Roger W. Brode U.S. EPA/OAQPS/AQAD Air Quality Modeling Group AERMET Training NESCAUM Permit Modeling Committee Annual Meeting New London, Connecticut.

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.
AERMOD Modeling System: Status and Updates Roger W. Brode U.S. EPA/OAQPS Air Quality Modeling Group Region 4 Modelers Meeting November 14, 2012 Atlanta,
Stratus. Outline  Formation –Moisture trapped under inversion –Contact layer heating of fog –Fog induced stratus –Lake effect stratus/strato cu  Dissipation.
Session 8, Unit 15 ISC-PRIME and AERMOD. ISC-PRIME General info. PRIME - Plume Rise Model Enhancements Purpose - Enhance ISCST3 by addressing ISCST3’s.
Why the Earth has seasons  Earth revolves in elliptical path around sun every 365 days.  Earth rotates counterclockwise or eastward every 24 hours.
Meteorological Data Issues for Class II Increment Analysis.
TCEQ Air Permits Division Justin Cherry, P.E. Ahmed Omar Stephen F. Austin State University February 28, 2013.
Weather and X/Q 1 Impact Of Weather Changes On TVA Nuclear Plant Chi/Q (  /Q) Kenneth G. Wastrack Doyle E. Pittman Jennifer M. Call Tennessee Valley Authority.
Session 2, Unit 3 Atmospheric Thermodynamics
Sensitivity of High-Resolution Simulations of Hurricane Bob (1991) to Planetary Boundary Layer Parameterizations SCOTT A. BRAUN AND WEI-KUO TAO PRESENTATION.
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:
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.
Introduction to the ISC Model Marti Blad NAU College of Engineering.
Characteristics of Isolated Convective Storms Meteorology 515/815 Spring 2006 Christopher Meherin.
Review of the Boundary Layer
Calculation of wildfire Plume Rise Bo Yan School of Earth and Atmospheric Sciences Georgia Institute of Technology.
Wind Driven Circulation I: Planetary boundary Layer near the sea surface.
Monin-Obukhoff Similarity Theory
ASSIMILATION OF GOES-DERIVED CLOUD PRODUCTS IN MM5.
Earth System Sciences, LLC Suggested Analyses of WRAP Drilling Rig Databases Doug Blewitt, CCM 1.
Prediction of Atlantic Tropical Cyclones with the Advanced Hurricane WRF (AHW) Model Jimy Dudhia Wei Wang James Done Chris Davis MMM Division, NCAR Jimy.
Understanding the USEPA’s AERMOD Modeling System for Environmental Managers Ashok Kumar Abhilash Vijayan Kanwar Siddharth Bhardwaj University of Toledo.
Xin Xi Aspects of the early morning atmospheric thermodynamic structure which affect the surface fluxes and BL growth through convection:
Understanding the USEPA’s AERMOD Modeling System for Environmental Managers Ashok Kumar University of Toledo Introduction.
Boundary Layer Convection Convection in the boundary layer occurs to transport heat moisture, and momentum from surface to free atmosphere Two common scenarios:
Observational and theoretical investigations of turbulent structures generated by low-Intensity prescribed fires in forested environments X. Bian, W. Heilman,
1/26 APPLICATION OF THE URBAN VERSION OF MM5 FOR HOUSTON University Corporation for Atmospheric Research Sylvain Dupont Collaborators: Steve Burian, Jason.
Meteorology & Air Pollution Dr. Wesam Al Madhoun.
Partnership for AiR Transportation Noise and Emission Reduction An FAA/NASA/TC-sponsored Center of Excellence MCIP2AERMOD: A Prototype Tool for Preparing.
Xin Xi Feb. 28. Basics  Convective entrainment : The buoyant thermals from the surface layer rise through the mixed layer, and penetrate (with enough.
USE THESE VALUES. e(T) = e s (T Dew ) PRACTICE WITH STABILITY.
Chapter 3 cont. (Heat & Temperatures). Heat & Temperature Basics temperature: the energy of molecular movement heat: a measure of the amount of energy.
Stable Atmosphere.
April Hansen et al. [1997] proposed that absorbing aerosol may reduce cloudiness by modifying the heating rate profiles of the atmosphere. Absorbing.
Observed Structure of the Atmospheric Boundary Layer
Diagnosis of Performance of the Noah LSM Snow Model *Ben Livneh, *D.P. Lettenmaier, and K. E. Mitchell *Dept. of Civil Engineering, University of Washington.
1 An Improved Approach To Updating Regulatory Dispersion Models 8 th Modeling Conference RTP, NC September 23, 2005.
Meteorology for modeling AP Marti Blad PhD PE. Meteorology Study of Earth’s atmosphere Weather science Climatology and study of weather patterns Study.
Air Pollution Meteorology Ñ Atmospheric thermodynamics Ñ Atmospheric stability Ñ Boundary layer development Ñ Effect of meteorology on plume dispersion.
Climate and Global Change Notes 17-1 Earth’s Radiation & Energy Budget Resulting Seasonal and Daily Temperature Variations Vertical Temperature Variation.
The Arctic boundary layer: Characteristics and properties Steven Cavallo June 1, 2006 Boundary layer meteorology.
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.
Roger W. Brode & James Thurman U.S. EPA/OAQPS/AQAD Air Quality Modeling Group AERMAP Training NESCAUM Permit Modeling Committee Annual Meeting Mystic,
AERMOD Modeling System Update Roger W. Brode U.S. EPA/OAQPS/AQAD/AQMG Research Triangle Park, NC NESCAUM Permit Modeling Committee Annual Meeting New London,
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 AERMAP Training NESCAUM Permit Modeling Committee Annual Meeting New London, Connecticut.
AERMOD Modeling System: Status and Updates Roger Brode & James Thurman U.S. EPA/OAQPS Air Quality Modeling Group 2009 NESCAUM PMC Annual Meeting Mystic,
Roger W. Brode U.S. EPA/OAQPS/AQAD Air Quality Modeling Group AERMOD Update: Status of AERSCREEN and AERSURFACE NESCAUM Permit Modeling Committee Annual.
Consequence Analysis Robert Wu South Coast Air Quality Management District.
A revised formulation of the COSMO surface-to-atmosphere transfer scheme Matthias Raschendorfer COSMO Offenbach 2009 Matthias Raschendorfer.
Comparisons of CALPUFF and AERMOD for Vermont Applications Examining differing model performance for a 76 meter and 12 meter (stub) stack with emission.
Meteorological Variables 1. Local right-hand Cartesian coordinate 2. Polar coordinate x y U V W O O East North Up Dynamic variable: Wind.
Urban Climate Characteristics
Boundary-Layer Meteorology and Atmospheric Dispersion
Surface Energy Budget, Part I
Monin-Obukhoff Similarity Theory
Meteorological Site Representativeness and AERSURFACE Issues
Consequence Analysis 2.1.
Aermet – Part 1 Course #423 Day 1 Afternoon
Air Pollution and Control (Elective- I)
Water Vapor Calculation
Introduction to Hands-on Activities
PURPOSE OF AIR QUALITY MODELING Policy Analysis
Meteorology & Air Pollution Dr. Wesam Al Madhoun
Presentation transcript:

Roger W. Brode U.S. EPA/OAQPS/AQAD Air Quality Modeling Group AERMET Training NESCAUM Permit Modeling Committee Annual Meeting New London, Connecticut May 31, 2007

Presentation Outline Brief History of AERMOD Basic Physics of Air Dispersion AERMET Scaling Parameters AERMOD Sensitivity Analysis with Example Meteorological Data Sets Meteorological Data Issues Recent AERMET Updates

Brief History of AERMOD Developed by AMS/EPA Regulatory Model Improvement Committee (AERMIC) Proposed as Replacement for ISCST3 April 2000 EPRI-sponsored PRIME Downwash Algorithms Incorporated in AERMOD in 2001 Notice of Data Availability (NDA) for AERMOD with PRIME Issued September 2003 Promulgated as EPA’s Preferred Model on December 9, 2005 One-year Grandfather Period Expired on December 9, 2006

AERMOD Design Criteria Up-to-date Science Simple – Captures Essential Physical Processes Robust – Applies Over Range of Meteorology Easily Implemented – Simple I/O, User-friendly Can Evolve – Easily Updated

Basic Physics of Dispersion Air dispersion is driven by two main forces – buoyancy effects and shear stress effects Buoyancy controlled by solar heating (day) and radiative cooling (night) Shear stress (friction) controlled by surface roughness elements and aerodynamic effects

Physics of Dispersion - Daytime Buoyancy caused by daytime solar heating generates large scale convective cells Convection causes rapid vertical spread of plumes and growth of the mixed layer Strength of convection controlled by solar angle (time-of-day and latitude), cloud cover and surface characteristics (albedo and Bowen ratio)

Physics of Dispersion - Daytime Albedo – Measure of reflectivity of surface, from 0 to 1 – Typical values ranges from about 0.1 for water to 0.6 or higher for full snow cover Bowen Ratio – Ratio of sensible to latent heat flux – Determines how much solar heating goes to evaporation of surface moisture – Ranges from about 0.1 (very wet) to 10 (very dry)

Physics of Dispersion - Nighttime Radiative cooling at night causes stable lapse rate to develop – suppresses propagation of turbulence Generation of turbulence dominated by friction-induced shear stress Shear stress or mechanical turbulence controlled by wind speed and surface roughness

Physics of Dispersion - Nighttime Surface Roughness Length (z o ) – Height at which wind speed goes to zero (0), based on theoretical logarithmic profile – Related to the surface roughness elements, but is not = height of elements – Ranges from about 0.001m (1mm) over water to 1.0m or higher for forests and urban areas – May vary by season and wind sector

AERMOD Similarity Theory Concepts Wind, temperature and turbulence are scaled with height based on Similarity Theory Mechanical (shear stress) turbulence scaled by friction velocity (u * ) Convective turbulence scaled by convective velocity scale (w * ) Monin-Obukhov length (L) stability parameter – Positive for stable conditions; negative for unstable – ~ Height at which friction and buoyant forces balance

Sensible Heat Flux – H If H > 0 PBL is convective; H < 0 stable Daytime – Convective B o = Bowen ratio R n = Net radiation Nighttime – Stable θ * = Temperature scale

Friction Velocity – u *

Monin-Obukhov Length – L Represents height at which mechanical (friction) and buoyant “forces” balance:

Monin-Obukhov Length (L) vs. PG Stability Class Roughness~L (m) PG Stability Class 0.1 m -12.5A -25B -65C --D +65E +30F Roughness~L (m) PG Stability Class 0.5 m -16A - 50B -100C --D +100E +50F

AERMOD CBL Treatment Turbulence in the CBL is driven by convection Convective cells grow during the day creating areas of updrafts and downdrafts AERMOD accounts for non-Gaussian vertical structure of dispersion in the CBL – Portions of plume released into updrafts vs. downdrafts are simulated separately – Full or partial penetration of plume through top of mixed layer also simulated

CBL Dispersion Comparisons – Crosswind Integration Concentrations ISCST3 AERMOD Tank Study

Convective Velocity Scale – w *

AERMOD Sensitivity Analysis Varied Surface Characteristics: – Albedo Test: α= (B o =1.0, z o =0.1) – Bowen Ratio Test: B o = (α=0.2, z o =0.1) – Surface Roughness Test: z o = m (α=0.2, B o =1.0) Varied Stack Heights and Buoyancy – 5,10,15,20,25,30,50,75,100,150,200m non-buoyant – 100,150,200m very buoyant (VB)

AERMOD Sensitivity Analysis Two meteorology sites: – Pittsburgh – Oklahoma City Rural and Urban (population=2.4 million) 24-hr and Annual averages

AERMOD Sensitivity Analysis Changes in albedo and Bowen ratio affect convective turbulence – ↓ Albedo ↑ Convective turbulence – ↑ Bowen ratio ↑ Convective turbulence Changes in surface roughness affect mechanical turbulence – ↑ Surface roughness ↑ Mechanical turbulence

Albedo – Normalized Percent Differences

Bowen Ratio – Normalized Percent Differences

Surface Roughness – Normalized Percent Differences

Sensitivity Analysis Conclusions AERMOD is most sensitive to changes in z o for low-level releases – This is due to the impact of changes in z o on mechanical turbulence: ↑ Surface roughness will ↑ Mechanical turbulence Some sensitivity to changes in albedo and Bowen ratio, especially for taller, more buoyant stacks – Lower albedo or higher Bowen ratio will increase convective turbulence and bring plume down quicker

AERMOD Sensitivity Example Ground-level volume source based on “haul road” example Meteorological data based on two sets of surface characteristics: – With snow cover: z o =0.01m, α = 0.6, B o = 1.5 – Without snow cover: z o =0.1m, α =0.16, B o = 0.8 Snow cover characteristics based on literal interpretation of look-up tables for winter, which assume continuous snow cover – sometimes overlooked by users

Sensitivity Example Results – 1hr

Sensitivity Example Results – 24hr

Identification/Selection of Met Data NWS surface data formats – CD144 (original data format – stands for “card deck” 144) – SCRAM ( ) – SAMSON (through 1990) – HUSWO ( ) – TD-3280 (24-hour records by element – NCDC may not support) – ISHD (TD-3505) – full archival format, not abbreviated format Note that ISHD surface data is reported in GMT, not local time! NWS upper air formats – TD-6201 – FSL

AERMET On-site Met Data Surface (single value) measurements – Functional: sky cover net radiation Solar radiation (insolation) temperature differences between levels mixing height surface roughness length surface friction velocity and others. – Not yet functional: surface heat flux & others.

AERMET On-site Met Data Tower (multi-level) measurements – Functional Height,  a,  w, temp., wind speed and direction Relative humidity – Not yet functional Vertical wind component  u,  e,  v Dew point

AERMET Meteorological Data Issues Automated Surface Observing System (ASOS) – NWS began replacing observer-based system with ASOS in 1992 – Hourly ASOS data now available for over 900 stations within U.S. – Ceilometer cloud cover limited to 12,000 feet – Cloud cover (total only – no opaque) reported by category (CLR, SCT, BKN, OVC) – Increased incidence of calms

ASOS vs. Observer-based Calms

AERMET Meteorological Data Issues Adoption of METAR standard for reporting weather observations – Began on July 1, 1996 – Introduced variable wind code (VRB) for wind speeds up to 6 knots – Variable winds now coded as missing WD with non- missing WS – previous versions of AERMET coded variable winds as calms – Added new cloud cover code (CLR, FEW, SCT, BKN, OVC)

AERMET Meteorological Data Issues Surface NWS data format issues – ASOS cloud cover data reported as four categories: CLR, SCT, BKN, OVC – METAR introduced fifth category: CLR, FEW, SCT, BKN, OVC – Abbreviated ISHD data does not include “FEW” cloud cover code – combined with “SCT” cloud code – TD-3280 cloud cover codes mapped to upper bound of category in tenths – Numerous additional cloud cover codes available in TD-3280 format not supported by AERMET – no plans to include these – Full archival ISHD format appears to be the best resource

AERMET Meteorological Data Issues ASOS station location uncertainties – Excel file with ASOS commission dates and station locations available on NCDC website appears to be unreliable for location information – Additional data available for about 200 ASOS stations as part of tropical cyclone wind study appears to be reliable – Many station locations appear to be off by several hundred meters (median value of about 500m) – Use of erroneous station locations in AERSURFACE could invalidate results

ASOS Met Station Locations – Cyclone Wind Study

ASOS Met Station Locations

Recent AERMET Update: Bug Fixes Significant changes to processing of NWS Integrated Surface Hourly Data (ISHD) surface data (TD-3505) – Selection of which record to process for hours with multiple records – Processing of cloud cover codes – Initialization of “additional” character variable – caused data from previous hours to be used – Identify “variable” winds as missing WD and non-missing WS rather than calm – Corrected problem that allowed observation hour to be incremented prematurely

Recent AERMET Update: Bug Fixes Corrected treatment of “variable” winds for TD-3280 format – coded as missing WD with non-missing WS Corrected treatment of missing data codes for WS/WD for HUSWO format Modified upper and lower bounds for surface pressure for FSL upper air data to avoid skipping valid soundings at high or low elevations Corrected bugs in calculation of critical solar angle – affecting transition hours

Recent AERMET Update: Enhancements Single AERMET executable – still needs to be run in 3 stages Optional station elevation for ISHD data to substitute for missing elevation in estimating surface pressure

Recent AERMET Update: Miscellaneous Impose lower limit of meters for user- specified surface roughness, for consistency with AERMOD model User-specified time window for ISHD data removed – default window of 30 minutes preceding the hour used Given range of changes affecting results, AERMOD modified to require reprocessing of met data with 06341

Unresolved AERMET Issues/ Planned Updates Format problem with some ISHD data may cause AERMET to crash – FIXISHD utility program – interim fix released in April Data (record-period) gap in ISHD data Inconsistencies between NWS surface data formats – TD-3280 sky cover codes – Abbreviated ISHD (TD-3505) sky cover

Questions