Search Results (10)

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) is a critical parameter for understanding climate change and maintaining hydrological balance across local and global scales. However, existing satellite LST products face trade-offs between spatial and temporal resolutions, making it challenging to provide all-weather LST with high spatiotemporal resolution. In this study, focusing on Chengdu city, a framework combining a spatiotemporal fusion model and machine learning algorithm was proposed and applied to retrieve hourly high spatial resolution LST data from Chinese geostationary weather satellite data and multi-scale polar-orbiting satellite observations. The predicted 30 m hourly LST values were evaluated against in situ LST measurements and Sentinel-3 SLSTR data on 11 August 2019 and 21 April 2022, respectively. The results demonstrate that validation based on the in situ LST, the root mean squared error (RMSE) of the predicted LST using the proposed framework are around 0.89 °C to 1.23 °C. The predicted LST is highly consistent with the Sentinel-3 SLSTR data, and the RMSE varies from 0.95 °C to 1.25 °C. In addition, the proposed framework was applied to Xi’an City, and the final validation results indicate that the method is accurate to within about 1.33 °C. The generated 30 m hourly LST can provide important data with fine spatial resolution for urban thermal environment monitoring. Full article

The transport and dispersion (T&D) of catalyst particles seeded by weather modification aircraft is crucial for assessing their weather modification effects. This study investigates the capabilities of the Chinese geostationary weather satellite FY-4A for identifying the physical response of cloud seeding with AgI-based catalysts and continuously monitoring its evolution for a weather event that occurred on 15 December 2019 in Henan Province, China. Satellite measurements are also used to verify an operational catalyst T&D algorithm. The results show that FY-4A exhibits a remarkable capability of identifying the cloud-seeding tracks and continuously tracing their evolution for a period of over 3 h. About 60 min after the cloud seeding, the cloud crystallization track became clear in the FY-4A tri-channel composite cloud image and lasted for about 218 min. During this time period, the cloud track moved with the cloud system about 153 km downstream (northeast of the operation area). An operational catalyst T&D model was run to simulate the cloud track, and the outputs were extensively compared with the satellite observations. It was found that the forecast cloud track closely agreed with the satellite observations in terms of the track widths, morphology, and movement. Finally, the FY-4A measurements show that there were significant differences in the microphysical properties across the cloud track. The effective cloud radius inside the cloud track was up to 15 μm larger than that of the surrounding clouds; the cloud optical thickness was about 30 μm smaller; and the cloud-top heights inside the cloud track were up to 1 km lower. These features indicate that the cloud-seeding catalysts led to the development of ice-phase processes within the supercooled cloud, with the formation of large ice particles and some precipitation sedimentation. Full article

A series of typhoons and tropical storms have produced extreme precipitation events in Vietnam during the first part of the 2020 monsoon season: events of this magnitude pose significant challenges to remote sensing Quantitative Precipitation Estimation (QPE) techniques. The weather-monitoring needs of modern human activities require that these challenges be overcome. In order to address this issue, in this work, seven precipitation products were validated with high spatial and temporal detail against over 1200 rain gauges in Vietnam during six case studies tailored around the most intense events of 2020. The data sources included the Vietnamese weather radar network, IMERG Early run and Final run, the South Korean GEO-KOMPSAT-2A and Chinese FengYun-4A geostationary satellites, DPR on board the GPM-Core Observatory, and European ERA5-Land reanalysis. All products were resampled to a standardized 0.02° grid and compared at hourly scale with ground stations measurements. The results indicated that the radars product was the most capable of reproducing the information collected by the rain gauges during the selected extreme events, with a correlation coefficient of 0.70 and a coefficient of variation of 1.38. However, it exhibited some underestimation, approximately 30%, in both occurrence and intensity. Conversely, geostationary products tended to overestimate moderate rain rates (FY-4A) and areas with low precipitation (GK-2A). More complex products such as ERA5-Land and IMERG failed to capture the highest intensities typical of extreme events, while GPM-DPR showed promising results in detecting the highest rain rates, but its capability to observe isolated events was limited by its intermittent coverage. Full article

Fine spectral detection can basically solve the problem of low vertical resolution at the 183 GHz water-vapor absorption line, and it is expected to become one of the main methods for next-generation geostationary and polar-orbiting satellites. Here, using data from Microwave Humidity Sounder II (MWHS-II) onboard the Chinese Fengyun 3D (FY-3D) satellite in the Global/Regional Assimilation and Prediction System (GRAPES) Four-Dimensional Variational (4D-Var) system of the China Meteorological Administration (CMA), we explore the assimilation application of the water-vapor absorption line at 183.31 ± 1 GHz, 183.31 ± 3 GHz and 183.31 ± 7 GHz, as well as 183.31 ± 1.8 GHz and 183.31 ± 4.5 GHz, two added channels, to assess the impact of adding the 183.31 ± 1.8 GHz and 183.31 ± 4.5 GHz sampling channels on data assimilation and numerical weather prediction. Our findings reveal a significant increase in the specific-humidity increment, which in the middle–upper troposphere is numerically much larger than in the lower troposphere. Specifically, the assimilation of 183.31 ± 1.8 GHz observations, positioned near the center of the water-vapor absorption line, results in a pronounced adjustment compared with the 183.31 ± 4.5 GHz observations. And under the strong constraint of the numerical model, the Root Mean Square Error (RMSE) of the wind field diminishes more significantly (by an average of 2–4%) after assimilating the water-vapor observations at greater heights. Full article

The Layered Precipitable Water Vapor (LPW) product derived from the Advanced Geosynchronous Radiation Imager (AGRI) onboard the first of the Chinese new generation geostationary satellite Fengyun-4A (FY-4A) has great significance for weather forecasting and climate monitoring of the Tibetan Plateau. To analysis and evaluation the reliability of the FY-4A/AGRI LPW, with respect to the complex terrain on the Southeastern Tibetan Plateau, the atmospheric precipitable water vapor values were calculated based on the radiosonde observations (RAOB TPW) of 11 radiosonde stations in the research area from 2019 to 2020, and a comparative analysis was performed with the FY-4A/AGRI LPW. The results indicated that: (1) FY-4A/AGRI LPW and RAOB TPW demonstrate excellent consistency in all of the vertical height layers, but the atmospheric precipitable water vapor was underestimated by FY-4A/AGRI LPW; (2) The mean values of FY-4A/AGRI LPW in various months were all lower than those of RAOB TPW. The low layer FY-4A/AGRI LPW was the most stable in precision from the dimension of months; and (3) The precision of FY-4A/AGRI LPW, and the deviation between FY-4A/AGRI LPW and RAOB TPW were related with RDLS. The evaluation results of the study demonstrated that FY-4A/AGRI LPW underestimated the total water vapor in the research area, but the Bias and RMSE values were relatively low. FY-4A/AGRI LPW had a relatively high precision, and the data from it had superior quality and stability in terms of time changes and spatial distribution. Therefore, the product can perfectly reflect the spatial and temporal variation of the atmospheric water vapor on the Southeastern Tibetan Plateau. Full article

The spatial coverage of atmospheric motion vectors (AMVs) over Southeast Asia (SEA) is mainly covered by the Himawari-8 (HIMA-8) and FengYun-2 (FY-2) series satellites in the Global Telecommunication System (GTS). With the launch of FengYun-4A (FY-4A), a new Chinese geostationary satellite, AMVs have enhanced the spatial and temporal resolution data along with allowing for more options of the spectral channels than the FY-2G. This study focuses on the preliminary quality assessments of the FY-2G, FY-4A and HIMA-8 AMVs during a three-month monsoon period, as well as the impact of assimilating AMVs on the numerical weather prediction (NWP) model over SEA. The results show that the qualities of the AMVs from the FY-2G and FY-4A are sensitive to different quality indicator (QI) values, but this is not the case for the HIMA-8. For QI values at 85%, FY-2G and FY-4A AMVs had a monthly mean feature in the monsoon period that were quite comparable to HIMA-8 AMVs, with a few exceptions in this area when three sets of AMVs were validated against NCEP/FNL operational global analysis data; however, the qualities of the AMVs from HIMA-8 were better overall than those from FY-2G and FY-4A. In addition, four experiments were conducted with and without an assimilation of AMVs with a QI at 85% available from FY-2G, FY-4A, and HIMA-8 to assess their impact on tropical cyclone (TC) PABUK from 1 to 4 January 2019. The findings demonstrate that the assimilation of three sets of AMVs diminishes the average initial position error and track forecast error after 42 h when compared to the control experiment. Nevertheless, none of the experiments’ analyses or forecasts of the TC intensity showed a statistically significant development. The findings for FY-2G and FY-4A AMVs may offer a direction forward for the FY AMVs series dataset for future implementation in the global data assimilation system of NWP models, similar to HIMA-8 AMVs, which shows a favourable performance in assimilating AMVs from assorted satellites for SEA forecasts. Full article

A physical retrieval algorithm has been developed for deriving the layered precipitable water vapor (LPWs) product from infrared radiances of the Advanced Geosynchronous Radiation Imager (AGRI) onboard FengYun-4A (FY-4A), the first of the new generation of Chinese geostationary weather satellites (FengYun-4, or FY-4 Series). The FY-4A AGRI LPWs are evaluated with different types of reference datasets based on Quality Control System (QCS), including those from Himawari-8 AHI (Advanced Himawari Imager), MODIS (Moderate Resolution Imaging Spectroradiometer), Radiosonde, ERA5 (European Centre for Medium-Range Weather Forecasts Reanalysis v5), NCEP (National Centers for Environmental Prediction) reanalysis and CMA (China Meteorological Administration) forecast product from global medium range numerical weather prediction (NWP) system. QCS is one of the important components of FY-4A ground segment, which mainly focuses on the satellite products’ evaluation and validation. It is found that the AGRI LPW product has a good agreement with different evaluating sources and the quality is favorable and stable. With the capability of frequent (5-min interval) observations over the East Asia and Western Pacific regions, the AGRI LPW products can be used to investigate the atmospheric temporal and spatial variations in the pre-landfall environment for typhoons. The QCS is a useful tool to monitor, evaluate, and validate the AGRI LPW products. Full article

This study applies the Advanced Radiative Transfer Modeling System (ARMS), which was developed to accelerate the uses of Fengyun satellite data in weather, climate, and environmental applications in China, to characterize the biases of seven infrared (IR) bands of the Advanced Geosynchronous Radiation Imager (AGRI) onboard the Chinese geostationary meteorological satellite, Fengyun–4A. The AGRI data are quality controlled to eliminate the observations affected by clouds and contaminated by stray lights during the mid–night from 1600 to 1800 UTC during spring and autumn. The mean biases, computed from AGRI IR observations and ARMS simulations from the National Center for Environmental Prediction (NCEP) Final analysis data (FNL) as input, are within −0.7–1.1 K (0.12–0.75 K) for all seven IR bands over the oceans (land) under clear–sky conditions. The biases show seasonal variation in spatial distributions at bands 11–13, as well as a strong dependence on scene temperatures at bands 8–14 and on satellite zenith angles at absorption bands 9, 10, and 14. The discrepancies between biases estimated using FNL and the European Center for Medium–Range Weather Forecasts Reanalysis–5 (ERA5) are also discussed. The biases from water vapor absorption bands 9 and 10, estimated using ERA5 over ocean, are smaller than those from FNL. Such discrepancies arise from the fact that the FNL data are colder (wetter) than the ERA5 in the middle troposphere (upper–troposphere). Full article

The Chinese Fengyun–4A geostationary meteorological satellite was successfully launched on 11 December 2016, carrying an Advanced Geostationary Radiation Imager (AGRI) to provide the observations of visible, near infrared, and infrared bands with improved spectral, spatial, and temporal resolution. The AGRI infrared observations can be assimilated into numerical weather prediction (NWP) data assimilation systems to improve the atmospheric analysis and weather forecasting capabilities. To achieve data assimilation, the first and crucial step is to characterize and evaluate the biases of the AGRI brightness temperatures in infrared channels 8–14. This study conducts the assessment of clear–sky AGRI full–disk infrared observation biases by coupling the RTTOV model and ERA Interim analysis. The AGRI observations are generally in good agreement with the model simulations. It is found that the biases over the ocean and land are less than 1.4 and 1.6 K, respectively. For bias difference between land and ocean, channels 11–13 are more obvious than water vapor channels 9–10. The fitting coefficient of linear regression tests between AGRI biases and sensor zenith angles manifests no obvious scan angle–dependent biases over ocean. All infrared channels observations are scene temperature–dependent over the ocean and land. Full article