- Quality Control of Canadian Radar Reflectivity Valliappa Lakshmanan1,2
- A Technique to Censor Biological Echoes in Radar Reflectivity Data
- IEEE GEOSC. AND REMOTE SENSING LETTERS, VOL. 1, PP. 192-195,2004 1 A Separable Filter for Directional Smoothing
- Quality Control of Weather Radar Data Using Texture Features and a Neural Network
- Model Verification Using Gaussian Mixture Models A Parametric, Feature-Based Method
- Quality Control of WSR-88D Reflectivity Data V Lakshmanan, Kurt Hondl, Gregory Stumpf, Travis Smith
- A Spatiotemporal Approach to Tornado Prediction V Lakshmanan
- High-resolution Radar Data and Products over the Continental United States
- VA LLIA P PA LA K S HM A NA N NATIONA L S E V E R E STOR M S LA B OR ATORY / U NIV E RS ITY
- Multiscale Storm Identification and Forecast V Lakshmanan
- Segmentation of Infrared Satellite Images V. Lakshmanan1,2
- VA LLIA P PA LA K S HM A NA N NATIONA L W EATHE R CE NTE R R E U P ROG R A M
- P2.2 Segmenting Radar Reflectivity Data using Texture V. Lakshmanan1,2
- 14.9: Real-time Merging of Multisource Data V Lakshmanan
- A Short Write-up On Wavelets V Lakshmanan
- A Real-Time, Three Dimensional, Rapidly Updating, Heterogeneous Radar Merger Technique
- A Simple Data-Driven Model for Streamflow Valliappa Lakshmanan1,2
- A Four-Dimensional Multiple-Source Weather Information System for Algorithms and
- Spatial Verification Using A True Metric Meijun Zhu1
- Tuning the Auto-Nowcaster Automatically Valliappa Lakshmanan1,21
- Generated using version 3.0 of the official AMS LATEX template A Statistical Approach to Mitigating Persistent Clutter
- An automated technique to categorize storm type Angelyn G. Kolodziej
- IEEE Geoscience and Remote Sensing Letters 1 Abstract--Weather radar products from the United States
- Generated using version 3.0 of the official AMS LATEX template A Gaussian Mixture Model Approach to Forecast
- An Automated Technique to Quality Control Radar Reflectivity Data
- Multiscale Storm Identification and Forecast V Lakshmanan1
- Censoring Biological Echoes in Weather Radar Images Valliappa Lakshmanan
- September 26, 2006 13:8 WSPC -Proceedings Trim Size: 11in x 8.5in icapr07 A technique for creating probabilistic spatio-temporal forecasts
- Nested Partitions Using Texture Segmentation V. Lakshmanan
- TEXTURE-BASED SEGMENTATION OF SATELLITE WEATHER IMAGERY V. Lakshmanan1,2
- VA LLIA P PA LA K S HM A NA N NATIONA L S E V E R E STOR M S LA B OR ATORY / U NIV E RS ITY
- Verification Verification
- VA L L I A P PA L A K S H M A N A N N AT I O N A L S E V E R E STO R M S L A B O R ATO RY / U N I V E RS I T Y
- The Simpler the Better Valliappa Lakshmanan1,2
- Introduction The Simpler the Better
- Introduction Conclusions
- Automated Real-time Extraction of Storm Properties from Gridded Fields
- The use of Lightning Mapping Array data in WDSS-II V Lakshmanan1,2
- Real-time Quality Control of Reflectivity Data Using Satellite Infrared Channel and Surface Observations
- Virtual Radar Volumes: Creation, Algorithm Access and Visualization
- Valliappa Lakshmanan National Severe Storms Laboratory &
- UNIVERSITY OF OKLAHOMA GRADUATE COLLEGE
- Support Vector Machines for Spatiotemporal Tornado INDRA ADRIANTO1
- Doppler weather radar based nowcasting of cyclone Ogni Soma Sen Roy1,
- 7.1: Quality Control of WSR-88D Data V Lakshmanan1
- Real-time, rapidly updating severe weather products for virtual globes Travis M. Smitha
- Evaluating a Storm Tracking Algorithm Objectively and without human truthing
- Introduction An Algorithm to Nowcast Lightning
- Reaching Scientific Consensus Through A Competition Valliappa Lakshmanan1,2
- Generated using version 3.0 of the official AMS LATEX template Visualizing Model Data Using A Fast Approximation of
- The 2008 Artificial Intelligence Competition Valliappa Lakshmanan
- 4.5: WDSS-II: An Extensible, Multi-source Meteorological Algorithm Development Interface
- An Algorithm to Nowcast Lightning Initiation and Cessation in Real-time
- The Warning Decision Support System Integrated Information
- 2.3: Creating spatio-temporal tornado probability forecasts using fuzzy logic and motion variability
- P2.1 Lossless Coding and Compression of Radar Reflectivity Data V Lakshmanan
- IDENTIFYING AND TRACKING STORMS IN SATELLITE V. Lakshmanan1,2
- 7.4: A Polar-Coordinate Real-Time Three-Dimensional Rapidly Updating Merger
- Overview of Radar Data Compression Valliappa Lakshmanan
- 15.14: Motion Estimator Based On Hierarchical Clusters V Lakshmanan
- An Objective Method of Evaluating and Devising Storm Tracking Algorithms
- An Efficient, General-Purpose Technique to Identify Storm Cells in Geospatial Images
- Predicting Turbulence Using Partial Least Squares Regression and an Artificial Neural Network
- Predicting Turbulence using Partial Least Squares Regression and an Artificial Neural Network
- Nowcasting of Thunderstorms from GOES Infrared and Visible V Lakshmanan
- J5.2 A Neural Network for Detecting and Diagnosing Tornadic Circulations using the Mesocyclone Detection and Near Storm
- Generated using version 3.1.2 of the official AMS LATEX template Image Processing of Weather Radar Reflectivity Data:1
- DETECTING CONVECTIVE INITIATION FROM RADAR
- Generated using version 3.1.1 of the official AMS LATEX template Range-Correcting Azimuthal Shear in Doppler Radar Data1
- Valliappa Lakshmanan1,2, Robert Rabin2, Jason Otkin3,John S. Kain2, 1University of Oklahoma & 2National Severe Storms Laboratory, Norman OK, USA
- Introduction Automated Way to Tune AutoNowCaster
- Valliappa Lakshmanan Automating the Analysis of
- Introduction Approximating
