Capabilities of satellite precipitation datasets to estimate heavy precipitation rates at different temporal accumulations

The importance of satellite datasets as alternative sources of precipitation information has been argued in numerous studies. Future developments in satellite precipitation algorithms as well as utilization of satellite data in operational applications rely on a more in-depth understanding of satellite errors and biases across different spatial and temporal scales. This paper investigates the capability of satellite precipitation data sets with respect to detecting heavy precipitation rates over different temporal accumulations. In this study, the performance of Tropical Rainfall Measuring Mission real time (TRMM-RT), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks and CPC MORPHing (CMORPH) is compared against radar-based gauge-adjusted Stage IV data. The results show that none of the high temporal resolution (3-h) datasets are ideal for detecting heavy precipitation rates. In fact, the detection skill of all products drops as the precipitation thresholds (i.e. 75 and 90 percentiles) increase. At higher temporal accumulations (6, 12 and 24 h), the detection skill improves for all precipitation products, with CMORPH showing a better detection skill compared to all other products. On the other hand, all precipitation products exhibit high false alarm ratios above the heavy precipitation thresholds, although TRMM-RT lead to a relatively smaller level of false alarms. These results indicate that further efforts are necessary to improve the precipitation algorithms so that they can capture heavy precipitation rates more reliably. Copyright © 2013 John Wiley & Sons, Ltd.