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This study presents a simulation framework for cloud and precipitation measurements via spaceborne millimeter wave radar composed of nine sub modules. To demonstrate the influence of the assumed physical parameters and optimizing the microphysical modeling of the hydrometeors, we first conducted a sensitivity analysis. The results indicated that the radar reflectivity was highly sensitive to the particle size distribution (PSD) parameter of the median volume diameter and particle density parameter, which can cause reflectivity variations of several to more than 10 dB. The variation in the prefactor of the mass-power relations that related to riming degree may result in an uncertainty of approximately 30–45 %. The particle shape and orientation also had a significant impact on the radar reflectivity. The spherical assumption may result in an average overestimation of the reflectivity by approximately 4–8 %, dependent on the particle shape and orientation modeling. Typical weather cases were simulated using optimal physical modeling accounting for the particle shapes, typical PSD parameters corresponding to the cloud precipitation types, mass-power relations for snow and graupel, and melting modeling. We present and validate the simulation results for a cold front stratiform cloud and a deep convective process with observations from W-band cloud profiling radar (CPR) on the CloudSat satellite. The simulated brightness band features, echo structure, and intensity showed good agreement with the CloudSat observations; the average relative error in the vertical profile was within 20 %. Our results quantify the uncertainty in the millimeter wave radar echo simulation that may be caused by the physical model parameters and provide a scientific basis for optimal forward modeling. They also provide suggestions for prior physical parameter constraints for the retrieval of the microphysical properties of clouds and precipitation.

The error characterization of rainfall products of spaceborne radar is essential for better applications of radar data, such as multisource precipitation data fusion and hydrological modeling. In this study, we analyzed the error of the near-surface rainfall product of the dual-frequency precipitation radar (DPR) on the Global Precipitation Measurement Mission (GPM) and modeled it based on ground C-band dual-polarization radar (CDP) data with optimization rainfall retrieval. The comparison results show that the near-surface rainfall data were overestimated by light rain and slightly underestimated by heavy rain. The error of near-surface rainfall of the DPR was modeled as an additive model according to the comparison results. The systematic error of near-surface rainfall was in the form of a quadratic polynomial, while the systematic error of stratiform precipitation was smaller than that of convective precipitation. The random error was modeled as a Gaussian distribution centered from −1 to 0 mm h−1. The standard deviation of the Gaussian distribution of convective precipitation was 1.71 mm h−1, and the standard deviation of stratiform precipitation was 1.18 mm h−1, which is smaller than that of convective precipitation. In view of the precipitation retrieval algorithm of DPR, the error causes were analyzed from the reflectivity factor (Z) and the drop size distribution (DSD) parameters (Dm, Nw). The high accuracy of the reflectivity factor measurement results in a small systematic error. Importantly, the negative bias of Nw was very obvious when the rain type was convective precipitation, resulting in a large random error. Significance Statement This study first compares the total and different rain types of near-surface rainfall measured by DPR and ground-based radar CDP, then separates the error of DPR near-surface rainfall into systematic and random errors and analyzes the possible causes of the error. The purpose of this study is to better apply the error model to applications such as optimal data fusion and hydrological modeling, and the analysis of the error can also provide a basis for improving the spaceborne precipitation retrieval algorithm.

With the increasing availability of precipitation radar data from space, enhancement of the resolution of spaceborne precipitation observations is important, particularly for hazard prediction and climate modeling at local scales relevant to extreme precipitation intensities and gradients. In this paper, the statistical characteristics of radar precipitation reflectivity data are studied and modeled using a hidden Markov tree (HMT) in the wavelet domain. Then, a high-resolution interpolation algorithm is proposed for spaceborne radar reflectivity using the HMT model as prior information. Owing to the small and transient storm elements embedded in the larger and slowly varying elements, the radar precipitation data exhibit distinct multiscale statistical properties, including a non-Gaussian structure and scale-to-scale dependency. An HMT model can capture well the statistical properties of radar precipitation, where the wavelet coefficients in each sub-band are characterized as a Gaussian mixture model (GMM), and the wavelet coefficients from the coarse scale to fine scale are described using a multiscale Markov process. The state probabilities of the GMM are determined using the expectation maximization method, and other parameters, for instance, the variance decay parameters in the HMT model are learned and estimated from high-resolution ground radar reflectivity images. Using the prior model, the wavelet coefficients at finer scales are estimated using local Wiener filtering. The interpolation algorithm is validated using data from the precipitation radar onboard the Tropical Rainfall Measurement Mission satellite, and the reconstructed results are found to be able to enhance the spatial resolution while optimally reproducing the local extremes and gradients.

The spaceborne precipitation radar onboard the Tropical Rainfall Measuring Mission satellite (TRMM PR) can provide good measurement of the vertical structure of reflectivity, while ground radar (GR) has a relatively high horizontal resolution and greater sensitivity. Fusion of TRMM PR and GR reflectivity data may maximize the advantages from both instruments. In this paper, TRMM PR and GR reflectivity data are fused using a neural network (NN)-based approach. The main steps included are: quality control of TRMM PR and GR reflectivity data; spatiotemporal matchup; GR calibration bias correction; conversion of TRMM PR data from Ku to S band; fusion of TRMM PR and GR reflectivity data with an NN method; interpolation of reflectivity data that are below PR’s sensitivity; blind areas compensation with a distance weighting-based merging approach; combination of three types of data: data with the NN method, data below PR’s sensitivity and data within compensated blind areas. During the NN fusion step, the TRMM PR data are taken as targets of the training NNs, and gridded GR data after horizontal downsampling at different heights are used as the input. The trained NNs are then used to obtain 3D high-resolution reflectivity from the original GR gridded data. After 3D fusion of the TRMM PR and GR reflectivity data, a more complete and finer-scale 3D radar reflectivity dataset incorporating characteristics from both the TRMM PR and GR observations can be obtained. The fused reflectivity data are evaluated based on a convective precipitation event through comparison with the high resolution TRMM PR and GR data with an interpolation algorithm.

The measurement error of differential reflectivity (ZDR), especially systematic ZDR bias, is a fundamental issue for the application of polarimetric radar data. Several calibration methods have been proposed and applied to correct ZDR bias. However, recent studies have shown that ZDR bias is time-dependent and can be significantly different on two adjacent days. This means that the frequent monitoring of ZDR bias is necessary, which is difficult to achieve with existing methods. As radar sensitivity has gradually been enhanced, large amounts of online solar echoes have begun to be observed in volume-scan data. Online solar echoes have a high frequency, and a known theoretical value of ZDR (0 dB) could thus allow the continuous monitoring of ZDR bias. However, online solar echoes are also affected by low signal-to-noise ratio and precipitation attenuation for short-wavelength radar. In order to understand the variation of ZDR bias in a C-band polarimetric radar at the Nanjing University of Information Science and Technology (NUIST-CDP), we analyzed the characteristics of online solar echoes from this radar, including the daily frequency of occurrence, the distribution along the radial direction, precipitation attenuation, and fluctuation caused by noise. Then, an automatic method based on online solar echoes was proposed to monitor the daily ZDR bias of the NUIST-CDP. In the proposed method, a one-way differential attenuation correction for solar echoes and a maximum likelihood estimation using a Gaussian model were designed to estimate the optimal daily ZDR bias. The analysis of three months of data from the NUIST-CDP showed the following: (1) Online solar echoes occurred very frequently regardless of precipitation. Under the volume-scan mode, the average number of occurrences was 15 per day and the minimum number was seven. This high frequency could meet the requirements of continuous monitoring of the daily ZDR bias under precipitation and no-rain conditions. (2) The result from the proposed online solar method was significantly linearly correlated with that from the vertical pointing method (observation at an elevation angle of 90&deg, ), with a correlation coefficient of 0.61, suggesting that the proposed method is feasible. (3) The day-to-day variation in the ZDR bias was relatively large, and 32% of such variations exceeded 0.2 dB, meaning that a one-time calibration was not representative in time. Accordingly, continuous calibration will be necessary. (4) The ZDR bias was found to be largely influenced by the ambient temperature, with a large negative correlation between the ZDR bias and the temperature.

A dual-band high-precision lightning imaging spectrometer (DHLIS) is designed to capture the fine spectrum of transient lightning. DHLIS improves the theoretical spectral resolution limit to 1 0 − 3 n m and broadens the spectral range to 30–40 nm by employing double echelle gratings in the arms of a spatial heterodyne spectrometer. Four strong and three fine spectral lines are selected to retrieve the temperature of the lightning channel using the Boltzmann plot method. The experimental results indicate that the inversion accuracy of the temperature obtained by inserting fine spectral lines is higher than that by implementation of four strong spectral lines.

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.

Circular synthetic aperture radar (SAR) imaging on the geosynchronous orbit has many potential advantages, such as high-resolution three-dimensional (3D) imaging and continuous surveillance of broad area. The mission considered is an L-band geosynchronous circular SAR (GEOCSAR) with full aperture measurement. Since the integration time of GEOCSAR can be as long as 24 h, the orbit altitude is about 36 000 km and the coverage may reach 1/3 of the Earth's surface. The spatial and temporal variation of the ionosphere may impose significant effects on 3D imaging performance of GEOCSAR. The effects of ionospheric spatial variation including vertical and horizontal structure, the ionospheric turbulence and the ionospheric temporal variation are discussed. The analysis shows that the temporal variation of the ionospheric electron content during the synthetic aperture and the ionospheric turbulence may severely affect the GEOCSAR focusing performance on the XY plane. The vertical and horizontal structure of the ionosphere will cause image shift, and the dispersion effect is negligible.

Circular SAR imaging on the geosynchronous orbit is an innovative SAR imaging mode, which can perform high resolution three-dimensional (3D) imaging with continuous observations for a large area. However, for geosynchronous satellites the orbit is severely disturbed by perturbing effects, and the orbit error will be an important factor influencing the SAR imaging on the geosynchronous orbit. In this paper, the effects of the orbit error on performances of 3D geosynchronous circular SAR (GEOCSAR) imaging are analyzed. The error model is firstly made, and then the orbit error propagating into the distance from the radar to the target (range) is derived. Furthermore, the effects of different components of the range error on the quality of focused SAR signals are deduced, based on the 3D GEOCSAR signal model with time-domain correlation algorithm. The analyses and simulations will quantify and verify the required accuracies of GEOCSAR orbit estimation for ensuring the 3D imaging quality.

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.

Circular synthetic aperture radar (SAR) imaging on the geosynchronous orbit is an innovative SAR imaging mode, which can produce high-resolution three-dimensional (3D) images and offer 24-h continuous surveillance of a broad area. In this paper, we use the geosynchronous circular synthetic aperture radar (GEOCSAR) to efficiently produce high-resolution 3D surface displacement maps by repeat pass interferometry. The coverage areas with GEOCSAR imaging can be mapped from different view directions; thus, a 3D displacement can be provided daily. First, we derive the connection between the GEOCSAR interferometric phase and the surface deformation for analytically interpreting its capability to resolve the three components of the deformation. Then, to retrieve the three components of the deformation, a retrieval matrix is deduced. Furthermore, analyses of the errors in the observation phase vector and retrieval matrix are described, and the way these errors propagate into the deformation vector is modeled. To reduce the errors of the deformation measurement, the retrieval matrix is optimized by choosing appropriate parameters and making the condition number of the matrix minimal. Finally, the simulation results validate the correctness of the analysis, and the 3D retrieval deformation is presented with three subapertures and shows an improvement of accuracy when the optimal retrieval matrix is used.

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.

The precipitation radar (PR) on the Tropical Rainfall Measuring Mission (TRMM) satellite has high vertical resolution, and ground radar (GR) has relatively good capability to detect weak precipitation and relatively good horizontal resolution. The joint utilization of PR and GR will be an important factor in maximizing the benefit to be reaped from both instruments. In this paper, we will blend the PR data and GR reflectivity in Nanjing, China based on image fusion algorithm. Integrating PR and GR mainly includes following steps: spatial-temporal matchup of PR and GR data, image fusion algorithms selection and quality evaluation of the fused image.

High resolution three dimensional (3D) imaging can be gained with geosynchronous circular SAR (GEOCSAR). Since the integration time of GEOCSAR may be as long as 24 hours, the effect of temporal variation of the tropospheric refractive index can't be ignored if well focused images are to be obtained. Based on the model of the diurnal variation of the refractivity, we present the reconstructed images affected by the phase error resulted from the temporal variation of the tropospheric refractive index. (3 pages)

The nadir track of geosynchronous satellite can be “figure-8”, circular or ellipse track which could be produced with appropriate orbit parameters. In this paper, the characteristics of the interferometric deformation detection for geosynchronous SAR with circular aperture are analyzed. The connection between the interferometric phase and the deformation of repeat pass geosynchronous SAR is derived, and its potential to get three-dimensional 3D deformation measurement is interpreted.