1. Walt Petersen and Kevin Knupp UAH/ESSC November 7, 2007 walt.petersen@nasa.gov UAH THOR Center Radar Infrastructure: Exploring QPE Algorithm Development for Operational Support of TVA River Management KBMX RSA 68 km KGWX UAH/NSSTC THOR Center and Hazardous Weather Testbed MIPS/NSSTC ARMOR KHTX 75 DD lobe 1 km Res. 1.5 km Res. LMA 100-500 m LMA Antenna NEXRAD ARMOR MIPS Profiler MAX ? MAX Outline Objectives for TVA-UAH interaction Radar QPE Problem, Dual-pol solution Brief overview of dual-pol How do we improve QPE UAH/NSSTC infrastructure Simple example data processing/flow Rainfall algorithm Where we are: Example products The future Appendix: BREAM

2. 88D Dual-pol upgrade imminent- improved QPE a primary driver. Can dual-pol QPE replace significant % of TVA gauge network? Demonstration project with ARMOR in advance of WSR-88D dual-pol upgrade Dual-pol rain rate estimator, NO gauge input 6-24 hour QPE over basin scales Real time web-products Facilitate/reintroduce radar QPE tailored to TVA needs for use in river management Future customer specific extensions (e.g., NOAA/NWS QPE/F products, site specific terrain corrections etc. HWT/COMET-NWS Synergies: pass products direct to WFO HUN- test utility, development E.g., Warm season precipitation event Favorable comparison to gauges- BUT much of the heaviest precip missed gauges (typical)!!! Heterogeneity of rain field presents problems for gauge-adjusted Z-R totals but not for dual-pol Dual-Pol QPE Applied to Operational Hydrology Walter A. Petersen, University of Alabama Huntsville

3. QPE: Problem with conventional radar-rainfall approaches: Reflectivity Factor (Z) - Rainfall Rate (R) Relations Sample of current operational relationships: Z = 300 R 1.4 - convective rain Z = 250 R 1.2 - tropical rain Z = 200 R 1.6 - summer stratiform rain Z = 130 R 2.0 - winter stratiform (eastern US) Z = 75 R 2.0 - winter stratiform (western US) NEXRAD measures rainfall using one variable- Z – at single polarization (H) Problem dates from 1940’s: Numerous rainfall- reflectivity relationships, which one is correct? Errors of 100-200% are common. Why? Measurement is sensitive to rain drop size distribution, presence of hail/ice/snow, and radar calibration. Unacceptable errors for high resolution hydrological application (e.g., flash flood nowcasting, runoff modeling) Even gauge corrections are still beholden to gauge calibration/error/sample mismatch- a problem at times. Z (dBZ) R (mm/hr) Z (dBZ) vs R(mm/hr) 50 vs 100 mm/hr at 50 dBZ over a valid range of observed DSDs!! 0 100 50 Proprietary information, Walter A. Petersen, University of Alabama Huntsville

4. Polarimetric Radar Variables 1.Reflectivity factor Z at horizontal polarization - Measure of drop size and concentration; most sensitive to SIZE (D 6 ) 2.Differential reflectivity Z DR - Measure of median drop diameter→ SIZE/SHAPE - Useful for rain / hail / snow discrimination→ SIZE/SHAPE 3.Differential phase Φ DP (Specific Differential Phase- K DP ) - Measure of content and size→ NUMBER/SHAPE - Immune to radar miscalibration, attenuation, and partial beam blockage 4.Copolar-correlation coefficient ρ hv - Indicator of mixed precipitation → SHAPE/PHASE/CANTING (Depolarization) - Useful for identifying non-meteorological scatterers U.S. Research NCAR NSSL CSU NASA UND NOAA ESRL UMASS UAH ARMOR Operational: NEXRAD, TV Advantages: Obtain a better description of particle types and shapes in a given volume of space More accurate rain rates (improved QPE) Hydrometeor ID and non-meteorological scatterers Consistent calibration U.S. Broadcast Huntsville New York Houston Chicago Tampa

5. Dual-Pol: Improved Quantitative Precipitation Estimation (QPE) and Hydrometeor Identification (HID) Radar “sees” Tumbling and lower dielectric strength makes hail look like a spheres Unless they start to melt… Hail/Graupel Melting Hail/Graupel (Toroid or ice core; looks like a huge drop) a b 1 mm 6 mm Axis ratio ~ 1 Axis ratio < 1 Rain Small Drops (1 mm) Large Drops (> 4 mm) Axis ratio decreases with size- more oblate Particle-Size Controlled Smaller ZDR Larger ZDR Smaller KDP Larger KDP vs Insects Rain vs Hail/Graupel Rain Small Drops Large Drops Smaller Z Larger Z Small Drops vs Large Drops Smaller KDP Larger KDP Larger ZDR Smaller ZDR Smaller # Larger # Number Concentration Controlled Microphysics Dual-Pol Interpretation Proprietary information, Walter A. Petersen, University of Alabama Huntsville

6. How do we get to “improved QPE”: UAH QPE Infrastructure Walter A. Petersen, University of Alabama Huntsville MAX: Mobile X-band Dual-Polarimetric Radar MAX OPS: Adaptive rapid sector/full volumes, RHI’s, vertically pointing Mobile targeted QPE studies; severe wx Raingauges and Disdrometers 2 Parsivel optical disdrometers, 2D Video Disdrometer (CSU) Geonor rain gauge, three tipping bucket, 1 WXT-510 impact gauge/disdrometer, CHARM rain gauge network, TVA gauge network ARMOR: C-band Dual-Polarimetric Radar Variables: Z, V, W, ZDR,  DP,KDP,  hv, LDR, HID ARMOR Standard Ops (24/7): 3-Tilt dual-pol scan every 5 min. 1-Tilt surveillance every 2.5 min. Significant Weather/Research Ops: Adaptive rapid sector/full volumes, RHI’s, vertically pointing MIPS: Mobile Integrated Profiling System 24/7 Ops Vertically pointing, adaptive vertical resolution, DSD, wind, temperature, pressure, humidity profiles

7. HID Z  hv ZDRKDP  DP Drizzle Lt. Rain Mod. Rain Heavy Rain Hail Hail/Rain Small Hail Rain/Sm. Hail Dry Snow Wet Snow Cloud Ice Crys. How do we get to “improved QPE”? ARMOR PPI at 19:38 UTC Use of raw variables takes more work for less experienced.......... Combined polarimetric variables offer a powerful means to discriminate liquid from frozen precipitation: Improved QPE, land surface hydrology, warning decision support. Proprietary information, Walter A. Petersen, University of Alabama Huntsville

8. How do we get to “improved QPE” UAH ARMOR Polarimetric Data Processing Walter A. Petersen, University of Alabama Huntsville NSSTC/UAH Raw IRIS files (w/Vaisala HCLASS) IRIS Images Clean using Z,  hv, var (  DP ) Correct Attenuation (Z)  Z and  ZDR =  KDP   Differential Attenuation (ZDR) Filter  DP, recompute KDP (adaptive FIR filtering) Compute Hybrid rain rates (R[KDP,ZDR,Z]) Write UF Compute HID Accumulated Rainfall Images/Tables Optional DD Level II (sweeps) WFO HUN (AWIPS, GR) UF TVA NetCDF ARMOR T1

9. ICE PRESENT? NO YES K DP  0.3 and Z H  35? R = R(K DP ) YE S NONO Z H  BAD? YE S R = R(Z H RAIN ) R=BAD NONO K DP  0.3 , Z H  35.0 dBZ Z DR  0.5 dB? YE S R > 50 mm/hr, dBZ > 50,or Z, ZDR corr. too large ? ZH > 30 dBZ, Z DR  0.5 dB? R = R(Z H,Z DR ) R = R(Z H ) ARMOR RAIN RATE ALGORITHM (1) R(K DP,Z DR ) (2) R(K DP ) (3) R(Z H,Z DR ) R = R(Z H ) GOOD DATA? YES NO R=BAD KDP ≥ 0.5? KDP< 0.5? YE S R = R(K DP ) YE S R =R(K DP,Z DR ) YE S R =R(Z H,Z DR ) no NONO YE S NONO UAH Rainfall algorithm Proprietary information, Walter A. Petersen, University of Alabama Huntsville 1-hr Accumulation 6-hr (N-hr) Accumulation

10. Product Access: http://www.nsstc.uah.edu/ARMOR/webimage Products All dual-pol variables for first 3 sweep elevations Hydrometeor types (fuzzy and table based) Rainfall 1-hour (image) Rainfall 6-hour (image) 6-hour TVA basin rain statistics (text) Operational Current image Last 10 image and 3-hour loops Scan comparisons between variables Automatic updating

11. UAH ARMOR RADAR 10/16/2007 (PM): 6-Hour Rainfall Accumulation Products 6-Hour Rainfall Accumulation Image Product Centered on ARMOR radar in Huntsville TVA Basins and 25 km range rings indicated with white contours. TVA gauge locations indicated as points Numeric table summarizing basin mean rainfall statistics (area mean, maximum, minimum and standard deviation of 1 km pixels in each basin). % RPxl = Percentage of basin area covered by > 0.005 inches of accumulated rainfall in the past 6-hours http://www.nsstc.uah.edu/ARMOR/webimage/

12. Tennessee River Basins MS AL GA NC KY TN VA SC (Unregulated area between Kentucky dam and mouth of Tennessee river, 710 Square Miles.) System refinements (more products, delivery methods, verification statistics etc.) Correction scheme for 88D (dual-pol tuning) Test terrain following rain-map with BREAM* database (*next talk) Topography: A problem area for radar i.Leave gauges in steep terrain for now ii.Target work (MAX/MIPS, other leveraged opportunities) site specific 88D corrections over terrain? 100 km Future TVA Work?

13. ROCKCASTLE AT BILLOWS 0.713 in. 0.027 in. 0.059 in. 0.367 in. 0.042 in. 0.000 in. WOLF CREEK LOCAL 0.504 in. 0.003 in. 0.069 in. 0.141 in. 0.029 in. 0.003 in. MOUTH OF OH TO BARKLEY DAM 0.000 in. 0.000 in. 0.000 in. 0.000 in. 0.000 in. 0.000 in. LAUREL R @ MUNICIPAL DAM @ CORBIN 0.255 in. 0.011 in. 0.035 in. 0.304 in. 0.068 in. 0.002 in. DOVER TO BARKLEY 0.000 in. 0.000 in. 0.003 in. 0.002 in. 0.000 in. 0.000 in. MOUTH OF TN TO KY DAM 0.000 in. 0.000 in. 0.000 in. 0.007 in. 0.000 in. 0.000 in. CUMB AB WILLIAMSBURG 0.074 in. 0.071 in. 0.090 in. 0.306 in. 0.111 in. 0.089 in. NORTH FORK HOLSTON RIVER NEAR GATE CITY 0.003 in. 0.873 in. 0.000 in. 0.774 in. 0.557 in. 0.603 in. CADIZ TO MOUTH 0.000 in. 0.000 in. 0.000 in. 0.000 in. 0.000 in. 0.000 in. CLINCH RIVER ABOVE TAZEWELL 0.021 in. 0.801 in. 0.026 in. 0.422 in. 0.331 in. 0.527 in........ Future: Test Utility of High-Resolution SPC WRF BASIN SPECIFIC QPF?