Diurnal variability of global precipitation: insights from hourly satellite and reanalysis datasets

Abstract. Accurate estimation of precipitation at the global scale is of utmost importance. Even though satellite and reanalysis products are capable of providing high-spatiotemporal-resolution estimations at the global level, they are associated with significant uncertainties that vary with regional characteristics and scales. The uncertainties among precipitation estimates, in general, are much higher at the sub-daily scale compared to daily, monthly, and annual scales. Therefore, evaluating these sub-daily estimations is of specific importance. In this context, this study explores the diurnal cycle of precipitation using all the currently available space-borne and reanalysis-based precipitation products with at least hourly resolution at the quasi-global scale (60° N–60° S), i.e. Integrated Multi-satellitE Retrievals for GPM (IMERG), Global Satellite Mapping of Precipitation (GSMaP), Climate Prediction Center Morphing (CMORPH), Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN), and ECMWF Reanalysis v5 (ERA5). The diurnal variability of precipitation is estimated using three parameters, namely, precipitation amount, frequency, and intensity, all remapped at a common resolution of 0.25° and 1 h. All the estimates represent the spatiotemporal variation across the globe well. Nevertheless, considerable uncertainties exist in the estimates regarding the peak precipitation hour, as well as the diurnal mean precipitation amount, frequency, and intensity. In terms of diurnal mean precipitation, PERSIANN shows the lowest estimates compared to the other datasets, with the largest difference observed over the ocean rather than over land. As for diurnal frequency, ERA5 exhibits the highest disparity among the estimates, with a frequency twice as high as that of the other estimates. Furthermore, ERA5 shows an early diurnal peak and highest variability compared to the other datasets. Among the satellite estimates, IMERG, GSMaP, and CMORPH exhibit a similar pattern, with a late-afternoon peak over land and an early-morning peak over the ocean. Overall, it emphasizes the need to integrate diverse datasets and exercise caution when relying solely on individual precipitation products to ensure a thorough understanding and precise analysis of global precipitation patterns.