Hydrometeorology and Polarimetric Radar How can Polarimetric radar aid in flash flood forecasting? James J. Stagliano, Jr.1, James L. Alford 1, Dean Nelson 1, J. William Conway 2, Barbara Gibson 3 and Don Hyde 3 1 Enterprise Electronics Corporation 2 Weather Decision Technologies 3 Choctawhatchee, Pea and Yellow Rivers Watershed Authority

Outline  Failure of Radar  Promise of Polarimetric Weather Radar  Choctawhatchee, Pea, Yellow Rivers Watershed  EEC Polarimetric Weather Radar  Conclusion

Failure of Radar  Numerous meteorological and physical effects limit accurate rainfall estimation –Marshall – Palmer (Z-R) Relationship  Different DSD  Varies from event to event  Different DSD  Varies within precipitation event  Mie Resonance Effects –Hail Contamination –Beam Blockage –Attenuation –Nonuniform beam filling –Height Above Ground  Wind dispersion  Precipitation Type (Melting Layer)

Failure of Radar to Deliver

Radar – Precipitation Estimation  Rainfall Estimates given by Marshall-Palmer Relationship –Dependent on sixth power of diameter Drop Size QuantityZ Water Volume 1 mm dBZ 17,160 mm 3 4 mm 1 36 dBZ 270 mm 3 A factor of 63 difference

Radar – Drop Size Distribution Similar Reflectivity, Different Drop Size Distributions

Scattering

Precipitation Scattering  Assumption is all scattering is Rayleigh –True for S band –Not true for C and X band (Ryzhkov, 2005) C-band: = 5.33 cm Mie ~ 4.5 mm  X-band: = 3.0 cm Mie ~ 2.5 mm 

Polarimetric Weather Radar  Transmits microwave energy in both horizontal and vertical polarizations –Able to measure entire scattering matrix –Much more information of scattering medium  Standard Base Moments: Z H, V H, W H  Polarimetric Base Moments : Z DR,  DP,  HV, L DR –Estimate the average Drop Size Density –Identify average particle shape  Hydrometeor Classification –Immunity from beam blockage –Variables change due to rainfall  Much better rainfall estimations

Conventional Weather Radar

Polarimetric Radar And Vertical Pulse

Polarimetric Weather Radar Enhanced SIDPOL TM (2 Patents Granted to EEC)

Vertical cross-sections of radar variables and results of classification. NCAR Spol radar. August 14, Florida LR – light rain, MR – moderate rain, HR – heavy rain, LD – large drops, R/H – rain / hail mixture, GSH – graupel / small hail, HA – hail, DS – dry snow, WS – wet snow, IH – horizontally oriented crystals, IV – vertically oriented crystals

Polarimetric Weather Radar  Raindrops are oblate spheroids –  Horizontal Returns greater than vertical returns –  Phase shift in horizontal is more than in vertical   DP increases with range through rain

Polarimetric Rainrate (Doviak and Zrnic, 1993)

Polarimetric RainRate (K DP ) Specific Differential Propagation Phase Independent of receiver/transmitter calibration Independent of attenuation Less sensitive to variations of size distributions (compared to Z) Immune to particle beam blocking Unbiased if rain is mixed with spherical hail Noisy at low rainrates

Radar RainRate Marshall - Palmer K DP Z h - Z DR K DP - Z DR

Polarimetric Radar vs. Gauge (Bringi and Chandrasekar, 2001)

Polarimetric Weather Radar Rainfall Measurements “Traditional” R(Z) estimate Polarimetric R(K DP ) estimate

Polarimetric Radar and Rainfall One hour point measurements: Radar estimates vs. gages

Areal Mean Rain Rate Bias Hail

Choctawhatchee, Pea, Yellow Rivers Watershed History  1990 – Watershed Authorities created throughout state –After devastating Elba Flash Floods –CPYRWA only active authority  SE AL only region in the state with extensive data on its water resources and needs –Initially just Choctawhatchee – Pea Rivers –Started with 3 rain gauges  COE Installed Network of Stage and Rain Gauges  1997 – Yellow River Added  1998 – Major Flood  2005 – Conecuh River under consideration

Choctawhatchee, Pea, Yellow Rivers Watershed

EEC C-Band Polarimetric Radar  Transmitter –1 MW  Split between channels –500 kW per channel –Magnetron  Coherent on Receive –PW 0.4, 0.8, 2.0  s –PRF 300 – 2000 Hz  EDRP-9 Signal Processor Intermediate Frequency MHz Digitization rate 80 MHz Linear dynamic range >100 dB Minimum discernable signal <110 dB Clutter suppression >50 dB Range resolution >45 meters PRF 1300 Hz Phase noise <-53 dBc integrated over the Nyquist co-interval  Greatly Improved Accuracy –Precipitation Estimates Improved Up to 40%  SidPol TM Radar is Now a True Hydrological Instrument –Better Clutter Identification and Elimination –True Precipitation Classification  Ice  Snow  Hail  Liquid Rain

EEC C-Band Polarimetric Radar  2 Installed –UK Met (250 kW) –EEC (1 MW)  2 more installations this year –Valpraiso University (Indiana, USA) (1 MW) –Austria (250 kW)

Hydrological Modelling - HDSS  QPE-SUMS –Multiple sensor integration for quantitative precipitation estimation –Accurate estimates of rainfall  Basin accumulation to forecast flood risk

Alabama Consortium  Statewide Consortium – Universities  University of South Alabama  University of Alabama - Huntsville  Auburn University  University of Alabama - Tuscaloosa –Federal Agencies  NASA –State Agencies  Watershed Management Authorities  State EMA –Local Agencies  County EMA’s  Local First Responders –EEC

Integration Research Laboratory  Fully integrated and operational hydrometeorological forecasting center  Located at EEC  Operational testbed for new technology  Data freely shared with consortium partners –All data streams integrated  Satellite  Radar  AWS sensors  NWP –Integration  Sensors  Software  Communications  Training – Evaluation  Sensors  Software  Communications

Future Work  Tropical –Convective  Hail shafts  Large Drops  More attenuation –Hurricanes  Run-off Modeling  AL Network –Hurricane Dissipation  ExtraTropical –Stratiform  Brightband  Smaller Drops –Snow / Ice  Run-off Modeling South Alabama Valpariso, Indiana