Analysis of CMAQ Performance and Grid-to- grid Variability Over 12-km and 4-km Spacing Domains within the Houston airshed Daiwen Kang Computer Science.

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.
Georgia Institute of Technology Evaluation of CMAQ with FAQS Episode of August 11 th -20 th, 2000 Yongtao Hu, M. Talat Odman, Maudood Khan and Armistead.
U.S. EPA Office of Research & Development October 30, 2013 Prakash V. Bhave, Mary K. McCabe, Valerie C. Garcia Atmospheric Modeling & Analysis Division.
The Use of High Resolution Mesoscale Model Fields with the CALPUFF Dispersion Modelling System in Prince George BC Bryan McEwen Master’s project
Quantifying CMAQ Simulation Uncertainties of Particulate Matter in the Presence of Uncertain Emissions Rates Wenxian Zhang, Marcus Trail, Alexandra Tsimpidi,
An initial linkage of the CMAQ modeling system at neighborhood scales with a human exposure model Jason Ching/Thomas Pierce Air-Surface Processes Modeling.
Application and Analysis of Kolmogorov- Zurbenko Filter in the Dynamic Evaluation of a Regional Air Quality Model Daiwen Kang Computer Science Corporation,
Three-State Air Quality Study (3SAQS) Three-State Data Warehouse (3SDW) 2008 CAMx Modeling Model Performance Evaluation Summary University of North Carolina.
Evaluation of the AIRPACT2 modeling system for the Pacific Northwest Abdullah Mahmud MS Student, CEE Washington State University.
Jenny Stocker, Christina Hood, David Carruthers, Martin Seaton, Kate Johnson, Jimmy Fung The Development and Evaluation of an Automated System for Nesting.
Office of Research and Development Atmospheric Modeling and Analysis Division, National Exposure Research Laboratory Application and evaluation of the.
Examination of the impact of recent laboratory evidence of photoexcited NO 2 chemistry on simulated summer-time regional air quality Golam Sarwar, Robert.
Five-year Progress in the Performance of Air Quality Forecast Models: Analysis on Categorical Statistics for the National Air Quality Forecast Capacity.
National/Regional Air Quality Modeling Assessment Over China and Taiwan Using Models-3/CMAQ Modeling System Joshua S. Fu 1, Carey Jang 2, David Streets.
Modeling Studies of Air Quality in the Four Corners Region National Park Service U.S. Department of the Interior Cooperative Institute for Research in.
Comparison of three photochemical mechanisms (CB4, CB05, SAPRC99) for the Eta-CMAQ air quality forecast model for O 3 during the 2004 ICARTT study Shaocai.
Importance of Lightning NO for Regional Air Quality Modeling Thomas E. Pierce/NOAA Atmospheric Modeling Division National Exposure Research Laboratory.
1 Using Hemispheric-CMAQ to Provide Initial and Boundary Conditions for Regional Modeling Joshua S. Fu 1, Xinyi Dong 1, Kan Huang 1, and Carey Jang 2 1.
Georgia Environmental Protection Division Uncertainty Analysis of Ozone Formation and Emission Control Responses using High-order Sensitivities Di Tian,
Impacts of Biomass Burning Emissions on Air Quality and Public Health in the United States Daniel Tong $, Rohit Mathur +, George Pouliot +, Kenneth Schere.
On the Model’s Ability to Capture Key Measures Relevant to Air Quality Policies through Analysis of Multi-Year O 3 Observations and CMAQ Simulations Daiwen.
Georgia Environmental Protection Division IMPACTS OF MODELING CHOICES ON RELATIVE RESPONSE FACTORS IN ATLANTA, GA Byeong-Uk Kim, Maudood Khan, Amit Marmur,
Georgia Institute of Technology Initial Application of the Adaptive Grid Air Quality Model Dr. M. Talat Odman, Maudood N. Khan Georgia Institute of Technology.
Assimilating AIRNOW Ozone Observations into CMAQ Model to Improve Ozone Forecasts Tianfeng Chai 1, Rohit Mathur 2, David Wong 2, Daiwen Kang 1, Hsin-mu.
A detailed evaluation of the WRF-CMAQ forecast model performance for O 3, and PM 2.5 during the 2006 TexAQS/GoMACCS study Shaocai Yu $, Rohit Mathur +,
Application of Models-3/CMAQ to Phoenix Airshed Sang-Mi Lee and Harindra J. S. Fernando Environmental Fluid Dynamics Program Arizona State University.
Applications of Models-3 in Coastal Areas of Canada M. Lepage, J.W. Boulton, X. Qiu and M. Gauthier RWDI AIR Inc. C. di Cenzo Environment Canada, P&YR.
U.S. EPA and WIST Rob Gilliam *NOAA/**U.S. EPA
William G. Benjey* Physical Scientist NOAA Air Resources Laboratory Atmospheric Sciences Modeling Division Research Triangle Park, NC Fifth Annual CMAS.
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.
C. Hogrefe 1,2, W. Hao 2, E.E. Zalewsky 2, J.-Y. Ku 2, B. Lynn 3, C. Rosenzweig 4, M. Schultz 5, S. Rast 6, M. Newchurch 7, L. Wang 7, P.L. Kinney 8, and.
1. How is model predicted O3 sensitive to day type emission variability and morning Planetary Boundary Layer rise? Hypothesis 2.
GOING FROM 12-KM TO 250-M RESOLUTION Josephine Bates 1, Audrey Flak 2, Howard Chang 2, Heather Holmes 3, David Lavoue 1, Mitchel Klein 2, Matthew Strickland.
Evaluation of Models-3 CMAQ I. Results from the 2003 Release II. Plans for the 2004 Release Model Evaluation Team Members Prakash Bhave, Robin Dennis,
Diagnostic Study on Fine Particulate Matter Predictions of CMAQ in the Southeastern U.S. Ping Liu and Yang Zhang North Carolina State University, Raleigh,
Seasonal Modeling of the Export of Pollutants from North America using the Multiscale Air Quality Simulation Platform (MAQSIP) Adel Hanna, 1 Rohit Mathur,
Evaluating temporal and spatial O 3 and PM 2.5 patterns simulated during an annual CMAQ application over the continental U.S. Evaluating temporal and spatial.
Opening Remarks -- Ozone and Particles: Policy and Science Recent Developments & Controversial Issues GERMAN-US WORKSHOP October 9, 2002 G. Foley *US EPA.
Robert W. Pinder, Alice B. Gilliland, Robert C. Gilliam, K. Wyat Appel Atmospheric Modeling Division, NOAA Air Resources Laboratory, in partnership with.
Office of Research and Development National Exposure Research Laboratory, Atmospheric Modeling and Analysis Division Office of Research and Development.
Evaluation of CMAQ Driven by Downscaled Historical Meteorological Fields Karl Seltzer 1, Chris Nolte 2, Tanya Spero 2, Wyat Appel 2, Jia Xing 2 14th Annual.
WRAP Stationary Sources Joint Forum Meeting August 16, 2006 The CMAQ Visibility Model Applied To Rural Ozone In The Intermountain West Patrick Barickman.
Assessment of aerosol direct effects on surface radiation in the northern hemisphere using two-way WRF-CMAQ model Jia Xing, Jonathan Pleim, Rohit Mathur,
Operational Evaluation and Model Response Comparison of CAMx and CMAQ for Ozone & PM2.5 Kirk Baker, Brian Timin, Sharon Phillips U.S. Environmental Protection.
Impact of Temporal Fluctuations in Power Plant Emissions on Air Quality Forecasts Prakash Doraiswamy 1, Christian Hogrefe 1,2, Eric Zalewsky 2, Winston.
U.S. Environmental Protection Agency Office of Research and Development Implementation of an Online Photolysis Module in CMAQ 4.7 Christopher G. Nolte.
Georgia Institute of Technology Evaluation of the 2006 Air Quality Forecasting Operation in Georgia Talat Odman, Yongtao Hu, Ted Russell School of Civil.
Office of Research and Development National Exposure Research Laboratory, Atmospheric Modeling and Analysis Division Office of Research and Development.
7. Air Quality Modeling Laboratory: individual processes Field: system observations Numerical Models: Enable description of complex, interacting, often.
Daiwen Kang 1, Rohit Mathur 2, S. Trivikrama Rao 2 1 Science and Technology Corporation 2 Atmospheric Sciences Modeling Division ARL/NOAA NERL/U.S. EPA.
Preliminary Evaluation of the June 2002 Version of CMAQ Brian Eder Shaocai Yu Robin Dennis Jonathan Pleim Ken Schere Atmospheric Modeling Division* National.
Ship emission effect on Houston Ship Channel CH2O concentration ——study with high resolution model Ye Cheng.
15th Annual CMAS Conference
Development of a Multipollutant Version of the Community Multiscale Air Quality (CMAQ) Modeling System Shawn Roselle, Deborah Luecken, William Hutzell,
Statistical Methods for Model Evaluation – Moving Beyond the Comparison of Matched Observations and Output for Model Grid Cells Kristen M. Foley1, Jenise.
A Performance Evaluation of Lightning-NO Algorithms in CMAQ
16th Annual CMAS Conference
Two Decades of WRF/CMAQ simulations over the continental U. S
C. Nolte, T. Spero, P. Dolwick, B. Henderson, R. Pinder
Predicting Future-Year Ozone Concentrations: Integrated Observational-Modeling Approach for Probabilistic Evaluation of the Efficacy of Emission Control.
Quantification of Lightning NOX and its Impact on Air Quality over the Contiguous United States Daiwen Kang, Rohit Mathur, Limei Ran, Gorge Pouliot, David.
Modeling the impacts of green infrastructure land use changes on air quality and meteorology—case study and sensitivity analysis in Kansas City Yuqiang.
Development of a 2007-Based Air Quality Modeling Platform
SELECTED RESULTS OF MODELING WITH THE CMAQ PLUME-IN-GRID APPROACH
Deborah Luecken and Golam Sarwar U.S. EPA, ORD/NERL
J. Burke1, K. Wesson2, W. Appel1, A. Vette1, R. Williams1
Simulation of Ozone and PM in Southern Taiwan
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:

Analysis of CMAQ Performance and Grid-to- grid Variability Over 12-km and 4-km Spacing Domains within the Houston airshed Daiwen Kang Computer Science Corporation, Research Triangle Park, NC, USA Golam Sarwar, Rohit Mathur AMAD/NERL, U.S. Environmental Protection Agency, Research Triangle Park, NC, USA

CMAQ modeling details Nested model runs using CMAQv4.7.1 with CB-05 chemistry Modeling domain – Large eastern US domain: 12-km grids – Nested Houston domain: 4-km grids Layer structure – 35 vertical layers with 20-m surface layer WRF generated for meteorological fields Initial and Boundary conditions – Large domain uses data from AQME II continental domain results – Small domain (4 km) uses results from the large domain (12 km)

Modeling domains Large domain with 12-km grid-cells Smaller domain with 4-km grid-cells

Emissions Point sources in Texas for VOC, NO X, CO - specialized inventory Point sources in Texas for other pollutants - NEI 2005 Other sources in Texas for all pollutants - NEI 2005 Sources outside Texas for all pollutants – NEI 2005 Mobile sources were estimated using MOBILE6 Biogenic sources were estimated using BEISv3.14 (off-line) Plume rise calculations for point sources are done in CMAQ

Illustration of grid structure for analysis km grid cell Observation site 4 km grid cell Each 12-km grid cell contains 9 4-km grid cells. The neighboring grid cells mentioned in this presentation include all the 9 grid cells surrounding an observation site. For traditional evaluation: OBS MOD (12-km) or OBS MOD (4-km) In this analysis: OBS All neighboring cells

Stats for Daily Maximum 8-hr O 3 at All AQS Sites within 4km Domain Metrics Paired (4km)Paired (12km) Best-Matching (4km Neighboring Cells) RMSE (ppb) NME (%) MB (ppb) NMB (%) R

Average Diurnal Profile for AQS Houston Sites 1.Majority of the observed values are within the range of mixing ratios simulated by the 4km grid cells (within a 12km grid cell). 2.The simulated values over the 4km and 12km resolutions differ mostly during nighttime. Daytime values are very similar and both overestimated

Time-series of Daily Max. 8-hr O 3 for AQS Houston sites 1.The simulated variability within a 12km grid can be as wide as 30 ppb with a mean value of 13 ppb. 2.About 1/3 of the observed daily max. 8-hr values are within the range simulated with finer resolution.

ECDF of Daily Max. 8-hr O 3 for AQS Houston Sites 1.The observed daily maximum 8-hr O 3 concentrations are within the distribution range constituted by the values from the neighboring cells. 2.At lower levels (20 to 50 ppb), the observed values are close to the low value edge, while at higher levels (>60 ppb), the observed values closer to or just beyond the high value edge of the band.

Sites located in the same 12km grid cell in Houston area The sites with the same numbers are located within the same 12km grid cell, while the pair of sites 8 is also located in the same 4km grid cell. Sites numbered 1 and 6 are out of this view.

The difference between observations and model simulations for sites located at the same 12 km grid cell Significant differences exist between the two sites which are located in the same 12km grid cell while in different 4km grid cells for both the observations and model simulations. At some site-pairs, the observed and modeled differences are opposite (s4 and s7)

The diurnal O 3 variation at two sites which are located in the same 12km grid cell but different 4km grid cells OBS1 OBS2 MOD1 MOD2 MOD1 OBS1 OBS2 MOD2

Time series of daily Max. 8-hr O 3 at two sites which are located in the same 12km grid cell but different 4km grid cells

The best-matching rate in the neighboring grid cells (hourly O 3 ) The best-match is one of the simulated values that is closest to an observation at a site from the neighboring grid cells surrounding the site; the best-matching rate (BMR) at one grid cell is the total best- matching points located at this grid cell divided by the total number of observed data points at the site. Mathematically: where BMR(i), the best-matching rate at cell (i), i=1,9; NP i, best-matched points located at cell (i); O(j), an observation (j); M(i, j), the corresponding simulated value (j) at cell (i); N, the total number of observations

The best-matching rate in the neighboring grid cells (Daily Max. 8-hr O 3 ) At some sites, the best- matching rate is dominantly higher at one grid cell than at other neighboring grid cells, that may suggest some subtle spatial configuration issues about emissions/meteorology inputs.

The diurnal variation at two sites which are located in the same 12km grid cell but different 4km grid cells for PM 2.5 MOD1 OBS1 MOD2 OBS2 OBS1 MOD2 MOD1

Conclusions Significant differences between the observations at sites which are located in the same 12 km grid cell indicate that it is necessary to perform model simulations using finer (4 km) resolutions over Houston airshed The performance measures over the 4 km resolution may not be necessarily better than over the coarser (12 km) resolution; it may be even worse if it is evaluated using the traditional evaluation metrics based on paired obs-mod data The distributional analysis of species concentrations among the neighboring grid cells may shed light on the subtle configuring problems associated with meteorological and/or emissions inputs which can help improve future model simulations

Conclusions The finer resolution provides additional information which enables distribution and probability evaluations which are more suitable measures to gauge a model’s performance To properly evaluate a 12km simulation, we need to perform the 4km simulation, and use the subsequent information to assess the 12km model performance

Acknowledgements David Wong, Sergey Napelenok, Shawn Roselle, James Godowitch, ST Rao Disclaimer The United States Environmental Protection Agency through its Office of Research and Development funded and managed the research described here. It has been subjected to Agency’s administrative review and approved for presentation.