The quantitative precipitation estimation accuracy of dual-polarization radar is affected by many factors. In order to better use dual-polarization radar to estimate precipitation and further improve the rainfall estimate accuracy, error analysis and modeling of radar precipitation estimation are needed. Based on observations of the C-band dual-polarization radar of Nanjing University of Information Science and Technology from 2015 to 2016, raindrop spectrometer observation data and rain gauge data in Nanjing, the error distribution of radar estimation precipitation is statistically analyzed, the gauge representativeness error variance is separated, and the error quantitative model based on random error and systematic error is established. Firstly, the data of dual polarization radar is preprocessed, and the radar rainfall formula is fitted by the data of raindrop spectrograph in the observation base of Nanjing University of Information Science and Technology. By comparing the four radar rainfall formula R (Z_H), R (Z_H, Z_DR), R (K_DP), R (K_DP, Z_DR) with the rain gauge, the estimation performance of each formula under different precipitation thresholds is analyzed. Then, the spatial correlation function of rain gauge data is estimated, the gauge representative error caused by spatial mismatch between radar and rain gauge is calculated, and the proportion of measurement error and parameter error in radar precipitation estimation error is analyzed. Based on the attribute and distribution rule of radar precipitation estimation errors, they are divided into random errors and systematic errors, and a quantitative model is established. Finally, based on the performance and error analysis of four radar rainfall formula, an optimized combination of dual polarization radar precipitation estimation algorithm is proposed. The results show that R (Z_H) and R (Z_H, Z_DR) have better performance for light precipitation estimation. When the rainfall threshold is greater than 2.5 mm/h, the advantage of K_DP becomes obvious. The gauge representative error caused by spatial mismatch cannot be ignored. Therefore, the point to area error should be eliminated when the radar resolution unit is large. The radar error is modeled according to the systematic error and random error, and it is found that the systematic error of radar near surface precipitation is proportional to the rainfall intensity in the form of linear function, and the double exponential model better represents the random error distribution. Through the performance analysis of radar rain measurement formula and dual polarization signal analysis, it is found that the optimized combination is better than the single rain algorithm in accuracy and stability.

Attenuation effect for spaceborne millimeter wave radar is severe. The microphysical properties of cloud and precipit- ation particles are quite different, and the attenuation effects caused by these different particles are different as well. Based on different cloud and precipitation scenarios simulated by the Weather Research and Forecasting model (WRF), the radar echoes after attenuation are obtained by a spaceborne millimeter wave radar simulator, and a sensitivity analysis of the attenuation characteristics of the particles is carried out to explain important factors that cause attenuation uncertainty. Results indicate that the simulated radar echoes after attenuation are in good agreement with the Cloud Profiling Radar (CPR) echoes. The echo structures and intensities are similar, and the differences in vertical profiles of the average reflectivity are within 20%. Through the sensitivity analysis of the attenuation coefficient (K), it is found that the attenuation caused by cloud and precipitation particles is most sensitive to water content (W) in the Particle Size Distribution (PSD), followed by the median volume diameter (D₀). The change of D₀ may cause the average relative error of the attenuation of rain to be around 25%. Snow and graupel are more affected by the density parameters (a and b) than by the slope parameter (Λ), and the uncertainty caused by a is approximately 25%. Under the same PSD parameters, the difference in attenuation coefficient caused by ice state and liquid state is approximately 90%. Considering the high sensitivity of attenuation to phase states, the K-W relationship in different phases is fitted for a cloud case measured by CPR. The attenuation correction result for the CPR is compared with the band correction result for the Dual-frequency Ka-band Precipitation Radar (DPR). It is found that the echo intensities of the two are quite close, and the uncertainty of the attenuation correction mainly comes from the difference between the inversion results of the liquid water content and the actual situation.

High-quality and accurate precipitation estimations can be obtained by integrating precipitation information measures using ground-based and spaceborne radars in the same target area. Estimating the true precipitation state is a typical inverse problem for a given set of noisy radar precipitation observations. The regularization method can appropriately constrain the inverse problem to obtain a unique and stable solution. For different types of precipitation with different prior distributions, the L₁ and L₂ norms were more effective in constraining stratiform and convective precipitation, respectively. As a combination of L₁ and L₂ norms, the Huber norm is more suitable for mixed precipitation types. This study uses different regularization norms to combine precipitation data from the C-band dual-polarization ground radar (CDP) and dual-frequency precipitation radar (DPR) on the Global Precipitation Measurement (GPM) mission core satellite. Compared to single-source radar data, the fused figures contain more information and present a comprehensive precipitation structure encompassing the reflectivity and precipitation fields. In 27 precipitation cases, the fusion results utilizing the Huber norm achieved a structural similarity index measure (SSIM) and a peak signal-to-noise ratio (PSNR) of 0.8378 and 30.9322, respectively, compared with the CDP data. The fusion results showed that the Huber norm effectively amalgamate the features of convective and stratiform precipitation, with a reduction in the mean absolute error (MAE; 16.1% and 22.6%, respectively) and root-mean-square error (RMSE; 11.7% and 13.6%, respectively) compared to the 1-norm and 2-norm. Moreover, in contrast to the fusion results of scale recursive estimation (SRE), the Huber norm exhibits superior capability in capturing the localized precipitation intensity and reconstructing the detailed features of precipitation.