This document discusses prognostic meteorological models and their use in dispersion modelling. It focuses on the Weather Research and Forecasting (WRF) model. It describes WRF's development and capabilities, and provides an example of its use in an air quality modelling case study in the Middle East. The key findings are that WRF can simulate source location meteorology with relative accuracy compared to observations, provides upper-air data at source locations, and is an alternative source of meteorological data for locations without observation systems. The document questions WRF's potential application in the UK.

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PROGNOSTIC METEOROLOGICAL MODELS
AND THEIR USE IN DISPERSION MODELLING
DMUG 2018
LONDON, UNITED KINGDOM
ARUN KANCHAN
TRINITY CONSULTANTS, INC.
APRIL 19, 2018

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Prognostic Meteorological Models and their
Use in Dispersion Modelling
AGENDA
1. INTRODUCTION
2. PROGNOSTIC MODEL OF FOCUS:
WEATHER RESEARCH AND FORECASTING
(WRF) MODEL
3. CASE STUDY: APPLICATION OF WRF FOR
AQM IN THE MIDDLE-EAST
4. FINDINGS AND CONCLUSIONS

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INTRODUCTION

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Meteorological Input Types Used in
Dispersion Modeling
 Site-Specific
Installed meteorological observation tower
Most accurate meteorological data at the source location
Installation/maintenance costs and data handling/quality control
procedures
 National Weather Service / Met Office
Routinely available
Generally not at the source location
May not be the most adequately representative data (e.g. complex
terrain, distance, etc.)
 Prognostic Meteorological Data
Three-dimensional mesoscale computer models: MM5 and WRF

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Discontinuance of MM5 and Advent of WRF

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PROGNOSTIC MODEL
OF FOCUS:
WEATHER RESEARCH
AND FORECASTING
(WRF) MODEL

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WRF
 Developed in the U.S. by National Center for
Atmospheric Research (NCAR)
Primary meteorological tool used by U.S. EPA
Continuous updates
Supported across a broad community
 Modeling across multiple scales

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WRF: Visualization

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Mesoscale Met Data
 “Mesoscale” is that domain of atmospheric
physics that takes place on the order of
approximately 100 km and about 1 hour
 Weather forecast models operate by computing
mesoscale physical parameters at hourly time
steps over the entire depth of the atmosphere
 Parameters are computed over a three-
dimensional grid that covers the globe and
multiple (approximately 30) levels of the
atmosphere

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Mesoscale Met Data
 MM data are used as an “initial guess” wind field
input to CALMET
 From this wind field, terrain effects and slope
flow are computed
 This wind field is then supplemented with
interpolated observations from surface and
upper air stations and smoothed to create the
final wind field

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Mesoscale Met Data
 MM data are compiled for each hour of the
analysis, at each MM grid point selected for the
following surface parameters
Date and time
Grid cell indices
Surface pressure
Past hour rainfall

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Mesoscale Met Data
 MM data are compiled for each hour of the
analysis, at each MM grid point selected for the
following upper air parameters at each
mandatory level (including surface) and
additional significant levels
Pressure
Elevation
Temperature/Dew point depression
Wind direction and speed

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WRF Guidance from U.S. EPA*
 Domain Selection
Large enough to capture meteorological
phenomena and topographic features that impact
source location
 Resolution
Typically two-way nesting method: parent and nest domains
Complex terrain of land/water interface: 4 km (or finer) nest
domain, 12 km for the parent
*Modeling Guidance for Demonstrating Attainment of Air Quality Goals for Ozone,
PM2.5 and Regional Haze, 2014

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Data Set Prepared for WRF
Processing
 Surface weather reports operationally collected by the
National Centers for Environmental Prediction (NCEP)
 NCEP FNL (Final) Operational Global Analysis data
which are on 1-degree by 1-degree grids prepared
operationally every six hours
 Global Upper Air Observational Weather Data which
are collected by the NCEP
 A daily, high-resolution, real-time, global, sea surface
temperature (RTG_SST) analysis developed by the
NCEP and Modeling and Analysis Branch (MMAB).

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WRF Data Inputs

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CASE STUDY:
APPLICATION OF WRF
FOR AQM IN THE
MIDDLE-EAST

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Modeling Approach – Puff Model
Propagation

Hour 1
Hour 2
Hour 3
Hour 4
Hour 1
Hour 2 Hour 3
Hour 4
Plume
Dispersion
Local
Winds
Source
Three dimensional advection and dispersion simulated
+ Chemistry
• Surface Data
• Upper Air
Data
• Overwater
Data
4 km
Variable

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FINDINGS AND
CONCLUSIONS

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CONCLUSION
 Prognostic Models simulate the source location with relative
accuracy
 Retrospective application grounded by actual observations through four-
dimensional data assimilation
 Improved representativeness relative to NWS data
 Provides upper-air data at source location
 Studies validate modeled data to be well correlated to balloon
observations
 Alternative source of met information for locations where
observation systems are not available
 Higher density of data for models such as CALPUFF

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APPLICATION IN THE U.K. ?

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Thank you!