Jan Mandel/Blog/2010 Dec 2011 Jan
From CCM
To the current blog page and the archive
These are mostly notes from our regular meetings. See also
- WRF-Fire commit graph (scroll right to see the dates and authors)
- list of all WRF-Fire pages on openwfm.org
- WRF-Fire development notes
December 6, 2010: Data Assimilation Seminar
Exascale computing
IMA Workshop last week Numerical Solutions of Partial Differential Equations: Fast Solution Techniques in IMA Thematic Year on Simulating Our Complex World: Modeling, Computation and Analysis, video:
- Ulrich Rüde Towards Exascale Computing: Multilevel Methods and Flow Solvers for Millions of Cores
- Xiao-Chuan Developing fast and scalable implicit methods for shallow water equations on cubed-sphere
- Roundtable/discussion: David Keys: Pax MPI is over
Further inks:
- DoE exascale solicitation FAQ with links to presentations
- Peter Strazdins, Exaflop Computing : Visions and Challenges: A thousand times better, not just for the High End
- (Exascale) software at NSF
- DARPA Exascale Software Study
- DOE ASCR Research Computer Science
- The Biggest Need: A New Model of Computation
Cloud computing
- Common Challenges in Manycore, Exascale, and Cloud Computing
- Cloud Computing -- Relevance to Enterprise
- Cloud Computing and Grid Computing 360-Degree Compared
- http://gpuscience.com/featured/word-count-with-pycuda-and-mapreduce-on-a-gpu/ original post with code
- Project Snowflock
- MapReduce tutorial
December 15-16: Wildfires session at AGU Fall meeting
Our presentations
- A. Kochanski, M. Jenkins, S. K. Krueger, J. Mandel, J. D. Beezley, C. B. Clements, Evaluation of The Fire Plume Dynamics Simulated by WRF-Fire pdf key, AGU Fall Meeting, 2010
- M. Jenkins, A. Kochanski, S. K. Krueger, W. Mell, R. McDermott, The Fluid Dynamical Forces Involved in Grass Fire Propagation, AGU Fall Meeting, 2010
- J. D. Beezley, A. Kochanski, V. Y. Kondratenko, J. Mandel, B. Sousedik, Simulation of the Meadow Creek fire using WRF-Fire, AGU Fall Meeting, 2010 (poster)
- Jan Mandel, Jonathan D. Beezley, Adam K. Kochanski, Volodymyr Y. Kondratenko, Bedrich Sousedik, Erik Anderson, and Joel Daniels, Wildland fire simulation by WRF-Fire, AGU Fall Meeting, 2010 (poster)
Cell simulations of long-term forest renewal and interaction with wildfires
- NH34A-01 A Conceptual Framework for Fire Ecology in a Changing Climate: Z. Gedalof
- NH34A-03 Stand-replacing patches within a ‘mixed severity’ fire regime: quantitative characterization using recent fires in a long- established natural fire area: B. Collins, S. Stephens
- NH33B-02 A forest-fire model with natural fire resistance Yoder, M R, Turcotte, D L, Rundle, J B, Glasscoe, M T, Donnellan, A
Combustion model and WFDS
- NH34A-05 Toward a detailed physical modelling of wildfires: physical considerations and numerical results. D. Morvan. See slides, also earlier paper from Eighth Symposium on Fire and Forest Meteorology, 2009 and earlier slides, and the European Fire Paradox project.
December 18: Facebook servers
December 22: Wavelets
- How to use Wavelab: Just trying to make sense of wavelets in the simplest case...
December 23: WRF-Fire
- Looking who cites our IEEE paper, I found some new ones:
- Jose Marcio Luna-Castaneda, Distributed, adaptive deployment for nonholonomic mobile sensor networks: theory and experiments, MS Thesis, University of New Mexico
- Hatef Monajemi, Data Assimilation for Shallow Water Waves: Application to Flood Forecasting, MS Thesis, Carleton University, 2009
- Jimy Dudhia, The Weather Research and Forecasting Model: 2010 Annual Update, 2010 WRF Users Workshop, 2010. Presentation: WRF Version 3.2: New Features and Updates
- Nina Dobrinkova and Georgi Jordanov, WRF-Fire wildfire modeling in the test area of Harmanli, Bulgaria. VI International Conference on Forest Fire Research, D. X. Viegas (Ed.), 2010
- Testing Volodymyr's ported code for improved fuel quadrature. Subroutine fuel_left_cell_3, my Matlab original is in standalone/matlab/fuel_burnt.m
- Getting started on the fire AMS paper: submitted abstract, files
December 25: Spectral EnKF
- Getting started on the AMS EnKF paper: submitted abstract, files
- Start from existing Osimorph papers and Matlab code for fire and epidemics, swap FFT for Wavelab
- Wavelet references and tutorials: Daubechies-1993-OBC, Cohen-1993-OBC, Donoho-1994-WTV, Graps-1995-IW, Buckheit-1995-WRR, Fournier-2000-IOW
- Spectral covariance references: Pannekoucke-2007-FPW, Berre-2000-ESM (diagonal spectral, used in AROME DA, see Yann's talk) , Deckmyn-2005-WA, Aimé's talk
- Multivariate and crosscovariances: Levy-2010-PDA, Berre-2000-ESM
December 30: EU wildfire projects
I have compiled a list of links to various EU projects sites. So far, I could not find any publicly accessible data, software, or manuals. Some have password-protected download links, others have only an email contact, which I did not pursue.
Fire Paradox
- Multinational EU project FP6-018505, 2007-2010
- Overview presentation local copy
- Fire behavior software from Fire Intuition
- Tiger 2D Fire Propagation Simulator, tracer based brochure local copy
- Vesta Large-Scale Fire Simulator, cell based
EU Fire Lab
MesoNH/ForeFire
Mesoscale atmosphere model Meso-nh, coupled with a tracer-based fire model.
- http://forefire.univ-corse.fr/
- http://mesonh.aero.obs-mip.fr/mesonh/
- Filippi et al., JAMES 2009 copy
- Filippi et al. Eighth Symposium on Fire and Forest Meteorology
- video
January 24: Data assimilation seminar: Dry run of the AMS presentations
January 25: AMS annual meeting
Our papers
- Jan Mandel, Jonathan D. Beezley, and Adam K. Kochanski, An overview of the coupled atmosphere-wildland fire model WRF-Fire, AMS 91st Annual Meeting, Seattle, WA, 23-27 January 2011, paper J7.1 abstract paper pdf paper files presentation pdf presentation files
- Jan Mandel, Jonathan D. Beezley, and Loren Cobb, Spectral and morphing ensemble Kalman filters, AMS 91st Annual Meeting, Seattle, WA, 23-27 January 2011, paper J12.7. abstract paper pdf paper sources presentation pdf presentation files
Computational intelligence methods and their applications to environmental science
- A. J. Cannon, A fexible nonlinear modeling framework for nonstationary generalized extreme value analysis in hydroclimatology, Hydrological Processes 24, 674-685, 2010.
- Generalized extreme value distribution for a long series of extreme values
- Caren Marzban, Sensitivity analysis in linear and nonlinear models: a review abstract slides
- ref. statistical experimental design
- Guido Cervone, Addressing wind direction uncertainty in source estimation through dynamic time warping
- Warping is a path through matrix... should be continuous and monotonous.. similar to morphing. The warping function is computed using dynamical programming... from discussion seems similar to tree search, but is polynomial time... really? even for nonconvex optimization?
Assimilation of observations into models: Advanced methods
- 3:30 PM J12.1 TOWARD THE ASSIMILATION OF IMAGES, Francois-Xavier Le Dimet, INRIA, Grenoble, France
- Satellite images now used qualitatively not quantitative way, very large data set cannot be directly use operationally, information not data, atmosphere "integral" of radiative properties of the atmosphere, information is borne by discontinuities; experiment: rotating tank 14m "Coriolis rotating platform" 1. pseudobservation - estimate velocities from images use as regular observation; 2 direct assimilation of images; temporal coherency of a sequence of images by law of conservation of brightness (seems much like what is done for radar images), solving conservation law by variational minimization plus Tichonov regularization; multiscale field optimization; image approach: add equation to make physical sense; Curvelet transform; extract structure by applying threshold to gradient of concentration.
- 3:45 PM J12.2 On the Gaussian approach to adaptive covariance inflation, Takemasa Miyoshi, University of Maryland, College Park, MD
- multiplicative inflation, additive, Anderson's Bayesian approach (2007, 2009), estimates for the inflation from sampling error estimate; localization - at each gridpoint separately; adaptive inflation accounts for model errors and limited ensemble size, 100%+ occasionally OK in limited region. Fixed inflation: spread too small over the denssely observed areas http://code.google.com/p/miyoshi
- 4:00 PM J12.3, Localization and correlation in ensemble Kalman filters, Jeffrey Anderson, NCAR, Boulder, CO
- impact of a single observation, regress increments onto each component of the state; localization is to find the multiplication of each regression coefficient; here, as a methodology to correct sampling error; need additional assumption on the prior; natural assumption about correlation of state and observation; minimize the multiplicative coefficient to minimize the RMSE; gives localization as a function of ensemble size N and sample correlation. Compares RMSE. "Spread is closer to the RMS error but still defficient". Ensembles size 20.
- 4:15 PM J12.4 A kernel-density based ensemble filter applicable to high-dimensional systems, Thomas M. Hamill, NOAA / ESRL, Boulder, CO; and J. S. Whitaker
- ref Anderson 2010 MWR pdf when prior is obviously nongaussian; cycled enkf can create nongaussian states all collapse except one; also for large ensembles Lawson and Hansen MWF 2003; from Anderson 2003: split update in 2 steps, compute increment then regress into state; "observation prior update"; more expensive, only when nongaussian by Kolmogorov-Smirnov test; nongaussianity detected only rarely; overall perturbation is superposition of old decaying structures and new ones, randomizes perturbation structures
- 4:30 PM J12.5 What should an “outer loop” for ensemble data assimilation look like? Craig H. Bishop, NRL, Monterey, CA; and D. Hodyss
- theoretically justifiable ensemble outer look approximates true nongaussian but less particles; fixed-truth error distribution; likelihood density; distribution of forecasts given guesses of truth rather than distribution of truth; would need K*64 members; jla: "outer loop" is a concept related 4DVAR
- 4:45 PM J12.6 Bias correction using analysis increments within an ensemble Kalman filter data assimilation, Ji-Sun Kang, University of Maryland, College Park, MD; and E. Kalnay and T. Miyoshi
- first perform analysis without bias correction, average forecast-analysis for every month - we have the estimated bias as a monthly mean, now perform another analysis with the bias correction; same with bias correction forecast-truth (or was it analysis-truth?) works better
- 5:00 PM J12.7 Spectral and morphing ensemble Kalman filters, Jan Mandel, University of Colorado, Denver, CO; and J. D. Beezley and L. Cobb
Predictability
January 30: WRF-Fire paper
Finishing the WRF-Fire model paper for GMD
January 31: Data assimilation seminar
- Jon's wavelet EnKF within matlab osimorph classes: epidemic, rain, hurricane, fire applications
- Abstract for JSM 2011: Spectral EnKF for epidemic
- Software structure: model and randomization in R (now in Ashok's Dropbox), data assimilation in Matlab (in the osimorph git repository)
- Hurricanes application and Fuqing Zhang