Operational Integrated System for wild-land fires...
Transcript of Operational Integrated System for wild-land fires...
Operational
Integrated System for wild-land fires
IS4FIRES
M.Sofiev1, J.Soares1, J. Hakkarainen1, E.
Ermakova2, R. Vankevich2
1.Finnish Meteorological Institute
2.Russian State Hydrometeorological University
Russian State
Hydrometeorological University
Outline
• Components and features of IS4FIRES
• From emission to AQ forecasting
• Specifics of IS4FIRES
diurnal variation of fire emission intensity
injection height of fire plumes
• Example of applications: fires 2012 in Eurasia
• Summary
TA
FRP
AOD
FRP
FRP
FRP
Plume top
CTM
(SILAM)
Emission factors
Diurnal variation
Emission flux
Injection height profile
Land use
Plume rise
param.
Offline
calibration
(Sofiev et al,
2009)
FRPFRPFRPFRPFRP
Fire information to emission: IS4FIRES v1.3
MODIS
MISR
SEVIRI
NRT&
offline
Offline
(Sofiev et al,
2012)
OfflineECMWF
Meteo
parameters
OfflineUSGS
Features of IS4FIRES v.1.3• Domains: global, European; resolution: 10km, daily + diurnal variation SEVIRI & VIIRS
• Timeliness: MODIS Rapid Response, persistence-based forecast
NRT fire alert for Finland (receiving antenna in Sodankyla)
• Primary scaling: FRP / TA to PM10 (Sofiev et al., 2009)
Cross-scaling (Andreae & Merlet, 2001): PM2.5, SO2, NO2 CO, NH3, HCHO
• Injection height:
crude ~(11.5) HABL
average injection profiles (global 3D map, 10 resolution)
dynamic (Sofiev et al, 2012)
• AQ impact: chemistry transport model SILAM
Fire plume forecasting: Europe, resolution of 20km and 1hr, horizon 72 hours
persistence assumption on fire behavior is the main limitation
• Data are available at
Emission 10km: http://is4fires.fmi.fi
Emission 0.5 deg: http://www.geiacenter.org
Forecasted PM fields: http://silam.fmi.fi
Plume rise: a new approach
• Plume rise: Briggs and others
Hot plume rises until is bended
out:
horizontal motion >> vertical
motion
Empirical parameterizations,
rise is limited for all stratification
types
• CAPE
Overheated parcel rises until in
equilibrium with surrounding air:
energy excess=0.
Rising through unstably
stratified layer increases energy
excess
U(z)
w(Tp,Ta,w0,..)
Data•MODIS active-fires: FRP
•ECMWF NWP fields: Habl (atmospheric boundary layer height), NFT (Brunt-Vaisala
frequency for free troposfere)
•MISR: plume height (Kahn et al., 2008; Mazzoni et al., 2007) to obtain α, β, γ, δ
coefficients α - ABL fraction passed “freely”
β - weight the contribution of the fire energy
γ - determines the power-law dependence on FRP
δ - determines the dependence on stability in the FT
Coefficient identification• MISR plume-height archive (fires in US, Siberia, Borneo, Africa, ~3000 cases)
• split the archive to learning and control dataset, perform fitting
• ranking fitting criterion: plume top within 500m from observed (approximate
accuracy of MISR itself)
Independent evaluation• CALIOP observations
Plume-rise formula (Sofiev et al, ACP, 2012)
' 2
' FTN
p ablH H FRP e
Comparison with other methods
0
1000
2000
3000
4000
0 1000 2000 3000 4000
Observed height
Ca
lcu
late
d h
eig
ht
High: 14%
Good: 42%
Low: 36%
0
1000
2000
3000
4000
0 1000 2000 3000 4000
Observed height
Ca
lcu
late
d h
eig
ht
High: 12%
Good: 37%
Low: 43%
0
1000
2000
3000
4000
0 1000 2000 3000 4000
Observed height
Ca
lcu
late
d h
eig
ht
Low: 17%
Good: 51%
High: 14%
0
1000
2000
3000
4000
0 1000 2000 3000 4000
Observed height
Ca
lcu
late
d h
eig
ht
Low: 17%
Good: 65%
High: 18%
Briggs’69
1-D model
BUOYANT
Briggs’84
New
formula
Hit rate (within 500m) for full MISR set (~2000 fires)
0
10
20
30
40
50
60
70
Briggs'69 Briggs'84 OND-86 BUOYANT Const =
1289m
New formula
Fire emission vertical profile: climatology
• Goal: 3-D mean global distribution of fire emission
supplement for existing fire emission databases
• Input
MODIS active-fires: FRP
ECMWF NWP fields: Habl, NFT
estimated diurnal variation using SEVIRI FRP
• Processing
two years (2001, 2008) ready (Sofiev et al, ACPD, 2012), full MODIS archive (2000-2012) on-going
sum-up the fire plumes, then normalize
fill-in gaps (no-fires grid cells with burns nearby)
• Evaluation
CALIOP
Height of 90% of emission injection, Feb, Aug
Injection profile, diurnal variation, Aug
Night time
Daytime
Recent fires (2012, July-November)
• July: Siberia and far east of Russia
• July-August: Northern America
• August: southern and south-eastern Europe
• September - October: Amazon basin
• October: Australia
• … and Africa non-stop, as usual…
5 July 2012: hemispheric-scale impact
Compare with: August, 2010, Russia
Moscow, 6.8.2010
Predicted (+16hrs):
1.5 mg PM10 / m3
Observed:
1.3 mg PM10 / m3
Virolahti, 8.8.2010
Predicted (+24hrs):
120 mg PM2.5 / m3
Observed:
140 mg PM2.5 / m3
Summary
• IS4FIRES data- and knowledge-base: http://is4fires.fmi.fi
Emission database, 2000-c.m. (daily, 10km, global, NRT)
GEIA database: 2000-2011 (daily, 0.5º, global)
• New semi-empirical method for injection height from wild-land fires
outperforms existing methods by 15-30% (fraction of good predictions)
can be recommended for 3-D models that possess fire intensity information
• Complementing the existing emission databases, the global map of “climatologic” injection profile from wild-land fires
accounts for diurnal/seasonal variation
targets all fire-related applications