Search Results (130)

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

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

Microwave sounding observations obtained from the National Oceanic and Atmospheric Administration (NOAA) and the European Meteorological Operational Satellite Program (METOP) satellites have been used for retrieving the cloud ice water path (IWP). However, the IWP algorithms developed in the past cannot be applied to the Fengyun-3F (FY-3F) microwave radiometers due to the differences in frequency of the primary channels and the fields of view. In this study, the IWP algorithm was tailored for the FY-3F satellite, and the retrieved IWP was compared with the fifth generation of reanalysis data from the European Centre for Medium-Range Weather Forecasts (ERA5) and the Meteorological Operational Satellite-C (METOP-C) products. The results indicate that the IWP distribution retrieved from FY-3F observations demonstrates strong consistency with the cloud ice distributions in ERA5 data and METOP-C products in low-latitude regions. However, discrepancies are observed among the three datasets in mid- to high-latitude regions. ERA5 data underestimate the frequency of high IWP values and overestimate the frequency of low IWP values. The IWP retrieval results from satellite datasets demonstrate a high level of consistency. Furthermore, an analysis of the IWP time series reveals that the retrieval algorithm used in this study better captures variability and seasonal characteristics of IWP compared to ERA5 data. Additionally, a comparison of FY-3F retrieval results with METOP-C products shows a high correlation and generally consistent distribution characteristics across latitude bands. These findings confirm the high accuracy of IWP retrieval from FY-3F data, which holds significant value for advancing IWP research in China. Full article

Fengyun-3G (FY-3G), successfully launched on 16 April 2023, is China’s first and the third in the world satellite dedicated to precipitation measurement. In this study, the reflectivity factors of the FY-3G satellite Precipitation Measurement Radar (PMR) are analyzed and compared with ground-based S-band dual-polarized radar (GR) data for typical precipitation events in parts of southern China during April–August 2024. By performing preprocessing and spatiotemporal matching, 169,657 matched pairs of FY-3G PMR and GR datasets are obtained, from which the agreement of reflectivity between FY-3G PMR and GR and the sensitivities to different precipitation types and phase states are evaluated. The results show that the reflectivity factors of FY-3G PMR and GR have a strong positive correlation, with an overall correlation coefficient of 0.82, especially in the stratiform precipitation. In addition, FY-3G PMR agrees with GR well in moderate precipitation, but systematically underestimates reflectivity in heavy rain rates and overestimates in light rain rates. Furthermore, FY-3G PMR has high accuracy in detecting liquid precipitation below the bright band, although with some underestimation of reflectivity for ice-phase precipitation above the bright band. Nevertheless, FY-3G PMR still provides valuable information on ice-phase precipitation. Overall, PMR has great potential for application in the monitoring of stratiform and liquid precipitation, but more complete processing is needed when applying PMR observations to heavy precipitation and complex meteorological conditions. Full article

Cloud optical thickness (COT) stands as a critical parameter governing the radiative properties of clouds. This study develops a convolutional neural network (CNN) model to retrieve the COT of single-layer non-precipitating clouds during nighttime using FY-4B satellite data. The model integrates multi-channel brightness, temperature, and geographic and temporal features, without relying on auxiliary meteorological data, using the multi-point averaged 532 nm COT from CALIPSO as ground truth for training. Performance evaluation demonstrates robust retrieval accuracy, achieving coefficients of determination (R²) of 0.88 and 0.73 for satellite zenith angles (SAZAs) < 70° and >70°, respectively. Key advancements include the incorporation of temporal features, the Squeeze-and-Excitation (SE) module, and a multi-point averaging technique, each validated through ablation experiments to reduce bias and enhance stability. Meanwhile, a model error analysis experiment was conducted that further clarified the performance boundaries of the model. These findings underscore the model’s capability to retrieve the COT of single-layer non-precipitating clouds during nighttime with high precision. Full article

This paper presents the development and outcomes of a cost-effective satellite ground station designed as a learning tool for satellite communication and wireless communication education. The study investigates accessible satellites and the methods for accessing them. The developed ground station has the capability to access satellites in the V, U, and L frequency bands, allowing it to receive a variety of satellite data. This includes full-disk meteorological images, high-resolution multispectral images, and scientific data from payloads of satellites in both low Earth orbit (LEO) and geostationary orbit (GEO). The ground station demonstrates capabilities similar to those of large organizations but at a significantly lower cost. This is achieved through a process of identifying educational requirements and optimizing the system for cost-efficiency. This paper presents the design demonstration, actual construction of the ground station, and results. Additionally, it compiles characteristics from real signal reception experiences from various satellites. Full article

The geostationary meteorological satellite Fengyun-4A (FY-4A) has rapidly advanced, generating abundant high spatiotemporal resolution atmospheric precipitable water vapor (PWV) products. However, remote sensing satellites are vulnerable to weather conditions, and these latest operational PWV products still require systematic validation. This study presents a comprehensive evaluation of FY-4A PWV products by separately using PWV data retrieved from radiosondes (RS) and the Global Navigation Satellite System (GNSS) from 2019 to 2022 in China and the surrounding regions. The overall results indicate a significant consistency between FY-4A PWV and RS PWV as well as GNSS PWV, with mean biases of 7.21 mm and −8.85 mm, and root mean square errors (RMSEs) of 7.03 mm and 3.76 mm, respectively. In terms of spatial variability, the significant differences in mean bias and RMSE were 6.50 mm and 2.60 mm between FY-4A PWV and RS PWV in the northern and southern subregions, respectively, and 5.36 mm and 1.73 mm between FY-4A PWV and GNSS PWV in the northwestern and southern subregions, respectively. The RMSE of FY-4A PWV generally increases with decreasing latitude, and the bias is predominantly negative, indicating an underestimation of water vapor. Regarding temporal differences, both the monthly and daily biases and RMSEs of FY-4A PWV are significantly higher in summer than in winter, with daily precision metrics in summer displaying pronounced peaks and irregular fluctuations. The classic seasonal, regional adjustment model effectively reduced FY-4A PWV deviations across all regions, especially in the NWC subregion with low water vapor distribution. In summary, the accuracy metrics of FY-4A PWV show distinct spatiotemporal variations compared to RS PWV and GNSS PWV, and these variations should be considered to fully realize the potential of multi-source water vapor applications. Full article

China’s FengYun 3E (FY3E) meteorological satellite, launched in 2021, is equipped with advanced instruments for comprehensive Earth observations. In this study, we compared outgoing longwave radiation (OLR) measurements from the FY3E satellite (FY3E OLR) and from a series of satellites operated by the National Oceanic and Atmospheric Agency (NOAA, United States of America; hereafter NOAA OLR) and analyzed the spatiotemporal differences between the datasets. We designed a new correction model, “DeepFM”, implementing both a factorization machine algorithm and a deep artificial neural network to minimize daily mean differences between the datasets. Then, we evaluated the spatiotemporal consistency between the corrected FY3E OLR and NOAA OLR data. The DeepFM model effectively reduced daily mean differences: after correction, the daily mean absolute bias and root-mean-square error decreased from 7.4 W/m² to 4.2 W/m² and from 10.3 W/m² to 6.3 W/m², respectively, indicating a notably improved spatiotemporal consistency between the corrected FY3E OLR and NOAA OLR data. Subsequently, we merged these datasets to generate a long-term OLR dataset suitable for climate analyses. This study provides a robust technological basis and innovative methodology for the dedicated application of China meteorological satellites to climate science. Full article

In April 2023, a major dust storm event in Lanzhou attracted widespread attention. This study provides a comprehensive analysis of the causes, progression, and dust sources of this event using multiple data sources and methods. Backward trajectory analysis using the HYSPLIT model was employed to trace the origins of the dust, while FY-2H satellite data provided high-resolution dust distribution patterns. Additionally, the MAIAC AOD product was used to analyze Aerosol Optical Depth, and concentration-weighted trajectory (CWT) analysis was used to identify key dust source regions. The study found that PM₁₀ played a dominant role in the storm, and the AOD values during the storm in Lanzhou were significantly higher than the annual average, highlighting the severe impact on regional air quality. Key meteorological conditions influencing the storm’s occurrence were analyzed, including the formation and eastward movement of a high-potential ridge, convection driven by diurnal temperature variations, and surface temperature increases coupled with decreased relative humidity, which together promoted the generation and development of dust. Backward trajectory and dust distribution analyses revealed that the dust primarily originated from Central Asia, western Mongolia, Xinjiang, and Gansu. From the 19th to the 21st, the dust distribution showed similarities between day and night, with a noticeable increase in dust concentration from night to day due to strong vertical atmospheric mixing. To mitigate the impacts of future dust storms, this study highlights both short-term and long-term strategies, including enhanced monitoring systems, public health advisories, and vegetation restoration in key source regions. Strengthening regional and international cooperation for transboundary dust management is also emphasized as critical for sustainable mitigation efforts. These findings are significant for understanding and predicting the causes, characteristics, and environmental impacts of dust storms in Lanzhou and the Northwestern region. Full article

This study evaluated the reliability of satellite-derived atmospheric temperature and humidity profiles derived from occultations of Fengyun-3D (FY-3D), the Constellation Observing System for Meteorology, Ionosphere, and Climate-2 (COSMIC-2), the Meteorological Operational Satellite program (METOP), and the microwave observations of NOAA Polar Orbital Environmental Satellites (POES) using various conventional sounding datasets from 2020 to 2021. Satellite-derived profiles were also used to explore the precursors of severe convective precipitations in terms of the atmospheric boundary layer (ABL) characteristics and convective parameters. It was found that the satellite-derived temperature profiles exhibited high accuracy, with RMSEs from 0.75 K to 2.68 K, generally increasing with the latitude and decreasing with the altitude. Among these satellite-derived profile sources, the COSMIC-2-derived temperature profiles showed the highest accuracy in the middle- and low-latitude regions, while the METOP series had the best performance in high-latitude regions. Comparatively, the satellite-derived relative humidity profiles had lower accuracy, with RMSEs from 13.72% to 24.73%, basically increasing with latitude. The METOP-derived humidity profiles were overall the most reliable among the different data sources. The ABL temperature and humidity structures from these satellite-derived profiles showed different characteristics between severe precipitation and non-precipitation regions and could reflect the evolution of ABL characteristics during a severe convective precipitation event. Furthermore, some convective parameters calculated from the satellite-derived profiles showed significant and rapid changes before the severe precipitation, indicating the feasibility of using satellite-derived temperature and humidity profiles as precursors to severe convective precipitation. Full article

The ever-increasing capacity of numerical weather prediction (NWP) models requires accurate flow information at higher spatial and temporal resolutions. The atmospheric motion vectors (AMVs) extracted from the Advanced Geostationary Radiation Imager (AGRI) mounted on the Fengyun-4A (FY-4A) satellite can provide information about atmospheric flow fields on small scales. This study focused on the assimilation of FY-4A AMVs and its impact on forecasts in the regional NWP system of the China Meteorological Administration—Beijing (CAM-BJ). The statistical characterization of FY-4A AMVs was firstly analyzed, and an optimal observation error in each vertical level was obtained. Three groups of retrospective runs over a one-month period were conducted, and the impact of assimilating the AMVs with different strategies on the forecasts of the CMA-BJ system were compared and evaluated. The results suggested that the optimal observation errors reduced the standard deviation of the background departures for U and V wind, leading to an improvement in the standard deviation in the corresponding analysis departures of about 8.3% for U wind and 7.3% for V wind. Assimilating FY-4A AMV data with a quality indicator (QI) above 80 and the optimal observation errors reduced the error of upper wind forecast in the CMA-BJ system. A benefit was also obtained in the error of surface wind forecast after 6 h of the forecasts, although it was not significant. For rainfall forecast with different thresholds, the score skills increased slightly after 6 h of the forecasts. There was an overall improvement for the overprediction of 24 h accumulated precipitation forecast including the AMVs, even when conventional observations were relatively rich. The application of FY-4A AMVs with a QI > 80 and adjustment to observation errors has a positive impact on the upper wind forecast in the CMA-BJ system, improving the score skill of rainfall forecasting. Full article

The latest-generation geostationary meteorological satellite, Fengyun-4B (FY-4B), equipped with the Geostationary Interferometric Infrared Sounder (GIIRS), offers high-spatiotemporal-resolution three-dimensional temperature structures. Its deployment serves as a critical complement to atmospheric temperature profile (ATP) observation in the Tibetan Plateau (TP). Based on radiosonde observation (RAOB) and the fifth-generation ECMWF global climate atmospheric reanalysis (ERA5), this study validates the availability and representativeness of FY-4B/GIIRS ATP products in the eastern TP region. Due to the issue of satellite zenith, this study focuses solely on examining the eastern TP region. Under a clear sky, FY-4B/GIIRS ATP exhibits good consistency with RAOB compared to cloudy conditions, with an average root mean square error (RMSE) of 2.57 K. FY-4B/GIIRS tends to underestimate temperatures in the lower layers while overestimating temperatures in the upper layers. The bias varies across seasons. Except for summer, the horizontal and vertical bias distribution patterns are similar, though there are slight differences in values. Despite the presence of bias, FY-4B/GIIRS ATP maintains a good consistency with observations and reanalysis data, indicating commendable product quality. These results demonstrate that it can play a vital role in augmenting the ATP observation network limited by sparse radiosonde stations in the eastern TP, offering crucial data support for numerical weather prediction, weather monitoring, and related meteorological research in this region. Full article

Extreme weather events like typhoons have become more frequent due to global climate change. Current typhoon monitoring methods include manual monitoring, mathematical morphological methods, and artificial intelligence. Manual monitoring is accurate but labor-intensive, while AI offers convenience but requires accuracy improvements. Mathematical morphology methods, such as brightness temperature perturbation (BTP) and a spatio-temporally consistent (STC) Scale-Invariant Feature Transform (SIFT), remain mainstream for typhoon positioning. This paper enhances BTP and STC SIFT methods for application to Fengyun 4A (FY-4A) Advanced Geosynchronous Radiation Imager (AGRI) L1 data, incorporating parallax correction for more accurate surface longitude and latitude positioning. The applicability of these methods for different typhoon intensities and monitoring time resolutions is analyzed. Automated monitoring with one-hour observation intervals in the northwest Pacific region demonstrates high positioning accuracy, reaching 25 km or better when compared to best path data from the China Meteorological Administration (CMA). For 1 h remote sensing observations, BTP is more accurate for typhoons at or above typhoon intensity, while STC SIFT is more accurate for weaker typhoons. In the current era of a high temporal resolution of typhoon monitoring using geostationary satellites, the method presented in this paper can serve the national meteorological industry for typhoon monitoring, which is beneficial to national pre-disaster prevention work as well as global meteorological research. Full article

This paper proposes a novel precipitation estimation method based on FY-4B meteorological satellite data (FY-4B_AI). This method facilitates the spatiotemporal matching of 125 features derived from the multi-temporal and multi-channel data of the FY-4B satellite with precipitation data at stations. Subsequently, a precipitation model was constructed using the light gradient boosting machine (LGBM) algorithm. A comparative analysis of FY-4B_AI and GPM/IMERG-L products for over 450 million station cases throughout 2023 revealed the following: (1) The results demonstrate that FY-4B_AI is more accurate than GPM/IMERG-L. Six of the eight evaluation indices exhibit superior performance for FY-4B_AI in comparison to GPM/IMERG-L. These indices include the mean absolute error (MAE), root mean square error (RMSE), relative error (RE), correlation coefficient (CC), probability of detection (POD), and critical success index (CSI). As for the MAE, the results are 1.67 (FY-4B_AI) and 1.92 (GPM/IMERG-L), respectively. The RMSEs are 3.68 and 4.07, respectively. The REs are 17.72% and 26.28%, respectively. The CCs are 0.44 and 0.36, respectively. The PODs are 61.84% and 47.31%, respectively. The CSIs are 0.30 and 0.27, respectively. However, with regard to the mean errors (MEs) and false alarm rates (FARs), FY-4B_AI (−0.88 and 62.85%, respectively) displays a slight degree of inferiority in comparison to GPM/IMERG-L (−0.80 and 62.21%, respectively). (2) An evaluation of two strong weather events to represent the spatial distribution of precipitation in different climatic zones revealed that both FY-4B_AI and GPM/IMERG-L are equally capable of accurately representing these phenomena, irrespective of whether the region in question is humid, as is the case in the southeast, or dry, as is the case in the northwest. Full article

Near-surface air temperature (T_a) estimation by geostationary meteorological satellites is mainly carried out under clear-sky conditions. In this study, we propose an all-weather T_a estimation method utilizing FY-4A Advanced Geostationary Radiation Imager (AGRI) and the Global Forecast System (GFS), along with additional auxiliary data. The method includes two neural-network-based T_a estimation models for clear and cloudy skies, respectively. For clear skies, AGRI LST was utilized to estimate the T_a (T_a,clear), whereas cloud top temperature and cloud top height were employed to estimate the T_a for cloudy skies (T_a,cloudy). The estimated T_a was validated using the 2020 data from 1211 stations in China, and the RMSE values of the T_a,clear and T_a,cloudy were 1.80 °C and 1.72 °C, while the correlation coefficients were 0.99 and 0.986, respectively. The performance of the all-weather T_a estimation model showed clear temporal and spatial variation characteristics, with higher accuracy in summer (RMSE = 1.53 °C) and lower accuracy in winter (RMSE = 1.88 °C). The accuracy in southeastern China was substantially better than in western and northern China. In addition, the dependence of the accuracy of the T_a estimation model for LST, CTT, CTH, elevation, and air temperature were analyzed. The global sensitivity analysis shows that AGRI and GFS data are the most important factors for accurate T_a estimation. The AGRI-estimated T_a showed higher accuracy compared to the ERA5-Land data. The proposed models demonstrated potential for T_a estimation under all-weather conditions and are adaptable to other geostationary satellites. Full article