Radar Palet e Home Dual Polarized Analysis & Diagnosis 1 Precipitation Phase – Radar Signatures Radar characteristics of precipitation types –Stratiform rain –Convective rain –Hail –Snow –Freezing Rain/Ice Pellets –Drizzle/Freezing Drizzle Conventional Radar and “The Bright Band” Doppler Radar spectral width – Doppler Texture Doppler and the “Virga Hole” Dual Polarized Applications

Radar Palet e Home Dual Polarized Analysis & Diagnosis 2 Dual Polarized Precipitation Phase Applications The dual polarization of radar was designed to yield detailed information on the characterisitcs of the hydrometeor. King Radar north of Toronto, Ontario has been operating a dual polarized radar since November Ongoing research and algorithm development will yield results in much the same way that research led to advances in Doppler Radar applications. Canada’s first Doppler Radar was established at King Radar early in 1985.

Radar Palet e Home Dual Polarized Analysis & Diagnosis 3 Differential Reflectivity (ZDR) Ratio of scattered H and V power returns (PHH/PVV) Function of the particle shape and orientation Independent of absolute calibration and particle concentration Affected by differential attenuation

Radar Palet e Home Dual Polarized Analysis & Diagnosis 4 Differential Reflectivity (ZDR) Drop shape as a function of size:

Radar Palet e Home Dual Polarized Analysis & Diagnosis 5 Differential Reflectivity (Z DR ) H/V Good discriminator between heavy rain and hail. For hail Z DR ~1 (hail is round and tumbling) For heavy rain Z DR is large since Z HH >>Z VV (big drops are Big Macs – large horizontally & smaller in the vertical) Low values for ground clutter (< 0) (buildings tend to be vertical…) High values for biological scatters (> 3) (birds and bugs have winds – horizontally larger than vertical dimension…) Intermediate values meteorological ZDR > 0  H dim greater ZDR < 0  V dim greater ZDR ~ 0  spherical or randomly oriented

Radar Palet e Home Dual Polarized Analysis & Diagnosis 6 Differential Reflectivity (Z DR ) Light rain ( dB) Moderate rain ( dB) Heavy rain ( dB) Rain/Hail ( dB) Dry snow ( dB) Wet snow ( dB) Ice crystals ( dB)

Radar Palet e Home Dual Polarized Analysis & Diagnosis 7 Correlation Coefficient – ρ HV Correlation of the time series of H and V returns Not quantitative measurement Independent of calibration, attenuation and particle concentration A measure of the variety of particle shapes and fall modes

Radar Palet e Home Dual Polarized Analysis & Diagnosis 8 Correlation Coefficient – ρ HV For radar bins with single particle shape Zh/Zv is constant in series of pulses. No variance equals 1 on “rhoHV” display Mixed particles.. Zh/Zv not constant and variance recorded as less than 1.

Radar Palet e Home Dual Polarized Analysis & Diagnosis 9 Correlation Coefficient – ρ HV ~ 1 for single type of hydrometeors in a preferred orientation (rain or snow) Decreases with increasing diversity of hydrometeor shapes and orientation More significant if particles wet or large and irregular

Radar Palet e Home Dual Polarized Analysis & Diagnosis 10 Fuzzy Logic to Radar Interpretation Inputs (radar variables) Decision process (membership functions) Output classification (hydrometeor type)

Radar Palet e Home Dual Polarized Analysis & Diagnosis 11 Types of Membership Functions Typical one-dimensional Trapezoid Membership Function

Radar Palet e Home Dual Polarized Analysis & Diagnosis 12 2D Trapezoid Membership Function 2-D TMF for Moderate Rain

Radar Palet e Home Dual Polarized Analysis & Diagnosis 13 “Simple” Membership Functions

Radar Palet e Home Dual Polarized Analysis & Diagnosis 14 King HCA (iParCA) IDL code emulating the NSSL algorithm Three schemes of particle classification: –Simple –Summer –Winter

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