Search Results (266)

The accuracy of numerical weather prediction largely depends on the quality of the initial conditions. Global Navigation Satellite System radio occultation (GNSS-RO) observations, with their high vertical resolution, play an important role in reducing initial condition errors. In this study, multiple simulations with different initialization times were conducted during the development of Typhoon BEBINCA using the WRF-GSI assimilation system to evaluate the impact of YunYao GNSS-RO observations on improving extreme weather simulation performance and to investigate the sensitivity of refractivity assimilation to different cloud microphysics parameterization schemes. The results show that assimilating YunYao GNSS-RO data significantly improves the consistency between the model initial fields and observations and enhances the analysis quality in the middle and upper troposphere. Compared with ERA5 reanalysis data, the assimilation experiments better reproduce the spatial and temporal evolution of key atmospheric variables, and the improvements persist from 36 h to 120 h forecast lead time. Statistical results from multiple initializations show that the maximum RMSE reductions exceed 0.2 K for temperature, 0.1 m s⁻¹ for wind speed, and geopotential height shows consistent improvements throughout the entire atmosphere. In addition, the assimilation experiments improve the simulation of Typhoon BEBINCA’s track and intensity. Statistical results from multiple initializations indicate that the 84 h track error is reduced by approximately 30 km on average, and the minimum central pressure bias is also reduced. Sensitivity experiments further show that the WSM6 microphysics scheme performs better in track forecasting, while the Thompson scheme is more suitable for intensity forecasting. Overall, YunYao GNSS-RO assimilation effectively improves typhoon forecast accuracy and demonstrates strong potential for operational applications. Full article

To address the degradation of zenith tropospheric delay (ZTD) prediction accuracy caused by time-varying noise and error accumulation in multi-step forecasting, this study proposes an integrated prediction model, named IBX, which combines improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), bidirectional long short-term memory (BiLSTM), and extreme gradient boosting (XGBoost). In the proposed framework, ICEEMDAN is first used to decompose the original ZTD series into components at different temporal scales. A three-criterion reconstruction strategy based on the Pearson correlation coefficient, dominant period, and sample entropy is then applied to obtain high-, medium-, and low-frequency subsequences with clearer physical meanings. BiLSTM and XGBoost are used to predict the reconstructed components, and their outputs are fused through a root mean square error (RMS)-based weighting strategy to improve forecasting robustness. Hourly ZTD data from 27 global navigation satellite system (GNSS) stations in China from 2011 to 2020 were used for model validation under 1–12 h rolling forecasting horizons. The results show that IBX achieves the best overall performance among the tested models. Its mean RMS and mean absolute error (MAE) over the 1–12 h horizons are 14.17 mm and 10.24 mm, respectively, which are 22.5% and 21.4% lower than those of the baseline BiLSTM model. Spatial and climate-region-based analyses further indicate that ZTD prediction accuracy is strongly affected by altitude, regional moisture conditions, and climate type. The proposed IBX model shows stable error suppression across heterogeneous station environments, especially in the temperate monsoon region and low-altitude regions with complex water vapor variability. These results demonstrate that IBX provides a reliable and physically interpretable approach for short- to medium-term ZTD forecasting and real-time atmospheric delay correction. Full article

In the period 2 to 4 March 2026, two rainstorms with intense convective weather occurred within and in the vicinity of Hong Kong, China, in the early rain season of the year in southern China. This is rather uncommon because the atmosphere is still generally stable (with very low or even zero value of convective available potential energy), and upper tropospheric divergence does not yet exist in the region climatologically. The rain episode is documented in this paper from both observational and forecasting aspects. On the observational side, a low-level vortex is found on and near the surface based on Doppler velocity measurements from a newly installed C-band solid-state weather radar. Combining the three-dimensional wind field as retrieved from the weather data and the measurements from the other ground-based remote-sensing meteorological equipment, the intense convection is mainly triggered by middle to lower tropospheric waves, and the vertical circulation in the atmospheric boundary layer may be stretched vertically upward to form the low-level vortex. In the second rainstorm, features of elevated thunderstorms are also identified. On the forecasting side, a high-resolution, limited-area atmosphere–ocean–wave coupled model manages to capture the occurrence and the timing of the heavy rain. The sub-seasonal forecast by a global model also provides a useful indication of the occurrence of above-normal rainfall over southern China, with a rather special feature of a deep and stationary westerly trough located to the north of the Indochina Peninsula. The microscale cyclone could be successfully picked up by the real-time run of a high-resolution numerical weather prediction model with data assimilation. This paper also discusses the weather service aspect of this rather unusual rainstorm episode. Full article

Atmospheric equatorial Kelvin waves—convective disturbances that manipulate tropical wind and rainfall patterns—can propagate eastward at speeds ranging from nearly stationary to 30 m/s, with variability determined by moist processes and advection by the background wind. Current studies on Kelvin waves lack a comprehensive climatology that explains how their structure and propagation speeds change in different background states. Thus, this work builds a variable regression model that uses ERA5 reanalysis data to reconstruct Kelvin waves during different background wind shear conditions and phases of the Madden–Julian Oscillation (MJO) and the El Niño–Southern Oscillation (ENSO) over the Pacific. Overall, Kelvin waves tend to speed up during background conditions that generate upper-tropospheric westerlies and slow down during upper-tropospheric easterlies. East Pacific Kelvin waves are faster than West Pacific Kelvin waves because of climatological westerly shear in the former and easterly shear in the latter. However, strong westerly shear over the East Pacific allows extratropical Rossby waves to impede on the Kelvin wave, while strong easterly shear over the West Pacific distorts classical Kelvin wave structure. The results provide references for weather prediction models to accurately resolve the interaction between Kelvin waves and background circulation. Full article

Based on summer ground-based microwave radiometer (MWR) observations over North China, this study systematically evaluates the accuracy of temperature and humidity profile products derived from the Vertical Atmospheric Sounding System (VASS) onboard the FY-3E satellite. The VASS products are compared with the numerical weather prediction (NWP) background fields as well as the ERA5 and CMA-RA 1.5 (CRA) reanalysis datasets. The results show that both ERA5 and CRA exhibit stable and reliable performance in representing temperature and humidity fields under both clear and cloudy conditions over North China. The temperature root mean square error (RMSE) generally ranges from 1.6 K to 2.6 K at different height levels (from 0 km to 10 km), while the RMSE of absolute humidity is approximately 0.4–2.7 g/m³. These results further confirm the reliability of CRA under both clear and cloudy conditions in this region. In contrast, the errors of the VASS products show pronounced variations with height, station, and weather conditions. A clear systematic underestimation of temperature is found at 1–3 km, with a mean bias of about −3.44 K. Humidity is also significantly underestimated in the boundary layer, with a mean bias of approximately −5.91 g/m³. Both temperature and humidity errors decrease rapidly with increasing height. Clear inter-station differences are also identified. Temperature errors show boundary-layer overestimation in Beijing, while Xingtai and Dingzhou exhibit systematic underestimation throughout most of the profile, with mean biases reaching −4.1 K and −3.3 K, respectively. Boundary-layer humidity underestimation is more pronounced in Xingtai and Dingzhou (approximately −6.6 g/m³) than in Beijing (−4.0 g/m³). Weather-based analysis indicates that clouds have a significant impact on the accuracy of the VASS products. Under cloudy conditions, the near-surface temperature mean bias shifts from overestimation under clear skies to underestimation. The magnitude of humidity underestimation under cloudy conditions is approximately twice that under clear conditions. Further comparison shows that the error characteristics of the NWP background fields in the lower and middle troposphere are partly similar to those of the VASS products. This suggests that the current retrieval algorithm still has limited capability to correct background field biases under complex weather conditions. These results provide scientific support for the selection of application scenarios and the optimization of retrieval algorithms for FY-3E/VASS temperature and humidity profile products, and they also support the reliable use of domestic reanalysis datasets in regional studies. Full article

To advance our understanding of atmospheric processes and climate dynamics, improved knowledge of outgoing long-wave radiation (OLR) spectral emission is essential. The FORUM mission, selected for the ninth cycle of the European Space Agency’s Earth Explorer programme, is specifically designed to address the long-standing observational gap in the far-infrared (FIR) spectral region. When combined with measurements from the IASI-NG instrument, FORUM will provide complete spectral coverage of Earth’s OLR emission (spanning 100 to 2760 cm⁻¹ wavenumber, or 3.62 to 100 μm wavelength), thereby enabling robust climate model validation and enhanced understanding of climate change processes. While IASI-NG’s primary mission is to support numerical weather prediction, FORUM is designed to measure key climate variables, which also enable the retrieval of atmospheric parameters in the troposphere and lower stratosphere. In this study, we assess the information content of FORUM and IASI-NG measurements for atmospheric profiling through a simulation-based approach. Synthetic retrieval products are generated using a linearized formulation of the retrieval transfer function, allowing an efficient and physically consistent evaluation of the sensitivity of the two instruments to atmospheric temperature and water vapor profiles. The analysis reveals a non-negligible sensitivity of FORUM to atmospheric temperature extending into the stratosphere, resulting in significant information content at altitudes higher than previously reported. This finding highlights the potential of far-infrared observations to contribute to atmospheric temperature profiling beyond the lower troposphere. The complementary capabilities of FORUM and IASI-NG suggest that their combined use can enhance the characterization of the atmospheric thermal structure. These results represent a first step toward evaluating the potential role of FORUM Level-2 products in future numerical weather prediction applications. Full article

Abnormal refraction phenomena such as atmospheric ducts due to temperature inversions or rapid decreases in humidity often happen in the lower troposphere over the sea and coastal area, which can make low-elevation signals in the duct layer propagate beyond the horizon, and the ray trajectories extend horizontally over long distances. This paper uses ray tracing technology based on a second-order Taylor approximation to accurately predict the low-elevation ray trajectories within atmospheric ducts. The meteorologic parameters at the heights traversed by the rays are extracted to accumulate atmospheric absorption attenuation by line-by-line calculations, and a high-precision prediction method for atmospheric absorption attenuation in over-the-horizon propagation links is established; meanwhile, we also implement visualization of atmospheric absorption attenuation changes along the ray trajectories in atmospheric duct environments. By comparing the results of the atmospheric absorption attenuation models for horizontal terrestrial paths in the ITU-R P.676 recommendation and GJB Z87-1997 in atmospheric duct environments, we found that the high-precision model proposed in this paper can improve the prediction accuracy of atmospheric absorption attenuation by about 15% in surface ducts and 28% in elevated ducts, significantly improving the propagation performance of low-elevation signals under atmospheric ducts and other abnormal refraction conditions for electronic systems such as surveillance, detection, communication, and navigation. Full article

Elevated tropospheric ozone (O₃) and global warming can affect nitrous oxide (N₂O) and nitric oxide (NO) emissions from rice paddies, but their interactive effect is poorly understood. Two-year field observations of soil properties were implemented to quantify the impact of elevated O₃ and warming on N₂O and NO fluxes and identify dominant regulatory factors. Results indicated that elevated O₃ reduced N₂O and NO fluxes, whereas warming increased N₂O and NO fluxes, relative to respective ambient conditions. The combined O₃ elevation and warming treatment resulted in reductions in N₂O and NO fluxes compared to the warming treatment. The N₂O and NO emissions under elevated O₃ and warming were primarily associated with significant changes in soil dissolved organic carbon (DOC) and NH₄⁺. Furthermore, under warming treatment, N₂O and NO emissions were also linked to significant variations in soil NO₃⁻, but were independent of soil microbial biomass carbon (MBC), microbial biomass nitrogen (MBN), and pH. This study found that high O₃ concentrations significantly suppress nitrogen oxide emissions from paddy fields under warming. This suppression necessitates the explicit integration of O₃ into agroecosystem emission prediction frameworks for accurate estimation of nitrogen oxide fluxes. Full article

To improve diagnostics and prediction of changes caused by increased impact of anthropogenic activity, it is necessary to increase the comparative analysis of measurements and modeling of ozone—one of the climatically important atmospheric gases due to the decisive influence of stratospheric ozone on the radiation balance of the Earth-atmosphere system and the role of tropospheric ozone, the third most significant anthropogenic factor contributing to the greenhouse effect. This task is particularly relevant for Russia, as its geographical location makes it more vulnerable to climate change than other countries, whereas its regional tendencies in ozone variability have not yet been studied in sufficient detail. An analysis of IKFS-2 tropospheric ozone content (TrOC) measurements for 2015–2022 revealed that in Siberian, Far Eastern, North Caucasian, and Southern federal districts of Russia TrOC maximum, caused by photochemical formation of ground-level ozone, is observed in July (up to 30–35 DU for monthly means in surface-400 hPa layer). In Northwestern federal district, TrOC maximum (up to 25–30 DU), determined by meridional transport, is observed in late spring. No statistically significant linear trends in TrOC are detected. The WRF-Chem model qualitatively describes the seasonal variations of TrOC as well as the anomalous increase in TrOC caused by forest fires. The variability of total ozone content (TOC) is analyzed by OMI (2005–2023) and IKFS-2 (2015–2022) measurements as well as by SOCOLv3 simulations. Ozone negative anomalies in spring (up to 15% for monthly means) are generally observed with positive Arctic oscillation index values and a westerly phase of Quasi-biennial oscillations. For the 2008–2022 period, a statistically significant increase in TOC (+1.6–1.7% per year) is obtained for European Russia and Western and Central Siberia in November. Full article

We develop a minimal thermodynamic model to predict Titan’s surface temperature based on radiative–convective equilibrium and the principle of maximum entropy production (MEP). The model retains only the essential atmospheric constituents: gaseous methane, which absorbs both longwave and near-infrared radiation, and stratospheric haze, which scatters and absorbs solar flux. Subject to Clausius–Clapeyron scaling of methane vapor pressure together with energy balances at the surface, tropopause, and stratopause, the model links the convective flux to the surface temperature, which exhibits a pronounced maximum due to competing radiative effects of tropospheric methane. As the surface warms, enhanced greenhouse effect would strengthen the convection, whereas the rising anti-greenhouse effect would suppress convection. The resulting convective peak corresponds to MEP, which thus selects a surface temperature slightly above methane’s triple point. To assess its long-term evolution, we consider a 20% dimmer early Sun and a hypothetical 20% enrichment of the oceanic methane. Even in combination, they only cool the surface by ~2 K, in sharp contrast to the ~20 K cooling inferred in studies that prescribe haze abundance. This study suggests a critical role of self-adjusting haze in providing the internal degree of freedom necessary for MEP closure, thereby stabilizing Titan’s temperature. Full article

Air pollution remains a major public-health concern, and exposure to particulate matter (PM), particularly PM₁₀ (with a diameter ≤ 10 µm), is associated with adverse respiratory and cardiovascular outcomes. Most research relies on a singular model for PM₁₀ surface estimation. This study is an assessment of a national-scale, daily PM₁₀ estimation framework for Lithuania (2019–2024), using a hybrid machine-learning method that combines Random Forest (RF) and extreme gradient boosting (XGBoost) algorithms. Hourly PM₁₀ observations were aggregated from 18 monitoring stations to obtain daily means and temporal means. The predictors integrated meteorological factors, such as temperature, wind, humidity, and precipitation, to determine satellite-based atmospheric composition from Sentinel-5P Tropospheric Monitoring Instruments (TROPOMI). Atmospheric components include nitrogen dioxide (NO₂), carbon monoxide (CO), sulfur dioxide (SO₂), ozone (O₃), formaldehyde (HCHO), and the absorbing aerosol index (AI). Moderate-Resolution Imaging Spectroradiometers (MODIS) were used to record land-surface temperature and static spatial descriptors, such as elevation, land cover, Normalized Difference Vegetation Index (NDVI), population, and road proximity. The dataset was partitioned temporally into training (70%), validation (20%), and testing (10%). The hybrid model achieved an improved accuracy, compared with single-model baselines, reaching a coefficient of determination (R²) of 0.739 in validation and R² = 0.75 in the tested dataset. Mean absolute error (MAE) was 3.15 µg/m³, and root mean square error (RMSE) was 3.98 µg/m³. The results indicate a slight tendency to overestimate PM₁₀ concentrations at lower concentration levels. Feature-importance analysis revealed that short-term temporal persistence is the key to daily PM₁₀ prediction, while meteorological variables provide secondary contributions. Temporal evaluation, using consecutive two-year windows, revealed a consistent improvement in predictive performance from 2019–2020 to 2023–2024, while station-level analysis showed moderate-to-strong agreement between the predicted and observed PM₁₀ concentrations across monitoring stations, with R² ranging from 0.455 to 0.760. This provides decision-support capabilities for air-quality management, the evaluation of mitigation measures, and integration of air-pollution considerations into sustainable urban planning strategies assessing public-health protection. Full article

This study uses a globally coupled climate framework to examine how regional differences in sulfate emissions, through both direct and indirect aerosol effects, regulate interactions between clouds and radiation and drive nonlinear thermodynamic and hydrological responses in the East Asia and South Asia summer monsoon region. We employ the Community Earth System Model to compare the Shared Socioeconomic Pathways 1–2.6 and 5–8.5 against the historical scenario with perturbations of anthropogenic sulfate. The results reveal regional contrasts in sulfate concentration and aerosol optical depth: direct shortwave radiation increases in East Asia, while South Asia experiences radiation weakening due to higher aerosol optical depth. Indirect aerosol effects induce cloud adjustments, with East Asia developing more low clouds and higher cloud droplet number concentrations and liquid water paths, leading to greater attenuation of surface shortwave radiation and changes in precipitation and convection. Over the Tibetan Plateau, a higher fraction of high clouds and changes in cloud-top heights jointly drive warming, raising net radiation and strengthening both latent-heat and sensible-heat release. South Asia exhibits a north–south oriented precipitation pattern, with intensified warm advection but a distribution shaped by upper and mid-tropospheric circulations. Overall, the coupling of cloud macro-distribution and cloud microphysics emerges as the principal driver, with direct and indirect effects amplifying nonlinear regional responses. To improve predictability, we advocate multi-model comparisons, observational constraints, tighter bounds on cloud-droplet size distributions, liquid water paths, and cloud droplet number concentrations. Full article

The tropospheric zenith wet delay (ZWD) serves as a pivotal parameter for atmospheric water vapour inversion. By converting it into precipitable water vapour, high-temporal-resolution atmospheric humidity monitoring becomes feasible, providing crucial support for enhancing short-term rainfall forecast accuracy. However, ZWD exhibits significant non-stationarity due to complex influencing factors, and traditional models struggle to achieve precise predictions across all scenarios owing to limitations in local feature extraction. This article employs a ZWD prediction method based on the dynamic temporal decomposition module of TimesNet, re-constructing one-dimensional high-frequency ZWD time series into two-dimensional tensors to overcome the technical limitations of conventional models. Comprehensively considering topographical characteristics, climatic features, and seasonal factors, experiments were conducted using 30 s ZWD data from 20 IGS stations. This dataset comprised four consecutive days of PPP solutions for each season in 2023. Through comparative experiments with CNN-ATT and Informer models, the global prediction accuracy, seasonal adaptability, and topographical robustness of TimesNet were systematically evaluated. Results demonstrate that under the input-prediction window configuration where each can achieve the optimal accuracy, TimesNet achieves an average seasonal Root Mean Square Error (RMSE) of 5.73 mm across all seasonal station samples, outperforming Informer (7.89 mm) and CNN-ATT (10.02 mm) by 27.4% and 42.8%, respectively. It maintains robust performance under the most challenging conditions—including summer severe convection, high-altitude terrain, and climatically variable maritime zones—while achieving sub-5 mm precision in stable environments. This provides a reliable algorithmic foundation for short-term precipitation forecasting in Global Navigation Satellite System (GNSS) real-time meteorology. Full article

The frequency and intensity of tropical cyclones (TCs) in the Arabian Sea have increased in recent decades, heightening concerns regarding regional vulnerability and forecasting difficulties. This study examines the impact of the Madden–Julian Oscillation (MJO) on TCs activity—formation, frequency, and severity—over the Arabian Sea from 1982 to 2021. This study analyzes variations in convection, vertical wind shear (VWS), sea level pressure (SLP), and relative humidity (RH) across different MJO phases utilizing the best-track data from the India Meteorological Department (IMD), the Real-Time Multivariate MJO (RMM) index, and reanalysis datasets from the National Oceanic and Atmospheric Administration (NOAA) and the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR). Results show that more than 80% of TCs form during the convectively active phases of the MJO (P1–P4). These phases have the most noticeable negative outgoing longwave radiation (OLR) anomalies, as well as higher mid-level moisture and low-pressure anomalies, which are good for cyclogenesis. On the other hand, suppressed phases (P6–P8) have positive outgoing longwave radiation, dry air in the middle troposphere, and high-pressure anomalies, which make it harder for TCs to form. While VWS is predominantly favorable during both active and inactive phases, thermodynamic and convective factors principally regulate the modulation of TC activity. The simultaneous presence of active MJO phases with positive Indian Ocean Dipole (pIOD) and neutral or El Niño conditions markedly increases TC frequency, highlighting a combined influence link between interannual–El Niño–Southern Oscillation (ENSO) and IOD– and intraseasonal (MJO) variability. Additionally, the association between MJO and the Indo-Pacific Warm Pool (IPWP) reveals that TC activity peaks during convectively active MJO phases under the second twenty years of this study, emphasizing the influence of large-scale oceanic warming on TC variability. These findings underscore the critical function of the MJO in regulating TC activity variability in the Arabian Sea and stress its significance for enhancing intraseasonal forecasting and disaster preparedness in the area. Full article

Simulated impact multipath (SIM) occurs when forward operators propagate Global Navigation Satellite System (GNSS) radio occultation (RO) signals through strongly nonspherical atmospheric structures, producing multivalued bending angles that cannot be assimilated directly. In this study, the relationships between SIM and planetary boundary layer (PBL) structures were quantified using COSMIC-2 RO observations and ERA5 reanalysis during two periods (January and July 2022). The results show that SIM affects ~36% of RO profiles, with more than 70% of cases occurring within 0.5 km above the diagnosed PBL top. By defining the simulated impact multipath height (SIMH) as the first detection level of SIM, we found that discarding data below the SIMH reduces bending angle biases by more than half and substantially decreases their scatter. These results provide direct physical evidence linking SIM to strong vertical gradients near PBL structures and establish a quantitative basis for simple, effective quality control, thereby improving weather prediction, particularly in the data-sparse tropical lower troposphere. Full article