With the unprecedented spaceborne precipitation radar(PR),the Tropical Rainfall Measuring Mission(TRMM) satellite has collected high-quality precipitation measurements for over ten years.The TRMM/PR data are nowadays extensively exploited in numerous meteorological and hydrological fields.Yet an artificial orbit boost of the TRMM satellite in August 2001 modulated the observation parameters,which inevitably affects climatological applications of the PR data and needs to be clarified.This study investigates the orbit boost effects of the TRMM satellite on the PR-derived precipitation characteristics.Both the potential impacts on precipitation frequency(PF) and precipitation intensity(PI) are carefully analyzed.The results show that the total PF decreases by 8.3% and PI increases by 4.0% over the tropics after the orbit boost.Such changes significantly exceed the natural variabilities and imply the strong effects of orbit boost on precipitation characteristics.The impacts on stratiform precipitation and convective precipitation are inconsistent,which is attributed to their distinct precipitation features.Further analysis reveal that the increased PI of stratiform precipitation is mainly due to the decreased frequencies of light precipitation,while the semi-constant PI of convective precipitation is caused by the concurrently decreased frequencies of light and heavy precipitation.A modification is applied to the post-boost PR precipitation data to retrieve the actual trends of tropical precipitation characteristics.It is found that the PI of total-precipitation approximately keeps invariable from 1998 to 2005.The total PF has no obvious trend over tropical oceans but decreases considerably over tropical lands.
The single-scattering albedo （SSA）, which quantifies radiative absorption capability, is an important optical property of aerosols. Ground-based methods have been extensively exploited to determine aerosol SSA but there were no satellite-based SSA measurements available until the advent of advanced remote sensing techniques, such as the Ozone Monitoring Instrument （OMI）. Although the overall accuracy of OMI SSA is estimated to approach 0.1, its regional availability is unclear. Four-year SSA daily measurements from three Aerosol Robotic Network （AERONET） sites in China （Xianghe, Taihu, and Hong Kong） are chosen to determine the accuracy of OMI SSA in specific locations. The results show that on a global scale, the OMI SSA is systematically higher （with a mean relative bias of 3.5% and a RMS difference of ~0.06） and has poor correlation with the AERONET observations. In the Xianghe, Taihu, and Hong Kong sites, the correlation coefficients are 0.16, 0.47, and 0.44, respectively, suggesting that the distinct qualities of OMI SSA depend on geographic locations and/or dominant aerosol environments. The two types of SSA data yield the best agreement in Taihu and the worst in Hong Kong; the differing behavior is likely caused by varying levels of cloud contamination. The good consistency of the aerosol variation between the two SSA datasets on a seasonal scale is promising. These findings suggest that the current-version OMI SSA product can be applied to qualitatively characterize climatological variations of aerosol properties despite its limited accuracy as an instantaneous measurement.
The Cloud Profiling Radar （CPR） onboard CloudSat is an active sensor specifically dedicated to cloud detection. Compared to passive remote sensors, CPR plays a unique role in investigating the occurrence of multi-layer clouds and depicting the internal vertical structure of clouds. However, owing to contamination from ground clutter, CPR reflectivity signals are invalid in the lowest 1 km above the surface, leading to numerous missed detections of warm clouds. In this study, by using 1-yr CPR and MODIS （Moderate Resolution Imaging Spectroradiometer） synchronous data, those CPR-missed oceanic warm clouds that are identified as cloudy by MODIS are examined. It is demonstrated that CPR severely underestimates the occurrence of oceanic warm clouds, with a global-average miss rate of about 0.43. Over the tropical and subtropical oceans, the CPR-missed clouds tend to occur in regions with relatively low sea surface temperature. CPR misses almost all warm clouds with cloud tops lower than 1 km, and the miss rate reduces with increasing cloud top. As for clouds with cloud tops higher than 2 kin, the negative bias of CPR-captured warm cloud occurrence falls below 3%. The cloud top height of CPR-missed warm clouds ranges from 0.6 to 1.2 kin, and these clouds mostly have evidently small optical depths and droplet effective radii. The vertically integrated cloud liquid water content of CPR-missed warm clouds is smaller than 50 g m 2 It is also revealed that CPR misses some warm clouds that have small optical depths or small droplet sizes, besides those limited in the boundary layer below about 1 km due to ground clutter.
Visible and infrared(VIR) measurements and the retrieved cloud parameters are commonly used in precipitation identification algorithms, since the VIR observations from satellites, especially geostationary satellites, have high spatial and temporal resolutions. Combined measurements from visible/infrared scanner(VIRS) and precipitation radar(PR) aboard the Tropical Rainfall Measuring Mission(TRMM) satellite are analyzed, and three cloud parameters, i.e., cloud optical thickness(COT), effective radius(Re), and brightness temperature of VIRS channel 4(BT4), are particularly considered to characterize the cloud status. By associating the information from VIRS-derived cloud parameters with those from precipitation detected by PR, we propose a new method for discriminating precipitation in daytime called Precipitation Identification Scheme from Cloud Parameters information(PISCP). It is essentially a lookup table(LUT) approach that is deduced from the optimal equitable threat score(ETS) statistics within 3-dimensional space of the chosen cloud parameters. South and East China is selected as a typical area representing land surface, and the East China Sea and Yellow Sea is selected as typical oceanic area to assess the performance of the new scheme. It is proved that PISCP performs well in discriminating precipitation over both land and oceanic areas. Especially, over ocean, precipitating clouds(PCs) and non-precipitating clouds(N-PCs) are well distinguished by PISCP, with the probability of detection(POD) near 0.80, the probability of false detection(POFD) about 0.07, and the ETS higher than 0.43. The overall spatial distribution of PCs fraction estimated by PISCP is consistent with that by PR, implying that the precipitation data produced by PISCP have great potentials in relevant applications where radar data are unavailable.
