1. Dual Polarimetric Radar Walt Petersen, NASA-MSFC Take away: A versatile and useful tool for research and operations Outline: Local network in the Tennessee Valley Basic variables A sample of research and operational applications

2. Why does NASA care about ground-based dual-polarimetric radar? NASA Precipitation Science Dual-pol radar provides the means to remotely sense precipitation processes over volumes of atmosphere not practically accessible by other means. Advancement of physically-based satellite retrieval algorithm development.

3. Basic Polarimetric Radar Variables 1.Reflectivity factor Z at horizontal (Z h ) or vertical (Z v ) polarization [Conventional radar measure] - Measure of drop size and concentration; Most sensitive to drop SIZE (D 6 ) 2.Differential reflectivity Z DR (Z h /Z v ) - Measure of median drop diameter→ SIZE/SHAPE - Useful for rain / hail / snow discrimination→ SIZE/SHAPE 3.Propagation differential phase KDP  (k h – k v ) - Measure of water content and drop size→ NUMBER/SHAPE - Immune to radar calibration, attenuation, partial beam blockage 4.Correlation coefficient ρ hv - Indicator of mixed precipitation → SHAPE/PHASE/CANTING (Depolarization) - Useful for identifying non-meteorological scatterers too! Advantages: Better description of various particle types/shapes in a given volume Determine size distribution- more accurate rain rates (improved QPE) Hydrometeor ID and non-meteorological scatterers (clutter!) Consistent calibration Z h, k h Z v, k v We need the measurement in H and V directions! Walter A. Petersen NASA MSFC VP-61 Variables……..

4. Some Tools: N. Alabama Dual-Pol Radars and Supporting Infrastructure Instruments/Infrastructure Radars: C and X-band dual-polarimetric VHF Lightning mapping array Mobile Integrated Profiling System (MIPS) Surface met./Sounding Network of Disdrometers, Rain gauges Model/Forecast R&D (NASA SPoRT) Science/Application: Operational: Severe weather, QPE, lightning, WRF model; NOAA-HWT, NWS-COMET, TVA Basic/Applied Research: QPE, Cloud Physics and kinematics, cloud electrification, boundary Layer and convective initiation Satellite: NASA satellite precipitation retrieval algorithms, NOAA-GLM risk reduction Technology Transfer : UAH: Graduate student education NWS-Huntsville: Ops/Training TVA: Radar applications WHNT-TV (other markets): Dual-pol applications, public awareness KBMX RSA 68 km KGWX UAH/NSSTC THOR Center and Hazardous Weather Testbed MIPS/NSSTC ARMOR KHTX 75 DD lobe 1 km Res. 1.5 km Res. LMA 100-500 m LMA Antenna NEXRAD ARMOR MIPS Profiler MAX ? MAX RESEARCH + TECHNOLOGY TRANSFER Academic, Govt./Public, Private Sectors

5. Jumping right in with both feet………….. Microphysics: 25 July 2007 Heavy rain/hail mix,large drops Z  hv ZDR Vert. Develop/Mixed Phase Ext.Mixed phase Glaciating Glaciated Demonstrating the Microphysical sequence in a T-storm: Antiquity of application.

6. Proprietary content: Walter A. Petersen NASA MSFC VP-61 Using DP Variables for Thunderstorm /Lightning Remote Sensing Project: NASA MSFC Support of launch operations at KSC/CCAFS Question: Can we exploit the dual-pol characteristics for lightning initiation and cessation nowcasting/ Phase shifts due to vertically-aligned ice crystals 1.E-field increase 2.Vertical alignment 3.Lightning 4.E-field decrease 5.Crystal relaxation

7. Proprietary content: Walter A. Petersen NASA MSFC VP-61 Cool season applications: Melting level asymmetry………. Veering in a pre-frontal zone Note asymmetries in ZDR and RHOHV “bright bands” Polarimetric variables, in particular RHOHV, are much more sensitive to presence of mixed phase VR ZDR  hv Melting level ID is a big problem in traditional radar QPE

8. ARMOR 1/29/2010: Cold Season Mixed Phase w/Complex FL height Problem: Temperatures around or just above freezing….. Where is it liquid, freezing and frozen? Z has only limited information In this case, RHOHV adds considerable information We could interpret individual variables (takes time/practice) or..........?

9. Combine Pol variables into “easy to digest” information: Hydrometeor ID Vivekanandan et al. (1999, BAMS) Vivekanandan et al., 1999 Liu and Chandrasekar, 2001 Originally (90’s) we used simple tables…. But boundaries between categories in nature are soft……move to “fuzzification”

10. ARMOR: 1/29/10 Cold Season Mixed Phase: Now add HID- easier to interpret

11. Dual-Pol HID: N. AL Downburst Case and Lightning ZeZe First precip echo 1639 UTC Low level rain w/ strong development at T< 0 o C Mixed phase development 1648-1652 UTC First Lightning 1655 UTC Whole mixed phase core falls by 1702 UTC, last CG lightning 1703 UTC, last IC 1705 First IC 1655 First CG 1702

12. QPE: Dual-Pol Selling point- Hybrid Polarimetric Rainfall Algorithms Premise: Combined variables account for clutter, DSD variability, and phase ID

13. Application for TVA : ARMOR Distributed Rainfall Products (AREPS) 1-hr Accum. 6-hr Accum. 6-hr Basin Mosaic Text file for 6-hour accumulation Accumulated and Generated Every 5-minutes Also transmitted to TVA: Basin 1-hour accumulations Gauge-location max, min and mean Operational Products: Image and Text Walter A. Petersen NASA MSFC VP-61

14. AREPS QPE Product Verification: ARMOR vs. TVA rain gauges (October 2007 – June 2008) Point Comparisons Bias = -10% (-0.99 mm) Error = 12% Critical: For operational applications a constant monitoring of calibration maintains precision and accuracy of product. Walter A. Petersen NASA MSFC VP-61

15. CHILL: Rainfall Accumulation Optimization Algorithm CHILL: Rainfall Accumulation NEXRAD Z-R Algorithm Colorado CSU-CHILL Radar ICE-Algorithm (R. Cifelli)

16. Tornado Debris (2/6/2008) KDP, RHOVH used to map debris trace Tropical Cyclone Rita Tornadoes Size sorting and spatial separation of drops- microphysics feedbacks on dynamics?

17. Dual Pol radar is an outstanding tool for exploring and detecting many different manifestations of weather. When implemented in network form, it will be the next leap in both research and operational applications. Having said that…………….

18. We should also admit that dual-pol radar is not the “pot of gold at the end of the precip remote sensing rainbow”……

19. It won’t completely replace our best rain gauges…………

20. It won’t solve all of our rainfall-related issues………….

21. In the end…..it will be the combination of Dual-pol radar with other observations and our ability to assimilate that ensemble of information into human and automated analysis/modeling systems that will be the key! DSD Particle types/phase Rain rate 3-D Precip structure and evolution Microphysical feedbacks to dynamics Hydrology and water budgets And more………. W. Petersen, NASA-MSFC