Hebei Meteorological Bureau

Abstract. Cloud vertical structure plays a central role in regulating Earth’s radiation balance and hydrological cycle, yet it remains poorly represented in weather and climate models due to limited high-resolution observations. Using a newly established national network of 80 Ka-band cloud radars, we provide the first high-spatiotemporal-resolution characterization of cloud vertical structure across China for 2024 and quantify its thermodynamic, dynamical, and land-surface controls. An improved retrieval algorithm accounting for height-dependent radar sensitivity and clutter suppression is applied to derive cloud boundaries. The national annual mean cloud occurrence frequency is 56.7 %, dominated by single-layer clouds (34.7 %), followed by two-layer (14.7 %) and multi-layer clouds (7.1 %). Single-layer clouds prevail over arid northwestern China, whereas multi-layer clouds are more frequent in humid southeastern regions. Cloud base height exhibits strong seasonality, with higher values in summer and lower values in winter, and distinctly lower bases over the Tibetan Plateau. Diurnally, summer clouds show a pronounced afternoon peak between 3 and 9 km, while winter clouds are mainly confined below 3 km with a near-sunrise maximum. Thermodynamic conditions exert primary control on cloud vertical development. Higher low-level humidity favors deeper clouds and higher tops, whereas stronger lower-tropospheric stability suppresses vertical growth. Wind shear generally limits cloud depth, though moderate shear may enhance organization under unstable conditions. Land-surface characteristics further modulate cloud base height, with higher bases over barren land and lower bases over forests. These results provide national-scale observational benchmarks for improving cloud parameterizations in numerical models.

<strong class="journal-contentHeaderColor">Abstract.</strong> The Statistical Uncertainty analysis-based Precipitation mERging (SUPER) methodology can optimally merge different precipitation datasets with minimal use of ground-based information and is therefore better suited for data-sparse regions. Although a proof-of-concept SUPER framework has already been introduced previously, it contains substantial uncertainties and is only available at a daily timescale, which is inadequate for land surface modeling. In response, we present here a new 3-hourly, 0.1-degree, global SUPER version 2 (v2) dataset, spanning 2000&ndash;2023. SUPER v2 is unique in three key aspects: i) it optimizes the number of input precipitation datasets, which reduces data redundancy and mitigates negative biases in extreme precipitation events; ii) it optimally evaluates its internal merging weights and filters out false-alarmed events without reliance on extensive gauge networks; and iii) it employs a multi-scale (i.e., monthly&ndash;daily&ndash;3-hourly) temporal correction/merging procedure that enhances the robustness of precipitation estimates. The SUPER v2 product is comprehensively evaluated using 5,972 independent gauges. Results show that it has a root-mean-squared-error of 3.64 mm d^&minus;1and correlation coefficient of 0.68 [-] for daily precipitation estimates. These error metrics outperform traditional approaches over 81 % to 86 % of the validation gauges. The superiority of SUPER v2 with regards to rain/no-rain classification skill is even more evident, with Heidke&rsquo;s Skill Score 22 % higher than commonly used datasets. Similar findings are also demonstrated in the 3-hourly SUPER v2 precipitation dataset. As such, SUPER v2 provides a unique opportunity for enhancing global-scale hydrology and land surface modeling &mdash; particularly for data sparse regions.

<strong class="journal-contentHeaderColor">Abstract.</strong> Agriculture on the North China Plain (NCP), home to over 300 million people, heavily relies on groundwater extraction to feed its irrigation systems. This has created a large groundwater depression zone up to 80 m deep, severely limiting sustainable groundwater extraction and crop-production. Effective recharge is essential to restore this depleted zone and secure future sustainability. Few studies, however, have quantified recharge delays and efficiencies in deep vadose zones with complex lithology. Here we simulated infiltration times and percolation velocities in the Ningbailong groundwater depression zone, a typical overexploited site in the NCP using HYDRUS-1D with measured borehole lithology and hydro-meteorological data. Two infiltration modes were considered: precipitation-fed and riverbed infiltration. Spatial distributions of infiltration times and percolation velocities were obtained, and recharge efficiencies were compared between these two infiltrations. Results showed that times for precipitation-fed recharge averaged 446 days and varied with lithology and thickness, from 10 days in western Taihang foothills (dominated by coarse sands) to 1,395 days in central/eastern plains (finer clays and loams). Riverbed recharge was markedly faster, averaging 91 days, indicating higher efficiency than precipitation under equivalent lithological conditions. Regression equations were derived to predict percolation velocities from vadose zone thickness and soil particle fractions. These findings elucidate how vadose zone thickness and lithology amplify recharge lags and control recharge efficiency. They also highlight the potential for managed aquifer recharge strategies, such as constructing infiltration basins for flood capture, offering strategies to reduce groundwater over-exploitation in similar depression zones.

In situ formed synergistic effects of amorphous carbon and MgS catalyzed the hydrogen sorption properties of MgH 2 -CBD.

On September 19, 2023, a rare autumn tornado outbreak, consisting of three successive tornadoes (including one EF3), occurred in northern Jiangsu, China, causing significant damage. This study presents a comprehensive analysis of this event using multisource data, including Doppler radar, ERA5 reanalysis, and surface observations. We find that pretornadic mesoscale vortices were triggered and intensified during the eastward movement of the Jianghuai cyclone, along low‐level shear lines on the warm side of a quasi‐stationary mesoscale front. The synoptic environment was characterized by an anomalously strong and persistent subtropical high, which provided record‐breaking moisture (precipitable water &gt;66 mm) and instability (CAPE &gt;2200 J kg −1 ), coupled with extreme low‐level wind shear (&gt;18 m s −1 in 0–1 km). These conditions created a classic yet extreme setting for tornadic supercells. At the storm scale, the tornadoes formed during the merging and intensification of multicell thunderstorms. Radar analysis revealed that the two strongest tornadoes (EF2 in Nancai and EF3 in Funing) exhibited clear supercell characteristics, including hook echoes and tornado vortex signatures (TVSs). Tornadogenesis in both cases coincided with the peak values of low‐level differential velocity (LLDV exceeding 40 m s −1 ). The Funing EF3 tornado was further confirmed by a prominent tornado debris signature (TDS) in dual‐polarization radar data. A key advance presented here is the identification of a hybrid tornadogenesis mechanism. While the storms displayed classic supercell dynamics (tilting and stretching of vertical vorticity), the process was significantly enhanced by the amplification of pre‐existing horizontal vorticity along successive, evolving meso‐β‐scale convergence centers. This mechanism, which shares characteristics with both supercellular and landspout processes, explains the sequential development of this “tornado cluster.” Our results underscore that in high‐shear, high‐instability environments, detailed analysis of mesoscale boundaries are crucial for anticipating tornadogenesis. This study enhances the understanding of autumn tornado outbreaks in eastern China and provides insights for improving monitoring and warning strategies for such rare but high‐impact events.

Quantitative evaluation of artificial precipitation enhancement effects remains a critical challenge in meteorological science. Traditional methods rely on idealized assumptions and control area selection, with limited applicability under complex terrain conditions. To address this problem, this paper proposes physics‐informed simple video prediction (SimVP) (PiSim), a hybrid physics‐informed disentangled framework designed to disentangle precipitation evolution. By employing the advection–diffusion equation (ADE) as an inductive bias within the latent space, we transform spatial comparative evaluation into a precise spatiotemporal sequence prediction task. The method adopts a dual‐branch architecture: The physics branch explicitly models deterministic macroscopic motion (advection and diffusion), whereas the data‐driven branch learns complex nonlinear residuals, effectively compensating for microphysical processes and local variations. Experimental results on the Hubei Province Swan radar dataset demonstrate that PiSim achieves a 5.5% improvement in MSE compared to the SimVP baseline, with pronounced advantages in heavy precipitation forecasting. Evaluation of 10 typical artificial precipitation enhancement operations shows hourly net rainfall increments ranging from 0.16 to 4.60 mm, which are highly consistent with historical records, validating the method’s effectiveness.

<strong class="journal-contentHeaderColor">Abstract.</strong> Aerosol-cloud interactions profoundly influence the properties of supercooled liquid water, which in turn play a critical role in aircraft icing. However, accurately quantifying aerosol emission inventories and their spatiotemporal distributions remains a major challenge. In this study, the Thompson-Eidhammer aerosol-aware microphysics scheme is applied to an in-flight icing event over the high-aerosol-concentration environment of the Sichuan Basin, China. Three numerical experiments with different initial aerosol number concentrations are conducted: Default, Climatology, and Copernicus Atmosphere Monitoring Service reanalysis (CAMS), representing clean and polluted conditions. All three experiments successfully reproduce the synoptic-scale spatial distribution of supercooled liquid water. Compared with the clean environment, the polluted scenarios simulate higher supercooled liquid water mass mixing ratios, greater cloud droplet number concentrations, smaller median volume diameters, and longer cloud system lifetimes. The experiments also reveal that stronger auto-conversion process in clean conditions suppresses supercooled liquid water formation, whereas enhanced accretion process in polluted environments promotes supercooled liquid water depletion. Comparison with in situ aircraft observations indicates that, among the three numerical experiments, the CAMS experiment performs best in capturing high supercooled liquid water contents and large median volume diameters. These findings highlight the importance of real-time aerosol input for improving the simulation of aerosol-cloud interactions and supercooled liquid water characteristics.