Abstract
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_RAW underestimate precipitation over South China. CMORPH_BLD and TRMM3BV42 show consistent with high CC (>0.8) but relatively large RMSE in summer.
Highlights
Precipitation is one of important factors in the global water cycle and plays a key role in the global energy system
Many products were developed by blending the MW and IR data, such as CMORPH (Climate Prediction Center’s morphing technique), PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks), TRMM (Tropical Rainfall Measuring Mission), GSMaP (Global Satellite Mapping of Precipitation)
This paper employed the PERSIANN_CDR products with a spatiotemporal resolution of 0.25◦/daily; these are calibrated by the Global Precipitation Climatology Project (GPCP) monthly gauge analysis [27]
Summary
Precipitation is one of important factors in the global water cycle and plays a key role in the global energy system. Remote sensing is an important method for retrieving high spatial and temporal resolution rainfall measurements over complex terrains and mountainous areas, where both rain gauge stations and ground radar are very limited or unavailable. Many products were developed by blending the MW and IR data, such as CMORPH (Climate Prediction Center’s morphing technique), PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks), TRMM (Tropical Rainfall Measuring Mission), GSMaP (Global Satellite Mapping of Precipitation). These precipitation products can help climate and hydrological research.
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