Mesoscale variability and drizzle in stratocumulus

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Mesoscale variability and drizzle in stratocumulus
Kim Comstock General Exam 13 June 2003
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EPIC 2001 Sc cruise
EPIC 2001 Sc cruise
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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

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Why are Sc important?

• Areal extent and persistence
• Effect on radiation budget

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Key parameter Sc albedo

• mean droplet size
• CCN ? aerosols
• cloud thickness
• turbulence, entrainment, drizzle
• diurnal and mesoscale variations
• horizontal variability
• mesoscale circulations
• drizzle?

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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?

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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.

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MMCR time-height section
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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, ZaRb , 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.

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Quantifying drizzle - method

• Evaporation-sedimentation model
• assumes truncated exponential drop-size
  distribution (DSD) with mean size r
• run with various rs 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.

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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

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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.

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Coupled BL
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Decoupled BL
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Drizzling BL
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Mesoscale variability
Goes 8 Visible 19 October 0545 Local Time
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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

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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.

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MCC comparisons

• Compare our coupled cell with closed cell from
  Rothermel and Agee (1980)

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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.

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Example
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EPIC Sc RHIs 17 October 2001 1058 UTC
2 km
19 km
0?
90?
180?
270?
dBZ
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EPIC Sc RHIs 17 October 2001 1058 UTC
2 km
19 km
0?
90?
180?
270?
m/s
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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)

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C-band composite
Cell 1
Cell 2
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Compositing/tracking preliminary results

• Examples from tracked drizzle cells

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Drizzles signature

• Air-sea temperature difference appears to be a
  good indication of drizzle occurring in the area.

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Drizzles signature
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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

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Buoy data

• Example of SST-Ta for 15 September 2001

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Buoy data
GOES 8 IR 1145 UTC
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Buoy data
GOES 8 Vis 1445 UTC
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Buoy data
GOES 8 Vis 1745 UTC
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Buoy data
GOES 8 Vis 2045 UTC
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Schedule
Date Goal
Summer 03 Submit Z-R paper
Summer 03 Compositing sizing of drizzle cells
Summer-Fall 03 Contribute to broken cell/drizzle paper
Fall 03 Submit mesoscale variability paper
Winter-Spring 04 C-band radial velocity analysis
Spring-Summer 04 DYCOMS II data analysis
Summer-Fall 04 Satellite time series analysis
Winter 05 Finish
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(No Transcript)
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LW as a proxy for cloud fraction
LW-sTa4 (W/m2)
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Drizzle and open cells
GOES image (color) and C-band reflectivity (gray
scale)
GOES image only
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(Less) drizzle and closed cells
GOES image (color) and C-band reflectivity (gray
scale)
GOES image only
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Evaporation-sedimentation model
r (mm)
N (/L)
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C-band Sc Volume Scan
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MCC closed cell

• Moyer
• Young
• 1994

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Tracking algorithm
Williams and Houze 1987