Search Results (573)

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

The spatial resolution (250–1000 m) of the FY-3D MERSI is too coarse for agricultural monitoring at the farmland scale (20–30 m). To achieve the winter wheat yield (WWY) at the farmland scale, based on FY-3D, a method framework is developed in this work. The enhanced deep convolutional spatiotemporal fusion network (EDCSTFN) was used to perform a spatiotemporal fusion on the 10 day interval FY-3D and Sentinel-2 vegetation indices (VIs), which were compared with the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM). In addition, a BP neural network was built to calculate the farmland-scale WWY based on the fused VIs, and the Aqua MODIS gross primary productivity product was used as ancillary data for WWY estimation. The results reveal that both the EDCSTFN and ESTARFM achieve satisfactory precision in the fusion of the Sentinel-2 and FY-3D VIs; however, when the period of spatiotemporal data fusion is relatively long, the EDCSTFN can achieve greater precision than ESTARFM. Finally, the WWY estimation results based on the fused VIs show remarkable correlations with the WWY data at the county scale and provide abundant spatial distribution details about the WWY, displaying great potential for accurate farmland-scale WWY estimations based on reconstructed fine-spatial-temporal-resolution FY-3D data. Full article

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

Hyperspectral infrared atmospheric sounding data, characterized by their high vertical resolution, play a crucial role in capturing three-dimensional atmospheric spatial information. The hyperspectral infrared atmospheric detectors HIRAS/HIRAS-II, mounted on the FY3D/EF satellite, have established an initial global coverage network for atmospheric sounding. The collaborative observation approach involving multiple satellites will improve both the coverage and responsiveness of data acquisition, thereby enhancing the overall quality and reliability of the data. In response to the increasing number of channels, the rapid growth of data volume, and the specific requirements of multi-satellite joint observation applications with infrared hyperspectral sounding data, this paper introduces an efficient storage and indexing method for infrared hyperspectral sounding data within a distributed architecture for the first time. The proposed approach, built on the Kubernetes cloud platform, utilizes the Google S2 discrete grid spatial indexing algorithm to establish a grid-based hierarchical model for unified metadata-embedded documents. Additionally, it optimizes the rowkey design using the BPDS model, thereby enabling the distributed storage of data in HBase. The experimental results demonstrate that the query efficiency of the Google S2 grid-based embedded document model is superior to that of the traditional flat model, achieving a query time that is only 35.6% of the latter for a dataset of 5 million records. Additionally, this method exhibits better data distribution characteristics within the global grid compared to the H3 algorithm. Leveraging the BPDS model, the HBase distributed storage system adeptly balances the node load and counteracts the detrimental effects caused by the accumulation of time-series remote sensing images. This architecture significantly enhances both storage and query efficiency, thus laying a robust foundation for forthcoming distributed computing. Full article

Mercury, a toxic heavy metal produced through both natural and anthropogenic processes, is found in all of Earth’s major systems. Mercury’s bioaccumulation characteristics in the human body have a significant impact on the liver, kidneys, brain, and muscles. In order to detect Hg²⁺ ions, a highly sensitive and specific fluorescent biosensor has been developed using a novel, modified seven amino acid peptide, FY7. The tyrosine ring in the FY7 peptide sequence forms a 2:1 complex with Hg²⁺ ions that are present in the water-based sample. As a result, the peptide’s fluorescence emission decreases with higher concentrations of Hg²⁺. The FY7 peptide’s performance was tested in the presence of Hg²⁺ ions and other metal ions, revealing its sensitivity and stability despite high concentrations. Conformational changes to the FY7 structure were confirmed by FTIR studies. Simultaneously, we designed a miniaturized setup to support an in-house-developed micro-volume capillary container for volume fluorometry measurements. We compared and verified the results from the micro-volume system with those from the commercial setup. The micro-volume capillary system accommodated only 2.9 µL of sample volume, allowing for rapid, sensitive, and selective detection of toxic mercury (II) ions as low as 0.02 µM. 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

In the context of rising temperatures and increasing humidity in Northwest China, substantial gaps remain in understanding the mechanisms of land–atmosphere cloud–precipitation coupling across the northeastern Tibetan Plateau (TP), Loess Plateau (LP), and Huangshui Valley (HV). This study addresses these gaps by investigating cloud properties and precipitation patterns utilizing the Fengyun-4 Satellite Quantitative Precipitation Estimation Product (FY4A-QPE) and ERA5 datasets. We specifically focus on Lanzhou, a pivotal city within the LP, and Xining, which epitomizes the HV. Our findings reveal that diurnal variations in precipitation are significantly less pronounced in the eastern regions compared to northeastern TP. This discrepancy is attributed to marked diurnal fluctuations in convective available potential energy (CAPE) and wind shear between 200 and 500 hPa. While both cities share similar wind shear patterns and moisture transport directions, Xining benefits from enhanced snowmelt and effective water retention in surrounding mountains, resulting in higher precipitation levels. Conversely, Lanzhou suffers from moisture deficits, with dry, hot winds exacerbating the situation. Notably, precipitation in Xining is strongly correlated with CAPE, influenced by diurnal variability, and intensified by valley and lake–land breezes, which drive afternoon convection. In contrast, Lanzhou’s precipitation exhibits a weak relationship with CAPE, as even elevated values fail to generate significant cloud formation due to insufficient moisture. The ongoing trends of warming and humidification may lead to improved precipitation patterns, especially in the HV, with potential ecological benefits. However, concentrated rainfall during summer afternoons and midnights raises concerns regarding extreme weather events, highlighting the susceptibility of the HV to geological hazards. This research underscores the need to further explore the uncertainties inherent in precipitation dynamics in these regions. Full article

Plastic surgery is a diverse speciality relevant to non-plastic doctors, as plastic surgeons frequently collaborate with other specialities and its basic principles are transferable across multiple specialities. Foundation-year (FY) doctors are the most junior doctors in the workforce and may need to apply plastic surgery knowledge and principles during their clinical duties. Despite this, formal plastic surgery education for junior doctors is limited, resulting in an educational gap. This study gains insight into the perceived confidence, knowledge gaps, skills, educational activities and needs related to plastic surgery. This qualitative study uses phenomenology through semi-structured individual interviews with eight FY doctors. Data was analysed using reflexive thematic analysis. This study revealed that plastic surgery features diversely in the work life of FYs, who often manage patients with a lack of knowledge and confidence, influencing patient care and FY wellbeing. FYs primarily acquire knowledge and confidence through experiential learning and individual initiative. A need for curriculum improvements was expressed. FYs are an essential part of the workforce who exhibited educational gaps and a lack of confidence in plastic surgery knowledge. We suggest improved integration of plastic surgery into the FY curriculum for improved FY knowledge and patient care. Full article

Efficient and accurate fishery forecasting is of great significance in ensuring the efficiency of fishery operations. This paper proposes a fishery forecasting method using a brightness temperature (TB) time series spatial feature extraction and fusion model. Using Indian Ocean bigeye tuna fishery data from 2009 to 2021 as a reference, this paper discusses the feasibility of fishery forecasting using FY-3 Microwave Radiation Imager (MWRI) Level 1 TB data. For this paper, we designed a deep learning network model for radiometer TB time series feature extraction (TimeTB-FishNet) based on the Convolutional Neural Network (CNN), Gated Recurrent Unit (GRU), and Attention mechanism. After expanding the dimensions of TB features, the model uses them together with spatiotemporal feature factors (year, month, longitude, and latitude) as features. By adding the GRU and Attention to the CNN, the CNN-GRU-Attention model architecture is established and can extract deep time series spatial features from the data to achieve the best results. In the model validation experiments, the TimeTB-FishNet model performed optimally, with a coefficient of determination (R²) of 0.6643. In the generalization experiments, the R² also reached 0.6261, with a root mean square error (RMSE) of 46.6031 kg/1000 hook. When the sea surface height (SSH) was introduced, the R² further reached 0.6463, with a lower RMSE of 45.1318 kg/1000 hook. The experimental results show that the proposed method and model are feasible and effective. The proposed model can directly use enhanced radiometer TB data without relying on lagging ocean environmental product data, performing deep temporal and spatial feature extraction for fishery forecasting. This method can provide a reference for the fishing of bigeye tuna in the Indian Ocean. Full article

Using the FY-3G/MWRI-RM observations, this paper proposes a precipitation retrieval method that combines the Synthetic Minority Over-sampling Technique with Light Gradient Boosting Machine (SMOTE-LGBM) and analyzes the impact of MWRI-RM channel settings on precipitation retrieval. The SMOTE-LGBM-based model consists of two LGBM models for precipitation identification and estimation, respectively. The SMOTE method is used to address the imbalance between precipitation and non-precipitation samples. Using the Integrated Multi-Satellite Retrievals for the Global Precipitation Measurement (IMERG) product as a reference, we validate the retrieved precipitation by the SMOTE-LGBM-based model with an independent testing dataset. The critical success indexes are 0.483 and 0.526, and the Pearson correlation coefficients are 0.611 and 0.645 for the ocean and land regions, respectively. The spatial distributions of the retrieved and IMERG accumulated precipitation in the testing dataset are similar. In addition, we visualize and analyze the cases of Meiyu and two typhoons. The results indicate that the SMOTE-LGBM-based model effectively represents the spatial distribution characteristics of precipitation and achieves high agreement with IMERG precipitation products. Overall, the SMOTE-LGBM-based model successfully retrieves precipitation from MWRI-RM and provides accurate precipitation products for FY-3G/MWRI-RM for the first time. Full article

Extreme precipitation events often present significant risks to human life and property, making their accurate prediction an essential focus of current research. Recent studies have primarily concentrated on exploring the formation mechanisms of extreme precipitation. Existing prediction methods do not adequately account for the combined terrain and atmospheric effects, resulting in shortcomings in extreme precipitation forecasting accuracy. Additionally, the satellite data resolution used in prior studies fails to precisely capture nuanced details of abrupt changes in extreme precipitation. To address these shortcomings, this study introduces an innovative approach for accurately predicting extreme precipitation: the multimodal attention ConvLSTM-GAN for extreme rainfall nowcasting (ER-MACG). This model employs high-resolution Fengyun-4A(FY4A) satellite precipitation products, as well as terrain and atmospheric datasets as inputs. The ER-MACG model enhances the ConvLSTM-GAN framework by optimizing the generator structure with an attention module to improve the focus on critical areas and time steps. This model can alleviate the problem of information loss in the spatial–temporal convolutional long short-term memory network (ConvLSTM) and, compared with the standard ConvLSTM-GAN model, can better handle the detailed changes in time and space in extreme precipitation events to achieve more refined predictions. The main findings include the following: (a) The ER-MACG model demonstrated significantly greater predictive accuracy and overall performance than other existing approaches. (b) The exclusive consideration of DEM and LPW data did not significantly enhance the ability to predict extreme precipitation events in Zhejiang Province. (c) The ER-MACG model significantly improved in identifying and predicting extreme precipitation events of different intensity levels. Full article

Biological decontamination strategies using microorganisms to adsorb aflatoxins have shown promising results for reducing the dietary exposure to these contaminants. In this study, the ability of inactivated biomasses of Lacticaseibacillus rhamnosus (LRB) and Saccharomyces cerevisiae (SCB) incorporated alone or in combination into functional yogurts (FY) at 0.5–4.0% (w/w) to adsorb aflatoxin B₁ (AFB₁) was evaluated in vitro. Higher adsorption percentages (86.9–91.2%) were observed in FY containing 1.0% LR + SC or 2.0% SC (w/w). The survival of mouse embryonic fibroblasts increased after exposure to yogurts containing LC + SC at 1.0–4.0% (w/w). No significant differences were noted in the physicochemical and sensory characteristics between aflatoxin-free FY and control yogurts (no biomass) after 30 days of storage. The incorporation of combined LRB and SCB into yogurts as vehicles for these inactivated biomasses is a promising alternative for reducing the exposure to dietary AFB₁. The results of this trial support further studies to develop practical applications aiming at the scalability of using the biomasses evaluated in functional foods to mitigate aflatoxin exposure. Full article

The spatial–temporal sampling errors arising from the differences in geographical locations and measurement times between co-located Global Navigation Satellite System (GNSS) radio occultation (RO) and radiosonde (RS) data represent systematic errors in the three-cornered hat (3CH) method. In this study, we propose a novel spatial–temporal sampling correction method to mitigate the sampling errors associated with both RO–RS and RS–model pairs. We analyze the 3CH processing chain with this new correction method in comparison to traditional approaches, utilizing Fengyun-3E (FY-3E) GNSS Occultation Sounder II (GNOS II) RO data, atmospheric models, and RS datasets from the Hailar and Xisha stations. Overall, the results demonstrate that the improved 3CH method performs better in terms of spatial–temporal sampling errors and the variances of atmospheric parameters, including refractivity, temperature, and specific humidity. Subsequently, we assess the error variances of the FY-3E GNOS II RO, RS and model atmospheric parameters in China, in particular the northern China and southern China regions, based on large ensemble datasets using the improved 3CH data processing chain. The results indicate that the FY-3E GNOS II BeiDou navigation satellite system (BDS) RO and Global Positioning System (GPS) RO show good consistency, with the average error variances of refractivity, temperature, and specific humidity being less than 1.12%², 0.13%², and 700%², respectively. A comparison of the datasets from northern and southern China reveals that the error variances for refractivity are smaller in northern China, while temperature and specific humidity exhibit smaller error variances in southern China, which is attributable to the differing climatic conditions. 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