Ground-based microwave radiometers (MWRs) are indispensable instruments for the continuous observation of atmospheric temperature and humidity profiles. The reliability of brightness temperature (TB) measurements and the accuracy of retrieved atmospheric profiles are fundamental to their effective use in both research and operational applications. In this study, we present a long-term dataset of multi-channel microwave brightness temperature observations and corresponding retrieved atmospheric profiles derived from the RPG-HATPRO MWR deployed at the Xianghe Integrated Observatory (XH) in Hebei Province, China, covering the period 2013-2022. Minute-level TB observations over the 10-year period were integrated with collocated infrared cloud detection data to establish a comprehensive dataset featuring detailed weather-related information. The quality of the observed TBs was carefully evaluated using a radiative transfer model. The results demonstrate excellent agreement between simulated and observed multi-channel TBs, with correlation coefficients typically exceeding 0.96 and mean biases within 1 K, confirming the stable and reliable performance of the XH MWR throughout the entire observation period. Based on the quality-controlled TBs, two retrieval schemes for atmospheric temperature and humidity profiles were developed using collocated radiosonde observations and ERA5 reanalysis data. For clear-sky conditions, an optimal estimation (OE)-based retrieval model was employed, whereas a deep neural network (DNN)-based model was designed for cloudy-sky retrievals. Validation against radiosonde measurements shows that both retrieval schemes achieved substantially improved accuracy up to 35 % for temperature and 25 % for humidity profiles compared with the retrieval approach provided by the manufacturer. Combining the two retrieval models with the 10-year quality-controlled TB dataset, we constructed a comprehensive data record characterized by decadal-scale coverage, high temporal resolution (1-10 min), and integrated MWR observations and retrieved profiles (10.5281/zenodo.20178914, Gong et al., 2025). The dataset captures changes in the frequency of surface-based inversion (SBI), from 6 % in summer to 68 % in winter. More frequent SBIs are associated with higher PM2.5 values, reaching

Supercooled liquid water (SLW) in mixed-phase clouds significantly influences precipitation efficiency and aviation safety. However, a comprehensive understanding of its vertical structure has been hampered by a lack of sustained, vertically resolved observations over the North China Plain. This study presents the first systematic analysis of SLW vertical distribution and microphysics in this region, utilizing a year-long dataset (2022) from synergistic ground-based instruments in Beijing. Our retrieval approach integrates Ka-band cloud radar, microwave radiometer, ceilometer, and radiosonde data, combining fuzzy-logic phase classification with a liquid water content inversion constrained by column liquid water path. Key findings reveal a distinct bimodal seasonality: SLW primarily occurs at mid-to-upper levels (4–7.5 km) during spring and summer, driven by convective lofting, while winter SLW is confined to lower altitudes (1–2 km) under stable atmospheric conditions. The temperature-dependent occurrence probability of SLW clouds has an annual maximum at −12 °C. The diurnal variation in SLW in summer shows peaks in the afternoon and at night, corresponding to convective cloud activity. Spring, autumn, and winter do not exhibit strong diurnal variations. Retrieved microphysical properties, including liquid water content and droplet effective radius, are consistent with in situ aircraft measurements, validating our methodology. This analysis provides a critical observational benchmark and offers actionable insights for improving cloud microphysics parameterizations in models and optimizing weather modification strategies, such as seeding altitude and timing, in this water-stressed region.

The dynamical and microphysical processes that govern the lifecycle of hail-producing deep convective clouds (DCCs) remain poorly understood, limiting severe weather prediction. Here, we dissect a severe hailstorm that occurred over Inner Mongolia using multi-source observations, including Himawari-8 satellite data, Doppler radar, and a lightning mapping network. Our analysis reveals a tightly coupled co-evolution of cloud-top microphysical properties, cloud-top kinematics, and electrical activity. A key finding is the synchronization during rapid updraft intensification of a collapsing cloud-top effective radius (from similar to 40 mu m to similar to 20 mu m) with a surge in total lightning flash rate. Rapid updrafts likely shorten particle residence time, limiting particle growth while accelerating mixed-phase collisions and non-inductive charging, thereby promoting lightning jump activity. Critically, these abrupt changes in updraft velocity and lightning activity preceded surface hailfall and peak rainfall by approximately 30-40 min and 2 h, respectively. This study provides quantitative evidence that the integration of satellite, radar, and lightning observations can elucidate the microphysical pathways leading to severe convective weather and offers valuable lead time for improved nowcasting.

Riming and aggregation are critical ice-phase microphysical processes in winter clouds, but their overlapping signatures and dynamic transitions pose challenges for conventional single-frequency radar detection. We introduce a novel gradient-based identification method using ground-based triple-frequency dual-polarization radar observations. By analyzing vertical gradients of triple-frequency radar variables, rather than their absolute values, we discern these microphysical processes through physically based thresholds that reflect particle growth regimes. This approach captures subtle spatiotemporal variations in riming and aggregation that conventional threshold methods would miss, particularly in resolving layered riming-aggregation transitions. The dynamic gradient-based method demonstrates enhanced physical consistency and adaptability near process boundaries, thereby improving the tracking of ice-particle evolution. These advances provide a pathway to refine microphysical parameterizations and enhance high-resolution snowfall forecasting.

On 23 June 2016, a supercell storm in Yancheng City, Jiangsu Province, China, produced two tornadoes: the first reached EF4 intensity and lasted approximately 50 min, while the second was rated EF2–EF3 and persisted for about 20 min. Evidence of cyclic tornadic mesocyclones within the supercell was captured by an S-band stationary Doppler radar, providing a rare case of a severe cyclic tornadic storm in China. Three discrete mesocyclones were identified during the storm’s evolution. The first occlusion persisted for approximately 42 min and was relatively prolonged; the submesocyclone-strength vortex associated with the old mesocyclone remained detectable for 18 min after the hook echo completed wrapping around itself. In contrast, the second occlusion evolved more rapidly and was completed within 18 min, during which the old and new mesocyclones rotated around each other. A temporal association was observed between the evolution of the reflectivity core and the occlusion and formation of the mesocyclones. The second and third mesocyclones formed approximately 24 and 18 min, respectively, after the reflectivity core weakened and lowered toward the surface.

Provides society information that may include news, reviews or technical notes that should be of interest to practitioners and researchers.

Frequent typhoon landfalls inflict substantial losses of life and property. Their genesis, rapid intensification, and track changes remain three key challenges in typhoon research. High spatiotemporal resolution in situ observations spanning the whole typhoon life cycle are urgently required. We propose deploying the Marine Weather Observer (MWO), a mobile oceanic observing system developed by the Institute of Atmospheric Physics, Chinese Academy of Sciences, for sustained networked typhoon observations. The experiment will deploy five to seven solar-powered MWOs in the South China Sea to measure marine surface and boundary layer meteorological parameters. This networked approach enables real-time in situ observations of typhoon internal dynamics and environmental conditions over the open ocean, supporting data assimilation and validation of numerical forecast models to improve predictions of typhoon tracks, intensity, and associated severe weather impacts.

Escalating global climate change has intensified the urgent demand for high-precision, real-time rainfall monitoring. While rainfall retrieval based on commercial microwave links (CMLs) currently faces key challenges, such as data scarcity and insufficient model generalization. The integration of core sixth-generation (6G) technologies within the Internet of Things (IoT) framework presents promising solutions. These emerging technologies offer inherent advantages, including wide coverage, low cost, easy deployment, and strong real-time capability. Building upon these strengths, it has significantly enhanced CMLs rainfall retrieval performance by integrating 6G's terahertz communication and ultralow latency transmission, IoT-enabled edge intelligence, integrated sensing and communication (ISAC), and artificial intelligence (AI) for data processing and real-time analysis, the performance of CML-based rainfall retrieval has been significantly enhanced. This provides a viable technical pathway for achieving subkilometer accuracy meteorological monitoring and building multidimensional sensing systems for smart cities. The main works of this article are as follows: 1) a comprehensive review of recent advances in CML-based rainfall retrieval, covering both model-driven and data-driven retrieval technical routes from fundamental principles; 2) designing and conducting a rainfall retrieval experiment using a long-short-term memory (LSTM) model based on real-world E-band CMLs measurements from Nanjing, China, which robustly verifies the high-precision retrieval potential of data-driven approaches in complex urban propagation environments; and 3) an in-depth analysis of emerging challenges, such as high-frequency data fusion, faced by CMLs rainfall retrieval technology in scenarios deeply integrated with 6th-generation IoT scenarios, along with discussion of potential technical solutions.

Surface heterogeneity drives the local-induced formation of clouds and their subsequent development through spatial variations in surface energy partitioning and boundary-layer processes. A central challenge, however, lies in disentangling the individual contributions of co-varying types of surface heterogeneity, such as topography, soil properties and land-use type. Using a combination of radiosonde observations, MODIS satellite data, and Large-eddy simulations, we separate the effects of topography and soil moisture in cloud formation over the Inner Mongolia grassland, China. Our results demonstrate a primary control of orographic forcing on cloud formation location, and a secondary control of soil moisture on cloud amount via surface sensible heat fluxes. Dynamically driven circulations initiate ascent on windward slopes, leading to convergence and cloud formation primarily over hills and lee side. Consequently, higher elevations with colder and wetter air corresponds to upward motion, creating a counter-intuitive correlation between vertical velocity and surface heat flux. Our analysis using a satellite-based soil moisture product reveals that high soil moisture suppresses cloud development by reducing sensible heat flux. These findings highlight the importance of jointly considering topography and soil moisture for improving the prediction of shallow cumulus occurrence in semi-arid regions.

Solar photovoltaic power is essential for achieving carbon neutrality, yet its output is highly sensitive to changes in air quality under future emission pathways. We integrate energy–economy, coupled meteorology–chemistry, and photovoltaic performance models to quantify how alternative carbon-neutrality pathways influence solar power generation via air quality changes in China. Here we show that a pathway emphasizing renewable-energy deployment yields the greatest air-quality improvements, boosting annual photovoltaic power generation by 57,526 ± 10,314 gigawatt-hours (GWh) relative to business-as-usual, equivalent to economic gains of US$5.18 ± 0.93 billion by 2060. These gains are driven primarily by aerosol–cloud interactions rather than aerosol–radiation interactions. In contrast, pathways relying on biomass or carbon capture achieve only about one-third of these gains. The largest gains occur in eastern and southern China, where electricity demand is highest. These findings highlight the importance of co-optimizing decarbonization and air-pollution mitigation to maximize the renewable energy benefits of carbon-neutrality strategies. This study reveals that the choice of carbon-neutrality pathway can determine the future solar energy gains from air-quality improvements. Renewable-focused strategies deliver the largest photovoltaic benefits, with aerosol–cloud interactions driving most of the enhancement.

This study investigates a severe summer convective hailstorm that occurred in Shanghai on 18 August 2019, using multisource meteorological datasets, with a particular focus on the innovative application of a single-polarization X-band array weather radar (AWR). Radiosonde data revealed high convective available potential energy and unstable atmospheric indices, while wind profiler radars (WPRs) showed initial easterly moisture transport near the ground and strong southwesterly flow aloft, both contributing significantly to intense convection. Ground-based automatic meteorological stations (AMSs) recorded abrupt temperature drops of approximately 10 °C and wind speed increases exceeding 20 m s−1, which aligned closely with the rapid expansion of the hailstorm. In addition, an integrated analysis of data from AWR, WPRs, and AMSs enabled detailed tracking of the storm’s evolution, providing deeper insights into the interplay between moisture transport and dynamic lifting. The AWR’s unique ability to capture divergence and vorticity fields at different altitudes revealed low-level convergence coupled with high-level divergence and cyclonic rotation, sustaining convective updrafts. This study underscores the value of high-resolution AWR data in capturing short-lived, intense precipitation processes, thereby enhancing our understanding of wind field structures and storm development. These findings highlight the comprehensive application of AWR data and the potential of this new high-spatiotemporal-resolution radar for investigating the mechanisms of short-lived severe convective processes.

On 13 August 2019, a severe convective precipitation event affected the Shanghai region. At 850 hPa, a low-level shear line influenced Shanghai with surface convergence, while at 700 hPa, an inversion layer separated warm, moist lower air from colder, drier air aloft, favoring convection. Observations also revealed vertical wind shear, facilitating additional convective growth. Observations from local automatic weather stations (AWSs) and wind profiler radars (WPRs) indicate that five minutes before rainfall began, ground heat and northerly winds collided, triggering the precipitation. Both the S-band Qingpu SA radar and a novel single-polarization X-band Array weather radar system (Array Weather Radar, AWR) with three phased-array radar frontends and one radar backend captured this event. Compared with the relatively coarse spatiotemporal resolution of the Qingpu SA radar, the AWR provides high-resolution wind-field data, enabling the derivation of horizontal divergence and vertical vorticity. A detailed analysis of reflectivity, divergence, and vorticity in the AWR’s overlapping detection areas shows that, during the development and mature stages of the cell’s lifecycle, the volume of echoes with Z > 25 dBZ consistently increases, whereas echoes with Z > 45 dBZ grow in an oscillatory pattern, reaching five peaks. Moreover, at the altitudes where Z > 45 dBZ appears, regions of cyclonic vorticity emerge.

Autonomous unmanned surface vehicles (USVs) offer transformative potential for collecting marine meteorological data under extreme weather conditions, yet their capability to provide reliable solar radiation measurements during typhoons remains underexplored. This study evaluates shortwave downward radiation (SWDR) data obtained by a solar-powered USV (developed by IAP/CAS, Beijing, China) that successfully traversed Typhoon Sinlaku (2020), compared with Himawari-8 satellite products. The SUSV acquired 1 min resolution SWDR measurements near the typhoon center, while satellite data were collocated spatially and temporally for validation. Results demonstrate that the USV maintained uninterrupted operation and power supply despite extreme sea states, enabling continuous radiation monitoring. After averaging, high-frequency SWDR data exhibited minimal bias relative to Himawari-8 to mitigate wave-induced attitude effects, with a mean bias error (MBE) of 13.64 W m−2 under cloudy typhoon conditions. The consistency between platforms confirms the SUSV’s capacity to deliver accurate in situ radiation data where traditional observations are scarce. This work establishes that autonomous SUSVs can critically supplement satellite validation and improve radiative transfer models in typhoon-affected oceans, addressing a key gap in severe weather oceanography.

This study presents a case study investigating the differences between local and non-local effects in aerosol transport during daytime and nighttime periods using observational data from a summer field campaign in the Southeastern Inner Mongolia Grassland, China. Skewness statistics and quadrant analysis were used for the first time in the terrestrial boundary layer which also employed to characterize the spatial distribution patterns of aerosols under varying boundary layer conditions. Aerosol transport primarily manifested non-local advection effects during daytime and nighttime periods. And for nighttime period, local and non-local effects were both observed with diminished mechanical turbulence and thermal turbulence and have little impact on aerosol number concentration. The elevated nocturnal number concentrations, peaking at 1500 cm⁻³, were linked to non-local advection mixed by wind shear-induced mechanical turbulence, further supported by size-resolved data showing a sharp increase in coarse particles ( 30 µg cm⁻³) consistent with regional inflow. This research addresses knowledge gaps regarding the coupled effects of non-local processes and turbulent activities on aerosol vertical transport in complex boundary layers, providing insights into air pollution formation and diffusion mechanisms.

Understanding frontal clouds and precipitation is crucial due to its increasing variability and intensity driven by global warming, which impacts agriculture, water resource management, and climate adaptation. Autumn precipitation in Central China is frequently induced by extratropical cyclones, often featuring a frontal cloud system influenced by warm conveyor belt (WCB). In this paper, the detailed microphysical processes of an autumn precipitation event were examined using reconstructed vertical profiles of polarimetric variables, supplemented with numerical simulation to better depict the microphysics evolution in different stages. Results showed that during the initial stage, a strong, inclined updraft and supersaturated layer influenced by WCB, enhanced the formation and depositional growth of ice particles above-10 degrees C level, then ice particles grew by riming in addition to aggregation in5-0 degrees C layer while precipitation particles coalescence and collecting cloud droplets occurred in warm cloud. As the vertical extent of supersaturated layer increased, the intensified process of collecting cloud droplets and vigorous riming process contributed to the maximum surface rainfall intensity. In the weakening stage, ice-phase processes attenuated before the warm cloud processes showed reducing tendencies. This decline is attributed to weakened inclined updraft and supersaturated layer falling below 0 degrees C level, which impaired the conditions necessary for ice-phase processes. It is concluded that the thermodynamic structure of WCB, namely the vertical extension of inclined updraft and the presence of supersaturated layer, significantly influenced both the ice-phase and liquid-phase microphysical processes of the frontal cloud system as well as the surface precipitation.

The mobile ocean weather observation system, named Marine Weather Observer (MWO), developed by the Institute of Atmospheric Physics (IAP), consists of a fully solar-powered, unoccupied vehicle and meteorological and hydrological instruments. One of the MWOs completed a long-term continuous observation, actively approaching the center of Typhoon Sinlaku from 24 July to 2 August 2020, over the South China Sea. The in situ and high-temporal-resolution (1 min) observations obtained from MWO were analyzed and evaluated through comparison with the observations made by two types of buoys during the evolution of Typhoon Sinlaku. First, the air pressure and wind speed measured by MWO are in good agreement with those measured by the buoys before the typhoon, reflecting the equivalent measurement capabilities of the two methods under normal sea conditions. The sea surface temperature (SST) between MWO and the mooring buoys is highly consistent throughout the observation period, indicating the high stability and accuracy of SST measurements from MWO during the typhoon evolution. The air temperature and relative humidity measured by MWO have significant diurnal variations, generally lower than those measured by the buoys, which may be related to the mounting height and sensitivity of sensors. When actively approaching the typhoon center, the air pressure from MWO can reflect some drastic and subtle changes, such as a sudden drop to 980 hPa, which is difficult to obtain by other observation methods. As a mobile meteorological and oceanographic observation station, MWO has shown its unique advantages over traditional observation methods, and the results preliminarily demonstrate the reliable observation capability of MWO in this paper.

ABSTRACT In a lake, the heat and energy budgets and evaporation are the most fundamental components of the regional weather and climate and are controlled by the interactions between the lake and the atmosphere. Poyang Lake is the largest freshwater lake in China. The surface area of this shallow lake, located in the central and lower catchment of the Yangtze River, southeastern China, varies across the year based on the precipitation. A steel platform was built in the northeast open-water area of the lake to measure surface energy fluxes and other related atmospheric/hydrologic variables during a cold season from 1 December 2020 to 28 February 2021. The results show the average water surface temperature was 1.25°C higher than the 2 m height air temperature, though temperature inversions occasionally occurred for short periods. The average wind speed and friction velocity were small, leading to weak turbulent mechanical mixing. Consequently, consistently positive and moderate latent and sensible heat fluxes (17.4 and 5.4 W/m2, respectively) values were produced due to the effects of turbulent mixing from thermal and mechanical factors. The monthly average albedo was large at mid-day (0.086). The low Bowen ratio (about 0.37) also indicates that more latent heat is released than sensible heat. When dry and cold air passed over the lake, the pressure of vapor and air temperature decrease significantly, the turbulent mechanical mixing is enhanced and the energy budget changed. As a consequence, the Ts-Ta, es-ea values, and sensible and latent heat fluxes all increase.

Aerosols are important atmospheric constituents with significant impacts on both regional air quality and the global climate and environment. Aerosol effects on radiation, clouds and precipitation are strongly related to the optical properties of aerosols, which vary considerably in space and time. This study reviewed the current understanding of the optical and radiative properties of aerosols and aerosol effects in China, and presented future research prospects. The Chinese aerosol remote sensing network (CARSNET) has been developed and expanded to include >80 stations across China. An internationally recognized radiometric calibration and data processing method has been developed to provide high-quality observational data of key optical properties. Ground lidars are used to provide information on the vertical aerosol optical properties on the spatiotemporal scales. The spatio-temporal distribution and evolution of aerosols are also analyzed using multi-satellite observations. Particle type and component content information are derived from aerosol component retrieval algorithm. Given the advantages of aerosol assimilation reanalysis products in terms of temporal resolution and spatial coverage, such information can more accurately characterize the climatological impact of aerosol optical properties and aerosol–climate interactions. Measurements obtained using the above methods can quantitatively reveal the influence of aerosol direct radiation effects in different regions, and support the exploration of the potential mechanisms of aerosol impacts on weather and climate from the perspective of ground-based remote sensing, which will help to guide the direction of future research.

The Microwave Radiation Imager for the Rainfall Mission (MWRI-RM) onboard the FengYun 3G satellite (FY3G), launched in April 2023, will provide massive brightness temperature (Tb) measurements at 17 frequencies from 10.65 GHz to 183 GHz. The operationally calibrated MWRI-RM Level 1B Tb data have been released since 23 October 2023. The MWRI-RM measurements are compared with the simultaneous measurements from the GPM Microwave Imager (GMI) to provide a first overview of the MWRI-RM measurement quality. A radiative transfer model (RTM) is performed for the double difference (DD) analysis. The results show that the Tbs of MWRI-RM are in good agreement with those of GMI, with mean bias error (MBE) and root-mean-square error (RMSE) values of less than 1 K for most channels. Larger differences are observed at the horizontal polarization of 36.5 and 89 GHz, with an RMSE of about 2 K. RTM simulations reflect that the Tb difference between MWRI-RM and GMI due to the different Earth incidence angle of the sensors is about 0.5 K over the sea. The DD analysis results suggest that MWRI-RM demonstrates quite similar performance to GMI for most channels, especially for the first used channels at 166 GHz and 183 GHz.