Warm Rain Detection &
Aerosol Impact on Cloud and Rain
Zhanqing Li
Feng Niu, Ruiyue Chen, Kwon-Ho Lee
University of Maryland, College Park, MD
Topics
„
Use of CloudSat, TRMM, Ground-based radars to
investigate if MODIS can
„
„
„
help detect drizzles
estimate rain rate
Use of ground-based (ARM) and space-borne (A-Train)
measurements to investigate
„
if aerosols have discernible long-term effects on
„
„
„
cloud height
cloud particle size
rainfall frequency
Topic I
„
Chen, R., R. Wood, Z. Li, R. Ferraro, F.-L. Chang, 2008, Studying the vertical
variation of cloud droplets effective radius using ship and space-borne remote
sensing data, J. Geophy. Res., 113, doi: 10.1029/2007/JD009596.
„
Chen, R., F. Chang, Z. Li, R. Ferraro and F. Weng, 2008: Impact of the
Vertical Variation of Cloud Droplet Size on the Estimation of Cloud Liquid
Water Path and Rain Detection. J. Atmos. Sci. 64, 3843-3853.
„
Chang, F.-L., Z. Li, 2003, Retrieving the vertical profiles of water-cloud
droplet effective radius: Algorithm modification and preliminary application,
J. Geophy. Res., 108, D(24), 4763, 10.1029/2003JD003906.
„
Chang, F.-L., Z. Li, 2002, Estimating the vertical variation of cloud droplet
effective radius using multispectral near-infrared satellite measurements, J.
Geophy. Res., 107.
Schematic Illustration of a Bispectral Retrieval
Procedure
τ'
0
(a)
(a) Conventional re retrievals by
assuming dre/dτ = 0.
3.7μm
10
1.6μm
20
4
τ'
0
6
8
10
12
14
16
(b)
3.7μm
10
1.6μm
20
4
τ'
0
6
8
10
12
14
(b) The linear-re retrievals with
dre/dτ = Δre/τtotal, where Δre =
13.1−11.8 μm as obtained from
the 3.7- (red) and 1.6-μm
(green) retrieved re values
shown in Figure (a).
16
(c) The optimal linear-re retrieval
for the two channels.
(c)
10
20
4
6
8
10
re (μm)
12
14
16
Chang and Li (JGR. 2002, 2003)
Validation with Ship-borne Radar
LWP(mm)
C-band reflectivity
60
60
20
0.7
50
50
0.6
10
40
40
0
30
-10
20
km
„
Estimation of cloud DER, DER profile, LWP from
MODIS measurements, 1x1 km, instantaneous.
Rain estimation at cloud base from C-band ship-based
radar during EPIC 2001, 0.5x0.5 km, 5-min averaged
km
„
0.5
0.4
30
0.3
20
0.2
-20
10
10
20
30
km
40
50
60
-30
10
0.1
10
Chen et al. (2008a)
20
30
km
40
50
60
0
Rain rate and Cloud Microphysics
10
1
10
1
C o rre latio n 0 .7 8
10
10
10
Rain Rate(mm/hr)
Rain Rate(mm/hr)
C orrelation 0.59
0
-1
-2
15
20
Re
10
base
(μ m )
25
30
10
10
10
0
-1
-2
0
0.1
0.2
0.3
0.4
0.5
0.6
L W P (m m )
1
Rain Rate(mm/hr)
C o rre latio n 0 .4 5
10
10
10
„
0
-1
DER at cloud base are
correlated more closely
with rain rate than
DER at cloud top
-2
15
20
R e top ( μ m )
25
30
Chen et al. (2008b)
DER and Drizzling
0.7
0.5
Drizzling, mean 18.55
Non-drizzling, mean 9.75
0.6
Drizzling, mean 20.74
Non-drizzling, mean 5.76
0.4
Probability
Probability
0.5
0.4
0.3
0.3
0.2
0.2
0.1
0.1
0
„
5
10
15
20
ReTop(μm)
25
30
0
5
10
15
20
ReBase(μm)
25
30
Drizzling increases DER at cloud base by 14.98,
increases DER at cloud top by 8.8
Topic II
Aerosol Impact on Rain
Frequency, Cloud
Thickness & Particle Size
Using Long-term Ground
and Global Satellite Data
Indirect Effects
Haywood and Boucher Revs. Geophys. 2000
Aerosol
Microphysical
Effect (AME)
1) Increased CCN - reduces reff
2) Drizzle suppression - increases LWC
Aerosol
Invigoration
Effect (AIE)
‘First’ indirect
effect (AME)
3) Increased cloud height
4) Increased cloud lifetime
‘Second’ indirect effect (AIE)
A lot of studies on aerosol effects on
precipitation with opposite findings
Aerosols (not including GCCN)
inhibit precipitation:
Kaufman and Nakajima 1993;
Borys et al, 1998; 2000;
Rosenfeld and Lensky 1998;
Rosenfeld 1999, 2000;
Aerosols can increase
precipitation:
Ohashi and Kida 2002
Shepherd and Burian 2003
Amiranashvili et al 2004
Filho et al (2004)
Khain et al (2004a,b)
Givati and Rosenfeld 2004
Tao et al (1995), Ferrier et al (1996)
Khain et al (2005-QJRMS)
Axel et al (2004);
Khain et al, 2001;
Axel and Beheng (2004)
Khain and Pokrovsky (2004);
Tao et al (2004)
Khain et al (2004a,b)
Van den Heever et al (2004)
Axel et al (2004); Tao et al (2004)
Lynn et al (2005a,b)
Teller and Levin(2006)
Lynn et al (2006)
Margaritz et al (2006)
Khain et al (2005, 2006)
Lynn et al (2006)
Jirak and Cotton (2006)
Khain and Pokrovsky (2006)
Rosenfeld IRS08 Talk
But little has been done for rain
frequency!
While rain amount and frequency change in
harmony in general, the impact of aerosol on
initiation of rain is likely to be more significant
than rain amount, as the latter is dictated more
by dynamics and abundance of available water
Datasets Used
„
„
„
Daily ARM SGP data 2003-2008 (~20000 data samples)
Most complete and highest quality measurements of
aerosol, cloud, atmospheric state
Key variables used:
„
„
„
„
„
„
Aerosol CN number concentration on the ground
Tipping bucket rain gauge
LWP from microwave radiometer
Cloud bottom and top heights from cloud radar & lidar
NOAA/NCAR Reanalysis
MODIS cloud particle size
Rainfall Frequency for clouds with
different liquid water path at SGP
(All-Season Data)
60.00%
2.00%
2
50.00%
LWP:0.4-0.8mm R2 = 0.037
1.60%
LWP:0.0-0.4mm R2 = 0.7803
40.00%
AIE ?
1.20%
30.00%
0.80%
AME ?
20.00%
0.40%
10.00%
0.00%
0.00%
0-1000
10002000
20003000
30004000
40005000
50006000
CN Number Concentration (1/cm^3)
Rainfall Frequency for Clouds
with Low LWP
Rainfall Frequency for Clouds
with High and Moderate LWP
LWP:>0.8mm R = 0.9088
Comparison of the Results Obtained
in All Seasons and in Summer Only
60.00%
LWP:>0.8mm
LWP:0.4-0.8mm
LWP:0.0-0.8mm
All
Slope=3.45E-5
Slope=-0.35E-5
Slope=-0.14E-5
Summer
Slope=6.6E-5
Slope=-0.25E-5
Slope=-0.07E-5
2.00%
1.60%
50.00%
1.20%
40.00%
30.00%
0.80%
20.00%
0.40%
10.00%
0.00%
0.00%
0-1000
10002000
20003000
30004000
40005000
50006000
CN Number Concentration (1/cm^3)
Rainfall Frequency for Clouds with
Low LWP
Rainfall Frequency for Clouds with
High and Moderate LWP
70.00%
Cloud Thickness for clouds with
different cloud base heights
3500
2
CBH:<1km R = 0.9718
Cloud Thickness (m)
3000
CBH:1km-2km R2 = 0.9159
2
CBH:2km-4km R = 0.1055
2500
2000
1500
1000
500
0
0-1000
10002000
20003000
30004000
40005000
50006000
CN number concentration (1/cm^3)
Most Direct Evidence of the Invigoration Effect !
Cloud Base Heights
3500
Cloud Base Height (m)
3000
y = -10.06x + 2969.9
R2 = 0.2024
2500
CBH:<1km
CBH:1km-2km
2000
y = 12.978x + 1404.7
R2 = 0.586
1500
CBH:2km-4km
Linear (CBH:2km-4km)
Linear (CBH:1km-2km)
Linear (CBH:<1km)
1000
y = 7.9095x + 330.05
R2 = 0.5579
500
0
0-1000
10002000
20003000
30004000
40005000
50006000
CN Number Concentration (/cm^3)
Cloud Top Heights
(for clouds of cloud base <1km)
Summer Only
All Seasons
7000
10000
5000
y = 237x + 4160.4
R2 = 0.8169
4000
3000
2000
y = 32.296x + 964.41
R2 = 0.7245
1000
Cloud Top Height (m)
6000
Cloud Top Height (m)
y = 907.49x + 3297.4
R2 = 0.82
9000
8000
7000
6000
CTH:>2km
CTH:>2km
CTH:<2km
CTH:<2km
Linear (CTH:>2km)
Linear (CTH:>2km)
4000(CTH:<2km)
Linear
Linear (CTH:<2km)
5000
3000
y = 22.14x + 1159.4
R2 = 0.2692
2000
1000
0
0
0-1000
10002000
20003000
30004000
40005000
50006000
CN Number Concentration (/cm^3)
0-1000
10002000
20003000
30004000
40005000
50006000
CN Number Concentration (/cm^3)
For clouds with CBH<1km, clouds are classified into two categories with
cloud top heights greater (blue) and less than (red) 2km.
Data
•Aqua/MODIS:
Level 3 daily aerosol product: AOD, Angstrom exponent
•Cloudsat: Level 2 daily products
2C-PRECIP-COLUMN product version 000: Precip_rate
2B-TAU product version 008: COD, mean Re
2B-GEOPROF-LIDAR product version 003: layer height
2B-CWC-RO product version 008: LWP, IWP
•Available DATA:
collected during Aug 2006~Aug. 2007
more will be downloading
•Outputs:
Scatter plots for cloud properties vs. Aerosol Index (daily observation)
Annual mean AI, COD, Re distribution maps (coincident only)
Global Non‐Precipitable Clouds
MODIS AI
Cloudsat COD
MODIS pixels were chosen where
Cloudsat COD exists.
AI=AODxAngstrom [Nakajima et al., 2001]
Cloudsat Re
Global Mean Aerosol Indirect Effect From CloudSat and MODIS Competition of two opposite effects
Meteorological
effects
Responses of
Rainfall frequency
to increasing CN
Invigoration
Increase rainfall
Effects
Suppress
rainfall
Microphysical
Effect
Depend critically
on cloudbase !!
Depend critically
on available water
Future Studies
Use of GLOBAL CloudSat-Calipso-MODIS-AMSR-E together with
LONG-TERM ground measurements to address the following questions
„
„
„
What are the NET effects of aerosols on cloud
(coverage, hydrometeor amount, height, etc.) and
precipitation on long-term and large scales?
Under what conditions, the aerosol’s microphysical
effect (AME) is dominant over the thermodynamic
effect (AIE), and vice versa; and what is the
determinant factor?
How do the AME and AIE depend on aerosol
properties, weather condition and cloud regimes?
Surface Pressure (hp)
Surface temperature (Degree Celsius)
982
25
20
15
10
T
5
0-1000
10002000
20003000
30004000
40005000
20
10
WV
10002000
30004000
40005000
CN Number Concentration (/cm^3)
50006000
LW P:0.4
LWP:
LWP:
LW P:0.0
972
P
10002000
20003000
30004000
40005000
50006000
CN Number Concentration (/cm^3)
6
5.5
5
LW
LW
4.5
LW
4
3.5
3
20003000
LWP:
0-1000
Surface Wind Speed (m/s)
Water Vapor (cm)
30
LW P:>0
974
6.5
40
0-1000
976
50006000
50
0
978
970
CN Number Concentration (per cm^3)
60
980
Wind
0-1000
10002000
20003000
30004000
40005000
50006000
CN Num ber Concentration (per cm ^3)