Mesoscale variability and drizzle in stratocumulus

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

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

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

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

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=aRb , from in-situ DSD data at cloud base and at the surface: • aircraft (N Atlantic) and surface (SE Pacific) data • linear least squares regression (log10Z, log10R) • 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)/ZCB) 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 ZCB to predict surface Rs.

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

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

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

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

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

22. EPIC Sc RHIs 17 October 2001 1058 UTC 2 km 19 km 0 90 180 270 dBZ

23. EPIC Sc RHIs 17 October 2001 1058 UTC 2 km 19 km 0 90 180 270 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. Time avg PDF of dBZ Average reflectivity Time (hr UTC) dBZ Compositing/tracking: preliminary results • Examples from tracked drizzle cells

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

31. WHOI BUOY Buoy data GOES 8 IR 1145 UTC

32. WHOI BUOY Buoy data GOES 8 Vis 1445 UTC

33. WHOI BUOY Buoy data GOES 8 Vis 1745 UTC

34. WHOI BUOY Buoy data GOES 8 Vis 2045 UTC

35. Schedule

37. LW as a proxy for cloud fraction LW-sTa4 (W/m2)

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 (mm) N (#/L)

41. C-band Sc Volume Scan

42. MCC – closed cell Moyer & Young 1994

43. Tracking algorithm Williams and Houze 1987