Search Results (350)

Radiometric consistency across satellite platforms is fundamental to producing high-quality Climate Data Records (CDRs). Because different cross-calibration methods have distinct advantages and limitations, comparative evaluation is necessary to ensure record accuracy. This study presents a comparative assessment of two widely applied calibration approaches—Simultaneous Nadir Overpass (SNO) and Double Difference (DD)—for the thermal infrared (TIR) bands of FY-3D MERSI. MODIS/Aqua serves as the reference sensor, while radiative transfer simulations driven by ERA5 inputs are generated with the Advanced Radiative Transfer Modeling System (ARMS) to support the analysis. The results show that SNO performs effectively when matchup samples are sufficiently large and globally representative but is less applicable under sparse temporal sampling or orbital drift. In contrast, the DD method consistently achieves higher calibration accuracy for MERSI Bands 24 and 25 under clear-sky conditions. It reduces mean biases from ~−0.5 K to within ±0.1 K and lowers RMSE from ~0.6 K to 0.3–0.4 K during 2021–2022. Under cloudy conditions, DD tends to overcorrect because coefficients derived from clear-sky simulations are not directly transferable to cloud-covered scenes, whereas SNO remains more stable though less precise. Overall, the results suggest that the two methods exhibit complementary strengths, with DD being preferable for high-accuracy calibration in clear-sky scenarios and SNO offering greater stability across variable atmospheric conditions. Future work will validate both methods under varied surface and atmospheric conditions and extend their use to additional sensors and spectral bands. Full article

Clouds cover nearly two-thirds of Earth’s surface, making reliable cloud mask data essential for remote sensing applications and atmospheric research. This study develops a TrAdaBoost transfer learning framework that integrates active CALIOP and passive MODIS observations to enable unified, high-accuracy cloud detection across FY-4A/AGRI, FY-4B/AGRI, and Himawari-8/9 AHI sensors. The proposed TrAdaBoost Cloud Mask algorithm (TCM) achieves robust performance in dual validations with CALIPSO VFM and MOD35/MYD35, attaining a hit rate (HR) above 0.85 and a cloudy probability of detection (${POD}_{cld})$ exceeding 0.89. Relative to official products, TCM consistently delivers higher accuracy, with the most pronounced gains on FY-4A/AGRI. SHAP interpretability analysis highlights that 0.47 μm albedo, 10.8/10.4 μm and 12.0/12.4 μm brightness temperatures and geometric factors such as solar zenith angles (SZA) and satellite zenith angles (VZA) are key contributors influencing cloud detection. Multidimensional consistency assessments further indicate strong inter-sensor agreement under diverse SZA and land cover conditions, underscoring the stability and generalizability of TCM. These results provide a robust foundation for the advancement of multi-source satellite cloud mask algorithms and the development of cloud data products integrated. Full article

Cloud detection is an indispensable step in satellite remote sensing of cloud properties and objects under the influence of cloud occlusion. Nevertheless, interfering targets such as snow and haze pollution are easily misjudged as clouds for most of the current algorithms. Hence, a robust cloud detection algorithm is urgently needed, especially for regions with high latitudes or severe air pollution. This paper demonstrated that the passive satellite detector Advanced Geosynchronous Radiation Imager (AGRI) onboard the FY-4A satellite has a great possibility to misjudge the dense aerosols in haze pollution as clouds during the daytime, and constructed an algorithm based on the spectral information of the AGRI’s 14 bands with a concise and high-speed calculation. This study adjusted the previously proposed cloud mask rectification algorithm of Moderate-Resolution Imaging Spectroradiometer (MODIS), rectified the MODIS cloud detection result, and used it as the accurate cloud mask data. The algorithm was constructed based on adjusted Fisher discrimination analysis (AFDA) and spectral spatial variability (SSV) methods over four different underlying surfaces (land, desert, snow, and water) and two seasons (summer and winter). This algorithm divides the identification into two steps to screen the confident cloud clusters and broken clouds, which are not easy to recognize, respectively. In the first step, channels with obvious differences in cloudy and cloud-free areas were selected, and AFDA was utilized to build a weighted sum formula across the normalized spectral data of the selected bands. This step transforms the traditional dynamic-threshold test on multiple bands into a simple test of the calculated summation value. In the second step, SSV was used to capture the broken clouds by calculating the standard deviation (STD) of spectra in every 3 × 3-pixel window to quantify the spectral homogeneity within a small scale. To assess the algorithm’s spatial and temporal generalizability, two evaluations were conducted: one examining four key regions and another assessing three different moments on a certain day in East China. The results showed that the algorithm has an excellent accuracy across four different underlying surfaces, insusceptible to the main interferences such as haze and snow, and shows a strong detection capability for broken clouds. This algorithm enables widespread application to different regions and times of day, with a low calculation complexity, indicating that a new method satisfying the requirements of fast and robust cloud detection can be achieved. Full article

This study investigates the impact of assimilating FY-3D Microwave Radiation Imager (MWRI) radiance data into the Weather Research and Forecasting (WRF) model, utilizing a 3D-Var data assimilation system, on the forecast accuracy of Typhoon Muifa (2022). The research focuses on the selection of data from different channels, land/ocean coverage, and orbits of the MWRI, along with the synergistic assimilation strategy with MWHS-2 data. Ten assimilation experiments were conducted, starting from 0600 UTC on 14 September 2022, covering a 42 h forecast period. The results show that after assimilating the microwave radiometer data, the brightness temperature deviation in the ocean area was significantly reduced compared to the simulation without data assimilation. This led to an improvement in the accuracy of typhoon track and intensity predictions, particularly for predictions beyond 24 h. Furthermore, the assimilation of land data and single-orbit data (particularly from the western orbit) further enhanced forecast accuracy, while the joint assimilation of MWHS-2 and MWRI data yielded additional error reductions. These findings underscore the potential of satellite data assimilation in improving typhoon forecasting and highlight the need for optimal land observation and channel selection techniques. Full article

Clouds are closely related to precipitation, as their type, microphysical characteristics, and dynamic properties determine the intensity, duration, and form of rainfall. While geostationary satellites offer continuous cloud-top observations, they cannot capture the full three-dimensional structure of clouds, limiting the accuracy of precipitation forecasting based on geostationary satellite data. However, cloud–precipitation relationships contain valuable physical information that can be leveraged to improve forecasting performance. To further enhance the precision of satellite precipitation forecasting, this study proposes a multi-channel satellite precipitation forecasting method that integrates cloud classification products. The method combines precipitation-prior information from Himawari-8 satellite cloud classification products with multi-channel satellite observations to generate precipitation forecasts for the next four hours. This approach further exploits the potential of satellite observations in precipitation forecasting. Experimental results show that integrating cloud classification products improves the Critical Success Index by 8.0%, improves the Correlation Coefficient by 5.8%, and reduces the Mean Squared Error by 3.0%, but increases the MAE by 4.5%. It is proven that this method can effectively improve the accuracy of multi-channel satellite precipitation forecasting. Full article

Ocean surface wind fields are crucial for marine environmental research and applications in weather forecasting, ocean disaster monitoring, and climate change studies. However, traditional wind retrieval methods often struggle with modeling complexity and ambiguity due to the nonlinear nature of geophysical model functions (GMFs), leading to increased computational costs and reduced accuracy. To tackle these challenges, this study establishes a sea surface wind field retrieval model employing a backpropagation (BP) neural network, which integrates multi-angular observations from the Wind Radar (WindRAD) sensor aboard the Fengyun-3E (FY-3E) satellite. Experimental results show that the proposed model achieves high precision in retrieving both wind speed and direction. The wind speed model achieves a root-mean-square error (RMSE) of 1.20 m/s for the training set and 1.00 m/s for the selected test set when using ERA5 data as the reference, outperforming the official WindRAD products. For wind direction, the model attains an RMSE of 23.99° on the training set and 24.58° on the test set. Independent validation using Tropical Atmosphere Ocean (TAO) buoy observations further confirms the model’s effectiveness, yielding an RMSE of 1.29 m/s for wind speed and 24.37° for wind direction, also surpassing official WindRAD products. The BP neural network effectively captures the nonlinear relationship between wind parameters and radar backscatter signals, showing significant advantages over traditional methods and maintaining good performance across different wind speeds, particularly in the moderate range (4–10 m/s). In summary, the method proposed herein significantly enhances wind field retrieval accuracy from space; it has the potential to optimize satellite wind field products and improve global wind monitoring and meteorological forecasting. Full article

High-resolution numerical weather prediction requires accurate cloud microphysical initial conditions to enhance forecasting capabilities for high-impact severe weather events such as convective storms. This study integrated Fengyun-2 (FY-2) geostationary satellite data (equivalent blackbody temperature and total cloud cover) and next-generation 3D weather radar reflectivity from the China Meteorological Administration (CMA) to construct cloud microphysical initial fields and evaluate their impact on the CMA-MESO 3 km regional model. An analysis of the catastrophic rainfall event in Henan on 20 July 2021, and a 92-day continuous experiment (May–July 2024) revealed that assimilating cloud microphysical variables significantly improved precipitation forecasting: the equitable threat scores (ETSs) for 1 h forecasts of light, moderate, and heavy rain increased from 0.083, 0.043, and 0.007 to 0.41, 0.36, and 0.217, respectively, with average hourly ETS improvements of 21–71% for 2–6 h forecasts and increases in ETSs for light, moderate, and heavy rain of 7.5%, 9.8%, and 24.9% at 7–12 h, with limited improvement beyond 12 h. Furthermore, the root mean square error (RMSE) of the 2 m temperature forecasts decreased across all 1–72 h lead times, with a 4.2% reduction during the 1–9 h period, while the geopotential height RMSE reductions reached 5.8%, 3.3%, and 2.0% at 24, 48, and 72 h, respectively. Additionally, synchronized enhancements were observed in 10 m wind prediction accuracy. These findings underscore the critical role of cloud microphysical initialization in advancing mesoscale numerical weather prediction systems. Full article

Clouds are important for Earth’s energy budget and water cycles, and precisely characterizing their vertical structure is essential for understanding their impact. Although passive remote sensing offers broad coverage and high temporal resolution, sensor and algorithmic limitations impede the accurate depiction of cloud vertical profiles. To improve estimates of their key structural parameters, e.g., cloud top height (CTH) and cloud vertical extent (CVE), we propose a multi-source collaborative optimization algorithm. The algorithm synergizes the wide-coverage FY-4A (FengYun-4A) and DSCOVR (Deep Space Climate Observatory) cloud products with high-precision CloudSat vertical profile data and establishes LightGBM-based CTH/CVE optimization models. The models effectively reduce systematic errors in the FY-4A and DSCOVR cloud products, lowering the CTH Mean Absolute Error (MAE) to 1.8 km for multi-layer clouds, an improvement of 4–8 km over the original. The CVE MAEs for single- and multi-layer clouds are ~2.5 km. Some bias remains in complex cases, e.g., multi-layer thin clouds at low altitudes, and error tracing analysis suggests this may be related to cloud layer number misclassification. The proposed algorithm facilitates daytime near-hourly cloud retrievals over China and neighboring regions. Full article

This study evaluates the performance of nine satellite and model-based daily surface soil moisture products, encompassing sixteen algorithm versions across mainland China to support sustainable land and water management. The assessment utilizes 2018 in situ measurements from over 2400 stations in China’s Automatic Soil Moisture Monitoring Network. All products were standardized to a 0.25° × 0.25° grid in the WGS-84 coordinate system through reprojection and resampling for consistent comparison. Daily averaged station observations were matched to product pixels using a 10 km radius buffer, with the mean station value as the reference for each time series after rigorous quality control. Results reveal distinct performance rankings, with SMAP-based products, particularly the SMAP_IB descending orbit variant, achieving the lowest unbiased root mean square deviation (ubRMSD) and highest correlation with in situ data. Blended products like ESA CCI and NOAA SMOPS, alongside reanalysis datasets such as ERA5 and MERRA2, outperformed SMOS and China’s FY3 products. The SoMo.ml product showed the broadest spatial coverage and strong temporal consistency, while FY3-based products showed limitations in spatial reliability and seasonal dynamics capture. These findings provide critical insights for selecting appropriate soil moisture datasets to enhance sustainable agricultural practices, optimize water resource allocation, monitor ecosystem resilience, and support climate adaptation strategies, therefore advancing sustainable development across diverse geographical regions in China. Full article

The measurement of atmospheric moisture content is essential for the monitoring of severe weather events and hydrological studies. This paper proposes a multivariate linear regression correction model that integrates elevation data with Global Navigation Satellite System (GNSS)-derived precipitable water vapor (PWV) to refine the water vapor content based on FY-4A satellite remote sensing data, thereby improving its accuracy. Taking Hong Kong as an experimental area, we investigated the correlation between GNSS PWV and FY-4A PWV, confirming the feasibility of utilizing GNSS PWV to calibrate FY-4A PWV. Subsequently, by examining the differences between the two PWV values, we found that the elevation of the stations affects the consistency of PWV measurement. Based on this finding, the elevation data are introduced to construct a multivariate linear regression correction model with a first-order polynomial. To evaluate the performance of the proposed model, a comparison with other correction models is made, including second-order polynomials and power functions. The results indicate that the elevation-integrated water vapor correction model improves the root mean square error (RMSE) by 27.4% and the MAE by 26.7%, and reduces the bias from 0.592 to nearly 0. Its accuracy surpasses that of second-order polynomial and power function models, demonstrating a considerable improvement in the precision of FY-4A. Full article

The remote sensing inversion of internal solitary waves (ISWs) enables the retrieval of ISW parameters and facilitates the analysis of their spatial variability. In this study, we utilize continuous optical imagery from the FY-4B satellite to extract real-time ISW propagation speeds throughout their evolution from generation to shoaling. ISW parameters are retrieved in the northern South China Sea based on the quantitative relationship between sea surface current divergence and ISW surface features in optical imagery. The inversion method employs a fully nonlinear equation with continuous stratification to account for the strongly nonlinear nature of ISWs and uses the propagation speed extracted from continuous imagery as a constraint to determine a unique solution. The results show that as ISWs propagate from deep to shallow waters in the northern South China Sea, their statistically averaged amplitude initially increases and then decreases, while their propagation speed continuously decreases with decreasing depth. The inversion results are consistent with previous in situ observations. Furthermore, a three-day consecutive remote sensing tracking analysis of the same ISW revealed that the spatial variation in its parameters aligned well with the abovementioned statistical results. The findings provide an effective inversion approach and supporting datasets for extensive ISW monitoring. Full article

The atmospheric boundary layer (ABL) and tropopause play critical roles in weather formation and climate change. This study initially focuses on the ABL height (ABLH), tropopause height (TPH), and temperature (TPT) retrieved from the integrated radio occultation (RO) profiles from FY-3E, FY-3F, and FY-3G satellites during September 2022 to August 2024. All three FY-3 series satellites are equipped with the RO payload of Global Navigation Satellite System Radio Occultation Sounder-II (GNOS-II), which includes open-loop tracking RO observations from the BeiDou navigation satellite system (BDS) and the Global Positioning System (GPS). The wavelet covariance transform method was used to determine the ABL top, and the temperature lapse rate was applied to judge the tropopause. Results show that the maximum ABL detection rate of FY-3/GNOS-II RO can reach up to 76% in the subtropical eastern Pacific, southern hemisphere Atlantic, and eastern Indian Ocean. The ABLH is highly consistent with the collocated radiosonde observations and presents distinct seasonal variations. The TPH retrieved from FY-3/GNOS-II RO profiles is in agreement with the radiosonde-derived TPH, and both TPH and TPT from RO profiles display well-defined spatial structures. From 45°S to 45°N and south of 55°S, the annual cycle of the TPT is negatively correlated with the TPH. This study substantiates the promising performance of FY-3/GNOS-II RO measurements in observing the ABL and tropopause, which can be incorporated into the weather and climate systems. Full article

This study aims to develop a high-resolution temperature dataset from 40 km to 110 km over China by machine learning techniques, with a horizontal resolution of 0.5° × 0.5° and vertical resolution of 1 km, utilizing measurements from SABER onboard the Thermosphere, Ionosphere, Mesosphere Energetics, and Dynamics (TIMED) and Fengyun 4A (FY-4A) satellites. Accurate temperature profiles play a critical role in understanding the atmospheric dynamics and climate change. However, because of the limitation of traditional detecting methods, the measurements of the upper stratosphere and mesosphere are rare. In this study, a new method is developed to construct a high-resolution temperature dataset over China in the middle atmosphere based on the XGBoost technique. The model’s performance is also validated based on rocket observations and ERA5 reanalysis data. The results indicate that the model effectively captures the characteristics of the vertical and seasonal variations in temperature, which provide a valuable opportunity for further research and improvement of climate models. The model demonstrates the highest accuracy below 80 km with RMSE < 12 K, while its performance decreases above 100 km, where RMSE can exceed 20 K, indicating optimal performance in the upper stratosphere and lower mesosphere regions. Full article

PM_2.5 pollution poses significant risks to human health and the environment, underscoring the importance of accurate PM_2.5 simulation. This study simulated a representative PM_2.5 pollution event using the Weather Research and Forecasting model coupled with chemistry (WRF-Chem), incorporating the assimilation of infrared atmospheric motion vector (AMV) data from the Fengyun-4A (FY-4A) satellite. A comprehensive analysis was conducted to examine the meteorological characteristics of the event and their influence on PM_2.5 concentration simulations. The results demonstrate that the assimilation of FY-4A infrared AMV data significantly enhanced the simulation performance of meteorological variables, particularly improving the wind field and capturing local and small-scale wind variations. Moreover, PM_2.5 concentrations simulated with AMV assimilation showed improved spatial and temporal agreement with ground-based observations, reducing the root mean square error (RMSE) by 8.2% and the mean bias (MB) by 15.2 µg/m³ relative to the control (CTL) experiment. In addition to regional improvements, the assimilation notably enhanced PM_2.5 simulation accuracy in severely polluted cities, such as Tangshan and Tianjin. Mechanistic analysis revealed that low wind speeds and weak atmospheric divergence restricted pollutant dispersion, resulting in higher near-surface concentrations. This was exacerbated by cooler nighttime temperatures and a lower planetary boundary layer height (PBLH). These findings underscore the utility of assimilating satellite-derived wind products to enhance regional air quality modeling and forecasting accuracy. This study highlights the potential of FY-4A infrared AMV data in improving regional pollution simulations, offering scientific support for the application of next-generation Chinese geostationary satellite data in numerical air quality forecasting. Full article

Land Surface Temperature (LST), as a core variable in the coupling of land–atmosphere energy transfers and ecological responses, relies heavily on the global coverage capacity of thermal infrared remote sensing (TIR-LST) for dynamic monitoring. Currently, the time reconstruction method of the TIR-LST products from China’s Fengyun polar-orbiting satellite under dynamic cloud interference remains under exploration. This study focuses on the Heihe River Basin in western China, and addresses the issue of cloud coverage in relation to the Fengyun-3C (FY-3C) satellite TIR-LST. An innovative spatiotemporal reconstruction framework based on multi-source data collaboration was developed. Using a hybrid ensemble learning framework of random forest and ridge regression, environmental parameters such as vegetation index (NDVI), land cover type (LC), digital elevation model (DEM), and terrain slope were integrated. A downscaling and multi-factor collaborative representation model for land surface temperature was constructed, thereby integrating the passive microwave LST and thermal infrared VIRR-LST from the FY-3C satellite. This produced a seamless LST dataset with 1 km resolution for the period of 2017–2019, with temporal continuity across space. The validation results show that the reconstructed data significantly improves accuracy compared to the original VIRR-LST and demonstrates notable spatiotemporal consistency with MODIS LST at the daily scale (annual R² ≥ 0.88, RMSE < 2.3 K). This method successfully reconstructed the FY-3C satellite’s 1 km level all-weather LST time series, providing reliable technical support for the use of domestic satellite data in remote sensing applications such as ecological drought monitoring and urban heat island tracking. Full article