# Presentation: 516816

## Multiple Sensor Severe Weather Applicationsfor NSSL’s WDSS-II

- Greg Stumpf
*Greg.Stumpf@noaa.gov*
- National Severe Storms Laboratory
- University of Oklahoma/CIMMS
- Norman, Oklahoma

## What is WDSS-II?

_**W**_arning _**D**_ecision _**S**_upport _**S**_ystem **– **_**I**_ntegrated _**I**_nformation
- WDSS-II is the result of 10+ years of research, application development, and operational testing at NSSL & NWS Forecast Offices
- WDSS-II is a capable real-time data ingest and processing system
- WDSS-II is also a powerful application development tool

## History of WDSS

- Legacy WDSS developed in the early 1990s
- Support for only single radar applications
- Level-II data displays for all tilts (“8-bit”).
- Test experimental applications and display concepts in real-time operations (>20 NWSFOs nationwide)
- Solicit feedback
- Compared with WSR-88D algorithms & PUP display
- Transferred algorithms to WSR-88D system
- SCIT / HDA / TDA / MDA implemented in WSR-88D
- Offline version known as WATADS
- Used by many as a case study analysis tool

## WDSS-II Requirements

- The limitations of the legacy WDSS were first addressed as early as 1997
- Initial requirements for WDSS-II
- Support multiple-radar and multi-sensor data integration
- Economical (Linux, PC graphics)
_**Support for algorithm/application developers**_
- Easy to add new products and concepts
- Seamless path from data ingest, processing, and output using standard formats
- Contemporary software development tools (e.g., concurrent versions system (CVS), auto-generated Doxygen documentation)
- Innovative 4D display tool for intermediate and final application output and case analysis
- Improve the pace of science and technology infusion

## WDSSII is not just a display!

- The display you see is only the tip of the iceberg

## NSSL Warning Research and Development Process

- Applied Research
- Field experiments, understanding storms, relating weather to remotely-sensed signatures.
- Application Development
- Algorithms, displays, image processing, artificial intelligence.
- Offline and real-time evaluation
- Testing at NWS offices.
- Feedback from evaluation leads to ultimate applications for users.

## The Warning Challenge

- How do operational warning forecasters distinguish between severe and non-severe, and tornadic and non-tornadic thunderstorms with the information they have?

## The Warning Challenge

- To reduce the uncertainty and improve the accuracy of a prediction, a warning forecaster will integrate more information about a storm as viewed by other radars and other sensors:

- Multiple radar data
- Algorithm information
- Trends
- Spotter reports
- Near-Storm Environment (NSE)
- Surface observations

- Lightning data
- Satellite data
- Statistical knowledge of past events
- Basic understanding of storm physics

## Severe Weather Applications (algorithms)

- It makes sense that the severe weather detection tools also integrate multiple-source information!
- Guidance tools
- “Safety Net”
- Are intended to aid the human decision maker
- Output should be effectively displayed and managed.
- Highest resolution data (e.g., radar data in native format).
- Novel ways to represent and “slice and dice” 4D data.

## Our Scientific Challenge

- What can NSSL do to provide the tools and knowledge to improve warnings?
- Improve decision-making efficiency
- Easy access to the most important info for decision making
- Improve public service
- New types of warning products
- Increase POD, Lead Time; Reduce FAR
- To satisfy Congress and the taxpayers All tools must be developed in a scientifically-sound manner.
- Positive quantifiable and defendable results versus “eye candy” and “bells and whistles”.

## New Severe Weather Algorithm Requirements

- NSSL’s objectives for new warning application development:
- Integrate multiple-radar and multiple-sensor information
- No longer single-radar specific
- Must input highest resolution data in native format
- More accuracy in detection and diagnosis (more “eyes” looking at storms).
- Must have rapid-update capability
- Uses _**virtual volume**_ scan concept
- Better lead time (no more waiting until end of volume scan for guidance).
- Must be scientifically sound

## SSAP algorithms:  SCIT, HDA, TDA, MDA
Signature detection based on single-radar data.
Disadvantages of single-radar algorithms:
Products generated at end of volume scan
Only 5-6 minute updates – storm evolution is fast
Poor sampling within cone-of-silence and at far ranges
Products all keyed to individual radar volume scan and radar domain (azimuth/range/elevation)

- Signature detection based on _single-radar data_.
- Disadvantages of single-radar algorithms:
- Products generated at end of volume scan
- Only 5-6 minute updates – storm evolution is fast
- Poor sampling within cone-of-silence and at far ranges
- Products all keyed to individual radar volume scan and radar domain (azimuth/range/elevation)

- Legacy WSR-88D Algorithms

## Many single radars provide many answers

## Many single radars provide many answers

**The “best” detection**

## Multiple Radar SSAP

- Runs traditional SSAP algorithms using multiple radar input (and NSE data).
- Can use adjacent radars to fill cones-of-silence.
- Outputs information rapid intervals (60 second updates); can be as fast as individual elevation scan updates using “virtual volume scans”.
- “Rapid update” also works in single-radar mode if coverage or outages dictate.
- _More accuracy and increased lead time_!

## Multiple radars provide one answer

## Multiple Radar SSAP

- _Multiple-sensor_:  Uses mean wind information from mesoscale model to “drift” 2D features in space and time.
- Products keyed to 4D earth-relative coordinate system (lat, lon, elevation, time).
- Radar data is time-synchronized and corrected to UTC.

## Multiple Radar SSAP

- Data from adjacent radar filled the cone-of-silence
- Complete VIL product

- Complete multi-radar data used for:
- VIL, POSH, MEHS

- Data from adjacent radars filled the cone-of-silence

## Multiple Radar SSAP

**30**

**32**

**25**

**32**

**25**

## Multiple Radar SSAP

## MR-SSAP Rapid Update

- 20 May 2000
- KSRX (Fort Smith)

## MR-SSAP Rapid Update

- 20 May 2000
- KINX (Tulsa)

## MR-SSAP Rapid Update

- 20 May 2000
- KTLX (OKC)

## MR-SSAP Rapid Update

- 20 May 2000
- Three Radars

## Multiple Radar SSAP

- Designed to be VCP independent, and can be integrated with other “gap-filling” radar platforms (_TDWR_, ASR, PAR, SMART-R, _NETRAD_, foreign radars, commercial radars).

## NETRAD and TDWR

**Earth curvature effects prevent 72% of the troposphere below 1 km from being observed using only the present WSR-88D network**
**TDWR and NETRAD data fills in the missing boundary layer measurements and offers higher resolution.**

## TDWR versus WSR-88D

- TDWR versus _WSR-88D_

## TDWR versus WSR-88D

## Multiple Radar SSAPUsing WSR-88D and TDWR

## Multiple Radar SSAPUsing WSR-88D and TDWR

- Multiple Radar SSAPUsing WSR-88D and TDWR

- No KTLX data above
- This level – cone-of-silence

## MR-SCIT Cell Table

- _Interactive_
- Left Click re-centers display to cell centroid
- Middle-click gives trend of parameter for that cell ID
- Can sort any column in ascending or descending order
- Color coding is configurable – based on severity
- SCIT table output in XML files (standard format), which can be used on other systems

## 3D Multiple-radar grid applications

- Mosaic data from multiple radars to create a 3D Cartesian lat/lon/ht grid.
- Uses time-weighting and inverse distance weighting schemes.
- Can also advect older data when running motion estimator (later slide).
- Run algorithms on _continuously-updating_ 3D grids:
- 3D reflectivity field for VIL, echo top, LRM, hail
- 3D velocity derivative fields for vortex (rotation) and wind shift (convergence) detection.
- Easy to integrate other sensor information (NSE, satellite, lightning, etc.).

## Multiple-Radar 3D Reflectivity Mosaic

## Multiple-Radar 3D Reflectivity Mosaic

- Continuously- Updating Grid

## Multiple-Radar 3D Reflectivity Mosaic

**01:20Z - 01:30Z, 2002-08-14 **

*cross section*

- Continuously- Updating Grid

## Multiple-Radar 3D Reflectivity Mosaic

- Continuously- Updating Grid

## Virtual Volume Concept

- VIL, CMP REF, etc.
- Rapidly updates for each new elevation scan (20-30 seconds).
- Single-radar

- VIL

## Virtual Volume Concept

- VIL, CMP REF, etc.
- Rapidly updates for each new elevation scan (20-30 seconds).
- Multi-radar

## Gridded Hail Products

- A new paradigm in hail information delivery
- Improves public service by giving them geo-spatial information on hail size versus a simple yes/no.
- Geospatial info also facilitates improved verification.
- Coupled with NSSL motion estimation algorithm, capability exists to predict short-term hail swaths.

## Gridded Hail Products

- Gridded hail diagnoses and hail accumulation products
- Integrates NSE data from mesoscale models

**Reflectivity (dB*****Z*****)**

**Probability of Severe Hail (>19 mm dia)**

## Gridded Hail Products

**Maximum Expected Hail Size (mm)**

**Reflectivity (dB*****Z*****)**

- Gridded hail diagnoses and hail accumulation products
- Integrates NSE data from mesoscale models

## Gridded Hail Products

**Two Hour Path of Max Hail Size (mm)**

**Reflectivity (dB*****Z*****)**

- Gridded hail diagnoses and hail accumulation products
- Integrates NSE data from mesoscale models

## Gridded Hail Products

**Two Hour Path of Hail Damage Potential**

**Reflectivity (dB*****Z*****)**

- Gridded hail diagnoses and hail accumulation products
- Integrates NSE data from mesoscale models

## Gridded Hail Products integrated with NSE data

- Easier to integrate with thermodynamic data from mesoscale model grids.
- Automated.
- Better spatial and temporal resolution.

## Virtual Volume High-Resolution Gridded Maximum Hail Size

- Multiple-radar mosaic
- Integrates NSE data from model
**Updates for each new elevation scan.**
**10-minute loop.**

## Multiple-Radar / Multiple-SensorHail Swaths and Cell Tracks

## Vortex Detection and Diagnosis (VDDA)

- Linear-Least Squares Derivatives (LLSD) of velocity
- Rotation and Divergence
- Multi-radar mosaic

**LLSD Rotation**

## Vortex Detection and Diagnosis (VDDA)

- Linear-Least Squares Derivatives (LLSD) of velocity
- Rotation and Divergence
- Multi-radar mosaic

**Six Hour Path of Rotational Shear**

## Vortex Detection and Diagnosis (VDDA)

**Six Hour Path of Rotational Shear**

- Linear-Least Squares Derivatives (LLSD) of velocity
- Rotation and Divergence
- May 3 1999 Tornado Paths from shapefile
- Multi-radar mosaic

## Vortex Detection and Diagnosis (VDDA)

**May 9-10 2003 OKC - Six Hour Path of Rotational Shear**

## Multi-scale Storm Segmentation Algorithms

- Use of multi-scale statistical (versus heuristic) approaches for storm, vortex, and boundary detection.
- Also can be used for Infrared satellite feature identification (e.g., storm cold cloud tops) or lightning flash density.

## A novel method of performing multi-scale segmentation of radar reflectivity data using statistical properties within the radar data itself. 
The method utilizes a K-Means clustering of texture vectors computed within the reflectivity scan.
Uses, besides the actual reflectivity value within a gate, the distribution of reflectivity values around that gate. 

- The method utilizes a K-Means clustering of texture vectors computed within the reflectivity scan.
- Uses, besides the actual reflectivity value within a gate, the distribution of reflectivity values around that gate.

- Multi-scale Storm Segmentation Algorithms

## Motion Estimation

- Actual dB*Z*

- 00 min

- Sophisticated technique using statistical segmentation and error analysis.
- Can be used on dB*Z*, IR satellite, VIL, lightning density, etc.
- Produces high-resolution motion field that can be used to predict hail, precipitation, rotation, lightning, etc.

## Motion Estimation

- Forecast dB*Z*

- Actual dB*Z*

- 30 min

- 30 min

## Motion Estimation

- Forecast dB*Z*

- Actual dB*Z*

- 60 min

- 60 min

## Motion Estimation

- Uses texture segmentation to extract multiple-scale components
- Advects textures
- Growth and Decay component
- This is a 60-minute loop
- 30-min actual data
- 30-min forecast

## Quality Control Neural Network (QCNN)

- Use multiple-sensor information to segregate precipitation echoes from non-precipitation echoes:
- Non-precipitating clear-air return
- Ground Clutter
- Anomalous Propagation (AP)
- Chaff
- Resulting clean “precipitation” field used as input to other applications (MDA, TDA, QPE)
- MDA and TDA false alarms are going to be a major issue when radars sample clear air return with more resolution (new VCPs, TDWR).
- Two stages:
- Radar-only (texture statistics from all three moments, vertical profiles)
- Radar, satellite, and surface temperature (for additional “cloud cover” product).

## Quality Control Neural Network (QCNN)

- Uses all three radar moments, and IR satellite and surface data to estimate cloud cover

- Original dB*Z *

## Quality Control Neural Network (QCNN)

- Uses all three radar moments, and IR satellite and surface data to estimate cloud cover

- Radar-only
- QCNN* *

## Quality Control Neural Network (QCNN)

- Uses all three radar moments, and IR satellite and surface data to estimate cloud cover

- Cloud Cover
- (Tsfc – Tsat)* *

## Quality Control Neural Network (QCNN)

- Uses all three radar moments, and IR satellite and surface data to estimate cloud cover

- Removed remaining
- Non-precip returns* *

- Kept precip cells* *

- Multiple-
- sensor
- QCNN* *

## Quality Control Neural Network (QCNN)

- Dramatic improvement over REC:

| Technique | POD | FAR | CSI |
| --- | --- | --- | --- |
| REC | .44 | .37 | .35 |
| QCNN | .90 | .41 | .55 |

## Quality Control Neural Network (QCNN)

- Original dB*Z *

## Quality Control Neural Network (QCNN)

- Radar Echo Classifier (REC)

## Quality Control Neural Network (QCNN)

## Quality Control Neural Network (QCNN)

- Long-term climatologies of MDA show numerous false alarms in non-storm echo.
- Application of QCNN shows over 90% reduction in the non-storm MDA false alarms and no change to detections within storm echo.

- After QC

- Before QC

## Multiple-sensorLightning Prediction

- Multiple-sensorLightning Prediction

- Under Development
- Using Neural Network for growth and decay
- Input data include multiple radars (MR), lightning density, and mesoscale model analyses.
- Uses WDSSII Motion estimation for future location

- MR Composite dB*Z*

- 15-min Lightning Density

- MR dB*Z* at -20 degC

- MR dB*Z* at 0 degC

## 3D Lightning

**3D Lightning Discharge Points**

- Example shows a single lightning discharge
- Points color-coded by height
- Red icons are ground impacts
- _Can also display CG or flash densities_
- With motion estimator, could advect lightning density field.

## 3D Lightning

**3D Lightning Discharge Points**

- Example shows another single lightning discharge
- Combined with radar data.
- 3D view is of vertical cross-section from an “airplane” angle.
- Can “fly” around 3D data.

## Mesoscale Model Data

**RUC20 Height of 273K Level**

- Can ingest from GRIB  or NetCDF formats
- Colormap is configurable
- Can also do contours
- Can be adapted for output from other models (e.g., MM5, WRF, local ADAS) as input to applications and display.

## Surface Observations

**Oklahoma Mesonet**

- Can input and display any surface observations (METAR, mesonet, etc).
- Display components are configurable.
- A variety of query tools are available.

## Satellite Data

- Ingest any satellite data encoded to NetCDF.
- For input to applications or display.

## Combining data from multiple sources

- WDSSII infrastructure such that if we can display any source or combination thereof, we can also ingest multiple-sources into applications.

**Visible Satellite and Radar Mosaic**

## Radars other thanWSR-88D

**OKC TDWR dB*****Z *****overlaid on KTLX WSR-88D dB*****Z***

- Terminal Doppler Weather Radar (TDWR)
- Research Polarimetric Radar
- Dopplers-On-Wheels
- Other Radars (commercial, ASR, PAR, etc).

- KTLX

- TDWR

## Radars other thanWSR-88D

**KOUN**

- Terminal Doppler Weather Radar (TDWR)
- Research Polarimetric Radar
- Dopplers-On-Wheels
- Other Radars (commercial, ASR, PAR, etc).

## Radars other thanWSR-88D

**Doppler-On-Wheels overlaid on WSR-88D**

- Terminal Doppler Weather Radar (TDWR)
- Research Polarimetric Radar
- Dopplers-On-Wheels
- Other Radars (commercial, ASR, PAR, etc).

## Radars other thanWSR-88D

**Doppler-On-Wheels overlaid on WSR-88D**

- Terminal Doppler Weather Radar (TDWR)
- Research Polarimetric Radar
- Dopplers-On-Wheels
- Other Radars (commercial, ASR, PAR, etc).

## NWSFO WDSSII Proof of Concept Test Objectives

- Jackson MS (Nov 02 – Mar 03); Wichita KS (Spr 03 - ?)
- Gain feedback on experimental multiple sensor severe weather applications for NWS.
- Multiple radars combined – all which provide some coverage of the entire NWSFO County Warning Area.
- Gain feedback on new 4D display concepts.
- NSSL staffing during severe weather season to better understand user requirements in real-time operational warning settings.
- Evaluation via observation and via feedback surveys.
- Toward eventual implementation into operational systems (e.g., AWIPS).

## New 4D display concepts

- Earth-relative coordinate system (not CWA centric)
- All data plotted at lat/lon/ht-MSL
- 3D sphere – no projections!
- Dynamic 3D cross-sections from any angle (vertical, CAPPI, tilted)
- Virtual Volume browser and looping (“All Tilts” and cross sections always have full volume).
- Continuous panning and zooming
- Unlimited image panes (as memory allows)
- Can fix on a point and fly in 3D (single frame) or in 4D (looping frames)

## Feedback

- Applications/Algorithms:
- Forecasters generally liked the multiple sensor and virtual volume concepts, but felt some could be improved
- Liked how cone-of-silence filled in by adjacent radars
- Liked 60 second update to SCIT tracks – more stable, and better trends.
- LLSD Rotation Tracks a big hit with JAN
- Tornado verification
- Felt the multi-radar hail algorithm still overwarned, although geospatial info helped with verification

## Feedback

- Display feedback:
- 3D data still very hard for some to latch on to (paradigm shift)
- Love it or hate it relationship
- 3D should be an option – not the only choice
- 3D analysis needs to be taught at the basic level – schools!  Training!
- Parallax issues
- Add height info to dynamic CAPPI
- Add isosurfaces, with transparency

## Feedback

- Display feedback:
- Some loved continuous pan/zoom.  Others liked stepwise versions on AWIPS.
- Liked multi-radar overlays; 8-bit available for all radars, all elevations.
- Liked volume browser (“All-Tilts”) feature with virtual volumes
- No time-height trends
- No real-time colormap editing
- Product and window management better on AWIPS
_**Hard to learn knobology of two systems (AWIPS and WDSSII).**_

## Feedback

- Overall Test feedback:
- Not enough time to use system (need longer test)
- Make experimental algorithm output available in AWIPS
- Get 3D concepts into AWIPS
- Make WATADSII available – to learn 3D concepts before using them in actual operations
- NSSL experience:
- More recent exposure to contemporary warning operations
- Fully-AWIPS centric
- New warning methodologies
- Mesoscale forecaster position
- Electronic spotter reports

## For Next Spring

- _Make experimental gridded output available in AWIPS!_
- Point data (icons, tables, tracks, trends) not as easy (Web browser?)
- New Multiple-Sensor Applications:
- User selectable LRMs, dBZ at constant Z and T levels, hgt above 0°C level, echo top divergence, VIL density.
- Enhanced HDA (cell and grid based) with velocity data and more NSE input
- QCNN – to remove non-precipitation echoes from storm algorithms
- QPESUMS
- Motion Estimation of reflectivity, precipitation accumulation, hail swaths, rotation tracks
- Lightning Prediction (multi-radar, satellite, surface, NSE)
- New data streams (both display and as input to algorithms):
- TDWR
- Mobile Doppler Radar (SMART-R)

## Our Vision

- Science and Technology Infusion Plan
- WDSSII test beds (at least one per region)
- Use WDSSII or its concept for a multi-radar and multi-sensor Products Generator for AWIPS
- ORPG only produces single-radar products
- 3D display concepts as an AWIPS “pop-up”
- Add virtual volume concept to AWIPS (e.g., all-tilts).
- More Warning Decision Making “team” interaction
- Include WATADSII into Advanced Warning Operations course as a high-resolution 4D radar base-data analysis tool
- WDM team helps with design phase of applications and display concepts

## WDSS-II

- The pace of this innovative development would not have been possible without the WDSSII as an effective R&D tool and testing platform.
- These are ideas that have been “in the queue” for many years at NSSL, only now coming to fruition.
- There are many more ideas that have yet to be implemented (some from NWS folks).
- Represents a quantum leap in the improvement of warning and situational awareness technology.

## Questions?

- Greg.Stumpf@noaa.gov
- http://www.nssl.noaa.gov/wrd
- ACKNOWLEDGEMENTS:
- Don Burgess, Kurt Hondl, Travis Smith, Arthur Witt, Lakshman, Kevin Manross, Claire Thomas, Jian Zhang