2005-10-31 satellite aerosol climatology

Satellite Aerosol Climatology

Satellite – Surface Visibility Analysis, 1976

• First satellite aerosol paper, F. Parmenter, 1972• The relationship to surface aerosol was qualitatively established

Regional Haze

Low Visibility Hazy ‘Blobs’Lyons W.A., Husar R.B. Mon. Weather Rev. 1976

SMS GOES June 30 1975

Smoke Quantification is Elusive!

Satellite Integral: Height, Size, Composition

Satellite Smoke Sensing Issues

Clouds, snow, night – no dataColumn AOT can be optically thick Smoke reflectance (blue, yellow,

white) is hard to interpret

Smoke Plumes over the Southeast

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

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

• At longer wavelength, the aerosol reflectance is insignificant.

R 0.68 m

G 0.55 m

B 0.41 m

0.41 m

0.87 m

•

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 Surface Bext

1200 ASOS Stations

Monte Carlo Dispersion Simulation

Surface wind 1200 Station ASOS

MM5?

z

T0

1 km

MIDDAY

NIGHT

MORNING

Mixingdepth

Subsidenceinversion

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’

SeaWiFS AOT – Summer 60 Percentile1 km Resolution

Seasonal Surface Reflectance, Eastern US

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

Satellite Aerosol Optical Thickness Climatology

SeaWiFS Satellite, Summer 2000 - 2003

20 Percentile

99 Percentile90 Percentile

60 Percentile

Satellite AOT – Time Fraction (0-100%)SeaWiFS Satellite, Summer 2000 - 2003

Dec, Jan Feb

Sep, Oct, NovJun, Jul, Aug

Mar, Apr, May

Information Techology Vision Scenario: Smoke ImpactREASoN Project: Application of NASA ESE Data and Tools to Particulate Air Quality Management (PPT/PDF)

• Scenario: Smoke form Mexico causes record PM over the Eastern US.

• Goal: Detect smoke emission and predict PM and ozone

concentrationSupport air quality management and transportation safety

• Impacts: PM and ozone air quality episodes, AQ standard exceedanceTransportation safety risks due to reduced visibility

• Timeline: Routine satellite monitoring of fire and smokeThe smoke event triggers intensified sensing and analysisThe event is documented for science and management use

• Science/Air Quality Information Needs:Quantitative real-time fire & smoke emission monitoring PM, ozone forecast (3-5 days) based on smoke emissions data

• Information Technology Needs:Real-time access to routine and ad-hoc data and modelsAnalysis tools: browsing, fusion, data/model integrationDelivery of science-based event summary/forecast to air

quality and aviation safety managers and to the public

Record Smoke Impact on PM Concentrations

rhusar@me.wustl.edu, stefan@me.wustl.edu

Smoke Event

Satellite Data Us in PM Management:A Retrospective Assessment

Rudolf B. HusarCAPITA, Washington University

Presented at A&WMA’s 97th Annual Conference and Exhibition

June 22-27, Indianapolis, IN

MexicanSmoke

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

Current Air Quality Information Ecosystem:

‘Organic’

DataMart

VIEWS

NEISGEI

AIRNow

AQMod

DAACs

ASOS

NEI

Emission

IDEA

GASP

Missions

WeaMod

Forecast

GloMod

FireInv

Data Federation Distributed,

UniformAQ Forecasting

AQ Compliance

Status and Trends

Network Assess.

Data Processing Filtering, Aggregation, Fusion

Info Products Reports, Websites

Mediators

Future Federated Air Quality Information System

Federated Air Quality Data System - Draft

Text 1Text 2

Wrappers

Where?

What?

When?

Federate Data

Structuring Slice & Dice

Explore Data

Viewers Programs

Integrate

Understand

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

Run and click PPT Slideshow to see chart animations

Networking Reuse

Non-intrusive Linking & Mediation

Inform Public

AQ Compliance

Forecast AQ

Status & Trends

Satellite Devel.

Network Asses.

Manage Hazards

………

Info Needs

Reports

Data Users

EmissionSurface Satellite

Model

Single Datasets

Providers

Data Providers

DataFed Apprach

 DataFed assumes spontaneous, autonomous emergence of AQ

data (a la nodes on Internet)

Non-intrusively wraps datasets for access by web services

WS-based mediators provide homogeneous data views e.g. geo-spatial, time...

Programming by End-user through Web Service composition  

SeaWiFS Satellite

SeaWiFS Satellite

Aerosol Chemical

Air Trajectory

Map Boarder

VIEW by Web Service Composition

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

Summary

• Major advances in fire detection (fire pixels, burn scars)

• Satellite and surface smoke detection is also advanced

• Still smoke quantification is elusive

• Need to integrate the available sensory information

• Propose an IT – mediated collaborative approach