# Presentation: 099891

## Aiding Severe Weather Forecasting

- _[Valliappa.Lakshmanan@noaa.gov](mailto:Valliappa.Lakshmanan@noaa.gov)_
- National Severe Storms Laboratory
- Norman OK, USA
- _[http://](http://w/)__[w](http://w/)_*ww.wdssii.org/*

## 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

## 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

## 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

## Single-radar/Multi-sensor algorithms

- Some single-radar (multi-sensor) algorithms in WDSS-II

- lakshman@ou.edu

## Multi-radar/multi-sensor algorithms

- A typical multi-radar deployment of WDSS-II

- lakshman@ou.edu

## 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

## 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

## 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

## Motion Estimation

- Uses K-Means clustering and Kalman filters

- Forecast dB*Z*

- Actual dB*Z*

- 30 min

- 30 min

- lakshman@ou.edu

## 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

## 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

## Example clusters

- Two different scales shown
- Both scales are tracked

- lakshman@ou.edu

## 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

## 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

## 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

## 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

## 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

## 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

## Post-event

- The polygons can be used to examine the decision-making process
- 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

## 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

## 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

## 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

## 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