Edward Mansell National Severe Storms Laboratory Donald MacGorman and Conrad Ziegler National Severe Storms Laboratory, Norman, OK Funding sources in the.

Slides:

Advertisements

Similar presentations

Some questions on convection that could be addressed through another UK field program centered at Chilbolton Dan Kirshbaum 1.

Advertisements

Simulating cloud-microphysical processes in CRCM5 Ping Du, Éric Girard, Jean-Pierre Blanchet.

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

Calibration of GOES-R ABI cloud products and TRMM/GPM observations to ground-based radar rainfall estimates for the MRMS system – Status and future plans.

Storm-scale pseudo-GLM lightning assimilation and explicit lightning forecast implemented within the WRF- ARW model Alexandre Fierro, Blake Allen (CIMMS/NOAA-

To perform statistical analyses of observations from dropsondes, microphysical imaging probes, and coordinated NOAA P-3 and NASA ER-2 Doppler radars To.

Eidgenössisches Departement des Innern EDI Bundesamt für Meteorologie und Klimatologie MeteoSchweiz Institut für Physik der Atmosphäre On the Value of.

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.

Assimilating Reflectivity and Doppler Velocity Observations of Convective Storms into Storm-Scale NWP Models David Dowell Cooperative Institute for Mesoscale.

Convective-scale diagnostics Rob Rogers NOAA/AOML Hurricane Research Division.

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.

Climate model grid meshes are too coarse to explicitly simulate storm system winds and therefore must rely on simplified models referred to as parameterizations.

On the relationship of in-cloud convective turbulence and total lightning Wiebke Deierling, John Williams, Sarah Al-Momar, Bob Sharman, Matthias Steiner.

Impact of Graupel Parameterization Schemes on Idealized Bow Echo Simulations Rebecca D. Adams-Selin Adams-Selin, R. D., S. C. van den Heever, and R. D.

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.

Ensemble Numerical Prediction of the 4 May 2007 Greensburg, Kansas Tornadic Supercell using EnKF Radar Data Assimilation Dr. Daniel T. Dawson II NRC Postdoc,

Microphysics complexity in squall line simulations. As high-resolution climate models increasingly turn towards an explicit representation of convection,

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

Towards an object-oriented assessment of high resolution precipitation forecasts Janice L. Bytheway CIRA Council and Fellows Meeting May 6, 2015.

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.

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,

Assimilating Reflectivity Observations of Convective Storms into Convection-Permitting NWP Models David Dowell 1, Chris Snyder 2, Bill Skamarock 2 1 Cooperative.

Lightning Outbreaks in the Eyewall MET 614 Seminar Antti Pessi.

Evaluation of the Latent heat nudging scheme for the rainfall assimilation at the meso- gamma scale Andrea Rossa* and Daniel Leuenberger MeteoSwiss *current.

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.

Comparison of Evaporation and Cold Pool Development between Single- Moment (SM) and Multi-moment (MM) Bulk Microphysics Schemes In Idealized Simulations.

WSN05 6 Sep 2005 Toulouse, France Efficient Assimilation of Radar Data at High Resolution for Short-Range Numerical Weather Prediction Keith Brewster,

Data assimilation, short-term forecast, and forecasting error

An ensemble study of HyMeX IOP6 and IOP7a Alan Hally (1,2), Evelyne Richard (1), Véronique Ducrocq (2) (1)LA, University of Toulouse, France (2)CNRM, Météo-France,

WMO International Cloud Modeling Workshop

Relationships between Lightning and Radar Parameters in the Mid-Atlantic Region Scott D. Rudlosky Cooperative Institute of Climate and Satellites University.

CCN effects on numerically simulated mixed-phase convective storms with Klaus D. Beheng, University Karlsruhe, Germany Alexander Khain, Hebrew University.

Storm tracking & typing for lightning observations Kristin Calhoun, Don MacGorman, Ben Herzog.

Towards parametrized GEC current sources for the CESM model FESD project meeting February 2014 Wiebke Deierling, Andreas Baumgaertner, Tina Kalb.

Don MacGorman 1,2, Ted Mansell 1, Kristin Kuhlman 2, Alex Fierro 2, Conrad Ziegler 1, Stephanie Weiss 2, Dave Rust 2 1 NOAA/National Severe Storms Laboratory.

Relationships between lightning flash rates over the tropics and subtropics and the vertical structures of precipitation radar reflectivity Chuntao Liu.

Feature-based (object-based) Verification Nathan M. Hitchens National Severe Storms Laboratory.

Toward a new parameterization of nitrogen oxides produced by lightning flashes in the WRF-AqChem model Christelle Barthe NCAR/ACD Previously at Laboratoire.

Alexander Ryzhkov Weather Radar Research Meteorological Applications of Dual-polarization Radar.

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.

DRAFT – Page 1 – January 14, 2016 Development of a Convective Scale Ensemble Kalman Filter at Environment Canada Luc Fillion 1, Kao-Shen Chung 1, Monique.

Assimilation of Lightning Data Using a Newtonian Nudging Method Involving Low-Level Warming Max R. Marchand Henry E. Fuelberg Florida State University.

Numerical Simulation and Prediction of Supercell Tornadoes Ming Xue School of Meteorology and Center for Analysis and Prediction of Storms University of.

“Real data OSSE” EnKF analysis of storm from 6 June 2000 (STEPS) Assimilated observed radar reflectivity and radial velocity. Activated electrification.

Lightning Mapping Technology & NWS Warning Decision Making Don MacGorman, NOAA/NSSL.

Vincent N. Sakwa RSMC, Nairobi

Lightning data assimilation in the Rapid Refresh and evaluation of lightning diagnostics from HRRR runs Steve Weygandt, Ming Hu, Curtis Alexander, Stan.

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

© 2000 Roger Edwards Investigating the Potential of Using Radar to Nowcast Cloud-to-Ground Lightning Initiation over Southern Ontario 18 th Annual GLOMW.

The Risks and Rewards of High-Resolution and Ensemble Modeling Systems David Schultz NOAA/National Severe Storms Laboratory Paul Roebber University of.

Assimilation of Pseudo-GLM Observations Into a Storm Scale Numerical Model Using the Ensemble Kalman Filter Blake Allen University of Oklahoma Edward Mansell.

Edward “Ted” Mansell NSSL/WRDD Thunderstorm Electrification and Lightning Simulation.

Global vs mesoscale ATOVS assimilation at the Met Office Global Large obs error (4 K) NESDIS 1B radiances NOAA-15 & 16 HIRS and AMSU thinned to 154 km.

Mesoscale Assimilation of Rain-Affected Observations Clark Amerault National Research Council Postdoctoral Associate - Naval Research Laboratory, Monterey,

Relationships between total lightning activity, microphysics, and kinematics during the 24 September 2012 HyMeX MCS system J.-F. Ribaud, O. Bousquet and.

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

Parameterization of Cloud Microphysics Based on the Prediction of Bulk Ice Particle Properties. Part II: Case Study Comparisons with Observations and Other.

and NM Tech team & many HyMeX participants

The DYMECS project A statistical approach for the evaluation of convective storms in high-resolution models Thorwald Stein, Robin Hogan, John Nicol, Robert.

Development of convective-scale data assimilation techniques for 0-12h high impact weather forecasting JuanzhenSun NCAR, Boulder, Colorado Oct 25, 2011.

Daniel Leuenberger1, Christian Keil2 and George Craig2

Inna M. Gubenko and Konstantin G

Sensitivity of WRF microphysics to aerosol concentration

Jimy Dudhia NCAR MWFR-WG

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

Presentation transcript:

Edward Mansell National Severe Storms Laboratory Donald MacGorman and Conrad Ziegler National Severe Storms Laboratory, Norman, OK Funding sources in the Office of Naval Research, NSF, NSSL, the National Research Council, and the Oklahoma State Regents Lightning at NSSL: Numerical Modeling and Data Assimilation

Storm electrification modeling: Basic understanding of electrification processes Lightning-storm relationships Lightning data assimilation (COAMPS) On mesoscale (>10km), control convection parameterization scheme. Storm-scale EnKF radar data assimilation Modeling Activities/Capabilities

Storm Model (COMMAS) Full dynamic, microphysical, and electrical simulation model Collisional charge separation, explicit small ion processes, branched lightning. Two-moment bulk microphysics: Predict particle concentrations (and mass) for all hydrometeors (droplets, rain, ice crystals, snow, graupel, hail) and simple bulk CCN. MPI capable [Mansell et al. 2002, 2005, (2009 in review), also Fierro et al, Kuhlman et al.]

Supercell Simluation

Small Storm Simluation

Lightning and Charge Structure Inferred charge structure from lightning sources + + – Model-simulated charge structure and lightning West -30km -25km East Altitude (km)

Sensitivity to CCN concentration Volume

Simulated Lightning Rate Correlations Isolated cells: 0.7 multiple cells: 0.5

Total Flash Rate Correlation Coefficient with ParameterIsolated StormsStorm Systems Maximum Elec. Field Graupel Volume Updraft Mass Flux (-10°C) Updraft Volume (>10 m s -1 ) Cloud Ice Mass Flux (- 30°C) Cloud Ice Mass Rain Mass Maximum Updraft

Assimilating Lightning Data o Unambiguous indicator of [electrified] convection o Mixed-phase ice precipitation (graupel) is present o Can complement radar data assimilation: Fill in radar voids: Mountains, oceans, other countries, radar drop-outs Potential use of flash rate relationships (storm intensity, e.g., updraft volume/mass flux) and satellite-based (e.g., GOES) total lightning detection (total flash rate in particular) [Mansell, Ziegler, and MacGorman, 2007]

Method is similar to Rogers et al. (2000) for radar assimilation. Force/suppress Kain-Fritsch based on presence/absence of lightning. Add up to 1.0 g/kg of moisture to get deep convection (10m/s updraft, 7km cloud depth). Allow KF scheme to generate precipitation rates and latent heating and evaporative cooling. (Other methods can be used to adjust or impose latent heating rates based on rainfall relations) LMA sources KS CO NE Case study with COAMPS on July 2000

Test caseSpin-up period: Obs. Precip vs. Control

Spin-up period: Obs. Precip vs. Assimilation

Spin-up period: Control vs. Assimilation

Surface Temperature (C) Warm-start Model Conditions: ControlAssimilation

01 UTC 21 July

02 UTC 21 July

0-6 hr Precip: Obs and forecasts Control Fcst from ltg. assim.

Summary Cloud modeling investigates basic aspects of lightning production by storms. Lightning data assimilation can be an effective means to initialize the effects of prior/current convection: Cold outflows better placed by forcing KF. Good potential for rapid update cycling and providing conditions for higher-resolution nest. Assimilation-based forecast shows more skill than control forecast for first few hours. Does little to correct errors in large scale fields, so range of forecast improvement limited ( < 6 hr).

Issues Must consider lightning location accuracy in terms of model resolution. What does a “flash” represent in the observing system? (large variations in flash extent) Method tied to data source. For resolved convection or convection-permitting EnKF, need to relate lightning to model variables or derived quantities. And/Or use lightning mainly to initiate deep convection.