruption. Column Umbrella Source a Source b Base (km) Top (km) Diameter (km) Fine ash fraction M (kg/s) 7.88 x x x 10 6 Figure 2. Dispersion model forecast for 0100 UTC, 14 Feb 2014 for a uniform column source (left most images) and an umbrella source (right most images). The forecast mass load (kg/m 2 ) for ACCESS-R is shown in (a) and (b) and ACCESS-GE11 is shown in (c) and (d).

5 Based on output from all ensemble members the percentage of members (or probability) that exceed a defined column load threshold at each grid point is calculated and output is provided. Thresholds at 2 and 4 g.m -2 have been identified as appropriate for management of risk by airlines. As the mass loads calculated here are much greater than indicated by the satellite data the results have relatively little value and are not presented. Regardless the results do show that representation of the ash plume as an umbrella for a large eruption like Kelut provides a better representation of the dispersion of ash compared with satellite observations. Conclusions and Future Developments The ensemble output shows a spread of possible outcomes for dispersion of the ash and provides guidance on the uncertainties when compared to the deterministic ACCESS-R output with a uniform column source term. Based on a qualitative assessment the simple umbrella source term provides an improved representation of the dispersion of ash for a large eruption that reaches the tropopause when compared with satellite observations. The mass load forecast by DEPS is too high by around 2 orders of magnitude when compared to the satellite estimate and this is largely due to uncertainties in the source term. These uncertainties may arise from: The MER based on Mastin et al (2009) for initialization of DEPS is too high for the observed height of the eruption column. Tupper et al, (2009) has reported this is a particular issue across the Maritime Continent where the latent instability is high Large uncertainties in the fine ash fraction appropriate for initialisation of dispersion models. Poor representation of cloud and precipitation in the tropics in NWP models that can have a significant impact on wet deposition processes in dispersion models. Future development of DEPS aims to integrate satellite based observations with the dispersion modelling, using inverse modelling techniques to better represent the source term (Zidikheri et al, 2017a,b). This should improve the calibration of the ensemble model and further improve available guidance. References Bessho, K., and coauthors, 2016: An Introduction to Himawari-8/9, Japan s New-Generation Geostationary Meteorological Satellites. J. Meteorol. Soc. Japan. Ser. II, 94, , doi: /jmsj Dare, R., 2015: Sedimentation of volcanic ash in the HYSPLIT dispersion model. Bureau of Meteorology, Melbourne, Australia, Dare, R. A., R. J. Potts, and A. G. Wain, 2016a: Modelling wet deposition in simulations of volcanic ash dispersion from hypothetical eruptions of Merapi, Indonesia. Atmos. Environ., 143, , doi: /j.atmosenv Dare, R. A., D. H. Smith, and M. J. Naughton, 2016b: Ensemble prediction of the dispersion of volcanic ash from the 13 February 2014 eruption of Kelut, Indonesia. J. Appl. Meteorol. Climatol., 55, , doi: /jamc-d

6 Draxler, R. R., and G. D. Hess, 1998: An Overview of the HYSPLIT_4 Modelling System for Trajectories, Dispersion, and Deposition. Aust. Meteorol. Mag., 47, Lucas, C., and L. Majewski, 2015: Evaluation of GEOCAT Volcanic Ash Algorithm for use in BoM. Bureau of Meteorology, Melbourne, Australia, 1-51 pp. Pavolonis, M. J., J. Sieglaff, and J. Cintineo, 2015a: Spectrally Enhanced Cloud Objects A generalized framework for automated detection of volcanic ash and dust clouds using passive satellite measurements: 1. Multispectral analysis. J. Geophys. Res. Atmos., 120, , doi:doi: /2014jd Pavolonis, M. J., J. Sieglaff, and J. Cintineo, 2015b: Spectrally Enhanced Cloud Objects A generalized framework for automated detection of volcanic ash and dust clouds using passive satellite measurements: 2. Cloud object analysis and global application. J. Geophys. Res. Atmos., 120, , doi:doi: /2014JD Puri, K., and Coauthors, 2013: Implementation of the initial ACCESS numerical weather prediction system. Aust. Meteorol. Oceanogr. J., 63, Stein, A. F., R. R. Draxler, G. D. Rolph, B. J. B. Stunder, M. D. Cohen, and F. Ngan, 2015: NOAA s HYSPLIT atmospheric transport and dispersion modeling system. Bull. Am. Meteorol. Soc., , doi: /bams-d Tupper, A., S. Carn, J. Davey, Y. Kamada, R. Potts, F. Prata, and M. Tokuno, 2004: An evaluation of volcanic cloud detection techniques during recent significant eruptions in the western Ring of Fire. Remote Sens. Environ., 91, 27 46, doi: /j.rse Tupper, A., C. Textor, M. Herzog, H. F. Graf, and M. S. Richards, 2009: Tall clouds from small eruptions: The sensitivity of eruption height and fine ash content to tropospheric instability. Nat. Hazards, 51, , doi: /s Webster, H. N., and Coauthors, 2012: Operational prediction of ash concentrations in the distal volcanic cloud from the 2010 Eyjafjallajkull eruption. J. Geophys. Res. Atmos., 117, 1 17, doi: /2011jd Zidikheri, M. J., C. Lucas, and R. J. Potts, 2017: Towards quantitative forecasts of volcanic ash dispersal: using satellite retrievals for optimal estimation of source terms. J. Geophys. Res. Atmos., doi:doi: /2017jd Zidikheri, M. J., C. Lucas, and R. J. Potts, 2017: Estimation of optimal dispersion model source parameters using satellite detections of volcanic ash. J. Geophys. Res. Atmos., doi:doi: /2017JD