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News Article
Site: www.rdmag.com

When a hail storm moved through Fort Worth, Texas on May 5, 1995, it battered the highly populated area with hail up to 4 inches in diameter and struck a local outdoor festival known as the Fort Worth Mayfest. The Mayfest storm was one of the costliest hailstorms in U.S history, causing more than $2 billion in damage and injuring at least 100 people. Scientists know that storms with a rotating updraft on their southwestern sides -- which are particularly common in the spring on the U.S. southern plains -- are associated with the biggest, most severe tornadoes and also produce a lot of large hail. However, clear ideas on how they form and how to predict these events in advance have proven elusive. A team based at University of Oklahoma (OU) working on the Severe Hail Analysis, Representation and Prediction (SHARP) project works to solve that mystery, with support from the National Science Foundation (NSF). Performing experimental weather forecasts using the Stampede supercomputer at the Texas Advanced Computing Center, researchers have gained a better understanding of the conditions that cause severe hail to form, and are producing predictions with far greater accuracy than those currently used operationally. To predict hail storms, or weather in general, scientists have developed mathematically based physics models of the atmosphere and the complex processes within, and computer codes that represent these physical processes on a grid consisting of millions of points. Numerical models in the form of computer codes are integrated forward in time starting from the observed current conditions to determine how a weather system will evolve and whether a serious storm will form. Because of the wide range of spatial and temporal scales that numerical weather predictions must cover and the fast turnaround required, they are almost always run on powerful supercomputers. The finer the resolution of the grid used to simulate the phenomena, the more accurate the forecast; but the more accurate the forecast, the more computation required. The highest-resolution National Weather Service's official forecasts have grid spacing of one point for every three kilometers. The model the Oklahoma team is using in the SHARP project, on the other hand, uses one grid point for every 500 meters -- six times more resolved in the horizontal directions. "This lets us simulate the storms with a lot higher accuracy," said Nathan Snook, an OU research scientist. "But the trade-off is, to do that, we need a lot of computing power -- more than 100 times that of three-kilometer simulations. Which is why we need Stampede." Stampede is currently one of the most powerful supercomputers in the U.S. for open science research and serves as an important part of NSF's portfolio of advanced cyberinfrastructure resources, enabling cutting-edge computational and data-intensive science and engineering research nationwide. According to Snook, there's a major effort underway to move to a "warning on forecast" paradigm -- that is, to use computer-model-based, short-term forecasts to predict what will happen over the next several hours and use those predictions to warn the public, as opposed to warning only when storms form and are observed. "How do we get the models good enough that we can warn the public based on them?" Snook asks. "That's the ultimate goal of what we want to do -- get to the point where we can make hail forecasts two hours in advance. 'A storm is likely to move into downtown Dallas, now is a good time to act.'" With such a system in place, it might be possible to prevent injuries to vulnerable people, divert or move planes into hangers and protect cars and other property. Looking at past storms to predict future ones To study the problem, the team first reviews the previous season's storms to identify the best cases to study. They then perform numerical experiments to see if their models can predict these storms better than the original forecasts using new, improved techniques. The idea is to ultimately transition the higher-resolution models they are testing into operation in the future. Now in the third year of their hail forecasting project, the researchers are getting promising results. Studying the storms that produced the May 20, 2013 Oklahoma-Moore tornado that led to 24 deaths, destroyed 1,150 homes and resulted in an estimated $2 billion in damage, they developed zero to 90 minute hail forecasts that captured the storm's impact better than the National Weather Service forecasts produced at the time. "The storms in the model move faster than the actual storms," Snook said. "But the model accurately predicted which three storms would produce strong hail and the path they would take." The models required Stampede to solve multiple fluid dynamics equations at millions of grid points and also incorporate the physics of precipitation, turbulence, radiation from the sun and energy changes from the ground. Moreover, the researchers had to simulate the storm multiple times -- as an ensemble -- to estimate and reduce the uncertainty in the data and in the physics of the weather phenomena themselves. "Performing all of these calculations on millions of points, multiple times every second, requires a massive amount of computing resources," Snook said. The team used more than a million computing hours on Stampede for the experiments and additional time on the Darter system at the National Institute for Computational Science for more recent forecasts. The resources were provided through the NSF-supported Extreme Science and Engineering Discovery Environment (XSEDE) program, which acts as a single virtual system that scientists can use to interactively share computing resources, data and expertise. Though the ultimate impacts of the numerical experiments will take some time to realize, its potential motivates Snook and the severe hail prediction team. "This has the potential to change the way people look at severe weather predictions," Snook said. "Five or 10 years down the road, when we have a system that can tell you that there's a severe hail storm coming hours in advance, and to be able to trust that -- it will change how we see severe weather. Instead of running for shelter, you'll know there's a storm coming and can schedule your afternoon." Ming Xue, the leader of the project and director of the Center for Analysis and Prediction of Storms (CAPS) at OU, gave a similar assessment. "Given the promise shown by the research and the ever increasing computing power, numerical prediction of hailstorms and warnings issued based on the model forecasts, with a couple of hours of lead time, may indeed be realized operationally in a not-too-distant future, and the forecasts will also be accompanied by information on how certain the forecasts are." The team published its results in the proceedings of the 20th Conference on Integrated Observing and Assimilation Systems for Atmosphere, Oceans and Land Surface (IOAS-AOLS); they will also be published in an upcoming issue of the American Meteorological Society journal Weather and Forecasting. "Severe hail events can have significant economic and safety impacts," said Nicholas F. Anderson, program officer in NSF's Division of Atmospheric and Geospace Sciences. "The work being done by SHARP project scientists is a step towards improving forecasts and providing better warnings for the public."


News Article
Site: www.scientificcomputing.com

When a hail storm moved through Fort Worth, TX, on May 5, 1995, it battered the highly populated area with hail up to four inches in diameter and struck a local outdoor festival known as the Fort Worth Mayfest. The Mayfest storm was one of the costliest hailstorms in U.S history, causing more than $2 billion in damage and injuring at least 100 people. Scientists know that storms with a rotating updraft on their southwestern sides — which are particularly common in the spring on the U.S. southern plains — are associated with the biggest, most severe tornadoes and also produce a lot of large hail. However, clear ideas on how they form and how to predict these events in advance have proven elusive. A team based at University of Oklahoma (OU) working on the Severe Hail Analysis, Representation and Prediction (SHARP) project works to solve that mystery, with support from the National Science Foundation (NSF). Performing experimental weather forecasts using the Stampede supercomputer at the Texas Advanced Computing Center, researchers have gained a better understanding of the conditions that cause severe hail to form, and are producing predictions with far greater accuracy than those currently used operationally. To predict hail storms, or weather in general, scientists have developed mathematically based physics models of the atmosphere and the complex processes within, and computer codes that represent these physical processes on a grid consisting of millions of points. Numerical models in the form of computer codes are integrated forward in time starting from the observed current conditions to determine how a weather system will evolve and whether a serious storm will form. Because of the wide range of spatial and temporal scales that numerical weather predictions must cover and the fast turnaround required, they are almost always run on powerful supercomputers. The finer the resolution of the grid used to simulate the phenomena, the more accurate the forecast; but the more accurate the forecast, the more computation required. The highest-resolution National Weather Service's official forecasts have grid spacing of one point for every three kilometers. The model the Oklahoma team is using in the SHARP project, on the other hand, uses one grid point for every 500 meters — six times more resolved in the horizontal directions. "This lets us simulate the storms with a lot higher accuracy," says Nathan Snook, an OU research scientist. "But the trade-off is, to do that, we need a lot of computing power — more than 100 times that of three-kilometer simulations. Which is why we need Stampede." Stampede is currently one of the most powerful supercomputers in the U.S. for open science research and serves as an important part of NSF's portfolio of advanced cyberinfrastructure resources, enabling cutting-edge computational and data-intensive science and engineering research nationwide. According to Snook, there's a major effort underway to move to a "warning on forecast" paradigm — that is, to use computer-model-based, short-term forecasts to predict what will happen over the next several hours and use those predictions to warn the public, as opposed to warning only when storms form and are observed. "How do we get the models good enough that we can warn the public based on them?" Snook asks. "That's the ultimate goal of what we want to do — get to the point where we can make hail forecasts two hours in advance. 'A storm is likely to move into downtown Dallas, now is a good time to act.'" With such a system in place, it might be possible to prevent injuries to vulnerable people, divert or move planes into hangers and protect cars and other property. Looking at past storms to predict future ones To study the problem, the team first reviews the previous season's storms to identify the best cases to study. They then perform numerical experiments to see if their models can predict these storms better than the original forecasts using new, improved techniques. The idea is to ultimately transition the higher-resolution models they are testing into operation in the future. Now in the third year of their hail forecasting project, the researchers are getting promising results. Studying the storms that produced the May 20, 2013 Oklahoma–Moore tornado that led to 24 deaths, destroyed 1,150 homes and resulted in an estimated $2 billion in damage, they developed zero to 90-minute hail forecasts that captured the storm's impact better than the National Weather Service forecasts produced at the time. "The storms in the model move faster than the actual storms," Snook says. "But the model accurately predicted which three storms would produce strong hail and the path they would take." The models required Stampede to solve multiple fluid dynamics equations at millions of grid points and also incorporate the physics of precipitation, turbulence, radiation from the sun and energy changes from the ground. Moreover, the researchers had to simulate the storm multiple times — as an ensemble — to estimate and reduce the uncertainty in the data and in the physics of the weather phenomena themselves. "Performing all of these calculations on millions of points, multiple times every second, requires a massive amount of computing resources," Snook says. The team used more than a million computing hours on Stampede for the experiments and additional time on the Darter system at the National Institute for Computational Science for more recent forecasts. The resources were provided through the NSF-supported Extreme Science and Engineering Discovery Environment (XSEDE) program, which acts as a single virtual system that scientists can use to interactively share computing resources, data and expertise. Though the ultimate impacts of the numerical experiments will take some time to realize, its potential motivates Snook and the severe hail prediction team. "This has the potential to change the way people look at severe weather predictions," Snook says. "Five or 10 years down the road, when we have a system that can tell you that there's a severe hail storm coming hours in advance, and to be able to trust that — it will change how we see severe weather. Instead of running for shelter, you'll know there's a storm coming and can schedule your afternoon." Ming Xue, the leader of the project and director of the Center for Analysis and Prediction of Storms (CAPS) at OU, gave a similar assessment. "Given the promise shown by the research and the ever-increasing computing power, numerical prediction of hailstorms and warnings issued based on the model forecasts, with a couple of hours of lead time, may indeed be realized operationally in a not-too-distant future, and the forecasts will also be accompanied by information on how certain the forecasts are." The team published its results in the proceedings of the 20th Conference on Integrated Observing and Assimilation Systems for Atmosphere, Oceans and Land Surface (IOAS-AOLS); they will also be published in an upcoming issue of the American Meteorological Society journal Weather and Forecasting. "Severe hail events can have significant economic and safety impacts," says Nicholas F. Anderson, program officer in NSF's Division of Atmospheric and Geospace Sciences. "The work being done by SHARP project scientists is a step towards improving forecasts and providing better warnings for the public."


Schenkman A.D.,University of Oklahoma | Xue M.,University of Oklahoma | Shapiro A.,University of Oklahoma | Brewster K.,Center for Analysis and Prediction of Storms | Gao J.,National Severe Storms Laboratory
Monthly Weather Review | Year: 2011

The impact of radar and Oklahoma Mesonet data assimilation on the prediction of mesovortices in a tornadic mesoscale convective system (MCS) is examined. The radar data come from the operational Weather Surveillance Radar-1988 Doppler (WSR-88D) and the Engineering Research Center for Collaborative Adaptive Sensing of the Atmosphere's (CASA) IP-1 radar network. The Advanced Regional Prediction System (ARPS) model is employed to perform high-resolution predictions of an MCS and the associated cyclonic line-end vortex that spawned several tornadoes in central Oklahoma on 8-9 May 2007, while the ARPS three-dimensional variational data assimilation (3DVAR) system in combination with a complex cloud analysis package is used for the data analysis. A set of data assimilation and prediction experiments are performed on a 400-m resolution grid nested inside a 2-km grid, to examine the impact of radar data on the prediction of meso-γ-scale vortices (mesovortices). An 80-min assimilation window is used in radar data assimilation experiments. An additional set of experiments examines the impact of assimilating 5-min data from the Oklahoma Mesonet in addition to the radar data. Qualitative comparison with observations shows highly accurate forecasts of mesovortices up to 80 min in advance of their genesis are obtained when the low-level shear in advance of the gust front is effectively analyzed. Accurate analysis of the low-level shear profile relies on assimilating high-resolution low-level wind information. The most accurate analysis (and resulting prediction) is obtained in experiments that assimilate low-level radial velocity data from the CASA radars. Assimilation of 5-min observations from the Oklahoma Mesonet has a substantial positive impact on the analysis and forecast when high-resolution low-level wind observations from CASA are absent; when the low-level CASA wind data are assimilated, the impact of Mesonet data is smaller. Experiments that do not assimilate low-level wind data from CASA radars are unable to accurately resolve the low-level shear profile and gust front structure, precluding accurate prediction of mesovortex development. © 2011 American Meteorological Society.


Johnson A.,University of Oklahoma | Wang X.,University of Oklahoma | Xue M.,University of Oklahoma | Kong F.,Center for Analysis and Prediction of Storms
Monthly Weather Review | Year: 2011

Twenty-member real-time convection-allowing storm-scale ensemble forecasts with perturbations to model physics, dynamics, initial conditions (IC), and lateral boundary conditions (LBC) during the NOAA Hazardous Weather Testbed Spring Experiment provide a unique opportunity to study the relative impact of different sources of perturbation on convection-allowing ensemble diversity. In Part II of this two-part study, systematic similarity/dissimilarity of hourly precipitation forecasts among ensemble members from the spring season of 2009 are identified using hierarchical cluster analysis (HCA) with a fuzzy object-based threat score (OTS), developed in Part I. In addition to precipitation, HCA is also performed on ensemble forecasts using the traditional Euclidean distance for wind speed at 10 m and 850 hPa, and temperature at 500 hPa. At early lead times (3 h, valid at 0300 UTC) precipitation forecasts cluster primarily by data assimilation and model dynamic core, indicating a dominating impact of models, with secondary clustering by microphysics. There is an increasing impact of the planetary boundary layer (PBL) scheme on clustering relative to the microphysics scheme at later lead times. Forecasts of 10-m wind speed cluster primarily by the PBL scheme at early lead times, with an increasing impact of LBC at later lead times. Forecasts of midtropospheric variables cluster primarily by IC at early lead times and LBC at later lead times. The radar and Mesonet data assimilation (DA) show its impact, with members withoutDAin a distinct cluster, through the 12-h lead time (valid at 1200 UTC) for both precipitation and nonprecipitation variables. The implication for optimal ensemble design for storm-scale forecasts is also discussed. © 2011 American Meteorological Society.


Johnson A.,University of Oklahoma | Wang X.,University of Oklahoma | Kong F.,Center for Analysis and Prediction of Storms | Xue M.,University of Oklahoma
Monthly Weather Review | Year: 2011

Convection-allowing ensemble forecasts with perturbations to model physics, dynamics, and initial (IC) and lateral boundary conditions (LBC) generated by the Center for the Analysis and Prediction of Storms for the NOAA Hazardous Weather Testbed (HWT) Spring Experiments provide a unique opportunity to understand the relative impact of different sources of perturbation on convection-allowing ensemble diversity. Such impacts are explored in this two-part study through an object-oriented hierarchical cluster analysis (HCA) technique. In this paper, an object-oriented HCA algorithm, where the dissimilarity of precipitation forecasts is quantified with a nontraditional object-based threat score (OTS), is developed. The advantages of OTS-based HCA relative to HCA using traditional Euclidean distance and neighborhood probability-based Euclidean distance (NED) as dissimilarity measures are illustrated by hourly accumulated precipitation ensemble forecasts during a representative severe weather event. Clusters based on OTS and NED are more consistent with subjective evaluation than clusters based on traditional Euclidean distance because of the sensitivity of Euclidean distance to small spatial displacements. OTS improves the clustering further compared to NED. Only OTS accounts for important features of precipitation areas, such as shape, size, and orientation, and OTS is less sensitive than NED to precise spatial location and precipitation amount. OTS is further improved by using a fuzzy matching method. Application of OTS-based HCA for regional subdomains is also introduced. Part II uses the HCA method developed in this paper to explore systematic clustering of the convection-allowing ensemble during the full 2009 HWT Spring Experiment period. © 2011 American Meteorological Society.

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