Validation studies have assessed the accuracy of satellite-based precipitation estimates at coarse scale (1° and 1 day or coarser) in the tropics, but little is known about their ability to capture the finescale variability of precipitation. Rain detection masks derived from four multisatellite passive sensor products [Tropical Amount of Precipitation with an Estimate of Errors (TAPEER), PERSIANN-CCS, CMORPH, and GSMaP] are evaluated against ground radar data in Burkina Faso. The multiscale evaluation is performed down to 2.8 km and 15 min through discrete wavelet transform. The comparison of wavelet coefficients allows identification of the scales for which the precipitation fraction (fraction of space and time that is rainy) derived from satellite observations is consistent with the reference. The wavelet-based spectral analysis indicates that the energy distribution associated with the rain/no rain variability throughout spatial and temporal scales in satellite products agrees well with radar-based precipitation fields. The wavelet coefficients characterizing very finescale variations (finer than 40 km and 2 h) of satellite and ground radar masks are poorly correlated. Coarse spatial and temporal scales are essentially responsible for the agreement between satellite and radar masks. Consequently, the spectral energy of the difference between the two masks is concentrated in fine scales. Satellite-derived multiyear mean diurnal cycles of rain occurrence are in good agreement with gauge data in Benin and Niger. Spectral analysis and diurnal cycle computation are also performed in the West Africa region using the TRMM Precipitation Radar. The results at the regional scale are consistent with the results obtained over the ground radar and gauge sites.

Satellite estimation of accumulated precipitation is an important facet of the study of the tropical water cycle. An advanced data merging approach using infrared geostationary imagery and microwave constellation based instantaneous rain rate estimates has been implemented in the framework of the Megha‐Tropiques and Global Precipitation Measurements missions. The Tropical Amount of Rainfall with Estimation of ERors (TAPEER) algorithm has been tailored to account for the loss of the MADRAS conical scanning radiometer by using the SAPHIR sounder rainfall detection capability, thanks to a novel two‐constellation implementation of the algorithm. A new bias correction module based on the TRMM PR observations is also presented. The performances of this new version of the product are reviewed with emphasis on West Africa. In particular, using data‐denial experiments, the contribution of SAPHIR data to the rainfall daily accumulation is quantified for various configurations of the microwave constellation and various algorithmic parameter selections. The results show that the daily accumulation statistics are well improved when SAPHIR is taken into the constellation. The improvements can be quantified using bulk statistics but are more evident following a frequency analysis. The pattern of the impact is a complex convolution of rainfall occurrence and of the Megha‐Tropiques mission original sampling. Over the 20°N–20°S belt, in zonal mean, the inclusion of SAPHIR data alters the daily accumulation substantially (more than 50% of the daily accumulation) more than 10% of the time and more than 20% when conditioned upon rainfall. Under both metrics, the improvement is majored in the 12°–17° latitude band where the Megha‐Tropiques mission sampling is at its maximum.