pre-printWe introduce several useful utilities in development for the creation and analysis of real wildland fire simulations using WRF and SFIRE. These utilities exist as standalone programs and scripts as well as extensions to other well known software. Python web scrapers automate the process of downloading and preprocessing atmospheric and surface data from common sources. Other scripts simplify the domain setup by creating parameter files automatically. Integration with Google Earth allows users to explore the simulation in a 3D environment along with real surface imagery. Postprocessing scripts provide the user with a number of output data formats compatible with many commonly used visualization suites allowing for the creation of high quality 3D renderings. As a whole, these improvements build toward a unified web application that brings a sophisticated wildland fire modeling environment to scientists and users alike

Beezley, Jonathan D.

Rosen, Paul Andrew

English

The University of Utah: J. Willard Marriott Digital Library

DATA MANAGEMENT AND ANALYSIS WITH WRF AND SFIREJonathan D. Beezley, Mavin Martin†, Paul Rosen†, Jan Mandel, Adam K. Kochanski‡Department of Mathematical and Statistical Sciences, University of CO Denver†Scientiﬁc Computing and Imaging Institute, University of Utah‡Department of Meteorology, University of UtahABSTRACTWe introduce several useful utilities in developmentfor the creation and analysis of real wildland ﬁresimulations using WRF and SFIRE. These utili-ties exist as standalone programs and scripts aswell as extensions to other well known software.Python web scrapers automate the process of down-loading and preprocessing atmospheric and surfacedata from common sources. Other scripts simplifythe domain setup by creating parameter ﬁles au-tomatically. Integration with Google Earth allowsusers to explore the simulation in a 3D environmentalong with real surface imagery. Postprocessingscripts provide the user with a number of outputdata formats compatible with many commonly usedvisualization suites allowing for the creation ofhigh quality 3D renderings. As a whole, these im-provements build toward a uniﬁed web applicationthat brings a sophisticated wildland ﬁre modelingenvironment to scientists and users alike.Index Terms— Data preprocessing, Data anal-ysis, Geophysics computing, Client-server systems1. INTRODUCTIONThe weather research and forecasting model (WRF)is a full-featured mesoscale weather model usedas a research tool and operational forecasting [1].SFIRE is an add-on wildland ﬁre forecasting modelcoupled with WRF [2]. An earlier version is avail-Supported by NSF grant AGS-0835579 and NIST Fire Re-search Grants Program grant 60NANB7D6144.able in WRF release as WRF-Fire.1 Because WRFis widely used in the research community, there area large number of standard tools available for creat-ing and visualizing its simulation outputs. However,SFIRE acts on domains that are signiﬁcantly higherresolution than the typical WRF simulation. In ad-dition, SFIRE uses surface variables on a reﬁnedsubgrid that is different from the standard atmo-spheric surface variables. These subgrid variablesand the higher resolution modeling typically re-quires special processing for input and visualizationthat is not supported by most commonly used tools.For this reason, we have begun to develop newsoftware that mimic and extend standard utilitiesfor data processing and visualization speciﬁcallytailored for use with SFIRE.2. MODEL INITIALIZATION AND DATAPREPROCESSINGAs computers become more powerful, there is in-creasing interest in simulating smaller scale phe-nomena using WRF than is typically associatedwith mesoscale weather forecasting. In particular,a typical SFIRE simulation occurs at mesh resolu-tions on the order of 10 m or less. Even the highestresolution surface data provided with WPS is sev-eral hundred times coarser. For these ﬁne scaledomains, the ability to import custom datasets intoWPS is essential for the initialization of a realis-tic simulation [3]. Resources such as the USGS’s1http://www.openwfm.org/wiki/WRF-Fire_development_notes5274978-1-4673-1159-5/12/$31.00 ©2012 IEEE IGARSS 2012seamless data server that provide open access tohigh quality surface data are generating a large in-terest in initializing WRF simulations using customdatasets. WPS provides a mechanism for thesedatasets through a simple binary ﬁle format that isdescribed in the WRF technical documentation [4];however, there is no standard API or GIS softwarecapable of writing to it. Users who lack sufﬁcienttechnical knowledge are currently unable to processthis data into WRF.Prior work related to WRF-Fire has lead tosmall utilities that are able to convert standard GISGeoTIFF ﬁles into Geogrid’s binary ﬁle format.The GeoTIFF speciﬁcation is commonly used andcan be provided as output from a wide range of GISapplications. TopoGrabber2 is a Python applicationbased on this work that is capable of download-ing and converting topological data automatically.More recently, modiﬁcations to Geogrid have beenwritten allowing it to read GeoTIFF ﬁles directly,and extensions to WPS standard programs havemade it possible to ingest GeoTIFF data directlyfrom the USGS into a WRF simulation [5]. Theimplemented changes to Geogrid allows a user toimport a USGS GeoTIFF dataset directly into theWRF workﬂow without the (often) difﬁcult anderror prone procedure of converting the data. TheGeoTIFF interface is compiled in as an optionalcomponent and is useful for both experienced GISusers with complicated workﬂows and those whojust want to replace a single data source. The im-plementation allows overriding erroneous metadatawithout modiﬁcation to the source images, and efﬁ-cient access to the data.With tools in place to automatically retrieve andintegrate known data sources into a simulation, itis now possible to automate nearly all of the stepsnecessary to initialize a simulation for WRF andSFIRE. A command-line client has been written us-ing Python to generate all required parameter andinput ﬁles for a simulation given user deﬁned do-main and ﬁre ignition speciﬁcations. This script iscurrently being integrated with a web applicationbased on Google Maps, providing users with a fullygraphical environment for designing a simulation on2http://laps.noaa.gov/topograbberreal surface imagery. This client application is cou-pled with a server located on a high performancecompute cluster that runs the simulation on demandand returns results back to the client as they are pro-cessed.3. MODEL EVALUATION AND DATAPOSTPROCESSINGBy default, WRF generates NetCDF binary ﬁlescontaining the raw simulation output. These ﬁlescontain a large number of variables that are usefulin analyzing the simulation output; however, theyare often too large to be transferred to the user’scomputer for visualization. Several utilities havebeen written that allow users to visualize the sim-ulation without downloading the full output. For aquick overview of the ﬁre propagation, it is possibleto generate a KMZ ﬁle containing a raster imageof any surface variable or a georeferenced polygonoutlining the burning region at a given timestep.This KMZ ﬁle can be opened in a number of GISapplications including Google Earth to be overlaidon a rendering of the earth’s surface. Google Earthallows the user to play back the images in the KMZﬁles as an animation with a timestamp indicatingthe simulation time. The web application in devel-opment uses this capability to display the results ofthe running simulation on top of Google Maps.There are a number of options for rendering a3D visualization of the simulation. VAPOR is anapplication developed at NCAR designed for visu-alization of standard WRF atmospheric simulations.The postprocessing utility wrf2vdf takes a stan-dard WRF NetCDF output ﬁle and generates a morecompact vdf ﬁle containing only the ﬁelds the userwishes to investigate. VAPOR features a very fastand easy to use GUI for generation of high qual-ity images; however, it does not support the reﬁnedgrids used by the ﬁre variables. When using VA-PORwith SFIRE, users are limited to low resolutionversions of ﬁre variables, which can result in blockyimages.Other popular 3D visualization applications in-clude Paraview, MayaVI, and VisTrails. These ap-plications all use the Visualization Toolkit (VTK)5275as a backend and do not have any built in capabil-ity for reading WRF output ﬁles. SFIRE containsa Python script wrf2vtk that can generate VTKcompatible ﬁles from WRF outputs at full resolu-tion. This script is much like VAPOR’s wrf2vdfin that it converts the data to a more compact repre-sentation and compresses the ﬁles for transfer to alocal computer.4. TOWARD A FIRE FORECASTING WEBPORTALAutomated scripting of the model setup procedurehas lead to the development of a web portal provid-ing users with a simple interface for running SFIREsimulations using real data. The user registers withthe site with a username and password. Upon log-ging in, he or she is presented with a Google Mapsinterface with instructions to click on a point on themap to create a new ignition. When clicked, a dia-log box appears allowing the user to customize theparameters of the simulation. The user is notiﬁedby email on completion of the simulation when theuser can return to the web portal to view the outputof this and all prior simulations. Currently, the out-put of the simulation displays as an animation theheat ﬂux into the atmosphere from the ﬁre. In thefuture, we plan to add the ability to visualize othersurface variables such as fuel types, winds, and ﬁredanger ratings.The web frontend is built on top of a GoogleMaps interface using its javascript interface. Thecore web application including user and contentmanagement is served by Django. The backendcomponent is executed at a remote site on a com-pute cluster accessible via ssh from the web server.The web application passes a string in the form ofsimple keyword=value pairs specifying parametersof the ﬁre simulation. These parameters includethe ignition location and time chosen by the user,domain size and resolution, and simulation run timeamong others. The web server executes an appli-cation script on the compute server providing thesimulation parameters and a session ID. The com-pute server initializes and executes the simulationwhile a post processing daemon watches for newoutput ﬁles. Whenever a new output ﬁle is detected,a visualization script is executed generating a KMZﬁle, which is then uploaded to the web server to bedisplayed to the user.5. CONCLUSION AND FUTURE WORKThe utilities presented here are designed to enhancethe usability of SFIRE for the average user by elimi-nating the need for complex data conversion or an indepth understanding of the underlying code. Theyare leading to a uniﬁed application where a user candesign a simulation through a series of graphical di-alogs, spawn the simulation on a remote computecluster, and visualize the output of the running sim-ulation locally. The development of such an appli-cation would provide an invaluable tool for researchand education.6. REFERENCES[1] J. Michalakes, J. Dudhia, D. Gill, T. Henderson,J. Klemp, W. Skamarock, and W. Wang, “TheWeather Reseach and Forecast model: Softwarearchitecture and performance,” 2004, Proceed-ings of the 11th ECMWF Workshop on the Useof High Performance Computing In Meteorol-ogy, 25-29 October 2004, Reading U.K. Ed.George Mozdzynski.[2] Jan Mandel, Jonathan D. Beezley, and Adam K.Kochanski, “Coupled atmosphere-wildland ﬁremodeling with WRF 3.3 and SFIRE 2011,”Geoscientiﬁc Model Development, vol. 4, pp.591–610, 2011.[3] Georgi Jordanov, Jonathan D. Beezley, NinaDobrinkova, Adam K. Kochanski, and JanMandel, “Simulation of the 2009 Harmanliﬁre (Bulgaria),” 8th International Conferenceon Large-Scale Scientiﬁc Computations, So-zopol, Bulgaria, June 6-10, 2011, 2011, Lecturenotes in Computer Science, Springer, to appear.Preprint available at arXiv:1001.1588.[4] Wei Wang, Cindy Bruye`re, Michael Duda,Jimy Dudhia, Dave Gill, Hui-Chuan Lin,5276Fig. 1. SFIRE web portal results page. Along with a standard Google Maps interface, the results portalcontains animation controls so the user can view the development of the ﬁre front. Prior simulations arestored on the user’s account page and can be shared with other users.John Michalakes, Syed Rizvi, Xin Zhang,Jonathan D. Beezley, Janice L. Coen, andJan Mandel, “ARW version 3 modelingsystem user’s guide,” Mesoscale & Mis-croscale Meteorology Division, NationalCenter for Atmospheric Research, July2010, http://www.mmm.ucar.edu/wrf/users/docs/user_guide_V3/ARWUsersGuideV3.pdf.[5] Jonathan D. Beezley, A. K. Kochanski, V. Y.Kondratenko, and J. Mandel, “Integratinghigh-resolution static data into WRF for realﬁre simulations,” Paper 6.3, Ninth Sym-posium on Fire and Forest Meteorology,Palm Springs, October 2011, 2011, http://ams.confex.com/ams/9FIRE/webprogram/Paper192276.html,retrieved December 2011.5277

Data management and analysis with WRF and SFIRE

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Data management and analysis with WRF and SFIRE

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