An Interactive Data-Driven HPC System for Forecasting Weather, Wildland Fire, and Smoke

We present an interactive HPC framework for coupled fire and weather simulations. The system is suitable for urgent simulations and forecast of wildfire propagation and smoke. It does not require expert knowledge to set up and run the forecasts. The core of the system is a coupled weather, wildland fire, fuel moisture, and smoke model, running in an interactive workflow and data management system. The system automates job setup, data acquisition, preprocessing, and simulation on an HPC cluster. It provides animated visualization of the results on a dedicated mapping portal in the cloud as well as delivery as GIS files and Google Earth KML files. The system also serves as an extensible framework for further research, including data assimilation and applications of machine learning to initialize the simulations from satellite data.

[1]  Jan Mandel,et al.  Do we need weather prediction models to account for local weather modifications by wildland fires , 2018 .

[2]  J. Mandel,et al.  Data assimilation of satellite fire detection in coupled atmosphere-fire simulation by wrf-sfire , 2014, 1410.6948.

[3]  Tomàs Margalef,et al.  Dynamic Data-Driven Genetic Algorithm for forest fire spread prediction , 2012, J. Comput. Sci..

[4]  Jonathan D. Beezley,et al.  New features in WRF-SFIRE and the wildfire forecasting and danger system in Israel , 2014 .

[5]  Minjeong Kim,et al.  Data assimilation for wildland fires , 2007, IEEE Control Systems.

[6]  Jonathan D. Beezley,et al.  Recent advances and applications of WRF–SFIRE , 2014 .

[7]  G. Powers,et al.  A Description of the Advanced Research WRF Version 3 , 2008 .

[8]  S. Parks Mapping day-of-burning with coarse-resolution satellite fire-detection data , 2014 .

[9]  Jonathan D. Beezley,et al.  Toward an integrated system for fire, smoke and air quality simulations , 2014, 1405.4058.

[10]  Xiaolin Hu,et al.  Analysis and Quantification of Data Assimilation based on Sequential Monte Carlo Methods for wildfire spread simulation , 2010, Int. J. Model. Simul. Sci. Comput..

[11]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[12]  Jonathan D. Beezley,et al.  ENSEMBLE KALMAN FILTERS IN COUPLED ATMOSPHERE-SURFACE MODELS , 2009 .

[13]  Branko Kosovic,et al.  A High Resolution Coupled Fire–Atmosphere Forecasting System to Minimize the Impacts of Wildland Fires: Applications to the Chimney Tops II Wildland Event , 2018 .

[14]  Chengquan Huang,et al.  Validation of GOES-16 ABI and MSG SEVIRI active fire products , 2019, Int. J. Appl. Earth Obs. Geoinformation.

[15]  John Michalakes,et al.  WRF-Fire: Coupled Weather–Wildland Fire Modeling with the Weather Research and Forecasting Model , 2013 .

[16]  Santiago Monedero,et al.  Assessing and reinitializing wildland fire simulations through satellite active fire data. , 2019, Journal of environmental management.

[17]  Jan Mandel,et al.  Assimilation of Satellite Active Fires Detection Into a Coupled Weather-Fire Model , 2016 .

[18]  G. Grell,et al.  A North American Hourly Assimilation and Model Forecast Cycle: The Rapid Refresh , 2016 .

[19]  Janice L. Coen,et al.  A Coupled AtmosphereFire Model: Convective Feedback on Fire-Line Dynamics , 1996 .

[20]  Arnaud Trouvé,et al.  Towards predictive simulation of wildfire spread at regional scale using ensemble-based data assimilation to correct the fire front position , 2014 .

[21]  C. Justice,et al.  The collection 6 MODIS active fire detection algorithm and fire products , 2016, Remote sensing of environment.

[22]  George Eastman House,et al.  Sparse Bayesian Learning and the Relevan e Ve tor Ma hine , 2001 .

[23]  Daniel Crawl,et al.  Data Assimilation of Wildfires with Fuel Adjustment Factors in farsite using Ensemble Kalman Filtering* , 2017, ICCS.

[24]  W. Schroeder,et al.  Use of spatially refined satellite remote sensing fire detection data to initialize and evaluate coupled weather‐wildfire growth model simulations , 2013 .

[25]  Jonathan D. Beezley,et al.  Coupled atmosphere-wildland fire modeling with WRF-Fire version 3.3 , 2011 .

[26]  Uang,et al.  The NCEP Climate Forecast System Reanalysis , 2010 .

[27]  Visible Infrared Imaging Radiometer Suite ( VIIRS ) 375 m Active Fire Detection and Characterization Algorithm Theoretical Basis Document , 2022 .

[28]  Jan Mandel,et al.  Data Likelihood of Active Fires Satellite Detection and Applications to Ignition Estimation and Data Assimilation , 2018, ArXiv.

[29]  Carlos Brun,et al.  A High Performance Computing Framework for Continental-Scale Forest Fire Spread Prediction , 2017, ICCS.

[30]  W. Schroeder,et al.  The New VIIRS 375 m active fire detection data product: Algorithm description and initial assessment , 2014 .

[31]  David D. Parrish,et al.  NORTH AMERICAN REGIONAL REANALYSIS , 2006 .

[32]  Martin Vejmelka,et al.  Data assimilation of dead fuel moisture observations from remote automated weather stations , 2014 .

[33]  J. Randerson,et al.  Mapping the Daily Progression of Large Wildland Fires Using MODIS Active Fire Data , 2014 .

[34]  H. Anderson Aids to Determining Fuel Models for Estimating Fire Behavior , 1982 .

[35]  D. Muñoz‐Esparza,et al.  An Accurate Fire‐Spread Algorithm in the Weather Research and Forecasting Model Using the Level‐Set Method , 2018 .

[36]  Kayo Ide,et al.  Towards Data-Driven Operational Wildfire Spread Modeling: A Report of the NSF-Funded WIFIRE Workshop , 2015 .

[37]  W. Hao,et al.  A VIIRS direct broadcast algorithm for rapid response mapping of wildfire burned area in the western United States , 2018, Remote Sensing of Environment.

[38]  Paul Rosen,et al.  Data management and analysis with WRF and SFIRE , 2012, 2012 IEEE International Geoscience and Remote Sensing Symposium.

[39]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[40]  Stanley G. Benjamin,et al.  The Real-Time Mesoscale Analysis at NOAA’s National Centers for Environmental Prediction: Current Status and Development , 2011 .

[41]  Andrew M. Stuart,et al.  Inverse problems: A Bayesian perspective , 2010, Acta Numerica.