Biomass Smoke Emissions and Transport:Community-based Satellite and Surface Data Analysis

R.B. HusarWashington University in St. Louis

Presented at

NARSTO Workshop on Innovative Methods forEmission-Inventory Development and Evaluation

Austin, TX ; October 14-17, 2003

Pattern of Fires over N. AmericaThe number of ATSR satellite-observed fires

peaks in warm seasonFire onset and smoke amount is unpredictable

Fire Pixel Count:

Western US

North America

Scientific Challenge: Description of smoke

• Gaseous concentration: g (X, Y, Z, T)

• Aerosol concentration: a (X, Y, Z, T, D, C, F, M)

• The ‘aerosol dimensions’ size D, composition C, shape F, and mixing M determine the impact on health, and welfare.

Dimension Abbr. Data SourcesSpatial dimensions X, Y Satellites, dense networks

Height Z Lidar, soundings

Time T Continuous monitoring

Particle size D Size-segregated sampling

Particle Composition C Speciated analysis

Particle Shape/Form F Microscopy

Ext/Internal Mixture M Microscopy

Particulate matter, incl. smoke is complex because of its multi-dimensionality

It takes at leas 8 independent dimensions to describe the PM concentration pattern

Technical Challenge: Characterization

• PM characterization requires many different instruments and analysis tools.

• Each sensor/network covers only a fraction of the 8-D PM data space.

• Most of the 8D PM pattern is extrapolated from sparse measured data.

Satellite-Integral

• Satellites, integrate over height H, size D, composition C, shape, and mixture dimensions; these data need de-convolution of the integral measures.

Smoke types: blue, yellow, white

Smoke from major fires comes in different colors, e.g. blue, yellow.

The chemical, physical and optical characteristics of smokes are not known

Can the reflectance color be used to classify smokes?

Can column AOT be retrieved for optically thick smoke? Multiple scattering, absorption?

California Smoke 1999 Quebec Smoke 2002

2002 Quebec SmokeGOES East & ASOS Bext & MPL Lidar

July 6, 2002 8:15, 16:15 EST

Smoke Plumes over the Southeast

• Satellite detection yields the origin and location is the shape of smoke plumes

R 0.68 m

G 0.55 m

B 0.41 m

0.41 m

0.87 m

• The influence of the smoke is to increase the reflectance ant short wavelength (0.4 m)

• At longer wavelength, the aerosol reflectance is insignificant.

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

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

Emission Model

Land Vegetation

Fire ModelRegional AQ

Model

Kansas Agricultural Smoke, April 12, 2003

Fire Pixels PM25 Mass, FRM65 ug/m3 max

Organics35 ug/m3 max

Ag Fires

SeaWiFS, Refl SeaWiFS, AOT Col AOT Blue

ASOS Visibility Monitoring System (1200 Sites)

• The Automated Surface Observing System, ASOS; weather every minute.

• The forward scattering (30-500) visibility sensor has a range 17 ft to 30 miles.

• The synoptic visibility data are truncated (<1/4, 1/4,..10+ miles)

• For smoke and haze events (vis. < 10 mile) truncation not a problem

Diurnal Cycle – Surface Bext, April 12, 2003

00 02 04 06

08 10 12 14

16 18 20 22

Night

Day

Night

High Night Bext Low Day Bext

Smoke

0504142345 HMS + GOES

0504142345 HMS + GOES

Current Air Quality Information ‘Ecosystem’ (Draft for Feedback)

AQ information includes emissions, ambient & satellite data and model outputs

The distributed data are produced and provided by agencies, mostly through portals

Providers have different access protocols, formats, and information usage conditions

This lack of interoperability causes the under-utilization of the rich data resources

Future Integrated AQ information System (Draft for Feedback)

DataMart

VIEWS

NEISGEI

AIRNow

AQMod

DAACs

ASOS

NEI

Emission

IDEA

GASP

Missions

WeaMod

Forecast

GloMod

FireInv

Data Federation Distributed, Virtual, Uniform

AQ Forecasting

AQ Compliance

Status and Trends

Network Assess.

Data Processing Filtering, Aggregation, Fusion

Info Products Reports, Websites

Data are maintained by custodians and exposed through ‘portals’ Mediators uniformly ‘wrap’ data and provide processing servicesAnalysts program the services to create application-specific productsResponsibility is shared among data providers and mediator/ integratorsESIPFed can provide the infrastructure and tools for the AQ info system

Mediators

Federated Air Quality Data System - Draft

Text 1Text 2

Wrappers

Where?

What?

When?

Federate Data

Structuring Slice & Dice

Explore Data

Viewers Programs

Integrate

Understand

Inform Public

AQ Compliance

Forecast AQ

Status & Trends

Satellite Devel.

Network Asses.

Manage Hazards

………

Info Needs

Reports

EmissionSurface Satellite

Model

Single Datasets

Providers

Networking Reuse

ESIP AQ Cluster 050510 Draft rhusar@me.wustl.edu

Run and click PPT Slideshow to see chart animations

Non-intrusive Linking & Mediation Data UsersData Providers

DataFed Description

DataFed VisionBetter air quality management and science through by effective use of relevant data 

DataFed GoalsFacilitate the access and flow of atmospheric data from provider to usersSupport the development of user-driven data processing value chainsParticipate in specific application projects  

Approach: Mediation Between Users and Data ProvidersDataFed assumes spontaneous, autonomous emergence of AQ data (a la Internet)Non-intrusively wraps datasets for access by web servicesWS-based mediators provide homogeneous data views e.g. geo-spatial, time...

End-user programming of data access and processing through WS composition (limited)

 

ApplicationsBuilding browsers and analysis tools for distributed monitoring data    Serve as data gateway for user programs; web pages, GIS, science toolsDataFed is currently focused on the mediation of air quality data

Anatomy of a Wrapper Service: TOMS Satellite Image Data

• Given the URL template and the image description, the wrapper service can access the image for any day, any spatial subset using a HTTP URL or SOAP protocol:

• Wrapper classes are available for geo-spatial (incl. satellite) images, SQL servers, text files,etc. The mediator classes are implemented as web services for uniform data access, transformation and portrayal.

src_img_width

src_

img_

heig

h t

src_margin_rightsrc_margin_left

src_margin_top

src_margin_bottom

src_lon_min src_lat_max

src_lat_min src_lon_max

Image Description for Data Access:

src_image_width=502 src_image_height=329

src_margin_bottom=105 src_margin_left=69 src_margin_right=69 src_margin_top=46

src_lat_min=-70 src_lat_max=70 src_lon_min=-180 src_lon_max=180

The daily TOMS images reside on the FTP archive, e.g. ftp://toms.gsfc.nasa.gov/pub/eptoms/images/aerosol/y2000/ea000820.gif

URL template: ftp://toms.gsfc.nasa.gov/pub/eptoms/images/aerosol/y[yyyy]/ea[yy][mm][dd].gif

Transparent colors for overlays

RGB(89,140,255) RGB(41,117,41) RGB(23,23,23) RGB(0,0,0)

SeaWiFS Satellite

SeaWiFS Satellite

Aerosol Chemical

Air Trajectory

Map Boarder

VIEW by Web Service Composition

Datasets Used in FASTNET

• Data are accessed from autonomous, distributed providers• DataFed ‘wrappers’ provide uniform geo-time referencing• Tools allow space/time overlay, comparisons and fusion

Near Real Time Data IntegrationDelayed Data Integration

Surface Air Quality AIRNOW O3, PM25 ASOS_STI Visibility, 300 sitesMETAR Visibility, 1200 sitesVIEWS_OL 40+ Aerosol Parameters

SatelliteMODIS_AOT AOT, Idea ProjectGASP Reflectance, AOTTOMS Absorption Indx, Refl.SEAW_US Reflectance, AOT

Model OutputNAAPS Dust, Smoke, Sulfate, AOTWRF Sulfate

Fire DataHMS_Fire Fire PixelsMODIS_Fire Fire Pixels

Surface MeteorologyRADAR NEXTRADSURF_MET Temp, Dewp, Humidity…SURF_WIND Wind vectorsATAD Trajectory, VIEWS locs.

A Sample of Datasets Accessible through ESIP MediationNear Real Time (~ day)

It has been demonstrated (project FASTNET) that these and other datasets can be accessed, repackaged and delivered by AIRNow through ‘Consoles’

MODIS Reflectance

MODIS AOT TOMS Index

GOES AOT

GOES 1km Reflec

NEXTRAD Radar

MODIS Fire Pix

NRL MODEL

NWS Surf Wind, Bext

FASTNET:

Inter-RPO pilot project, through NESCAUM, 2004

Web-based data, tools for community use

Built on DataFed infra-structure, NSF, NASA

Project fate depends on sponsor, user evaluation

Some of the Tools Used in FASTNET

– Data Catalog– Data Browser– PlumeSim, Animator– Combined Aerosol Trajectory Tool (CATT)

Consoles: Data from diverse sources are displayed to create a rich context for exploration and analysis

CATT: Combined Aerosol Trajectory Tool for the browsing backtrajectories for specified chemical conditions

Viewer: General purpose spatio-temporal data browser and view editor applicable for all DataFed datasets

Midwest HazeCam Image ConsoleImage Archive and Browser

• Hourly Midwest HazeCam Images are archived by DataFed data access system• Archived images for all cameras can be browsed through this console• HazeCam URL for a day: http://www.datafed.net/consoles/MWH_WebCams.asp?image_width=400&image_height=300&datetime=2005-01-31T13:00:00

• URL for a site and day: http://webapps.datafed.net/datasets/webcam/cincinnati/20050131-13mwhcincinnati.jpg

• URLs can be embedded as links into emails, bookmarks, web pages, PPT and PDF files.

Midwest HazeCam Image Browser

Select date and time Set image size and time MW HazeCam ConsoleOther FASTNET

Consoles

Aerosol Event Catalog: Web pages

• Catalog of generic ‘web objects’ – pages, images, animations that relate to aerosol events

• Each ‘web object’ is cataloged by location, time and aerosol type.

DIURNAL CYCLE OF SURFACE HEATING/COOLING:

z

T0

1 km

MIDDAY

NIGHT

MORNING

Mixingdepth

Subsidenceinversion

NIGHT MORNING AFTERNOON

2000-2004 SeaWiFS Satellite AOTDaily and Climatology, 1 km resolution

Ready to be used by the community!

Bad Data

Idaho & Cal

Smoke

5 Year Median AOT, JJA EUS Haze

Atlanta

Appalachian AOT ‘hole’

Seasonal Surface Reflectance, Eastern US

April 29, 2000, Day 120 July 18, 2000, Day 200 October 16, 2000, Day 290