Assimilating Smoke Data for Air Quality Analysis

Transcription

1 Assimilating Smoke Data for Air Quality Analysis Matt Mavko TEMPO Early Adopters Meeting April 11, 2018 Western Regional Air Partnership

2 Why Include Fire in Air Quality Models? EPA s Fire NEI estimated 1.6 million tons of PM 2.5 emitted in 2014 Major source category for many planning applications Large episodic events affect air quality Human health and safety NAAQS attainment

3 Effort and Quality Tradeoffs Completeness Characterize as many burns as possible, everywhere Every burn is classified (WF, Rx, Ag, etc) Coordination Consensus Technological Advancement Cost Regulatory Deadlines Timeliness Operational/Forecasting Exceptional Events Demonstrations Air Quality Planning Coherence Use the same models and inputs for all fires Highest quality source data Consistency Stable methodology year-to-year Similar methods across jurisdictions

4 Pathways For Modeling Smoke Operational Forecasting Retrospective Analysis Activity Detected real-time; est. size Gather reports, detections Emissions Source categories Chemistry None sometimes, but no other source categories Similar methods All fires classified WF, Rx, Ag Full chemistry and all source categories Transport Forecast meteorology Year-specific meteorology

5 Operation Smoke Models and Their Inputs Model Component Fire location Blue Sky Daily Operational Runs NOAA s HMS (GOES, MODIS, AVHRR, VIIRS) HRRR Smoke FireWork AIRPACT 5 NOAA/NWS National AQ VIIRS AND MODIS AVHRR, MODIS, VIIRS Fuel Characteristics FCCS v2 3BEM CWFIS FCCS v2 FCCS v2 Fuel Loading FCCS v2 3BEM/WF_ABBA CWFIS FCCS v2 FCCS v2 Fuel Consumption Consume 3 3BEM FBS Consume 3 Consume 3 Fire Emissions FEPS 3BEM FEPS FEPS Consume 3 Fire Emissions Temporal Profile Plume Rise Characterization HMS HMS FEPS Gaussian FEPS FEPS Constant FEPS Briggs Heat flux = FRP/Burnt Area. Briggs Briggs (PBL from WRF; Heat Flux from FEPS) Briggs using HMSderived fire heat rate

6 Fire Tracking and Analysis Systems Resources and Benefits Smoke Management Systems Implement needs of smoke management rules Accept and store information needed to support decision-making Allow for rapid communication with burners Provide a platform for efficient information exchange Foster coordination among agencies Provide public access to elements of SMP Maintain an historical record FETS and WRAPTools EI resource, automated data collection, quality control, provide data (EIs and model) Provide modeling data for national and regional studies Next-generation modeling impacts and emission factors (VBS) Near-Term Applications Resource for EE demonstrations Implementation of revised ozone standard 6

7 Fire Activity Fuels Fire Weather Consumption Emission Factors Plume Rise Temporal Profiles Airborne Remote Sensing Regional Reporting Modeling Modeling Laboratory Measurements Field Measurements Parameterization/Heuristic Parameterization/Heuristic Smoke Mgt Reports; ICS Reports; TIR WF perimeter flights, RS point detection (HMS), NDBI (Landsat) Smoke Mgt Reports; ICS Reports; FCCS (Landsat) Smoke Mgt Reports; WIMS/RAWS; NOAA Smoke Mgt Reports; CONSUME/FOFEM; NDBI (Landsat) Compiled from literature, match to fuels and consumption Briggs; Flaming Phase Consumption Index; FRP; CALIPSO/OMI WRAP Diurnal Profiles; FEPS; Gaussian

8 Elevated Ozone due to Fire: Idaho Case Study Aug 10 Aug 31, 2008 South Barker Nature North Minidoka East Slide Rock Ridge

9 Rx fires & O 3 monitor Observed O 3 (monitor on map) and modeled fire contribution Model Source Apportionment at monitor location (Rx fire is green, max 2ppb) Rx Fire Contribution to Elevated O 3 in Arizona 10/1/2008 2ppb modeled contribution Relevant to lowered standard

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11 Glacier NP EPA split of natural and anthropogenic Organic Carbon Mass (OCM) Light Extinction OCM Episodic Natural OCM Routine Natural OCM Anthropogenic CAMx PSAT Apportioned Organic Carbon Mass Light Extinction Black Carbon Non-fire Natural Non-US Wildfire US Rx Fire US Wildfire Anthropogenic

12 Fire Activity Fuels Fire Weather Consumption Emission Factors Plume Rise Temporal Profiles Airborne Remote Sensing Regional Reporting Modeling Modeling Laboratory Measurements Field Measurements Parameterization/Heuristic Parameterization/Heuristic Real-time (operational) and retrospective GOES/AVHRR/MODIS Landsat Retrospective Landsat Retrospective Landsat Operational and Retrospective VIIRS FRP CALIPSO/OMI

13 Possible TEMPO Applications Air Quality Analysis Trace smoke between fire and monitor Understory burn detection (where there s smoke, there s ) Aerosol index climatology for anomaly detection Plume Rise Characterization Improvements Build on methods using CALIPSO and OMI Vertical distribution of smoke? Temporal Profile Improvements for Short-Lived Fires Regional practices for managed burning (timing) Duration Reducing Uncertainties Bracketing fuels and consumption Validating model ozone chemistry SOA formation? --downwind impacts and characterizing aged smoke (or at least size distribution?)