1. Single Frequency Observations of Warm Clouds and Precipitation Edward Luke with Pavlos Kollias, Frederic Tridon, Stefan Kneifel, and Alessandro Battaglia Short Course on Research Applications of Radar Doppler Spectra Garmisch-Partenkirchen, Germany August 31, 2014

2. On a Mac or Linux: java -classpath netcdfAll-4.3.jar:BNLSpecVisToolkit-current.jar SpectraVisualizationToolkit On Windows (a semicolon replaces the colon): Java -classpath netcdfAll-4.3.jar;BNLSpecVisToolkit-current.jar SpectraVisualizationToolkit Visualizer Software Startup Use the correct path to the two.jar files in the above command if they are not in your current directory. BNLSpecVisToolkit-1.0.10.jar  BNLSpecVisToolkit-current.jar

3. Non-precipitating Cloud Particles have negligible fall velocities, making them tracers of air motion.  = f(  t,  s ) V air

4. Spectral Broadening Due to Shear

5. A Simple Simulation of Broadening Due to Turbulence

6. Non-Rayleigh Features Observing Precipitation with a Cloud Radar—Why Mie? (Lhermitte, 1988; Kollias et al. 2002)

7. Air Velocity Retrieval in Stratiform Rain

8. DSD Retrieval in Stratiform Rain Measured Forward Model Drop size distribution Radar cross section Retrieval Incomplete Gamma Fit Power spectrum

9. Vertical Air Velocities and Rainfall DSD Vertical air velocity to an accuracy of 10 cm/s Exploration of Drop Size Distribution (DSD) parameters in time and height Excellent agreement with surface disdrometer measurements (Giangrande et al., 2010) N(D) = N 0 e -ΛD

10. Spectrum Skewness in the Azores A marked microphysical transition occurs near midday, as indicated in this time-height plot of spectrum skewness. autoconversion dominated accretion dominated

11. Spectrum Skewness Captures Information Missed by Spectrum Width  = f(  t,  s,  DSD )

12. Spectrum Skewness Captures Information Missed by Spectrum Width  = f(  t,  s,  DSD )

13. Drizzle Onset in the Doppler Spectrum Drizzle first appears here The Doppler spectrum of cloud droplets without drizzle is very close to symmetrical due to action of small-scale turbulence. Thus, early drizzle growth should impose a deviation (positive skewness) from the near-zero skewness of the background (cloud PSD Doppler spectrum).

14. Doppler spectra skewness and microphysics Cloud Drizzle Cloud dominates 1 st, 3 rd and 6 th moments of N(D) Cloud dominates 1 st, 3 rd and 6 th moments of N(D) Cloud dominates 1 st, 3 rd ; Drizzle dominates 6 th moment of N(D) Cloud dominates 1 st, 3 rd ; Drizzle dominates 6 th moment of N(D) Kollias et al., 2011a, JGR Starting and transition state

15. Doppler Spectrum Skewness – A Powerful Radar Observable Sensitive to Drizzle Onset Drizzle radar reflectivity higher than cloud radar reflectivity Cloud radar reflectivity higher than drizzle radar reflectivity Cloud+drizzle radar Doppler spectrum (Z cloud >Z drizzle ) Cloud+drizzle radar Doppler spectrum (Z cloud <Z drizzle ) Cloud-only radar Doppler spectrum

16. Positive Doppler spectra skewness (indicator of drizzle onset) is more sensitive than reflectivity to identify drizzle onset Change in skewness Change in reflectivity

17. Luke and Kollias, 2013 Cloud/Drizzle Spectrum Partitioning Key assumption 1: On average, cloud power is equal on either side of spectrum peak. Key assumption 2: Drizzle particle fall velocities generally exceed the spectrum broadening due to dynamics (violated by heavier drizzle).

18. Drizzling Stratocumulus Retrievals Luke and Kollias, 2013, JTECH

19. Insect Clutter Spectra Luke et. al., 2008, JTECH

20. Insect Clutter Detection Luke et. al., 2008, JTECH

21. In Summary We have looked at some properties and retrieval approaches for radar Doppler spectra representative of Drizzling warm marine stratocumulus Continental stratiform rain Fair weather cumulus Clear air clutter

22. http://www.gim.bnl.gov/armclouds/rad ar_spectra_shortcourse_erad2014/