A NASA Model for Improving the Lightning NOx Emission Inventory for CMAQ William Koshak 1, Maudood Khan 2, Arastoo Biazar 3, Michael Newchurch 3, Richard.

Slides:

Advertisements

Similar presentations

Mission: Apply NASA measurement systems and unique Earth science research to improve the accuracy of short-term (0-24 hr) weather prediction at the regional.

Advertisements

SPoRT Products in Support of the GOES-R Proving Ground and NWS Forecast Operations Andrew Molthan NASA Short-term Prediction Research and Transition (SPoRT)

Transitioning unique NASA data and research technologies to operations SPoRT Total Lightning Activities and Updates Geoffrey Stano NASA SPoRT / ENSCO,

SPoRT Activities in Support of the GOES-R and JPSS Proving Grounds Andrew L. Molthan, Kevin K. Fuell, and Geoffrey T. Stano NASA Short-term Prediction.

NASA AQAST 6th Biannual Meeting January 15-17, 2014 Heather Simon Changes in Spatial and Temporal Ozone Patterns Resulting from Emissions Reductions: Implications.

Cloud Flash Evaluation Issues and Progress Report Don MacGorman, NOAA/NSSL Al Nierow, FAA Dennis Boccippio, NASA/MSFC.

Lightning NOx measurements during and after DC3 Harold Peterson: USRA Arastoo Biazar, Mike Newchurch, Wesley Cantrell: UAH GLM Science Meeting, Huntsville,

Lightning Climatology Oklahoma Lightning Mapping Array May - September, Paul Krehbiel New Mexico Tech September 28, 2005.

Vaisala TLS200, Technological Advancements for VHF total lightning mapping / Nikki Hembury and Ron Holle - Vaisala / Southern Thunder Conference 2011.

Deep Convective Clouds and Chemistry (DC3)* Field campaign to study the impact of midlatitude deep convection on the upper troposphere-lower stratosphere.

Variability in Ozone Profiles at TexAQS within the Context of an US Ozone Climatology Mohammed Ayoub 1, Mike Newchurch 1 2, Brian Vasel 3 Bryan Johnson.

Hayden Oswald 1 and Andrew Molthan 2 NASA Summer Intern, University of Missouri, Columbia, Missouri 1 NASA SPoRT Center, NASA/MSFC, Huntsville, Alabama.

Inter-comparison of Lightning Trends from Ground-based Networks during Severe Weather: Applications toward GLM Lawrence D. Carey 1*, Chris J. Schultz 1,

1 Comparative Lightning Characteristics of a Tornadic and Non-Tornadic Oklahoma Thunderstorm on April , 2006 Amanda Sheffield Purdue University.

Abstract Using OMI ozone profiles as the boundary conditions for CMAQ calculations significantly improves the agreement of the model with ozonesonde observations.

Lightning The LDAR II Network Nicholas W. S. Demetriades, Ronald L. Holle and Martin J. Murphy Vaisala, Inc., Tucson, Arizona HEAT Project - First Planning.

Development of a Lightning NOx Algorithm for WRF-Chem Amanda Hopkins Hansen Department of Meteorology Florida State University Henry.

Science Mission Directorate National Aeronautics and Space Administration transitioning unique NASA data and research technologies The SPoRT Center – Infusing.

Transitioning unique NASA data and research technologies to operations GOES-R Proving Grounds Fifth Meeting of the Science Advisory Committee November,

Proxy Data and VHF/Optical Comparisons Monte Bateman GLM Proxy Data Designer.

Improving Cloud Simulation in Weather Research and Forecasting (WRF) Through Assimilation of GOES Satellite Observations Andrew White Advisor: Dr. Arastoo.

Cloud Correction and its Impact on Air Quality Simulations Arastoo Pour Biazar 1, Richard T. McNider 1, Andrew White 1, Bright Dornblaser 3, Kevin Doty.

Ability of GEO-CAPE to Detect Lightning NOx and Resulting Upper Tropospheric Ozone Enhancement Conclusions When NO emissions from lightning were included.

The Lightning Warning Product Fifth Meeting of the Science Advisory Committee November, 2009 Dennis Buechler Geoffrey Stano Richard Blakeslee transitioning.

Update on GOES Radiative Products Richard T. McNider, Arastoo Pour Biazar, Andrew White University of Alabama in Huntsville Daniel Cohan, Rui Zhang Rice.

Earth Science Division National Aeronautics and Space Administration 18 January 2007 Paper 5A.4: Slide 1 American Meteorological Society 21 st Conference.

Earth-Sun System Division National Aeronautics and Space Administration SPoRT SAC Nov 21-22, 2005 Southern Thunder Alliance and LMA Assessments Steve Goodman.

Land Processes Group, NASA Marshall Space Flight Center, Huntsville, AL Response of Atmospheric Model Predictions at Different Grid Resolutions Maudood.

Importance of Lightning NO for Regional Air Quality Modeling Thomas E. Pierce/NOAA Atmospheric Modeling Division National Exposure Research Laboratory.

Ken Cummins 1, with help from: Richard J. Blakeslee 2, Lawrence D. Carey 3, Jeff C. Bailey 3, Monte Bateman 4, Steven J. Goodman 5 1 University of Arizona,

Estimating the Influence of Lightning on Upper Tropospheric Ozone Using NLDN Lightning Data Lihua Wang/UAH Mike Newchurch/UAH Arastoo Biazar/UAH William.

Remote Sensing and Modeling of the Georgia 2007 Fires Eun-Su Yang, Sundar A. Christopher, Yuling Wu, Arastoo P. Biazar Earth System Science Center University.

Richard Blakeslee, NASA/MSFC with a host of partners: NASA, NOAA, NMT, UAH, UF, UMD, GATech July 2011 Southern Thunder (ST11) Workshop Norman, OK.

NASA SPoRT’s Pseudo Geostationary Lightning Mapper (PGLM) GOES-R Science Week Meeting September, 2011 Huntsville, Alabama Geoffrey Stano ENSCO, Inc./NASA.

Vaisala Total Lightning Network Update / Nikki Hembury / Southern Thunder Conference 2011.

Christopher J. Schultz 1, Walter A. Petersen 2, Lawrence D. Carey 3* 1 - Department of Atmospheric Science, UAHuntsville, Huntsville, AL 2 – NASA Marshall.

AN ENHANCED SST COMPOSITE FOR WEATHER FORECASTING AND REGIONAL CLIMATE STUDIES Gary Jedlovec 1, Jorge Vazquez 2, and Ed Armstrong 2 1NASA/MSFC Earth Science.

Proving Ground Activities with Aviation Weather Center, Storm Prediction Center and NASA SPoRT GLM Science Meeting Huntsville, Alabama 20 September 2012.

EOS Aura Science Team Meeting, October, 2008, Columbia, MD AURA OMI Ozone Profiles for Large-scale CMAQ Boundary Conditions with Lightning Effects.

Evaluating ammonia (NH 3 ) predictions in the NOAA National Air Quality Forecast Capability (NAQFC) using in situ aircraft measurements William Battye,

111 SPoRT SAC, Nov 2009 transitioning unique NASA data and research technologies to operations Forecasting Lightning Threat Using Cloud Models With Explicit.

The Value of Air Quality Forecasting in the Mid-Atlantic Region Gregory Garner – Penn State Meteorology Advisers: Anne Thompson, William Ryan 2011 National.

Use of TES, AIRS and other satellite data for evaluation of air quality modeling efforts by the Texas Commission on Environmental Quality Fall AGU 2007.

Evaluation of the Pseudo-GLM GLM Science Meeting Huntsville, Alabama September 2013 Geoffrey Stano – NASA SPoRT / ENSCO Inc. Kristin Calhoun – NOAA.

2015 GLM Annual Science Team Meeting: Cal/Val Tools Developers Forum 9-11 September, 2015 DATA MANAGEMENT For GLM Cal/Val Activities Helen Conover Information.

Transitioning unique NASA and NOAA data and research technologies to operations SPoRT AWIPS II Activities Jason Burks

Comparison of CMAQ Lightning NOx Schemes and Their Impacts Youhua Tang 1,2, Li Pan 1,2, Pius Lee 1, Jeffery T. McQueen 4, Jianping Huang 4,5, Daniel Tong.

Methods for Incorporating Lightning NO x Emissions in CMAQ Ken Pickering – NASA GSFC, Greenbelt, MD Dale Allen – University of Maryland, College Park,

Impact of Lightning-NO Emissions on Eastern United States Photochemistry During the Summer of 2006 as Determined Using the CMAQ Model Dale Allen, Dept.

Transitioning research data to the operational weather community Overview of GOES-R Proving Ground Activities at the Short-term Prediction Research and.

Lawrence Carey 1, William Koshak 2, Harold Peterson 3, Retha Matthee 1 and A. Lamont Bain 1 1 Department of Atmospheric Science, University of Alabama.

Sub-hourly Variation of Tropospheric Ozone Profiles Measured by the Huntsville DIAL Shi Kuang 1*, John Burris 2, M. J. Newchurch 1*, Steve Johnson 3, and.

Photo by Chuck Doswell Ground Flash Fraction Retrieval Algorithm GLM Science Meeting December 1-3, 2010, Huntsville, AL Dr. William Koshak, NASA/MSFC/VP61.

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

Influence of Lightning-produced NOx on upper tropospheric ozone Using TES/O3&CO, OMI/NO2&HCHO in CMAQ modeling study M. J. Newchurch 1, A. P. Biazar.

A Summary of the NASA Lightning Nitrogen Oxides Model (LNOM) And Recent Results 10 th Annual CMAS Conference, Chapel Hill, NC October 24-26, 2011 William.

Total Lightning Characteristics in Mesoscale Convective Systems Don MacGorman NOAA/NSSL & Jeff Makowski OU School of Meteorology.

LNOx Influence on Upper Tropospheric Ozone Lihua Wang 1, Mike Newchurch 1, Arastoo Biazar 1, Williams Koshak 2, Xiong Liu 3 1 Univ. of Alabama in Huntsville,

Operational Use of Lightning Mapping Array Data Fifth Meeting of the Science Advisory Committee November, 2009 Geoffrey Stano, Dennis Buechler, and.

M. Newchurch 1, A. Biazar 1, M. Khan 2, B. Koshak 3, U. Nair 1, K. Fuller 1, L. Wang 1, Y. Park 1, R. Williams 1, S. Christopher 1, J. Kim 4, X. Liu 5,6,

Deep Convective Clouds and Chemistry (DC3) Field Program.

Application of OMI Ozone Profiles in CMAQ

LNOx Influence on Upper Tropospheric Ozone Mike Newchurch1, Lihua Wang1, Arastoo Biazar1, Williams Koshak2, Xiong Liu3 1Univ. of Alabama in Huntsville,

1st TEMPO Applications Workshop

The 96th AMS Annual Meeting

16th Annual CMAS Conference

Impact of GOES Enhanced WRF Fields on Air Quality Model Performance

Impact of lightning-NO emissions on eastern United States photochemistry during the summer of 2004 as determined using the CMAQ model Dale Allen – University.

WARM SEASON CONVECTIVE WIND CLIMATOLOGY FOR THE CCAFS/KSC AREA

Partitioning the LIS/OTD lightning climatological dataset into

Presentation transcript:

A NASA Model for Improving the Lightning NOx Emission Inventory for CMAQ William Koshak 1, Maudood Khan 2, Arastoo Biazar 3, Michael Newchurch 3, Richard McNider 3 1 Earth Science Office, NASA-MSFC, Huntsville, AL Universities Space Research Association, Huntsville, AL University of Alabama in Huntsville, Huntsville, AL 35805

Motivation  State & local air quality agencies use CMAQ to determine compliance with the National Ambient Air Quality Standards (NAAQS), so CMAQ predictions must be accurate.  Currently, emissions from lightning are either omitted or are poorly represented in CMAQ. The variability of lightning is typically (and unrealistically) ignored.  CMAQ model predictions suffer as a result, especially in the middle and upper troposphere.

LNOM  Lightning Nitrogen Oxides Model (LNOM)  Presently under development at NASA/MSFC  Builds spatio-temporal NOx emission inventory for CMAQ  Employs state-of-the-art lightning measurements in a unique way to model the variable nature of lightning

LNOM Part of an Ongoing Project … Improve Boundary & Initial Conditions Improve Lightning NOx Emission Inventory NASA Data (Aura/TES, CALIPSO/CALIOP) CMAQ NASA LNOM Improved Forecasts Improved Decisions RPC Project (UAH, Mike Newchurch et al.)

Basic Datasets Employed by LNOM  National Lightning Detection Network (NLDN) »Ground Flash Number, N g »Ground Flash Location, (x,y) »Ground Flash multiplicity, m »Ground Flash Peak Current, I  LIS/OTD Lightning Climatology Data »Provides satellite-inferred climatological “Z s ” ratio (# cloud flashes to # ground flashes). »Cloud Flash Number Estimation: N c = Z s N g  VHF Lightning Channel Mapping Data »Used to generate Channel Length pdfs, L »Channel altitude statistics, z

Current US VHF Networks LMA networks (NMT) North Alabama (MSFC) Oklahoma City (OU & NSSL) White Sands Missile Range (Army) Washington, D.C. Area (MSFC, NOAA, NMT) LDAR II networks (Vaisala) Dallas-Fort Worth Houston (Texas A&M, NSF) Kennedy Space Center (Air Force) [Adapted from D. Buechler, Univ. of Alabama in Huntsville]

LNOM Details:

LNOM Details (cont.)

Summary  Development of LNOM continues.  Implementation of LNOM is described in the paper »H. Peterson (NASA Post Doc.) is presently writing IDL code to produce channel length probability distribution functions from North Alabama LMA data. »H. Peterson is considering ways to employ radar data to improve cloud flash count in a CMAQ grid volume.  There is a desire to assess the specific impact of LNOM on CMAQ results by applying LNOM to an August 2006 baseline run of CMAQ (see paper for details).