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Precipitation is a key aspect of the climate system. In this paper, the dependability of five satellite precipitation products (TRMM [Tropical Rainfall Measuring Mission] 3BV42, PERSIANN [Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks] CDR, GSMaP [Global Satellite Mapping of Precipitation] RENALYSIS, CMORPH [Climate Prediction Center's morphing technique] BLD and CMORPH_RAW) were compared with in situ measurements over China for the period of 2005 to 2013. To completely evaluate these precipitation products, the annual, seasonal and monthly precipitation averages were calculated. Overall, the Huaihe River and Qinlin mountains are shown to have heavy precipitation to the southeast and lighter precipitation to the northwest. The comparison results indicate that Gauge correction (CMORPH_BLD) improves the quality of the original satellite products (CMORPH_RAW), resulting in the higher correlation coefficient (CC), the low relative bias (BIAS) and root mean square error (RMSE). Over China, the GSMaP_RENALYSIS outperforms other products and shows the highest CC (0.91) and lowest RMSE (0.85 mm/day) and all products except for PERSIANN_CDR exhibit underestimation. GSMaP_RENALYSIS gives the highest of probability of detection (81%), critical success index (63%) and lowest false alarm ratio (36%) while TRMM3BV42 gives the highest of frequency bias index (1.00). Over Tibetan Plateau, CMORPH_RAW demonstrates the poorest performance with the biggest BIAS (4.2 mm/month) and lowest CC (0.22) in December 2013. GSMaP_RENALYSIS displays quite consistent with in situ measurements in summer. However, GSMaP_RENALYSIS and CMORPH_RAWunderestimate precipitation over South China. CMORPH_BLD and TRMM3BV42 show consistent with high CC (>0.8) but relatively large RMSE in summer. [ABSTRACT FROM AUTHOR]

The fine-mode aerosol optical depth (AODf) is an important parameter for the environment and climate change study, which mainly represents the anthropogenic aerosols component. The Polarization and Anisotropy of Reflectances for Atmospheric Science coupled with Observations from a Lidar (PARASOL) instrument can detect polarized signal from multi-angle observation and the polarized signal mainly comes from the radiation contribution of the fine-mode aerosols, which provides an opportunity to obtain AODf directly. However, the currently operational algorithm of Laboratoire d'Optique Atmosphérique (LOA) has a poor AODf retrieval accuracy over East China on high aerosol loading days. This study focused on solving this issue and proposed a grouped residual error sorting (GRES) method to determine the optimal aerosol model in AODf retrieval using the traditional look-up table (LUT) approach and then the AODf retrieval accuracy over East China was improved. The comparisons between the GRES retrieved and the Aerosol Robotic Network (AERONET) ground-based AODf at Beijing, Xianghe, Taihu and Hong_Kong_PolyU sites produced high correlation coefficients (r) of 0.900, 0.933, 0.957 and 0.968, respectively. The comparisons of the GRES retrieved AODf and PARASOL AODf product with those of the AERONET observations produced a mean absolute error (MAE) of 0.054 versus 0.104 on high aerosol loading days (AERONET mean AODf at 865 nm = 0.283). An application using the GRES method for total AOD (AODt) retrieval also showed a good expandability for multi-angle aerosol retrieval of this method. [ABSTRACT FROM AUTHOR]