Search Results (822)

Satellite data play an irreplaceable role in global observation data systems. Effective comprehensive application of satellite products will inevitably improve numerical weather prediction. FengYun-4 (FY-4) series satellites can provide not only radiance data but also retrieval data with high temporal and spatial resolutions. To evaluate the potential benefits of the combination application of FY-4 Advanced Geostationary Radiance Imager (AGRI) products on Typhoon Saola analysis and forecast, two group of experiments are set up with the Weather Research and Forecasting model (WRF). Compared with the benchmark experiment, whose sea surface temperature (SST) is from the National Centers for Environmental Prediction (NCEP) reanalysis data, the SST replacement experiments with FY-4 A/B SST products significantly improve the track and precipitation forecast, especially with the FY-4B SST product. Based on the above results, AGRI clear-sky and all-sky assimilations with FY-4B SST are implemented with a self-constructed AGRI assimilation module. The results show that the AGRI all-sky assimilation experiment can obtain better analyses and forecasts. Furthermore, it is proven that the combination application of AGRI radiance and SST products is beneficial for typhoon prediction. 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

The study aims to develop vertical radar reflectivity profiles based on lightning density data from the Geostationary Lightning Mapper (GLM) on the GOES-16 satellite in the subtropical region of Brazil. The primary objective is to improve the assimilation of lightning data in numerical weather prediction models. The methodology involves the analysis of polarimetric radar data from Chapecó-SC and Jaraguari-MS, spanning from January 2019 to December 2023, and their correlation with lightning data from the GLM. Radar reflectivity profiles were created for different lightning density classes, categorized into six classes based on geometric progression. Results show a significant relationship between lightning activity and radar reflectivity, with distinct profiles for convective and stratiform events. These findings demonstrate the potential of using GLM data to enhance short-term weather forecasting, particularly for severe weather events. The study concludes that the integration of GLM data into weather models can lead to more accurate predictions of intense precipitation events, contributing to better preparedness and response strategies. Full article

Global dust events have become more frequent due to climate change and increased human activity, significantly impacting air quality and human health. Previous studies have mainly focused on determining atmospheric dust pollution levels through atmospheric parameter simulations or AOD values obtained from satellite remote sensing. However, research on the quantitative description of dust intensity and its cross-regional transport characteristics still faces numerous challenges. Therefore, this study utilized Fengyun-4A (FY-4A) satellite Advanced Geostationary Radiation Imager (AGRI) imagery, Cloud-Aerosol Lidar, and Infrared Pathfinder Satellite Observation (CALIPSO) lidar, and other auxiliary data, to conduct three-dimensional spatiotemporal monitoring and a cross-regional transport analysis of two typical dust events in the Beijing–Tianjin–Hebei (BTH) region of China using four dust intensity indices Infrared Channel Shortwave Dust (Icsd), Dust Detection Index (DDI), dust value (DV), and Dust Strength Index (DSI)) and the HYSPLIT model. We found that among the four indices, DDI was the most suitable for studying dust in the BTH region, with a detection accuracy (POCD) of >88% at all times and reaching a maximum of 96.14%. Both the 2021 and 2023 dust events originated from large-scale deforestation in southern Mongolia and the border area of Inner Mongolia, with dust plumes distributed between 2 and 12 km being transported across regions to the BTH area. Further, when dust aerosols are primarily concentrated below 4 km and PM10 concentrations consistently exceed 600 µg/m³, large dust storms are more likely to occur in the BTH region. The findings of this study provide valuable insights into the sources, transport pathways, and environmental impacts of dust aerosols. 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

An equatorial plasma bubble (EPB) is characterized by ionospheric irregularities which disturb radio waves by causing phase and amplitude scintillations or even signal loss. It is becoming increasingly important in space weather to assure the reliability of radio systems in both space and on the ground. This paper presents a newly established GNSS ionospheric observation network (GION) around the north equatorial ionization anomaly (EIA) crest in south China, which has a longitudinal coverage of ∼30° from 94°E to 124°E. The measurement with signals from geostationary earth orbit (GEO) satellites of the BeiDou navigation satellite system (BDS) is capable of separating the temporal and spatial variations of the ionosphere. A temporal fluctuation of TEC (TFT) parameter is proposed to characterize EPBs. The longitude of the EPBs’ generation can be located with TFT variations in the time–longitude dimension. It is found that the post-sunset EPBs have a high degree of longitudinal variability. They generally show a quasiperiodic feature, indicating their association with atmospheric gravity wave activities. Wave-like structures with different scale sizes can co-exist in the same night. Full article

The Geostationary Interferometric InfraRed Sounder (GIIRS) provides a novel opportunity to acquire high-spatiotemporal-resolution atmospheric information. Previous studies have demonstrated the positive impacts of assimilating GIIRS radiances from either long-wave temperature or middle-wave water vapor bands on modeling high-impact weather processes. However, the impact of assimilating both bands on forecast skill has been less investigated, primarily due to the non-identical geolocations for both bands. In this study, a locally cloud-resolving global model is utilized to assess the impact of assimilating GIIRS observations from both long-wave and middle-wave bands. The findings indicate that the GIIRS observations exhibit distinct inter-channel error correlations. Proper inflation of these errors can compensate for inaccuracies arising from the treatment of the geolocation of the two bands, leading to a significant enhancement in the usage of GIIRS observations from both bands. The assimilation of GIIRS observations not only markedly reduces the normalized departure standard deviations for most channels of independent instruments, but also improves the atmospheric states, especially for temperature forecasting, with a maximum reduction of 42% in the root-mean-square error in the lower troposphere. These improvements contribute to better performance in predicting heavy rainfall. Full article

High-resolution (2 km) high-frequency (hourly) SST data from 2015 to 2021 provided by the Advanced Himawari Imager (AHI) onboard the Japanese Himawari-8 geostationary satellite were used to study spatial and temporal variability of the China Coastal Front (CCF) in the South China Sea. The SST data were processed with the Belkin and O’Reilly (2009) algorithm to generate monthly maps of the CCF’s intensity (defined as SST gradient magnitude GM) and frontal frequency (FF). The horizontal structure of the CCF was investigated from cross-frontal distributions of SST along 11 fixed lines that allowed us to determine inshore and offshore boundaries of the CCF and calculate the CCF’s strength (defined as total cross-frontal step of SST). Combined with the results of Part 1 of this study, where the CCF was documented in the East China Sea, the new results reported in this paper allowed the CCF to be traced from the Yangtze Bank to Hainan Island. The CCF is continuous in winter, when its intensity peaks at 0.15 °C/km (based on monthly data). In summer, when the Guangdong Coastal Current reverses and flows eastward, the CCF’s intensity is reduced to 0.05 °C/km or less, especially off western Guangdong, where the CCF vanishes almost completely. Owing to its breadth (50–100 km, up to 200 km in the Taiwan Strait), the CCF is a very strong front, especially in winter, when the total SST step across the CCF peaks at 9 °C in the Taiwan Strait. The CCF’s strength decreases westward to 6 °C off eastern Guangdong, 5 °C off western Guangdong, and 2 °C off Hainan Island, all in mid-winter. Full article

As a contribution to the Integrated Marine Observing System (IMOS), the Bureau of Meteorology introduces new reprocessed Himawari-8 satellite-derived Sea Surface Temperature (SST) products. The Radiative Transfer Model and a Bayesian cloud clearing method is used to retrieve SSTs every 10 min from the geostationary satellite Himawari-8. An empirical Sensor Specific Error Statistics (SSES) model, introduced herein, is applied to calculate bias and standard deviation for the retrieved SSTs. The SST retrieval and compositing method, along with validation results, are discussed. The monthly statistics for comparisons of Himawari-8 Level 2 Product (L2P) skin SST against in situ SST quality monitoring (iQuam) in situ SST datasets, adjusted for thermal stratification, showed a mean bias of −0.2/−0.1 K and a standard deviation of 0.4–0.7 K for daytime/night-time after bias correction, where satellite zenith angles were less than 60° and the quality level was greater than 2. For ease of use, these native resolution SST data have been composited using a method introduced herein that retains retrieved measurements, to hourly, 4-hourly and daily SST products, and projected onto the rectangular IMOS 0.02 degree grid. On average, 4-hourly products cover ≈$10\%$ more of the IMOS domain, while one-night composites cover ≈$25\%$ more of the IMOS domain than a typical 1 h composite. All available Himawari-8 data have been reprocessed for the September 2015–December 2022 period. The 10 min temporal resolution of the newly developed Himawari-8 SST data enables a daily composite with enhanced spatial coverage, effectively filling in SST gaps caused by transient clouds occlusion. Anticipated benefits of the new Himawari-8 products include enhanced data quality for applications like IMOS OceanCurrent and investigations into marine thermal stress, marine heatwaves, and ocean upwelling in near-coastal regions. Full article

By utilizing top-of-atmosphere radiation (TOAR) data from China’s new generation of geostationary satellites (FY-4A and FY-4B) along with interpretable machine learning models, near-surface particulate matter concentrations in China were estimated, achieving hourly temporal resolution, 4 km spatial resolution, and 100% spatial coverage. First, the cloudless TOAR data were matched and modeled with the solar radiation products from the ERA5 dataset to construct and estimate a fully covered TOAR dataset under assumed clear-sky conditions, which increased coverage from 20–30% to 100%. Subsequently, this dataset was applied to estimate particulate matter. The analysis demonstrated that the fully covered TOAR dataset (R² = 0.83) performed better than the original cloudless dataset (R² = 0.76). Additionally, using feature importance scores and SHAP values, the impact of meteorological factors and air mass trajectories on the increase in PM₁₀ and PM_2.5 during dust events were investigated. The analysis of haze events indicated that the main meteorological factors driving changes in particulate matter included air pressure, temperature, and boundary layer height. The particulate matter concentration products obtained using fully covered TOAR data exhibit high coverage and high spatiotemporal resolution. Combined with data-driven interpretable machine learning, they can effectively reveal the influencing factors of particulate matter in China. Full article

Hail poses a significant meteorological hazard in China, leading to substantial economic and agricultural damage. To enhance the detection of hail and mitigate these impacts, this study presents an ensemble machine learning model (BPNN+Dtree) that combines a backpropagation neural network (BPNN) and a decision tree (Dtree). Using FY-4A satellite and ERA5 reanalysis data, the model is trained on geostationary satellite infrared data and environmental parameters, offering comprehensive, all-day, and large-area hail monitoring over China. The ReliefF method is employed to select 13 key features from 29 physical quantities, emphasizing cloud-top and thermodynamic properties over dynamic ones as input features for the model to enhance its hail differentiation capability. The BPNN+Dtree ensemble model harnesses the strengths of both algorithms, improving the probability of detection (POD) to 0.69 while maintaining a reasonable false alarm ratio (FAR) on the test set. Moreover, the model’s spatial distribution of hail probability more closely matches the observational data, outperforming the individual BPNN and Dtree models. Furthermore, it demonstrates improved regional applicability over overshooting top (OT)-based methods in the China region. The identified high-frequency hail areas correspond to the north-south movement of the monsoon rain belt and are consistent with the northeast-southwest belt distribution observed using microwave-based methods. Full article

Satellite communication systems, especially multi-beam low-Earth-orbit (LEO) satellites, could cater to the needs of different industrial applications through flexible resource allocation. Unfortunately, due to the wide coverage of LEO satellites, the data exchange within the LEO satellite networks suffers from the risk of eavesdropping and malicious jamming, which could severely degrade the performance of the industrial production process. To address such challenges, this paper introduces a multi-dimensional resource allocation strategy to facilitate covert communication within the multi-beam LEO satellite network. Our approach ensures the rate requirements of different user equipments while preventing the detection of communication signals by an eavesdropping geostationary orbit (GEO) satellite. Specifically, we formulate an optimization problem that jointly optimizes satellite beam-hopping scheduling, frequency band allocation, and the transmit power of different user equipments, under the covertness constraint. By introducing auxiliary binary variables, we transform this optimization problem into a Mixed-Integer Linear Programming (MILP) problem, which allows us to utilize machine learning-based techniques for efficient solution finding. The simulation results demonstrate the effectiveness of our proposed scheme. Full article

Recently, increasing heat and drought events have threatened the resilience of Chinese fir forests. Trees primarily respond to these threats by downregulating photosynthesis including through stomatal limitation that causes a drop in productivity at noon (known as the midday depression). However, the effects of these events on midday and afternoon GPP inhibition are rarely analyzed on a fine timescale. This may result in negligence of critical responses. Here, we investigated the impact of climatic events on the midday depression of photosynthesis at a subtropical fir forest in Huitong from 2016 to 2022 using data from the Himawari 8 meteorological satellite and flux tower. Our results indicated that the highest number of midday depression occurred in 2022 (126 times) with the highest average temperature (29.1 °C). A higher incidence of midday depression occurred in summer and autumn, with 48 and 34 occurrences, respectively. Compound drought, heat, and drought events induced increases in midday depression at 74.3%, 66.0%, and 47.5%. Thus, trees are more likely to adopt midday depression as an adaptive strategy during compound drought and heat events. This study can inform forest management and lead to improvements in Earth system models. Full article

In this study, we present the first results of detecting ionospheric irregularities using non-typical GPS observations recorded onboard the Geostationary Operational Environmental Satellites (GOES) mission operating at ~35,800 km altitude. Sitting above the GPS constellation, GOES can track GPS signals only from GPS transmitters on the opposite side of the Earth in a rather unique geometry. Although GPS receivers onboard GOES are primarily designed for navigation and were not configured for ionospheric soundings, these GPS measurements along links that traverse the Earth’s ionosphere can be used to retrieve information about ionospheric electron density. Using the radio occultation (RO) technique applied to GPS measurements from the GOES–16, we analyzed variations in the ionospheric total electron content (TEC) on the links between the GPS transmitter and geostationary GOES GPS receiver. For case-studies of major geomagnetic storms that occurred in September 2017 and August 2018, we detected and analyzed the signatures of storm-induced ionospheric irregularities in novel and promising geostationary GOES GPS observations. We demonstrated that the presence of ionospheric irregularities near the GOES GPS RO sounding field of view during geomagnetic disturbances was confirmed by ground-based GNSS observations. The use of RO observations from geostationary orbit provides new opportunities for monitoring ionospheric irregularities and ionospheric density. Full article

Thermally induced vibrations usually affect spacecraft equipped with light and slender appendages such as booms, antennas or solar panels. This phenomenon occurs when a thermal shock, resulting from the sudden cooling and warming phases at the entrance and exit from eclipses, triggers mechanical vibrations. The study proposed hereafter concerns the modeling and prediction of jitter of thermal origin in a long and thin plate with a sun-pointing attitude in geostationary orbit. The system’s temperature and dynamics are described by a set of equations expressing the two-way coupling between the thermal bending moment and the shape of the panel. The structure is discretized and reduced to a one-degree-of-freedom simplified model able to identify a mechanism of thermal pumping that could lead to instability. Finally, the results of the analysis are compared with those obtained with a more accurate FEM modelization. Full article