Deep learning for X-band radar quantitative precipitation estimation using polarimetric measurements

Accurate estimation of surface precipitation with high spatial and temporal resolution is crucial for disaster weather detection and decision-making regarding water resources management. Polarimetric weather radar is an important instrument for quantitative precipitation estimation (QPE). Conventional parametric approaches, such as the radar reflectivity (Z) and rain rate (R) relations, cannot fully represent the spatial and temporal variability of clouds and precipitation due to parameterization errors and dependence on raindrop size distribution (DSD). Furthermore, these relations estimate rainfall on a grid-by-grid basis, preventing the incorporation of spatial information into precipitation estimation.In recent years, machine learning has made rapid advancements in non-linear fitting and feature extracting. Since 2020, multiple studies constructed MLP or CNN-based QPE models that used polarimetric radar observations to retrieve precipitation. These researches have consistently demonstrated that machine learning algorithms perform better than traditional parametric methods in different regions and climatic conditions(Chen & Chandrasekar, 2021; Li et al., 2023; Osborne et al., 2023; Tian et al., 2020; Zhang et al., 2021; Zhou et al., 2023).The aforementioned studies have highlighted the immense potential of deep learning for radar QPE, but they are based on S-band radar data. Because X-band radar has a shorter wavelength, the electromagnetic scattering characteristics of hydrometeors differ from those of S-band radar, especially for specific differential phase (kdp), which is closely related to rainfall. Furthermore, X-band radars have different spatial resolutions from S-band radars, which indicates that directly applying a model trained with S-band radar data to X-band radar data may introduce biases. Therefore, we develop a CNN-based QPE model using polarimetric measurements from X-band radars and compare its performance against traditional parametric methods. The input data for the CNN model is a matrix with dimensions (6, 9, 9). The matrix is composed of two matrices of size (3, 9, 9), which is the polarimetric measurements from the two lowest scan elevation angles and 9*9 surrounding range gates. This allows the input data to capture the spatial and physical characteristics of the precipitation field. The results reveal that the CNN-based model not only enhances the accuracy of radar QPE with a diminished bias but also provides a more precise depiction of the spatial distribution of precipitation in comparison to conventional methods.