estimates of biomass burning particulate matter (pm2.5) emissions from the goes imager

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Estimates of Biomass Burning Particulate Matter (PM2.5) Emissions from the GOES Imager 0 20 40 60 80 100 3 45 87 129 171 213 255 297 339 17 59 101 143 185 227 269 311 353 31 73 115 157 199 241 283 325 3 45 87 129 171 213 255 297 339 D ay ofyear VCI 0 1 2 3 4 5 M oisturecatogery Needle leaf forests in the western USA Grass in the western USA Shrubsin the western U SA Broadleaf forests in the eastern U SA M ixed forests in the eastern U SA 2002 2005 2004 2003 Xiaoyang Zhang , Shobha Kondragunta, and Chris Schmidt Fuel loading derived from MODIS land product Fuel Moisture category derived from weekly NOAA GVIx vegetation health condition Half hourly PM2.5 emissions from GOES fires Annual PM2.5 emissions from GOES fires

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Estimates of Biomass Burning Particulate Matter (PM2.5) Emissions from the GOES Imager. Xiaoyang Zhang , Shobha Kondragunta, and Chris Schmidt. Fuel Moisture category derived from weekly NOAA GVIx vegetation health condition. Fuel loading derived from MODIS land product. - PowerPoint PPT Presentation

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Page 1: Estimates of Biomass Burning Particulate Matter (PM2.5) Emissions from the GOES Imager

Estimates of Biomass Burning Particulate Matter (PM2.5) Emissions from the GOES Imager

0

20

40

60

80

100

3 45 87 129

171

213

255

297

339

17 59 101

143

185

227

269

311

353

31 73 115

157

199

241

283

325

3 45 87 129

171

213

255

297

339

Day of year

VC

I

0

1

2

3

4

5

Moi

stur

e ca

toge

ry

Needle leaf forests in the western USA Grass in the western USAShrubs in the western USA Broadleaf forests in the eastern USAMixed forests in the eastern USA

2002 200520042003

Xiaoyang Zhang , Shobha Kondragunta, and Chris Schmidt

Fuel loading derived from MODIS land product

Fuel Moisture category derived from weekly NOAA GVIx vegetation health condition

Half hourly PM2.5 emissions from GOES fires

Annual PM2.5 emissions from GOES fires