Electrified Simulations of Hurricane Rita (2005) with Comparisons to LASA Data Steve Guimond 1,2, Jon Reisner 2, Chris Jeffery 2 and Xuan-Min Shao 2 1.

Slides:



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

Advanced Research WRF High Resolution Simulations of Hurricanes Katrina, Rita and Wilma (2005) Kristen L. Corbosiero, Wei Wang, Yongsheng Chen, Jimy Dudhia.
To perform statistical analyses of observations from dropsondes, microphysical imaging probes, and coordinated NOAA P-3 and NASA ER-2 Doppler radars To.
Sensitivity of High-Resolution Simulations of Hurricane Bob (1991) to Planetary Boundary Layer Parameterizations SCOTT A. BRAUN AND WEI-KUO TAO PRESENTATION.
Predicting lightning density in Mediterranean storms based on the WRF model dynamic and microphysical fields Yoav Yair 1, Barry Lynn 1, Colin Price 2,
Atmospheric structure from lidar and radar Jens Bösenberg 1.Motivation 2.Layer structure 3.Water vapour profiling 4.Turbulence structure 5.Cloud profiling.
Lightning and Storm Electricity Research Don MacGorman February 25–27, 2015 National Weather Center Norman, Oklahoma.
Principal Rainband of Hurricane Katrina as observed in RAINEX Anthony C. Didlake, Jr. 28 th Conference on Hurricanes and Tropical Meteorology April 29,
HWRF Model Sensitivity to Non-hydrostatic Effects Hurricane Diagnostics and Verification Workshop May 4, 2009 Katherine S. Maclay Colorado State University.
Convective-scale diagnostics Rob Rogers NOAA/AOML Hurricane Research Division.
Aerosol effects on rain and hail formation and their representation using polarimetric radar signatures Eyal Ilotovich, Nir Benmoshe and Alexander Khain.
Impact of the 4D-Var Assimilation of Airborne Doppler Radar Data on Numerical Simulations of the Genesis of Typhoon Nuri (2008) Zhan Li and Zhaoxia Pu.
The Hurricane Weather Research & Forecasting (HWRF) Prediction System Next generation non-hydrostatic weather research and hurricane prediction system.
Evidence of Strong Updrafts in Tropical Cyclones using Combined Satellite, Lightning, and High-Altitude Aircraft Observations Christopher S. Velden*, Sarah.
Towards Developing a “Predictive” Hurricane Model or the “Fine-Tuning” of Model Parameters via a Recursive Least Squares Procedure Goal: Minimize numerical.
Chris Birchfield Atmospheric Sciences, Spanish minor.
Electrified Simulations of Hurricane Rita (2005) with Comparisons to LASA Data Steve Guimond 1,2, Jon Reisner 2, Chris Jeffery 2 and Xuan-Min Shao 2 1.
In this work we present results of cloud electrification obtained with the RAMS model that includes the process of charge separation between ice particles.
Background Research Applications Philip Hayes The Florida State University.
The Rapid Intensification of Hurricane Karl (2010): Insights from New Remote Sensing Measurements Collaborators: Anthony Didlake (NPP/GSFC),Gerry Heymsfield.
Simulating Supercell Thunderstorms in a Horizontally-Heterogeneous Convective Boundary Layer Christopher Nowotarski, Paul Markowski, Yvette Richardson.
Remote Sensing and Modeling of Hurricane Intensification Steve Guimond and Jon Reisner Atmospheric Dynamics EES-2 FSU.
Study Design and Summary Atmospheric boundary layer (ABL) observations were conducted in Sapporo, Japan from April 2005 to July Three-dimensional.
IMPROVING VERY-SHORT-TERM STORM PREDICTIONS BY ASSIMILATING RADAR AND SATELLITE DATA INTO A MESOSCALE NWP MODEL Allen Zhao 1, John Cook 1, Qin Xu 2, and.
Lightning: Charge Separation Mechanisms, Detection and Applications Kaitlyn Suski May 29, 2009 SIO 209
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.
Lightning Outbreaks in the Eyewall MET 614 Seminar Antti Pessi.
Rapid Intensification of Hurricane Earl (2010): Vorticity and Mass Flux Budgets 1. Motivation: Various studies have emphasized the importance of different.
Outline Background Highlights of NCAR’s R&D efforts A proposed 5-year plan for CWB Final remarks.
High resolution simulations of microphysics and electrification in a hurricane-like vortex and a TOGA COARE oceanic squall line Alexandre Fierro School.
Hurricane Microphysics: Ice vs Water A presenation of papers by Willoughby et al. (1984) and Heymsfield et al. (2005) Derek Ortt April 17, 2007.
Methods for Introducing VHTs in Idealized Models: Retrieving Latent Heat Steve Guimond Florida State University.
Data assimilation, short-term forecast, and forecasting error
Frank J. LaFontaine 1, Robbie E. Hood 2, Courtney D. Radley 3, Daniel J. Cecil 4, and Gerald Heymsfield 5 1 Raytheon Information Solutions, Huntsville,
Edward Mansell National Severe Storms Laboratory Donald MacGorman and Conrad Ziegler National Severe Storms Laboratory, Norman, OK Funding sources in the.
Ensemble Kalman filter assimilation of Global-Hawk-based data from tropical cyclones Jason Sippel, Gerry Heymsfield, Lin Tian, and Scott Braun- NASAs GSFC.
Field Mill. Charging by induction Initially neutral Introduce a charge, creating an electric field E.
Overview of Tropical Cyclones AOS 453 April 2004 J. P. Kossin CIMSS/UW-Madison.
The Rapid Intensification of Hurricane Karl (2010): Insights from New Remote Sensing Measurements Anthony Didlake (NPP/GSFC),Gerry Heymsfield (GSFC), Paul.
Why is it important to HS3 science to estimate convective vertical velocity accurately? Ed Zipser and Jon Zawislak Dept. of Atmospheric Sciences University.
Steve Guimond. Main driver of hurricane genesis and intensity change is latent heat release Main driver of hurricane genesis and intensity change is.
Electrified Simulations of Hurricane Rita (2005) with Comparisons to LASA Data Steve Guimond 1,2, Jon Reisner 2, Chris Jeffery 2 and Xuan-Min Shao 2 1.
Assimilation of Lightning Data Using a Newtonian Nudging Method Involving Low-Level Warming Max R. Marchand Henry E. Fuelberg Florida State University.
Determining Key Model Parameters of Rapidly Intensifying Hurricane Guillermo(1997) Using the Ensemble Kalman Filter Chen Deng-Shun 16 Apr, 2013, NCU Godinez,
Meeting the challenge of obtaining and interpreting observations of deep convection in tropical disturbances and hurricanes by Ed Zipser, Jon Zawislak,
Doppler Lidar Winds & Tropical Cyclones Frank D. Marks AOML/Hurricane Research Division 7 February 2007.
Lightning Mapping Technology & NWS Warning Decision Making Don MacGorman, NOAA/NSSL.
Understanding Convection in Relation to the Non-aerosol Environment ASR Science Team Meeting, Tyson’s Corner, VA, March 17, 2015 Robert Houze With help.
Science Questions What is role of hot towers in TC intensification and RI? Are they a cause of intensification or an effect? How does wind and temperature.
HOT TOWERS AND HURRICANE INTENSIFICATION Steve Guimond Florida State University.
Assimilation of Pseudo-GLM Observations Into a Storm Scale Numerical Model Using the Ensemble Kalman Filter Blake Allen University of Oklahoma Edward Mansell.
1 Simulations of Rapid Intensification of Hurricane Guillermo with Data assimilation Using Ensemble Kalman Filter and Radar Data Jim Kao (X-2, LANL) Presentation.
INNER CORE STRUCTURE AND INTENSITY CHANGE IN HURRICANE ISABEL (2003) Shuyi S. Chen and Peter J. Kozich RSMAS/University of Miami J. Gamache, P. Dodge,
Mesoscale Assimilation of Rain-Affected Observations Clark Amerault National Research Council Postdoctoral Associate - Naval Research Laboratory, Monterey,
Shuyi S. Chen Rosenstial School of Marine and Atmospheric Science University of Miami Overview of RAINEX Modeling of 2005 Hurricanes In the eye of Katrina.
Relationships between total lightning activity, microphysics, and kinematics during the 24 September 2012 HyMeX MCS system J.-F. Ribaud, O. Bousquet and.
HOT TOWERS AND HURRICANE INTENSIFICATION Steve Guimond Florida State University.
Evolution of Hurricane Isabel’s (2003) Vortex Structure and Intensity
Sensitivity to the Representation of Microphysical Processes in Numerical Simulations during Tropical Storm Formation Penny, A. B., P. A. Harr, and J.
Rosenstial School of Marine and Atmospheric Science
NOAA Intensity Forecasting Experiment (IFEX)
Hurricane Vortex X L Converging Spin up Diverging Spin down Ekman
A LATENT HEAT RETRIEVAL IN A RAPIDLY INTENSIFYING HURRICANE
Tong Zhu and Da-Lin Zhang 2006:J. Atmos. Sci.,63,
Tong Zhu and Da-Lin Zhang
Lightning Potential Index (J/Kg) (Yair et al.2010,JGR)
Lightning Potential Index (J/Kg) (Yair et al.2010,JGR)
Lightning Potential Index (J/Kg) (Yair et al.2010,JGR)
Lightning Potential Index (J/Kg) (Yair et al.2010,JGR)
Lightning Potential Index (J/Kg) (Yair et al.2010,JGR)
Presentation transcript:

Electrified Simulations of Hurricane Rita (2005) with Comparisons to LASA Data Steve Guimond 1,2, Jon Reisner 2, Chris Jeffery 2 and Xuan-Min Shao 2 1 Florida State University 2 Los Alamos National Laboratory

Motivation Improve understanding and forecasting of TC intensification

Latent Heat Updraft Background Vortex Microphysics Hurricane Intensification Roadmap Eddy Heat and Momentum Fluxes Balanced response Adjustment Symmetric heating Asymmetric heating Adjustment Balanced response Adjustment Intensity and Structure Change Nolan and Grasso (2003)

Motivation Improve understanding and forecasting of TC intensification –Convective obs hard to come by over ocean Doppler radar coverage very sparse Lightning fills gaps in convective monitoring ?

Latent Heat Updraft Background Vortex Microphysics Hurricane Intensification Roadmap Eddy Heat and Momentum Fluxes Balanced response Adjustment Symmetric heating Asymmetric heating Adjustment Balanced response Adjustment Intensity and Structure Change Nolan and Grasso (2003) Lightning Collisions & Charging

Motivation Improve understanding and forecasting of TC intensification –Convective obs hard to come by over ocean Doppler radar coverage very sparse Lightning fills gaps in convective monitoring ? –Understand relationship between latent heating and lightning –When/where to add energy to system –Differences in where most lightning located (i.e. Molinari et al. 1999; Cecil and Zipser 2002; Squires and Businger 2007) Detection efficiency issue Is eyewall lightning important indicator of structural change ?

New Research Tools –Observational component Los Alamos Sferic Array (LASA; Shao et al. 2000) –Existing VLF array »Records full EMP (allows detection of intracloud and cloud- to-ground strokes) »Lat/Lon, time, height? New Dual VLF-VHF 4-D lightning mapping array –Deployed along banks of Gulf of Mexico –VLF (~2000 km range) –VHF (~500 km range) »Provides precise height retrieval Combine with existing radars –NOAA P-3s, 88Ds, ELDORA?, EDOP?

New Research Tools –Theoretical component Advanced atmospheric model HIGRAD (Reisner et al. 2004) –Compressible Navier-Stokes, non-hydrostatic, explicit convection –Differentiable (smooth) numerics with greatly reduced time errors (option) –Option to use a particle-based (Lagrangian) cloud model which overcomes bin limitations. Coupled to electrification model (Mansell et al. 2005) –Non-inductive charging mechanism using Saunders scheme –Discharge model requires significant tuning »Based on limiters »Tuned to hurricanes based on Fierro et al. (2002)

Do Hot Towers Produce Lightning? Hot Towers or Vortical Hot Towers (i.e. Montgomery et al. 2006) –Deep convection  reach or penetrate trop –Strong, rotating updrafts –Embedded in warm-core cyclone over ocean Effects on microphysics? Next slides… –ER-2 Doppler Radar observations of Hot Towers Linear Depolarization Ratio (LDR) –particle canting angle or asymmetry »horizontal dimension larger than vertical –dielectric constant (i.e. wet or dry) Retrieved vertical velocities (nadir beam) –Lightning Instrument Package (LIP) Aircraft (20 km) electric field mills (x,y,z components) ~1 s sampling, ~200 m horizontal resolution

Hot Tower #1: CAT 2 Dennis (2005) -8 to -15 dB  large, wet, asymmetric ice to large, wet snow aggregates -13 to -17 dB  medium, wet graupel or small hail -18 to -26 dB  small, dry ice particles to dry, low density snow

Hot Tower #2: CAT 4 Emily (2005) -8 to -15 dB  large, wet, asymmetric ice to large, wet snow aggregates -13 to -17 dB  medium, wet graupel or small hail -18 to -26 dB  small, dry ice particles to dry, low density snow

Hurricane Rita Simulations Current configuration –Grid 1,980 km on a side; 4 km inner mesh, stretch to 20 km 35 m stretching to 15 km –Relaxation boundary conditions –Weak, top gravity wave absorber Initialization –Barotropic vortex, max wind of 40 m/s –Initial forcing from Key West 88D reflectivity Storm-centered, gridded, native 1 km –ECMWF operational analyses for large scale –Satellite SSTs, High-res topography

Structure of Latent Heat

HIGRAD vs. LASA Model Observations

Combining Structure and Magnitude

Testing algorithm in model

P-3 Doppler Radar Results

Rita WSR-88D Animation

LASA observations of Rita

P-3 Doppler Radar LH in Guillermo(1997)

P-3 Radar LH: Thermodynamic Sensitivity

New method for LH retrievals –Ability to accept some errors in water budget –Local tendency, radar-derived parameters –LH magnitude relatively insensitive to thermo –Sensitive to vertical velocity (most important) Test mass continuity vertical velocity or use EDOP? ~30 minute radar sampling does nothing for water budget Local tendency à order of mag. less than Qnet Incorporate WSR-88Ds for tendency, heating evolution Hybrid method –Doppler radar and dropsonde Conclusions and Future Work

Future Hurricane Rita Simulations Configuration – km inner mesh –Key West 88D derived Symmetric, baroclinic vortex Symmetric latent heat retrieval (Guimond 2008) –Extended Kalman Filtering data assimilation with LASA (with J. Kao)

Acknowledgments LANL Hurricane Lightning Team References Reisner et al. (2005) Mansell et al. (2005) Molinari et al. (XXXX) Cecil et al. (XXXX)