| Sunday, 17 January 2010 |
| 7:30 AM-9:00 AM, Sunday
Short Course Registration |
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| 9:00 AM-6:00 PM, Sunday
Annual Meeting Registration Begins |
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| 12:00 PM-4:00 PM, Sunday
Weatherfest |
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| 3:00 PM-4:00 PM, Sunday, B314
First-Time Attendee Briefing |
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| 5:00 PM-6:00 PM, Sunday, B314
Annual Meeting Review and Fellows Awards |
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| 6:00 PM-7:00 PM, Sunday, Exhibit Hall B2
Fellows Reception |
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| Monday, 18 January 2010 |
| 7:30 AM-5:30 PM, Monday
Registration Open |
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| 9:00 AM-10:30 AM, Monday, Thomas Murphy Ballroom 1 and 2
Presidential Forum |
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| 10:30 AM-11:00 AM, Monday
Coffee Break in Meeting Room Foyer |
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| 12:00 PM-1:30 PM, Monday
Lunch Break |
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| 2:30 PM-4:00 PM, Monday, Exhibit Hall B2
Formal Poster Viewing with Coffee Break |
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| 5:30 PM-7:30 PM, Monday, Exhibit Hall B1
Opening of the Exhibit Hall with Reception |
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| Tuesday, 19 January 2010 |
| 9:45 AM-11:00 AM, Tuesday, Exhibit Hall B2
Formal Poster Viewing with Coffee Break |
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| 11:00 AM-6:00 PM, Tuesday, Exhibit Hall B1
Exhibits Open |
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| 12:00 PM-1:30 PM, Tuesday, B208
Kuettner Symposium Luncheon |
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| 12:00 PM-1:30 PM, Tuesday
Lunch Break (Cash and Carry in Exhibit Hall) |
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| 1:30 PM-3:00 PM, Tuesday, B204
Session 1 Applications of Artificial Intelligence Methods to Problems in Environmental Science: Part I |
Chair: Michael Richman, CIMMS/Univ. of Oklahoma, Norman, OK
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| 1:30 PM | 1.1 | Development of neural network convection parameterizations for climate models using CRM simulations and ARM data
Vladimir M. Krasnopolsky, IMSG at NCEP/NWS/NOAA, Camp Springs, MD; and M. S. Fox-Rabinovitz, P. Rasch, Y. Kogan, and A. Belochitski |
| 2:00 PM | 1.2 | Data assimilation through machine learning methods
Robin C. Gilbert, University of Oklahoma, Norman, OK; and M. Richman, L. M. Leslie, and X. Wang |
| 2:15 PM | 1.3 | Linear and nonlinear postprocessing of ensemble forecasts 
Ranran Wang, University of Washington, Seattle, WA; and C. Marzban |
| 2:30 PM | 1.4 | Improving snowfall accumulation predictions by post-processing ensemble forecasts with an Artificial Neural Network
Tyler C. McCandless, Penn State University, University Park, PA; and S. E. Haupt and G. Young |
| 2:45 PM | 1.5 | Optimization of neural network performances by means of exogenous input variables for the forecast of ozone pollutant in Rome urban area
Armando Pelliccioni, ISPESL, Monteporzio Catone, Italy; and F. Pungi, S. Lucidi, and V. La Torre |
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| 3:00 PM-3:30 PM, Tuesday, Exhibit Hall B1
Coffee Break in Exhibit Hall |
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| 3:30 PM-5:30 PM, Tuesday, B308
Joint Session 1 Applications of Artificial Intelligence Techniques to Air Pollution Problems (Joint between the 16th Conference on Air Pollution Meteorology and the 8th Conference on Artificial Intelligence Applications to Environmental Science) |
Cochairs: Sue Ellen Haupt, Penn State Univ., University Park, PA; Michael J. Brown, LANL, Los Alamos, NM
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| 3:30 PM | J1.1 | Evaluation of sensor placement techniques
Ian H. Griffiths, RiskAware Ltd, Bristol, United Kingdom; and I. Bush |
| 3:45 PM | J1.2 | Comparative investigation of source term estimation algorithms using FUSION field trial 2007 data
Nathan Platt, Institute for Defense Analyses, Alexandria, VA; and D. Deriggi |
| 4:00 PM | J1.3 | Using regression-based source detection algorithm for source location with FFT-07 data
Randolph J. Evans, ENSCO, Inc., Melbourne, FL; and S. E. Masters and M. A. Kienzle |
| 4:15 PM | J1.4 | Source Term Characterization of FFT07 Data using a Genetic Algorithm
Luna M. Rodriguez, Penn State Univ., University Park, PA; and S. E. Haupt, G. Young, A. J. Annunzio, and K. J. Schmehl |
| 4:30 PM | J1.5 | Combined Methods from Entity and Field Frameworks to Determine the Source Characteristics of a Contaminant
Andrew J. Annunzio, Penn State Univ., University Park, PA; and S. E. Haupt, G. Young, and L. M. Rodriguez |
| 4:45 PM | J1.6 | A forensic approach to source location applied to FFT-07 data
Shawn Rottmann, ENSCO, Melbourne, FL; and A. Siegel |
| 5:00 PM | J1.7 | Machine Learning for the Source Detection of Atmospheric Emissions
Guido Cervone, George Mason University, Fairfax, VA; and P. Franzese |
| 5:15 PM | J1.8 | Real-time fusion of sensor data to achieve improved situational awareness
Ian H. Griffiths, RiskAware Ltd, Bristol, United Kingdom; and M. Bull and L. Carrivick |
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| Wednesday, 20 January 2010 |
| 8:30 AM-10:00 AM, Wednesday, B204
Session 2 Applications of Artificial Intelligence Methods to Problems in Environmental Science: Part II |
Chair: Philippe E. Tissot, Texas A&M University-Corpus Christi, Corpus Christi, TX
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| 8:30 AM | 2.1 | Optimization of neural net training using patterns selected by cluster analysis: a case-study of ozone prediction level
Armando Pelliccioni Sr., ISPESL, Monteporzio Catone, Italy; and R. Cotroneo and F. Pungė |
| 8:45 AM | 2.2 | Visualize, analyze and mine satellite imagery using GLIDER software tool
Rahul Ramachandran, Univ. of Alabama, Huntsville, AL; and S. Graves, T. Berendes, M. Maskey, C. Chidambaram, S. A. Christopher, P. Hogan, T. Gaskins, and M. Smith |
| 9:00 AM | 2.3 | Design and validation of a neural network system for forecasting and monitoring fog events in the UAE using SEVIRI-MSG data
Abdulla Bushahab, Emirates Institution for Advanced Science & Technology, Dubai, United Arab Emirates; and H. Ghedira, K. Mubarak, A. Dawood, and H. Al Ahmad |
| 9:15 AM | 2.4 | Prediction of skew surge by a fuzzy decision tree
Samantha J. Royston, University of Bristol / Proudman Oceanographic Laboratory, Liverpool, Merseyside, United Kingdom; and K. Horsburgh and J. Lawry |
| 9:30 AM | 2.5 | Analyzing the effects of low level boundaries on tornadogenesis through spatiotemporal relational data mining
David John Gagne II, University of Oklahoma, Norman, OK; and T. A. Supinie, A. McGovern, J. B. Basara, and R. A. Brown |
| 9:45 AM | 2.6 | Capturing relationships between coherent structures and convectively-induced turbulence using Spatiotemporal Relational Random Forests
Jennifer Abernethy, NCAR/RAL, Boulder, CO; and T. A. Supinie, A. McGovern, and J. K. Williams |
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| 10:00 AM-10:30 AM, Wednesday
Coffee Break in Meeting Room Foyer |
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| 10:30 AM-12:00 PM, Wednesday, B204
Session 3 Third Annual AMS Artificial Intelligence Forecasting Contest: Methods and Results |
Chair: Matthew J. Pocernich, NCAR, Boulder, CO
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| 10:30 AM | 3.1 | The Third Annual Artificial Intelligence Forecasting Competition
Stephen Sullivan, UCAR, Boulder, CO; and M. J. Pocernich and J. Abernethy |
| 10:45 AM | 3.2 | Probabilistic Turbulence Prediction using Random Forests
Zhengzheng Li, The University of Oklahoma, Norman, OK; and T. A. Supinie |
| 11:00 AM | 3.3 | Predicting Turbulence Using a Neural Network
Valliappa Lakshmanan, CIMMS/Univ. of Oklahoma, NOAA/NSSL, Norman, OK |
| 11:15 AM | 3.4 | Turbulence Probability using Principal Component Analysis and Support Vector Machine Approaches
Kimberly L. Elmore, CIMMS/Univ. of Oklahoma and NOAA/NSSL, Norman, OK; and M. Richman |
| 11:30 AM | 3.5 | Statistical Turbulence Prediction
Walter C. Kolczynski Jr., Penn State University, University Park, PA; and S. E. Haupt |
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| 11:00 AM-6:30 PM, Wednesday
Exhibits Open |
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| 12:00 PM-1:30 PM, Wednesday
Lunch Break (Cash and Carry in Exhibit Hall) |
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| 2:30 PM-4:00 PM, Wednesday, Exhibit Hall B2
Formal Poster Viewing with Coffee Break |
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| 2:30 PM-4:00 PM, Wednesday, Exhibit Hall B2
Joint Poster Session Applications of Artificial Intelligence Techniques to Air Pollution Problems (Joint between the 16th Conference on Air Pollution Meteorology and the 8th Conference on Artificial Intelligence Applications to Environmental Science) |
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| 2:30 PM-4:00 PM, Wednesday, Exhibit Hall B2
Poster Session Applications of Artificial Intelligence Methods to Problems in Environmental Science |
| | 453 | Ranking severe weather outbreaks using a multivariate index
Chad M. Shafer, University of Oklahoma, Norman, OK ; and C. A. Doswell III |
| | 454 | Using support vector machines to predict the type and relative severity of severe weather outbreaks
Chad M. Shafer, University of Oklahoma, Norman, OK ; and M. Richman, L. M. Leslie, and C. A. Doswell III |
| | 455 | Using a K-Means clustering and watershed segmentation algorithm to automatically classify convective storm types
Angelyn G. Kolodziej, CIMMS/Univ. of Oklahoma and NOAA/NSSL, Norman, OK; and V. Lakshmanan |
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| 4:00 PM-5:45 PM, Wednesday, B204
Joint Session 11 Probabilistic Forecasting for the Next Generation Air Transportation System (NextGen) (Joint between the 8th Conference on Artificial Intelligence Applications to Environmental Science, the 20th Conference on Probability and Statistics in the Atmospheric Sciences, the 14th Conference on Aviation, Range, and Aerospace Meteorology, and the Presidential Forum) |
Cochairs: John K. Williams, NCAR, Boulder, CO; John J. Murray, NASA Langley Research Center, Hampton, VA; Jun Du, NOAA/NWS/NCEP, Camp Springs, MD
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| 4:00 PM | J11.1 | A proposed framework for estimating and reducing hourly-updated forecast uncertainty products for aviation applications in NextGen
Stan Benjamin, NOAA/ESRL/GSD, Boulder, CO; and G. DiMego |
| 4:15 PM | J11.2 | Advances in the Collaborative Storm Prediction for Aviation (CoSPA)
James O. Pinto, NCAR/RAL, Boulder, CO; and J. K. Williams, M. Steiner, D. Albo, S. Dettling, W. Dupree, D. Morse, H. Iskenderian, T. Xiaofeng, M. Wolfson, C. Reiche, S. Weygandt, S. Benjamin, and C. Alexander |
| 4:30 PM | J11.3 | Probabilistic forecasting of ceiling and visibility at CONUS terminals: Development progress
Paul H. Herzegh, NCAR, Boulder, CO; and B. Lambi, J. Cowie, G. Wiener, R. Bateman, and J. Black |
| 4:45 PM | J11.4 | Using a genetic algorithm to estimate source term parameters of volcanic ash clouds
Kerrie J. Schmehl, Penn State University, State College, PA; and D. Truesdell and S. E. Haupt |
| 5:00 PM | J11.5 | Techniques for providing probabilistic forecasts of turbulence for NextGen
Matthew J. Pocernich, NCAR, Boulder, CO; and R. D. Sharman and J. K. Williams |
| 5:15 PM | J11.6 | Regionalized probabilistic turbulence forecasting using machine learning with in-situ data
Jennifer Abernethy, NCAR/RAL, Boulder, CO; and R. D. Sharman and J. K. Williams |
| 5:30 PM | J11.7 | A probabilistic global turbulence nowcast and forecast system
John K. Williams, NCAR, Boulder, CO; and C. Kessinger, R. D. Sharman, W. F. Feltz, and A. Wimmers |
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| 5:30 PM-6:30 PM, Wednesday, Exhibit Hall B1
Reception in Exhibit Hall (Cash Bar) |
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| 7:00 PM-9:00 PM, Wednesday, Thomas Murphy Ballroom 1-4
AMS Annual Awards Banquet |
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| Thursday, 21 January 2010 |
| 7:30 AM-8:30 AM, Thursday, B208
Washington Symposium Breakfast |
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| 9:45 AM-1:30 PM, Thursday
Exhibits Open |
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| 9:45 AM-11:00 AM, Thursday, Exhibit Hall B2
Formal Poster Viewing with Coffee Break |
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| 11:00 AM-12:15 PM, Thursday, B305
Joint Session 10 Verification of Probabilistic Forecasts (Joint between the 20th Conference on Probability and Statistics in the Atmospheric Sciences, the 14th Conference on Aviation, Range, and Aerospace Meteorology, and the 8th Conference on Artificial Intelligence Applications to Environmental Science) |
Cochairs: Bjarne Hansen, EC, Dorval, QC Canada; Barbara G. Brown, NCAR, Boulder, CO; Valliappa Lakshmanan, CIMMS/Univ. of Oklahoma, NOAA/NSSL, Norman, OK
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| 11:00 AM | J10.1A | A comparison between raw ensemble output, Bayesian model averaging and logistic regression using ECMWF ensemble precipitation reforecasts
Maurice J. Schmeits, KNMI, De Bilt, Netherlands; and C. J. Kok |
|  | J10.1 | The three dimensions of prediction and service
Philip Chadwick, EC, Toronto, ON, Canada |
| 11:15 AM | J10.2 | Determining Optimal Thresholds for Inland Locations of Tropical Cyclone Incremental Wind Speed Probabilities to Support the Provision of Expressions of Uncertainty within Text Forecast Products
Pablo Santos, NOAA/NWS, Miami, FL; and M. DeMaria and D. W. Sharp |
| 11:30 AM | J10.3 | Verification of aviation turbulence detection, nowcast, and forecast products
John K. Williams, NCAR, Boulder, CO; and M. J. Pocernich, R. D. Sharman, and J. Abernethy |
| 11:45 AM | J10.4 | Evaluation of a probabilistic convective nowcast for CoSPA
D. Ahijevych, NCAR, Boulder, CO; and J. Williams, S. Dettling, H. Cai, and M. Steiner |
| 12:00 PM | J10.5 | Choosing a scoring rule for verification of forecast probability distributions: Continuous ranked probability score or ignorance score?
Jonathan R. Moskaitis, Naval Research Laboratory, Monterey, CA |
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| 3:00 PM-3:30 PM, Thursday
Coffee Break in Meeting Room Foyer |
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| 5:00 PM-5:05 PM, Thursday
AMS 90th Annual Meeting Adjourns |
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