1991 Pinatubo Volcanic Simulation Using ATHAM Model Song Guo, William I Rose, Gregg J S Bluth Michigan Technological University, Houghton, Michigan Co-Workers.

Slides:

Advertisements

Similar presentations

The University of Reading Helen Dacre The Prediction And Observation Of Volcanic Ash Clouds During The Eyjafjallajökull Eruption Helen Dacre and Alan Grant.

Advertisements

Robin Hogan Julien Delanoe University of Reading Remote sensing of ice clouds from space.

Boundary Layer Clouds & Sea Spray Steve Siems, Yi (Vivian) Huang, Luke Hande, Mike Manton & Thom Chubb.

A thermodynamic model for estimating sea and lake ice thickness with optical satellite data Student presentation for GGS656 Sanmei Li April 17, 2012.

The Problem of Parameterization in Numerical Models METEO 6030 Xuanli Li University of Utah Department of Meteorology Spring 2005.

Requirements for monitoring the global tropopause Bill Randel Atmospheric Chemistry Division NCAR.

Evaluation of ECHAM5 General Circulation Model using ISCCP simulator Swati Gehlot & Johannes Quaas Max-Planck-Institut für Meteorologie Hamburg, Germany.

Electroscavenging of Condensation and Ice- Forming Nuclei Brian A. Tinsley University of Texas at Dallas WMO Cloud Modeling Workshop,

ADMS ADMS 3.3 Modelling Summary of Model Features.

Atmospheric structure from lidar and radar Jens Bösenberg 1.Motivation 2.Layer structure 3.Water vapour profiling 4.Turbulence structure 5.Cloud profiling.

The Centre for Australian Weather and Climate Research A partnership between CSIRO and the Bureau of Meteorology The Effect of Turbulence on Cloud Microstructure,

Relationships between wind speed, humidity and precipitating shallow cumulus convection Louise Nuijens and Bjorn Stevens* UCLA - Department of Atmospheric.

1 Global Observations of Sulfur Dioxide from GOME Xiong Liu 1, Kelly Chance 1, Neil Moore 2, Randall V. Martin 1,2, and Dylan Jones 3 1 Harvard-Smithsonian.

The Role of Aerosols in Climate Change Eleanor J. Highwood Department of Meteorology, With thanks to all the IPCC scientists, Keith Shine (Reading) and.

Spectral microphysics in weather forecast models with special emphasis on cloud droplet nucleation Verena Grützun, Oswald Knoth, Martin Simmel, Ralf Wolke.

In order to fully understand the three main effects of volcanic eruptions on global climate change it is important to first look at the two main factors.

Module 1: Themes in Physical Geography Topic 3: Weather Unit A : Atmospheric heating, motion (winds) and moisture Unit B : Weather systems (air masses,

Warm Up 3/4/08 True or False: The seasons are caused by changes in Earth’s distance from the sun. False Does land or water heat more rapidly? Land heats.

Volcanoes and the Atmosphere Rich Stolarski 22 June 2012 Pinatubo.

Tianfeng Chai 1,2, Alice Crawford 1,2, Barbara Stunder 1, Roland Draxler 1, Michael J. Pavolonis 3, Ariel Stein 1 1.NOAA Air Resources Laboratory, College.

Cyclone composites in the real world and ACCESS Pallavi Govekar, Christian Jakob, Michael Reeder and Jennifer Catto.

Different heterogeneous routes of the formation of atmospheric ice Anatoli Bogdan Institute of Physical Chemistry, University of Innsbruck Austria and.

T. Elperin, A. Fominykh and B. Krasovitov Department of Mechanical Engineering The Pearlstone Center for Aeronautical Engineering Studies Ben-Gurion University.

Session 4, Unit 7 Plume Rise

Rick Russotto Dept. of Atmospheric Sciences, Univ. of Washington With: Tom Ackerman Dale Durran ATTREX Science Team Meeting Boulder, CO October 21, 2014.

Applications and Limitations of Satellite Data Professor Ming-Dah Chou January 3, 2005 Department of Atmospheric Sciences National Taiwan University.

In this work we present results of cloud electrification obtained with the RAMS model that includes the process of charge separation between ice particles.

An Example of the use of Synthetic 3.9 µm GOES-12 Imagery for Two- Moment Microphysical Evaluation Lewis D. Grasso (1) Cooperative Institute for Research.

Chapter 17.1 Atmospheric Characteristics

Real-Time Estimation of Volcanic Ash/SO2 Cloud Height from Combined UV/IR Satellite Observations and Numerical Modeling Gilberto A. Vicente NOAA National.

Non-hydrostatic Numerical Model Study on Tropical Mesoscale System During SCOUT DARWIN Campaign Wuhu Feng 1 and M.P. Chipperfield 1 IAS, School of Earth.

Third annual CarboOcean meeting, 4.-7.December 2007, Bremen, Segschneider et al. Uncertainties of model simulations of anthropogenic carbon uptake J. Segschneider,

WMO workshop, Hamburg, July, 2004 Some aspects of the STERAO case study simulated by Méso-NH by Jean-Pierre PINTY, Céline MARI Christelle BARTHE and Jean-Pierre.

WRF Volcano modelling studies, NCAS Leeds Ralph Burton, Stephen Mobbs, Alan Gadian, Barbara Brooks.

Fanglin Yang Work Done at Climate Research Group

Modern Era Retrospective-analysis for Research and Applications: Introduction to NASA’s Modern Era Retrospective-analysis for Research and Applications:

Georgia Institute of Technology Initial Application of the Adaptive Grid Air Quality Model Dr. M. Talat Odman, Maudood N. Khan Georgia Institute of Technology.

High-Resolution Simulation of Hurricane Bonnie (1998). Part II: Water Budget Braun, S. A., 2006: High-Resolution Simulation of Hurricane Bonnie (1998).

Earth: Our Home. Earth Bio/Facts Diameter: 12,742 km Relative Mass (Earth = 1): 1 Density (kg/m 3 ): 5220 Distance from Sun (AU): 1 Length of Day: 24.

The University of Reading Helen Dacre The Eyjafjallajökull eruption: How well were the volcanic ash clouds predicted? Helen Dacre and Alan Grant Robin.

On the Definition of Precipitation Efficiency Sui, C.-H., X. Li, and M.-J. Yang, 2007: On the definition of precipitation efficiency. J. Atmos. Sci., 64,

A Cloud Resolving Modeling Study of Tropical Convective and Stratiform Clouds C.-H. Sui and Xiaofan Li.

Chapter 3 Introduction to the Atmosphere.  Supplies oxygen for humans & animals  Supplies carbon dioxide (CO 2 ) for plants  Helps maintain water supply.

Potential of the ATHAM model for use in air traffic safety Gerald GJ Ernst Geological Institute, University of Ghent, Gent, Belgium Christiane Textor Lab.

High-Resolution Simulation of Hurricane Bonnie (1998). Part II: Water Budget SCOTT A. BRAUN J. Atmos. Sci., 63,

04/12/011 The contribution of Earth degassing to the atmospheric sulfur budget By Hans-F. Graf, Baerbel Langmann, Johann Feichter From Chemical Geology.

Atmospheric Moisture.

1. The atmosphere 2 © Zanichelli editore 2015 Characteristics of the atmosphere 3 © Zanichelli editore 2015.

Modeling. How Do we Address Aerosol-Cloud Interactions? The Scale Problem Process Models ~ 10s km Mesoscale Models Cloud resolving Models Regional Models.

Towards parameterization of cloud drop size distribution for large scale models Wei-Chun Hsieh Athanasios Nenes Image source: NCAR.

NUMERICAL SIMULATION CLOUDS AND PRECIPITATION CAUSED CATASTROPHIC FLOODS ALONG THE ELBE RIVER IN AUGUST 2002 Krakovskaia S.V., Palamarchuk L.V. and Shpyg.

Chien Wang Massachusetts Institute of Technology A Close Look at the Aerosol-Cloud Interaction in Tropical Deep Convection.

Active and passive microwave remote sensing of precipitation at high latitudes R. Bennartz - M. Kulie - C. O’Dell (1) S. Pinori – A. Mugnai (2) (1) University.

The University of Reading Helen Dacre The Prediction And Observation Of Volcanic Ash Clouds During The Eyjafjallajökull Eruption Helen Dacre and Alan Grant.

Orographic Banner Clouds 2. EULAG Workshop Björn Brötz, Daniel Reinert, Volkmar Wirth Institute for Atmospheric Physics/ University of Mainz 2 nd EULAG.

THE INFLUENCE OF WIND SPEED ON SHALLOW CUMULUS CONVECTION from LES and bulk theory Louise Nuijens and Bjorn Stevens University of California, Los Angeles.

Atmospheric Gravity Waves

Impact of Cloud Microphysics on the Development of Trailing Stratiform Precipitation in a Simulated Squall Line: Comparison of One- and Two-Moment Schemes.

1 Detailed Microphysical Model Simulations of Freezing Drizzle Formation Istvan Geresdi Roy Rasmussen University of Pecs, Hungary NCAR Research funded.

Part II: Implementation of a New Snow Parameterization EXPLICIT FORECASTS OF WINTER PRECIPITATION USING AN IMPROVED BULK MICROPHYSICS SCHEME Thompson G.,

Influences of Particle Bulk Density of Snow and Graupel in Microphysics-Consistent Microwave Brightness Temperature Simulations Research Group Meeting.

GEORGE H. BRYAN AND HUGH MORRISON

Water Budget of Typhoon Nari(2001)

Quantifying uncertainty in volcanic ash forecasts

Assessing Volcanic Ash Hazard by Using the CALPUFF System

Properties of the Atmosphere

I. What? ~ II. Why? ~ III. How? Modelling volcanic plumes with WRF

D. C. Stolz, S. A. Rutledge, J. R. Pierce, S. C. van den Heever 2017

Atmospheric modelling of HMs Sensitivity study

Presentation transcript:

1991 Pinatubo Volcanic Simulation Using ATHAM Model Song Guo, William I Rose, Gregg J S Bluth Michigan Technological University, Houghton, Michigan Co-Workers Christiane Textor 1, Hans-F. Graf 1, Michael Herzorg 2 1 Max-Planck Institute for Meteorology, Hamburg, Germany 2 University of Michigan, Ann Arbor, Michigan

Photos of Volcanic Plume from Mt. Pinatubo Eruption

Outline Introduction and Motivation Summary of initial input parameters for ATHAM model simulation Simulation results from model ATHAM Comparison with satellite observation Future Work and Outlook

Introduction and Motivation Why is remote sensing useful to study volcanic plumes and their interaction with the atmosphere? Why is modeling work needed to study volcanic plumes and their interaction with atmosphere?

Why Pinatubo? (Objective) Pinatubo eruption is the largest eruption of the satellite remote sensing era (Hourly GMS, AVHRR, TOMS) Pinatubo eruption had the largest global environmental and climatic impacts Pinatubo eruption had the largest impact on stratospheric ozone depletion

Objective (continue) Some results from ATHAM can be compared with satellite observations – shape of the plume – movement of the plume – gas phase SO 2 amount – gas and particle separation Some model results cannot be measured by satellite observations - H 2 O entrained from the ambient air - microphysics process - ash-hydrometer aggregation - volcanic gas scavenging

Brief Introduction to ATHAM (Active Tracer High Resolution Atmospheric Model) 3d formulated (2d Cartesian coordinates, 2d cylindrical coordinates) 127 × 127 (× 127) grid points model domain: 50 km vertical, 200 km horizontal simulation time: several hours

Brief Introduction to ATHAM (Assumptions) Dynamic equilibrium Thermal equilibrium Ash is an active cloud or ice condensation nuclei Ash is covered with water or ice is treated as a pure hydrometeors

Brief Introduction to ATHAM (Modules) Dynamics: transport of gas-particle-mixture including tracers (advection and thermodynamics) Turbulence: entrainment of ambient air Microphysics: development of ash- hydrometeorss Scavenging: redistribution of volcanic gases in hydrometeors

GMS Images Showing the Growth and Movement of Volcanic Plume from Holasek et al., 1996, JGR, Vol. 101, No. B12, 27,635-27,655 The Plume is quite symmetrical for ~ 2-3 hrs after eruption The Plume expends ~100km/hr (~80km/hr)for the first (second) hour after eruption The Plume is heavily influenced by Typhoon Yuya after 2-3 hrs after eruption

Summary of Input Parameters to ATHAM 2d cylindrical coordinate simulation is used : simulation time : 120 min. duration of eruption: 180 min Geometry of the volcano: mountain height: 1200m diameter of the crater: 680m Volcanic forcing: magma temperature: 1073K eruption velocity: 360 m/s mass eruption rate: 4.5×10 8 kg/s density of ash: 1100 kg/m 3 Ash size distribution: 2 classes of gamma distribution radius of smaller ash particle: 25  m radius of larger ash particle: 90  m Weight percentage: small and large particles : 46% each gas (water vapor): 8% (6.4%) Atmospheric Profile: no real time observation combine pre-eruption in-situ and nearby real-time sounding observation (no hurricane effect is considered for first 2 hours simulation)

Sounding ProfilesStandard Tropical Profile Temperature Relative Humidity Wind Speed

Mt. Pinatubo Volcanic Plume Altitude from Holasek et al., (1996)

Highest Plume Altitude from ATHAM Simulation

Vertical Wind Distribution with the Larger Plume Height Simulation

Vertical Wind Distribution with Pinatubo Initial Conditions (19 min.)

In Situ Temperature Anomalous after 6 minutes of eruption

Total Ash Particles (19 minutes after eruption)

Total Ash Particles after 55 minutes of eruption

Total Ash Particles after 115 minutes of eruption

Ash Particle Results After 19 Minutes Eruption (a) Sum Small Ash(b) Sum Large Ash © Gas Fraction (d) Ice

Ash Particle Results after 55 Minutes of Eruption (a) Sum Small Ash(b) Sum Large Ash © Gas Fraction(d) Ice

Ash Particle Results After 115 Minutes of Eruption (a) Sum Small Ash(b) Sum Large Ash © Gas Fraction(d) Ice

Schematic of Microphysics Processes in Volcanic Plume

Hydrometeor Results After 19 Minutes of Eruption (a) Water Vapor(b) Cloud Water © Cloud Ice(d) Graupel

Hydrometeor Results After 55 Minutes of Eruption (a) Water Vapor(b) Cloud Water © Cloud Ice(d) Graupel

Hydrometeor Results After 115 Minutes of Eruption (a) Water Vapor(b) Cloud Water © Cloud Ice(d) Graupel

SO 2 Scavenging Results After 19 Minutes of Eruption (a) gas phase SO 2 (b) SO 2 in cloud water © SO 2 in cloud ice (d) SO 2 in graupel

SO 2 Scavenging Results After 55 Minutes of Eruption (a) gas phase SO 2 (b) SO 2 in cloud water © SO 2 in cloud ice(d) SO 2 in graupel

SO 2 Scavenging Results After 115 Minutes of Eruption (a) gas phase SO 2 (b) SO 2 in cloud water © SO 2 in cloud ice (d) SO 2 in graupel

Summary of intermediate results Ice phase hydrometeors (ash-hydrometeor aggregations) are dominant, larger ash particles travel horizontally faster than small ones The Plume’s horizontal travelling velocity (most probably caused by gravity) is quite consistent with the satellite image Gas phase volcanic gases (SO 2, HCl, H 2 S) coexist with different gas-hydrometeor mixtures Vertical falling particle velocity increases due to the ash- hydrometeor aggregation

Summary of intermediate results (continue) No significant gas-particle separation is observed. Possible explanations: - 2d symmetrical simulation (no wind effect included) - simulation time is too short - no typhoon Yunya influence yet Plume height is lower than Holasek et al. (1996) suggest. Possible explanations: - according to the dynamic, turbulent, microphysics processes considered, the plume cannot reach ~40km with the known eruption rate - uncertainties from initial input conditions (atmospheric temperature profile, vent temperature and diameter, weight percentage …)

Outlook and Future Work 2d cartesian coordinate simulation (wind effect) is needed, especially for longer simulation with potential influence from typhoon Yunya 3d simulation is necessary for a more realistic and better description assemble and confirm initial input conditions more precisely, with sensitivity tests to match the plume with the satellite results laboratory study of incorporation and adsorption of volcanic gases into ash-hydrometeor aggregates comparison of gas phase SO 2 with TOMS results, and considering SO 2 releases due to ice sublimation, to study the variation and fate of SO 2 in the volcanic cloud comparison of ash property results with AVHRR and TOMS results more in detail study the large particle removal rate by increasing the particle size if possible, use a regional chemical model to further study SO 2 transportation add more tracers (?)