View source for DDDAS: Data Dynamic Simulation for Disaster Management

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<b>NSF Award No:</b>[https://www.fastlane.nsf.gov/servlet/showaward?award=0325314 0325314], [https://www.fastlane.nsf.gov/servlet/showaward?award=0324989 0324989], [https://www.fastlane.nsf.gov/servlet/showaward?award=0324988 0324988], [https://www.fastlane.nsf.gov/servlet/showaward?award=0324876 0324876], [https://www.fastlane.nsf.gov/servlet/showaward?award=0324910 0324910]
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[[File:Nuggett_fig01.gif]]
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<p><strong>Project Title:</strong><br />
ITR/NGS: Collaborative Research: DDDAS: Data Dynamic Simulation for Disaster Management</p>
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<p>Investigators:</p>
<p>Jan Mandel, Anatolii Puhalski, Craig Johns, Leopoldo</span> P. Franca, Craig C. Douglas, Janice L. Coen,   Anthony Vodacek, Robert Kremens,   Guan Qin</p>
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<b>Institution:</b><br />
University of Colorado at Denver, University of Kentucky, National Center for Atmospheric Research, Rochester Institute of Technology, Texas A&amp;M University
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<strong>Website:</strong><br />
[http://www-math.cudenver.edu/~jmandel/fires math.ucdenver.edu/~jmandel/fires]</td>
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<b>Description of Graphic Image:</b><br /> Simulated sparse measurements at the   locations marked by triangles are assimilated into a highly nonlinear model of temperature at the front of an advancing fire. The red line is the truth   and the green points are an ensemble of simulations. The standard Ensemble Kalman Filter (EnKF) method   matches the data points well, but it does not approximate the truth away from   data points (b). In several assimilation steps, this would result in a   breakdown of the filter. A new stabilized method provides good match for the whole solution and a stable filtering process (c).
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<p><strong>Project Description and Outcome</strong></p>
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<p><em>Ideas:</em>The goal of this project is to provide a data driven real-time atmosphere-wildfire model with data acquired from weather data streams,   sensors on location, and airborne images. The project is developing new data   driven assimilation methods for highly nonlinear problems. The model consists   of an ensemble of simulation. The data assimilation methods modify the model   from data that arrives while the model is running.</p>
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<p><em>Tools:</em>A data   driven massively parallel software framework was developed to link data   assimilation algorithms, data acquisition, and an ensemble of simulations.</p>
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[[File:Nuggett_fig02.jpg]]</td>
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<p><b>Description of Graphic Image:</b><br />Left to right and top to bottom: The <em style="mso-bidi-font-style:normal">Reference</em> solution represents the truth. Data assimilation by a standard <em>ENKF </em>algorithm results in an unstable   solution because of the nonlinear behavior of wildfire. Stabilization gives   the regularized solution <em>ENKF+reg</em>. Without data assimilation, the solution would   develop as in the <em>Comparison</em>; the data assimilation shifts the model towards the truth. The model state is a   probability distribution, visualized in the two ENKF figures as the  superposition of transparent temperature profiles of ensemble members.</p>
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