DataFed Support for

EPA’s Exceptional Event Rule

R.B. HusarWashington University in St. Louis

Presented at the workshop:Satellite and Above-Boundary Layer Observations

for Air Quality Management

January, 11-12, 2012, Baltimore, MD

1976 - Satellite Detection of Regional Haze Event over the Midwest

Regional Haze

Lyons W.A., Husar R.B. Mon. Weather Rev. 1976

SMS GOES June 30 1975 Daily Haze Maps Surface Visual Range Data

Hazy ‘Blobs’

Mexican Smoke Event, May 1998Smoke sweeps through Eastern USTOMS, SeaWiFS, monitors show daily smoke Airports close, surface concentrations at max--------------------------NC, OK attribute Ozone violation to smokeThey request waivers for exceedances

Record Smoke Impact on PM Concentrations

Smoke EventData shows that O3 DEPLETION under smokeHence, the NC & OK ozone violations can not be due to smoke-generated excess ozone

EE Rule and Satellites

• The enforcement of NAAQS is normally based on standardized surface-based observations, “Federal/Equivalent Reference Methods”

• The EE Rule allows multiple lines of observational evidence ..demonstrating the occurrence of the event, including:

…satellite-derived pixels indicating the presence of fires; satellite images of the dispersing smoke; Identification of the spatial pattern of the affected area (the size, shape, and area of geographic coverage)….

‘But for’ demonstration videoGeorgia Smoke, May 2007

Legitimate EE Flag: The Exceedance would not Occur,

But For the Exceptional Event

Example EE Tool in DataFed: Anayst’sConsole Near-Real-Time browser of EE-relevant data

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

Console LinksMay 07, 2007, May 08, 2007May 09, 2007May 10, 2007May 11, 2007May 12, 2007May 13, 2007May 14, 2007May 15, 2007

Estimation of emissions from EE sources Determination of Policy-Relevant BackgroundUnderstanding qualitative features of events

Satellites and EER: The Future

OMI Tropo NO2

Sweat Water fire in S. Georgia (May 2007)

Estimation of emissions from EE sources Needed for modeling, Quantification of ‘but for’

Sweat Water fire in S. Georgia (May 2007)

Estimation of emissions from EE sources Needed for modeling, Quantification of ‘but for’

OMI Tropo NO2

Kansas Agricultural Smoke, April 12, 2003

PM25 Mass, FRM65 ug/m3 max

Organics35 ug/m3 max

Fire Pixels

Ag Fires

SeaWiFS, Refl SeaWiFS, AOT Col AOT Blue

Kansas Grass Smoke Emission Estimation

Day 3, 87 T/day

Day 2: 1240 T/d

Mass Extinction Efficiency: 5 m2/gSeaWiFS AOD: April 9-11, 2003

Day 1: ~100 T/day

Real-Time Smoke Emission Estimation:Local Smoke Model with Data Assimilation

Emission ModelLand Vegetation

Fire Model

e..g. MM5 winds, plume model

Local Smoke Simulation Model

AOT Aer. Retrieval

Satellite Smoke

Visibility, AIRNOW

Surface Smoke

Assimilated Smoke Pattern

Continuous Smoke Emissions

Assimilated Smoke Emission for Available Data

Fire Pixel, Field Obs

Fire Loc, Energy

Assimilated Fire Location, Energy

NOAA, NASA, NFS NOAA, NASA, NFS NOAA, EPA, States

EER-Relevant Background: What is Natural/Normal??

Regional Haze Rule: Natural AerosolThe 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

Color Satellites: Qualitative visualizers of EesImproves general understanding

On April 19, 1998 a major dust storm occurred over the Gobi Desert

The dust cloud was seen by SeaWiFS, TOMS, GMS, AVHRR satellites

China

Mongolia

Korea

EER Decision Support System (DSS)

The Regional Haze Rule has been supported by the VIEWS DSSEER tech support was ad hoc through States (e.g. Texas), DataFed and others

Earth Ob-servations

Emission

Model

Satellite

Monitorig Network

Data Pool

Societal Benefit

Informing the Public

Protecting Health

Global Policies

Atmosph. Science

Facilitation of a Data Sharing NetworkMore effective use and reuse of data through a Data Pool

Data & Tool HubsHazMAP..

RSIG..

GIOVANNI

DataFed

States

AIRNow-Public

VIEWS – RHR

FASTNET –EER…

Earth Ob-servations

Emission

Model

Satellite

Monitorig Network

Data Pool

AQAST

TF-HTAP

Others ...

ScienceTeams

Decision Support

Societal Benefit

Informing the Public

Protecting Health

Global Policies

Atmosph. Science

AQ CoP Motto: Connecting and Enabling Other

Integrating Initiatives

• Satellites and EER• Estimation of emissions from EE sources • Determination of Policy-Relevant Background• Understanding qualitative features of events

• Impediments to Satellite data use• Data access Networking• Management/Coordination Workgroups? ‘CoPs’?

Summary

Fast forward 25 years

• Air quality data are sparse in space, time, composition

• Qualitative satellite, visibility data show synoptic AQ

• Science of regional AQ poor• AQ regulations are mild

Richer AQ data from surface network, satellites, etc.Regional AQ is quantitatively observedScience has improved … Regulations became much tighter

ca. 1975 ca. 2000

EER Evolution

• 1998 ‘Color’ satellite images, surface obs. offer compelling evidence of EEs, EPAs OAQPS issues memo outlining EE flagging procedure

• 1998-2007 Development of the EE Rule– Development of EE flagging procedure– Guidance through detailed case studies– States, other Agencies and (RHR) Researchers analyze many EEs

• 2007 - EE Rule implementation

Accessible datasets for the Barcelona Demo

Sahara Dust over Southern EuropeInteroperability Demo through GEOSS

Sahara Dust

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

Application-Task-Centric Workspace Example: EventSpaces

Catalog - Find Dataset

Specific Exceptional Event

Harvest Resources

Temporal Signal Decomposition and Event

Detection

• First, the median and average is obtained over a region for each hour/day (thin blue line)

• Next, the data are temporally smoothed by a 30 day moving window (spatial median - red line; spatial mean – heavy blue line). These determine the seasonal pattern.

EUS Daily Average 50%-ile, 30 day 50%-ile smoothing

Deviation from %-ile

Event : Deviation > x*percentile

Median Seasonal Conc.

Mean Seasonal Conc.

Average

Median

• Finally, the hourly/daily deviation from the the smooth median is used to determine the noise (blue) and event (red) components

Tools/Methods for for Regional AQ – Climate Analysis