MGCPN: An Efficient Deep Learning Model for Tibetan Plateau Precipitation Nowcasting Based on the IMERG Data

The sparse and uneven placement of rain gauges across the Tibet Plateau (TP) impedes the acquisition of precise, high-resolution precipitation measurements, thus challenging the reliability of forecast data. To address such a challenge, we introduce a model called Multisource Generative Adversarial Network - Convolutional Long Short-Term Memory (GAN-ConvLSTM) for Precipitation Nowcasting (MGCPN), which utilizes data products from the global precipitation measurement (GPM) Integrated Multi-satellite Retrievals for GPM (IMERG) data, offering high spatiotemporal resolution precipitation forecasts for upcoming periods ranging from 30 to 300 minutes. The results of our study confirm that the implementation of the MGCPN model successfully addresses the problem of underestimating and blurring precipitation results that often arise with increasing forecast time. This issue is a common challenge in precipitation forecasting models.  Furthermore, we have used multisource spatiotemporal datasets with integrated geographic elements for training and prediction to improve model accuracy. The model demonstrates its competence in generating precise nowcasting precipitation forecasts using IMERG data, thereby offering valuable support for precipitation research and forecasting in the TP region. The metrics results obtained from our study further emphasize the notable advantages of the MGCPN model, it outperforms the other considered models in the probability of detection (POD), critical success index, Heidke Skill Score and mean absolute error, especially showing improvements in POD by approximately 33%, 19%, and 8% compared to Convolutional Gated Recurrent Unit (ConvGRU), ConvLSTM, and small Attention-UNet (SmaAt-UNet) models.