International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B7. Istanbul 2004 
  
2) Long-term variation of r, over China 
Next we examine long-term change of r,. Due to calibration 
uncertainty, here we do not mention x and N,, whose estimate t 
is used for. Fig. 4 indicates the time-series of r, from 1985 to 
1994 over China area. We can observe gradual decreasing 
trends for both oceanic and continental clouds with gap in 1989. 
This gap is caused by satellite platform change from NOAA-9 
to NOAALI. The seasonal change of r,, such as large values in 
July and small values in January, would be driven by 
precipitation (scavenging of aerosols) discussed in existing 
studies (Han et al. 1994, Kawamoto et al. 2001). Apart from 
shorter time scale like seasonal scale, long-term the absolute 
values do not seem to be realistic. As pointed out for the global 
results in Kawamoto and Nakajima (2003), we need to be 
careful to use satellite data for the long-term analysis. There 
exist some factors that generate artifacts such as satellite 
platform change, degradation of sensors, calibration 
uncertainties and orbital shift and so on. As for orbital shift, 
NOAA-9 (1985 — 1988) had more than 2hours, and NOAA-11 
(1989 — 1994) had more than 3hours difference in the equator 
crossing time (ETC) from the launch to the end of the mission. 
The difference of ECT would bring diurnal change to the 
analysis. This means that obtained time-series of r, are mixed 
with diurnal, interannual changes and technical influence. To 
further understand the absolute variation of r,, we need to make 
efforts to separate the influence caused by the above factors. 
Summary 
We have performed remote sensing analysis of low-level water 
clouds, targeting over China area. First, we describe the annual 
mean characteristics of cloud parameters. Their land/ocean 
contrasts of t, r, and N, (larger x and N,, and smaller r, over 
land) is consistent with Twomey's idea. Their geographical 
patterns over land are implicated in anthropogenic SO2 
emission (Kawamoto et al. 2003). 
  
over ocean 
emma over land 
  
  
LA 
w 
   
_. (micron) 
= N 
N e 
— 
- 
Qo 
A 
e 
o 
Effective Particle Radius 
© 
o 
  
  
  
  
85 90 95 
Year 
Fig.4 Time series of low-cloud effective particle size 
Then we showed long-term trend of r, Absolute values 
generally decrease, indicating seasonal cycle with a gap due to 
satellite platform change in 1989. This steep decrease would not 
be realistic. This trend would be caused mainly by artifacts as 
T16 
pointed out in global case in Kawamoto and Nakajima (2003). 
For future tasks, it is urgent to cope with reducing artifacts 
caused by the above factors such as sensor degradation, orbital 
shift, etc.. And to use recent sensors such as MODIS (Moderate 
resolution imaging spectroradiometer) and GLI (Global Imager), 
comparison with historical AVHRR results would be of interest 
to study the variation of cloud fields. 
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