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