1. Mesoscale variability and drizzle in stratocumulus Kim Comstock General Exam 13 June 2003

2. EPIC 2001 Sc cruise image courtesy of Rob Wood x EPIC 2001 Sc cruise

3. Data set Meteorological measurements on ship and buoy (T, q, U, LW, SST) Ceilometer MMCR and C-band radar GOES satellite imagery EPIC 2001 Sc data

4. Why are Sc important? Areal extent and persistence Effect on radiation budget

5. Key parameter: Sc albedo mean droplet size –CCN  aerosols cloud thickness –turbulence, entrainment, drizzle diurnal and mesoscale variations horizontal variability –mesoscale circulations –drizzle?

6. Central Questions To understand the physical processes that govern variability in Sc albedo, we must answer the following questions: What is the structure and life cycle of Sc? What is the role of drizzle in mesoscale variability? What role does the diurnal cycle play?

7. Goals: using EPIC data to address central questions Determine drizzle cell properties from C- band radar. Obtain and physically interpret signatures of mesoscale variability from ship and buoy time series. Estimate amount of drizzle and relate to mesoscale variability. Analyze diurnal cycle and determine how it modulates all of the above.

8. MMCR time-height section -60 -40 -20 0 15 dBZ hourly cloud top hourly LCL hourly cloud base

9. Quantifying drizzle We have reflectivity (Z) over a wide area around the ship from the C-band radar, but we want to know rain rate (R) information. No suitable Z-R relationships exist for drizzle. We developed Z-R relationships, Z=aR b, from in-situ DSD data at cloud base and at the surface: –aircraft (N Atlantic) and surface (SE Pacific) data –linear least squares regression (log 10 Z, log 10 R) Ideally, we want to know R at the surface.

10. Quantifying drizzle - method Evaporation-sedimentation model –assumes truncated exponential drop-size distribution (DSD) with mean size r –run with various r’s and drop concentrations Obtain model reflectivity profiles (Z( z )/Z CB ) and compare with MMCR profiles. –infer DSD for each MMCR profile –use model to extrapolate cloud base DSD characteristics to the surface (get surface R) Develop “bi-level” Z-R relationship using cloud base Z CB to predict surface R s.

11. Quantifying drizzle - results Apply bi-level Z-R to C-band cloud reflectivity data to obtain area-averaged rain rate at the surface. Average drizzle rates for EPIC Sc –0.93 mm/day at cloud base (range 0.3-3) –0.13 mm/day at the surface (range 0.02-0.6) Uncertainties due to –C-band calibration (  2.5 dBZ) –Z-R fitting procedure

12. Diurnal cycle At night the BL tends to be well mixed (coupled). During the day, the BL is less well mixed (decoupled). It tends to drizzle most during the early morning.

13. Coupled BL U U T cloud thickness 410 ± 60 m cloud base 930 ± 30 m ~ 30 km

14. Decoupled BL cloud thickness 310 ± 110 m cloud base 930 ± 60 m

15. Drizzling BL cloud thickness 415 ± 150 m cloud base 890 ± 110 m

16. Mesoscale variability Goes 8 Visible 19 October 0545 Local Time

17. Summary of previous work Though the diurnal signal is dominant, mesoscale structure is an integral part of the dynamics of the Sc BL. BL time series classified as coupled, decoupled or drizzling. There is a significant amount of drizzle in the SE Pacific BL, and it is associated with increased mesoscale variability

18. Future work Compare Sc mesoscale structure with previous studies of mesoscale cellular convection (MCC) Further examine radar data for 2-D and 3-D information –circulations (also use DYCOMS II and possibly TEPPS Sc) –compositing/tracking Analyze buoy time series for mesoscale variability in relation to “drizzle”.

19. MCC comparisons Compare our coupled cell with closed cell from Rothermel and Agee (1980) q

20. Radial velocities EPIC C-band volume-scan radial velocities are probably unusable due to pointing errors associated with these scans. Vertical RHI scans appear less susceptible to error, so the radial velocity data (in the RHIs) may be useful for qualitatively looking at 3-D circulations in the BL. TEPPS volume scans and DYCOMS II vertically-pointing radar data are other possibilities.

21. Example 00 90  180  270  15 km 30 km

22. EPIC Sc RHIs 17 October 2001 1058 UTC 00 90  180  270  2 km19 km dBZ

23. EPIC Sc RHIs 17 October 2001 1058 UTC 00 90  180  270  2 km19 km m/s

24. Comparison with DYCOMS II Anticipate receiving DYCOMS II aircraft data (vertically-pointing MMCR data and time series) –look for circulations associated with closed cells and drizzling conditions –look at variability associated with drizzle (flight RF02)

25. C-band composite Cell 1 Cell 2

26. Compositing/tracking: preliminary results Examples from tracked drizzle cells Time avg PDF of dBZAverage reflectivity Time (hr UTC) dBZ

27. Drizzle’s signature Air-sea temperature difference appears to be a good indication of drizzle occurring in the area.

28. Drizzle’s signature

29. Drizzle climatology Will apply air-sea  T analysis to year- long buoy time series to determine –frequency and persistence of drizzle –diurnal cycle information –cloud fraction associated with drizzle Longwave radiation can be used as a proxy for cloud fraction in the buoy data series. –relationship to satellite images

30. Buoy data Example of SST-Ta for 15 September 2001 satellite overpasses

31. Buoy data GOES 8 IR 1145 UTC WHOI BUOY

32. Buoy data GOES 8 Vis 1445 UTC WHOI BUOY

33. Buoy data GOES 8 Vis 1745 UTC WHOI BUOY

34. Buoy data GOES 8 Vis 2045 UTC WHOI BUOY

35. Schedule DateGoal Summer 03Submit Z-R paper Summer 03Compositing & sizing of drizzle cells Summer-Fall 03Contribute to broken cell/drizzle paper Fall 03Submit mesoscale variability paper Winter-Spring 04C-band radial velocity analysis Spring-Summer 04DYCOMS II data analysis Summer-Fall 04Satellite – time series analysis Winter 05Finish

37. LW as a proxy for cloud fraction LW-  T a 4 (W/m 2 )

38. Drizzle and open cells GOES image (color) and C-band reflectivity (gray scale) GOES image only

39. (Less) drizzle and closed cells GOES image (color) and C-band reflectivity (gray scale) GOES image only

40. Evaporation- sedimentation model r (  m) N (#/L)

41. C-band Sc Volume Scan

42. MCC – closed cell Moyer & Young 1994

43. Tracking algorithm Williams and Houze 1987