  Obtaining reliable precipitation estimation with high resolutions in time and
space is of great importance to hydrological studies. However, accurately
estimating precipitation is a challenging task over high mountainous complex
terrain. The three widely used precipitation measurement approaches, namely
rainfall gauge, precipitation radars, and satellite-based precipitation
sensors, have their own pros and cons in producing reliable precipitation
products over complex areas. One way to decrease the detection error
probability and improve data reliability is precipitation data merging. With
the rapid advancements in computational capabilities and the escalating volume
and diversity of earth observational data, Deep Learning (DL) models have
gained considerable attention in geoscience. In this study, a deep learning
technique, namely Long Short-term Memory (LSTM), was employed to merge a
radar-based and a satellite-based Global Precipitation Measurement (GPM)
precipitation product Integrated Multi-Satellite Retrievals for GPM (IMERG)
precipitation product at hourly scale. The merged results are compared with the
widely used reanalysis precipitation product, Multi-Radar Multi-Sensor (MRMS),
and assessed against gauge observational data from the California Data Exchange
Center (CDEC). The findings indicated that the LSTM-based merged precipitation
notably underestimated gauge observations and, at times, failed to provide
meaningful estimates, showing predominantly near-zero values. Relying solely on
individual Quantitative Precipitation Estimates (QPEs) without additional
meteorological input proved insufficient for generating reliable merged QPE.
However, the merged results effectively captured the temporal trends of the
observations, outperforming MRMS in this aspect. This suggested that
incorporating bias correction techniques could potentially enhance the accuracy
of the merged product.
