National Weather Service Dual-Polarization Radar Technology Photo courtesy of NSSL

National Weather Service Acknowledgements Paul Schlatter and Andrew NWS Warning Decision Training Branch Norman, OK Paul Schlatter and Andrew NWS Warning Decision Training Branch Norman, OK

National Weather Service What Is Dual-Polarization Radar Technology Exactly?

National Weather Service Why Is Dual-Polarization Technology Important? Conventional Radar Dual-Polarization Radar Conventional radar tells us about the size of objects Dual-polarization radar tells us about the size, shape, & variety of objects – = Size ( | + –) = Shape σ ( | + –) = Variety

National Weather Service Highlights of How Dual-pol Data Will Aid Decision Makers 1.Improved detection and mitigation of non-weather echoes (insects, clutter, etc.) 2.Melting layer identification 3.Hydrometeor classification (precip type) 4.New severe storm signatures 5.Better precipitation estimation 1.Improved detection and mitigation of non-weather echoes (insects, clutter, etc.) 2.Melting layer identification 3.Hydrometeor classification (precip type) 4.New severe storm signatures 5.Better precipitation estimation

National Weather Service List of New Products via Dual-Pol  Differential Reflectivity (ZDR)  Correlation Coefficient (CC)  Hydrometeor Classification Algorithm (HCA)  Melting Layer Detection Algorithm (MLDA)  Specific Differential Phase (KDP)  9 NEW Precipitation Estimation Products  Differential Reflectivity (ZDR)  Correlation Coefficient (CC)  Hydrometeor Classification Algorithm (HCA)  Melting Layer Detection Algorithm (MLDA)  Specific Differential Phase (KDP)  9 NEW Precipitation Estimation Products

National Weather Service Differential Reflectivity (ZDR) Simple definition: Ratio of Horizontally and Vertically Oriented Power Returns Size and shape dependent Values of -2 to +6 dB Simple definition: Ratio of Horizontally and Vertically Oriented Power Returns Size and shape dependent Values of -2 to +6 dB

National Weather Service Differential Reflectivity (ZDR) ZDR < 0 Indicates the presence of large liquid drops. Most horizontal ZDR ~ 0 Indicates large hail or hail shafts without a lot of liquid water ZDR ~ 0 Indicates large hail or hail shafts without a lot of liquid water

National Weather Service Physical Interpretation Spherical (small rain drops and large hail) Horizontal (large rain drops, melting hail, insects, etc.) Vertical (i.e. vertically oriented ice crystals, sometimes large hail) ZDR ~ 0 dBZDR > 0 dBZDR < 0 dB PvPv PhPh PvPv PhPh PvPv PhPh Z h ~ Z v Z h > Z v Z h < Z v

National Weather Service Reflectivity vs ZDR Is there hail and if so where is it, exactly? Area of ZDR ~ 0 (blues)

National Weather Service Updraft Detection  “ZDR columns”: regions of liquid water found above the environmental 0 o C height (freezing level)  Water droplets “flattened” by intense updraft (ZDR > 0)  “ZDR columns”: regions of liquid water found above the environmental 0 o C height (freezing level)  Water droplets “flattened” by intense updraft (ZDR > 0)

National Weather Service Differential Reflectivity (ZDR) Simple definition: Measures the relationship between the horizontal and vertical power returns ZDR is useful for: Detecting the presence of liquid drops (especially large drops) Detecting updrafts containing liquid water above the freezing layer Detecting severe-sized hail Simple definition: Measures the relationship between the horizontal and vertical power returns ZDR is useful for: Detecting the presence of liquid drops (especially large drops) Detecting updrafts containing liquid water above the freezing layer Detecting severe-sized hail

National Weather Service Correlation Coefficient (CC) Simple definition: The VARIETY or DIVERSITY of shapes reflected back to the radar “Spectrum width” for reflectivity Values range from 0 to 1 Simple definition: The VARIETY or DIVERSITY of shapes reflected back to the radar “Spectrum width” for reflectivity Values range from 0 to 1

National Weather Service What Do We Mean by Variety? Small Variety Example When the radar detects objects similar in size and type, the correlation coefficient is high

National Weather Service What Do We Mean by Variety? Large Variety Example When the radar detects a variety of object sizes and types, the correlation coefficient is lower

National Weather Service Physical Interpretation Non-Meteorological (birds, insects, etc.) Uniform Precip (rain, snow, etc.) Non-Uniform or Mixed Precip (hail, melting snow, etc.) Shapes are complex and highly variable. Horizontal and vertical pulses will behave very differently with these objects Also GIANT hail, larger than golfballs, because so few of these stones among much smaller objects Shapes are fairly simple and do not vary much. Horizontal and vertical pulses behave very similarly with these objects Precip vs NonPrecip Shapes can be complex and are MIXED phase. Horizontal and vertical pulses behave somewhat differently with these objects Melting Layer Detection High Diversity Low CC (< 0.85) Low Diversity High CC (> 0.97) Diverse Moderate CC (0.85 to 0.95)

National Weather Service Melting Layer Helps both aviation & surface transportation better resolve precipitation type issues Identifies areas of icing Helps both aviation & surface transportation better resolve precipitation type issues Identifies areas of icing Liquid Melting Frozen

National Weather Service Winter Precip Are all these precip types the same? Right is uniform (either all rain or all snow) Center and left are mixed precip

National Weather Service Precip vs NonPrecip  Chaff, AP/Ground Clutter, Birds/Insects Standard Reflectivity (Z) Correlation Coefficient(CC)  Chaff, AP/Ground Clutter, Birds/Insects Standard Reflectivity (Z) Correlation Coefficient(CC)

National Weather Service Precip vs NonPrecip 0.5 o Base Reflectivity 0.5 o Correlation Coefficient Biological Targets Developing Showers

National Weather Service Very Large Hail Detection Is there very large hail here? Area of low CC

National Weather Service Correlation Coefficient (CC) Simple definition: Measure of similarity between the horizontally and vertically oriented pulses in a volume (VARIETY) Lower CC values useful for identifying: Non-weather targets Melting layer Very large hail Simple definition: Measure of similarity between the horizontally and vertically oriented pulses in a volume (VARIETY) Lower CC values useful for identifying: Non-weather targets Melting layer Very large hail

National Weather Service Hydrometeor Classification Algorithm (HCA) Simple definition: Uses “fuzzy logic” algorithm to make a best guess at radar echo classification on every elevation angle scan Combines ZDR, CC and other data HCA is useful for: Identifying precipitation type Hail detection Biological target detection Ground clutter removal Simple definition: Uses “fuzzy logic” algorithm to make a best guess at radar echo classification on every elevation angle scan Combines ZDR, CC and other data HCA is useful for: Identifying precipitation type Hail detection Biological target detection Ground clutter removal

National Weather Service  Algorithm makes best guess of dominant radar echo type –For every radar elevation angle Hydrometeor Classification Algorithm Lgt/mod rain Heavy rain Hail “Big drops” Graupel Ice crystals Dry snow Wet snow Unknown AP or Clutter Biological Currently 11 Classification Options

National Weather Service Hydrometeor Classification Algorithm

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National Weather Service Summary  Dual-Polarization Radar: Provides more information about observed targets (i.e., size, shape, & variety) Allows forecasters to make better decisions (hopefully) about issuing products Enables your local office to improve service during hazardous weather  Dual-Polarization Radar: Provides more information about observed targets (i.e., size, shape, & variety) Allows forecasters to make better decisions (hopefully) about issuing products Enables your local office to improve service during hazardous weather Hail Mixed w/Rain BigDrops HeavyRain Rain

National Weather Service Ken Drozd NWS Tucson x 223 Ken Drozd NWS Tucson x 223