Mesoscale variability and drizzle in stratocumulus

Kim Comstock

General Exam13 June 2003

EPIC 2001 Sc cruise

image courtesyof Rob Wood

x

EPIC 2001 Sc cruise

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

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

Key parameter: Sc albedo

• mean droplet size– CCN aerosols

• cloud thickness– turbulence, entrainment, drizzle

•diurnal and mesoscale variations• horizontal variability

– mesoscale circulations– drizzle?

Central QuestionsTo 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?

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.

MMCR time-height section

-60 -40 -20 0 15 dBZ

hourly cloud top

hourly LCL

hourly cloud base

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.

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.

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

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.

Coupled BL

U

UT

cloud thickness 410 ± 60 mcloud base 930 ± 30 m

~ 30 km

Decoupled BL

cloud thickness 310 ± 110 mcloud base 930 ± 60 m

Drizzling BL

cloud thickness 415 ± 150 mcloud base 890 ± 110 m

Mesoscale variabilityGoes 8 Visible19 October

0545 Local Time

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

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”.

MCC comparisons Compare our coupled cell with

closed cell from Rothermel and Agee (1980)

q

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.

Example

0

90

180

270

15 km

30 km

EPIC Sc RHIs 17 October 2001 1058 UTC0

90

180

270

2 km 19 km

dBZ

EPIC Sc RHIs 17 October 2001 1058 UTC0

90

180

270

2 km 19 km

m/s

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)

C-band compositeCell 1Cell 2

Compositing/tracking: preliminary results

• Examples from tracked drizzle cells

Time avg PDF of dBZ Average reflectivity

Time (hr UTC)dBZ

Drizzle’s signature• Air-sea temperature difference

appears to be a good indication of drizzle occurring in the area.

Drizzle’s signature

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

Buoy data• Example of SST-Ta for 15

September 2001 satellite overpasses

Buoy dataGOES 8 IR 1145 UTC

WHOI BUOY

Buoy dataGOES 8 Vis 1445 UTC

WHOI BUOY

Buoy dataGOES 8 Vis 1745 UTC

WHOI BUOY

Buoy dataGOES 8 Vis 2045 UTC

WHOI BUOY

ScheduleDate GoalSummer 03 Submit Z-R paperSummer 03 Compositing & sizing of drizzle

cellsSummer-Fall 03 Contribute to broken

cell/drizzle paperFall 03 Submit mesoscale variability

paperWinter-Spring 04 C-band radial velocity analysisSpring-Summer 04 DYCOMS II data analysisSummer-Fall 04 Satellite – time series analysisWinter 05 Finish

LW as a proxy for cloud fractionLW

-T a4 (

W/m

2 )

Drizzle and open cellsGOES image (color) and C-band reflectivity (gray

scale)GOES image only

(Less) drizzle and closed cells

GOES image (color) and C-band reflectivity (gray

scale)GOES image only

Evaporation-sedimentatio

n model

r (m)

N (#/L)

C-band Sc Volume Scan

MCC – closed cell

Moyer&

Young 1994

Tracking algorithm

Williams and Houze 1987