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Rudolf B. Husar
Washington University, St. Louis
Seminar Presented at
Environmental Protection Agency, Research Triangle Park
February 5, 2013
Exceptional Event Analysis for 2012
Smoke and Dust Events
Exceptional Events: Past, Present, Future
Data, Models, Tools: Decision Support Systems
NRL NAAPS Model: Advancement in Science
Mexican Smoke over the E. U, May 1998
Record Smoke Impact on PM Concentrations
Smoke Event
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 spring
Sahara in summer
Regional-scale fine dust
events are mainly
from
intercontinental transport
Fine Dust Events, 1992-2003 - VIEWS
ug/m3
EE Rule Features – RH Perspective
• Not rigid, weight of evidence approach
• Not based on single FRM measurement
• Encourages use of diverse data and models
• Satellites, real-time, model etc.
• There is a role of science in AQ Management
• EE events demand science, particularly the ‘but for’ clause
Near-Real-Time Data for May 11, 07 GA Smoke
Displayed on DataFed Analysts Console
Pane 1,2: MODIS visible satellite images – smoke pattern
Pane 3,4: AirNOW PM2.5, Surf. Visibility – PM surface conc.
Pane 5,6: AirNOW Ozone, Surf. Wind – Ozone, transport pattern
Pane 7,8: OMI satellite Total, Tropospheric NO2 – NO2 column conc.
Pane 9,10: OMI satellite Aerosol Index, Fire P-xels – Smoke, Fire
Pane 11,12: GOCART, NAAPS Models of smoke – Smoke forecast
1
10
2 4
5 8
7
6
3
9 12
11
Console Links
May 07, 2007,
May 08, 2007
May 09, 2007
May 10, 2007
May 11, 2007
May 12, 2007
May 13, 2007
May 14, 2007
May 15, 2007
‘But for’ demonstration: May 2007 Georgia Smoke
Red backtrajectories pass through
source area
NO2 form Fires Evidence: OMI
Sweat Water fire in S.
Georgia (May 2007)
NO2 form Fires Evidence: OMI
Sweat Water fire in S.
Georgia (May 2007)
2007: Exceptional Event Rule in
Federal Register
EPA-Approved EE Flags, 2006-2011
EE flags were added to FRM PM2.5 and ozone data in AQS
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 Decline
Expected 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: 2009-2012
NASA 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 Jones
Chattanooga-Hamilton County Air Pollution Control Bureau
Claire Aiello
WHNT TV, Huntsville, Alabama
Atmospheric Science Brown Bag Mini-Seminar, October 23, 2012
Fort Payne (Kim Pendergrass)
Colbert Heights Mountain
Looking East, (Carter Watkins)
Lake Guntersville
Monte Sano (Rebekah Bynum)
Donegal Drive, looking west
(Megan Hayes, WHNT)
WHNT TV photographs
Monte 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 Ceilometer
Mobile 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 System
NAAPS
by
D. Westphal et al, NRL
Why I luv NAAPS:
1. Assimilates satellite aerosol optical thickness and fire pixels
2. Provides 4D aerosol structure for dust, smoke, sulfate, sea salt
3. Open access to 10 years of global simulations (via DataFed)
Navy Aerosol Analysis and Prediction System
September 11, 2011
Key:
Smoke = blue
Dust = green
Sulfate = 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 aerosols
Current 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 – Aeronet
The Gold Standard for Satellite Calibration
http://webapps.datafed.net/datafed.aspx?page=Aeronet/Aeronet_MODIS/AOT_MODIS_Aeronet_Bias
AERONET Sun photometer – MODIS AOD Comparison
Very 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 elusive
EOMs are generally autonomous and quite separate activities
Software tools are available to support EOM reconciliation
But closing the EOM loop requires “interoperability” of people and machines
4D Dust, Smoke, Sulfate
Vertical 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 documentation
The 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 PM2.5
Summer
EPA PM2.5 – CMAQ Model PM2.5 Bias
Winter
OBSERVATION MODEL
PM2.5 BIAS
Tool to Iteratively Reduce the Bias
Actual closure to be worked out by the AQ community
DJF JJA SON
MAM
Nitrate
Low in DJF
Add nitrate source
Inverse modeling of
VIEWS Nitrate
Organics
Low in DJF
Improved smoke by
combined chemical,
satellite, space-time
Fine Dust
Low in MAM
Add Sahara, local dust
Dust and smoke BC for
CMAQ – e.g. NAAPS
Bio. Organics
High MAM & JJA
Reduce biogenic OC
Adjust source trem
NAAPS Dust, July
VIEWS NO3 DJF
CMAQ
EPA PM2.5 – CMAQ Model PM2.5 Bias
Winter
Many Thanks:
• Kari Hoijarvi
• Erin Robinson
• Rich Poirot
• Doug Westphal
• Neil Frank

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130205 epa ee_presentation_subm

  • 1. Rudolf B. Husar Washington University, St. Louis Seminar Presented at Environmental Protection Agency, Research Triangle Park February 5, 2013 Exceptional Event Analysis for 2012 Smoke and Dust Events Exceptional Events: Past, Present, Future Data, Models, Tools: Decision Support Systems NRL NAAPS Model: Advancement in Science
  • 2. Mexican Smoke over the E. U, May 1998 Record Smoke Impact on PM Concentrations Smoke Event
  • 3. 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
  • 4. Continental/Hemispheric Dust Events over the US Gobi dust in spring Sahara in summer Regional-scale fine dust events are mainly from intercontinental transport Fine Dust Events, 1992-2003 - VIEWS ug/m3
  • 5. EE Rule Features – RH Perspective • Not rigid, weight of evidence approach • Not based on single FRM measurement • Encourages use of diverse data and models • Satellites, real-time, model etc. • There is a role of science in AQ Management • EE events demand science, particularly the ‘but for’ clause
  • 6. Near-Real-Time Data for May 11, 07 GA Smoke Displayed on DataFed Analysts Console Pane 1,2: MODIS visible satellite images – smoke pattern Pane 3,4: AirNOW PM2.5, Surf. Visibility – PM surface conc. Pane 5,6: AirNOW Ozone, Surf. Wind – Ozone, transport pattern Pane 7,8: OMI satellite Total, Tropospheric NO2 – NO2 column conc. Pane 9,10: OMI satellite Aerosol Index, Fire P-xels – Smoke, Fire Pane 11,12: GOCART, NAAPS Models of smoke – Smoke forecast 1 10 2 4 5 8 7 6 3 9 12 11 Console Links May 07, 2007, May 08, 2007 May 09, 2007 May 10, 2007 May 11, 2007 May 12, 2007 May 13, 2007 May 14, 2007 May 15, 2007
  • 7. ‘But for’ demonstration: May 2007 Georgia Smoke Red backtrajectories pass through source area
  • 8. NO2 form Fires Evidence: OMI Sweat Water fire in S. Georgia (May 2007)
  • 9. NO2 form Fires Evidence: OMI Sweat Water fire in S. Georgia (May 2007)
  • 10. 2007: Exceptional Event Rule in Federal Register EPA-Approved EE Flags, 2006-2011 EE flags were added to FRM PM2.5 and ozone data in AQS
  • 11. Foreign Sources Pattern of EE flagged PM2.5 data, 2006-2012 Domestic Sources
  • 12. 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
  • 13. 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
  • 14. PM2.5 EE Flag Decline Expected 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
  • 15. 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
  • 16. O3 ‘Violation’ Trends Number of CONUS Stations with O3>75 ppb 2010-2013 1993-2013 Apr-Oct 2012
  • 17. Exceptional Event Decision Support System (EE DSS) EE_CATT Screencast NASA Grant: 2009-2012 NASA 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)
  • 19. October 2012 MT-KS Dust Event 121015_Montana_Dust Event
  • 20. October 2012 Dust Event: Passage of the dust plume
  • 21. 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 Jones Chattanooga-Hamilton County Air Pollution Control Bureau Claire Aiello WHNT TV, Huntsville, Alabama Atmospheric Science Brown Bag Mini-Seminar, October 23, 2012
  • 22. Fort Payne (Kim Pendergrass) Colbert Heights Mountain Looking East, (Carter Watkins) Lake Guntersville Monte Sano (Rebekah Bynum) Donegal Drive, looking west (Megan Hayes, WHNT) WHNT TV photographs Monte Sano (Jacks Camera Network, WHNT) Oklahoma Dust Plume Event over Huntsville, Alabama, October 19, 2012
  • 24. 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 Ceilometer Mobile 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)
  • 25. 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
  • 26. Application-Task-Centric Workspace Example: EventSpaces Catalog - Find Dataset Specific Exceptional Event Harvest Resources
  • 27. EE DSS Links CATT – General EE_CATT 121015_Montana_Dust Event 110414_Kansas_Smoke Event Screencast Anomaly Map
  • 28. Navy Aerosol Analysis and Prediction System NAAPS by D. Westphal et al, NRL Why I luv NAAPS: 1. Assimilates satellite aerosol optical thickness and fire pixels 2. Provides 4D aerosol structure for dust, smoke, sulfate, sea salt 3. Open access to 10 years of global simulations (via DataFed)
  • 29. Navy Aerosol Analysis and Prediction System September 11, 2011 Key: Smoke = blue Dust = green Sulfate = red D. Westphal
  • 30. 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 aerosols Current 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.
  • 31. Aerosol Optical Thickness – Aeronet The Gold Standard for Satellite Calibration
  • 33. VIEWS Sulfate – NAAPS Sulfate OBSERVATION MODEL SO4 BIAS
  • 34. Goal: ‘Reconciliation’, ‘Harmonization’…’Closure’ By iterative refinement of of Emissions, Observations and Models HTAP, 2010 Reconciling emissions, observations and models (EOMs), has been elusive EOMs are generally autonomous and quite separate activities Software tools are available to support EOM reconciliation But closing the EOM loop requires “interoperability” of people and machines
  • 35. 4D Dust, Smoke, Sulfate Vertical 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 documentation The DataFed Browser now incorporates vertical cross section views
  • 36. 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.
  • 37. Satellite-Surface PM relationship The NAAPS helps separating local and ‘global’ dust but much work is needed
  • 38. OBSERVATION MODEL PM2.5 BIAS EPA PM2.5 – CMAQ Model PM2.5 Summer
  • 39. EPA PM2.5 – CMAQ Model PM2.5 Bias Winter OBSERVATION MODEL PM2.5 BIAS
  • 40. Tool to Iteratively Reduce the Bias Actual closure to be worked out by the AQ community DJF JJA SON MAM Nitrate Low in DJF Add nitrate source Inverse modeling of VIEWS Nitrate Organics Low in DJF Improved smoke by combined chemical, satellite, space-time Fine Dust Low in MAM Add Sahara, local dust Dust and smoke BC for CMAQ – e.g. NAAPS Bio. Organics High MAM & JJA Reduce biogenic OC Adjust source trem NAAPS Dust, July VIEWS NO3 DJF CMAQ EPA PM2.5 – CMAQ Model PM2.5 Bias Winter
  • 41. Many Thanks: • Kari Hoijarvi • Erin Robinson • Rich Poirot • Doug Westphal • Neil Frank

Editor's Notes

  1. The Exceptional Event that triggered the fist regulatory response by EPA was the May 1998 Mexican smoke event. The color images from the NASA SeaWiFS satellite (louched in 1997) allowed the detection and following the evolution of the smoke event.Based on the record smoke impact on the PM10 concentration over the entire Eastern US, the EPA issued a memorandum to the States, as a precursor of the Exceptional Event Rule, promulgated in 2007.------------------Since time immemorial, smoke from forest fires, wind-blown dust storms and other ‘exceptional events’ have punctuated the air quality with extreme concentrations of atmospheric particulates and gases. However, historically the spatio-temporal pattern of air quality during such events was sparse and patchy. This has changed dramatically during the sensing revolution of the 1990s, in particular through the near-real-time availability of color satellite images. Satellites the became the primary sensory inputs for event detection and spatio-temporal characterization. Satellite observations were also responsible for formally including Exceptional Events into the AQ management process. When combined with routine surface-based monitoring data …In the past, the definition and documentation of events has been subjective, dependent on the analyst, the is event type etc.The routine overall characterization of detected events is accomplished by the rich real-time data through delivered through the Analysts ConsolesObjective event definition is now possible through spatio-temporal statistical parameters derivable from routine monitoring dataand their causes is still poorly understood and largely unpredictable.
  2. The spatial distribution of EE flags attributed to Mexican and Canadian fires (left). EE flags attributed to prescribed and wild fires within the US (right). The time series below each map shows the number of flags throughout the country for each day. The spatial distribution of EE flags attributed to African and Asian dust. (left). EE flags attributed to dust origination within the US(right).
  3. NAAPS general description. Species are sulfate, dust, smoke, and sea salt. Aspects impacted by NASA remote sensing products are specification of sources and initialization.Each term in the equation can dominate at one time or another.  At a source grid point, S is dominant.  One grid point away, advection could be largest.  In a cloud R is largest. etc.Image is a composite of geostationary clouds, fires from FLAMBE (pink and yellow dots), and NAAPS aerosol plume forecasts (see legend). It shows 1) production and transport of anthropogenic aerosol in northern latitudes (red), 2) Saharan dust over the Atlantic being scavenged by two tropical cyclones, and 3) smoke from thousands of African fires transported to S. America.
  4. NAAPS initialization benefits from the current 2-D assimilation of aerosol optical depth (AOD) provided by satellite remote sensing (MODIS and MISR) and also 3-D extinction values derived from the CALIPSO CALIOP lidar. The initial conditions are thus improved by assimilating these two parameters.
  5. The August 2012 Western US smoke is a Continental-scale event.While the NAAPS AOT column concentration stretches over much of the continent (>5000 km), the smoke impact on the surface is diminished by about 1000 km. This indicates long distance smoke transport aloft. In fact the PM2.5 concentrations reported in Airnow, do not show smoke impact except near the fires in Idaho.