LWP can be improved when compared with the calculation based on a constant re profile. The improved cloud LWP calculation is given by

Chen et al. (2007) applied the linear-re retrieval method to the Aqua-MODIS-observed water clouds (Tc > 273 K) over tropical and subtropical oceans between 40° S and 40° N. The Aqua satellite also carries an Advanced Microwave Scanning Radiometer (AMSR-E) instrument for measuring cloud LWP and precipitation information. Passive microwave radiometers have been used to measure cloud LWP (Curry et al., 1990; Greenwald et al., 1993; Wentz, 1997; Grody et al., 2001). Since the microwave cloud LWP retrieval is based on the radiances emitted by cloud droplets, it is applicable for observations during day and night. However, the microwave retrieval has difficulty over land due to the large variability of land surface emis-sivity at microwave frequencies. Another disadvantage of the microwave measurements is their large footprints, which have a much coarser spatial resolution (-15 km) than cloud imagers like MODIS (-1km). The MODIS-retrieved re profile along with the t retrieval can be useful for measuring cloud LWP for both land and ocean areas.

Chen et al. (2007) compared one day (1 January 2003) of coincident Aqua MODIS and AMSR-E overcast regions and reported the effects of the re vertical variation on the MODIS-derived cloud LWP. Their study shows that while the overall mean values are similar between the MODIS and AMSR-E, the instantaneous cloud LWP retrievals differ markedly, which may be caused by precipitation and/or uncertainties in both the MODIS and AMSR-E retrieval algorithms. In comparisons of the AMSR-E retrievals and MODIS standard products (MOD06) that assumed a constant re as given by Eq. (7), the MODIS cloud LWP tended to be underestimated when the re profile increased from cloud top to cloud base, and overestimated when the re profile decreased from cloud top to cloud base. But in comparisons with the MODIS-improved cloud LWP retrievals that accounted for the re vertical variation, the mean biases and root-mean-square errors between the AMSR-E- and MODIS-derived LWPs were reduced.

Chen et al. (2007) further suggest that the manner in which re varies with height has the potential for discriminating precipitative and nonprecipitative warm water clouds. For precipitating clouds, the re at the cloud top is smaller than the re at the cloud base; whereas for non-precipitating clouds, the re is smaller toward the cloud base. Figure 8 illustrates the differences of the MODIS-retrieved re at the cloud top minus the re at the cloud base for the overcast warm regions (-13 km) based on the AMSR-E pixel scale. The data were obtained from Aqua MODIS on 2 July 2003 and were divided into precipitating [Fig. 8(a)] and non-precipitating [Fig. 8(b)] clouds using the operational AMSU-E rainfall products (Wentz and Meissner, 1999). For AMSR-E-flagged precipitating clouds, re at the cloud top are generally larger than re at the cloud base; whereas for AMSR-E-flagged nonprecipitating clouds, it is generally the opposite. There is the possibility that precipitating clouds are not detected by the operational AMSR-E algorithm, as many re at the cloud base are significantly larger than re at the cloud top [cf. Fig. 8(b)].

The conventional satellite remote sensing techniques for detecting precipitation rely on the measurements of cold cloud temperatures and/or large ice particles for convective clouds (Arkin and Ardanuy, 1989). They mainly detect deep convection and heavy precipitation clouds, but have overlooked many warm precipitating clouds within shallow stratocumulus cloud systems. As such, the total precipitation on global scales may be underestimated. Liu et al. (1995) utilized a microwave emission technique to detect precipitating warm clouds, and found that clouds with Tc > 273 K contributed to 14%

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