Abstract
Radar beam blockage is an important error source that affects the quality of weather radar data. An echo-filling network (EFnet) is proposed based on a deep learning algorithm to correct the echo intensity under the occlusion area in the Nanjing S-band new-generation weather radar (CINRAD/SA). The training dataset is constructed by the labels, which are the echo intensity at the 0.5° elevation in the unblocked area, and by the input features, which are the intensity in the cube including multiple elevations and gates corresponding to the location of bottom labels. Two loss functions are applied to compile the network: one is the common mean square error (MSE), and the other is a self-defined loss function that increases the weight of strong echoes. Considering that the radar beam broadens with distance and height, the 0.5° elevation scan is divided into six range bands every 25 km to train different models. The models are evaluated by three indicators: explained variance (EVar), mean absolute error (MAE), and correlation coefficient (CC). Two cases are demonstrated to compare the effect of the echo-filling model by different loss functions. The results suggest that EFnet can effectively correct the echo reflectivity and improve the data quality in the occlusion area, and there are better results for strong echoes when the self-defined loss function is used.
Highlights
Weather radar is an important means of cloud precipitation physics research and precipitation monitoring and early warning
Terrain blockage results in a radar observation blind area, which causes a reduction in the radar beam power and further influences the multiradar mosaic and other radar secondary products, such as quantitative precipitation estimation (QPE), which depends on echo intensity [2,3]
The performance of model compiled with the self-defined loss function is slightly worse than that with mean square error (MSE), in which the average value of mean absolute error (MAE) increases by 0.0646 and explained variance (EVar) and CC decrease by 0.0067 and 0.0019, respectively, and it is mainly because the model with 8 ofstrong self-defined loss function highlights the strong echoes more but the proportion of echo is relatively small in the test dataset
Summary
Weather radar is an important means of cloud precipitation physics research and precipitation monitoring and early warning. The DEM-based beam blockage correction method was based on the equations of radar beam propagation in a standard atmosphere proposed by Battan [4]. According to the occlusion rate calculated by the DEM, a filling method using the average value of gates is proposed, which can correct the echo data in the azimuth of a small blocking coefficient using the adjacent plan position indicator (PPI) in real time [8]. The echoes in lowelevation, completely blocked areas are filled by the data in high-elevation, unblocked areas using the statistical analysis of VPR, and the filling effect is evaluated by contrasting the reflectivity factor and radar QPE values before and after filling [19].
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