1. Aiding Severe Weather Forecasting
National Severe Storms Laboratory
Norman OK, USA

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.
21 February 2019

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.
21 February 2019

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.
21 February 2019

5. Single-radar/Multi-sensor algorithms
Some single-radar (multi-sensor) algorithms in WDSS-II
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6. Multi-radar/multi-sensor algorithms
A typical multi-radar deployment of WDSS-II
21 February 2019

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
21 February 2019

8. Display The WDSS-II display WDSS-II tools exist for
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
21 February 2019

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
21 February 2019

10. Motion Estimation Uses K-Means clustering and Kalman filters
30 min
30 min
Actual dBZ
Forecast dBZ
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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
21 February 2019

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)
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13. Example clusters Two different scales shown Both scales are tracked
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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.
21 February 2019

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
21 February 2019

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 …
21 February 2019

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
21 February 2019

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 …
21 February 2019

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?
21 February 2019

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
21 February 2019

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
21 February 2019

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.
21 February 2019

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)
21 February 2019

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
21 February 2019