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International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B7. Istanbul 2004
incorporated new treatments on thermal emission and water
vapor absorption in the atmosphere. A pre-computed numerical
table (lookup table) was prepared to compare with satellite
observed radiances, and it determined the best-matched
combination from the comparison to be the retrieved cloud
properties. To construct lookup table, they used a general
radiative transfer (RT) code which solves RT with a combined
discrete-ordinate-matrix-operator method (Nakajima and
Tanaka 1986, 1988) and LOWTRAN-7 gas absorption model
(Kneizys et al. 1988). About ancillary data for actual data
analysis, NCEP/NCAR reanalysis data were used for input
meteorological data such as humidity, pressure and temperature
profiles, and the minimum value for the month was determined
as the monthly ground albedo. Retrieved results were well
validated with in situ aircraft measurements (Kawamoto et al.
2001).
This algorithm was applied to AVHRR (Advanced Very High
Resolution Radiometer) GAC (Global Area Coverage) radiance
data for 1985 - 1988 (NOAA-9) and 1989 - 1994 (NOAA-11).
4-month analysis (April, July, October and December) was
performed, and the annual mean was taken as their arithmetic
average. As for calibration of sensor signals, the calibration
constants from Rao and Chen (1994) were used for channel 1
visible channel, and those from on-board internal blackbody for
channel 3 near-infrared and channel 4 infrared channels.
Potentially analyzed pixels were selected whose satellite zenith
angles less than 25 degrees in order to avoid the effect of cloud
inhomogeneity. According to Iwabuchi and Hayasaka (2002),
that effect would be reduced for the most part when viewed
from angles less than about 25 degrees. For efficient processing,
target area was divided into 0.5-degree spatial segment and one
segmented-box stores 100 pixels. We constructed this
segmented data daily for the analysis period, and analyzed one
pixel which had the median value of visible reflectivities among
those classified as cloudy in each segmented box.
3. Results and discussion
1) The annual-mean cloud characteristics over China
First, we begin with the annual features of cloud properties
over target area in 1990. Fig.1 shows the annual mean of x.
Generally x over the land is larger than that over the ocean.
Especially large t is observed in the southern part of China.
This cloud would be occurred mainly due to active convection.
These general features are consistent with ISCCP (International
satellite Cloud Climatology Project) statistics (Rossow et al.
1996). Fig.2 shows the annual mean of r,. Unlikez, r, over land
is smaller than that over the ocean. Particularly r, over the
eastern and central parts of China is smaller, although that over
the north and west part are larger. Fig.3 shows the annual mean
of the integrated cloud droplet number N,.
N, is estimated from x and r, with the assumed size distribution
of log-normal. We find large N, areas for small r, and large T.
Values of N, reported here are compatible with the preceding
study (Han et al., 1998). These features on r, t and N, can be
explained by Twomey effect. An idea was proposed that
additional aerosol particles can decrease the cloud particle size
and increase the cloud optical depth, with increasing cloud
droplet number (Twomey, 1977). In general, acrosols are less
over ocean, and more over land. The above phenomena are
consistent with this idea. Note that cloud properties near the
east coast are affected by continental airflow, that is, r, is
smaller, while t and N, are larger over land compared to those
of remote oceanic area.
As stated above, patterns of cloud properties over land are not
geographically uniform. Kawamoto et al. (2003) investigated
their relationships with SO2 emission, which is location-
775
dependent. SO2 emission is more in highly populated coastal
areas and inland industrial cities, and SO2 is known as a
precursor gases for sulfate aerosols. They found good
agreement between the two.
0 5 10 15 20
Fig.l The annual-mean of low-cloud optical depth
6 8 5° 1 13] 15 16 18
Fig.2 The annual-mean of low-cloud effective particle size
(1/em?)
0.00e+00 5.00e+06 1.00e+07
Fig.3 The annual-mean of low-cloud droplet number