12-14 July 2005 – 1 st NWS Svr Wx Warning Technology User MeetingGreg Stumpf – CIMMS/MDL The New NSSL - MDL Partnership: CIMMS / University of Oklahoma NWS Meteorological Development Laboratory Decision Assistance Branch Location: National Severe Storms Laboratory, Norman, OK CIMMS / University of Oklahoma NWS Meteorological Development Laboratory Decision Assistance Branch Location: National Severe Storms Laboratory, Norman, OK Gregory J. Stumpf New Multiple- Radar/Sensor Application R&D for Warning Decision Making

12-14 July 2005 – 1 st NWS Svr Wx Warning Technology User MeetingGreg Stumpf – CIMMS/MDL NSSL’s Vision in 2003 for NWS Warning Improvement Support NWS Science and Technology Infusion Plan NSSL pitching idea to NWS to make WDSSII a Multi-Sensor Products Generator for AWIPS (to supplement ORPG) u ORPG only produces single-radar products Warning test beds (at least one per region) using WDSSII to feed products to AWIPS Introduce 4D radar analysis concepts as an AWIPS “pop-up” option NWS Warning Decision Making “team” interaction; training Motivate WDM team to aid with the design phase of new warning applications and display concepts Include WDSSII into WDTB Advanced Warning Operations Course as a high-resolution 4D radar base-data analysis tool Support NWS Science and Technology Infusion Plan NSSL pitching idea to NWS to make WDSSII a Multi-Sensor Products Generator for AWIPS (to supplement ORPG) u ORPG only produces single-radar products Warning test beds (at least one per region) using WDSSII to feed products to AWIPS Introduce 4D radar analysis concepts as an AWIPS “pop-up” option NWS Warning Decision Making “team” interaction; training Motivate WDM team to aid with the design phase of new warning applications and display concepts Include WDSSII into WDTB Advanced Warning Operations Course as a high-resolution 4D radar base-data analysis tool

12-14 July 2005 – 1 st NWS Svr Wx Warning Technology User MeetingGreg Stumpf – CIMMS/MDL The First Step My NSSL position was moved into the NWS Meteorological Development Laboratory Decision Assistance Branch My new boss: Dr. Stephan Smith My location remained at NSSL in Norman Act as a liaison between severe weather research and application development at NSSL and NWS warning operations program Develop AWIPS testbed for new remote-sensing technologies and new multiple-sensor warning applications My NSSL position was moved into the NWS Meteorological Development Laboratory Decision Assistance Branch My new boss: Dr. Stephan Smith My location remained at NSSL in Norman Act as a liaison between severe weather research and application development at NSSL and NWS warning operations program Develop AWIPS testbed for new remote-sensing technologies and new multiple-sensor warning applications

12-14 July 2005 – 1 st NWS Svr Wx Warning Technology User MeetingGreg Stumpf – CIMMS/MDL NSSL’s Mission To enhance NOAA’s capabilities to provide accurate and timely forecasts and warnings of hazardous weather events. NSSL accomplishes this mission, in partnership with the National Weather Service (NWS), through a balanced program of research to advance the understanding of weather processes research to improve forecasting and warning techniques development of operational applications and transfer of understanding, techniques, and applications to the NWS. NSSL is the sole NOAA agency responsible for the R&D of new applications and technology to improve NWS severe weather warning decision making. To enhance NOAA’s capabilities to provide accurate and timely forecasts and warnings of hazardous weather events. NSSL accomplishes this mission, in partnership with the National Weather Service (NWS), through a balanced program of research to advance the understanding of weather processes research to improve forecasting and warning techniques development of operational applications and transfer of understanding, techniques, and applications to the NWS. NSSL is the sole NOAA agency responsible for the R&D of new applications and technology to improve NWS severe weather warning decision making.

12-14 July 2005 – 1 st NWS Svr Wx Warning Technology User MeetingGreg Stumpf – CIMMS/MDL Needs Assessment

12-14 July 2005 – 1 st NWS Svr Wx Warning Technology User MeetingGreg Stumpf – CIMMS/MDL Needs Assessment More frequent algorithm updates (not at end of volume scan) Intermediate products (help to understand final output, understand science, build expertise) Better data QC (to remove false alarms outside storms) Multi-radar integration (cones-of-silence, far ranges, terrain blockage) NSE integration (automated, more frequent updates, better spatial resolution, for HDA, SCIT-RU, multi-radar integration). More frequent algorithm updates (not at end of volume scan) Intermediate products (help to understand final output, understand science, build expertise) Better data QC (to remove false alarms outside storms) Multi-radar integration (cones-of-silence, far ranges, terrain blockage) NSE integration (automated, more frequent updates, better spatial resolution, for HDA, SCIT-RU, multi-radar integration).

12-14 July 2005 – 1 st NWS Svr Wx Warning Technology User MeetingGreg Stumpf – CIMMS/MDL The NWS Severe Weather Warning Challenge How do operational warning forecasters distinguish between severe and non-severe, and tornadic and non-tornadic thunderstorms with the information they have?

12-14 July 2005 – 1 st NWS Svr Wx Warning Technology User MeetingGreg Stumpf – CIMMS/MDL The NWS Severe Weather 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 (WSR-88D, TDWR) Near-Storm Environment (NSE) Surface observations Upper Air data Lightning data Satellite data Multiple radar data (WSR-88D, TDWR) Near-Storm Environment (NSE) Surface observations Upper Air data Lightning data Satellite data Algorithm guidance Trends Spotter reports Statistical knowledge of past events Basic understanding of storm physics Algorithm guidance Trends Spotter reports Statistical knowledge of past events Basic understanding of storm physics

12-14 July 2005 – 1 st NWS Svr Wx Warning Technology User MeetingGreg Stumpf – CIMMS/MDL Severe Weather Warning Decision Making Applications It makes sense that the NWS severe weather detection, diagnosis, and prediction tools also integrate multiple-sensor information! “Multiple-sensor” integration is not a new concept However, the MS concept has yet to be fully realized within NWS warning applications (still mostly single- radar based) It makes sense that the NWS severe weather detection, diagnosis, and prediction tools also integrate multiple-sensor information! “Multiple-sensor” integration is not a new concept However, the MS concept has yet to be fully realized within NWS warning applications (still mostly single- radar based)

12-14 July 2005 – 1 st NWS Svr Wx Warning Technology User MeetingGreg Stumpf – CIMMS/MDL New Severe Weather Algorithm Requirements Objectives for new warning application development: Integrate multiple-radar and multiple-sensor information u No longer single-radar specific u Must input highest resolution data in native format u More accuracy in detection and diagnosis (oversampling - more “eyes” looking at storms). Must have rapid-update capability u Uses virtual volume scan concept u Better lead time (no more waiting until end of volume scan for guidance). Must be scientifically sound Objectives for new warning application development: Integrate multiple-radar and multiple-sensor information u No longer single-radar specific u Must input highest resolution data in native format u More accuracy in detection and diagnosis (oversampling - more “eyes” looking at storms). Must have rapid-update capability u Uses virtual volume scan concept u Better lead time (no more waiting until end of volume scan for guidance). Must be scientifically sound

12-14 July 2005 – 1 st NWS Svr Wx Warning Technology User MeetingGreg Stumpf – CIMMS/MDL ORPG Algorithms: SCIT, HDA, TDA, MDA, etc. 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) No automated tuning for different near-storm environments ORPG Algorithms: SCIT, HDA, TDA, MDA, etc. 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) No automated tuning for different near-storm environments Legacy WSR-88D Severe Weather Applications

12-14 July 2005 – 1 st NWS Svr Wx Warning Technology User MeetingGreg Stumpf – CIMMS/MDL Many single radars provide many different answers KJAN VIL = 34 KLIX VIL = 52 KMOB VIL = 45

12-14 July 2005 – 1 st NWS Svr Wx Warning Technology User MeetingGreg Stumpf – CIMMS/MDL KLIX VIL = 52 The “best” detection? Many single radars provide many different answers

12-14 July 2005 – 1 st NWS Svr Wx Warning Technology User MeetingGreg Stumpf – CIMMS/MDL Multiple Radar Algorithms Storms are oversampled, especially in cones-of-silence and at far ranges from single radars. Outputs information in rapid intervals; 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. Multiple radars and rapid update lead to more stable tracks and trends Products keyed to 4D earth-relative coordinate system (lat, lon, elevation, time). Designed to be VCP independent, and can be integrated with other “gap- filling” radar platforms (TDWR, ASR, PAR, SMART-R, NETRAD/CASA, foreign radars, commercial radars). Storms are oversampled, especially in cones-of-silence and at far ranges from single radars. Outputs information in rapid intervals; 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. Multiple radars and rapid update lead to more stable tracks and trends Products keyed to 4D earth-relative coordinate system (lat, lon, elevation, time). Designed to be VCP independent, and can be integrated with other “gap- filling” radar platforms (TDWR, ASR, PAR, SMART-R, NETRAD/CASA, foreign radars, commercial radars).

12-14 July 2005 – 1 st NWS Svr Wx Warning Technology User MeetingGreg Stumpf – CIMMS/MDL Multiple radars provide one answer KMOB KLIX KJAN

12-14 July 2005 – 1 st NWS Svr Wx Warning Technology User MeetingGreg Stumpf – CIMMS/MDL 3D Multiple-radar grid applications Mosaic multiple radar data to create a 3D Cartesian lat/lon/ht grid. Uses time-weighting and power-density (distance) weighting schemes. Intelligently handles terrain blockage, interpolation in sparse grid cells Can advect older data when running a motion estimator. Run algorithms on continuously-updating 3D grids (“virtual volumes”) – the data are nearly LIVE: 3D reflectivity field for MaxRef, VIL, echo top, LRM, LRA, hail, Cell ID 3D velocity derivative fields for vortex (rotation) and wind shift (convergence) detection. Easy to integrate other sensor information (NSE, satellite, lightning, etc.) on similar grids. e.g., Thermodynamic info for hail diagnosis. Mosaic multiple radar data to create a 3D Cartesian lat/lon/ht grid. Uses time-weighting and power-density (distance) weighting schemes. Intelligently handles terrain blockage, interpolation in sparse grid cells Can advect older data when running a motion estimator. Run algorithms on continuously-updating 3D grids (“virtual volumes”) – the data are nearly LIVE: 3D reflectivity field for MaxRef, VIL, echo top, LRM, LRA, hail, Cell ID 3D velocity derivative fields for vortex (rotation) and wind shift (convergence) detection. Easy to integrate other sensor information (NSE, satellite, lightning, etc.) on similar grids. e.g., Thermodynamic info for hail diagnosis.

12-14 July 2005 – 1 st NWS Svr Wx Warning Technology User MeetingGreg Stumpf – CIMMS/MDL Multiple-Radar 3D Reflectivity Mosaic

12-14 July 2005 – 1 st NWS Svr Wx Warning Technology User MeetingGreg Stumpf – CIMMS/MDL Multiple-Radar 3D Merging 3D Grid information: Create a 3D Lat-Lon-Height grid of 3D “voxels” Current resolution: 0.01  x 0.01  x 1 km Current domain: covers the entire CWAs of OUN, FTW, and TSA, plus a “buffer” Radars: Level-II data from KTLX, KINX, KSRX, KVNX, KICT, KDDC, KAMA, KLBB, KFDR, KFWS, KDYX (later: KGRK, and even later: CONUS!) Each 3D grid voxel “knows” which radars are sensing it (this info is cached) If terrain blocks a radar’s view of a voxel, that radar is not used for that voxel Latest elevation scan of data from any radar is used, replacing the previous version (“virtual volumes”). 3D Grid information: Create a 3D Lat-Lon-Height grid of 3D “voxels” Current resolution: 0.01  x 0.01  x 1 km Current domain: covers the entire CWAs of OUN, FTW, and TSA, plus a “buffer” Radars: Level-II data from KTLX, KINX, KSRX, KVNX, KICT, KDDC, KAMA, KLBB, KFDR, KFWS, KDYX (later: KGRK, and even later: CONUS!) Each 3D grid voxel “knows” which radars are sensing it (this info is cached) If terrain blocks a radar’s view of a voxel, that radar is not used for that voxel Latest elevation scan of data from any radar is used, replacing the previous version (“virtual volumes”).

12-14 July 2005 – 1 st NWS Svr Wx Warning Technology User MeetingGreg Stumpf – CIMMS/MDL Multiple-Radar 3D Merging Data are QCed, to remove non-precipitation echoes (e.g., AP) Older data are advected forward in time using a motion estimator Data are interpolated between elevation scans For each radar sensing a voxel, the radar info is weighted based on a power-density function (inversely proportional to distance). Internal 3D grid is updating continuously, but new product grids are generated every 60 seconds (can be faster!). Data are QCed, to remove non-precipitation echoes (e.g., AP) Older data are advected forward in time using a motion estimator Data are interpolated between elevation scans For each radar sensing a voxel, the radar info is weighted based on a power-density function (inversely proportional to distance). Internal 3D grid is updating continuously, but new product grids are generated every 60 seconds (can be faster!).

12-14 July 2005 – 1 st NWS Svr Wx Warning Technology User MeetingGreg Stumpf – CIMMS/MDL 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 Multiple Sensor Information (two stages): Radar (texture statistics from all three moments, vertical profiles) Radar, satellite, and surface temperature (“cloud cover”) Resulting clean “precipitation” field used as input to other applications (MDA, TDA, QPE, LLSD) MDA and TDA false alarms are going to be a major issue when radars sample clear air return with more resolution (new VCPs, TDWR). Use multiple-sensor information to segregate precipitation echoes from non-precipitation echoes: Non-precipitating clear-air return Ground Clutter Anomalous Propagation (AP) Chaff Multiple Sensor Information (two stages): Radar (texture statistics from all three moments, vertical profiles) Radar, satellite, and surface temperature (“cloud cover”) Resulting clean “precipitation” field used as input to other applications (MDA, TDA, QPE, LLSD) MDA and TDA false alarms are going to be a major issue when radars sample clear air return with more resolution (new VCPs, TDWR).

12-14 July 2005 – 1 st NWS Svr Wx Warning Technology User MeetingGreg Stumpf – CIMMS/MDL Uses all three radar moments, and IR satellite and surface data to estimate cloud cover Original dBZ Quality Control Neural Network (QCNN)

12-14 July 2005 – 1 st NWS Svr Wx Warning Technology User MeetingGreg Stumpf – CIMMS/MDL Uses all three radar moments, and IR satellite and surface data to estimate cloud cover Radar-only QCNN Radar-only QCNN Quality Control Neural Network (QCNN)

12-14 July 2005 – 1 st NWS Svr Wx Warning Technology User MeetingGreg Stumpf – CIMMS/MDL Uses all three radar moments, and IR satellite and surface data to estimate cloud cover Cloud Cover (T sfc – T sat ) Cloud Cover (T sfc – T sat ) Quality Control Neural Network (QCNN)

12-14 July 2005 – 1 st NWS Svr Wx Warning Technology User MeetingGreg Stumpf – CIMMS/MDL Uses all three radar moments, and IR satellite and surface data to estimate cloud cover Removed remaining Non-precip returns Removed remaining Non-precip returns Kept precip cells Multiple- sensor QCNN Multiple- sensor QCNN Quality Control Neural Network (QCNN)

12-14 July 2005 – 1 st NWS Svr Wx Warning Technology User MeetingGreg Stumpf – CIMMS/MDL Original dBZ Quality Control Neural Network (QCNN)

12-14 July 2005 – 1 st NWS Svr Wx Warning Technology User MeetingGreg Stumpf – CIMMS/MDL Quality Control Neural Network (QCNN)

12-14 July 2005 – 1 st NWS Svr Wx Warning Technology User MeetingGreg Stumpf – CIMMS/MDL A novel method of performing multi-scale segmentation of image data (e.g., radar reflectivity) using statistical properties within the image data itself. The method utilizes a K-Means clustering of texture vectors computed within the image; clusters are hierarchical. Uses, besides the actual values on the image grid, the distribution of values around each grid point. A novel method of performing multi-scale segmentation of image data (e.g., radar reflectivity) using statistical properties within the image data itself. The method utilizes a K-Means clustering of texture vectors computed within the image; clusters are hierarchical. Uses, besides the actual values on the image grid, the distribution of values around each grid point. Multi-Scale Storm Segmentation

12-14 July 2005 – 1 st NWS Svr Wx Warning Technology User MeetingGreg Stumpf – CIMMS/MDL 2D Motion Estimation Uses K-means texture segmentation to extract multiple-scale components Advects multiple-scale textures Growth and Decay component Can track and trend individual multiple-scale textures 2D motion field (u, v) used to advect older data in 3D dBZ grid. This is a 60-minute loop 30-min actual data 30-min forecast Uses K-means texture segmentation to extract multiple-scale components Advects multiple-scale textures Growth and Decay component Can track and trend individual multiple-scale textures 2D motion field (u, v) used to advect older data in 3D dBZ grid. This is a 60-minute loop 30-min actual data 30-min forecast

12-14 July 2005 – 1 st NWS Svr Wx Warning Technology User MeetingGreg Stumpf – CIMMS/MDL Multiple-Radar 3D Reflectivity Mosaic Continuously- Updating Grid

12-14 July 2005 – 1 st NWS Svr Wx Warning Technology User MeetingGreg Stumpf – CIMMS/MDL Multiple-Radar 3D Reflectivity Mosaic 01:20Z - 01:30Z, cross section Continuously- Updating Grid

12-14 July 2005 – 1 st NWS Svr Wx Warning Technology User MeetingGreg Stumpf – CIMMS/MDL Multiple-Radar 3D Reflectivity Mosaic Continuously- Updating Grid

12-14 July 2005 – 1 st NWS Svr Wx Warning Technology User MeetingGreg Stumpf – CIMMS/MDL Multiple-Radar 3D Reflectivity Mosaic Proposed CONUS Testbed

12-14 July 2005 – 1 st NWS Svr Wx Warning Technology User MeetingGreg Stumpf – CIMMS/MDL Multiple-Radar 3D Reflectivity Mosaic Filling the cones-of- silence Single Radar Filling the cones-of- silence Single Radar

12-14 July 2005 – 1 st NWS Svr Wx Warning Technology User MeetingGreg Stumpf – CIMMS/MDL Multiple-Radar 3D Reflectivity Mosaic Filling the cones-of- silence Multiple radars Filling the cones-of- silence Multiple radars

12-14 July 2005 – 1 st NWS Svr Wx Warning Technology User MeetingGreg Stumpf – CIMMS/MDL Multiple Radar Cell ID

12-14 July 2005 – 1 st NWS Svr Wx Warning Technology User MeetingGreg Stumpf – CIMMS/MDL Single Radar Cell ID VIL "hole" as the storm goes through the cone-of-silence. Single Radar:KINX Cone-Of- Silence

12-14 July 2005 – 1 st NWS Svr Wx Warning Technology User MeetingGreg Stumpf – CIMMS/MDL Multiple Radar Cell ID No VIL "hole" as the storm goes through the cone-of-silence. The VIL maxxes out within the cone- of-silence. The upward trend of max VIL is only observed by integrating multiple- radars. Trend information is smoother (fewer sharp peaks and valleys) and is available at more rapid intervals (60 seconds versus 5 minutes). The data are nearly “live”. Cell tracking tends to be much more stable. Time association techniques are employed every 60 seconds (more rapidly), instead of every 5-6 minutes (per volume scan) where there is a greater likelihood of storm evolution and storm centroid "jumping". Single Radar:KINX Multi-Radar: KINX, KICT, KSGF, KSRX, KTLX Single Radar:KINX Multi-Radar: KINX, KICT, KSGF, KSRX, KTLX Increased Lead Time: 83 minutes

12-14 July 2005 – 1 st NWS Svr Wx Warning Technology User MeetingGreg Stumpf – CIMMS/MDL New Tools for Hail Diagnosis Using Conventional Radar Taking the concept of cell-based HDA to a grid Integrate multi-radar and NSE information Provide intermediate products Provide other “popular” hail-diagnosis products Taking the concept of cell-based HDA to a grid Integrate multi-radar and NSE information Provide intermediate products Provide other “popular” hail-diagnosis products

12-14 July 2005 – 1 st NWS Svr Wx Warning Technology User MeetingGreg Stumpf – CIMMS/MDL VIL versus SHI Both use “vertically integrated dBZ” dBZ profiles can come from A Storm cell A 3D grid (integrate hold (lat, lon) constant A 3D grid (integrate along a tilt) VIL integrates the entire profile, and caps dBZs at 56 to remove ice contamination Severe Hail Index (SHI) integrates only the profile above the melting layer, and excluded dBZ below 40, to include ice. Both use “vertically integrated dBZ” dBZ profiles can come from A Storm cell A 3D grid (integrate hold (lat, lon) constant A 3D grid (integrate along a tilt) VIL integrates the entire profile, and caps dBZs at 56 to remove ice contamination Severe Hail Index (SHI) integrates only the profile above the melting layer, and excluded dBZ below 40, to include ice. Plot of hail kinetic energy flux (used to calculate SHI; solid curve), and liquid water content (used to calculate VIL; dashed curve), as a function of reflectivity.

12-14 July 2005 – 1 st NWS Svr Wx Warning Technology User MeetingGreg Stumpf – CIMMS/MDL Severe Hail Index (SHI) No reflectivities below 40 dBZ are used, all reflectivities above 50 dBZ are used, and reflectivities between 40 and 50 dBZ are linearly weighted from 0 to 1 (a proxy to the curve shown in Fig. 2). Furthermore, only reflectivities (meeting the above criteria) above the melting layer are considered. Reflectivities between the 0  C and -20  C levels are weighted from 0 to 1, and all reflectivities (meeting the above criteria) above the -20  C level are considered. Temperature profile is made available from RUC 00h analysis grids. Maximum Expected Size of Hail (MESH; inches) = 0.1 * (SHI) 0.5 No reflectivities below 40 dBZ are used, all reflectivities above 50 dBZ are used, and reflectivities between 40 and 50 dBZ are linearly weighted from 0 to 1 (a proxy to the curve shown in Fig. 2). Furthermore, only reflectivities (meeting the above criteria) above the melting layer are considered. Reflectivities between the 0  C and -20  C levels are weighted from 0 to 1, and all reflectivities (meeting the above criteria) above the -20  C level are considered. Temperature profile is made available from RUC 00h analysis grids. Maximum Expected Size of Hail (MESH; inches) = 0.1 * (SHI) 0.5

12-14 July 2005 – 1 st NWS Svr Wx Warning Technology User MeetingGreg Stumpf – CIMMS/MDL Cell versus Grid dBZ profile from cell: Pros: Follows max dBZ, inherent tilt Cons: SCIT frequently misses detection of entire dBZ profile dBZ profile from 3D grid: Pros: There is always a complete dBZ profile, multiple-radar, motion estimation minimizes apparent tilt due to fast motion Cons: Vertical integration may not capture storm tilt (BUT: we are working on this issue) dBZ profile from cell: Pros: Follows max dBZ, inherent tilt Cons: SCIT frequently misses detection of entire dBZ profile dBZ profile from 3D grid: Pros: There is always a complete dBZ profile, multiple-radar, motion estimation minimizes apparent tilt due to fast motion Cons: Vertical integration may not capture storm tilt (BUT: we are working on this issue)

12-14 July 2005 – 1 st NWS Svr Wx Warning Technology User MeetingGreg Stumpf – CIMMS/MDL Tilted Storm Cores Future: Tilted Integration VIL – Vertical Integration

12-14 July 2005 – 1 st NWS Svr Wx Warning Technology User MeetingGreg Stumpf – CIMMS/MDL Tilted Storm Cores VIL – Tilted Integration Future: Tilted Integration Storm Cores projected to location of hail fall, not under echo overhangs Cleaner image Grid can replace cell-based value Multi-radar SCIT and/or NSE can be used to develop 2D grid of expected storm tilt angle Future: Tilted Integration Storm Cores projected to location of hail fall, not under echo overhangs Cleaner image Grid can replace cell-based value Multi-radar SCIT and/or NSE can be used to develop 2D grid of expected storm tilt angle

12-14 July 2005 – 1 st NWS Svr Wx Warning Technology User MeetingGreg Stumpf – CIMMS/MDL Cell versus Grid Cell-based VIL or SHI: Only one value per volume scan Always at end of volume scan Single-radar NSE data is sparse, and must be manually-input Multi-radar Grid based VIL or SHI: Geospatial information: where in cell is largest hail falling? Can accumulate grid over time for hail swaths Much easier for event verification (know where to make the probing calls). Storms are oversampled by multiple-radars, especially in cones-of-silence Output is essentially live (rapid update). Cell-based VIL or SHI: Only one value per volume scan Always at end of volume scan Single-radar NSE data is sparse, and must be manually-input Multi-radar Grid based VIL or SHI: Geospatial information: where in cell is largest hail falling? Can accumulate grid over time for hail swaths Much easier for event verification (know where to make the probing calls). Storms are oversampled by multiple-radars, especially in cones-of-silence Output is essentially live (rapid update).

12-14 July 2005 – 1 st NWS Svr Wx Warning Technology User MeetingGreg Stumpf – CIMMS/MDL Intermediate and Popular products It’s nice to have the “final answer”, but what ingredients went into the gridded MESH? dBZ relative to temperature altitudes u Reflectivity at 0  C, Reflectivity at -20  C u Height of 50 dBZ above -20  C altitude Echo tops of various dBZ thresholds u 50 dBZ Echo Top Eliminates the arduous task of using all-tilts and data sampling, as well as mental multi-radar and NSE integration, to determine these values for each and every storm at every time What values correspond to what hail sizes? You tell us! It’s nice to have the “final answer”, but what ingredients went into the gridded MESH? dBZ relative to temperature altitudes u Reflectivity at 0  C, Reflectivity at -20  C u Height of 50 dBZ above -20  C altitude Echo tops of various dBZ thresholds u 50 dBZ Echo Top Eliminates the arduous task of using all-tilts and data sampling, as well as mental multi-radar and NSE integration, to determine these values for each and every storm at every time What values correspond to what hail sizes? You tell us!

12-14 July 2005 – 1 st NWS Svr Wx Warning Technology User MeetingGreg Stumpf – CIMMS/MDL Rapidly-Updating Gridded Products from 3D Mosaic Shown: Maximum Expected Hail Size (MEHS) “Virtual Volume” updates for each new elevation scan. Integrates NSE thermodynamic data from model 10-minute loop Shown: Maximum Expected Hail Size (MEHS) “Virtual Volume” updates for each new elevation scan. Integrates NSE thermodynamic data from model 10-minute loop

12-14 July 2005 – 1 st NWS Svr Wx Warning Technology User MeetingGreg Stumpf – CIMMS/MDL Gridded Hail Products integrated with NSE data Easier to integrate with thermodynamic data from mesoscale model grids. Automated. Better spatial and temporal resolution. Easier to integrate with thermodynamic data from mesoscale model grids. Automated. Better spatial and temporal resolution.

12-14 July 2005 – 1 st NWS Svr Wx Warning Technology User MeetingGreg Stumpf – CIMMS/MDL Gridded data can be accumulated to give “hail swath”. Geo-spatial information on hail size versus a simple yes/no per cell. Geospatial info facilitates improved verification. Gridded data can be accumulated to give “hail swath”. Geo-spatial information on hail size versus a simple yes/no per cell. Geospatial info facilitates improved verification. Rapidly-Updating Gridded Products from 3D Mosaic

12-14 July 2005 – 1 st NWS Svr Wx Warning Technology User MeetingGreg Stumpf – CIMMS/MDL Examples May 20, dBZ Echo Top Height of 50 dBZ Above -20  C MESH MESH 2hr Swath Reflectivity at -20  C 1 km MSL Reflectivity Reflectivity at 0  C

12-14 July 2005 – 1 st NWS Svr Wx Warning Technology User MeetingGreg Stumpf – CIMMS/MDL Vortex Detection and Diagnosis (VDDA) Linear-Least Squares Derivatives (LLSD) of velocity Azimuthal and Radial Shear Multi-radar mosaic of 0-4 km shear Azimuthal Shear can be accumulated in time. Linear-Least Squares Derivatives (LLSD) of velocity Azimuthal and Radial Shear Multi-radar mosaic of 0-4 km shear Azimuthal Shear can be accumulated in time. LLSD Azimuthal Shear

12-14 July 2005 – 1 st NWS Svr Wx Warning Technology User MeetingGreg Stumpf – CIMMS/MDL Modeled Rankine Vortex (Northern Hemisphere) Vortex Detection and Diagnosis (VDDA) Mesocyclone Simulated WSR-88D Velocity Azimuthal Shear (LSD) Cyclonic Shear Anticyclonic Shear Radial Shear (LSD) Convergence Divergence

12-14 July 2005 – 1 st NWS Svr Wx Warning Technology User MeetingGreg Stumpf – CIMMS/MDL Linear Least Squares Derivative (LSSD) Rotational shear (u s ) is calculated on a local neighborhood surrounding each range gate (a range-dependent variable size mask), where:  s ij V ij w ij u s =  (Δs ij ) 2 w ij V ij is the radial velocity, s ij is the azimuthal distance from the center of the kernel to the point (i,j), and w ij is a uniform weight function. Because us is derived from only the radial component of the wind, they are approximations of one half the vertical vorticity (“half vorticity”, hereafter), respectively, assuming a symmetric wind field. Rotational shear (u s ) is calculated on a local neighborhood surrounding each range gate (a range-dependent variable size mask), where:  s ij V ij w ij u s =  (Δs ij ) 2 w ij V ij is the radial velocity, s ij is the azimuthal distance from the center of the kernel to the point (i,j), and w ij is a uniform weight function. Because us is derived from only the radial component of the wind, they are approximations of one half the vertical vorticity (“half vorticity”, hereafter), respectively, assuming a symmetric wind field.

12-14 July 2005 – 1 st NWS Svr Wx Warning Technology User MeetingGreg Stumpf – CIMMS/MDL Multiple Radar Azimuthal Shear First, QCNN is run to id only precipitation echoes, “stamp” those out of the velocity field, and then the resulting field is dilated to include a small clear air buffer around storms. Azimuthal shear is calculated for each single radar (since it is radar coordinate- system specific) for every sample volume in the 0-3 km MSL layer. In addition, the 0.5 degree tilt is always used, regardless if it has an altitude above 3 km MSL. The maximum value in the vertical column in this layer is projected to a 2D polar grid. Using the same merging techniques as the dBZ data (but for a 2D grid) the azimuthal shear single radar grids are combined into a multi-radar grid. The maximum positive azimuthal (cyclonic) shear over a 6 hour period is plotted to produce “Rotation Tracks”. First, QCNN is run to id only precipitation echoes, “stamp” those out of the velocity field, and then the resulting field is dilated to include a small clear air buffer around storms. Azimuthal shear is calculated for each single radar (since it is radar coordinate- system specific) for every sample volume in the 0-3 km MSL layer. In addition, the 0.5 degree tilt is always used, regardless if it has an altitude above 3 km MSL. The maximum value in the vertical column in this layer is projected to a 2D polar grid. Using the same merging techniques as the dBZ data (but for a 2D grid) the azimuthal shear single radar grids are combined into a multi-radar grid. The maximum positive azimuthal (cyclonic) shear over a 6 hour period is plotted to produce “Rotation Tracks”.

12-14 July 2005 – 1 st NWS Svr Wx Warning Technology User MeetingGreg Stumpf – CIMMS/MDL Vortex Detection and Diagnosis (VDDA) Six Hour Path of Rotational Shear Linear-Least Squares Derivatives (LLSD) of velocity Rotation and Divergence May Tornado Paths from shapefile Multi-radar mosaic Linear-Least Squares Derivatives (LLSD) of velocity Rotation and Divergence May Tornado Paths from shapefile Multi-radar mosaic

12-14 July 2005 – 1 st NWS Svr Wx Warning Technology User MeetingGreg Stumpf – CIMMS/MDL Rotation Track Usefulness Real-time: A simple diagnostic of the radial velocity data Provides, in one image, information about the past locations and the past trend of intensity. Doesn’t suffer from centroid matching failures (in 3D and 4D), threshold failures, etc, as does the MDA and TDA Post-event: Very useful for verification – first guess at where strongest rotation tracked – send survey teams there. Eliminates need to manually replay radar data and track the mesos. Real-time: A simple diagnostic of the radial velocity data Provides, in one image, information about the past locations and the past trend of intensity. Doesn’t suffer from centroid matching failures (in 3D and 4D), threshold failures, etc, as does the MDA and TDA Post-event: Very useful for verification – first guess at where strongest rotation tracked – send survey teams there. Eliminates need to manually replay radar data and track the mesos.

12-14 July 2005 – 1 st NWS Svr Wx Warning Technology User MeetingGreg Stumpf – CIMMS/MDL Vortex Detection and Diagnosis (VDDA) May OKC - Six Hour Path of Rotational Shear

12-14 July 2005 – 1 st NWS Svr Wx Warning Technology User MeetingGreg Stumpf – CIMMS/MDL Multiple-sensor CG Lightning Prediction Uses “Radial Basis Function” (RBF) for initiation, growth, decay. Input data include multiple radars (MR), lightning density, and mesoscale model analyses “Self-training” using live CG data. Also uses WDSSII Motion Estimator to advect fields For possible NWS “Lightning Warnings” Uses “Radial Basis Function” (RBF) for initiation, growth, decay. Input data include multiple radars (MR), lightning density, and mesoscale model analyses “Self-training” using live CG data. Also uses WDSSII Motion Estimator to advect fields For possible NWS “Lightning Warnings” MR Composite dBZ 15-min Lightning Density MR dBZ at -20  C MR dBZ at 0  C

12-14 July 2005 – 1 st NWS Svr Wx Warning Technology User MeetingGreg Stumpf – CIMMS/MDL Summary of WDSSII AWIPS Products Multi-Radar/Sensor WDSSII to AWIPS Volume Browser Products: MESH MESH 2hr Swath dBZ at 0C dBZ at -20C Note: Volume Browser treats the grid as a grid of points, thus runs an OBAN which results in somewhat smoothed data. Possible Additional Grids: 3D Lightning Mapping Array (LMA) u Vertically-Integrated source density u Vertically-integrated flash density Quantitative Precipitation Estimation and Segregation Using Multiple Sensors (QPESUMS) u Instantaneous Rain Rate u 1 hr and 24 hr accumulation Multi-Radar/Sensor WDSSII to AWIPS Volume Browser Products: MESH MESH 2hr Swath dBZ at 0C dBZ at -20C Note: Volume Browser treats the grid as a grid of points, thus runs an OBAN which results in somewhat smoothed data. Possible Additional Grids: 3D Lightning Mapping Array (LMA) u Vertically-Integrated source density u Vertically-integrated flash density Quantitative Precipitation Estimation and Segregation Using Multiple Sensors (QPESUMS) u Instantaneous Rain Rate u 1 hr and 24 hr accumulation Height of 50 dBZ above -20C 50 dBZ Echo Tops 6-hour Rotation Tracks Height of 50 dBZ above -20C 50 dBZ Echo Tops 6-hour Rotation Tracks

12-14 July 2005 – 1 st NWS Svr Wx Warning Technology User MeetingGreg Stumpf – CIMMS/MDL AWIPS Examples MESH

12-14 July 2005 – 1 st NWS Svr Wx Warning Technology User MeetingGreg Stumpf – CIMMS/MDL AWIPS Examples 0  C

12-14 July 2005 – 1 st NWS Svr Wx Warning Technology User MeetingGreg Stumpf – CIMMS/MDL AWIPS Examples -20  C

12-14 July 2005 – 1 st NWS Svr Wx Warning Technology User MeetingGreg Stumpf – CIMMS/MDL Experimental Warning Application Testbeds First ever AWIPS development system was installed at NSSL Ideally, at least one AWIPS severe weather warning testbed per region SR: Norman, plus ? CR: Boulder, plus ? WR: Support for hydro apps, plus ? ER: Sterling, plus ? Similar in concept to WDSS and WDSSII testbeds Application developer staffing during severe weather operations Feedback via surveys, etc. Proposed Spring ’05 activities: Experimental svr wx grids in D2D (gridded hail and hail swath, rotation tracks). Proposed Spring ’06 activities: Four-dimensional Stormcell Investigator (FSI) More WDSSII grids in AWIPS, perhaps as part of SCANprocessor. First ever AWIPS development system was installed at NSSL Ideally, at least one AWIPS severe weather warning testbed per region SR: Norman, plus ? CR: Boulder, plus ? WR: Support for hydro apps, plus ? ER: Sterling, plus ? Similar in concept to WDSS and WDSSII testbeds Application developer staffing during severe weather operations Feedback via surveys, etc. Proposed Spring ’05 activities: Experimental svr wx grids in D2D (gridded hail and hail swath, rotation tracks). Proposed Spring ’06 activities: Four-dimensional Stormcell Investigator (FSI) More WDSSII grids in AWIPS, perhaps as part of SCANprocessor.

12-14 July 2005 – 1 st NWS Svr Wx Warning Technology User MeetingGreg Stumpf – CIMMS/MDL The WATADS replacement! Linux OS Available for FTP download at A large number of new experimental multi-sensor warning applications Can run archive cases with Level-II data, from single or multiple radars, and with other sensor data (e.g., RUC20) Innovative 4D display tool for intermediate and final application output and case analysis (the WDSSII GUI, or “wg”) API support for multi-sensor severe weather and flash flood application development “Peer-to-peer” support via an electronic forum: The WATADS replacement! Linux OS Available for FTP download at A large number of new experimental multi-sensor warning applications Can run archive cases with Level-II data, from single or multiple radars, and with other sensor data (e.g., RUC20) Innovative 4D display tool for intermediate and final application output and case analysis (the WDSSII GUI, or “wg”) API support for multi-sensor severe weather and flash flood application development “Peer-to-peer” support via an electronic forum: Warning Decision Support System – Integrated Information (WDSS-II)

12-14 July 2005 – 1 st NWS Svr Wx Warning Technology User MeetingGreg Stumpf – CIMMS/MDL NSSL Forum Open to all in NWS and academia Follow the “Register” link User name should be format “First Last”, and please include your Location when signing up Topics: WDSSII Support Multiple-Sensor Severe Weather Applications Quantitative Precipitation Estimation 4D Base Radar Data Analysis Warning Decision Making Theory Open to all in NWS and academia Follow the “Register” link User name should be format “First Last”, and please include your Location when signing up Topics: WDSSII Support Multiple-Sensor Severe Weather Applications Quantitative Precipitation Estimation 4D Base Radar Data Analysis Warning Decision Making Theory

12-14 July 2005 – 1 st NWS Svr Wx Warning Technology User MeetingGreg Stumpf – CIMMS/MDL Resources Go to: “NSSL Experimental Products in AWIPS” Look for links to papers on: New multi-sensor applications Multi-radar Merging Hail Diagnosis Linear Least Squares Derivatives Go to: “NSSL Experimental Products in AWIPS” Look for links to papers on: New multi-sensor applications Multi-radar Merging Hail Diagnosis Linear Least Squares Derivatives