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3-D Radar Mosaic and Initial Q2 Development Plans

3-D Radar Mosaic and Initial Q2 Development Plans. Jian Zhang 1 , Ken Howard 2 , and Steve Vasiloff 2 1 University of Oklahoma, Norman, OK 2 National Severe Storms Lab, Norman, OK. Outline. NMQ Components Overview Single Radar Process 2-D Radar Mosaic 3-D Radar Mosaic

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3-D Radar Mosaic and Initial Q2 Development Plans

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  1. 3-D Radar Mosaic and Initial Q2 Development Plans Jian Zhang1, Ken Howard2, and Steve Vasiloff2 1University of Oklahoma, Norman, OK 2National Severe Storms Lab, Norman, OK Q2 Workshop, Norman, OK

  2. Outline • NMQ Components Overview • Single Radar Process • 2-D Radar Mosaic • 3-D Radar Mosaic • Initial Q2 Development Plans • Outlook

  3. Outline • NMQ Components Overview • Single Radar Process • 2-D Radar Mosaic • 3-D Radar Mosaic • Initial Q2 Development Plans • Outlook

  4. NMQ Overview Flowchart Radar Ingest & QC Satellite 2D/3D Radar Mosaic Hydro Model* Rain Gauge QPF QPE Sfc Obs & Sounding Precip Products Mosaic Products Hydro Products Lightning Verification Users Model

  5. NMQ Overview Flowchart Radar Ingest & QC Satellite 2D/3D Radar Mosaic Hydro Model* Rain Gauge QPF QPE Sfc Obs & Sounding Precip Products Mosaic Products Hydro Products Lightning Verification Users Model

  6. NMQ Philosophy • An open R&D system • Dynamic enhancements/improvements to scientific components • Real-time 24/7 testing and evaluation on CONUS domain to address real-world problems • A real-time verification system • Cost-effective algorithms for operational benefits • Incorporation of new data as they become available • A common framework for joint scientific research and development

  7. Data Ingest • Radar • WSR-88D, level-II and level-III (140+radars) • Canadian radar network (~35 radars, efforts undergoing) • TDWR (ongoing, limited data availability) • CASA/gap-filling radars (future) • Dual-pol radar data (future)

  8. Data Ingest (Cont.) • Satellite • GOES IR imagery data (Tb) • For QC and radar-satellite QPE • GOES sounder data (ECA) • For QC • Other (GOES multi-spectral, exploring) • Auto Estimator (efforts undergoing) • GMSRA (future) GOES Multi-Spectral Rainfall Algorithm • SCaMPR (future) Self-Calibrating Multivariate Precipitation Retrieval

  9. Data Ingest (cont.) • Rain Gauge • NCEP/USGS hourly gage data • OK mesonet • Additional gage networks (mesowest, LCRA, prism) • Other?

  10. Data Ingest (cont.) • Model (RUC 20km, hourly analysis) • Upper Air Sounding • Lightning • Surface Observations (ASOS) (future) • Other?

  11. Outline • NMQ Components Overview • Single Radar Process • 2-D Radar Mosaic • 3-D Radar Mosaic • Initial Q2 Development Plans • Outlook

  12. Single Radar Process • Reflectivity QC (dynamically evolving effort!) • Noise filter • Sun beam filter • Terrain based QC (hybrid scan) • Horizontal texture and vertical structure based QC • Temporal continuity based QC Satellite based QC • Satellite based QC • Dual-pol data (future) • Velocity Dealiasing

  13. Noise Filter

  14. Sunbeam Filter

  15. Horizontal and Vertical Structure Based QC

  16. Temporal Continuity QC To remove the hardware testing pattern: Check sudden increase in echo coverage between consecutive volume scans

  17. Effective Cloud Amount

  18. Single Radar Process (cont.) • Reflectivity climatology • Brightband Identification • Precipitation typing • (1-good strat rain; 2- bad strat rain; 3-good strat snow; 4- bad strat snow; 5-mixed phase; 6-convective). • Hybrid scan reflectivity and the associated height • Composite reflectivity (QC and UnQC) and the associated height • Vertical Profile of Reflectivity (VPR) • VPR-adjusted hybrid scan reflectivity

  19. Convective Precip Flags Composite Reflectivity Convective/Stratiform Segregation • dBZ > 50 in any bin or, • dBZ > 30 at temperatures < -10 C or, • 1 lightning flash

  20. Bright Band Identification (BBID)(Gourley and Calvert, 2003, WAF) • 3-D Reflectivity Field • Find Layer of Higher Reflectivity • Vertical Reflectivity Gradient • Spatial/Temporal Smoothing

  21. Precipitation type classification • Stratiform rain/snow • Composite refl. • Precip. type

  22. Single Radar Process (cont.) • 3-D Single Radar Cartesian (SRC) Grid reflectivity (QC’d and UnQC’d) • 3-D SRC reflectivity (QC’d with VPR gap-filling) • Multi-scale storm tracking • 3-D SRC grid with synchronization

  23. Single Radar Cartesian Grid R = 460km for coastal radars and 300km for other radars. X R Horizontal grid (~1km x 1km) Vertical grid (31 levels)

  24. o + o o o + o o 3-D Spherical to Cartesian Transformation(Zhang et al. 2005, JTECH) No BB: Vertical linear interpolation No BB BB exists: Vertical and horizontal linear interpolation BB

  25. Convective Case1: RHI, 263° Raw Interpolated

  26. Stratiform Case 2: RHI, 0° Raw Interpolated

  27. Outline • NMQ Components Overview • Single Radar Process • 2-D Radar Mosaic • 3-D Radar Mosaic • Initial Q2 Development Plans • Outlook

  28. 2-D Radar Mosaic • Composite reflectivity (QC’d and UnQC’d) and associated height • Hybrid scan reflectivity (QC’d, with and without VPR-adjustment) • Precipitation type • Radar coverage maps (spatial and temporal) • Layered composite reflectivity (e.g., the lowest 4 tilts)

  29. 2D Hybrid Scan Refl Mosaic

  30. 2D HYBREF height AGL

  31. Strat Rain (good) Convective (good) Bright Band (bad) Frozen (bad) 2D Precipitation Type Mosaic

  32. Outline • NMQ Components Overview • Single Radar Process • 2-D Radar Mosaic • 3-D Radar Mosaic • Initial Q2 Development Plans • Outlook

  33. 3-D Radar Mosaic • 3-D multi-radar mosaic grid • QC’d • UnQC’d • QC’d with VPR gap-filling • 2-D derived products: • Composite reflectivity and the associated height • Hybrid scan reflectivity and associated height • Hail products (SHI, POSH, MEHS) • VIL and VILD • ETOP • Layered composite reflectivity

  34. Computational Tiles

  35. Cross Sections from 3-D Mosaic Dallas Hail Storm, 5/5/1995

  36. Vertical Cross Section Loop (W-E)

  37. Outline • NMQ Components Overview • Single Radar Process • 2-D Radar Mosaic • 3-D Radar Mosaic • Initial Q2 Development Plans • Outlook

  38. Q2 Components • Radar QPE • Satellite QPE • Rain gage QPE • Multi-sensor QPEs • Radar+satellite (& model and sounding) • Radar+gage • Radar+satellite+gage

  39. Radar QPE • Rain rate • Derived from: • Hybrid scan reflectivity from 3-D radar mosaic (QC’d, with and without VPR gap-filling) • Layer composite reflectivity of the lowest 4 tilts (from 2D radar mosaic) • Different Z-R relationships based on 2D mosaic precip type field • 1km x 1km, update every 5 min • Accumulations (1- to 72-h or longer)

  40. Z-R relationships Oklahoma Convective Oklahoma Stratiform Taiwan

  41. Satellite QPE • Products from existing algorithms: • Hydro (Auto) Estimator • GMSRA • SCaMPR

  42. Rain Gauge QPE • Individual stations • Objective analysis -- gridded gage products (e.g., ADAS) • Issues: • Bad data • Spatial representativeness of gage obs • Non-uniform and sparse gage distributions • Terrain effects • Real-time latency

  43. Radar-satellite QPE • Radar rain rate - satellite Tb regressions • Multiple regressions -- one for each weather regimes • Initial weather regimes are defined by: • Surface temperature zones (hourly RUC surface analysis) • Regression using data pairs within a running hourly window • Rain rate averaged for each 1 deg Tb bin • Derive a dynamic exponential regression to the data in a least square fit sense • Various rules to prevent an ill-conditioned regression

  44. Radar-satellite QPE (Contd.) • Satellite rain rate • Apply regression curves to the Tb field in each weather regimes and obtain rain rate • Distance weighted mean across boundaries between different weather regimes • Use rain/no-rain mask (defined by radar obs and satellite) • Accumulations (1-72h)

  45. Surface Temp Regression Equation Satellite CTT Satellite/Radar Regression Regresses co-located satellite Tb with stratiform R from radar. One for each weather regimes. Radar Rainrate Updates regression curves hourly and purges old data

  46. Surface Temp Regr. Eqn Q2 Rainfall Rate Satellite CTT Generating Multi-sensor Rate Regression parameters are used to calibrate cloud-top temperature field by supplying precipitation rates

  47. Radar-gage QPE • Pre-defined bias regions (radar umbrella? basins? weather regimes?) • Regional radar/gage bias adjustment • Compute mean radar/gage bias for each bias region • Adjust radar QPE using the bias • Smoothing over the boundaries between bias regions • Point radar/gage bias adjustment • Compute radar/gage bias at each gage station • Objective analysis of the point biases • Adjust radar QPE using the gridded bias field • Bias is based on hourly accumulation • Adjustment is performed in real-time dynamically

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