Shobha Kondragunta and Ivan CsiszarNOAA/NESDIS

Amy HuffPennsylvania State University

Hai Zhang and Pubu CirenIMSG at NOAA

Xiaoyang ZhangSouth Dakota State University

February 24, 2015

Tracking the impact of wildfires on exo-urban regions using high resolution SNPP VIIRS aerosol products

Image courtesy of king5.com

2

This presentation focuses on

•Aerosol Optical Thickness (AOT)

•Smoke mask

•Fire hotspots

VIIRS Products

•Carlton complex fires

•San Diego fires

•Funny River fires

Case Studies

•enhanced Infusing satellite Data into Environmental Applications

eIDEA

GOES-R/JPSS synergy

1

2

3

Wildfires have detrimental effect on human health and economy

www.nifc.gov

4

VIIRS fire and aerosol products are validated and ready for operational use

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Washington state Carlton Complex fires

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Alaska Funny River fire smoke mask

Thick Smoke Thin Smoke

Data artifacts

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San Diego fires

Fire hotspots

High resolution display of AOD pixels at city road/neighborhood (750 m to 1.2 km) scale

High aerosol

concentrations

Low aerosol concentrati

ons

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Enhancements to IDEA (eIDEA) – proxy burned area

California Rim Fire 2013: Landsat TM Burned Scar with VIIRS hotspots overlaid

Fire hot spots tend to correlate with burned area

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Enhancements to IDEA (eIDEA) – proxy burned area

0 100 200 300 400 500 600 7000

100

200

300

400

500

600Slope= 0.594R2=0.69p<0.001Number of TM burn scar=258

Number of VIIRS hotspots

TM-B

urn

Scar

Are

a (k

m2 )

Sq. km per hot spot

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Enhancements to IDEA (eIDEA) – smoke exposure

AOT PM2.5

Relative Risk FactorsBurnett et al., EHP, 2014

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Enhancements to IDEA (eIDEA) – fire category

Adapt Ichoku et al (RSE, 2008) methodology that uses Fire Radiative Power (FRP) to classify fires into five different categories

MODIS FRP Range (MW) Category

< 100 1

100 - 500 2

500 - 1000 3

1000 - 1500 4

>1500 5

Individual fire hot spots can

be of different

categories within a fire

complex

Real time information

can help with resource allocation

(manpower) in suppressing

fires

Numbers for VIIRS TBD

Key features of eIDEA…to deploy as NWS Western region demo

Access to high resolution VIIRS aerosol and fire products1

Fire category2

Burned area

Mortality risk factors for smoke exposure4

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Information accessible on web and any portable device of choice to aid fire managers in short-term resource

allocation planning and long-term assessment of land management policies

Pixel level AOT

GOES-R ABI and JPSS VIIRS aerosol product synergy

• Algorithm for AOT is/can be similar.

• Algorithm for smoke/dust mask different. ABI does not have deep blue channel.

• Product applications are similar. IDEA will showcase GOES-R strengths (5-min snapshots of CONUS). Huff et al., EM, 2014 documents first ever near real time experiment AQPG did with simulated ABI aerosol products disseminated to air quality forecasters as if GOES-R was in orbit.

• Questions remain about applications for aerosol particle size parameter (aka Angstrom Exponent) – next slide

Aerosol Particle Size Parameter

-0.8 -0.4 0.0 0.4 0.8 1.2 1.6 2.0 2.4 2.80.0

5.0E3

1.0E4

1.5E4

2.0E4-0.8 -0.4 0.0 0.4 0.8 1.2 1.6 2.0 2.4 2.8

Cou

nts

Haze

-0.8 -0.4 0.0 0.4 0.8 1.2 1.6 2.0 2.4 2.80.0

5.0E3

1.0E4

1.5E4

2.0E4

Dust

Cou

nts

-0.8 -0.4 0.0 0.4 0.8 1.2 1.6 2.0 2.4 2.80.0

5.0E3

1.0E4

1.5E4

2.0E4

SmokeC

ount

s

Angstorm Exponent (AE)

Ground-based AERONET Data

SNPP VIIRSsmoke

haze

ABI aerosol particle size parameter will have limited ability to separate different types of aerosols

Summary

• SNPP VIIRS aerosol products available through IDEA website to air quality forecasters and other users. Latency is 2 hours. Goal is 20 minutes.

• eIDEA website to provide value-added fire and smoke information for real time monitoring.

• VIIRS fire and aerosol products have the potential to contribute to air quality and land management policies.