Towards Location-Specific Precipitation Projections Using Deep Neural Networks
This provides a more accurate alternative for weather forecasting and spatial analysis at individual locations, representing a paradigm shift rather than an incremental improvement.
This study tackled the problem of station-specific precipitation estimation by proposing two deep neural network architectures that incorporate various meteorological parameters, achieving superior performance over traditional Kriging methods across multiple evaluation metrics on a validation set from 1980-2019 data.
Accurate precipitation estimates at individual locations are crucial for weather forecasting and spatial analysis. This study presents a paradigm shift by leveraging Deep Neural Networks (DNNs) to surpass traditional methods like Kriging for station-specific precipitation approximation. We propose two innovative NN architectures: one utilizing precipitation, elevation, and location, and another incorporating additional meteorological parameters like humidity, temperature, and wind speed. Trained on a vast dataset (1980-2019), these models outperform Kriging across various evaluation metrics (correlation coefficient, root mean square error, bias, and skill score) on a five-year validation set. This compelling evidence demonstrates the transformative power of deep learning for spatial prediction, offering a robust and precise alternative for station-specific precipitation estimation.