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Development of a Tool for Downscaling of Operational Climate Forecasts to Regional and Local Fire Indices. 1,2 Nicole M ölders & 1 Gerhard Kramm University of Alaska Fairbanks 1 Geophysical Institute 2 College of Natural Sciences, and Mathematics. Motivation. - PowerPoint PPT Presentation
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Development of a Tool for Downscaling of Operational Climate Forecasts to
Regional and Local Fire Indices
1,2Nicole Mölders & 1Gerhard Kramm
University of Alaska Fairbanks1Geophysical Institute
2College of Natural Sciences, and Mathematics
Motivation
Wildfires reduce visibility => affect land and air trafficReleased aerosols and trace gases reduce air quality PM2.5 may affect healthDestruction of propertiesNatural treat with high occurrence nearly worldwide…
Agencies need data for planning fire management several months ahead a fire seasonA tool to provide suitable data for support in decision making and planning is required
Wildfires occur worldwide
Alaska has long wildfire history
High temporal variability in wildfire frequency
and area burned
Modified after Mölders and Kramm (2006)
Fire weather assessment difficult ahead of a season or when data are sparse
Operational climate model predictions are too coarseHigher resolution non-linearly slows down the CPU and turnaround time Only 7 first class sites in Interior Alaska21 additional sites of unknown data quality run by volunteers for limited amount of quantitiesClouds or smoke from existing fire may affect remote sensing
None of the above methods permits 3 months ahead assessment of fire risk as required by regional fire agencies for planning
Moisture, temperature and wind dependent Fosberg Fire Weather Index typically used for fire risk assessment
Equilibrium moisture content
Fosberg Fire Weather Index
(FFWI)
Wind factor
Moisture damping coefficient
Wind speed
Temperature
Relative humidity
after Goodrick (2002)
Fosberg Fire Weather Index obtained from climate simulations not very helpful
Arctic Ocean
Alaska Canada
FFWI (-.-)
Modified Fosberg Fire Weather Index (mFFWI) by inclusion of Keetch-Byram Drought Index (KBDI) and fuel availability
),T,y,x,t(f)T,P,P,y,x,t(fKBDI cfdailymax,dailymax,annualdaily
Spatially varyingfuel availability factor
FFWI Modified FFWI
observations model
Heterogeneous precipitation distribution requires spatial fuel availability factor
30-average observed precipitation according to the GPCC (color)and as simulated by CCSM (solid lines)
Dryness yields to high fuel availability
Arctic Ocean
Arctic Ocean
mFFWI obtained from climate simulations lacks regional details
Improvement compared to FFWI, but too coarse
modified FFWI (-.-)
FFWI (-.-)
Arctic Ocean
Arctic Ocean
Schematic to bridge operational climate forecasts to regional and local mFFWI
operationalclimate
forecasts
WRF
WRFnested runs in endangered
areas
weeklymFFWI maps
for quick-looks
6-hourlyforcing data
data base of climate model data plus derived mFFWI,regional model data plus derived mFFWI,
observations
statistical analysis toderive relationships, evaluation,
improvement, etc.
observations
dailymFFWI maps foranalysis
dailymFFWI
local maps
if required
Schematic of time staggering for mFFWI ensembles
operational climate forecasts
WRF simulations every x days for 5d
time
loca
l mF
FW
I
mFFWI form various WRFruns started at different times envelop
of mFFWI
start procedure 3 month ahead a fire season for guesstimates
repeat procedure until end of the fire season if a “real” month is over
Application of WRF for 4 km x 4 km grid increments
6-27-2005 1400 AST
Great horizontal variability of fuel availability factorwithin the Interior
Importance of consideration of fuel availability within the region
6-27-2005 1400 AST 6-27-2005 1400 AST
Modified FFWI derived from scare meteorological observations often shows lower fire risk than FFWI
Fires in June 2005
2005/06/14 2215 UT Fires in eastern Alaska Aquahttp://rapidfire.sci.gsfc.nasa.gov/gallery/?search=alaska&date
2005/06/26 2050 UT Fires in Alaska and Yukon Territory Terra http://rapidfire.sci.gsfc.nasa.gov/gallery/?search=alaska&date
Evaluation of simulated (m)FFWI difficult
Not every area with high (m)FFWI burns
Network density (4 sites in Interior Alaska)
Complex terrain, low representativeness
Random errors due to initial and boundary conditions or observations
Systematic errors from consistent misrepresentation of geometrical, physical, or numerical factors
Error propagation in measurements/simulations
Actual fires and burned areas affect temperature, humidity, precipitation
Overall evaluation recommended
Small uncertainty of (m)FFWI from propagation of measurement errors
2
2n
1i ii
Where stands for FFWI or mFFWI and is RH, T, v, P
Assume: (RH)= 5% (T)= 0.5K (v)= 0.5m/s (P)= 0.01inch
Note that uncertainty of FFWI and mFFWI will only differ if precipitation occurs
Note that McGrath and Northway airports are outside the WRF model domain!
WRF well provides modified FFWI
Forecast errors propagate in mFFWISimulation started 6-11-2005 0600 UTvalid for 6-11-2005 2300 UT (1400 AST)
Simulation started 6-7-2005 0600 UTvalid for 6-11-2005 2300 UT (1400 AST)
Errors in P yield errors in soil moisture, FAF, KBDI, mFFWIErrors in T yield errors in RH, FFWI, mFFWIErrors in RH yield errors in FFWI, mFFWIErrors in v yield errors in FFWI, mFFWI
=> Estimate uncertainty of mFFWI with Gaussian Error Propagation (GEP) principles
Suitability of WRF for fire risk assessment
Simulation length mattersMean FFWI and mFFWI obtained by WRF at the 4 observational sites are the same as for observationsErrors between (m)FFWI derived from WRF and observations are within the range of observational uncertaintyRMSE are lower for mFFWI than FFWIDetermination of (m)FFWI more difficult in mountainous than relatively flat terrainMeans of (m)FFWI derivded from WRF and observations do not differ significantly (95% confidence) according to a t-test, but variance does according to f-testWRF is suitable for fire weather forecast
Acknowledgements
We thank
Edward O'Lenic and the organizing team for inviting us
Zhao Li, Debasish Pai Mazumder, Ted Fathauer for collaboration
You for your attention
Released aerosols and trace gases reduce air quality
Courtesy: J. Connor
Fires, smoke, and burn scars in Alaska and Yukon Territory in August 2005
2005/08/10 2210 UT(false color) Aqua http://rapidfire.sci.gsfc.nasa.gov/gallery/?search=alaska&date
Large fire scars increase of upward transport and development of a non-classical mesoscale circulation
0500 AST 1100 AST
2100 AST1400 AST
From Mölders & Kramm 2006
Cloud formation changes due to wildfire scars
0500 AST 1100 AST
2100 AST1400 ASTFrom Mölders & Kramm 2006