Assessment of Satellite-Based Precipitation Estimates over Egypt

Ground precipitation measurements face obstacles in many regions of Egypt, where gauge stations are sparse. It is thus necessary to find reliable sources such as satellite-based precipitation products, which provide uninterrupted precipitation time-series with high spatial resolution. This work evaluated the performance of four well-known global satellite precipitation products, i.e., Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR), Tropical Rainfall Measuring Mission (TRMM3B42V7), Integrated Multi-satellitE Retrievals for global precipitation measurement-Final (IMERG-F), and Global Satellite Mapping of Precipitation (GSMaP)-Gauge, against gauged data of 23 stations in Egypt. GSMaP-Gauge revealed its outstanding abilities over the other three products in detecting rainfall occurrences and estimating the amount of rainfall. Further, Global Satellite Mapping of Precipitation (GSMaP-Gauge) was corrected by three commonly used bias-correction methods: linear scaling; local intensity scaling; and empirical quantile mapping (EQM). All three methods are effective in reducing biases to some extent, especially on the annual scale, with EQM being the best for most cases. This study demonstrates that satellite products can provide an alternative source of rainfall information for Egypt.