Typhoon 14 (Maemi), Sep./11/2003
GLI 250m 2.2µm / 1.6µm / 0.46µm

Estimation of cloud verticalinhomogeneity by multi-wavelengthtechnique

Takashi Y. Nakajima (Tokai University)
Keiji Imaoka (JAXA/EORC), Hirohiko Masunaga (Nagoya University),
Teruyuki Nakajima (Univ. Tokyo), Kentaroh Suzuki (Univ. Tokyo), andYoshitaka Muraji (ESCoT)SWIR 3.7µm
Microwave
SWIR 2.2 µm

WideNarrow

Contents• Motivations• Cloud observations by Passive Sensors– GLI global scale analysis of clouds. (ATSK3R)– GLI-AMSR combined analyses– GLI SWIR Sounding Technique• Application for the cloud droplet growing process studies.
– Observation VS Model, optical thickness (τc) andeffective radius (re),• SummaryObserving….

Revealing…
cloud-aerosolinteractions

Horizontal and Vertical
distributions ofcloud properties

Better understanding therole of aerosols andclouds forclimate change.

Motivation

Validating…
Cloud model v.s.
Remote sensing

Modeling…
Simulates themby Cloud ModelCloud observations by Passive Sensors• Horizontal Inhomogeneity for wide area
– Column quantity of cloud optical and microphysical properties
using passive imagers.
(AVHRR, ADEOS-II/GLI, Terra/MODIS, etc..）
Han et al. 1994, N and N 1995, Kawamoto et al. 2000 and others.

• Vertical Inhomogeneity for wide area
– Combined use of visible-infrared imager and microwave
scanners (VIRS-TMI on TRMM, GLI-AMSR on ADEOS-II)
Masunaga et al. (2002), Today’s talk.

– SWIR Sounding Technique (MODIS, GLI, etc..)
Suggested by Nakajima and King (1990), Today’s talk.

Cloud optical thickness

τcApr. 2003
WarmWater
Cloud
Version2ThickThin
Effective particle radius

ReSmallLargeNakajima and Nakajima(2005, SPIE)

Aerosol Optical Thickness

τaApr. 2003
AerosolThin

Thick
Angstrom Exponent

Version2Å
GLI

Large particles

Small particles

2ch method by Higurashi

Estimations of the VERTICAL
inhomogeneity of wide- area cloudproperties using passive sensors.

Tilt angle: ±20°

ＧＬＩ sensor

1600km
GLIAMSRFlow Chart
35

Ratio

Re(Mic/VIS)Re(SWIR/VIS)(SWIR

(SWIR&Vis)

Re(Mic/VIS)Re(SWIR/VIS)<1(SWIR&Vis)

Re(Mic/VIS)Re(SWIR/VIS)>1(SWIR&Vis)

New aspects in GLI/AMSR
combination
Algorithm and concept from Masunaga et al. (JGR 2002)Passed study(Masunaga et al.)Present study

Satellite(Sensor)

TRMM(VIRS+TMI)ADEOS2(GLI+AMSR)

Latitude Region+- 35 degs.

> +- 60 degs

Sensor Swath

720km

1,600km

Nadir IFOVOrbit(Sun synchronous ?)2km
NODiurnal Cycle

1km
YES (10:30 LT)
AMApplications of satellite measured data forprocess studies of
CLOUD DROPLET GROWINGPost-RainingEffective Radius re

I will show re-τc plots in the following viewgraphs(2) Raining
Drizzling(3)

Collision-coagulation(1) Pre-Raining
Nucleation, Condensation

Optical Thickness τcre-τc diagram
is a correlation between re& τc in a selected area

re-τc diagram (Simulated by Bin Model)
2W = ρ w reτ c3

τ c = Qext πre N c (Qext ≈ 2)
2

Nc=3×105/cm2

Nc=1×106/cm2

reaerosol effectdynamical effectτc

Nc=3×106/cm2
W=400g/m2
W=300g/m2
W=200g/m2
Suzuki,K. et al. (2006)

Summary
Horizontal and Vertical inhomogeneity of cloud properties.
– (H) Consistent with previous results such as AVHRR and MODIS.
– (H) Strong aerosol-cloud interaction appeared in Eastern Asia.
– (V) Combined analyses of GLI and AMSR are tentatively presented.
Advantage : Wider latitude area than passed study by TRMM
– (V) SWIR sounding technique using 1.6, 2.2, 3.7 is presented.

re-τc scatter plots comparisons (Observation VS Model).
– Typical scatter plots that present, pristine, polluted and mixture, werefound.

Future works
Long term data analyses (AVHRR,GLI,MODIS…)- To monitor the variability of cloud (and aerosol) coverage and properties
more than decades-long period.

- To give more sample data for model development studies.
More detailed investigations (by pixel by pixel analyses)- To make the most practical understanding of cloud growing process.
→ Higher spatial and temporal resolution of cloud dataset.
(ATSEG analysis -> L2A_OA analysis)

Comparison with, aerosol products, cloud radar data.
Contribute better forecasting of global climate change.

Thank you very much!
Conferences & Publications in 2006
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Kikuchi et al. (JGR, 2006)…………………………….. cloud observation
Nakajima TY et al. (SPIE@GOA, 2006)……………… cloud and model
Nakajima T et al. (SPIE@GOA, 2006)……………….. observation vs modelSuzuki et al. (SOLA, 2006)……………………………. cloud bin-method
Nakajima TY. (“Tenki” by JMA, 2006)………………. GSS simulator
Nakajima TY. (APMS@Kanazawa, 2006)……………. vertical structure
Suzuki et al. (AMS@Wisconsin, 2006)……………….. cloud bin-method
Nakajima T. (AMS@Wisconsin, 2006)……………….. observation vs model
Nakajima TY. (AMS@Wisconsin, 2006)………………horizontal distribution
Nakajima TY. (“Parity” by Maruzen, 2007)……………showing GLI image