Meteorological Data Issues for Class II Increment Analysis.

Slides:



Advertisements
Similar presentations
AIR POLLUTION AND METEOROLOGY
Advertisements

21M062007D The Shaw Group Inc. ® An Analytical Screening Technique to Estimate the Effect of Cooling Ponds on Meteorological Measurements – A Case Study.
Training course: boundary layer; surface layer Parametrization of surface fluxes: Outline Surface layer (Monin Obukhov) similarity Surface fluxes: Alternative.
Joint GABLS-GLASS/LoCo workshop, September 2004, De Bilt, Netherlands Interactions of the land-surface with the atmospheric boundary layer: Single.
Session 8, Unit 15 ISC-PRIME and AERMOD. ISC-PRIME General info. PRIME - Plume Rise Model Enhancements Purpose - Enhance ISCST3 by addressing ISCST3’s.
Performance of Air Quality Models in Urban Areas  Objectives and Motivation  St. Louis study and ISC urban  Model Improvements  Performance of Improved.
TCEQ Air Permits Division Justin Cherry, P.E. Ahmed Omar Stephen F. Austin State University February 28, 2013.
Session 2, Unit 3 Atmospheric Thermodynamics
Use of Prognostic Meteorological Model Output in Dispersion Models Eighth Modeling Conference Research Triangle Park, NC.
The Use of High Resolution Mesoscale Model Fields with the CALPUFF Dispersion Modelling System in Prince George BC Bryan McEwen Master’s project
AERMET 8 TH Modeling Conference RTP, NC September 22 – 23, 2005 Presented by: Desmond T. Bailey
Reading: Text, (p40-42, p49-60) Foken 2006 Key questions:
Will Pendergrass NOAA/ARL/ATDD OAR Senior Research Council Meeting Oak Ridge, TN August 18-19, 2010 Boundary–Layer Dispersion Urban Meteorology 5/20/2015Air.
Meteorological Driver for CTM Freie Universität Berlin Institut für Meteorologie Eberhard Reimer.
ADMS ADMS 3.3 Modelling Summary of Model Features.
1 AirWare : R elease R5.3 beta AERMOD/AERMET DDr. Kurt Fedra Environmental Software & Services GmbH A-2352 Gumpoldskirchen AUSTRIA
Atmospheric Analysis Lecture 3.
Rapid Update Cycle Model William Sachman and Steven Earle ESC452 - Spring 2006.
Comparison of Eddy Covariance Results By Wendy Couch, Rob Aves and Larissa Reames.
Ranjeet S Sokhi, Nutthida Kitwiroon and Lia Fragkou Atmospheric Science Research Group (ASRG) University of Hertfordshire Modelling of Urban Air Quality.
BlueSky Implementation in CANSAC Julide Kahyaoglu-Koracin Desert Research Institute - CEFA CANSAC Workshop Riverside, CA May 2006 Julide Kahyaoglu-Koracin.
Introduction to the ISC Model Marti Blad NAU College of Engineering.
COST 715 Meteorology Applied to Urban Air Pollution Problems September 98-September 2003 Problem Issues covered Successes Conclusions.
Wind Driven Circulation I: Planetary boundary Layer near the sea surface.
Session 1, Unit 1 Course Overview. Introduction Course – ENV 7335 Air Quality Modeling Instructor – Yousheng Zeng, Ph.D., P.E. Prerequisite – ENV 7331.
Air Pollution Potential and Fire Weather Forecasting Anthony R. Lupo Atms Sci 4310 / 7310 Lab 9.
Page 1© Crown copyright Distribution of water vapour in the turbulent atmosphere Atmospheric phase correction for ALMA Alison Stirling John Richer & Richard.
Dispersion Modeling 101: ISCST3 vs. AERMOD
Understanding the USEPA’s AERMOD Modeling System for Environmental Managers Ashok Kumar Abhilash Vijayan Kanwar Siddharth Bhardwaj University of Toledo.
Understanding the USEPA’s AERMOD Modeling System for Environmental Managers Ashok Kumar University of Toledo Introduction.
1/26 APPLICATION OF THE URBAN VERSION OF MM5 FOR HOUSTON University Corporation for Atmospheric Research Sylvain Dupont Collaborators: Steve Burian, Jason.
Partnership for AiR Transportation Noise and Emission Reduction An FAA/NASA/TC-sponsored Center of Excellence MCIP2AERMOD: A Prototype Tool for Preparing.
Meteorological Data Analysis Urban, Regional Modeling and Analysis Section Division of Air Resources New York State Department of Environmental Conservation.
Lecture 8 Evapotranspiration (1) Evaporation Processes General Comments Physical Characteristics Free Water Surface (the simplest case) Approaches to Evaporation.
Estimating the Optimal Location of a New Wind Farm based on Geospatial Information System Data Dec Chungwook Sim.
Dispersion conditions in complex terrain - a case study of the January 2010 air pollution episode in Norway Viel Ødegaard Norwegian Meteorological.
Stable Atmosphere.
Cristina Gonzalez-Maddux ITEP
Toulouse IHOP meeting 15 June 2004 Water vapour variability within the growing convective boundary layer of 14 June 2002 with large eddy simulations and.
Regional Modeling Joseph Cassmassi South Coast Air Quality Management District USA.
CITES 2005, Novosibirsk Modeling and Simulation of Global Structure of Urban Boundary Layer Kurbatskiy A. F. Institute of Theoretical and Applied Mechanics.
Introduction to Modeling – Part II
1 Tracer Experiments Barrio Logan Working Draft Do Not Cite or Quote Tony Servin, P.E. Shuming Du, Ph.D. Vlad Isakov, Ph.D. September 12, 2002 Air Resources.
Air Pollution Meteorology Ñ Atmospheric thermodynamics Ñ Atmospheric stability Ñ Boundary layer development Ñ Effect of meteorology on plume dispersion.
Intro to Modeling – Terms & concepts Marti Blad, Ph.D., P.E. ITEP
Processes in the Planetary Boundary Layer
Chapter 9 Winds: Small scale and local systems. Scales of motion Smallest - microscale (few meters or less) Middle - Mesoscale (few to about 100 km) Large.
Stephen F. Austin State University February 27, 2014 Justin Cherry, P.E. Reece Parker TCEQ Air Permits Division.
1 THE AERMOD MODELING SYSTEM AN OVERVIEW FOR THE 8 TH MODELING CONFERENCE SEPTEMBER 22, 2005.
AERSCREEN Status and Update James Thurman, Ph.D. U.S. EPA/OAQPS/AQAD Air Quality Modeling Group 2009 NESCAUM PMC Annual Meeting Mystic, CT.
Roger W. Brode U.S. EPA/OAQPS/AQAD Air Quality Modeling Group AERMET Training NESCAUM Permit Modeling Committee Annual Meeting New London, Connecticut.
Dirk Van MaerckeIMAGINE Final Conference, Budapest, Meteorological effects from theory till operational use… Dirk van Maercke CSTB 24, rue.
Roger W. Brode U.S. EPA/OAQPS/AQAD Air Quality Modeling Group AERMOD Update: Status of AERSCREEN and AERSURFACE NESCAUM Permit Modeling Committee Annual.
Comparisons of CALPUFF and AERMOD for Vermont Applications Examining differing model performance for a 76 meter and 12 meter (stub) stack with emission.
Modeling of heat and mass transfer during gas adsorption by aerosol particles in air pollution plumes T. Elperin1, A. Fominykh1, I. Katra2, and B. Krasovitov1.
Development of the two-equation second-order turbulence-convection model (dry version): analytical formulation, single-column numerical results, and.
Remote Sensing ET Algorithm— Remote Evapotranspiration Calculation (RET) Junming Wang,
Lecture 8 Evapotranspiration (1)
Meteorological Site Representativeness and AERSURFACE Issues
WindNinja Model Domain/Objective
AERLINE: Air Exposure Research model for LINE sources
Air Pollution and Control (Elective- I)
Radiation Fog Forecasting Using a 1-D Model
Models of atmospheric chemistry
MODELING AT NEIGHBORHOOD SCALE Sylvain Dupont and Jason Ching
Introduction to Modeling – Part II
Meteorology & Air Pollution Dr. Wesam Al Madhoun
Temperature in a free surface flow
TFMM – Trends Available Meteorological Forcing
Atmospheric modelling of HMs Sensitivity study
Presentation transcript:

Meteorological Data Issues for Class II Increment Analysis

Modeling Domain For Class II Analysis and Meteorological Approach to the Analysis 1. Microscale - Individual Sources or Close Grouping of Sources Onsite data – Site Specific Analysis, 1 year of data Offsite data – Screening Analysis,, Multiple years of data Gaussian Models – up to 100km (50km + S.I.R.) 2. Mesoscale – Increment Consumption over large area, urban growth Onsite data – Site Specific Analysis, 1 year of data Offsite data – Screening Analysis, Multiple years of data Gaussian Models – up to 100km (50km + S.I.R.) Puff Models – 100s of km

Models and Meteorological Pre- Processors for Class II Analysis  ModelsMet Processors  SCREEN3 Met. internal to Model  ISCST3 MPRM, PCRAMMET  AERMOD AERMET  CALPUFF CALMET

MPRM and PCRAMMET - Input  Meteorological Input Parameters – Wind Speed, Wind Direction, Temperature, Opaque Cloud Cover, Ceiling Height, Upper Air Data, Precipitation Data  Data Formats - CD144 (surface data) Sampson (surface data support) TD5600 (upper air data) TD9689 (estimated mixing heights) TD3240 (hourly precipitation) On-site (site specific data)

MPRM and PCRAMMET - Output  Wind Speed  Wind Direction  Temperature  Stability Class  Urban Mixing Height  Rural Mixing Height

AERMET - Input  Meteorological Input Parameters – Multi-Level WS, WD, and Temperature, Opaque Cloud Cover, Ceiling Height, RH, Pressure, Surface Heat Flux, Friction Velocity, and Roughness Length, Delta- T, Solar Radiation, Upper Air Data  Data Formats - CD144, SCRAM, SAMPSON (surface data) TD 3280 (surface data t) TD6201 (upper air data) On-site (site specific data)

AERMET - Output  Boundary Layer File  sensible heat flux  surface friction velocity  convective velocity scale  potential temp. gradient above mixing height  convectively-driven mixing height  mechanically-driven mixing height  Monin-Obukhov length  surface roughness length  Bowen ratio  albedo  WS, WD, and anemometer. height  temperature and measurement height used  Profile File  Measurement height  WD, WS  Temperature  Standard Dev. of Lateral WD  Standard Dev. of Vertical WS

Meteorological Parameters Individual Issues

 Wind Speed Linear Relationship to Concentration Collection Height Terrain Channeling Surface Roughness  Wind Direction Persistency Collection Height Terrain Channeling Surface Roughness / Obstructions Range of Representation

Temperature Vertical Profile (Delta-T) Stability  Surface Roughness / Obstructions (sigma theta)

Mixing Height (ISC) Based on two observation Low mixing heights in early morning. Urban vs. Rural Determinations

Convective and Mechanical Mixing Heights (AERMOD) Data availability for calculations Sensitive to Surface Roughness Plume splitting (partial penetration) and transport above the mixing layer

Surface Roughness, Albedo, and Bowen Ratio NWS site are generally flat areas Use Met site or Source site Surface Characteristics How do you evaluate an areas Model sensitivity to parameters

General Issues Domain Size Straight Line Transport up to 100 km (model limited to 50 km) Generating meteorological parameters of domains extending out hundreds of kilometers – poor resolution Formulation of Equations and Algorithms /Transferring into Code Data Formats and Availability Obsolete Onsite Data Sets (Intro of AERMOD)

General Issues Available Resources to Collect Data and Perform Analysis Range of Representation – Horizontal and Vertical Surface Influences – Roughness, Obstructions, Terrain Effects, Albedo, Bowen Ratio Available Meteorological Parameters/Collection Rate / QA Cost – Purchasing, generating and collecting data

The End