Elaine M. Prins

NOAA/NESDIS/ORA/STARAdvanced Satellite Products Branch – Madison, WI

Grass Valley, CA

elaine.prins@ssec.wisc.edu

Joleen M. FeltzChris C. Schmidt

UW-MadisonCooperative Institute

for Meteorological Satellite Studies

Overview of GOES Fire Products and Their ApplicationsWorkshop on Air Quality Applications of Satellite Data

NOAA NESDIS Center for Satellite Applications and Research (STAR), 4 May 2004

UW-MadisonCooperative Institute for Meteorological Satellite Studies (CIMSS)

NOAA/NESDIS/STAR/CORPAdvanced Satellite Products Branch (ASPB)

National Aeronauticsand Space Administration

Grass Valley

Applications of Meteorological Satellite Fire Products

Hazards Detection and Monitoring

Each year millions of acres of forest and grassland are consumed by wildfire resulting in loss of life and property with significant economic costs and

environmental implications.

- Although the capabilities of current operational meteorological satellites are limited, they can provide valuable regional and global fire products in near real-time, and are critical for fire detection and monitoring in remote locations and developing countries.

Global Change and Air Quality Monitoring

Biomass burning is a distinct biogeochemical process that plays an important role in terrestrial ecosystem processes and global climate change

- Land use and land cover change monitoring:Fire is used in the process of deforestation and agricultural management. Approximately 85% of all

fires occur in the equatorial and subtropical regions and are not adequately documented.

- Estimates of atmospheric emissions: Biomass burning is a major source of trace gases and an abundant source of aerosols

NO, CO2 (40%), CO (32%), O3(38%), NOX, N2O, NH3, SOX, CH4(10%), NMHC (>20%) , POC (39%)

- Within the Framework Convention on Climate Change (FCCC) countries need to report on greenhouse gas emissions including those from biomass burning.

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The Basics of GOES Satellite Infrared Fire Detection

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GOES-12 (East) Imager Characteristics

Band Wavelength IGFOV Sampled Subpoint NEDT (microns) (km) Resolution (km) 1 0.53-.77 1.0x1.0 0.57x1.0 10-bit data 2 3.76-4.03 4.0x4.0 2.3x4.0 .21 K @ 300 K 3 5.77- 7.33 4.0x4.0 2.3x4.0 .16 K @ 230 K 4 10.23-11.24 4.0x4.0 2.3x4.0 .10 K @ 300 K 5 NA 6 12.96-13.72 8.0x8.0 2.3x8.0 .18 K

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Current U.S. Geostationary Coverage and Fire Monitoring Characteristics

Oversampling in the East/West direction with a sub-sampled res of 2.3x4.0 km High temporal resolution: every 15 minutes over portions of North America, half-hourly

elsewhere, capability for 1-minute imaging in Super Rapid Scan Operational mode. GOES-12 band 2 has an elevated saturation temperature of ~337 K.

Elevated GOES-12 band 2 saturation temperature gives improved fire characterization.

GOES-10 saturates at ~322K resulting in non-fire saturation points during peak heating hours. Fire size detectability limits with an average fire temperature of 750K:

Equator: .15 ha 50°N: .32 ha

Fire Monitoring Characteristics

GOES-10 (West) Imager Characteristics

Band Wavelength IGFOV Sampled Subpoint NEDT (microns) (km) Resolution (km) 1 0.53-.72 1.0x1.0 0.57x1.0 10-bit data 2 3.78-4.03 4.0x4.0 2.3x4.0 .23 K @ 300 K 3 6.47-7.03 8.0x8.0 2.3x8.0 .30 K @ 230 K 4 10.2-11.2 4.0x4.0 2.3x4.0 .14 K @ 300 K 5 11.5-12.5 4.0x4.0 2.3x4.0 .26 K @ 300 K

Animations of Wildfire ABBA composite image products are being provided via anonymous ftp and the web every half-hour.

Displays include three overviews and 35 regional views providing coverage of the entire Western Hemisphere.

Examples of Regional View Sectors

University of Wisconsin-Madison CIMSS/ASPTGOES-10/-12 Half-hourly Wildfire ABBA Web Distribution

http://cimss.ssec.wisc.edu/goes/burn/wfabba.html

Examples of the GOES Wildfire ABBA Monitoring System in the Western Hemisphere

http://cimss.ssec.wisc.edu/goes/burn/wfabba.html

Fire Detection Using Rapid Scan ImageryCase Studies in the Western U.S. During the 2002 Fire Season

Rodeo/Chediski Complex: Largest Wildfire in Arizona’s

Recorded History

Size: > 480,000 acresCost: > $170 million

Start Date of Rodeo Fire: 18 June 2002

Official report time by suspected arsonist: 23:11 UTC

Initial detect in post-processed GOES-11 image: 23:07 UTC

Rodeo/Chediski Complex in Arizona

2303 UTC 2307 UTC 2315 UTC 2320 UTC

Using rapid scan GOES-11 data, the WF_ABBA was able to identify several wildfires in imagery near/at the initial reported start times during the 2002 fire season in the Western U.S.

UW-Madison/CIMSS/ASPT

GOES-11 Rapid Scan Visible Imagery Highlights the Hayman Fire

Denver WebCam: Decreased visibility due to smokeDate: 9 June 2002Time: 3:06 pm (21:06 UTC)

Denver WebCam: Blue Skies over DenverDate: 9 June 2002Time: 9:06 am (15:06 UTC)

Animation of GOES-11 Rapid Scan Visible Imagery (1 km)Time Period: 22:07, 9 June 2002 – 00:50, 10 June 2002Example of plume-driven fire

CSU/CIRA/RAMM Team

GOES WF_ABBA Diurnal Monitoring of WildfiresIn the Western U.S.

Half-hourly GOES alpha-blended imagery provide insight into diurnal variation in fire and weather

Currently products are available on-line within half-hour of image receipt

Goal is to provide fire products within 5 minutes for regional sectors in rapid scan mode this year. Rapid scan mode can be requested by the fire weather community.

Los Angeles

San Diego

GOES-10 WF_ABBA Alpha-Blended Imagery(GOES Visible, IR, WF_ABBA, USGS GLCC)

26 October – 29 October 2003

In 2002 a validation effort was conducted in the new frontier of Acre, Brazil. The GOES-8 WF_ABBA identified 84% of the 88 fires that were monitored by on-site survey teams; the majority of the fires not detected with the GOES WF_ABBA were cloud-covered. Most of the fires were less than 10 hectares in size.

Throughout the Western Hemisphere GOES Wildfire Automated Biomass Burning Algorithm (WF_ABBA) trend analyses are providing insight

into biomass burning activity associated with land-use and land-cover applications for climate

change and carbon cycle studies.

Applications of the GOES Wildfire ABBA for Land-Use/Land-Cover Change Studies

GOES-8 Wildfire ABBA Composite Fire Product for WesternAmazonia in Acre, Brazil Date: June – October, 2002

(Cost share with NASA ESE and LBA funding)

Wildfires and Agricultural Burning Show Strong Diurnal Cycle

2002 Validation Study in Quebec

Courtesy of Michel MoreauEnvironment Canada, Meteorological Ser vices, Quebec Region

Quebec

Red markers indicate fires first detected by the WF_ABBA. Filled red circles are fire pixels that were only detected by the WF_ABBA

In one case, the WF_ABBA detected a fire 17 days in advance of the first fire agency report. This fire eventually burned more than 55,000 hectares. This fire was located in Northern Quebec where there is no need for systematic daily detection by SOPFEU

WF_ABBA Results for the 2002 Quebec Fire Season

Fire Pixel Flag Positive Fire Detections

Possible Fire Detections

False Detections

Processed 2775 (3030) 20 (20) 52 (133)

Saturated 1979 (2001) 0 (0) 0 (6)

Cloud Covered 1978 (2120) 2 (3) 3 (11)

High Possibility 598 (689) 1 (1) 5 (170)

Medium Possibility 61 (88) 9 (12) 18 (197)

Low Possibility 448 (539) 75 (113) 168 (1660)

Filtered96% confirmed1% possible3% false (primarily low poss.)

Unfiltered78% confirmed2% possible20% false (primarily low poss.)

Real-time Assimilation of the Wildfire ABBA Fire Products into the NAAPS Model

Applications of the GOES Wildfire ABBA in Modeling Programs

Real-time Assimilation at the University of Sao Paulo and CPTEC/INPE into the RAMS model

Point Sources for 13 August 2002

RAMS CO Product

RAMS PM2.5 Product

GOES-8 WF_ABBA Fire Product

Real-time Assimilation into the Naval Research LaboratoryNavy Aerosol Analysis and Prediction System (NAAPS)

GOES WF_ABBA Fire Product22 August 2003 at 17:45 UTC

NAAPS Smoke Optical Depth22 August 2003 at 18:00 UTC

GOES-8 ABBA Fire and MACADA Cloud Products Used in Study to Model and Predict Future Fire Activity at UNH

Collaboration with Univ. of New Hampshire Inst. for Study of Earth, Oceans, and Space

Other Modeling Efforts and Collaborations

Real-time Air Quality Modeling at NASA/Langley: Real-time assimilation into the RAQMS model as part of IDEA (Infusing satellite Data into Environmental Applications) Fire Emissions and Regional Air Quality Modeling at NCAR: Assimilation into the U.S. EPA Community Multiscale Air Quality model in support of the 2002 SMOCC campaign in Brazil

Climate Modeling at NASA/GSFC: Assimilation into the GOCART model

Intermediate Deforestation ScenarioPredicted increase in futureregional fire activity: 22%

Complete Deforestation ScenarioPredicted increase in future regional fire activity: 123% N

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GOES-10 WF_ABBA Detected Fires: 20–27 August 2000

MOPITT CO Max.

Smoke and cumulus from large fires

Comparison between WF_ABBA Fire Observations and MOPITT CO ProductPacific Northwest United States

Idaho

Montana NorthDakota

MOPITT Total Column CO: 25–27 August 2000

MOPITT carbon monoxide composite is courtesy of J. Warner (NCAR) and the MOPITT Science team

Future GOES Imager (ABI) Band

Wavelength Range (μm)

Central Wavelength

(μm) Sample Objective(s)

1 0.45-0.49 0.47 Daytime aerosol-over-land, Color imagery 2 0.59-0.69 0.64 Daytime clouds fog, insolation, winds 3 0.84-0.88 0.86 Daytime vegetation & aerosol-over-water, winds 4 1.365-1.395 1.38 Daytime cirrus cloud 5 1.58-1.64 1.61 Daytime cloud water, snow 6 2.235 - 2.285 2.26 Day land/cloud properties, particle size, vegetation 7 3.80-4.00 3.90 Sfc. & cloud/fog at night, fire 8 5.77-6.6 6.19 High-level atmospheric water vapor, winds, rainfall 9 6.75-7.15 6.95 Mid-level atmospheric water vapor, winds, rainfall

10 7.24-7.44 7.34 Lower-level water vapor, winds & SO2 11 8.3-8.7 8.5 Total water for stability, cloud phase, dust, SO2 12 9.42-9.8 9.61 Total ozone, turbulence, winds 13 10.1-10.6 10.35 Surface properties, low-level moisture & cloud 14 10.8-11.6 11.2 Total water for SST, clouds, rainfall 15 11.8-12.8 12.3 Total water & ash, SST 16 13.0-13.6 13.3 Air temp & cloud heights and amounts

Current GOES ImagersMODIS/MTG/ Aircraft, etc

ABI Bands

MSG/AVHRR/Sounder(s)

Band 7: Saturation temperature of 400K

GOES-R Simulated 3.9 micron DataPadua/Grand Prix FiresDate: 27-Oct-03 Time: 09:50 UTC

GOES-12 Simulated 3.9 micron DataPadua/Grand Prix FiresDate: 27-Oct-03 Time: 09:50 UTC

GOES-R and GOES-I/M Simulations of Southern California Fires Using MODIS Data: 27-October-2003 at 0950 UTC

Brightness Temperature (K)

Overview and Future Plans

Current international environmental meteorological satellites were not specifically designed for fire monitoring and have limitations in this application.

In the Western Hemisphere GOES WF_ABBA fire products are providing new insights into diurnal, spatial, seasonal and interannual biomass burning activity with applications in hazards, global change, and emissions monitoring.

The International Global Observing Strategy GOFC/GOLD Fire Program has recommended development and operational implementation of a consistent global geostationary fire product utilizing GOES/MSG/MTSAT. Demonstration in 2005.

Future plans- Implement a Rapid Scan WF_ABBA for hazards applications, with products available within 5 minutes - Go Global: Adapt GOES WF_ABBA to GOES-9, MSG, MTSAT-1R - Transfer global WF_ABBA to NESDIS Operations- Participate in multi-sensor validation and intercomparison studies- Get ready for the next generation geostationary platform (ABI)