Analysis of aerosol, cloud, and watervapor using GLI NUV, O2 absorption,and IR channels

Makoto KUJI
Nara Women's University, Japan

GLI Workshop in Tsukuba, Jan. 30, 2007

Contents
Algorithm developments:
1. Retrieval of cloud heights• using the following GLI Channels:
VIS, Oxygen A-band, NIR, TIR;

2. Retrieval of aerosol amount• using GLI NUV channels;

3. Retrieval of water vapor amount• using GLI NIR channels;

Global analysis of cloud geometricalproperties
using ADEOS-II / GLI datafor radiation budget studies

Makoto Kuji
Nara Women's University, Japan

Teruyuki Nakajima
CCSR, University of Tokyo, Japan

Objective•

Cloud has an influence on the Earth radiation budget– For Shortwave and/or Longwave flux at TOA and/or surface;– Depending upon optical, microphysical and geometrical properties;• Optical thickness, particle size, and layer height;•There still exists some uncertainty in the estimation of surface
radiation budget among observations and/or simulations;•Retrieval of cloud geometrical properties (ie, cloud layer height)– Reducing uncertainty of downward longwave flux;– Improvement of surface radiation budget;– Air-/space-borne imager-type remote sensing data
are useful for extended cloud systems;

ZtIRCloud (Tc)

ZbIRSurfaceApproach•

ADEOS-II / GLIADEOS-Ⅱ– A nadir looking imager with 1km
footprint;• Four-channel algorithm:
Visible, Oxygen A-band, NIR, andTIR;•Retrieval of cloud properties:
τc, re, zt, and Δz;•Application to Global observation
dataset;http://sharaku.eorc.jaxa.jp/
http://www.eoc.jaxa.jp/

GLI global data (678nm; Mar. 20, 2003)
180˚W
90˚N

120˚W60˚W0˚60˚E

120˚E(EQ)90˚S* Segment dataset: GAC type dataset divided every 60˚ in longitude

180˚E

Cloud top height (Mar. 20, 2003)
180˚W
90˚N

120˚W60˚W0

˚60˚E120˚E

180˚E(EQ)90˚S* Future improvement: cloud type flag (water or ice) for cirrus cloud contamination

Cloud geometrical thickness (Mar. 20, 2003)
180˚W
90˚N120˚W

60˚W0

˚60˚E120˚E

180˚E(EQ)90˚S* Future improvement: multilayered cloud system for a single layer application

Cloud bottom height (Mar. 20, 2003)
180˚W

120˚W

60˚W0˚60˚E

120˚E

90˚N(EQ)

90˚S* A by-product with top height and geometrical thickness

180˚E

Summary (cloud)•A four-channel algorithm was developed and applied to theADEOS-II / GLI global dataset to retrieve cloud geometricalproperties as well as optical and microphysical ones.

•An initial global analysis and preliminary results reveal
 Algorithm works as a whole, but more elaborate analyses arenecessary Surface condition: albedo, land/ocean, temerature, and pressure; Atmospheric profile: temperature, relative humidity, and pressure; Cirrus screening: water vapor absorbing band or thermal IR bands;
 Sun glint screening: scan geometry;•Future works
 Validation studies with active sensors; Further global analyses for radiation budget and cloud physics
studies;Characterization of sulfate haze overEast Asia retrieved with satellite andground-based remote sensing data
*M. Kuji, M. Hibino, Y. Kondo, S. Hayashida
Nara Women's University, Japan

M. Shiobara, M. Yabuki
National Institute of Polar Research, JapanK. HaraFukuoka University, JapanH. Kobayashi
Yamanashi University, JapanT. Hayasaka, S. Satake
Research Institute for Humanity and Nature, Japan

July 31, 2006
APRS @ Kanazawa, JAPANObjective• Air pollution accompanying with recent rapid
economic growth in East and South Asia has apotential to influence our environment.
• It is important to investigate aerosol type anddistribution with remote sensors and groundbased measurements.
• In this study, we characterize a haze event
around Japan in autumn of 2003 withobservations and chemical transport modelsimulations.

エアロゾルの主な発生要因

ADEOS-Ⅱ/GLIGlobal Imager (GLI)
Operation:December 2002 to October 2003
Channels: 36
from 380nm (NUV) to 12 µm (TIR)
Recursive period: 4 days
Spatial resolution: 1km (IFOV) and1600km (Swath)

ADEOS-Ⅱ

GLI obs.

Data to be analyzed
Date: September 16, 2003
Band: Near Ultraviolet (NUV);
380nm (ch1) and 400nm (ch2)Scene: around Japan
http://sharaku.eorc.jaxa.jp/
http://www.eoc.jaxa.jp/より

GLI image (Sept. 16, 2003)

Gradation

NIR (866nm)

NUV (400nm)

Shipborne measurements with R/V Shirase

Aerosol retrieval with NUV observation
Direct MethodSoot

Direct method– Originally developed with
TOMS dataset (UV;several 10km);– Applying to GLI dataset(NUV; 1km);– Look up table approach;

Reflectance ratio (380nm/400nm)

•00.5
1.0
1.5

Dust like

Sulfuric aerosol
1.0<AOT<1.5
Retrieval of aerosol type andoptical thickness

・・・
5.0

Sea salt

Sulfuric

Reflectance (400nm)

Retieved aerosol optical thickness (Sulfuric)
GLI

Non absorbing(sulfuric)consistent
September
16,
2003
aroundJapanShirase
Non abs. (sulfuric)GLI

AOT 0.38
R/V Shirase

Sulfuric
Amami aerosol dominated in a wide area.
Sulfuric→consistentcoming from Asian continent ?!
Ground LIDAR
Sphere (sulfuric)AOT 0.43

Aerosol Optical Thickness

Summary (aerosol)• GLI NUV (380 nm) enables us to retrieve Asian Haze
properties in 1-km resolution;– Comparison to the in situ observations, such as surface
sampling as well as lidar and skyradiometer, indicated theretrieved optical thickness was reasonable;• Combined analyses of surface, satellite, and model
simulation are useful (necessary) to characterizeaerosol properties– Chemical type, amount, microphysics, distribution,transportation (source and sink), and so on;• Aerosol layer height is one of the greatest error
sources for absorbing aerosols;– LIDAR observation will help us;– O2 A-band (763nm) will be a possible candidate in GLIanalysis;

Retrieval of precipitable waterusing ADEOS-II / GLI
Near Infrared data
Makoto Kuji*
Nara Women's University, Japan

Nobuyuki Kikuchi
JAXA / EORC, Japan

Akihiro Uchiyama
Meteorological Research Institute, Japan

Objectives•

Water vapor: Integrated amount (precipitable water);– A key parameter in an aerosol-cloud interaction as well asradiation budget study;•Utility: Atmospheric product (clear sky) or correction;– Over land, in particular (AMSR only over ocean);•Applications to ADEOS-II / GLI;
– The MODIS algorithm (King et al. 1992; Kaufman and Gao 1992);•

Advantage of GLI analysis:– on a pixel-by-pixel (about 1 km2) basis;– Fully synchronized data set with other channels;– Cloud and ecosystem flags from GLI processing flow;– Atmospheric correction for surface products, e.g., vegetation,snow/ice, etc.;

Approach• Radiance Ratio Method:– Near Infrared (ADEOS-II / GLI Application);• Water vapor absorbing band (1135 nm), and;• Non-absorbing band (1050 or 1240 nm);– Daytime Retrieval;– Under a clear sky condition;– Surface albedo: high or moderate (over land);– Aerosol loading: up to moderate (1.0 @ 500 nm);• Analysis and Validation:– Implemented to EORC (ATSK6p);– GLI analysis: April and August, 2003 @ Global;– Comparison to radiosonde observations, and so on;– Re-evaluation of algorithm;

Physical background(Water Vapor)

GLI Chs.

()*+,-./0123,1.,45023,6.)

2*R1135 /(R1050 + R1240)!"#
2!a=0.0!"$$"$$!a=0.1!a=1.0$"%$"&$"'$"#

7$!$$!7$

8,60*29,:)*2;,6<2=++>#$$* We adopt the calibration curve under Lambertiansurface (50% reflectivity) condition without aerosol
loading in this analysis;

Calibration (Bright Surface; Lambertian 50%)

April 2003

Precipitable water with GLI (over Land; 2ch);
Seasonal contrast•– Good seasonal contrast;•August 2003

Comparison between
spring (April) and summer(August);•GLI precipitable water wascorrected after thevalidation with radiosonde;
Precipitable water overocean is retrieved withAMSR;(provided by courtesy of JAXA EORC)

Re-validation with radiosondes•

Good correlation
between GLI andRadiosondes;•

Even with a timelag by a fewhours; A poster
presentation by
Dr. N. Kikuchi ofJAXA EORC;

N. Kikuchi (2007)

Summary (water vapor)• GLI NIR enables us to retrieve precipitablewater over land in 1-km resolution under aclear-sky condition;• Global analysis over land
– Combined results with water vapor over oceanwith AMSR;– Good seasonal variation;• Re-evaluation of calibration curve– Improvement of water vapor amount estimation;– Re-analysis on a global basis in the future;

Final Summary
Table 1. The development status (self-evaluation)
Algorithm

Globalanalysis

Validation(precision)

Paper
preparation

Relevant Channel

Cloud heights

***

***

**

**

O2 A-band

Aerosol typeover land

**

**

**

**

NUV

Water vapor
amount

***

***

***

**

WV-NIR

Water vapor
profile*****WV-IR (#)*** Almost done; ** Ongoing; * Future works;# A possible retrieval algorithm for water vapor profile with GLI water vapor IR bands;