Travis Smith NSSL / OU / CIMMS The Hazardous Weather Testbed / Experimental Warning Program.

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Presentation transcript:

Travis Smith NSSL / OU / CIMMS The Hazardous Weather Testbed / Experimental Warning Program

Warn-on-Forecast Kickoff Workshop – Feb 18, 2010 Experimental Warning Program one half of the Hazardous Weather Testbed, focused on short-fused severe weather hazards EWP “Research” (NSSL / WRD / SWAT + friends) – develop severe weather warning applications and techniques to enhance warning decision-making EWP “Operations” – collaborative evaluation of new techniques, applications, observing platforms, and technologies 2 Warn-on-Forecast Kickoff Workshop – Feb 18, 2010

EWP Research 3 Warn-on-Forecast Kickoff Workshop – Feb 18, 2010 We work at the crossroads of nearly everything in warning decision-making research.

Warn-on-Forecast Kickoff Workshop – Feb 18, 2010 Example: Multi-sensor data fields Show physical relationships between data fields from multiple sensors Storm tracks and trends can be generated at any spatial scale, for any data fields Future state predicted through extrapolation shows skill out to about an hour Near-surface reflectivity -20 C (~6.5 km AGL) Total Lightning Density Max Expected Size of Hail 44

Warn-on-Forecast Kickoff Workshop – Feb 18, 2010 EWP Operations Forecaster / researcher collaboration participants All NWS regions International visitors Valuable feedback! Science and Technology showcase Currently 6 weeks annually – could expand 5 Warn-on-Forecast Kickoff Workshop – Feb 18, 2010

EWP Research / Operations and Warn-on-Forecast What are the best approaches for radar data QC for assimilation into models? How well are storm-scale processes depicted by data assimilation and model forecasts? How will WoF information be used/visualized in NWS operations? How will it be conveyed to the many different types of end-users? How do we manage this paradigm shift? (deterministic versus probabilistic warning guidance) 6

Warn-on-Forecast Kickoff Workshop – Feb 18, 2010 Radar QC: current “best practices” and future capabilities Examples, not an exhaustive list: 2D velocity dealiasing (Zhongqi and Wiener) Staggered PRT range/velocity ambiguity reduction (Torres et al.) Reflectivity QC neural network (Lakshamanan) Dual-Pol Clutter Mitigation Decision algorithm (Hubbert et al.) First step: human-QC’d data set of case studies for evaluation. 7 Before After

Warn-on-Forecast Kickoff Workshop – Feb 18, 2010 Collaborative evaluation and feedback Do models accurately depict: current storm structure? range of possible predicted storm evolutions? How to best visualize the data? Help forecasters understand the data 8 10 m/s updraft Ice and liquid water

Warn-on-Forecast Kickoff Workshop – Feb 18, Evaluation Example: SHAVE Phone calls to conduct surveys Student-run, student-led Remote high resolution verification of: Hail Wind damage Flash floods SHAVE VERIFICATION

Warn-on-Forecast Kickoff Workshop – Feb 18, 2010 Integration into NWS operations Integrate: new science new technologies With: new concepts of operations new products and services 10 HWT Collaboration (early and often!) Operational Implementation

Warn-on-Forecast Kickoff Workshop – Feb 18, 2010 The meteorologist is the expert on interpreting the hazard and its uncertainty. The meteorologist cannot anticipate everyone’s exposure and response time How can weather hazard information be made more adaptable to those that do know their own exposure and response time? Sociology of Probabilistic Warning Guidance Little Sioux Camp Concrete Dome Home

Getting “there” from “here” Statistics-based uncertainty / human-assisted & automated extrapolation “Warn on detection” (deterministic) Blended statistics / extrapolation w/ data assimilation NWP “Warn on forecast” Existing storms Newly initiated convection Present Forecast convection (doesn’t yet exist) WSR-88DDual-Pol RadarPhased Array RadarGap-filling radar Future GOES-R

Warn-on-Forecast Kickoff Workshop – Feb 18, 2010 The EWP “ résumé ” Radar interpretation / analysis / visualization Severe weather warning applications Multi-sensor data blending and extrapolation-based nowcasting Radar data QC / Human QC of data Data mining of large data sets Building enhanced verification data sets Good relationships with many operational NWSFOs / regions (MICs, SOOs, WCMs, forecasters) Live where research and operations meet. 13