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Polarization information can greatly improve the ability of detection, parameter retrieval, and classification for entomological radar. In recent, the instantaneous fully polarimetric measurement technique is used in entomological radar, so as to relax the restriction of invariant orientation during observation compared to the typical vertical-looking radar with linear-polarized rotation configuration. However, the fully polarimetric measurement is based on a multichannels’ radar system, and the imbalance and crosstalk between channels, namely, polarimetric errors, will severely affect the measurement of the polarimetric scattering matrix (PSM). Thus, polarimetric calibration is essential to obtain the accurate polarization information of targets. In contrast to inefficient and manual calibration methods, a data-driven polarimetric calibration method is proposed based on the assumption of reciprocity and bilateral symmetry of insects in this article. The polarimetric errors are elaborately decomposed into two components, which could be estimated by reciprocity and bilateral symmetry, respectively. In addition, because the proposed method is data-driven, the calibration process could be automatically executed to compensate for the temporal-variant polarimetric errors induced by temperature change and, thus, suitable for the long-term operation of entomological radar. Simulations and experiments are carried out to evaluate the performance of the proposed calibration method. Results show that it could achieve high accuracy when the number of insects used as calibrators is large enough. The proposed method has been applied in Ku-band high-resolution fully polarimetric entomological radars for cross-border migratory insect observation in Yunnan province, China. [ABSTRACT FROM AUTHOR]

The X-band dual-polarization radar can provide raindrop size distribution (DSD) data at a continuous spatiotemporal scale; however, the traditional empirical-formula-based DSD retrieval methods for X-band radar are limited by high uncertainty and poor generality such as differences due to rainfall type, seasons, and geographical environment. This study identified strong correlations between polarimetric radar variables and various climatic and geographical elements. Two random-forest-based DSD retrieval models, one using only X-band radar parameters as the model input (RF-DSD) and the other containing multielement inputs as well (RF-M-DSD), were established using datasets from 18 sites in China. This study also evaluated the proposed models using different error indices over three test sites. Compared with the traditional DSD retrieval model for X-band dual-polarization radar, RF-DSD and RF-M-DSD showed improved predictive ability for all DSD parameters. The root-mean-square error (RMSE) values for $D_{0}$ (median volume diameter parameter), ${\text {log}}_{10}N_{w}$ (normalized intercept parameter), and $\mu $ (shape parameter) were 27%, 55%, and 39% lower, respectively, for RF-DSD, and 48%, 68%, and 43% lower, respectively, for RF-M-DSD. Finally, the DSD retrieval models were applied on one CLA-12A X-band radar in the Jiangsu Province. The DSD retrieval results suggest that the proposed models perform well on radar observation data with low uncertainty especially for $D_{0}$ and ${\text {log}}_{10}N_{w}$. Summary reveals that the RF-M-DSD performs superior and has stable DSD retrieval capabilities across different geographical areas, which can provide reliable radar-DSD retrieval data for areas with scarce disdrometer data. [ABSTRACT FROM AUTHOR]

China’s first Mars probe Tianwen-1 has successfully landed on the southern Utopia Planitia of Mars on May 15, 2021. The Zhurong rover is first equipped with a full-polarimetric Mars Rover Penetrating Radar (FP-RoPeR) system, aiming to map the subsurface fine structure and to find the potential underground water ice. However, different from the previous water ice detection of orbital radar, the FP-RoPeR signals will be affected by the induced field rotation (IFR) if electromagnetic (EM) waves propagate through rough interfaces. Therefore, in this article, we assess the IFR effects from rough interfaces on the circular polarization ratio (CPR) response of FP-RoPeR data, which is a significant parameter for water ice detection. The theoretical computation and numerical validation indicate that the depth, the number of rough interfaces, and relative permittivity are three vital parameters that affect the IFR effects; depth plays a more important role than the other two for FP-RoPeR system. The CPR estimation result will be with greater error in the shallow region (0–1 m). The relative error in the region of depth greater than 1 m can be guaranteed to be under 10%. [ABSTRACT FROM AUTHOR]

Monsoon coastal cities often suffer from extreme rain-induced flooding and severe hazard. However, the associated physical mechanisms and detailed storm structures are poorly understood due to the lack of high-resolution data. This study presents an analysis of a thunderstorm that produces extreme hourly rainfall (EXHR) of 219 mm over the Guangzhou megacity on the southern coast of China using integrated multiplatform observations and a four-dimensional variational Doppler radar analysis system. Results indicate that weak environmental flows and convectively generated weak cold pool facilitate the formation of a quasi-stationary storm, while onshore warm and moist flows in the boundary layer (BL) provide the needed moisture supply. The 219-mm EXHR is attendant by a shallow meso- $\gamma $ -scale vortex due to stretching of intense latent heating-induced convergence, which, in turn, helps organize convective updrafts into its core region. Lightning and dual-polarization radar observations reveal active warm-rain (but weak mixed-phase) microphysical processes, with raindrop size distribution (RSD) closer to marine convection. In contrast, another storm develops about 4 h earlier and only 35 km to the northwest, but with more lightning, higher cloud tops, more graupel and supercooled liquid water content, more continental RSD, little evidence of rotation, and much less rainfall; they are attributable to the presence of larger convective available potential energy resulting from the urban heat island effects and less moisture supply in the BL. These results highlight the importance of using multisource remote sensing data sets in understanding the microphysical and kinematic structures of EXHR-producing storms. [ABSTRACT FROM AUTHOR]

The ground-based radar quantitative precipitation estimation (QPE) faces various challenges including the overestimation caused by the bright band (BB) in the stratiform region and the underestimation in mountainous areas when the terrain-enhanced precipitation occurs at the levels below ground-based radar measurements. The vertical precipitation structure provided by spaceborne radars, i.e., the Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) and the Global Precipitation Measurement mission (GPM) Dual-frequency PR (DPR), is valuable for mitigating the above problems. Since the spaceborne radars and ground-based radars usually operate in different frequencies, e.g., the TRMM PR and the KuPR of GPM DPR work in Ku-band (13.8 and 13.6 GHz, respectively) and the ground-based radars in western China work in C-band (5.4 GHz), the reflectivity conversion from Ku-band to C-band is necessary before the vertical profile of reflectivity (VPR) measured by spaceborne radars can be utilized to improve the ground-based radar QPE in western China. This study presents a conversion method using GPM DPR measurements, i.e., the drop size distribution (DSD) for different precipitation types (the stratiform with/without BB and the convective cases) and particle phases (the solid, melting, and liquid). Using the T-matrix method, the reflectivity difference between Ku-band and C-band is found and the Ku-band to C-band conversion relations are derived with the linear regression. These conversion relations have been validated by matching and comparing the converted C-band reflectivity with the C-band ground-based radar measurements. The results demonstrate the effectiveness and reliability of the conversion. This method can be extended for the reflectivity conversion in other frequencies and can facilitate the incorporation of reflectivity measurements from various instruments. [ABSTRACT FROM AUTHOR]

Incoherent scatter radar (ISR), as one of the most powerful ionospheric detection methods on the ground, can obtain a variety of important ionospheric parameters with high spatiotemporal resolution over the entire ionosphere. A high-power large-aperture phased array (PA) ISR, named Sanya ISR (SYISR), is under construction at Sanya (18.3°N, 109.6°E), China. Two remote receive-only arrays of SYISR will be built in the future to form a tristatic ISR system, which will be the only multistatic ISR in a low-latitude region. Estimating the signal-to-noise ratio (SNR) is the key task for analyzing the detection accuracy and evaluating the radar performance. In this article, the SNR of the SYISR tristatic system is simulated for the first time using a suitable PA bistatic radar equation. The parameters of the designed SYISR tristatic system are used in the simulation. Subsequently, the SNR spatial variability of the bistatic PA radar and bistatic equivalent parabolic dish (EPD) radar is analyzed and compared. The results show that the radar system constants, effective scattering volume, and spatial variable terms are the three different sources of factors affecting the SNR. They jointly determine the spatial distribution characteristics of the SNR. Moreover, the PA SNR is significantly different from the EPD SNR numerically and morphologically. The PA SNR is weaker than the EPD SNR in the whole observational space. In the horizontal direction, the PA SNR has a single-peak structure, while the EPD SNR has a double-peak structure. The root cause of these differences is that the beamwidth of a PA changes with the beam directions, while that of the EPD is fixed. This research provides a theoretical basis for the design of tristatic PA ISR in hardware optimization and error analysis, which will be shown in another article. [ABSTRACT FROM AUTHOR]

In this study, as part of an effort to study snowfall characteristics and quantify winter precipitation in East China, we investigated the microphysical properties of snowfall, including size, shape, density, and terminal velocity using a 2-D video disdrometer (2-DVD) and a weighing precipitation gauge in Nanjing (NJ), East China during the winters of 2015–2019. We obtained larger snow density and terminal velocity values than those reported in the literature for this region. Higher snow density could account for higher snowflake terminal velocity, after removing the effects of observation altitude and surface temperature. We then fit the snow particle size distributions (PSDs) to the gamma model and explored the interrelationships among the model parameters and snowfall rate (SR). The relationship between radar reflectivity factor ($Z_{e}$) and SR was derived based on snow PSD measurements and the snow density relation. Using this $Z_{e}-\mathrm {SR}$ relationship, the estimated liquid-equivalent SRs are obtained from S-band NJ radar data collected during several snowfall events. Radar-inferred SRs showed reasonable agreement with those measured on the ground, with a mean absolute error of 16% for the collected snowfall events in NJ. [ABSTRACT FROM AUTHOR]

An inverse mapping table (IMT) method is proposed in this article to retrieve the raindrop size distribution (RSD) parameters from X-band polarimetric weather radar data. In the IMT method, a forward mapping database from three parameters of a gamma-type RSD to polarimetric radar variables is first built based on the scattering simulations under ideal atmospheric conditions, and then an inverse mapping database is derived. In particular, given a fixed shape parameter ($\mu $) of RSD, the intersection of horizontal reflectivity ($Z_{\text {H}}$) and differential reflectivity ($Z_{\text {DR}}$) contour lines is first obtained in the domain of total number concentration ($N_{\text {T}}$) and median volume diameter (MVD) $D_{0}$ ; and the inverse mapping relationship between $Z_{H}$ and $Z_{\text {DR}}$ to $N_{T}$ and $D_{0}$ at a fixed $\mu $ value is derived to form a single layer of IMT. Then, the monotonic relationship between $\mu $ and the specific differential propagation phase shift ($K_{\text {DP}}$) or backscatter differential phase ($\delta _{\text {CO}}$) can aid in determining $\mu $ and a single layer of IMT. Thus, the inverse mapping database from polarimetric observations to the three gamma-type RSD parameters $\mu $ , $N_{\text {T}}$ , and $D_{0}$ can be established. Demonstration studies during a convective rainfall event and a large-scale rainfall event which occurred in northeastern China are carried out to examine the performance of this IMT method compared to a constrained-gamma (C-G) method that uses empirical relations between RSD parameters. The results show that the IMT method has a better performance in the convective case and similar performance in the large-scale continuous rainfall case. [ABSTRACT FROM AUTHOR]

A new approach is presented in this paper to produce Digital Elevation Model (DEM) in mountainous areas with steep slope using ERS-½ tandem data. In order to reduce the impact of phase errors on the Interferometric Synthetic Aperture Radar (InSAR)-generated DEM, an external DEM such as that from Shuttle Radar Topography Mission (SRTM) is utilized in this approach. The proposed algorithm includes two steps: The first step is to model and remove phase trends with a linear regression analysis before converting phase to height; the second step is to filter unreliable height points before interpolating the DEM from the InSAR height map. The critical points are the following: 1) determining the one-to-one correspondence between the interferogram and the SRTM DEM before knowing the InSAR-derived elevation values and 2) estimating the elevation range of every pixel from SRTM DEM. To solve the first problem, an iteratively geocoding algorithm is performed. A DEM interpolation error model solves the second one. For InSAR data processing, the SRTM DEM is not only usable for modeling systematic phase errors but also for filtering gross height errors. The experiments in Zhangbei and the Three Gorges areas in China show that our approach has improved the accuracy of the resulting DEMs significantly without any ground control points. [ABSTRACT FROM AUTHOR]