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Innovative Techniques to Improve Weather Observations

Innovative Techniques to Improve Weather Observations. Sebastián Torres Weather Radar Research. Time series data. The What Weather Radar Signal Processing. Why does this pixel have this color? What does it represent?. Meteorological variables. Weather Radar Signal Processing.

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Innovative Techniques to Improve Weather Observations

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  1. Innovative Techniques to Improve Weather Observations Sebastián TorresWeather Radar Research

  2. Time series data The WhatWeather Radar Signal Processing Why does this pixel have this color?What does it represent? Meteorological variables Weather Radar Signal Processing • Smaller amounts of data • Understandable • Large amounts of data • Unintelligible Separation and classification of echoesMitigation of sampling artifacts NSSL Laboratory Review February 17-19, 2009

  3. The Why, Who, and HowThe Big Picture and The Players • NOAA Strategic Goals • “Increase lead-time and accuracy for weather and water warnings and forecasts” • “Improve predictability of the onset, duration, and impact of hazardous and severe weather and water events” • “Increase development, application, and transition of advanced science and technology to operations and services” • All weather-radar-centric endeavors benefit • Research charters, partners, and customers • NEXRAD Product Improvement • Data Quality • MPAR NSSL Laboratory Review February 17-19, 2009

  4. RelevanceWhy are we really doing this? • Four basic needs to improve weather observations • Effective quality control • Faster updates • Better accuracy • Greater coverage • Improvements at the source • Benefits carry over downstream • Enabled by technology • Feasible real-time implementation Data Acquisition BENEFITS Product Generation Users NSSL Laboratory Review February 17-19, 2009

  5. Effective Quality ControlMotivation • Radar data is messy! • Users and algorithms need clean data Ground Clutter Total US wind power capacity has increased more than 6 times over the past decade Wind Farms Storms Clutter Filter OFF Ground Clutter& Weather Clutter Filter OFF NSSL’s Clean-AP Filter Clutter Filter ON NSSL Laboratory Review February 17-19, 2009

  6. Faster UpdatesMotivation • Faster update times are needed to provide forecasters a greater opportunity to see first signs of potentially severe weather from quickly evolving phenomena 62% Courtesy of Randy Steadham (ROC) NSSL Laboratory Review February 17-19, 2009

  7. Legacy Resolution 250 m 250 m 1 km 250 m 250 m Super-Resolution 1 deg 0.5 deg 0.5 deg Better AccuracyMotivation • Range Oversampling • Super-Resolution LegacyNo Oversampling 1/8 Reflectivity EvolutionaryOversampling & Whitening Tornado outbreak in Oklahoma City, 9 May 2003from Curtis et al (2003) Reflectivity NSSL Laboratory Review February 17-19, 2009

  8. Greater CoverageMotivation AcquisitionParameters Echoes appear in the wrong place! Echoes appear in the right place! Reflectivity Reflectivity Where is the storm? Echoes are obscured! Echoes are recovered! Radar Signal Processing Doppler Velocity Doppler Velocity Surprised by strong winds? NSSL Laboratory Review February 17-19, 2009

  9. Phase CodingMitigation of Range and Velocity Ambiguities • Initial research • Sponsored by NWS’s Radar Operations Center • Collaboration with NCAR • Proof of concept • Supported by KOUN upgrades • Technology transfer • Integrated SZ-2 into signal processing pipeline • Support • Operational issues • Refinements • Evolution • Other phase codes • Other techniques PAST Purple denotes unrecoverable data Doppler Velocity LegacyNo Phase Coding PRESENT Doppler Velocity EvolutionaryPhase Coding FUTURE NSSL Laboratory Review February 17-19, 2009

  10. Phase Coding PerformanceAn Operational Example KTLX radar in Oklahoma City 30 Mar 2007, 0.5 deg elevation Courtesy of Jami Boettcher (WDTB) Notice switch of scanning strategies: from VCP 12 (phase coding OFF)to VCP 212 (phase coding ON) NSSL Laboratory Review February 17-19, 2009

  11. Quality and PerformanceAre we doing things right? • Performance • Technology transfer • Phase Coding, Super Resolution, Staggered PRT, Dual Polarization, etc. • Teaching and advising • Quality • Publications • OAR Outstanding Scientific Paper Award • Technical reports • Theses and dissertations • US Patent • NEXRAD Technical Advisory Committee endorsement • Awards • NOAA’s Bronze Medal Award • User satisfaction NSSL research supports career development OU students are exposed to the latest technology Super-resolution received NOAA’s Bronze Medal award NSSL research is “blessed” by the TAC before becoming operational Upgrades impact the whole NEXRAD Network Range oversampling received a US Patent NSSL Technical Reports are key pieces of the technology transfer process Ivic et al., JTECH 2003 Our research is driven by users’ needs (Weather Bureau Forecast Office ,1926) NSSL Laboratory Review February 17-19, 2009

  12. Present and Future TrendsOur strategy for success • The path ahead • Technology transfer (NEXRAD) • Evolutionary techniques (NWRT) • Future radar technologies (MPAR) • Synergistic connections • NEXRAD Data Quality team • Collaboration with the • Challenges • Hiring and retaining EE’s NSSL Laboratory Review February 17-19, 2009

  13. Conclusions • Developing techniques to improve weather observations • Improvements at the source • Driven by four basic needs • Benefits carry over to all radar-centric applications • Demonstrated successful technology transfer • Synergistic collaborations • User satisfaction • Performing cutting-edge research • Evolutionary techniques • Future technologies Questions? NSSL Laboratory Review February 17-19, 2009

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