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Radar Calibration Radar Snowfall Estimation Automated Precipitation Detection, Amount and Typing

Radar Calibration Radar Snowfall Estimation Automated Precipitation Detection, Amount and Typing. Paul Joe, Brian Sheppard, Nick Kouwen Isztar Zawadzki, Norman Donaldson. Outline. General Comments Wx Radar/Precip Measurements Radar Calibration Workshop Automated Snowfall Amounts/Typing.

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Radar Calibration Radar Snowfall Estimation Automated Precipitation Detection, Amount and Typing

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  1. Radar CalibrationRadar Snowfall Estimation Automated Precipitation Detection, Amount and Typing Paul Joe, Brian Sheppard, Nick Kouwen Isztar Zawadzki, Norman Donaldson

  2. Outline • General Comments Wx Radar/Precip Measurements • Radar Calibration Workshop • Automated Snowfall Amounts/Typing

  3. Conclusions • Radar for QPE not there yet • Can work well in certain situations • Long range and large areas problems are being solved • But not demonstrated to the hydrology community

  4. General Comments • Since 1948/52 • Z related to Drop Size Distributions • Z = nD6 ; R~D4.5 • Z = 200 R 1.6 • “valid for rain and snow” • many more since then (+/- 3 dB) • Z related to R in ‘ideal situation’ • short range • Z related to snow water equivalent • DSD is only one of the factors • long range • Joss and Waldvogel 1990

  5. Snow/Rainfall Estimation Factors • Space-time sampling • Ground Clutter/Anomalous Propagation • Vertical Profile Correction/Bright Band • Calibration • Attenuation • Partial Beamfilling • DSD Z-R/Rain Classification and type • convective vs stratiform • wet bulb technique (rain vs snow) • Drop Size Distribution • Z-R convective, Z-R stratiform, Z-A • Wind drift problem for snow (application dependent)

  6. Ideal SituationClose Range Difference due to DSD (Joss)

  7. Snowfall • 3 seasons of very good comparisions • well calibrated radar • well sited gauge • consistent snow type • low wind speed • better than rain results • effective climatological ZS relationships • low snow gradients

  8. Low Level Scanning 0o GC removal

  9. Network Processing (QA) Using two radars goes along way toward filling gaps

  10. Vertical Profile of Reflectivity and Range Convective Stratiform Snow

  11. Vertical Profile of Reflectivity and Range

  12. Radar Calibration Workshop Summary • Complex system - Stable electronic calibration but consistent? • Rainfall as a “success criteria” • Expansive definition of calibration • various criteria of success • calibration, performance measurements, validation, adjustment • Inter-radar/network comparisons • cross-radar comparisons including TRMM (GPM) • Validation • problematic standards • trend to use basins • Successful absolute external target “calibrations” • Prevalent use of the Sun

  13. Calibration and Validation(Andy White Nexrad ROC) • Calibration is the process of quantitatively defining system responses to known, controlled signal inputs. • Validation is the process of assessing by independent means the quality of the data products derived from system inputs. • Validation is a natural adjunct to calibration. • Calibration - validation inseparable Mean Reflectivity Error of -1.47 dB, σ = 1.34 dB before solar cal

  14. Calibration with a DSD gage (POSS) Using the Z-R relationship for each day or by event(Zawadzki, MRO; Sheppard and Joe, MSC) By Event and ZR coefficients 34% to 7.5% error reduction

  15. Range Correction with Raingauges ApproachTo Resolve a Heterogeneous Network(Daniel Michelson, SMHI) Range Correction See also USBR studies

  16. Calibration Measurements • Transmitter: • Frequency, PRF, Average RF power • Antenna: • Gain, Beamwidths • Receiver: • Output vs. RF input, IF filter loss, [Range] Uncertainty Analysis

  17. Inter-radar Comparisons TRMM & WSR88D(Meneghini, NASA) TRMM PR Stability ~0.5 dB; Consistent device Compare with WSR88D Inter-radar Comparisons(Asko Huuskonen FMI)

  18. Needs • Consistency of calibration • Redundant measurements • Error estimates • Use small basins to integrate the measurements (USWRP QPE workshop) - overcome scale and time sampling (Uijlenhoet, Princeton)

  19. High Temporal Snow Rate Measurements Experiment to assess our ability to provide 1 minute information on precipitation type and amount.

  20. Vaisala FD12P Biral VPF730 Fwd Scattering, capacitance Fwd & Back Scattering, pulse detection of terminal velocity POSS WiVis Microwave CW Doppler Fischer-Porter Manual Obs IR scintillation, spectral analysis of fluctuations, fwd scattering

  21. POSS and Snowfall 1 minute data

  22. Window Matching vs Minutely Matching • Minutely matching - too many ways for mismatch • Develop “window matching” to try to mimic how an observation is made • “sensor observation time window” is defined • to match the uncertainty in manual observation period • “percentage of window” is defined • predominant type is determined • to match the persistence criteria • “observation window” determined by an “observer report” • one event = one observation • no report = no event = no comparison (even if sensor reports)

  23. IdentificationHeike Skill Score All show skill!

  24. How are we doing with the various steps? (Zawadzki) Removing non meteorological data: Filtering at signal processing Morphology of reflectivity and Doppler Polarization diversity VPR correction for beam height and shadows: Morphology of reflectivity Adjustments with gages (N N, Optimization) Probability Matching Correction for attenuation: Networks of radars? Polarization diversity? Z-R relations: Polarization diversity? Morphology of reflectivity? Validation: not enough

  25. Summary • Progress on many fronts • user success criteria • network consistency needed • radar adjustments • calibration • automated high temporal measurements • Much more to do • complete correction system needed • need to demonstrate • Need a standard for high temporal rate measurements • necessary but not sufficient demonstration

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