For satellite brightness temperature images, researchers are constantly pursuing higher resolutions to obtain more detailed meteorological information. In this paper, a novel deep-learning-based modelling approach, named Zero-Sample Gradient Guidance Spatial Resolution Enhancement (ZSGRE), is developed explicitly for microwave radiometers. The detailed model, including mathematical derivation and key parameters, is presented. Subsequently, the proposed approach is applied in four scenarios: synthetic scene, simulated geographical brightness temperature, practical measurement of microwave radiometer in Fengyun-3D (FY-3D), and a cyclone analysis on the Atlantic. Compared with other methods, the proposed ZSGRE method improves 2.51% of SSIM (structural similarity), enhances 2.3 dB of PSNR (Peak Signal-to-Noise Ratio), and decreases 15.8% of IFOV (Instantaneous Field of View). Such applications demonstrate ZSGRE’s significant performance: zero-sample preparation and spatial resolution enhancement.

Spaceborne scatterometers are active non-imaging radar systems, become one of the most effective sensors in the field of quantitative remote sensing for global observation. However, their nominal resolution of 25 to 50 km limits their applicability in scenarios requiring higher resolution. In this paper, an improved adaptive bilateral-total-variation regularization reconstruction algorithm with Lorentzian norm (LABTV+ RR algorithm) is specifically designed for the world’s first dual-frequency scatterometer Fengyun-3E Wind Radar (FY-3E WindRAD) data. LABTV+ RR algorithm introduces dynamically adaptive regularization parameters based on our previously proposed LABTV RR algorithm, which effectively suppresses the noise while maintaining the image texture details, enabling more flexible adaptation to different image contents and noise levels. In addition, the performance of the LABTV+ RR algorithm is further optimized by employing Barzilai-Borwein (BB) stepsize, which dynamically adjusts the stepsize and accelerates the convergence speed of the solution. In this study, the effectiveness of the LABTV+ RR algorithm is validated by comparing actual data and simulated images. The spatial response function (SRF) derived from the actual antenna patterns is used to validate the algorithm’s performance on FY-3E WindRAD Level 1B (L1B) data. Specifically, the algorithm is tested on C-band with a spatial resolution pixel size of 25 km×0.25 km and Ku-band data with a spatial resolution pixel size of 10 km×0.25 km. The validation includes both horizontally polarized (HH-pol) and vertically polarized (VV-pol) transmitted and received signals. Additionally, two representative regions in China are chosen as the study regions to demonstrate the algorithm’s capability to enhance resolution to 3.125 km in both C-band and Ku-band. Furthermore, a multi-pass reconstruction strategy is proposed to achieve an even higher resolution pixel size of 1.5625 km for FY-3E WindRAD C-band and Ku-band data. The study has demonstrated the effectiveness of the proposed LABTV+ RR algorithm in enhancing the resolution of FY-3E WindRAD data, as evidenced by both qualitative visual assessments and quantitative evaluation metrics.

The first scatterometer onboard Chinese meteorological satellites is a dual-frequency and dual-polarization scatterometer named Wind Radar (WindRAD). It uses an advanced fan-beam conical scanning mechanism to acquire wind vector observation of global ocean surfaces and other geophysical parameters. Since the launch in 2021, external calibration using ground-based active radar calibrator (ARC) has been carried out to evaluated WindRAD in-orbit situation and observation accuracy. Satellite-ground integrated external calibration process is proposed, and optimal satellite-ground observation mode is specially designed for the WindRAD, greatly reducing the complexity of satellite-ground interaction and improving the efficiency of external calibration observation. This article proposes the algorithm of WindRAD external calibration, with comprehensive consideration of scientific nature and engineering realizability. WindRAD in-orbit observation data as well as ARC data were used to calculate the real antenna patterns and absolute calibration coefficients of each polarization for both C- and Ku-bands. The evaluation results revealed that the in-orbit antenna pattern hadn’t changed much compared with the prelaunch test result, which for the first time confirmed the correctness of the key parameters used in WindRAD calibration. Moreover, the calibration accuracy is better than 1 dB. For the first time, the stability of WindRAD in orbit is confirmed using external active reference target.

This study exploits the unique capabilities of the FY-3E WindRAD scatterometer, the first spaceborne dual-frequency (Ku- and C-band) and dual-polarization (hhandvv) rotating fan-beam scanning measurements, to investigate the backscatter characteristics of open water (OW), first-year ice (FYI), and multi-year ice (MYI) under different seasonal, wavelength, and polarization conditions throughout 2022 in the Arctic. Four types of feature parameters were defined for systematic analysis based on WindRAD swath data. It is concluded that the mean backscatter coefficient σp,λand the wavelength gradient ratioGRpare key indicators for distinguishing between FYI and MYI, with the Ku-band exhibiting superior performance outside the melt season due to enhanced volume scattering from desalinated ice and bubble structures. During melting, however, both ice types become indistinguishable as meltwater increases dielectric loss and reduces penetration depth. Furthermore, the standard deviation of the backscatter coefficient Δσp,λand the polarization ratio γλprove highly effective in separating sea ice from OW with the C-band showing particular advantage owing to a wider incidence angle range and stronger angular sensitivity of Bragg scattering over water. The γλapproaches 1 for both FYI and MYI due to depolarizing rough surfaces, whereas OW exhibits lower values dominated by Bragg scattering. This study provides a systematic observational basis for exploring the benefits of dual-frequency joint detection in enhancing sea ice monitoring capabilities, providing vital support for the development and refinement of algorithms for FY-3E WindRAD operational sea ice products.

The first C- and Ku-band dual-frequency scatterometer (WindRAD) onboard the Chinese FengYun-3E (FY-3E) satellite was successfully launched in July 2021. The WindRAD scatterometer uses an advanced fan-beam conical scanning mechanism to acquire wind vector data of global ocean surfaces and other geophysical parameters through backscattering measurements of the Earth. This article provides an introduction to the WindRAD instrument, an overview of the data preprocessing, and assessment of WindRAD measurements. The numerical weather prediction-based ocean calibration (NOC) approach, natural targets, and cross-calibration against the Ku-band scatterometer onboard the HY-2B satellite based on collocated backscatter measurements, were used to validate WindRAD backscatter results. The evaluation results revealed that the performance of the WindRAD data is generally in good agreement with other scatterometer data currently in use, while WindRAD backscatter data may contain nonlinear calibration issues that require further investigation. WindRAD has the ability to provide high-quality global backscattering measurements, which can be used for the inversion of various geophysical parameters and assimilation applications.

The short-wave infrared Polarization and Multi-Angle Imager (PMAI) onboard Fengyun-3 precipitation satellite is a new spaceborne imaging polarimeter for clouds and aerosols, with polarization channels of 1030, 1370, and 1640 nm. This study presents a detailed description and assessment of the calibration model of PMAI. For radiometric intensity calibration, multiple parameters in the radiometric model are fitted into a single coefficient to simplify calibration. Results show that the radiometric calibration uncertainty of the full image plane is better than 0.02, and the calibration coefficient increases as field of view increases. The maximal unsaturated incident radiance of all channels is equivalent to 100% albedo, and signal-to-noise ratio at the referenced radiance is greater than 115 and 182 for the polarized and unpolarized channels, respectively. The response of all channels shows high linearity and good uniformity of the full image plane. Based on results of intensity calibration, a polarization calibration model using a fully linear polarized light source is introduced with a polarization measurement matrix established by a simplified method and a calculation method. Assessment of polarization measurement indicates that the uncertainties of the obtained degree of linear polarization (DoLP) and angle of linear polarization (AoLP) based on the two methods are highly consistent. When fully linearly polarized light is incident, the measurement error of DoLP using the simplified polarization measurement matrix is within 0.02 and that of AoLP is less than 1°. Therefore, the simplified radiometric intensity and polarization calibration model meets the measurement accuracy requirements and improves the calibration efficiency.

The spaceborne microwave scatterometer (SCAT) is a radar for quantitatively measuring the backscatter coefficient of the Earth’s surface. Its main application is the measurement of wind speed and direction near the sea surface with a wide swath and high precision. So far, many SCATs have been launched in orbit, including SeaWinds by the United States, the ASCAT series by Europe, and the HY-2 series scatterometers by China, which contribute to improved weather forecasts and typhoon positioning services. The Fengyun-3E satellite wind scatterometer or WindRad, launched by China in July 2021, is the first Ku and C dual-band SCAT with the highest spatial resolution. In this study, the rotation characteristics and calibration parameters of WindRad in the prelaunch stage and after its early test and verification in orbit were investigated. The prelaunch test results demonstrated the excellent performance of WindRad, which can fully meet its quantitative application requirements. In-orbit early tests demonstrated the stable operation of WindRad, with signal characteristics consistent with the design specifications. The Ku and C dual-band earth surface backscatter coefficient projection and the sea surface wind field inversion were also investigated. The preliminary results confirmed the ability of WindRad to provide a high-quality ocean wind measurement and the improved capability for different applications, including sea ice detection and soil moisture measurement.

This study develops a machine learning (ML)-based multilayer cloud detection algorithm for the passive Advanced Himawari Imager (AHI) aboard the geostationary Himawari-8 satellite. AHI measurements in the 0.64-, 1.6-, 2.3-, 3.9-, 7.3-, 8.6-, 11.2-, and 12.4-$\mu \text{m}$channels, their combinations, geolocations, and observational geometries are used as predictors, and collocated active CloudSat and CALIPSO data are used to accurately label multilayer cloud pixels as the reference/truth of the predictand. We develop an ML-based daytime model (ML-Day) that utilizes all the aforementioned predictors and an all-time one (ML-All) that excludes the solar channel-dependent variables. Among four ML algorithms, the random forest (RF) performs slightly better than the artificial neural network, K-nearest neighbor, and support vector machines. By comparing with the merged CloudSat and CALIPSO product, the ML-Day model correctly identifies ~89% single-layer clouds and ~70% multilayer clouds, outperforming the Moderate Resolution Imaging Spectroradiometer (MODIS) operational multilayer cloud product (~80% and ~40% given by Marchantet al.). The success rates of ML-All for single-layer and multilayer clouds also reach ~85% and ~64%, respectively. The misclassification of our algorithm is mostly caused by missing optically thin clouds, a drawback of most radiometers without the 1.38-$\mu \text{m}$channel. Furthermore, with multilayer cloud pixels well detected by our algorithm, the AHI operational cloud top height retrievals are found to be larger biased due to multilayer cloud occurrence and might be improved by considering cloud vertical structures.

Nominal grid data of geostationary remote sensing satellites are fundamental for generating the subsequent products. It can be obtained by normalized projection models, mainly based on the imaging mode. However, there are only definitions and primary descriptive equations for the normalized geostationary projection (NGP) model in the existing literature, while the corresponding imaging mode and the physical interpretation are missing, thus hindering the understanding of the produced nominal grid dataset as well as the subsequent products based on the grid. This paper first derived the imaging mode for NGP based on the limited literature. In addition, another new imaging mode was introduced and analyzed based on NGP. The corresponding projection model [nonstandard normalized geostationary projection (NNGP)] was proposed, which is entirely consistent with the situation of America's Geostationary Operational Environmental Satellite-R Series (GOES-R) and Chinese Fengyun-4A (FY-4A). Furthermore, this paper proposed a novel nominal projection model for frame imaging, which is consistent with the imaging mode of China's Gaofen-4. Finally, extensive experiments were designed to comparatively analyze the three nominal grids and demonstrate a detailed difference. By providing a theoretical basis for nominal grid selection, this research is highly significant for the efficient near-real-time production and further applications of geostationary images, as well as the conversion between different datasets resulting from different nominal grid data. In addition, our models are sufficiently tested during the on-orbit running of FY-4A, the first satellite of China's second-generation three-axis stabilized geostationary meteorological satellite series. The algorithms provide the technical support for the high-precision image navigation and registration and play a significant role in robustly producing the meteorological data with similar quality to those from GOES-R.

Based on the data simulated by satellite radar simulator unit, two frequencies have been chosen and dual-frequency retrieval algorithm of cloud microwave parameters has been studied. Results suggest that: (1) 94/220GHz is sensitive to the tiny change of drop size parameter and a large frequency difference is advantage to the parameter retrieval. So, 94/220GHz can be chosen as the detection frequency of space borne cloud radar in the future after the consideration of detective ability, attenuation and manufacturing level in the industrial sector. (2) The relationship between dual wavelength ratio and median volume diameter rely on the particle density. DWR always increases with D0if particle density changes with the diameter. By contrast, DWR will fluctuate with D0when particle density is a constant. So the retrieval of a constant density is very difficult. (3) Backward iteration retrieval algorithm of dual frequency can be applied by 94/220GHz and the retrieval results are coincident with the true value. What's more, the precision of retrieval is influenced by system noise and calibration precision. So, the noise and calibration precision should be controlled under 1dBZ in order to satisfy the retrieval precision need.

China's first airborne Ku/Ka-band precipitation radar field campaign was carried out in 2010. Valuable data were collected by experimental aviation and simultaneous observations. Primary results of Ku/Ka-band precipitation radar field campaign are presented. Satellite-airplane-ground data comparisons are carried out in detail to validate airborne radar's ability in precipitation measurement. Important performance characteristics of airborne dual-frequency precipitation radar, including detection sensitivity, sidelobe level, dynamic range and actual range resolution, are presented to indicate the accuracy and sensitivity of the radar. Results show that the dual-frequency radar is qualified for the development of future spaceborne dual-frequency radar in China.

This paper describes field campaign situations of first spaceborne rain radar in China, including related background introduction, instrument parameters-, field campaign atmospheric condition and so on. What's more, based on the high precision Goddard Cumulus Ensemble (GCE) model, this paper focused on the calculation of two main rain parameter relations (k Zeand RZerelations) for Ka band. Then, by using this filed campaign observation data and the rain parameter relations mentioned above, the rain attenuation corrections were done and rain profiles were retrieved. Finally, some rain-profiling retrieval results were shown. Comparing with the local weather situation, the retrieval results are reasonable.

A Dual-frequency rain radar is planned In FY3(02)-E satellite, which is planed to be launched in 2016. In present, A dual-frequency airborne radar (ADPR) was developed in China, Field campaigns were carried out from July to October in 2010. To validate the instruments performance, Radar data from different sources were collected during the field campaign, include TRMM-PR 2A21/2A25 data products, car-borne X/ka band Doppler radar and ground weather radar data. Results from field campaign shows that, the overall performance of ADPR Ku/Ka radar are fulfilled with the requirement of china space-borne dual frequency ram radar(CDPR).

In China, two precipitation measurement radars (PMR) have been developed and deployed on aircrafts to carry out field experiments since July 2010. As one important part, calibration experiments using the active radar calibrator (ARC) are conducted for the airborne PMRs. Then those radar parameters deduced from external calibration are used to preprocess radar measurements to obtain the effective radar reflectivity factor. The results of calibration and data preprocessing are also analyzed in this paper.