DDDAS: Data Dynamic Simulation for Disaster Management - Revision history

Erikj at 19:26, 8 March 2010

2010-03-08T19:26:02Z

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Erikj at 19:25, 8 March 2010

2010-03-08T19:25:32Z

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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.~~<strong><o:p></o:p></strong>~~</p>		<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:~~<o:p></o:p>~~</em>A data driven massively parallel software framework was developed to link data assimilation algorithms, data acquisition, and an ensemble of simulations.</p>		<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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	~~<p><strong><!--~~[~~if gte vml 1]><v~~:~~shape id="_x0000_i1026" type="#_x0000_t75" style='width:6in;height:324.6pt'> <v:imagedata src="/wf-nuggets-05_files/image003.jpg" mce_src="/wf-nuggets-05_files/image003~~.jpg~~" o:title="fire2d_00033" /> </v:shape><![endif]--><!--[if !vml~~]~~--><img alt="Figure 2" src="images/documents/figures/nuggett_fig02.jpg" v:shapes="_x0000_i1026" width="576" height="433" /><!--[endif~~]~~--></strong></p>~~		[[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.~~<o:p></o:p>~~</p>		<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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Erikj at 19:19, 8 March 2010

2010-03-08T19:19:24Z

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	~~<p><img alt="Figure 1" src="images/documents/figures/nuggett_fig01~~.gif~~" width="329" height="290" /></p>~~		[[File:Nuggett_fig01.gif]]
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Erikj at 19:13, 8 March 2010

2010-03-08T19:13:17Z

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	~~<p>~~<b>Institution:</b></p>		<b>Institution:</b><br />
	~~<p>~~University of Colorado at Denver, University of Kentucky, National Center for Atmospheric Research, Rochester Institute of Technology, Texas A&M University~~</p>~~		University of Colorado at Denver, University of Kentucky, National Center for Atmospheric Research, Rochester Institute of Technology, Texas A&M University
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	~~<p>~~<strong>Website:</strong></p>		<strong>Website:</strong><br />
	~~<p>~~[http://www-math.cudenver.edu/~jmandel/fires math.ucdenver.edu/~jmandel/fires]~~</p>~~		[http://www-math.cudenver.edu/~jmandel/fires math.ucdenver.edu/~jmandel/fires]</td>
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	<p>Description of Graphic Image:<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).~~</p>~~		<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><em>Ideas:~~<o:p></o:p>~~</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.<strong><o:p></o:p></strong></p>		<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.<strong><o:p></o:p></strong></p>
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	<p><~~strong~~>Description of Graphic Image:<br /> </~~strong~~>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.<o:p></o:p></p>		<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.<o:p></o:p></p>
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Erikj at 19:09, 8 March 2010

2010-03-08T19:09:47Z

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	<p><strong>Project Title:~~<o:p></o:p>~~</strong></p>		<p><strong>Project Title:</strong><br />
	~~<p>~~ITR/NGS: Collaborative Research: DDDAS: Data Dynamic Simulation for Disaster Management~~<o:p></o:p>~~</p>		ITR/NGS: Collaborative Research: DDDAS: Data Dynamic Simulation for Disaster Management</p>
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	<p>Investigators:</p>		<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~~<strong><o:p></o:p></strong>~~</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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	<p>Institution:</p>		<p><b>Institution:</b></p>
	<p~~><b~~>University of Colorado at Denver, University of Kentucky, National Center for Atmospheric Research, Rochester Institute of Technology, Texas A&M University~~<o:p></o:p>~~</p>		<p>University of Colorado at Denver, University of Kentucky, National Center for Atmospheric Research, Rochester Institute of Technology, Texas A&M University</p>
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	<p><strong>Website:~~</span><o:p></o:p>~~</strong></p>		<p><strong>Website:</strong></p>
	<p>~~<a href="~~http://www-math.cudenver.edu/~jmandel/fires">math.ucdenver.edu/~jmandel/fires~~</a><o:p></o:p>~~</p>		<p>[http://www-math.cudenver.edu/~jmandel/fires math.ucdenver.edu/~jmandel/fires]</p>
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	<p>Description of Graphic Image:<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).</p>		<p>Description of Graphic Image:<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).</p>
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