Jan Mandel/Blog/2013 Jul Sep

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June 23-July 5: Institute of Computer Science, CAS, Prague

July 1-5: Preconditioning of Iterative Methods, Prague

June 6: Fuel moisture forecast

June 15: Haar measure

July 16: NDVI and fuels

July 17: LETKF convergence

July 20: Integrating WRF tracers in the WRF-SFIRE-CHEM coupling

July 21: How weather can help trigger an explosive fire

July 23: LETKF convergence

  • Now the files in the framework.

July 28: Wavelets

July 29: LETKF convergence

Aug 2: FireFlux paper appeared

Aug 6: Satellite observations and NASA

Aug 6: Combining ensemble Kalman filter and particle filter

Aug 8: The economics of wildland fires

The California Fire Economics Simulator

Resource allocation and optimization

  • Donovan, Geoffrey H. and Rideout, Douglas B., An integer programming model to optimize resource allocation for wildfire containment, Donovan-2003-IPM link
  • Thompson, Matthew P. and Calkin, David E. and Finney, Mark A. and Gebert, Krista M. and Hand, Michael S., A risk-based approach to wildland fire budgetary planning, Thompson-2013-RAW link
  • Finney, Mark and Grenfell, Isaac C. and McHugh, Charles W., Modeling Containment of Large Wildfires Using Generalized Linear Mixed-Model Analysis Finney-2009-MCL link
  • Wilson R.S., P.L. Winter, L.A. Maguire, and T. Ascher, 2011: Managing Wildfire Events: Risk-Based Decision Making among a Group of Federal Fire Managers. Risk Analysis, 31(5), 805-18. Wilson-2011-MWF doi ucd

Aug 9: BlueSky

Aug 10: CU-Boulder budget cuts

Aug 17: Reading some papers



  • Bishop and Toth 1999, Ensemble Transformation and Adaptive Observations Bishop-1999-ETA doi ucd
  • Bishop et al 2001, Adaptive Sampling with the Ensemble Transform Kalman Filter. Part I: Theoretical Aspects Bishop-2001-ASE doi ucd
  • Hunt et al 2007, Efficient data assimilation for spatiotemporal chaos: A local ensemble transform Kalman filter Hunt-2007-EDA doi ucd
  • Lorenc 2003, The potential of the ensemble Kalman filter for NWP—a comparison with 4D-Var Lorenc-2003-PEK doi ucd
  • Wang and Bishop 2003, A Comparison of Breeding and Ensemble Transform Kalman Filter Ensemble Forecast Schemes Wang-2003-CBE doi ucd


Aug 19: Fire Danger Rating

Aug 26: Data assimilation seminar: Lp laws of large numbers in Hilbert spaces

  • abstract
  • lecture notes
  • Le Gland et al, 2011, Large sample asymptotics for the ensemble Kalman filter, LeGland-2011-LSA link, Woyczynski, 1980, On Marcinkiewicz-Zygmund laws of large numbers in Banach spaces and related rates of convergence, Woyczynski-1980-MLL link, Gine, Central limit theorems and weak laws of large numbers in certain {B}anach spaces Gine-1983-CLT doi ucd
  • References from Troy: Real Analysis: Modern Techniques and Their Applications, Gerald B. Folland Folland-1999-RAM, Measure Theory and Probability, Malcolm Adams and Victor Guillemin Adams-1996-MTP

Aug 27: Mike McCourt: Stable Kernel-Based Interpolation using Gaussian Eigenfunctions

Aug 31: Satellite fire detection

Sep 1: Satellite indices and fuel moisture

  • Dennison et al., Use of Normalized Difference Water Index for monitoring live fuel moisture", Dennison-2005-UND doi ucd
  • Evaluating satellite and climate data-derived indices as fire risk indicators in savanna ecosystems Verbesselt-2006-ESC doi ucd "Several studies have demonstrated that NDWI is strongly related to the quantity of water per unit area but not related to the quantity of water per unit of dry vegetation weight [7], [10]. Consequently, NDWI is not directly related to FMC..." "NDWI exhibited an improved perforfmance over NDVI when studying the relation of each index with fire activity. " "The logistic regression model with one index (e.g., NDWI) alone therefore should not be used as a reliable predictor of fire behavior." On a seasonal/decadal scale.
  • Evaluating remotely sensed live fuel moisture estimations for fire behavior predictions in Georgia, USA, Dasgupta-2007-ERS doi ucd: Graph of live woody FMC predicted by NDWI (Fig. 3 and 4) then used in FARSITE to adjust spread rates (Fig 5) (Finney-1998-FFA link). Live fuel moisture is a part of fuel description (page 18), assumed in FARSITE to be constant. Live fuel moisture affects the spread rate per BEHAVE (Andrews-1986-BFB link) but it is unclear how exactly.
  • Retrieval of real-time live fuel moisture content using MODIS measurements Hao-2007-RRL doi ucd: direct estimate from MODIS raw data, not through NDWI
  • Combining NDVI, surface temperature with a function of the day of year for the estimation of live fuel moisture content in forest fire danger rating Chuvieco-2004-CNS doi ucd: not constant at all? Especially in grasslands (Fig. 4).
  • Special issue The Application of Remote Sensing to Fire Research in the Eastern United States of Remote Sensing of Environment link ucd

Sep 1: Fires by minimal arrival time

Sep 7: Fire Tornado

Sep 8: Giving Employers What They Don't Really Want

  • "93 percent of the employers surveyed said that a demonstrated capacity to think critically, communicate clearly, and solve complex problems is more important than [a candidate's] undergraduate major."
  • "more than nine in 10 employers surveyed said it was important that job candidates demonstrate ethical judgment and integrity; intercultural skills; and the capacity for continued new learning. More than 75 percent of employers say they want more emphasis on five key areas, including critical thinking, complex problem-solving, written and oral communication, and applied knowledge in real-world settings."
  • "Those are not skills optimally developed through passive learning in lecture settings, including MOOCs. Rather, they are skills developed through active learning in settings that encourage dialogue, give-and-take, real-world problem-solving, and active mentorship."
  • AACU survey
  • Chronicle survey

Sep 8: Translation of British polite phrases

Sep 9: Data assimilation seminar: Evan Kwiatkowski: Asymptotic Convergence of the Local Ensemble Transform Filter

Sep 11: Colibri MPI testing

Sep 17: Joint Mathematics Meetings 2014 abstracts

Sep 21: Corsica overview paper including the Israel wildfire system

Sep 22: Convergence of square root ensemble Kalman filters

Sep 25: DDDAS Workshop at ICCS 2012 in Barcelona

Sep 26: The National Wildfire Prediction System in Israel we helped to build

See also

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