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Innovative Methods for Evaluating Meteorological Model Performance during the Central California Air Quality Studies. Neil Wheeler, Kenneth Craig, and Clinton MacDonald Sonoma Technology, Inc. Petaluma, California Presented at the
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Innovative Methods for Evaluating Meteorological Model Performance during the Central California Air Quality Studies Neil Wheeler, Kenneth Craig, and Clinton MacDonald Sonoma Technology, Inc. Petaluma, California Presented at the Sixth Annual Community Modeling and Analysis System (CMAS) Conference October 1-3, 2007 Chapel Hill, North Carolina STI-3229
Introduction • Prior Measurement, Analysis, and Modeling Studies • The Question • The Central California Air Quality Studies (CCAQS) • California Regional PM10/PM2.5 Air Quality Study (CRPAQS) • Central California Ozone Study (CCOS)
Central California Air Quality Studies • Multi-year • Meteorological and Air Quality Monitoring • Quality Assurance and Quality Control • Data Analysis • Emission Inventory Development • Meteorological and Air Quality Modeling • Back to basics…
Meteorological Assessment • Objective: Assess the readiness of meteorological data and models to drive the air quality simulation models • Issues investigated: • the sufficiency of data precision, accuracy, bias, consistency, and time-resolution; • The adequacy and validity of measurement methods; • the ability of models to represent important processes and phenomena; and • new model evaluation techniques.
Model Performance Evaluations • Typical Operational Evaluations Focus on “important” Parameters • Statistical • Graphical – Temporal and Spatial Comparisons; Animations • Diagnostic and Sensitivity Simulations
“Innovative” Methods (1 of 2) • Data-Based Analysis: Understanding Processes and Phenomena • Community Modeling and Analysis System (CMAS): 1997 – 2007 • Analysis Replication • Derived and Integrated Parameters • Transport Statistics • Flux Calculations • Trajectories and Tracers
“Innovative” Methods (2 of 2) • Process-Based Analysis • Assess Meteorology with an AQM • Assess Processes and Performance between Sources and Receptors but… • Synthesis • Relate Physical and Chemical Processes • Multi-Parameter Analysis • “Big Picture”
Examples • Based on Important Data Analysis Findings • Tracer Concentration Distribution • Wildfires and Ozone Aloft • Flux Calculations and Transports Statistics • Plume Rise • Carbon vs. Nitrate Aerosols • Recirculation • Nighttime Nitrate Formation Aloft • Fog and Stratus • Soil Temperature-Air Temperature-Fog-Mixing Heights
Tracer Distribution • CRPAQS: MM5-CAMx with 1 ppm initial concentration • Analysis after 60 hours: • Surface concentration • Peak tracer concentrations by region • Mass balance
Maximum Predicted Temperatures September 19, 2000
Aircraft Spirals 35.9N 19.5W 37.0N 120.1W
Ozone Correlation by Level 7.5 km 1.8 km 0.25 km 0.9 km
Wild Fire Tracers 16 km
Transport Statistics RWP MM5
Vertical Wind Profiles RWP CAMx Input MM5
Extent of Fog • MM5 tends to overestimate the extent of fog and stratus.
Summary • Think beyond traditional approaches • Analysis • Multi-method • Multi-parameter • Phenomena and Processes • Synthesis • Challenge models to replicate the synthesis • Maybe then the atmosphere will behave as models predict
Acknowledgements • The evaluation methods discussed in this paper were developed over the past decade with funding from many agencies. Analyses and evaluations specific to the CCAQS were funded by the San Joaquin Valleywide Air Pollution Study Agency. The statements and conclusions in this paper are those of the authors and not necessarily those of the California Air Resources Board, the San Joaquin Valleywide Air Pollution Study Agency, or its Policy Committee, their employees or their members. The mention of commercial products, their source, or their use in connection with the material reported herein is not to be construed as actual or implied endorsement of such products.
Parting Thought • Why aren’t meteorological models instrumented with process analysis tools like photochemical grid models?