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.

Slides:

Advertisements

Similar presentations

Office of Research and Development National Exposure Research Laboratory, Atmospheric Modeling and Analysis Division Changes in U.S. Regional-Scale Air.

Advertisements

A PERFORMANCE EVALUATION OF THE ETA - CMAQ AIR QUALITY FORECAST MODEL FOR THE SUMMER OF 2004 CMAS Workshop Chapel Hill, NC 20 October, 2004.

Improving the Representation of Atmospheric Chemistry in WRF William R. Stockwell Department of Chemistry Howard University.

CO 2 in the middle troposphere Chang-Yu Ting 1, Mao-Chang Liang 1, Xun Jiang 2, and Yuk L. Yung 3 ¤ Abstract Measurements of CO 2 in the middle troposphere.

Integrating satellite observations for assessing air quality over North America with GEOS-Chem Mark Parrington, Dylan Jones University of Toronto

Effects of Urban-Influenced Thunderstorms on Atmospheric Chemistry Kenneth E. Pickering Department of Meteorology University of Maryland HEAT Planning.

Weather Research & Forecasting Model (WRF) Stacey Pensgen ESC 452 – Spring ’06.

Evaluation of Real-time Air Quality Forecasts from WRF-NMM/CMAQ and NMMB/CMAQ Using Discover-AQ P-3B Airborne Measurements Youhua Tang 1,2, Jeffery T.

Effects of climate change on future wildfire and its impact on regional air quality Hyun Cheol Kim, Dae-Gyun Lee, and Daewon Byun 1 Institute for Multidimensional.

Impact of Mexico City on Regional Air Quality Louisa Emmons Jean-François Lamarque NCAR/ACD.

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

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

Air Resources Laboratory Yunsoo Choi 12, Daewon Byun 1, Pius Lee 1, Rick Saylor 1, Ariel Stein 12, Daniel Tong 12, Hyun-Cheol Kim 12, Fantine Ngan 13,

A Case Study Using the CMAQ Coupling with Global Dust Models Youhua Tang, Pius Lee, Marina Tsidulko, Ho-Chun Huang, Sarah Lu, Dongchul Kim Scientific Applications.

Jerold Herwehe 1, Kiran Alapaty 1, Chris Nolte 1, Russ Bullock 1, Tanya Otte 1, Megan Mallard 1, Jimy Dudhia 2, and Jack Kain 3 1 Atmospheric Modeling.

1 Air Quality Reanalysis (Configuration for 2010 HTAP production) AQAST-9 June 2-4, 2015, St Louis, MO Greg Carmichael 1, Pius Lee 2, Brad Pierce 3, Dick.

Template Improving Sources of Stratospheric Ozone and NOy and Evaluating Upper Level Transport in CAMx Chris Emery, Sue Kemball-Cook, Jaegun Jung, Jeremiah.

Page1 PAGE 1 The influence of MM5 nudging schemes on CMAQ simulations of benzo(a)pyrene concentrations and depositions in Europe Volker Matthias, GKSS.

Comparison of NO X emissions and NO 2 concentrations from a regional scale air quality model (CMAQ-DDM/3D) with satellite NO 2 retrievals (SCIAMACHY) over.

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

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

Earth&Atmospheric Sciences, Georgia Tech Modeling the impacts of convective transport and lightning NOx production over North America: Dependence on cumulus.

Heidy Plata 1, Ezinne Achinivu 1, Szu-Ting Chou 1, Sheryl Ehrman 1, Dale Allen 2, Kenneth Pickering 2♦, Thomas Pierce 3, James Gleason 3 1 Department of.

Georgia Environmental Protection Division Uncertainty Analysis of Ozone Formation and Emission Control Responses using High-order Sensitivities Di Tian,

Modeling volcanic and marine emissions for Hawaii Air Quality Forecast 10/24/2015Air Resources Laboratory1 Daniel Tong*, Pius Lee, Rick Saylor, Mo Dan,

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.

MELANIE FOLLETTE-COOK KEN PICKERING, PIUS LEE, RON COHEN, ALAN FRIED, ANDREW WEINHEIMER, JIM CRAWFORD, YUNHEE KIM, RICK SAYLOR IWAQFR NOVEMBER 30, 2011.

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

Preliminary Study: Direct and Emission-Induced Effects of Global Climate Change on Regional Ozone and Fine Particulate Matter K. Manomaiphiboon 1 *, A.

1 Air Quality Reanalysis (Configuration for 2010 HTAP production) 14 th CMAS, Chapel Hill, NC October 5-7, 2015 Greg Carmichael 1, Pius Lee 2, Youhua Tang.

Melanie Follette-Cook Christopher Loughner (ESSIC, UMD) Kenneth Pickering (NASA GSFC) CMAS Conference October 27-29, 2014.

Rick Saylor 1, Barry Baker 1, Pius Lee 2, Daniel Tong 2,3, Li Pan 2 and Youhua Tang 2 1 National Oceanic and Atmospheric Administration Air Resources Laboratory.

Evaluation of modeled surface ozone biases as a function of cloud cover fraction Hyun Cheol Kim 1,2, Pius Lee 1, Fong Ngan 1,2, Youhua Tang 1,2, Hye Lim.

Use of OMI Data in Monitoring Air Quality Changes Resulting from NO x Emission Regulations over the United States K. Pickering 1, R. Pinder 2, A. Prados.

Sensitivity of Air Quality Model Predictions to Various Parameterizations of Vertical Eddy Diffusivity Zhiwei Han and Meigen Zhang Institute of Atmospheric.

Office of Research and Development National Exposure Research Laboratory, Atmospheric Modeling and Analysis Division Using Dynamical Downscaling to Project.

Impact of lightning-NO and radiatively- interactive ozone on air quality over CONUS, and their relative importance in WRF-Chem M a t u s M a r t i n i.

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

Impact of Meteorological Inputs on Surface O 3 Prediction Jianping Huang 9 th CMAS Annual Conference Oct. 12, 2010, Chapel, NC.

U.S. EPA and WIST Rob Gilliam *NOAA/**U.S. EPA

1 Air Quality : National AQ Forecasting Capability surface O 3 and PM 2.5 Presented By: Pius Lee (OAR/ARL) Contributors: Jeffery McQueen, Jianping Huang,

1 Impact on Ozone Prediction at a Fine Grid Resolution: An Examination of Nudging Analysis and PBL Schemes in Meteorological Model Yunhee Kim, Joshua S.

Diagnostic Study on Fine Particulate Matter Predictions of CMAQ in the Southeastern U.S. Ping Liu and Yang Zhang North Carolina State University, Raleigh,

Improved understanding of global tropospheric ozone integrating recent model developments Lu Hu With Daniel Jacob, Xiong Liu, Patrick.

Comparison of NOAA/NCEP 12km CMAQ Forecasts with CalNEX WP-3 Measurements Youhua Tang 1,2, Jeffery T. McQueen 2, Jianping Huang 1,2, Marina Tsidulko 1,2,

Robert W. Pinder, Alice B. Gilliland, Robert C. Gilliam, K. Wyat Appel Atmospheric Modeling Division, NOAA Air Resources Laboratory, in partnership with.

Global high-resolution marine isoprene emission derived from VIIRS-SNPP and MODIS-Aqua ocean color observations 1/25/2016Air Resources Laboratory1 Daniel.

Air Resources Laboratory 1 Comprehensive comparisons of NAQFC surface and column NO 2 with satellites, surface, and field campaign measurements during.

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.

The Impact of Lateral Boundary Conditions on CMAQ Predictions over the Continental US: a Sensitivity Study Compared to Ozonsonde Data Youhua Tang*, Pius.

W. T. Hutzell 1, G. Pouliot 2, and D. J. Luecken 1 1 Atmospheric Modeling Division, U. S. Environmental Protection Agency 2 Atmospheric Sciences Modeling.

Sensitivity of PM 2.5 Species to Emissions in the Southeast Sun-Kyoung Park and Armistead G. Russell Georgia Institute of Technology Sensitivity of PM.

Convective Transport of Carbon Monoxide: An intercomparison of remote sensing observations and cloud-modeling simulations 1. Introduction The pollution.

Picture: METEOSAT Oct 2000 Tropospheric O 3 budget of the South Atlantic region B. Sauvage, R. V. Martin, A. van Donkelaar, I. Folkins, X.Liu, P. Palmer,

On the Verification of Particulate Matter Simulated by the NOAA-EPA Air Quality Forecast System Ho-Chun Huang 1, Pius Lee 1, Binbin Zhou 1, Jian Zeng 6,

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,

WRF-Chem Modeling of Enhanced Upper Tropospheric Ozone due to Deep Convection and Lightning During the 2006 AEROSE II Cruise Jo nathan W. Smith 1,2, Kenneth.

PREMAQ: A New Pre-Processor to CMAQ for Air Quality Forecasting Tanya L. Otte*, George Pouliot*, and Jonathan E. Pleim* Atmospheric Modeling Division U.S.

Daniel Tong NOAA Air Resources Lab & George Mason University

15th Annual CMAS Conference

Transport Working Group

Modeling Ozone in the Eastern U. S

A Performance Evaluation of Lightning-NO Algorithms in CMAQ

16th Annual CMAS Conference

17th Annual CMAS Conference

Potential Performance differences of the National Air Quality Forecasting Capability when upgrading the Chemical Transport Model Pius Lee1, Youhua Tang1,2,

Quantification of Lightning NOX and its Impact on Air Quality over the Contiguous United States Daiwen Kang, Rohit Mathur, Limei Ran, Gorge Pouliot, David.

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

Chris Misenis*, Xiaoming Hu, and Yang Zhang

The Value of Nudging in the Meteorology Model for Retrospective CMAQ Simulations Tanya L. Otte NOAA Air Resources Laboratory, RTP, NC (In partnership with.

Presentation transcript:

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 1,2,3, Hyun-Cheol Kim 1,2, Min Huang 1,3, Dale Allen 6, and Ken Pickering 7 1. NOAA Air Resources Laboratory, 5830 University Research Court, College Park, MD Cooperative Institute for Climate and Satellites, University of Maryland, College Park, MD Center for Spatial Information Science and Systems, George Mason University, Fairfax, VA NCEP Environmental Modeling Centers, 5830 University Research Court, College Park, MD I.M Systems Group Inc., Rockville, MD Department of Atmospheric and Oceanic Science, University of Maryland, College Park, MD NASA Goddard Space Flight Center, Greenbelt, MD 20771

Lightning Emission Process used in CMAQ NLDN (National Lightning Detection Network) data Map to CMAQ grid Calculate Total monthly Lightning flash Count over each grid Model’s Convective Precipitation (CP) Rate Model’s flash count (monthly total) Mean LTratio used in CMAQ NLDN/Model 1 mm/hr => 147 flashs Inline Lightning NOx emission 1 flash => 500 moles NO over land

WRF-ARW Setting (12km CONUS, 42 layers up to 50hPa) SchemesRemarks and Reference Advection Runge-Kutta 3 advection scheme Wicker and Skamarock (2002) Shortwave radiationDudhiaDudhia (1989) Longwave radiationRRTMMlawer et al. (1997) PBL turbulent mixingYonsei University SchemeHong et al. (2006) Cloud Micro Physics WRF single-moment, 6-class scheme Hong and Lim (2006) Cumulus ParameterizationKain-Fritsch scheme Kain (2004) Surface layer heat/momentum exchange MM5 Similarity SchemeZhang and Anthes (1982) Land surface exchange Unified Noah Land Surface Model Tewari et al. (2004)

CMAQ Setting (12km CONUS, 42 layers up to 50hPa) CB05tucl-Aero6 Chemical mechanism NEI2011 area emission Mobile emission: 2005 mobile 6 project to year 2011 Point sources: 2010 CEM + DOE Annual Energy Outlook (DOE, 2012) Biogenic Emission: BEIS 3 inline (CMAQ 5.0.2)

CMAQ Lightning counts (flash numbers) derived from modeled CP rate show location and time shifting compared to NLDN lightning data. CP rate derived flash counts (July 2011) before being scaled to NLDN monthly totals

Their Correlation is poor

NLDN derived LNOx emission (NLDN1)

CMAQ Default LNOx (from modeled convective Precipitation rate, LTGN-A) versus NLDN- derived LNOx (NLDN1)

Issues in current Lightning NOx scheme Highly depends on meteorological model’s convective precipitation rate for its time, location and strength, even with monthly NLDN data constrains. Lightning NOx emission over ocean is set to zero. Lightning NOx emission rate (500 moles NO/ stroke) is too high Lightning stroke rate over ocean change to 1 mm/hr (CP) => 9 strokes according to Pessi and Businger (2009) Lightning NOx emission rate changes to 43.2 moles/flash (Skamarock et al., 2003) (LTGN-B)

Comparison with Discover-AQ 2011 P-3B aircraft data

The default CMAQ LNOx emission rate was too high, and degraded the model performance.

Similar Thing can also been for surface AIRNow ozone comparison

Summary We tested the CMAQ 5.0.2’s lightning NOx emission module using hourly WRF-ARW convective precipitation rate and with constraint of monthly NLDN data. Its lightning counts show offsets in locations and timing, compared with NLDN lightning data. Its emission rate per flash may be too high. The LNOx’s wet scavenging and deposition needs further examination. Reducing the LNOx emission rate can significantly reduce that high NOx bias, though its overestimation is still evident in some cases. Using original NLDN data to derive LNOx emission for retrospective simulations looks more trustable, but it cannot be used in forecast. The current NLDN1 method needs significant improvements: application of NLDN detection efficiencies and inclusion of appropriate vertical distributions of flash channels Lightning data derived from modeled convective precipitation rate is still quite uncertain for its location, time and strength.