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W.A. Petersen NASA – Marshall Space Flight Center, Huntsville, AL P. N. Gatlin, L. D. Carey

THE USE OF DUAL-POLARIMETRIC RADAR DATA TO IMPROVE RAINFALL ESTIMATION ACROSS THE TENNESSEE RIVER VALLEY. W.A. Petersen NASA – Marshall Space Flight Center, Huntsville, AL P. N. Gatlin, L. D. Carey University of Alabama in Huntsville – Earth Systems Science Center, Huntsville, AL

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W.A. Petersen NASA – Marshall Space Flight Center, Huntsville, AL P. N. Gatlin, L. D. Carey

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  1. THE USE OF DUAL-POLARIMETRIC RADAR DATA TO IMPROVE RAINFALLESTIMATION ACROSS THE TENNESSEE RIVER VALLEY W.A. Petersen NASA – Marshall Space Flight Center, Huntsville, AL P. N. Gatlin, L. D. Carey University of Alabama in Huntsville – Earth Systems Science Center, Huntsville, AL S. R. Jacks Tennessee Valley Authority, Knoxville, TN

  2. Motivation • Reduction of costs associated with maintenance of large rain gauge network • Provide a custom-tailored rainfall product specific to the end-user’s needs • Independent validation of ARMOR rain rate algorithms • Ground-validation for TRMM satellite measurements

  3. 112 sub-basins 1840 km2 189 rain gauges maintained by TVA Tennessee River Watershed KY TN NC SC MS GA AL • 11 sub-basins within 100 km of the ARMOR dual-pol. radar

  4. Advanced Radar for Meteorological & Operational Research • Location: • Huntsville International Airport, Huntsville, AL (Altitude 206m) • C-band dual-polarimetric Doppler radar • Simultaneous transmit and receive of H, V • Variables: Z, V, W, ZDR, ΦDP, ρhv • Operations: • 24-hrs a day / 7 days • Rain volumetric scans at least every 5-min (tilts: 0.7°,1.5 °,2.0 °) • Also used in research mode (e.g., RHIs, full volumes, vertically pointing scans) • Routine calibration: • Receiver calibrations • Solar scans • Self-consistency amongst variables • Comparisons with TRMM and rain gauges

  5. ARMOR Rainfall Estimation Processing System (AREPS) Grid rain rates (1 km2 spacing) Raw Iris Files T1-line NSSTC ARMOR Summation of rain rates Compute point and areal N-hr rainfall estimates End-user

  6. ICE PRESENT? 1-hr Accumulation 6-hr (N-hr) Accumulation no no YES R = R(ZH) GOOD DATA? KDP 0.3 and ZH  35? YES R = R(KDP) NO YES NO ZH BAD? R = R(ZHRAIN) YES NO R=BAD R=BAD NO KDP 0.3, ZH 35.0 dBZ ZDR 0.5 dB? NO NO ZH > 30 dBZ, ZDR 0.5 dB? YES R = R(ZH) YES (1) R(KDP,ZDR) (2) R(KDP) (3) R(ZH,ZDR) R = R(ZH,ZDR) R > 50 mm/hr, dBZ > 50 ,or Z, ZDR corr. too large ? YES R = R(KDP) ARMOR RAIN RATE ALGORITHM YES R =R(KDP,ZDR) KDP ≥ 0.5? YES R =R(ZH,ZDR) KDP< 0.5?

  7. AREPS Coverage • 100 km from ARMOR • 11 sub-basins • 42 rain gauges

  8. AREPS Distributed Rainfall Products • Rainfall products created every 5-min: • 1-hr and 6-hr basin/sub-basin rainfall statistics (mean, max, min, etc) • Rainfall at critical locations (e.g., dams) • rainfall accumulation images (1-hr, 6-hr) • Text files transmitted every hour to TVA • Contain previous hour’s rainfall • used as input by inflow model input 1-hr rainfall (also create 6-hr rainfall) 6-hr Basin Mosaic 6-hour accumulation statistics

  9. Verification: Point Comparisons ARMOR vs. TVA rain gauges(October 2007 – June 2008) Before Calibration Correction After Correction • Original bias and error targets achieved (+/-20%, +/-25% respectively) • Constant monitoring of calibration maintains precision and accuracy of product Bias = -17% (-1.80 mm) Error = 18% Bias = -10% (-0.99 mm) Error = 12% Radar Rainfall Estimate Improved

  10. Verification: Sub-basinsARMOR vs. rain gauge-derived areal mean(January 2008 – July 2008) • Radar rainfall estimates averaged over each sub-basin • rain-gauge network used by TVA to compute Theissen polygon values to represent each sub-basin • Radar underestimates sub-basin rainfall by only 8% • Random error = 20% • Largely attributed to Theissen polygons (i.e, density of rain gauge network with respect to sub-basin boundaries) Radar derived accum. (mm) Gauge derived accum. (mm)

  11. 6-Hour Rain Accumulation (in): 12 – 6 PM, 7/9/2008 2 1 3 1 Result: Distributed Radar Rainfall Measurement Benefits TVA Gauge-Estimated Basin Means vs. Radar BASIN GAUGE (in) ARMOR (in) Decatur-Wheeler 0.79 0.25 Guntersville-Decatur0.430.46 Upper Bear Creek 0.00 0.06 Town Creek 0.00 0.12 • Why are their gauge-radar differences? • Case 1 (no gauge rain when there is rain) • Rain narrowly missed gauge, but radar captured • Case 2 (isolated gauge “deluge”) • Single gauge located in heavy rain maximum- single point translated to entire basin- results in overestimate of basin mean • Case 3 (Gauge and radar match) • More gauges, broader rain distribution • Water management impacts? • How might the application of distributed rainfall measurements be extended?

  12. What’s next? • Employ NCAR hydrometeor identification algorithm to remove clutter and improve precipitation calculations • Correct for partial beam blockage • Use ARMOR to polarimetrically “tune” nearby NEXRAD until upgraded • Examine radar dominated rainfall estimates in a distributed model vs gauge only estimates

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