Circular synthetic aperture radar (SAR) imaging on the geosynchronous orbit has significant potential advantages, such as high resolution three‐dimensional imaging, large coverage, but it also has challenges to overcome. One major problem limiting the high‐resolution geosynchronous circular SAR (GEOCSAR) performances is the atmospheric effects including the tropospheric and ionospheric effects. In this study, the authors mainly consider the tropospheric effects on L‐band GEOCSAR imaging. Since the integration time of GEOCSAR may be as long as 24 h, and the coverage can reach 1/3 of the Earth's surface, the effects of the spatial and temporal variation of the tropospheric refractive index cannot be ignored if well focused images are to be obtained. The analysis shows that the temporal variation of the tropospheric refractive index may cause severe degradation of the SAR images; the spatial variation of the refractive index and the tropospheric turbulence are not the important factor limiting the GEOCSAR imaging performance.

Dual-polarization radar provides information about precipitation microphysics through drop size distribution and hydrometeor classification, and, therefore, can produce improvement in quantitative precipitation estimation. Rainfall relations combination is an optimization algorithm; however, optimally selecting the rainfall relation is challenging in dual-polarization rainfall estimation. In this study, an adaptive rainfall algorithm is developed using a logistic regression model to guide the choice of the optimal radar rainfall relation. The logistic model is established according to the matched dual-polarization radar data and rain gauge data. Only liquid particles are considered for the rainfall estimation determined by the hydrometeor classification of dual-polarization radar, and the polarimetric rainfall relations are obtained with a neural network algorithm based on the disdrometer data. The proposed algorithm is validated with C-band dual-polarization radar data, and the results show that the adaptive algorithm outperforms the single rainfall relation and conventional combination algorithm.

The geosynchronous circular synthetic aperture radar (GEOCSAR) is an innovative SAR system, which can produce high resolution three-dimensional (3D) images and has the potential to provide 3D deformation measurement. With an orbit altitude of approximately 36 000 km, the orbit motion and orbit disturbance effects of GEOCSAR behave differently from those of the conventional spaceborne SAR. In this paper, we analyze the effects of orbit errors on GEOCSAR imaging and interferometric processing. First, we present the GEOCSAR imaging geometry and the orbit errors model based on perturbation analysis. Then, we give the GEOCSAR signal formulation based on imaging geometry, and analyze the effect of the orbit error on the output focused signal. By interferometric processing on the 3D reconstructed images, the relationship between satellite orbit errors and the interferometric phase is deduced. Simulations demonstrate the effects of orbit errors on the GEOCSAR images, interferograms, and the deformations. The conclusions are that the required relative accuracy of orbit estimation should be at centimeter level for GEOCSAR imaging at L-band, and that millimeter-scale accuracy is needed for GEOCSAR interferometric processing.

Reflectivity factor bias caused by radar calibration errors would influence the accuracy of Quantitative Precipitation Estimations (QPE), and further result in spatial discontinuity in Multiple Ground Radars QPE (MGR-QPE) products. Due to sampling differences and random errors, the associated discontinuity cannot be thoroughly solved by the single-radar calibration method. Thus, a multiple-radar synchronous calibration approach was proposed to mitigate the spatial discontinuity of MGR-QPE. Firstly, spatial discontinuity was solved by the intercalibration of adjacent ground radars, and then calibration errors were reduced by referring to the Ku-Band Precipitation Radar (KuPR) carried by the Global Precipitation Measurement (GPM) Core Observatory as a standard reference. Finally, Mosaic Reflectivity and MGR-QPE products with spatial continuity were obtained. Using three S-band operational radars covering the lower reaches of the Yangtze River in China, this method was evaluated under four representative precipitation events. The result showed that: (1) the spatial continuity of reflectivity factor and precipitation estimation fields was significantly improved after bias correction, and the reflectivity differences between adjacent radars were reduced by 78% and 82%, respectively; (2) the MGR-QPE data were closer to gauge observations with the normalized absolute error reducing by 0.05 to 0.12.