Remote sensing of clouds
from A-Train!

Mini-Workshop on A-Train Science (March 8, 2013), 15:20-15:40 (20)
Faculty of Engineering Bldg.1 (Room-15), The University of Tokyo!

!"#$%&'()%"!
•! Clouds exert an important influence on the water and energybalances and processes, thus, more observations arerequired.
•! We have long history of the passive sensing of clouds, using
the NOAA, ADEOS2, TRMM, Terra/Aqua, ENVISAT, !
•! Recently, active sensing open the door toward better
understanding of clouds, in terms of cloud evolution process. !

Synergistic analysis of clouds : CloudSat + Aqua/MODIS!

Cloud Radar!Vertical structure"!
Lidar(Vertical)!

Imager!Horizontal structure"!
Microwave(LWP etc.)!

3

Cloud properties were drastically modified
by volcanic ash!
!Mt. Kilauea, Hawaii 2$'3)%"-4"-.//1"!

*'+,-.//0!

*'+,.//1,-2$'3)%"-!

CDR!

567-89:2();2-3<$)(=2-$<&4'>?6<#<-$-@2$$<A-BC6!D*=+%$4#EF-G-5*H5CB-I2JK/.-

CDR!

CDR!
LM,///NF!
LO,///NF!

Due to SO2 release,
CDR decreased 15"m !#12"m- 5 W/m2 change in SW radiation (Eguchi
http://www.darkroastedblend.com/2007/11/hawaiian-volcanoes-beauty-terror.html! et al. 2011)!

Directions of the cloud research!
•! Climate change study!Long term record
AVHRR, MODIS, GLI, etc, and GCOM in future!

•! Cloud evolution process!3-D observation
CloudSat, Calipso, EarthCARE, + Passive sensors•! Observation + Model simulation!!

5Simulation of the cloud evolution
5 hours simulation !

Condensatio
nalgrowth!

by Bin-method (Suzuki et al. 2010a)!

-! Need investigating
consistency/difference
between model andobservation

Aerosol 20 bins (0.01-1um)
Cloud 60 bins (3 – 3000um)

6

New visualization method of the radar reflectivity,CFODD (Contoured Frequency by Optical Depth Diagram) !
A-Train flight direction !!

A case of,!
14µm<CDR<16µm!

MODIS CDR (µm)!

Rain mode!

Drizzle mode!

MODIS COT!

Cloud particle!

g!

cin!Sli

In Cloud Optical Depth!

Gathering global
observations!

CloudSat Radar Reflectivity!
Radar Reflectivity [dBZ]!

Visualizing Cloud Growth from space!
Nakajima et al. (JAS, 2010b), Suzuki et al. (JAS, 2010b)!

Condensation!

Collision!

CDR (by MODIS)=10-12"%!

CDR (by MODIS)=14-16"%!

Coalescence!
CDR (by MODIS)=25-30"%!

July 2006, One month data of the Aqua/MODIS and CloudSat/CPR!

Synergistic analysis of clouds (2) :VIS-IR + Microwave!

•! TRMM VIRS (VIS-IR imager! retrieves"–! COT"–! CDR_virs"•! TRMM TMI (Microwave scanner) retrieves"–! LWP (Liquid Water Path)"•! (Combination of VIRS and TMI….)"–! CDR_tmi ~ LWP / COT"•! CDR_tmi / CDR_virs"-> Vertical structure of clouds"~ Precipitability!

Masunaga et al. (JGR 2002a, 2002b) for TRMM VIRS-TMI!
Nakajima et al. (RSSJ, 2009) for ADEOS-2 GLI+AMSR!

9"Diagnosis of the Aerosol Effects
using CFODD!
Suzuki et al. (2012, JGR in press)!
X - 46SUZUKI ET AL.: AEROSOL EFFECT ON PRECIPITATION

Pristine!
AI=0.01-0.1!

0"! MODIS Cloud products"! MODIS Aerosol Products"! CloudSat CPR Reflectivity!

-20

2.4

2.440eAI=0.01-0.1!

340

2.5

ICOD

3.5(f) NICAM/AI=0.3-1.0
403.5

20

340

2.5

43.520340Rain formationin the modelis 60faster60
22-20 than020-20020-200observation
Z [dBZ]Z [dBZ]Z [dBZ]ee

220

AI=0.3-1.0!

AI=0.1-0.3!

02.2e(e) NICAM/AI=0.1-0.3

420

Figure 3.

2.6

2.2(d) NICAM/AI=0.01-0.1

602060due to22Signaturesof60 rain suppression
020-20020-200aerosols
Zincreased[dBZ]
Z [dBZ]Z [dBZ]

0ICOD

A Global Cloud
Resolving model!

2.640e

NICAMSPRINTARS

32.8

ICOD

202.26002.8ICOD

2.4

40(c) A-Train/AI=0.3-1.03

2.8
2.6

AI=0.3-1.0!

(b) A-Train/AI=0.1-0.3

320Polluted!

AI=0.1-0.3!

(a) A-Train/AI=0.01-0.10ICOD

A-TrainObservation!

Moderate!

ICOD

AI = ! a"

2.5
220e

Vertical cloud microphysical structures depicted in the form of the contoured

frequency diagram of radar reflectivity profile rescaled as a function of in-cloud optical depth10(ICOD) and classified according to aerosol index (AI) into (left to right) pristine (AI=0.01-0.1),
moderate (AI=0.1-0.3) and polluted (AI=0.3-1.0) conditions obtained from (a, b and c) the A-3-D Spectral Bin Microphysical model (SBM) simulation!
[Sato et al. 2012]!
2018

JOURNAL OF THE ATMOSPHERIC SCIENCES

VOLUME 69

Rich CCN!

Poor CCN!

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FIG. 4. CFODDs at t 5 6 h obtained from (a)–(e) polluted and (f)–(j) pristine experiments, divided by RE 5 (a),(f) 5–10, (b),(g) 10–15,(c),(h) 15–20, (d),(i) 20–25, and (e),(j) 25–30 mm. Arrows and rectangles are described in the text, and Arabic and capital Greek numbers
are used for rectangles and arrows, respectively.

found that hexagonal cell structures (i.e., open cell structures) are produced in the pristine experiment. Precipi-of cloud field changes caused by aerosol effects (Twomey1974; Albrecht 1989).

Research plots
for understanding retrieved cloud properties !
Vertical! +drizzle"!
NumericalExperiment!

Satellite!

Nakajima T. Y. et al. 2010a[2-layer model]!

Nakajima T. Y. et al. 2010b[CFODD]!

Zhang and Platnick et al., 2011!

Chang and Li, 2002, 2003
(R vertical profile)

Nakajima T. Y. et al. 2010a[FIRE]!

3D-RT!

Zinner et al., 2010 [LES+3DRT]!

Nagao T. M. et al. 2013(in revision)[Bin-model + 1DRT]!

Seethala and Horváth, 2010
(Validate MOD06)

In-situ!

Sub-pixel
Horizontal&!

Painemal et al, 2011
(Validation of MOD06)

Strategy of cloud observation!
GCOM-W
Satellite Observations! GCOM-C!

Altitude!

Bin-method
Simulation!

Radius!
Comparison between
Sat. vs Models.!

Summary!
•! Need more observations of clouds from satellites for,–! generating cloud climatology database–! investigating cloud evolution process•! The CFODD presents–! cloud evolution process, clearly.
–! results are consistent with past studies by TRMM,ADEOS2!
–! useful for model evaluations.
•! A Doppler capability of the EarthCARE/CPR improves ourunderstanding of cloud evolution process (2015-).

Selected papers (2010-2013)!
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Nagao, T. M., K. Suzuki, and T. Y. Nakajima, 2013: Interpretation of multiwavelength-retrieved droplet effective radii for warm water clouds interms of in-cloud vertical inhomogeneity by using spectral bin microphysics cloud model. J. Atmos. Sci., in revision.
Koike, M., and Coauthors, 2012: Measurements of Regional-Scale Aerosol Impacts on Cloud Microphysics over the East China Sea:
Possible Influences of Warm Sea Surface Temperature over the Kuroshio Ocean Current. J. Geophys. Res., 117, D17205, doi:
10.1029/2011JD017324.
Letu, H., T. Y. Nakajima, and T. M. Nagao, 2012: Development of the ice crystal scattering database for the GCOM-C/SGLI satellite mission:
investigating the refractive index grid system and potential retrieval error, App. Opt., 51, 6172-6178.
Sato Y., T. Y. Nakajima, and T. Nakajima, 2012: Investigation of the vertical structure of warm cloud microphysical properties using the cloud
evolution diagram, CFODD, simulated by three-dimensional spectral bin microphysical model. J. Atmos. Sci., 69, 2012-2030.
Nakajima, T. Y.,2012: Algorithm development for the satellite measurements of cloud properties, Tenki. 59.11, 3-16. (in Japanese )Suzuki, K., G. L. Stephens, S. C. van den Heever, T. Y. Nakajima, 2011: Evaluation of the warm rain formation process in cloud-resolving
models using joint CloudSat and MODIS observatoins, J. Atmos. Sci.,68, 2655-2670.
Eguchi, K., and I. Uno, K. Yumimoto, T. Takemura, T. Y. Nakajima, M. Uematsui, and Z. Liu, 2011: Modulation of cloud droplets and radiation
over the North Pacific by sulfate aerosol erupted from Mount Kilauea, SOLA, 7, 77-80.
Ishida, H., T. Y. Nakajima, T. Yokota, N. Kikuchi, and H. Watanabe, 2011:Investigation of GOSAT TANSO-CAI cloud screening ability
through an inter-satellite comparison., J. Appl. Meteor. and Climat., 50, 1571-1586.
Dim, J., H. Murakami, T. Y. Nakajima, B. Nordell, A. Heidinger, and T. Takamura, 2011: The Recent State of the Climate: Driving
Components of Cloud Type Variability., J. Geophys. Res.,116, D11117, doi:10.1029/2010JD014559.
Nakajima, T. Y., T. Tsuchiya, H. Ishida, T. N. Matsui, and H. Shimoda, 2011: Cloud detection performance of spaceborne visible-to-infraredmultispectral imager. Applied Optics,50, 2601-2616
Masunaga, H., and Coauthors, 2010: Satellite data simulator unit (SDSU) : A multi-sensor, multi-spectral satellite simulator package. Bull.
Amer. Meteor. Soc., 91, 1625-1632. doi:10.1175/2010BAMS2809.1
Suzuki, K., T. Y. Nakajima, and G. L. Stephens, 2010b: Particle growth and drop collection efficiency of warm clouds as inferred from joint
CloudSat and MODIS observations. Journal of the Atmospheric Sciences, 67, 3019-3032.
Suzuki, K., T. Nakajima, T. Y. Nakajima, and G. L. Stepehns, 2010a: Effect of droplet activation process on microphysical properties of warmclouds. Environmental Research Letters,5, 024012.
Nakajima, T.Y., K. Suzuki, and G. L. Stephens, 2010b: Droplet growth in warm water clouds observed by the A-Train. Part I: Sensitivity
analysis of the MODIS-derived cloud droplet size., J. Atmos. Sci., 67, 1884-1896.
Nakajima, T.Y., K. Suzuki, and G. L. Stephens, 2010a: Droplet growth in warm water clouds observed by the A-Train. Part II: A Multi-sensor
view., J. Atmos. Sci., 67, 1897-1907.
Suzuki, K., T. Nakajima, T. Y. Nakajima, and A. P. Khain, 2010:A Study of Microphysical Mechanisms for Correlation Patterns between
Droplet Radius and Optical Thickness of Warm Clouds with a Spectral Bin Microphysics Cloud Model. J. Atmos. Sci., 67, 1126-1141.