Biocomplexity Project: N-deposition Model Evaluation UCR, CE-CERT, Air Quality Modeling Group Model Performance Evaluation for San Bernardino Mountains.

Slides:

Advertisements

Similar presentations

WRAP RMC Phase II Wind Blown Dust Project ENVIRON International Corporation and University of California, Riverside August 24, 2004.

Advertisements

2002 MM5 Model Evaluation 12 vs. 36 km Results Chris Emery, Yiqin Jia, Sue Kemball-Cook, and Ralph Morris ENVIRON International Corporation Zion Wang UCR.

Regional Haze Modeling: Recent Modeling Results for VISTAS and WRAP October 27, 2003, CMAS Annual Meeting, RTP, NC University of California, Riverside.

Center for Environmental Research and Technology/Air Quality Modeling University of California at Riverside Model Performance Metrics, Ambient Data Sets.

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.

Coupled NMM-CALMET Meteorology Development for the CALPUFF Air Dispersion Modelling in Complex Terrain and Shoreline Settings Presented at: European Geoscience.

U.S. EPA Office of Research & Development October 30, 2013 Prakash V. Bhave, Mary K. McCabe, Valerie C. Garcia Atmospheric Modeling & Analysis Division.

Computer Weather Forecasts for Wind Energy Plants Khanh T. Tran AMI Environmental 206 Black Eagle Ave, Henderson, NV 89015(702) http://

Preliminary Results CMAQ and CMAQ-AIM with SAPRC99 Gail Tonnesen, Chao-Jung Chien, Bo Wang, UC Riverside Max Zhang, Tony Wexler, UC Davis Ralph Morris,

An initial linkage of the CMAQ modeling system at neighborhood scales with a human exposure model Jason Ching/Thomas Pierce Air-Surface Processes Modeling.

Three-State Air Quality Study (3SAQS) Three-State Data Warehouse (3SDW) 2008 CAMx Modeling Model Performance Evaluation Summary University of North Carolina.

1 Modelled Meteorology - Applicability to Well-test Flaring Assessments Environment and Energy Division Alex Schutte Science & Community Environmental.

Integration of CMAQ into the Western Macedonia environmental management system A. Sfetsos 1,2, J. Bartzis 2 1 Environmental Research Laboratory, NCSR Demokritos.

Evaluation of the AIRPACT2 modeling system for the Pacific Northwest Abdullah Mahmud MS Student, CEE Washington State University.

EXAMPLE 1 Describe the correlation of data Describe the correlation of the data graphed in the scatter plot. a. The scatter plot shows a positive correlation.

Operational Air Quality and Source Contribution Forecasting in Georgia Georgia Institute of Technology Yongtao Hu 1, M. Talat Odman 1, Michael E. Chang.

October 17, 20065th Annual CMAS Conference1 Photochemical Modeling Investigation of an Extended Winter PM Episode in Central California 1. Air Resources.

Regional Haze Modeling RPO Update Gary Kleiman, NESCAUM National RPO Meeting, Dallas, TX December 3, 2002.

University of California Riverside, ENVIRON Corporation, MCNC WRAP Regional Modeling Center WRAP Regional Haze CMAQ 1996 Model Performance and for Section.

Developing a High Spatial Resolution Aerosol Optical Depth Product Using MODIS Data to Evaluate Aerosol During Large Wildfire Events STI-5701 Jennifer.

CMAQ Evaluation Preliminary 2002 version C WRAP 2002 Visibility Modeling: Annual CMAQ Performance Evaluation using Preliminary 2002 version C Emissions.

Development of PM2.5 Interpollutant Trading Ratios James Boylan and Byeong-Uk Kim Georgia EPD – Air Protection Branch 2012 CMAS Conference October 16,

2004 Workplan WRAP Regional Modeling Center Prepared by: Gail Tonnesen, University of California Riverside Ralph Morris, ENVIRON Corporation Zac Adelman,

Center for Environmental Research and Technology University of California, Riverside Bourns College of Engineering Evaluation and Intercomparison of N.

PM2.5 Model Performance Evaluation- Purpose and Goals PM Model Evaluation Workshop February 10, 2004 Chapel Hill, NC Brian Timin EPA/OAQPS.

UC Riverside FEJF Meeting, Las Vegas, NV Dec 8, 2004 UNC/CEPENVIRON Corp. WRAP/RMC Fire Sensitivity Modeling Project Mohammad Omary, Gail Tonnesen WRAP.

Models-3 Users’ Workshop Raleigh, North Carolina October 21-23, 2002 Recent Applications of MM5, SMOKE, and CMAQ in Canada J. Wayne Boulton*, Mike Lepage,

Modeling Overview For Barrio Logan Community Health Neighborhood Assessment Program Andrew Ranzieri Vlad Isakov Tony Servin Shuming Du October 10, 2001.

Ozone MPE, TAF Meeting, July 30, 2008 Review of Ozone Performance in WRAP Modeling and Relevance to Future Regional Ozone Planning Gail Tonnesen, Zion.

AoH/MF Meeting, San Diego, CA, Jan 25, 2006 Source Apportionment Modeling Results and RMC Status report Gail Tonnesen, Zion Wang, Mohammad Omary, Chao-Jung.

Science Investigation Discussion of Results to Date & Future Work Red Deer Particulate Matter Information Session Maxwell Mazur

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.

A comparison of PM 2.5 simulations over the Eastern United States using CB-IV and RADM2 chemical mechanisms Michael Ku, Kevin Civerolo, and Gopal Sistla.

Georgia Environmental Protection Division IMPACTS OF MODELING CHOICES ON RELATIVE RESPONSE FACTORS IN ATLANTA, GA Byeong-Uk Kim, Maudood Khan, Amit Marmur,

PM Model Performance in Southern California Using UAMAERO-LT Joseph Cassmassi Senior Meteorologist SCAQMD February 11, 2004.

WRAP Experience: Investigation of Model Biases Uma Shankar, Rohit Mathur and Francis Binkowski MCNC–Environmental Modeling Center Research Triangle Park,

University of California Riverside, ENVIRON International Corporation, MCNC WRAP Regional Modeling Center Overview of WRAP Regional Haze Modeling Activities.

OThree Chemistry MM5/CAMx Model Diagnostic and Sensitivity Analysis Results Central California Ozone Study: Bi-Weekly Presentation 2 T. W. Tesche Dennis.

Center for Environmental Research and Technology/Environmental Modeling University of California at Riverside Fire Plume Rise WRAP (FEJF) Method vs. SMOKE.

Operational Evaluation and Comparison of CMAQ and REMSAD- An Annual Simulation Brian Timin, Carey Jang, Pat Dolwick, Norm Possiel, Tom Braverman USEPA/OAQPS.

Evaluation of the VISTAS 2002 CMAQ/CAMx Annual Simulations T. W. Tesche & Dennis McNally -- Alpine Geophysics, LLC Ralph Morris -- ENVIRON Gail Tonnesen.

Center for Environmental Research and Technology/Air Quality Modeling University of California at Riverside CMAQ Model Performance Evaluation with the.

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.

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.

Analysis of Ozone Modeling for May – July 2006 in PNW using AIRPACT3 (CMAQ) and CAMx. Robert Kotchenruther, Ph.D. EPA Region 10 Nov CMAQ O 3 Prediction.

October 1-3, th Annual CMAS Meeting Comparison of CMAQ and CAMx for an Annual Simulation over the South Coast Air Basin Jin Lu 1, Kathleen Fahey.

WRAP Regional Modeling Center, Attribution of Haze Meeting, Denver 7/22/04 WRAP 2002 Visibility Modeling: Emission, Meteorology Inputs and CMAQ Performance.

WRAP Regional Modeling Center, Attribution of Haze Meeting, Denver CO 7/22/04 Results from January/July CMAQ Source Apportionment Modeling Gail Tonnesen,

AoH/MF Meeting, San Diego, CA, Jan 25, 2006 WRAP 2002 Visibility Modeling: Summary of 2005 Modeling Results Gail Tonnesen, Zion Wang, Mohammad Omary, Chao-Jung.

WRAP RMC Phase II Wind Blown Dust Project Results & Status ENVIRON International Corporation and University of California, Riverside Dust Emission Joint.

Evaluation of CAMx: Issues Related to Sectional Models Ralph Morris, Bonyoung Koo, Steve Lau and Greg Yarwood ENVIRON International Corporation Novato,

1 Projects:/WRAP_RMC/Presents/ADEQ_Feb ppt Western Regional Air Partnership (WRAP) Regional Modeling Center (RMC) Preliminary Fire Modeling Results.

1 DRAFT Report for Air Quality Analysis on Cumulative Emissions, Barrio Logan Tony Servin, P.E. Modeling Support Section Planning and Technical Support.

Center for Environmental Research and Technology/Air Quality Modeling University of California at Riverside CCOS 2000 Model Intercomparison: Summary of.

WRAP Workshop on Fire, Carbon and Dust – Sacramento, CA - May 23-24, 2006 WRAP RMC Phase II Wind Blown Dust Project Regional Modeling Center ENVIRON; UCR.

V:\corporate\marketing\overview.ppt CRGAQS: CAMx Sensitivity Results Presentation to the Gorge Study Technical Team By ENVIRON International Corporation.

The application of Models-3 in national policy Samantha Baker Air and Environment Quality Division, Defra.

WRAP Technical Work Overview

Stephen Reid, Hilary Hafner, Yuan Du Sonoma Technology, Inc.

Predicting PM2.5 Concentrations that Result from Compliance with National Ambient Air Quality Standards (NAAQS) James T. Kelly, Adam Reff, and Brett Gantt.

Statistical Methods for Model Evaluation – Moving Beyond the Comparison of Matched Observations and Output for Model Grid Cells Kristen M. Foley1, Jenise.

WRAP RMC Phase II Wind Blown Dust Project

VISTAS Grid Resolution Sensitivity

SELECTED RESULTS OF MODELING WITH THE CMAQ PLUME-IN-GRID APPROACH

Evaluating Revised Tracking Metric for Regional Haze Planning

J. Burke1, K. Wesson2, W. Appel1, A. Vette1, R. Williams1

7th Annual CMAS Conference

WRAP RMC Windblown Dust Emission Inventory Project Summary

Update on 2016 AQ Modeling by EPA

Results from 2018 Preliminary Reasonable Progress Modeling

Presentation transcript:

Biocomplexity Project: N-deposition Model Evaluation UCR, CE-CERT, Air Quality Modeling Group Model Performance Evaluation for San Bernardino Mountains (SBM) Monitoring Sites Gail Tonnesen, Chao-Jung Chien, Zion Wang, Mohammad Omary CE-CERT, UCR April 25, 2006

Biocomplexity Project: N-deposition Model Evaluation UCR, CE-CERT, Air Quality Modeling Group Objectives UCR Fire Lab has long term HNO3 data in Western Riverside County that can be used in air quality model evaluations. CE-CERT models have been previously evaluated using several ambient data networks but N species data is sparse in Riverside County. Goal: Compare CE-CERT model predictions with Fire Lab N data.

Biocomplexity Project: N-deposition Model Evaluation UCR, CE-CERT, Air Quality Modeling Group SBM monitoring data ~11 monitoring stations being evaluated (data from UCR Fire Lab). 2-week long sampling periods from April through October in 2002; total of 11 periods. Gaseous HNO3 (ug/m3) data.

Biocomplexity Project: N-deposition Model Evaluation UCR, CE-CERT, Air Quality Modeling Group Air Quality Modeling Results Modeling system: CMAQ, MM5, SMOKE. –Case 1: WRAP 2002 Base C annual run: Modeling domain: US continent; 36km grid spacing, 148 x 112 grid cells. –Case 2: Ndep 2002 base annual run: Modeling domain: State of California; 4km grid spacing, 144 x 225 grid cells.

Biocomplexity Project: N-deposition Model Evaluation UCR, CE-CERT, Air Quality Modeling Group Selected Summary Statistical Results

Biocomplexity Project: N-deposition Model Evaluation UCR, CE-CERT, Air Quality Modeling Group All Days at Each Site Model to Data Comparisons

Biocomplexity Project: N-deposition Model Evaluation UCR, CE-CERT, Air Quality Modeling Group All Sites and All Days Comparisons 36kwrap vs. 4kndep

Biocomplexity Project: N-deposition Model Evaluation UCR, CE-CERT, Air Quality Modeling Group Time series and scatter plots for all days at site: AO 36kwrap vs. 4kndepAmb. vs. 36kwrap vs. 4kndep

Biocomplexity Project: N-deposition Model Evaluation UCR, CE-CERT, Air Quality Modeling Group Time series and scatter plots for all days at site: BF 36kwrap vs. 4kndepAmb. vs. 36kwrap vs. 4kndep

Biocomplexity Project: N-deposition Model Evaluation UCR, CE-CERT, Air Quality Modeling Group Time series and scatter plots for all days at site: BP 36kwrap vs. 4kndepAmb. vs. 36kwrap vs. 4kndep

Biocomplexity Project: N-deposition Model Evaluation UCR, CE-CERT, Air Quality Modeling Group Time series and scatter plots for all days at site: CP 36kwrap vs. 4kndepAmb. vs. 36kwrap vs. 4kndep

Biocomplexity Project: N-deposition Model Evaluation UCR, CE-CERT, Air Quality Modeling Group Time series and scatter plots for all days at site: GV 36kwrap vs. 4kndepAmb. vs. 36kwrap vs. 4kndep

Biocomplexity Project: N-deposition Model Evaluation UCR, CE-CERT, Air Quality Modeling Group Time series and scatter plots for all days at site: HP 36kwrap vs. 4kndepAmb. vs. 36kwrap vs. 4kndep

Biocomplexity Project: N-deposition Model Evaluation UCR, CE-CERT, Air Quality Modeling Group Time series and scatter plots for all days at site: HV 36kwrap vs. 4kndepAmb. vs. 36kwrap vs. 4kndep

Biocomplexity Project: N-deposition Model Evaluation UCR, CE-CERT, Air Quality Modeling Group Time series and scatter plots for all days at site: KP 36kwrap vs. 4kndepAmb. vs. 36kwrap vs. 4kndep

Biocomplexity Project: N-deposition Model Evaluation UCR, CE-CERT, Air Quality Modeling Group Time series and scatter plots for all days at site: MC 36kwrap vs. 4kndepAmb. vs. 36kwrap vs. 4kndep

Biocomplexity Project: N-deposition Model Evaluation UCR, CE-CERT, Air Quality Modeling Group Time series and scatter plots for all days at site: OS 36kwrap vs. 4kndepAmb. vs. 36kwrap vs. 4kndep

Biocomplexity Project: N-deposition Model Evaluation UCR, CE-CERT, Air Quality Modeling Group Time series and scatter plots for all days at site: SP 36kwrap vs. 4kndepAmb. vs. 36kwrap vs. 4kndep

Biocomplexity Project: N-deposition Model Evaluation UCR, CE-CERT, Air Quality Modeling Group Spatial Overlay Plots (SBM data shown in diamonds)

Biocomplexity Project: N-deposition Model Evaluation UCR, CE-CERT, Air Quality Modeling Group

Biocomplexity Project: N-deposition Model Evaluation UCR, CE-CERT, Air Quality Modeling Group

Biocomplexity Project: N-deposition Model Evaluation UCR, CE-CERT, Air Quality Modeling Group

Biocomplexity Project: N-deposition Model Evaluation UCR, CE-CERT, Air Quality Modeling Group Summary Model performance is reasonably good compared to other model evaluation studies: –longer term averages of model and data tend to show better performance than do hourly or daily comparisons. Better model performance with 4km grid. The data has more spatial variability than does the model, even at the finer 4km grid: –use of finer grid and better land use data might improve the model spatial resolution.

Biocomplexity Project: N-deposition Model Evaluation UCR, CE-CERT, Air Quality Modeling Group Conclusions The model performs reasonably well, and these model results can be used in future analyses, however, additional effort should be made to improve the accuracy and spatial resolution of the model predictions.