130205 epa exc_event_seminar
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Rudolf B. HusarWashington University, St. Louis
Seminar Presented atEnvironmental Protection Agency, Research Triangle Park
February 5, 2013
Exceptional Event Analysis and Decision Support System
Exceptional Events: Past, Present, FutureData, Models, Tools: Decision Support SystemsNRL NAAPS Model: Advancement in Science
Mexican Smoke over the E. U, May 1998
Record Smoke Impact on PM Concentrations
Standard65 un/m3
The day-max ozone is depressed under the smoke plume!
Asian Dust Cloud over N. America
On April 27, the dust cloud arrived in North America.
Regional average PM10 concentrations increased to 65 mg/m3
In Washington State, PM10 concentrations exceeded 100 mg/m3
Asian Dust 100 mg/m3
Hourly PM10
Continental/Hemispheric Dust Events over the US
Gobi dust in springSahara in summer
Regional-scale fine dust events are mainly
from intercontinental transport
Fine Dust Events, 1992-2003 - VIEWSug/m3
EE Rule Features – RH Perspective• Not rigid but flexible weight of evidence approach
• Not based on single FRM measurement• Encourages use of diverse data and models
• Satellites, real-time data, emission-transport model etc.• There is a role of science in AQ Management
• EE flagging demands science, particularly the ‘but for’ clause
Near-Real-Time Data for May 11, 07 GA SmokeDisplayed on DataFed Analysts Console
Pane 1,2: MODIS visible satellite images – smoke patternPane 3,4: AirNOW PM2.5, Surf. Visibility – PM surface conc.Pane 5,6: AirNOW Ozone, Surf. Wind – Ozone, transport patternPane 7,8: OMI satellite Total, Tropospheric NO2 – NO2 column conc.Pane 9,10: OMI satellite Aerosol Index, Fire P-xels – Smoke, FirePane 11,12: GOCART, NAAPS Models of smoke – Smoke forecast
1
10
2 4
5 876
3
9 1211
Console LinksMay 07, 2007, May 08, 2007May 09, 2007May 10, 2007May 11, 2007May 12, 2007May 13, 2007May 14, 2007May 15, 2007
‘But for’ demonstration: May 2007 Georgia Smoke
Red back-trajectories pass through source area
Sweat Water fire in S. Georgia
Jacksonville
Tampa
Miami
Atlanta
Tropospheric NO2 form May 2008 GA Fire
NO2 form Fires Evidence: OMI
Sweat Water fire in S. Georgia (May 2007)
Exceptional Event Rule in Federal Register in 2007
PM2.5 and Ozone EE flags were added to the monitoring data
Next we show the EPA-Approved EE Flags for PM2.5, 2006-2011
EE Rule Implementation, 2007-2012
Foreign Sources
Pattern of EE flagged PM2.5 data, 2006-2012
Domestic Sources
Trend of PM2.5 Concentration
A reason for the 2006-2010 EE flag decline is the overall reduction in PM2.5 concentrations.
2000-2003 avg. 2009-2012 avg.
As a result of PM2.5 decline, the number of >35ug/m3 samples has also declined dramatically. No NAAQS exceedances-no EE flag.
>35 ug/m3 station count
Regional Haze Rule: Natural Aerosol
The goal is to attain natural conditions by 2064;Baseline during 2000-2004, first Natural Cond. SIP in 2008;SIP & Natural Condition Revisions every 10 yrs
PM2.5 EE Flag DeclineExpected Increase in 2012
Trend of EE flags for PM2.5between 2006 and 2012 based on the official EPA AQS database. The EE flags declined tenfold between 2007-8 and 2010-11
However, in 2012 the average AOT from smoke was higher than any year between 2006 and 2012, probably due the severe drought. The EE flags for 2012 are expected to rise again in 2012.
Drought Anomaly, 2012
Smoke Super-Events in 2012: August 1- August 20 20
June 20- July 10
Average Smoke Emission Rate
Average Smoke Optical Depth
Average Smoke Surface Concentration
O3 ‘Violation’ Trends Number of CONUS Stations with O3>75 ppb
2010-2013
1993-2013
Apr-Oct 2012
Exceptional Event Decision Support System (EE DSS)
EE_CATT Screencast
NASA Grant: 2009-2012NASA and NAAPS Products for Air Quality Decision Making, D. Westphal, PI, R. Husar, CoI
Washington University, McDonald Academy for Global Energy and Environmental Partnership (MAGEEP)
Ozone Exceedances Apr-Sep 2011
October 2012 MT-KS Dust Event
121015_Montana_Dust Event
October 2012 Dust Event: Passage of the dust plume
Oklahoma Dust Plume Event over Huntsville, Alabama October 19, 2012
NSSTC Rooftop Camera, Looking East, without and with Dust
Kevin Knupp, Michael Newchurch, Udaysankar Nair, Dustin Phillips, David Bowdle, Shih Kuang, Wesley Cantrel,University of Alabama in Huntsville
Kathy JonesChattanooga-Hamilton County Air Pollution Control Bureau
Claire AielloWHNT TV, Huntsville, Alabama
Atmospheric Science Brown Bag Mini-Seminar, October 23, 2012
Sunrise in Madison (TBD)
Fort Payne (Kim Pendergrass)Colbert Heights Mountain
Looking East, (Carter Watkins)
Lake Guntersville(Jacks Camera Network WHNT)
Monte Sano (Rebekah Bynum)
Sunrise in Decatur (Allison)
Donegal Drive, looking west(Megan Hayes, WHNT)
WHNT TV photographs provided by Claire AielloMonte Sano
(Jacks Camera Network, WHNT)
Oklahoma Dust Plume Event over Huntsville, Alabama, October 19, 2012
October 2012 Dust Event
Oklahoma Dust Plume Event over Huntsville, Alabama, October 19, 2012
http://vortex.nsstc.uah.edu/mips/data/current/ceilometer/
Time-Height Cross-Section of Relative Signal Intensity from Vertically Viewing CeilometerMobile Integrated Profiling System (MIPS), University of Alabama in Huntsville
Altitude in km, time in Coordinated Universal Time (CDT = UTC - 05:00), intensity in false color
Peak at ~17 UTC(12 N EDT)
EE DSS Tools: Data System Architecture
• Data are accessible from the Air Quality Data Network (ADN) by the AQ Community Catalog• ADN is facilitated by the GEO AQ Community of Practice (GEO AQ CoP), including R. Husar’s group.• The generic client tools (red boxes) are for processing and visualization; used in many applications• Specialized Application Tools are dedicated to specific applications, e.g. event detection
Application-Task-Centric Workspace Example: EventSpaces
Catalog - Find Dataset
Specific Exceptional Event
Harvest Resources
EE DSS Links
CATT – General
EE_CATT
121015_Montana_Dust Event
110414_Kansas_Smoke Event
Screencast
Anomaly Map
Navy Aerosol Analysis and Prediction SystemNAAPS
by D. Westphal et al, NRL
Why I luv NAAPS:1. Assimilates satellite aerosol optical thickness and fire pixels2. Provides 4D aerosol structure for dust, smoke, sulfate, sea salt3. Open access to 10 years of global simulations (via DataFed)
Navy Aerosol Analysis and Prediction System
September 11, 2011Key:Smoke = blueDust = greenSulfate = red
D. Westphal
NASA Data for NAAPS Initialization
Forecasting is an initial value problem: Requires the 3-D distribution of aerosol concentration at the start of the forecast:
: Assimilation of previous forecast + information from remote sensing of aerosolsCurrent capabilities:
Aerosol Optical Depth (AOD; 2-D) (MODIS and MISR)Extinction (3-D) (CALIPSO)
+ Land/Ocean MODIS+ Land/Ocean MISR
Natural run + Ocean MODIS+ land/Ocean MISR
Multiple aerosol sensors are critical for assimilation.
Aerosol Optical Thickness – AeronetThe Gold Standard for Satellite Calibration
http://webapps.datafed.net/datafed.aspx?page=Aeronet/Aeronet_MODIS/AOT_MODIS_Aeronet_Bias
AERONET Sun photometer – MODIS AOD ComparisonVery low bias except in blue sites
Kanpur, IN
VIEWS Sulfate – NAAPS Sulfate
OBSERVATION MODEL
SO4 BIAS
Goal: ‘Reconciliation’, ‘Harmonization’…’Closure’
By iterative refinement of of Emissions, Observations and Models
HTAP, 2010
Reconciling emissions, observations and models (EOMs), has been elusiveEOMs are generally autonomous and quite separate activitiesSoftware tools are available to support EOM reconciliationBut closing the EOM loop requires “interoperability” of people and machines
4D Dust, Smoke, SulfateVertical Cross Section Views
Vertical dust cross sections at about 120 (surface plume) and 1000 km (elevated plume).Knowing the vertical structure of smoke and dust plumes is critical to EE documentationThe DataFed Browser now incorporates vertical cross section views
Long-Term Model Data 2006-Now
A significant fraction of the dust vertical column is in the troposphere and it is ‘global’ Most of surface dust is local and highly variable in space and time
Emission
Vertical AOT
Surface Conc.
Satellite-Surface PM relationship
The NAAPS helps separating local and ‘global’ dust but much work is needed
OBSERVATION MODEL
PM2.5 BIAS
EPA PM2.5 – CMAQ Model PM2.5Summer
EPA PM2.5 – CMAQ Model PM2.5 BiasWinter
OBSERVATION MODEL
PM2.5 BIAS
Tool to Iteratively Reduce the Bias Actual closure to be worked out by the AQ community
DJF JJA SONMAM
NitrateLow in DJF
Add nitrate sourceInverse modeling of
VIEWS Nitrate
OrganicsLow in DJF
Improved smoke bycombined chemical, satellite, space-time
Fine DustLow in MAM
Add Sahara, local dustDust and smoke BC for CMAQ – e.g. NAAPS
Bio. OrganicsHigh MAM & JJA
Reduce biogenic OCAdjust source trem
NAAPS Dust, July
VIEWS NO3 DJFCMAQ
EPA PM2.5 – CMAQ Model PM2.5 BiasWinter
Many Thanks:
• Kari Hoijarvi• Erin Robinson• Rich Poirot• Doug Westphal• Neil Frank