6/29/2005 Q2 Workshop, Norman, OK 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  Initial Q2 Development Plans  Outlook

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

Radar Satellite Sfc Obs & Sounding Lightning Model QPE Ingest & QC & QC Rain Gauge HydroModel* NMQ Overview Flowchart PrecipProducts HydroProducts Users QPF 2D/3DRadarMosaic MosaicProducts Verification

Radar Satellite Sfc Obs & Sounding Lightning Model QPE Ingest & QC & QC Rain Gauge HydroModel* NMQ Overview Flowchart PrecipProducts HydroProducts Users QPF 2D/3DRadarMosaic MosaicProducts Verification

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

Data Ingest  Radar WSR-88D, level-II and level-III (140+radars) 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)

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

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

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

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

Single Radar Process  Reflectivity QC (dynamically evolving effort!) Noise filter Noise filter Sun beam filter Sun beam filter Terrain based QC (hybrid scan) 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 Velocity Dealiasing

Noise Filter

Sunbeam Filter

Horizontal and Vertical Structure Based QC

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

Effective Cloud Amount

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

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

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

Precipitation type classification  Stratiform rain/snow  Precip. type  Composite refl.

Single Radar Process (cont.) 3-D Single Radar Cartesian (SRC) Grid reflectivity (QC’d and UnQC’d) 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

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

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

Convective Case1: RHI, 263° Raw Interpolated

Stratiform Case 2: RHI, 0° Raw Interpolated

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

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)

2D Hybrid Scan Refl Mosaic

2D HYBREF height AGL

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

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

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

Computational Tiles

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

Vertical Cross Section Loop (W- E)

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

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

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)

Z-R relationships Taiwan Oklahoma Convective Oklahoma Stratiform

Satellite QPE  Products from existing algorithms:  Hydro (Auto) Estimator  GMSRA  SCaMPR

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

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

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)

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

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

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

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

Outlook  Radar QPE  Improve radar data QC  VPR/range correction  Additional data streams  Continue improving precip typing including identification of warm rain process  More adaptive Z-R relationships  Gage QPE  Improved gage QC  Adaptive influence of radius for objective analysis  Non-uniform spatial distributions  Terrain effects (mountain mapper?)

Precipitation typing  Warm/cold rain  Cold rain echo core (dbZ)  Warm rain echo core (dbZ) -10°C time height  time height

New Data Streams (e.g. TDWR) Better coverage at lower atmosphere Higher spatial resolution near urban areas

Outlook ( Contd.)  Radar-satellite QPE  Refine weather regimes for satellite-radar regressions  Multi-variable regression using multi-spectral satellite data (SCaMPR concepts)  Systematic verification  Extensive case studies from different weather regimes  Real-time verification of all products  Quantification of uncertainties in different QPEs

6/29/2005 Q2 Workshop, Norman, OK THANK YOU!