WDSS-II Overview. Valliappa Lakshmanan (Lak) University of Oklahoma & National Severe Storms Laboratory

Transcription

1 WDSS-II Overview Valliappa Lakshmanan (Lak) University of Oklahoma & National Severe Storms Laboratory 1

2 What is WDSS-II? Second-generation of Warning Decision Support System (WDSS), primarily about Integrated Information (II) Three components: Multi-sensor algorithms for severe weather diagnosis & nowcasting Virtual globe display of weather products Application Programming Interface (API) for developing algorithms 2

3 Who uses WDSS-II? Free (restricted) Use General public can get products in real-time Average of 4 million downloads per week in 2012 Free for research use Over 50 universities, research labs Used operationally, in real-time within US government FAA, NOAA/SPC, etc. After-the-event products used for damage surveys in NWS Commercial Use Licensed by OU to several private companies who then support Mobile phone apps, TV stations, international customers, etc. Licensed by OU for real-time, operational use internationally Taiwan, Korea, Australia, India, Dubai, etc. 3

4 4 An Example WDSS-II Algorithm Maximum Expected Size of Hail (MESH) Radar reflectivity from all the radar in the country merged into 3D grid Reflectivity at -20C, 0C From model analysis Accumulated in time Painstakingly validated by phone calls Produced in real-time Ortega, Kiel L., Travis M. Smith, Kevin L. Manross, Angelyn G. Kolodziej, Kevin A. Scharfenberg, Arthur Witt, Jonathan J. Gourley, 2009: The Severe Hazards Analysis and Verification Experiment. Bull. Amer. Meteor. Soc., 90,

5 Creation of MESH 5 Lakshmanan, Valliappa, Travis Smith, Gregory Stumpf, Kurt Hondl, 2007: The Warning Decision Support System Integrated Information. Wea. Forecasting, 22,

6 6 Ingest and Output

7 The preferred data formats WDSS-II algorithms read and write only: Netcdf for gridded data 2D polar data (azimuth, range) Cylindrical equidistant (latitude, longitude) XML for tabular data Specific attributes, variable names, etc. required 7

8 Data Ingestors WDSS-II comes with a set of tools to ingest data And convert them into the netcdf/xml required ldm2netcdf (Level-II), sigmetingest (sigmet files), GridIngest (Grib2), satingest, etc. All these tools work in real-time Monitor a directory, convert any files that show up Make sure that file is complete Move the file into the monitored directory (not copy) Level-II files ldm2netcdf netcdf files + Index records To provide custom data to WDSS-II, write custom ingestor 8

9 Data Converters WDSS-II algorithms provide output in netcdf This netcdf can be read directly by many programs (ArcGIS, Matlab, etc.) Set flag in w2config/misc/dataformat: cdmcompliant=true Files bigger, but portable to other programs WDSS-II also provides a number of converters w2pngconverter, w2geotiff, w2gribconv, w2maptiles, etc. These also work in real-time (converting products as they are produced) Netcdf files w2pngconvert Png snapshots 9

10 WDSS-II display options The WDSS-II display is mainly used by algorithm developers and to carry out experiments Most organizations license WDSS-II, run the algorithms and pipe the output to their own display programs There are two WDSS-II display programs: wg which comes with the WDSS-II distribution (OpenGL, Linux) wg2 which is Java-based and multi-platform and is available from: Another option is to use converters such as w2geotiff, w2maptiles, w2pngconvert, etc. and send data directly to web pages Programs such as IDV, Matlab, etc. can also read netcdf 10

11 Mirroring and Dissemination Common to mirror these products as they are produced Push products onto a LDM queue w2mirror can also be used to archive products onto slower disks for long-term storage 11

12 12 Distribution to Google Maps

13 13 Single radar algorithms

14 Legacy SSAP Algorithms Severe Storms Analysis Program: Implemented into WSR-88D: SCIT - Storm Cell ID and Tracking algorithm HDA - Hail Detection Algorithm TDA - Tornado (TVS) Detection Algorithm MDA - Mesocyclone Detection Algorithm TDA+ - Tornado (TVS) Detection Algorithm (with Tracking) Others: DDPDA - Damaging Downburst Prediction & Detection Algorithm ISCIT Improved Storm Cell ID and Tracking algorithm EHDA - Enhanced Hail Diagnosis Algorithm (with NSE) 14

15 15 NEXRAD Feature Detection Algorithms

16 Legacy WSR-88D Algorithms Legacy 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) 16

17 Running netssap Single Radar Data Reflectivity Velocity w2alignradials w2alignradials nse AlignedReflectivity AlignedVelocity TemperatureLevels netssap CellTable MesoTable TvsTable 17

18 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). Geared to help forecaster make warning decisions 18

19 19 WDSS-II Display and Products

20 Multi-Radar, Multi-Sensor Algorithms 20

21 Virtual Volume Concept VIL, CMP REF, etc. Rapidly updates for each new elevation scan (20-30 seconds). Single-radar VIL R. Lynn and V. Lakshmanan, ``Virtual radar volumes: Creation, algorithm access and visualization,'' in 21st Conf. on Severe Local Storms, (San Antonio, TX), Amer. Meteor. Soc.,

22 3D Multiple-radar grid applications Mosaic data from multiple radars to create a 3D Cartesian lat/lon/ht grid. Uses inverse distance weighting schemes for most products 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.). 22

23 23 Multiple-Radar 3D Reflectivity Mosaic

24 Multiple-Radar 3D Reflectivity Mosaic Continuously- Updating Grid 24

25 Multiple-Radar 3D Reflectivity Mosaic 01:20Z - 01:30Z, Continuously- Updating Grid 25

26 Multiple-Radar 3D Reflectivity Mosaic Continuously- Updating Grid 26

27 27 MR-SSAP Multi-Radar Update

28 Gridded Hail Products A new paradigm in hail information delivery Grid-based vs Stormcell-based Combined 3D Refl grid with other sensors 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. 28

29 Reflectivity plotted at temperature levels Reflectivity at 0 o C Reflectivity at -20 o C Composite reflectivity Applications combines radar data and numerical model data to plot reflectivity at constant temperature levels. Hail algorithm uses reflectivity at 0 o C and -20 o C. This allows forecaster to see the inner-workings of the algorithm. 29

30 Gridded Hail Products integrated with NSE data Easier to integrate with thermodynamic data from mesoscale model grids. Automated. Better spatial and temporal resolution. 30

31 Gridded Hail Products Reflectivity (dbz) Probability of Severe Hail (>19 mm dia) Maximum Expected Hail Size (mm) Two Hour Path of Max Hail Size (mm) 31

32 Virtual Volume High-Resolution Gridded Maximum Hail Size Multipleradar mosaic Integrates NSE data from model Updates for each new elevation scan. 10-minute loop. 32

33 Reflectivity Velocity Rotational shear Rotation tracks 34

34 Rotation Tracks Motion and relative strength of the circulation signature is evident 35

35 Vortex Detection and Diagnosis Linear-Least Squares Derivatives (LLSD) of velocity Rotation and Divergence May Tornado Paths from shapefile Multi-radar mosaic Six Hour Path of Rotational Shear 36

36 Multi-Doppler Wind Analysis View of the same vortex from multiple radars Simulated radar data from a storm-scale model 37

37 Multi-Doppler Wind Analysis Multi-Doppler analysis provides 2D wind vectors in real-time Wind vectors computed from simulated radar data 38

38 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. 39

39 Multi-scale Storm Segmentation Algorithms 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. 40

40 Motion Estimation 00 min Sophisticated technique using statistical segmentation and error analysis. Can be used on dbz, IR satellite, VIL, lightning density, etc. Produces high-resolution motion field that can be used to predict hail, precipitation, rotation, lightning, etc. Actual dbz 41

41 Motion Estimation 30 min 30 min Actual dbz Forecast dbz 42

42 Motion Estimation 60 min 60 min Actual dbz Forecast dbz 43

43 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 44

44 Quality Control Neural Network (QCNN) Use multiple-sensor information to segregate precipitation echoes from nonprecipitation 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). 45

45 Original dbz Radar-only QCNN 46 Cloud Cover (T sfc T sat ) Multi-sensor QCNN

46 QCNN: Raw data 47

47 QCNN: QCed 48

48 Questions? Please contact: 49