Lightning Potential Index (J/Kg) (Yair et al.2010,JGR)

Slides:



Advertisements
Similar presentations
Lightning data assimilation techniques for convective storm forecasting with application to GOES-R Geostationary Lightning Mapper Alexandre Fierro, Blake.
Advertisements

WMO International Cloud Modeling Workshop, July 2004 A two-moment microphysical scheme for mesoscale and microscale cloud resolving models Axel Seifert.
Forecasting convective outbreaks using thermodynamic diagrams. Anthony R. Lupo Atms 4310 / 7310 Lab 10.
Predicting lightning density in Mediterranean storms based on the WRF model dynamic and microphysical fields Yoav Yair 1, Barry Lynn 1, Colin Price 2,
Lightning and Storm Electricity Research Don MacGorman February 25–27, 2015 National Weather Center Norman, Oklahoma.
Aerosol effects on rain and hail formation and their representation using polarimetric radar signatures Eyal Ilotovich, Nir Benmoshe and Alexander Khain.
In this work we present results of cloud electrification obtained with the RAMS model that includes the process of charge separation between ice particles.
Remote Sensing and Modeling of Hurricane Intensification Steve Guimond and Jon Reisner Atmospheric Dynamics EES-2 FSU.
The IOP6 (24 September 2012) heavy precipitation event over Southern France: observational and model analysis Lagouvardos, K. (1), Kotroni, V. (1), Bousquet.
A preliminary experiment on the simulation of thunderstorm electrification through GRAPES Yijun Zhang Chinese Academy of Meteorological Sciences, Beijing,
Lightning: Charge Separation Mechanisms, Detection and Applications Kaitlyn Suski May 29, 2009 SIO 209
On the possibility of sprites on other planets Yoav Yair and Roy Yaniv.
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.
Electricité atmosphérique et Production d’oxydes d’azote par les éclairs: Etat des lieux et perspectives Christelle Barthe 1 et Jean-Pierre Pinty Laboratoire.
DYMECS: Dynamical and Microphysical Evolution of Convective Storms (NERC Standard Grant) University of Reading: Robin Hogan, Bob Plant, Thorwald Stein,
WRF Volcano modelling studies, NCAS Leeds Ralph Burton, Stephen Mobbs, Alan Gadian, Barbara Brooks.
High resolution simulations of microphysics and electrification in a hurricane-like vortex and a TOGA COARE oceanic squall line Alexandre Fierro School.
Modern Era Retrospective-analysis for Research and Applications: Introduction to NASA’s Modern Era Retrospective-analysis for Research and Applications:
Bauru November 2004 Modelling interpretation of in situ H2O, CH4 and CO2 measured by  SDLA balloon borne instrument (SF2 and SF4 flights). N. Huret(1),G.
Edward Mansell National Severe Storms Laboratory Donald MacGorman and Conrad Ziegler National Severe Storms Laboratory, Norman, OK Funding sources in the.
Possible Aerosol Effects on Lightning Activity and Structure of Hurricanes Khain, A., N. Cohen, B. Lynn, and A. Pokrovsky, 2008: Possible aerosol effects.
Field Mill. Charging by induction Initially neutral Introduce a charge, creating an electric field E.
Toward a new parameterization of nitrogen oxides produced by lightning flashes in the WRF-AqChem model Christelle Barthe NCAR/ACD Previously at Laboratoire.
High-Resolution Simulation of Hurricane Bonnie (1998). Part II: Water Budget SCOTT A. BRAUN J. Atmos. Sci., 63,
Assimilation of Lightning Data Using a Newtonian Nudging Method Involving Low-Level Warming Max R. Marchand Henry E. Fuelberg Florida State University.
Lightning Mapping Technology & NWS Warning Decision Making Don MacGorman, NOAA/NSSL.
Case 5: Tracer Transport in Deep Convection STERAO-1996 From Dye et al. (2000)
Cheng-Zhong Zhang and Hiroshi Uyeda Hydroshperic Atmospheric Research Center, Nagoya University 1 November 2006 in Boulder, Colorado Possible Mechanism.
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.
Deep Convective Clouds and Chemistry (DC3) Field Program.
Sensitivity to the Representation of Microphysical Processes in Numerical Simulations during Tropical Storm Formation Penny, A. B., P. A. Harr, and J.
Dynamics I: Basic Equations and approximations Adilson W
Japan Meteorological Agency / Meteorological Research Institute
Influences of Particle Bulk Density of Snow and Graupel in Microphysics-Consistent Microwave Brightness Temperature Simulations Research Group Meeting.
Parameterization of Cloud Microphysics Based on the Prediction of Bulk Ice Particle Properties. Part II: Case Study Comparisons with Observations and Other.
Hugh Morrison & Jason A. Milbrandt JAS (2015), p
Advisors: Fuqing Zhang and Eugene Clothiaux
Paper Review Jennie Bukowski ATS APR-2017
1. Background for Cloud Physics
By SANDRA E. YUTER and ROBERT A. HOUZE JR
Water Budget of Typhoon Nari(2001)
Inna M. Gubenko and Konstantin G
ATOC 4720 class24 Clouds and storms 1. Extratropical cyclonic storms
Sensitivity of WRF microphysics to aerosol concentration
Characterizing the response of simulated atmospheric boundary layers to stochastic cloud radiative forcing Robert Tardif, Josh Hacker (NCAR Research Applications.
Precipitation 18.3 Brain Pop: Snowflakes Brain Pop: Rainbows.
NRL POST Stratocumulus Cloud Modeling Efforts
Concurrent Sensitivities of an Idealized Deep Convective Storm to Parameterization of Microphysics, Horizontal Grid Resolution, and Environmental Static.
Generation of Simulated GIFTS Datasets
Guy Dagan, Ilan Koren, Orit Altaratz, Yoav Lehahn  iScience 
Conrick, R., C. F. Mass, and Q. Zhong, 2018
Tong Zhu and Da-Lin Zhang 2006:J. Atmos. Sci.,63,
Review of Roesenfeld et al
Dual-Aircraft Investigation of the Inner Core of Hurricane Nobert
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)
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)
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:

Lightning Potential Index (J/Kg) (Yair et al.2010,JGR) Lightning Potential Index (LPI) is a measure of the potential charge separation that leads to lightning flashes in convective TSs. It is calculated from model simulated updraft and microphysical fields within the charge separation region of clouds between (0 o C and - 20 o C), where the non inductive mechanism involving collisions of ice and graupel particles in the presence of supercooled water is most effective (Saunders, 2008). So, the LPI is defined as the volume integral of the total mass flux of ice and liquid water within the “charging zone” in a developing thundercloud. The LPI (J kg-1) and is defined as, Where V is the volume of air in the layer between 0oC and -20oC, w is the vertical wind component (m s-1), and qs, qi and qg are the model-computed mass mixing ratios for snow, cloud ice, and graupel respectively (in kg kg-1).€ is a dimensionless number that has a value between 0 and 1 and is defined by, Where Ql is the total liquid water mass mixing ratio (kg kg-1 )and Qi is the ice fractional mixing ratio (kg kg-1 ) defined by, In essence, € is a scaling factor for the cloud updraft and attains a maximal value when the mixing ratios of supercooled liquid water and of the combined ice species (the total of cloud ice, graupel, and snow) are equal., calculation of the LPI from the cloud-resolving atmospheric model output fields can provide maps of the microphysics based potential for electrical activity and lightning flashes.

IC:2019041700

IC:2019041700

Day1 IC:2019041700

Day2 2019041700

IC: 2019041700 Day1

IC: 2019041700 Day2

48 Hour Forecast IC: 2019041700 Hourly evolution of IMD: WRF (3km) Derived :Lightning Potential Index (J/kg)

48 Hour Forecast IC: 2019041700 Threat Level : LOW: LPI < 0.001 and >0.005 Threat Level : Moderate: LPI :< 0.01 and > 0.001 Threat Level : High : LPI > 0.01

IC: 2019041700 48 Hour Forecast SCP: Supercell Composite parameter Hourly evolution of IMD: WRF (3km) Derived : Supercell Composite Parameter Threat Level : LOW: 3 < SCP < 5 Moderate: 5 < SCP < 7 High : SCP > 7

LAYRH : Layer Mean Relative Humidity IC: 2019041700 48 Hour Forecast Hourly evolution of IMD: WRF (3km) Derived :Layer Mean Relative Humidity Threat Level : LOW: 20 < LAYRH < 40 Moderate: 40 < LAYRH < 60 High : LAYRH > 60