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The chitosan-stabilized ferrous sulfide nanoparticles were loaded on biochar to prepare a composite material FeS-CS-BC for effective removal of hexavalent chromium in water. BC and FeS-CS-BC were characterized by Brunauer–Emmett–Teller (BET), scanning electron microscopy (SEM), X-ray diffraction (XRD), and Fourier transform infrared spectroscopy (FTIR) analyses. Batch experiments were employed to evaluate the Cr(VI) removal performance. The experimental results showed that the removal rate of Cr(VI) by FeS-CS-BC(FeS:CS:BC = 2:2:1) reached 98.34%, which was significantly higher than that of BC (44.58%) and FeS (79.91%). In the pH range of 2–10, the removal of Cr(VI) by FeS-CS-BC was almost independent of pH. The limitation of coexisting anions (Cl − 、SO 4 2− 、NO 3 − ) on Cr(VI) removal was not too obvious. The removal of Cr(VI) by FeS-CS-BC was fitted with the pseudo-second-order dynamics, which was a hybrid chemical-adsorption reaction. The X-ray photoelectron spectroscopy (XPS) analysis result showed that Cr(VI) was reduced, and the reduced Cr(VI) was fixed on the surface of the material in the form of Cr(VI)–Fe(III). Graphical abstract Removal of hexavalent chromium from wastewater by FeS-CS-BC composite synthesized by impregnation.

Gravity waves play a crucial role in the evolution of convective systems. The unique thermal structure of elevated convection occurring above a stable boundary layer facilitates the generation and propagation of gravity waves. This study focuses on an elevated convection event over Central China on the night of 2–3 February 2024. The WRF model, combined with terrain sensitivity experiments, is employed to analyze the characteristics of gravity waves and the effects of terrain variability. The event consists of two elevated convection clusters; the first triggers gravity waves on its southwestern side, which subsequently initiates the second convection cluster. The gravity waves exhibit a horizontal wavelength of 25 km and a period of 17.5 min, propagating eastward. Below an altitude of 3 km, a stable wave duct layer is present, overlain by an unstable overreflective zone. This stratification creates an ideal channel for ducted gravity wave propagation, which is essential for maintaining the waves. Sensitivity experiments confirm that convective forcing alone is sufficient to generate the observed gravity waves. Although the terrain lies within the stable boundary layer, its ruggedness modulates the distribution of waves and indirectly influences the organization of elevated convection.

Satellite microwave-sounding radiometer data assimilation under clear-sky conditions typically requires the exclusion of precipitation-affected field-of-view (FOV) regions. However, the traditional scatter index (SI) and cloud liquid water path (CLWP)-based precipitation sounding algorithms from earlier NOAA microwave sounders are built on window channels which are not available from FY-3C/D MWTS-II. To address this limitation, this study establishes a nonlinear relationship between multispectral visible/infrared data from the FY-2F geostationary satellite and microwave sounding channels using an artificial intelligence (AI)-driven approach. The methodology involves three key steps: (1) The spatiotemporal integration of FY-2F VISSR-derived products with NOAA-19 AMSU-A microwave brightness temperatures was achieved through the GEO-LEO pixel fusion algorithm. (2) The fused observations were used as a training set and input into a random forest model. (3) The performance of the RF_SI method was evaluated by using individual cases and time series observations. Results demonstrate that the RF_SI method effectively captures the horizontal distribution of microwave scattering signals in deep convective systems. Compared with those of the NOAA-19 AMSU-A traditional SI and CLWP-based precipitation sounding algorithms, the accuracy and sounding rate of the RF_SI method exceed 94% and 92%, respectively, and the error rate is less than 3%. Also, the RF_SI method exhibits consistent performance across diverse temporal and spatial domains, highlighting its robustness for cross-platform precipitation screening in microwave data assimilation.

Fengyun-4B (FY-4B) is the first operational satellite from China’s latest generation of geostationary meteorological satellites. It is equipped with the Geostationary Interferometric Infrared Sounder (GIIRS), which is able to obtain highly accurate atmospheric temperature and humidity profiles through hyperspectral detection in long- and mid-wave infrared spectral bands. In this study, the accuracy of the FY-4B/GIIRS temperature and humidity profile retrievals over two months is evaluated using radiosonde observations and ERA5 reanalysis data. We go a step further to investigate the impact of the satellite retrievals on assimilation and forecasts for Typhoons Chaba and Ma-on in 2022. Results reveal that the root-mean-square difference (RMSD) for the FY-4B/GIIRS temperature and humidity profile retrievals were within 1 K and 1.5 g/kg, respectively, demonstrating high overall accuracy. Moreover, assimilating temperature and humidity profiles from FY-4B/GIIRS positively impacts model analysis and prediction, improving typhoon track and intensity forecasts. Additionally, improvements have been discovered in predicting precipitation, particularly with high-magnitude rainfall events.

All‐sky radiance assimilation has been extensively developed to provide additional information for numerical weather prediction under cloudy conditions. Microwave radiances are particularly sensitive to hydrometeors, which can be used to initialize hydrometeor directly if the hydrometeor control variables (HCVs) are available. However, the effects of HCVs statistical structure and their multivariate correlation on all‐sky radiance assimilation remain unclear. In this study, five HCVs are introduced into the variational assimilation system. The characteristics of hydrometeor background errors are analyzed, and the combined effect with the observation operator is discussed. Then a 3D Variational all‐sky assimilation framework with HCVs is modified to assimilate Fengyun‐3C/D Microwave Humidity Sounder‐II radiance. It is shown that hydrometeors are initialized by radiance directly, and the thermodynamic fields are adjusted accordingly. The characteristics of multi‐variables increments are associated with both the characteristics of HCVs in background error and the Jacobians in observation operator. Furthermore, cycle assimilation and forecast experiments for three typhoon cases are conducted. It is found that the difference between observed and analyzed brightness temperatures decreases when HCVs are activated, and the hydrometeors analysis fields are more consistent with observations. Additionally, the typhoon intensity forecasts are improved with enhanced double warm‐core and the secondary circulation. This paper analyzes the characteristics of variational all‐sky assimilation framework with HCVs, and demonstrates the potential value of HCVs for variational all‐sky radiance assimilation. Plain Language Summary Accurate forecasting of clouds and precipitation remains a challenging task in numerical weather prediction. Effective utilization of observation under cloudy conditions through data assimilation is crucial for improving the weather prediction. Current typical all‐sky assimilation methods can only indirectly assimilate cloud and precipitation in the model trajectory. In this study, the impact of enabling such direct updates is explored through the modification of the radiance assimilation framework. It is found that the enhanced radiance assimilation framework can better understand the cloud and precipitation information and further influence the thermal and dynamical processes. Typhoon cases are used to test this new assimilation framework and found that the modeled hydrometeors are closer to observations and improves typhoon intensity prediction. This study demonstrates a potential approach by directly updating hydrometeors using satellites radiance and lead to more accurate weather predictions, especially for typhoon cases. Key Points A 3D‐Var all‐sky radiance assimilation framework for FY‐3C/D MWHS‐II with hydrometeors control variables (HCVs) is established Hydrometeors are initialized by radiance directly, through the combined effect with background error and the observation operator Better analysis of hydrometeors leads to improved typhoon inner‐core structure, thus enhancing the ability of typhoon intensity prediction

Highly pathogenic avian influenza (HPAI) H5 viruses have recently been documented in mammals including humans, posing a major threat to global public health. To prevent a potential H5 pandemic, it is critical to elucidate the antigenic evolutionary pattern and identify key drivers underlying its evolution. In this work, we construct a comprehensive antigenic map of H5 influenza viruses spanning their evolutionary history and classified three antigenic clusters with no cross-neutralization. The first corresponds to ancestral clades, the second to 2.3.4.4* clades being predominant since 2010, and the third to 2.3.4.4 h clade. Despite the gradually increasing genetic distances from ancestral to 2.3.4.4* to 2.3.4.4 h, their antigenic evolution does not follow the same progressive pattern: the antigenic distance between 2.3.4.4 h and ancestral is smaller than that between 2.3.4.4* and ancestral. This divergence is associated with two distinct mutation patterns at six key amino acid positions: (1) persistent mutations at positions 88 (N > R > S), 199 (D > N > S), and 205 (K > N > D), and (2) reversible mutations at positions 131 (Q > L > Q), 139 (S > P > S), and 289 (N > H > N). These findings not only reveal the antigenic evolution mechanism of H5 influenza, but also provide important guidance for vaccine strain selection and broad-spectrum vaccine development. This research uncovered a critical inconsistency between the genetic and antigenic evolution of Highly pathogenic avian influenza H5 viruses using a pseudovirus tool and identified the key amino acid substitutions driving this divergence, which provides crucial insights for vaccine development and pandemic preparedness.

Accurate and long leading time flood forecasting is very important for flood disaster mitigation. It is an effective method to couple the Quantitative Precipitation Forecast (QPF) products provided by Numerical Weather Prediction (NWP) models to a distributed hydrological model with the goal of extending the leading time for flood forecasting. However, the QPF products contain a certain degree of uncertainty and would affect the accuracy of flood forecasting, especially in the mountainous regions. Radar data assimilation plays an important role in improving the quality of QPF and further improves flood forecasting. In this paper, radar data assimilation was applied in order to construct a high-resolution atmospheric-hydrological coupling model based on the WRF and WRF-Hydro models. Four experiments with conventional observational and radar data assimilation were conducted to evaluate the flood forecasting capability of this coupled model in a small-medium sized basin based on eight typical flood events. The results show that the flood forecast skills are highly QPF-dependent. The QPF from the WRF model is improved by assimilating radar data and further increasing the accuracy of flood forecasting, although both precipitation and flood are slightly over-forecasted. However, the improvements by assimilating conventional observational data are not obvious. In general, radar data assimilation can improve flood forecasting effectively in a small-medium sized basin based on the atmospheric-hydrological coupling model.

Precipitation detection is an essential step in radiance assimilation because the uncertainties in precipitation would affect the radiative transfer calculation and observation errors. The traditional precipitation detection method for microwave only detects clouds and precipitation horizontally, without considering the three-dimensional distribution of clouds. Extending precipitation detection from 2D to 3D is expected to bring more useful information to the data assimilation without using the all-sky approach. In this study, the 3D precipitation detection method is adopted to assimilate Microwave Temperature Sounder-2 (MWTS-II) onboard the Fengyun-3D, which can dynamically detect the channels above precipitating clouds by considering the near-real-time cloud parameters. Cycling data assimilation and forecasting experiments for Typhoons Lekima (2019) and Mitag (2019) are carried out. Compared with the control experiment, the quantity of assimilated data with the 3D precipitation detection increases by approximately 23%. The quality of the additional MWTS-II radiance data is close to the clear-sky data. The case studies show that the average root-mean-square errors (RMSE) of prognostic variables are reduced by 1.7% in the upper troposphere, leading to an average reduction of 4.53% in typhoon track forecasts. The detailed diagnoses of Typhoon Lekima (2019) further show that the additional MWTS-II radiances brought by the 3D precipitation detection facilitate portraying a more reasonable circulation situation, thus providing more precise structures. This paper preliminarily proves that 3D precipitation detection has potential added value for increasing satellite data utilization and improving typhoon forecasts.

Genetic analyses showed nearly 30 amino acid mutations occurred in the spike protein of the Omicron variant of SARS-CoV-2. However, how these mutations occurred and changed during the generation and development of Omicron remains unclear. In this study, 6.7 million (all publicly available data from 2020/04/01 to 2022/04/01) SARS-CoV-2 genomes were analyzed to track the origin and evolution of Omicron variants and to reveal the genetic pathways of the generation of core mutations in Omicron. The haplotype network visualized the pre-Omicron, intact-Omicron, and post-Omicron variants and revealed their evolutionary direction. The correlation analysis showed the correlation feature of the core mutations in Omicron. Moreover, we found some core mutations, such as 142D, 417N, 440K, and 764K, reversed to ancestral residues (142G, 417K, 440N, and 764N) in the post-Omicron variant, suggesting the reverse mutations provided sources for the emergence of new variants. In summary, our analysis probed the origin and further evolution of Omicron sub-variants, which may add to our understanding of new variants and facilitate the control of the pandemic.

To make better use of microwave radiance observations for data assimilation, removal of radiances contaminated by hydrometeor particles is one of the most important steps. Generally, all observations below the middle troposphere are eliminated before the analysis when precipitation is present. However, the altitude of the cloud top varies; when the weighting function peak height of a channel is higher than the altitude of the cloud top, observations are not affected by the absorption or scattering of cloud particles. Thus, the radiative transfer calculation can be performed under a clear sky scenario. In this paper, a dynamic channel selection (DCS) method was developed to determine the radiance observations unaffected by clouds under cloudy conditions in assimilation. First, the sensitivity of cloud liquid water (CLW) profiles to radiance from the microwave temperature sounding frequencies was analyzed. It was found that the impact of CLW on transmittance can be neglected where the cloud top height is below the weighting function peak height. Second, three lookup tables were devised through analysis of the impact of cloud fraction and cloud top height on radiance, which is the basis of the DCS method. The unified cloud top height of the Microwave Temperature Sounder (MWTS)-2 fields of view (FOVs) can be calculated by remapping the cloud mask and cloud top height data from the Medium Resolution Spectral Imager-2 (MERSI-2). Observations from various channels may be removed or retained based on real-time dynamic unified cloud top height data. Twelve-hour and long-term time-series brightness temperature simulation experiments both showed that an increase in the amount of observations used for data assimilation of more than 300% can be achieved by application of DCS, but this had no effect on the amount of error. Through DCS, areas of strong precipitation can be accurately identified and removed, and more observations above cloud top height can be included in the data assimilation. The application of DCS to data assimilation will greatly improve the data utilization rate, and therefore allow for more accurate characterization of upper atmospheric circulation.