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FireMAFS project: Gomez- Dans , Spessa , Wooster, Lewis

Assessment and optimisation of SPITFIRE using EO data, and Bayesian probability and Markov Chain Monte Carlo (MCMC) techniques. FireMAFS project: Gomez- Dans , Spessa , Wooster, Lewis. *. *. LPJ: Lund Potsdam Dynamic Vegetation Model

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FireMAFS project: Gomez- Dans , Spessa , Wooster, Lewis

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  1. Assessment and optimisation of SPITFIRE using EO data, and Bayesian probability and Markov Chain Monte Carlo (MCMC) techniques FireMAFS project: Gomez-Dans, Spessa, Wooster, Lewis

  2. * * LPJ: Lund Potsdam Dynamic Vegetation Model SPITFIRE: Spread and Intensity of Fire and Emissions Model LPJ  SPITFIRE… Above-ground fuel load. SPITFIRE LPJ… Post-fire plant mortality and above-ground biomass unburnt. * * * By-passing the vegetation dynamics and soil hydrology components of LPJ. *

  3.  Improved PFT densities and distribution

  4.  Improved fuel load magnitudes and distribution

  5. uncalibrated MODIS satellite calibrated

  6. White = 0% disparity Light pink ~ 1% disparity Dark red ~ 20% disparity This gives a basis to further investigate structural and parameterisation problems with the fire model without having to worry too much about errors emanating from the vegetation model itself.

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