Aiding Severe Weather Forecasting

1. Aiding Severe Weather Forecasting Valliappa.Lakshmanan@noaa.gov National Severe Storms Laboratory Norman OK, USA http://www.wdssii.org/

2. What is WDSS-II? • The Warning Decision Support System – Integration Information (WDSS-II) • An integrated set of loosely coupled tools for: • Severe weather diagnosis • A collection of meteorological algorithms for severe weather analysis, diagnosis and prediction • Hail, tornadoes, wind, lightning, storm tracking • Image processing • Statistical validation • Ground-truth verification • Users chain the tools together to accomplish their tasks. lakshman@ou.edu

3. WDSS-II algorithms • WDSS-II algorithms are essentially data filters • Takes some data as input • Produces new data as output • One can specify the scientific/validation processing in the middle • Without having to worry about data ingest, data formats, notification, etc. • But provide a library of common computations on the typical data used. lakshman@ou.edu

4. WDSS-II in the forecast office • How can WDSS-II help at the forecast office? • New algorithms • Interactive 4D display capabilities • Multi-sensor case studies • Post-event validation • Continuous learning • WDSS-II may not be the “official” solution • But the official solutions should draw on the lessons we have learnt. lakshman@ou.edu

5. Single-radar/Multi-sensor algorithms • Some single-radar (multi-sensor) algorithms in WDSS-II lakshman@ou.edu

6. Multi-radar/multi-sensor algorithms • A typical multi-radar deployment of WDSS-II lakshman@ou.edu

7. WDSS-II in the forecast office • How can WDSS-II help at the forecast office? • New algorithms • Interactive 4D display capabilities • Multi-sensor case studies • Post-event validation • Continuous learning lakshman@ou.edu

8. Display • The WDSS-II display • Provides 4D analysis capabilities • Interactive slicing and dicing • Can display all kinds of products • Configurable and extensible • Not tied to particular sites, product codes or times. • On Linux and Windows • WDSS-II tools exist for • Export to GIS, image and spread-sheet formats lakshman@ou.edu

9. WDSS-II in the forecast office • How can WDSS-II help at the forecast office? • New algorithms • Interactive 4D display capabilities • Multi-sensor case studies • Clustering and storm tracking • Storm-attribute trends • Post-event validation • Continuous learning lakshman@ou.edu

10. Motion Estimation • Uses K-Means clustering and Kalman filters 30 min 30 min Actual dBZ Forecast dBZ lakshman@ou.edu

11. Need for new approach • Traditional centroid tracking • Accurate at small scales, but not at large scales • Inaccurate when storms merge or split • Possible to extract trends from the information • Flow-based tracking • Cross-correlation, Lagrangian methods, etc. • Are accurate at large scales, but not at small scales • Not useful in decision support because trends of storm properties can not be extracted lakshman@ou.edu

12. K-Means clustering • K-Means clustering is a hybrid approach • Cluster the input data to find clusters • Like centroid-based tracking methods • But at different scales. • Track the clusters using flow-based methods (minimization of cost-functions) • Like flow-based methods • Does not involve cluster matching (e.g: Titan) lakshman@ou.edu

13. Example clusters • Two different scales shown • Both scales are tracked lakshman@ou.edu

14. Extrapolation • Smooth the motion estimates • spatially using OBAN techniques (Gaussian kernel) • temporally using a Kalman filter (assuming constant velocity) • Repeat at different scales and choose scale appropriate to extrapolation time period. lakshman@ou.edu

15. WDSS-II in the forecast office • How can WDSS-II help at the forecast office? • New algorithms • Interactive 4D display capabilities • Multi-sensor case studies • Clustering and storm tracking • Storm-attribute trends • Post-event validation • Continuous learning lakshman@ou.edu

16. Trends • The clusters can be used to extract trends of any gridded field. • Configurable to extract minimum, maximum, count, sum, time-delta, etc. of gridded fields within cluster • Even fuzzy combination of multiple fields • Extremely useful for warning decision making! • Statistical properties of storms • Which clusters are convective? • Trends in rain-rates … • Which storms intensified after a warning was issued? • Trends in cloud-top temperatures … lakshman@ou.edu

17. WDSS-II in the forecast office • How can WDSS-II help at the forecast office? • New algorithms • Interactive 4D display capabilities • Multi-sensor case studies • Post-event validation • Continuous learning lakshman@ou.edu

18. Polygon statistics • Using cluster trends is useful for deriving storm properties. • What about extracting statistics around a fixed location? • Validating probabilistic guidance • Maybe at areas of particular interest? • NASA launch sites • Sporting events • WDSS-II has a tool … lakshman@ou.edu

19. Statistics of watch and warning polygons • WDSS-II can provide polygon statistics from any gridded field(s) • And these polygons can change with time • Watch and warning polygons • Improved validation of watches and warnings. • Does it help to say that 90% of the time that a tornado watch is issued, low-level shear greater than X is observed on radar within the watch area? lakshman@ou.edu

20. Post-event • The polygons can be used to examine the decision-making process • Post-event • For case-studies • Easy to run through a whole bunch of data from various sensors • Examine the behavior of various gridded fields. • Compare to reports, radar observations, etc. • Export to GIS/image/spreadsheet formats • Move from anecdotal to statistical lakshman@ou.edu

21. WDSS-II in the forecast office • How can WDSS-II address some of the issues at the forecast office? • New algorithms • Interactive 4D display capabilities • Multi-sensor case studies • Post-event validation • Continuous learning lakshman@ou.edu

22. Continuous Learning • In real-time … • The polygons can be watched in real-time • The statistics updated in real-time • On observations that arrive in near real-time • Why not do the “post-event” analysis during the event? • Continuous feedback on existing watches • Forecasters can mark certain areas and indicate characteristics they are interested in. • And the automated monitoring can tell them if/when those characteristics are met. • More information to emergency managers • Based on polygons being “watched” for certain characteristics. lakshman@ou.edu

23. Current uses of WDSS-II in the NWS • WDSS-II is a leading edge system • Provides capabilities not yet in the “official” National Weather Service systems. • But getting these capabilities in hasn’t been easy • The Storm Prediction Center • defines daily threat areas • launch a WDSS-II domain • automatically configures the data ingest and starts the algorithms. • HPCC project: WDSS-II products into GRIB2, GEMPAK and onto N-AWIPS • Pioneer grant: increase size of WDSS-II domain to near-CONUS scale • NWS forecast offices • WDSS-II products are converted into AWIPS format and piped to AWIPS displays in several NWS forecast offices. • But the AWIPS display is too restrictive. Therefore … • The 4D WDSS-II display can be implemented as a separate app on AWIPS but controlled from within D2D. • Consider WDSS-II concepts for next redesign of AWIPS? • Algorithm development capabilities • 4D visualization • Multi-sensor algorithms • Adaptive algorithms (forecaster-algorithm feedback loop) lakshman@ou.edu

24. Summary • How can WDSS-II help in the forecast office? • New algorithms • Better guidance • Interactive 4D display capabilities • Improved analysis • Multi-sensor case studies • More science in the forecast office • Post-event validation • Better metrics • Continuous learning • Growing warning decision making expertise lakshman@ou.edu