Search Results (64)

A new quantitative precipitation estimation (QPE) method for Himawari-9 (H9) and Fengyun-4B (FY4B) satellites has been developed based on cloud top brightness temperature (TBB). The 24-hour, 6-hour, and hourly rainfall estimates of H9 and FY4B have been compared with rain gauge datasets and precipitation estimation data from the GPM IMERG V07 (IMERG) and Global Precipitation Satellite (GSMaP) products, especially based on the case study of landfalling super typhoon “Doksuri” in 2023. The results indicate that the bias-corrected QPE algorithm substantially improves precipitation estimation accuracy across multiple temporal scales and intensity categories. For extreme precipitation events (≥100 mm/day), the FY4B-based estimates exhibit markedly better performance. Furthermore, in light-to-moderate rainfall (0.1–24.9 mm/day) and heavy rain to rainstorm ranges (25.0–99.9 mm/day), its retrievals are largely comparable to those from IMERG and GSMaP, demonstrating robust consistency across varying precipitation intensities. Therefore, the new QPE retrieval algorithm in this study could largely improve the accuracy and reliability of satellite precipitation estimation for extreme weather events such as typhoons. Full article

This study presents an objective scheme to construct hurricane bogus vortices based on satellite microwave scatterometer observations of sea surface wind vectors. When specifying a bogus vortex using Fujita’s formula, the required parameters include the center position and the radius of the maximum gradient of sea level pressure (R₀). We first propose determining the tropical cyclone (TC) center position as the cyclonic circulation center obtained from sea surface wind observations and then establishing a regression model between R₀ and the radius of 34-kt sea surface wind of scatterometer observations. The radius of 34-kt sea surface wind (R₃₄) is commonly used as a measure of TC size. The center positions determined from HaiYang-2B/2C/2D Scatterometers, MetOp-B/C Advanced Scatterometers, and FengYun-3E Wind Radar compared favorably with the axisymmetric centers of hurricane rain/cloud bands revealed by Advanced Himawari Imager observations of brightness temperature for the western Pacific landfalling typhoons Doksuri, Khanun, and Haikui in 2023. Furthermore, regression equations between R₀ and the scatterometer-determined radius of 34-kt wind are developed for tropical storms and category-1, -2, -3, and higher hurricanes over the Northwest Pacific (2022–2023). The bogus vortices thus constructed are more realistic than those built without satellite sea surface wind observations. Full article

As China’s second operational Geostationary Interferometric Infrared Sounder, Fengyun-4B/GIIRS can provide temporally and spatially continuous atmospheric temperature profile (ATP) and atmospheric humidity profile (AHP) information, which can be used in cold wave monitoring and other meteorological applications. In this study, radiosonde observations and ERA5 reanalysis are used to assess the quality of operational Fengyun-4B/GIIRS ATP and AHP products released by the National Satellite Meteorological Centre (NSMC). The results are as follows: (1) Compared to Fengyun-4A/GIIRS, due to the improvement in the instruments, the usability of Fengyun-4B/GIIRS is enhanced, and the influence of clouds and land surfaces reduces its usability under clear-sky conditions and below 900 hPa. (2) The current operational quality-flagged algorithm can identify the Fengyun-4B/GIIRS ATP and AHP products with different accuracies well, providing beneficial information to users. Taking radiosonde observations as a reference, the RMSEs of the Fengyun-4B/GIIRS ATP and AHP products with the best quality (with the quality flag of “very good”) are around 1.5K and below 2 kg/kg, respectively, which is better than those of the Fengyun-4A/GIIRS ATP product. (3) Compared to the ERA5 reanalysis, due to the different coefficients in the retrieval algorithm, systematic overestimation and underestimation occur for the Fengyun-4B/GIIRS ATP product under clear-sky conditions and cloudy-sky conditions, respectively. (4) The biases and RMSEs of the Fengyun-4B/GIIRS ATP and AHP products have significant dependence on the satellite zenith angles when the angles are larger than 50°, but when the angles are smaller than 50°, the dependence is negligible. Full article

Aerosols significantly impact the brightness temperature (BT) in thermal infrared (IR) channels, and ignoring their effects can lead to relatively large observation-minus-background (OMB) bias in radiance calculations. The accuracy of aerosol datasets is essential for BT simulations and bias reduction. This study incorporated aerosol reanalysis datasets from the Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) and Copernicus Atmosphere Monitoring Service (CAMS) into the Advanced Radiative Transfer Modeling System (ARMS) to compare their impacts on BT simulations from the Geostationary Interferometric Infrared Sounder (GIIRS) and their effectiveness in reducing OMB biases. The results showed that, for a sandstorm event on 10 April 2023, incorporating total aerosol data from the MERRA-2 improved the BT simulations by 0.56 K on average, surpassing CAMS’s 0.11 K improvement. Dust aerosols notably impacted the BT, with the MERRA-2 showing a 0.17 K improvement versus CAMS’s 0.06 K due to variations in the peak aerosol level, thickness, and column mass density. Improvements for sea salt and carbonaceous aerosols were concentrated in the South China Sea and Bay of Bengal, where the MERRA-2 outperformed CAMS. For sulfate aerosols, the MERRA-2 excelled in the Bohai Sea and southern Bay of Bengal, while CAMS was better in the northern Bay of Bengal. These findings provide guidance for aerosol assimilation and retrieval, emphasizing the importance of quality control and bias correction in data assimilation systems. Full article

Fengyun-3F (FY-3F) satellite was launched in 2023 with a MicroWave Temperature Sounder (MWTS) and a MicroWave Humidity Sounder (MWHS) onboard. This study evaluates the in-orbit performances of these two instruments and compares them with similar instruments onboard FY-3E and NOAA-20 satellites. It is found that the polarization of FY-3F MWHS at channel 1 is different from FY-3E from the quasi-horizontal to quasi-vertical, whereas the rest of the channels are revised to quasi-horizontal polarization. FY-3F MWTS performance at the upper air channels is, in general, better than FY-3E MWTS, with 0.3 K smaller in biases (O-B) and 0.13 K lower in standard deviation. The striping noise between FY-3E and 3F MWHS is similar in magnitude for most of the channels. The FY-3F can form a satellite constellation with the FY-3E and NOAA-20, enabling better monitoring of many weather events, such as typhoons and hurricanes, through the use of all three satellites. Using the Global-Scene Dependent Atmospheric Retrieval Testbed (GSDART), Typhoon Yagi warm cores are retrieved from both MWTS/MWHS and ATMS. It is shown the warm core structures of Typhoon Yagi are consistent with the three satellites in terms of their magnitudes and locations. Full article

In this study, we utilized ECMWF Reanalysis of the Global Climate at Atmospheric Resolution 5 (ERA5) data, FengYun-4B satellite (FY-4B) data, a Wind3D 6000 Three-Dimensional Scanning Laser Wind Radar, and raindrop spectrum data to analyze the circulation background, convective cloud changes, boundary layer wind field variations, and precipitation drop size spectrum characteristics of a severe convective rainfall process that occurred on 3 April 2024 in the eastern margin of the Tibetan Plateau. The findings indicated the following: (1) The rain belt of this precipitation event showed a southwest–northeast trend. During the vigorous development of convection, the rainfall intensity and total precipitation at the station increased, with a wider raindrop spectrum, and the raindrop spectrum of this precipitation process was unimodal. (2) On 3 April, the interaction between the eastward movement of the plateau trough at 500 hPa and the upper-level jet stream at 200 hPa in the eastern Tibetan Plateau and the Sichuan Basin area, along with the necessary conditions for precipitation, such as energy and moisture, led to severe convective rainfall. (3) This intense convective precipitation process was caused by the vigorous convective clouds that developed in the eastern part of the Tibetan Plateau. As these clouds developed and moved eastward out of the plateau, they precipitated with increased turbulence intensity at the station, leading to the generation of intense convective activities at the site. (4) One hour before the precipitation, there were significant increases in horizontal wind speed, vertical air velocity, and turbulence intensity within the boundary layer, and there were also significant changes in the horizontal wind direction. The results obtained can provide important theoretical references for the prediction of severe convective rainfall and the performance of numerical simulations thereon. 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

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

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

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

The Advanced Geostationary Radiation Imager (AGRI) sensor on board the geostationary satellite Fengyun-4B (FY-4B) is capable of capturing particles in different phases in the atmospheric environment and acquiring aerosol observation data with high spatial and temporal resolution. To understand the quality of the Land Aerosol (LDA) product of AGRI and its application prospects, we conducted a comprehensive evaluation of the AGRI LDA AOD. Using the 550 nm AGRI LDA AOD (550 nm) of nearly 1 year (1 October 2022 to 30 September 2023) to compare with the Aerosol Robotic Network (AERONET), MODIS MAIAC, and Himawari-9/AHI AODs. Results show the erratic algorithmic performance of AGRI LDA AOD, the correlation coefficient (R), mean error (Bias), root mean square error (RMSE), and the percentage of data with errors falling within the expected error envelope of $\pm (0.05+0.15\times {\mathrm{AOD}}_{AERONET})$ (within EE15) of the LDA AOD dataset are 0.55, 0.328, 0.533, and 34%, respectively. The LDA AOD appears to be overestimated easily in the southern and western regions of China and performs poorly in the offshore areas, with an R of 0.43, a Bias of 0.334, a larger RMSE of 0.597, and a global climate observing system fraction (GCOSF) percentage of 15% compared to the inland areas (R = 0.60, Bias = 0.163, RMSE = 0.509, GCOSF = 17%). Future improvements should focus on surface reflectance calculation, water vapor attenuation, and more suitable aerosol model selection to improve the algorithm’s accuracy. Full article

The Fengyun-4B (FY-4B) satellite is currently the primary operational geostationary meteorological satellite in China, replacing the previous FY-4A satellite. The advanced geostationary radiation imager (AGRI) aboard the FY-4B satellite provides an operational sea surface temperature (SST) product with a high observation frequency of 15 min. This paper conducts the first data quality assessment of operational SST products from the FY-4B/AGRI using quality-controlled measured SSTs from the in situ SST quality monitor dataset and foundation SSTs produced by the operational sea surface temperature and sea ice analysis (OSTIA) system from July 2023 to January 2024. The FY-4B/AGRI SST product provides a data quality level flag on a pixel-by-pixel basis. Accuracy evaluations are conducted on the FY-4B/AGRI SST product with different data quality levels. The results indicate that the FY-4B/AGRI operational SST generally has a negative mean bias compared to in situ SST and OSTIA SST, and that the accuracy of the FY-4B/AGRI SST, with an excellent quality level, can meet the needs of practical applications. The FY-4B/AGRI SST with an excellent quality level demonstrates a strong correlation with in situ SST and OSTIA SST, with a correlation coefficient R exceeding 0.99. Compared with in situ SST, the bias, root mean square error (RMSE), and unbiased RMSE (ubRMSE) of the FY-4B/AGRI SST with an excellent quality level are −0.19, 0.66, and 0.63 °C in daytime, and −0.15, 0.70, and 0.68 °C at night, respectively. Compared with OSTIA SST, the bias, RMSE, and ubRMSE of the FY-4B/AGRI SST with an excellent data quality level are −0.10, 0.64, and 0.63 °C in daytime, and −0.13, 0.68, and 0.67 °C at night. The FY-4B/AGRI SST tends to underestimate the sea water temperature in mid–low-latitude regions, while it tends to overestimate sea water temperature in high-latitude regions and near the edges of the full disk. The time-varying validation of FY-4B/AGRI SST accuracy shows weak fluctuations with a period of 3–4 months. Hourly accuracy verification shows that the difference between the FY-4B/AGRI SST and OSTIA SST reflects a diurnal effect. However, FY-4B/AGRI SST products need to be used with caution around midnight to avoid an abnormal accuracy. This paper also discusses the relationships between the FY-4B/AGRI SST and satellite zenith angle, water vapor content, wind speed, and in situ SST, which have an undeniable impact on the underestimation of the FY-4B/AGRI operational SST. The accuracy of the FY-4B/AGRI operational SST retrieval algorithm still needs to be further improved in the future. Full article

This study examines the Vertical Total Electron Content (VTEC) estimation performance of multi-instruments on a global scale during different ionospheric conditions. For this purpose, GNSS-based VTEC data from Global Ionosphere Maps (GIMs), COSMIC (F7/C2)—Feng–Yun 3C (FY3C) radio occultation (RO) VTEC, SWARM–VTEC, and JASON–VTEC were utilized. VTEC assessments were conducted on three distinct days: geomagnetic active (17 March 2015), solar active (22 December 2021), and quiet (11 December 2021). The VTEC values of COSMIC/FY3C RO, SWARM, and JASON were compared with data retrieved from GIMs. According to the results, COSMIC RO–VTEC is more consistent with GIM–VTEC on a quiet day (the mean of the differences is 4.38 TECU), while the mean of FY3C RO–GIM differences is 7.33 TECU on a geomagnetic active day. The range of VTEC differences between JASON and GIM is relatively smaller on a quiet day, and the mean of differences on active/quiet days is less than 6 TECU. Besides the daily comparison, long-term results (1 January–31 December 2015) were also analyzed by considering active and quiet periods. Results show that Root Mean Square Error (RMSE) values of COSMIC RO, FY3C RO, SWARM, and JASON are 5.02 TECU, 6.81 TECU, 16.25 TECU, and 5.53 TECU for the quiet period, and 5.21 TECU, 7.07 TECU, 17.48 TECU, and 5.90 TECU for the active period, respectively. The accuracy of each data source was affected by solar/geomagnetic activities. The deviation of SWARM–VTEC is relatively greater. The main reason for the significant differences in SWARM–GIM results is the atmospheric measurement range of SWARM satellites (460 km–20,200 km (SWARM A, C) and 520 km–20,200 km (SWARM B), which do not contain a significant part of the ionosphere in terms of VTEC estimation. Full article

Satellite precipitation products can help improve precipitation estimates where ground-based observations are lacking; however, their relative accuracy and applicability in data-scarce areas remain unclear. Here, we evaluated the accuracy of different satellite precipitation datasets for the Lancang River Basin, Western China, including the Tropical Rainfall Measuring Mission (TRMM) 3B42RT, the Global Precipitation Measurement Integrated Multi-satellitE Retrievals (GPM IMERG), and Fengyun 2G (FY-2G) datasets. The results showed that GPM IMERG and FY-2G are superior to TRMM 3B42RT for meeting local research needs. A subsequent bias correction on these two datasets significantly increased the correlation coefficient and probability of detection of the products and reduced error indices such as the root mean square error and mean absolute error. To further improve data quality, we proposed a novel correction–fusion method based on window sliding data correction and Bayesian data fusion. Specifically, the corrected FY-2G dataset was merged with GPM IMERG Early, Late, and Final Runs. The resulting FY-Early, FY-Late, and FY-Final fusion datasets showed high correlation coefficients, strong detection performances, and few observation errors, thereby effectively extending local precipitation data sources. The results of this study provide a scientific basis for the rational use of satellite precipitation products in data-scarce areas, as well as reliable data support for precipitation forecasting and water resource management in the Lancang River Basin. Full article

In response to the need for a space security situation assessment during orbit, the multi-satellite space environmental risk prediction and early warning system is based on the detection results of the space weather payload of the Fengyun 4A and 4B satellites, as well as the prediction results of the National Space Weather Center, for the first time. By comprehensively utilizing some open-source data, it is the first time that we have achieved a 24 h advanced prediction of the space environment high-energy proton, low-energy particle, and high-energy electron risks for the safety of the Fengyun-series high-orbit satellites, and a real-time warning of satellite single-event upset, surface charging, and deep charging risks. The automation system has preliminarily achieved an intelligent space risk assessment for the safety of multiple stationary meteorological satellites, effectively improving the application efficiency of the space environmental data and the products of Fengyun-series satellites. The business status is stable in operation, and the resulting error between the predicted results of various risk indices and the measured data was less than one level. The warning accuracy was better than 90%. This article uses the system for the first time, to use Fengyun satellite data to, accurately and in a timely manner, predict and warn us about the low-energy particles and surface charging high-risk levels of the Fengyun 4A and 4B satellites during the typical space weather event on 21 April 2023, in response to the impact of complex spatial environmental factors on the safety of Fengyun-series high-orbit satellites. The construction and operation of a multi-satellite space environmental risk prediction and early warning system can provide a reference for the safety work of subsequent satellite ground system operations. Full article