To address the scarcity of observational data constraining in-depth research on topographic precipitation, the Cangshan Mountain Precipitation Experiment (CAMPEX) was launched by the Chinese Academy of Meteorological Sciences and Yunnan Provincial Meteorological Bureau. Focused on the quasi-north–south-oriented Dali Cangshan Mountain, a region with a pronounced precipitation enhancement effect in Southwest China, CAMPEX aims to elucidate how steep meso-γ-scale terrain modulates large-scale systematic precipitation and locally triggered convection. For this purpose, three core observation zones across the mountain peak and the eastern and western valleys are equipped with X-band and Ka-band radars, microwave radiometers, disdrometers, Doppler wind lidars, and other advanced instruments. The 5-yr experiment has completed 2 intensive observing periods to date (20 June–31 October 2024 and 1 May–31 October 2025), capturing 17 systematic precipitation events and 14 locally triggered convective processes under dominant weather conditions such as shear lines, typhoon remnants, frontal systems, southern branch troughs, and weak synoptic-forcing scenarios. Advances in cross-instrument quality control, multi-radar mosaics, and three-dimensional (3D) wind-field retrieval over complex terrain, have enabled comprehensive monitoring of precipitation evolution, filling previous radar blind zones. The campaign has produced high-resolution datasets including 3D winds, thermodynamic profiles, and cloud microphysics. Another distinct feature of CAMPEX is a hectometer-resolution numerical forecasting system designed particularly for the Cangshan region, operating synchronously with the field campaign. Preliminary analysis confirms the reliability of the acquired data and has provided potential insights into how the Cangshan terrain enhances precipitation of different types. This paper outlines the design and implementation of CAMPEX, and presents the dataset and early scientific findings obtained thus far.
There exist significant diurnal asymmetries in the physicochemical properties of atmospheric aerosols and their environmental and climatic effects. Such asymmetries stem from the diurnal variations of emission sources, the fundamental transformation of the thermal structure of the planetary boundary layer, and the alternating dominance of photochemical and heterogeneous chemical processes. However, traditional observational paradigms face enormous technical bottlenecks at night, resulting in a “half-day blind spot” in understanding the aerosol life cycle. This review systematically summarizes the capabilities and limitations of ground-based in-situ observations, active remote sensing, satellite measurements, and numerical models in revealing the mechanisms of diurnal aerosol variations. We point out that the current technical system suffers from a “diurnal–nocturnal separation” issue: ground-based point observations lack vertical dimensionality, lidar signal-to-noise ratio degrades during daytime, and passive satellite remote sensing fails at night due to the absence of light sources. This separation limits our ability to accurately assess the net radiative forcing of aerosols and their feedback effects on the evolution of the planetary boundary layer. Finally, we prospectively demonstrate the revolutionary potential of artificial intelligence (AI) technologies, particularly physics-informed machine learning and deep learning, in fusing multi-source heterogeneous data, reconstructing all-weather three-dimensional aerosol fields, and identifying key processes. By constructing an “AI-defined” intelligent observational network, we expect to achieve a cognitive leap from “phenomenon” to “mechanism” in aerosol research, providing unprecedented scientific support for climate prediction and pollution control.
Current data-driven weather forecasting models demonstrate superior performance yet remain dependent on reanalysis or analysis fields from numerical models, inheriting their systematic errors. To address this constraint, we present FengYuan, an end-to-end global weather forecasting model that directly processes multi-source observational data to generate forecasts. FengYuan employs a modular architecture with two components: a data assimilation module (FengYuan-DA) that integrates observational data from satellites, surface stations, and radiosondes with background fields, and a forecasting module (FengYuan-Forecasting) that generates medium-range global forecasts. The system uses a staged training strategy, first training the FengYuan-Forecasting module on ERA5 reanalysis data, then iteratively optimizing the FengYuan-DA module using the forecasting module outputs. Evaluation on the 2022 test set shows that the FengYuan-Forecasting module achieves performance comparable to state-of-the-art Artificial Intelligence (AI) models and significantly outperforms ECMWF’s Integrated Forecasting System (IFS), with enhanced capabilities for regional forecasting over East Asia. The FengYuan-DA module achieves substantial improvements, showing remarkable spatial consistency with ERA5 reanalysis, low global root-mean-square errors, and effective assimilation of multi-source observational signals. The complete end-to-end system of FengYuan, initialized with FengYuan-DA analysis fields, achieves forecast quality very close to the reanalysis-driven version while consistently outperforming IFS throughout the 10-day forecast period, demonstrating that FengYuan successfully bridges observations to forecasts while maintaining high accuracy.
The China Meteorological Administration (CMA) Chemistry–Weather Coupled Model (CMA-CW), combined with a two-option chemical data assimilation (DA) module and an anthropogenic emission processing module, has been developed for operational prediction of fog–haze and sand and dust (SD) storms (SDSs). The chemistry–weather (CW) two-way feedback is realized through aerosol–cloud–radiation interactions. The fog–haze and SD predictions are evaluated and compared with those by CMA Unified Atmospheric Chemistry Environment model (CUACE) and Asian Dust Aerosol Model 3 (ADAM3) models based on the WMO platform. Analyses show that the 24–72-h threat scores (TSs) of PM2.5 and visibility from CMA-CW are obviously higher than those from CMA-CUACE, especially for the high PM2.5 and low visibility levels. Spatiotemporal variations of PM2.5 and visibility in some subregions, e.g., Northeast China (NEC), Beijing–Tianjin–Hebei (Jing–Jin–Ji or JJJ), Sichuan basin (SCB), Yangtze River Delta (YRD), and Pearl River Delta (PRD), demonstrate that CMA-CW successfully reproduces most regional fog–haze events and basically captures the peaks, troughs, and variation timings of PM2.5 and visibility. The prediction accuracy for fog–haze processes is about 82
In 2024, the China Meteorological Administration (CMA) set up a strategic objective—to build a global science and technology (S T) powerhouse in meteorology—and since then a series of related initiatives has been launched. To guide the high-quality development of the meteorological enterprise, this strategic objective highlights an urgent need for a clear indicator system to assess meteorological S T capacity and to optimize innovation stimulating mechanisms. By analyzing international trends and integrating the recent achievements in S T powerhouse theories in fields such as space science in China, this study constructs a comprehensive indicator system based on scientific rigor, systematicity, representativeness, comparability, and operability. The system is structured across five dimensions, including “basic research and original innovation”, “breakthroughs in key core technologies”, “international influence and leadership”, “cultivation and aggregation of high-level talent”, and “S T governance systems and capabilities”, in alignment with the CMA mission of “precise monitoring, accurate forecasting, and refined services”. The validity of this system is evaluated through a comparative analysis of the meteorological S T capabilities of China, Europe, and the United States. Preliminary results indicate that the proposed indicator system effectively reflects the S T innovation capacities of major global meteorological players, demonstrating significant application potential. Furthermore, it helps identify the current strengths and weaknesses in China’s meteorological S T development. This provides a basis for concentrating efforts on high-quality development and offers a strategic reference for achieving the goal of becoming a meteorological S T powerhouse at an early stage.
A thermally driven “Super Vortex” circulation often develops over the Qingzang Plateau (QZP; namely, the Qinghai–Xizang Plateau), which is characterized by cyclonic convergence in the lower troposphere and anticyclonic divergence in the upper troposphere, a structure dynamically analogous to the conditional instability of the second kind (CISK) mechanism in tropical cyclones. Meanwhile, the latent heat released by cloud and precipitation also constitutes a primary component of the QZP’s thermal forcing, reinforcing a self-excited positive feedback that maintains this “Super Vortex” dynamic structure. South Asia–sourced aerosols are transported to the Mount Qomolangma (MQ) region, which are closely associated with the “Super Vortex” dynamic structure. These aerosols and the apparent heat source generate a pronounced cloud–precipitation activation effect, thereby enhancing deep convection over the complex terrain of the QZP. The strong buoyancy and shear terms in the QZP boundary layer, induced by inhomogeneous low-level air density over the large-scale terrain, also possess an “efficient” convection triggering capability. From a global perspective, the large-scale terrain boundary layers exhibit general universality in modulating the cloud and precipitation triggering processes. The warming–wetting trend over the QZP and its associated convective activity are closely associated with atmospheric moisture cycling and energy transport arising from trans-hemispheric air–sea interactions. Sea surface temperatures (SSTs) in key oceanic regions of both hemispheres exhibit coherent interdecadal variability with that of low cloud cover (LCC) over the QZP. Furthermore, Rossby wave sources triggered by SST variations over these regions exert non-negligible impacts on the upper-tropospheric circulation of the “Super Vortex” dynamic structure and its convective activity. Deep convective systems that frequently occur over the QZP further enhance the vertical transport efficiency through the “chimney effect”, rapidly conveying energy and moisture into the mid- and upper troposphere. In doing so, they constitute a critical component of the global atmospheric energy and moisture cycles, highlighting the global influence of the QZP “Super Vortex” dynamic structure on atmospheric moisture cycling and climate variability.
Current subseasonal forecasting of extreme temperature in China faces challenges due to relatively low capability of the subseasonal-to-seasonal (S2S) models. Benefiting from ensemble forecasting, the extreme forecast index (EFI) is an effective approach to tackling the challenge; however, it usually requires abundant ensemble members (i.e., dynamic models), while individual S2S models usually have a limited sample of members that constrain their performance in constructing EFI. To address this issue, this study proposes a novel subseasonal EFI approach for temperature utilizing the S2S multi-model ensemble (MME) to provide a much larger ensemble size. This approach, applied to both a bias-corrected MME (MME-C) and a direct MME (MME-D), is evaluated for high temperature scenarios in comparison with the ECMWF single model forecasts. The results show that MME-C, by removing the drifted climatology of individual model and replacing it with observed climatology, can maximally use the unbiased large-sample ensemble information to decently construct EFI. For verification of subseasonal forecast of extremely high temperatures over China, both MME approaches for the EFI construction outperform the ECMWF single model, e.g., threat score (TS) of MME-C can reach 0.52 for 27-day forecasts, and MME-C achieves overall higher skills than MME-D. The decreased skills with lead time exhibit variations across different regions and both of the MME approaches show higher temporal correlation coefficient (TCC) skills in the northern regions such as Northwest and Northeast China and achieve smaller errors across the entire China. Using larger-sample MME information, the MME-C approach proves to be an effective tool for improving subseasonal early warnings of extreme high-temperature risks.
The Ensemble Prediction System (EPS) provides reliable precipitation forecasts. However, constrained by computational resources, its relatively coarse spatial resolution directly limits its capability to predict high-impact severe rainfall events. Given that downscaling to super-resolution offers a computationally efficient and highly practical solution to enhance forecast resolution, this study develops a Self-Attention-Enhanced Convolutional Neural Network (SAECNN) for downscaling coarse ensemble forecasts of precipitation over North China, an area that has frequently experienced severe rainfall in recent years. The SAECNN integrates a self-attention mechanism and inception-style module. It is trained through a two-step process using pairs of high-resolution (HR) and low-resolution (LR) precipitation data under a composite loss function. The model is trained by using 3-h accumulated summer precipitation data from 2010 to 2019 obtained from the ECMWF ERA5-Land reanalysis dataset. Subsequently, taking the Global Ensemble Prediction System of ECMWF (ECMWF-GEPS) as an example, the SAECNN is applied to the LR GEPS to generate HR precipitation ensemble forecasts. Ablation experiments demonstrate that the combination of Huber loss and mean absolute error with minimized missed rate, along with the two-step training strategy, effectively reduces forecast errors. Independent validation against bilinear-interpolated forecasts of the ECMWF-GEPS during 2020–2021 demonstrates that SAECNN yields realistic and detailed precipitation forecasts, reducing the probabilistic forecast bias (ranked probability score) by 8
The Arctic is a critical region for global climate change, where temperature variations profoundly influence sea ice dynamics and atmospheric circulation. Harsh conditions limit in-situ observations in the Arctic, making reanalysis datasets indispensable, while the reliability of these reanalysis products requires systematic validation. This study evaluates the performance of the China Meteorological Administration Global Atmospheric Reanalysis (CMA-RA) and the ECMWF Reanalysis v5 (ERA5) in representing 2-m air temperature (Ta) and surface temperature (Ts) against in-situ observations from a total of 1658 buoys of the International Arctic Buoy Programme (IABP) and Cold Regions Research and Engineering Laboratory of the United States (CRREL) during 2001–2024. The results demonstrate strong consistency between the two reanalysis products and buoy observations, with correlation coefficients (R) of ⩾ 0.94 for Ta and ⩾ 0.88 for Ts in reproducing seasonal and interannual variations. For Ta, CMA-RA outperforms ERA5 under extreme cold conditions but exhibits a cold bias in long-term simulations (−0.30 ± 1.50°C), whereas ERA5 shows a warm bias (1.13 ± 2.28°C). Both CMA-RA and ERA5 yield high correlations with buoy observations across Arctic subregions. For Ts, both CMA-RA (−4.15 ± 3.55°C) and ERA5 (−2.52 ± 3.21°C) display cold biases, with the largest deviations occurring in winter (< −4°C) and the smallest in summer (> −1.6°C). The relatively higher Ts observed by buoys reveals consistent cold biases in CMA-RA and ERA5, suggesting that model physics, surface parameterizations, and data assimilation strategies jointly contribute to these systematic discrepancies. This study provides a comprehensive spatial evaluation and a relatively long continuous temporal assessment of the performance of CMA-RA and ERA5 over the Arctic to date.
Low-level atmospheric turbulence impacts urban air quality and poses substantial risks to aviation safety, underscoring a critical demand for reliable short-term early warning. Radar wind profilers (RWPs) provide continuous vertical observations of wind fields, emerging as a key tool for low-level turbulence monitoring. However, existing turbulence forecasting relies mostly on diagnostic or retrieval approaches, which are often prone to systematic biases and not tailored for short-term operational early warning. In this study, we develop an operational early-warning model for low-level turbulence using a data-driven machine learning approach, leveraging RWP observations. Drawing on long-term observations at Shiyan Station in Shenzhen of Guangdong Province, we integrate RWP measurements with estimates of in situ turbulence dissipation rate (ε), as derived from ultrasonic anemometers installed at 160 and 320 m on a meteorological tower. A random forest model is trained to predict the mean ε in the subsequent 30 minutes, with predictors consisting of preceding half-hourly averaged RWP variables. The results demonstrate that the proposed model effectively captures the nonlinear relationship between RWP observations and ε, delivering robust predictive performance. The reliability of model exhibits a clear dependence on atmospheric and seasonal conditions. Under clear-sky scenarios and during autumn and winter, the model achieves enhanced stability, with correlation coefficients exceeding 0.7. In contrast, prediction uncertainty increases under complex conditions characterized by low wind speeds and weak turbulence signals. Further error analysis reveals systematic associations between prediction errors and observed spectral width, wind speed, and true turbulence intensity magnitude. Notably, applying threshold constraints to observed spectral width further improves the reliability of model predictions. This study provides a promising pathway to advancing turbulence monitoring and short-term forecasting in complex urban environments, with direct implications for low-level aviation safety.
Reasonable exploration and utilization of cloud water resources (CWR) provide a key approach to solve water security challenges. Using the 1° × 1° diagnostic cloud water resource dataset for China (i.e., CWR-DQ V1.0, 2000–2019), alongside the Northern Hemisphere polar vortex intensity index (NHPVI), the western Pacific subtropical high ridge position index (WPSHRP), and the ERA5 reanalysis data, this study systematically investigates the spatiotemporal evolution of CWR over the North China Region (NCR) and associated underlying dynamical mechanisms. Beyond the influence of complex mountainous terrain, a pronounced “seesaw effect” between the NHPVI and WPSHRP is found to regulate CWR variability. Correlation analysis reveals a significant synchronous negative correlation between CWR and NHPVI (−0.66), with positive correlation being the maximum when CWR leads WPSHRP by 2 months (0.58), and negative correlation being the largest when CWR lags WPSHRP by 5 months (−0.63). A stronger western Pacific subtropical high (WPSH) coupled with a weaker polar vortex (PV) in summer favors higher CWR, whereas a weaker WPSH coupled with a stronger PV in winter leads to lower CWR. The seesaw effect acts via anomalous atmospheric circulations: a stronger and northward-shifted (weaker and southward-shifted) WPSH combined with a weaker (stronger) PV induces anomalous southeasterly (northwesterly) winds over the NCR, favoring higher (lower) regional CWR. A case study in 2010 further confirms this mechanism. Collectively, PV, WPSH, wind fields, and topography shape the dynamic framework modulating CWR spatiotemporal evolution over the NCR. These findings improve the understanding of CWR variability and its driving mechanisms, and support the optimization of cloud seeding strategies to relieve regional water scarcity.
The Southwest Vortex (SWV) is a major meso-α-scale system responsible for heavy rainfall over the Sichuan basin, yet the role of dust aerosols—particularly their ice-nucleating (IN) effects—in modulating SWV precipitation remains poorly understood. In particular, whether and how such effects vary across different radial regions and evolutionary stages of the vortex has not been systematically examined. Clarifying this structural dependence is critically important for reducing uncertainties in heavy-rainfall forecasts over complex terrain regions of Southwest China, where long-range transported dust frequently intersects with SWV activities but is largely overlooked in operational models. Using the Weather Research and Forecasting Model version 4.3 (WRF v4.3) with the Thompson aerosol-aware microphysics scheme, we simulate a quasi-stationary SWV event that occurred on 12–13 August 2020. Three sensitivity experiments—LowDust, MediumDust, and HighDust—are designed based on the 20-yr August climatology of the the ECMWF Copernicus Atmosphere Monitoring Service (CAMS) global reanalysis (2003–2022), with hydrophilic cloud condensation nuclei (CCN) concentrations held constant across all runs to isolate the pure IN effects of dust. The vortex domain is divided into inner (0–60 km), middle (60–120 km), and outer (120–180 km) radial zones to assess structural dependencies. Precipitation responses to increasing dust loading exhibit strong spatial heterogeneity and non-monotonic behavior. During the mature stage, as dust increases from Low to Medium, precipitation in the inner circle rises from 0.47 to 0.67 mm (10 min)−1, while it declines in the middle and outer circles. Further increasing dust from Medium to High reverses this trend. During the decaying stage, inner-circle precipitation decreases monotonically, whereas the middle and outer circles show an initial increase followed by a decrease. These disparities are governed by a vortex dynamic “competitive effect” that redistributes liquid water among regions according to local convective intensity, thereby altering snow/graupel production, latent heat release, and ultimately precipitation distribution. Dust IN effects on microphysics and precipitation are strongly dependent on convective intensity and vortex structure, becoming negligible during the dissipating stage. This structural dependence provides a process-level framework for understanding aerosol–precipitation interactions in mesoscale vortex systems and offers a physical basis for improving their representation in regional numerical weather prediction models.
Tropical cyclones (TCs) in the Bay of Bengal (BoB) significantly influence precipitation anomalies over the Qinghai–Xizang Plateau (QXP) in early summer and autumn. During midsummer (July–August), TCs rarely occur due to the strong environmental vertical wind shear, while cyclonic vortices (CVs) remain active over the BoB region. How midsummer CVs influence QXP rainfall remains poorly understood. This study investigates midsummer QXP precipitation anomalies and their linkage with BoB CVs based on daily precipitation data from 108 stations on the QXP and hourly ECMWF reanalysis 5 (ERA5) data with 0.25° resolution from 1980 to 2023. The results show that precipitation amount and intensity, extreme precipitation amount and frequency, and maximum daily precipitation amount in midsummer increase significantly in the northeastern QXP but decrease insignificantly in the Hengduan Mountains area. BoB CVs primarily occur in BoB coastal areas north of 15°N, presenting a deep vertical structure extending into the upper troposphere during midsummer. The CV frequency is positively (negatively) correlated with precipitation amount in the northeastern QXP (southern QXP), while its intensity shows a roughly opposite correlation pattern. In other words, QXP precipitation distribution patterns exhibit certain similarities between years with high (low) CV frequency and weak (strong) intensity. Composite analysis reveals that during high (low) CV frequency years, the vortex circulation extends northwestward (southeastward) to facilitate the transport of water vapor associated with the CV towards the northeastern (southern) QXP. During strong (weak) intensity years, the western Pacific subtropical high (WPSH) retreats eastward (extends westward), resulting in warm moist air related to the CV convergence over the southeastern QXP (northeastern QXP) region. This highlights the key role of BoB CVs in modulating QXP midsummer precipitation anomaly. The increasing (decreasing) trend in the CV frequency (intensity) from 1980 to 2023 may contribute to the abnormal increase in midsummer rainfall amount over the northeastern QXP.
Rapid environmental changes over the Qinghai–Xizang Plateau (QXP) demand improved knowledge of formaldehyde (HCHO) distributions, but observational constraints have left their spatiotemporal patterns largely unexplored. This study performed ground-based remote sensing measurements of HCHO using the multi-axis differential optical absorption spectroscopy (MAX-DOAS) technology from August 2021 to March 2023 in Lhasa (29.66°N, 91.14°E; 3552.5 m above sea level). The tropospheric HCHO vertical column densities (VCDs) were retrieved, their temporal variations and relationship with surface wind, air temperature, and local Sang offering activity were investigated, and the corresponding Tropospheric Monitoring Instrument (TROPOMI) satellite product was validated. It is found that the seasonal variation patterns of tropospheric HCHO VCDs presented two peaks, one in summer and the other in winter. The diurnal variation patterns of tropospheric HCHO VCDs during the daytime presented a “W” shape in spring and summer, and a “U” shape in autumn and winter. No significant changes in the levels and variation patterns of tropospheric HCHO VCDs were found during the Coronavirus Disease 2019 (COVID-19) lockdown period compared to those in a normal year. The dominant transport path for HCHO in Lhasa was along the river valley. The trends of HCHO variations with air temperature were opposite between cold and warm conditions. On average, the local Sang offering activities enhanced the HCHO levels in Lhasa. TROPOMI did not capture the significant seasonal variations of tropospheric HCHO VCDs observed by multi-axis differential optical absorption spectroscopy (MAX-DOAS). Overall, these results are beneficial to air pollution control and satellite validation over the high and complex QXP.
The Yunnan–Guizhou quasi-stationary front (YGQSF) governs sharp weather contrasts on its two sides over Southwest China in winter. Its movement is closely related to synoptic-scale weather on the daily timescale, but exhibits significant diurnal variations. Accurately quantifying the frontal movement is vital to improving weather forecasts in this area. Based on the fifth generation ECMWF reanalysis (ERA5) data for a chosen region of high frontal activity, this study proposes a new method using the meridionally averaged potential temperature gradient to quantify frontal movement and investigate the YGQSF characteristics in its daily westward-moving (WM) and eastward-moving (EM) processes. The results show that both the WM and EM fronts exhibit a consistent diurnal cycle: eastward movement during 0800–1600 Beijing Time (BJT), followed by westward movement thereafter. For WM fronts, the large-scale circulation usually features cold-air outbreaks, with prevailing easterly winds and cold advection over Southwest China. Stronger cold-air advection weakens the eastward movement and amplifies the subsequent westward movement. Consequently, the front exhibits net westward propagation on the diurnal timescale. For EM fronts, the large-scale circulation pattern at 850 hPa is opposite to that of WM fronts. This pattern presents a weakening cold-air intrusion, and the EM fronts often appear following the cold-air outbreak. Additionally, EM fronts generally exhibit weaker intensity and a more westward location. Compared with those of WM fronts, the cloud cover on both sides of EM fronts is lower, resulting in stronger radiative heating of the cold air east of the fronts before 1600 BJT. Warm advection at frontal locations further promotes the eastward movement of EM fronts. Together, these factors lead to a net eastward retreat of YGQSF on the daily timescale. In sum, WM fronts are dynamically driven by large-scale cold advection, whereas EM fronts are thermodynamically driven by local cloud-radiative heating. Forecasting of frontal movements must distinguish between synoptic-scale dynamical forcing and local diurnal thermal contrasts.
Accurate precipitation forecasting over the Qinghai–Xizang Plateau (QXP) remains a major challenge in numerical weather prediction. During 3–4 September 2024, extreme rainfall struck the Hehuang Valley in the northeastern QXP, with hourly precipitation exceeding 60 mm and daily records broken. Both global and regional models failed to capture the intensity and location of this event, motivating a diagnostic investigation into sources of the simulation error. This study uses a 3-km convection-permitting WRF simulation verified against surface observations, satellite precipitation, ERA5, and radar data. Mesoscale convective systems (MCSs) are tracked by using the PyFLEXTRKR algorithm. Three sets of sensitivity experiments are conducted: wind nudging toward ERA5 to isolate the role of winds from low-level moisture transport, a 31-member WRF ensemble to identify bias sources, and an initial wind-field replacement between a poorly-performing and a better-performing ensemble member. The model primarily failed to reproduce the observed northeastward-moving MCS, linked to a deficient mid-tropospheric southwesterly flow. Successful ensemble members systematically exhibited a stronger southwesterly, with a more eastward-extended westerly trough and a deepened mesoscale low. The initial wind-field replacement experiment confirmed that correcting the initial large-scale wind field recovered the southwesterly steering flow and significantly improved the precipitation simulation. By contrast, artificially enhancing the southeasterly moisture transport via wind nudging toward ERA5 only marginally improved the rainfall and did not correct the MCS track, demonstrating that the southeasterly wind bias was not the primary error source. The enhanced southwesterly flow steered the MCS into the Hehuang Valley, where orographic and frontal lifting, along with abundant moisture, jointly intensified convection. The simulation failure is mainly attributable to a deficient mid-tropospheric southwesterly steering flow originating from errors in the initial large-scale wind field rather than to an underestimation of southeasterly moisture transport. Accurate representation of this steering flow is therefore essential for forecasting extreme precipitation in complex terrain. The findings highlight the value of ensemble-based sensitivity diagnostics and suggest that targeted assimilation of upper-air wind observations could improve operational precipitation forecasts over the northeastern QXP.
Urbanization and atmospheric aerosols individually influence fog, yet their synergistic effects and coupled feedback mechanisms remain poorly quantified. This study aims to systematically disentangle the thermal, dynamic, and aerosol-radiative contributions of urbanization and pollution to a persistent dense fog event, and to elucidate the positive feedback loop connecting urban development, pollutant emissions, fog evolution, and boundary layer processes. A heavy fog episode over the Yangtze River Delta during 23–30 November 2018 was simulated by using the Weather Research and Forecasting model with Chemistry (WRF-Chem) over three two-way nested domains (finest resolution: 3 km) and 40 vertical levels. Four sensitivity experiments were designed against a baseline simulation (BASE): replacing urban with cropland (CTL1), turning off anthropogenic heat (CTL2), reducing building height (CTL3), and switching off aerosol radiative effects while retaining urban surfaces (CTL4). The model was validated against surface observations from eight national stations and ERA5 reanalysis, showing correlation coefficients of 0.85 for both 2-m temperature and relative humidity. The results show that urbanization significantly suppresses fog development. Relative to suburbs, the urban heat island weakens the nocturnal near-surface inversion by 0.3°C and elevates the mean planetary boundary laye height (HPBL) by 20 m. Consequently, urban areas exhibit lower nocturnal relative humidity (94
During the boreal summer, the westerlies of the South Asian monsoon prevail over Southwest China, flowing perpendicularly to the north–south-oriented mountains. However, the initiation mechanism of nocturnal convection under weak synoptic forcing in this region remains unclear. This study combines radar observations, convection-resolving Weather Research and Forecasting (WRF) simulations, and terrain sensitivity experiments to investigate the influence of low-level winds and complex terrain on nocturnal convection initiation (CI) around Cangshan Mountain, a typical north–south-oriented range in Southwest China. Diagnostic analysis of vertical velocity acceleration indicates that CI is associated with persistent dynamic forcing and rapid enhancement of thermal buoyancy forcing, and the terrain oriented perpendicular to the flow plays a crucial role in this process. Thermodynamically, the topography of Cangshan Mountain depresses the level of free convection (LFC) and lifting condensation level (LCL) to below 700 hPa via moisture accumulation and enhanced instability in the western valleys, resulting in pronounced thermal asymmetry between the eastern and western flanks of the mountain. Dynamically, low-level winds influenced by Cangshan Mountain and a mountain range to its west generate persistent convergence within the thermally favorable environment, which induces ascending motion above both the LFC and LCL. During the hour before CI, the LFC within the western valleys gradually decreases, while the convergence above the LFC and LCL persistently intensifies, forcing the maintenance and enhancement of ascending motion. This process simultaneously promotes water vapor condensation, thereby enhancing thermal buoyancy forcing and ultimately leading to CI. Terrain sensitivity experiments reveal that scaling down the Cangshan Mountain elevation weakens its topographic barrier effect, raising the LFC and reducing low-level convergence at the CI location, thereby suppressing CI. This study identifies a terrain-elevated convection mechanism, where parallel mountains lower the LFC thermodynamically while raising the convergence layer dynamically. The proposed mechanism provides a reference for improving convection forecasting in complex terrain under weak synoptic forcing.
Quasi-linear convective systems (QLCSs) frequently produce severe weather in North China, with multiple QLCSs often forming successively within a short period. However, their formation mechanisms under such conditions remain unclear. This study investigated the formation mechanisms of three successive QLCSs that affected Beijing and its vicinity from the afternoon to evening on 12 June 2022, using in-situ and remote sensing observations as well as high-resolution Weather Research and Forecasting model simulations with a 9-, 3-, 1-km nested grid. The event occurred within the trough of a mature cold vortex, which induced dry westerlies over the western mountainous areas and moist southerlies over the eastern plains of North China. Diurnal solar heating further intensified the zonal thermal and moisture contrasts between the eastern and western domains. Therefore, a southwest–northeast-oriented dryline with a dynamical confluence persisted along the eastern Taihang Mountains. To the west of the dryline, sustained boundary layer turbulent activities eliminated convective inhibition. Meanwhile, episodic weak cold advections induced by the cold vortex modestly increased the convective available potential energy over the northern mountains, providing energy for the generation of convective cell clusters (CCs). These sequentially initiated CCs propagated southeastward under the steering airflow, with distinct outflow boundaries forming at their leading edges. Crucially, the continuous merging between the southeastward-moving CC outflow boundaries and the quasi-stationary dryline strengthened the low-level convergence both in depth and intensity along segments of the dryline zone. The intensified dynamical lifting ultimately triggered convection along these segments, corresponding to the successive formation of three QLCSs. This paper highlights the synergistic effects of cold-vortex forcing, complex-terrain modulation, and dryline–outflow boundary interactions on the formation of organized convection over mountainous areas. The findings benefit the forecasting and early warning of similar severe convective events in North China.
The operational utility of S-band weather radars—the backbone of the China New Generation Weather Radar (CINRAD) network—is severely compromised by ground clutter from dense high-rise buildings and surrounding mountainous terrain in megacities. Although over 170 units have been upgraded to dual-polarization, the extent to which such clutter systematically degrades data quality, hydrometeor classification, and quantitative precipitation estimation (QPE) has not been quantified across diverse urban environments. Meanwhile, X-band radars are being deployed extensively as gap-fillers, yet a systematic comparison of their clutter susceptibility relative to S-band systems—and whether dense X-band networking can actively compensate for S-band observational deficits—remains absent. Addressing this knowledge gap is critical for optimizing multi-band collaborative observation strategies and improving severe weather nowcasting in densely populated metropolitan areas. In this study, observations from 25 S-band and 50 X-band radars during the 2024 flood season in Beijing, Hangzhou, and Guangzhou are analyzed. A long-term statistical averaging method is applied to accumulated data from large-scale precipitation events to isolate systematic clutter signatures from random precipitation variability. The study systematically compares the ground clutter impact characteristics between S-band and X-band radars deployed across three Chinese megacities, and quantitatively evaluates the mitigation efficacy of dense X-band radar networking in clutter-affected regions of S-band radars. It is found that for S-band radars, dual-polarization anomalies at low elevations account for 35
