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Abstract: Crop phenological information provides key support for the refined management of agricultural production, early warning and assessment of meteorological disasters, and the guarantee of national food security. Satellite remote sensing offers an effective way to obtain phenological information for major crops over large areas and becomes an important research topic in current agricultural remote sensing. This paper systematically reviews the technical framework of satellite remote sensing extraction for major crop phenological information, summarises the research progress from three aspects: data sources, time-series data reconstruction, and phenological parameter extraction methods, analyses the existing challenges, and explores future development trends. Studies show that data sources for crop phenology remote sensing monitoring can be mainly divided into two categories: satellite sensors with high temporal resolution but medium-low spatial resolution, and sensors with medium-high spatial resolution but low temporal resolution. The Normalised Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and two-band Enhanced Vegetation Index (EVI2) are the most widely used vegetation indices, each with its own advantages and limitations under different vegetation coverage conditions. Time-series data reconstruction is a critical preprocessing step for phenology extraction. Mainstream methods include Maximum Value Composite, filtering, curve fitting, spatiotemporal data fusion, and optical-microwave data fusion. Among these, multi-source data fusion (especially optical and microwave fusion) and deep learning techniques become important approaches to alleviate cloud contamination and resolve the trade-off between temporal and spatial resolution. Crop phenological parameter extraction methods are grouped into post-season methods and in-season methods. The former relies on full growth-season time-series curves and uses thresholding, curve feature point detection, shape matching, etc. These techniques are relatively mature but retrospective. The latter enables near-real-time phenology extraction, mainly including shape matching and trend-based methods, which meet the real-time requirements of agricultural production. However, related studies remain limited due to insufficient effective observations and other constraints. At present, the satellite remote sensing extraction of major crop phenological information still faces several challenges: (1) impacts of cloud and fog on valid data availability; (2) uncertainty in field-scale monitoring caused by mixed pixels; (3) lack of robust near-real-time monitoring methods; (4) insufficient application depth and level of domestic satellite data. Future research should focus on four key directions: first, improve satellite sensor performance and develop multi-source data fusion to enhance the temporal, spatial and spectral resolution of remote sensing data; second, optimise phenology extraction algorithms, strengthen real-time monitoring and short-term prediction of crop phenology, and improve the biological interpretability of remote sensing results; third, integrate data assimilation, artificial intelligence and crop models to enhance regional crop phenology forecasting capabilities and support meteorological disaster early warning; fourth, establish a satellite-ground integrated observation system, promote the application of domestic satellite data in crop phenology monitoring, and reduce dependence on foreign satellite data. Accurate crop phenology remote sensing extraction technologies will further improve the refinement level of agricultural meteorological services and provide important technical support for addressing climate change and ensuring food security.
Abstract: Global meteorological observing systems face severe challenges including the explosive growth of observing elements, steep increases in spatiotemporal resolution of data, and the integration of multi-source heterogeneous datasets. There is an urgent need to establish a unified and scalable evaluation framework to support the scientific design and operational construction of observing systems. As China’s meteorological observing capabilities expand toward three-dimensional coverage, evaluation targets become increasingly complex, and observation-assimilation technologies advance rapidly, the China Meteorological Administration proposes the concept of “integrated development of observation and forecasting”. Quantitative assessment of observing system contributions to numerical models and forecasts becomes an essential requirement for observing system development. This paper systematically reviews research progress in numerical model-based observing system evaluation methods and their operational applications worldwide, while providing prospective discussions on future development pathways. Current mainstream observing system evaluation methods fall into three categories: Observing System Experiments (OSE) primarily assess the actual contributions of existing observing systems through data denial experiments to quantify impacts on numerical weather prediction, and while highly flexible, OSE incurs substantial computational costs. Observing System Simulation Experiments (OSSE) evaluate the potential value of proposed observing systems through simulated observations, effectively guiding system design but heavily dependent on observation operator accuracy. Forecast Sensitivity to Observation methods (FSO/EFSO) dynamically parse observation contributions to forecast errors through adjoint models or ensemble approaches, offering high computational efficiency, though FSO requires adjoint model development while EFSO necessitates ensemble forecasting systems. These methods serve different purposes with distinct characteristics, collectively forming a methodological framework for quantitative observing system evaluation and optimal design. Notably, all these approaches are inevitably influenced by background field quality and reference truth selection. Recently, with emerging demands for multi-sphere observations, expanding Earth observing system assessments to encompass oceanic, land surface, and other spheres becomes a significant development trend. Operational institutions worldwide conduct extensive practices in observing system evaluation with remarkable achievements. Numerous case studies demonstrate the complementary value of observations from different types and platforms, providing direct scientific evidence for optimising observing network layouts, including station density adjustment, element configuration optimisation, and platform type combinations. The specific implementation of evaluation methods heavily relies on underlying data assimilation technologies such as three-dimensional/four-dimensional variational assimilation and Local Ensemble Transform Kalman Filter. Persistent evaluation demands drive assimilation technology advancement, while progress in hybrid assimilation, particle filter assimilation, and other novel techniques enables more refined and reliable evaluation results. For new remote sensing observing systems including microwave radiometers, phased array radars, and hyperspectral instruments, observation operator development becomes a critical bottleneck constraining their evaluation capabilities. Evaluation targets are gradually expanding from traditional atmospheric observations toward multi-sphere Earth system observations encompassing oceans, land surfaces, and cryosphere, with assessing the comprehensive influence of observation information in multi-sphere coupling processes—such as ocean-atmosphere and land-atmosphere interactions—emerging as a current research hotspot. These international practices demonstrate that continuously deepening evaluation methodology research, closely integrating assimilation technology innovation, actively exploring and applying novel observations, and expanding toward Earth system perspectives constitute key pathways for enhancing observing system effectiveness and forecast accuracy. Looking ahead, guided by the China Meteorological Administration’s “Observation as Service” concept, China’s observing system evaluation operations rely on autonomous evaluation technologies, pursuing paths of technological self-reliance, multi-sphere coupling, sector coordination, and open-source sharing. First, leveraging the CMA’s autonomous numerical models and following WMO’s Rolling Requirements Review (RRR) guiding principles, an integrated “assimilation-evaluation-optimal design” workflow is established to achieve closed-loop management encompassing design optimisation, impact quantification, and error source tracing. Second, Earth system multi-sphere evaluation capabilities are constructed, keeping pace with new assimilation technology developments such as nonlinear assimilation techniques addressing non-Gaussian problems and novel remote sensing observation operators, while strengthening research and development of three-dimensional observing evaluation technologies. Third, evaluation metrics expand from traditional “accuracy” dimensions toward full-chain “sectoral requirements-socioeconomic impacts,” directly serving national strategies including disaster prevention and carbon neutrality goals. Fourth, international cooperation is strengthened through active participation in WMO affairs, conducting joint international observing system evaluations and optimal design, proactively promoting open-source sharing of autonomous evaluation tools, and forming continuously improving, globally shared evaluation platforms to internationalise indigenous indicator systems. Ultimately, through continuous exploration of deep observation-service integration mechanisms, China comprehensively advances its observing system evaluation capabilities from leapfrog development to leading positions.
Abstract: A novel and efficient performance assessment technique for weather radar systems is developed to address the operational challenges associated with the increasing scale of radar deployments, particularly in regions like Zhejiang Province. With the expansion of meteorological observation networks, weather radars become vital tools for real-time monitoring and forecasting. However, traditional methods for calibrating and verifying radar performance are typically time-consuming, labour-intensive, and technically complex. These limitations pose significant barriers to the frequent inspection and rapid troubleshooting needed to maintain the operational health of dense radar systems. To overcome these challenges, an innovative solution is proposed that leverages a lightweight unmanned aerial vehicle (UAV) equipped with a custom-designed power measurement unit and a stable, high-fidelity signal source module. This compact, mobile platform is capable of performing real-time, in-situ radar performance assessments with minimal logistical overhead. The hardware modules are engineered to meet stringent industry and national standards in terms of signal accuracy, frequency stability, power resolution, and electromagnetic compatibility, ensuring the reliability of the collected data. The UAV is flown into the radar’s detection zone, where it emits pre-configured signals that interact with the radar system. Meanwhile, it collects reflected signal data and records the radar’s response under controlled test conditions. Parameters such as transmitted power, system gain, beam pointing direction, and reflectivity under potential interference are captured and analysed. This allows for a thorough evaluation of radar performance metrics to determine whether the system operates within its expected performance envelope. Extensive field validation shows excellent consistency between measured results and theoretical expectations. Specifically, the standard deviation of absolute deviations in transmitted power and receiver gain is maintained within 1 dB, and the deviation in beam pointing accuracy is kept under 0.1 degrees. These findings demonstrate the method’s reliability, reproducibility, and suitability for operational deployment. This UAV-based approach for radar performance evaluation provides significant advantages over traditional techniques. It reduces testing time and cost, increases flexibility, and minimises downtime. It is applicable across various radar configurations, including stationary, mobile, and ship-based systems, and can be deployed even in remote or hard-to-access environments where fixed calibration systems are not feasible. The technique is especially useful for routine inspections, emergency diagnostics, acceptance testing of new radar units, and long-term performance tracking. In summary, this UAV-based radar performance assessment technique represents a substantial advancement in the operational support and maintenance of modern weather radar systems. By offering a rapid, portable, and highly accurate method for performance testing, it contributes to enhanced reliability, safety, and efficiency in meteorological observation and forecasting operations. With further refinement, it may serve as a foundation for future intelligent and autonomous radar maintenance platforms.
Abstract: Accurate surface shortwave radiation data is critical for solar energy resource assessment, photovoltaic (PV) power plant siting, and grid power scheduling. The Gansu Plateau is one of the regions with the richest solar resources in China, but its complex terrain and variable climate pose challenges for satellite remote sensing retrieval. The new-generation geostationary meteorological satellite, FY-4B, offers high spatiotemporal resolution radiation products, yet its accuracy over the complex terrain of the plateau requires systematic evaluation. This study aims to reveal the error distribution characteristics of the FY-4B Downward Shortwave Radiation (DSR) product over the Gansu Plateau and explores the feasibility of using machine learning algorithms to improve data accuracy, thereby providing high-quality data support for regional renewable energy operations. Five representative national radiation observation stations in the Gansu Plateau (Dunhuang, Minqin, Yuzhong, Xifeng, and Jiuquan) are selected as benchmarks. The minute-level ground observation data of one year from April 2024 to March 2025 are collected. Through spatiotemporal matching, the ground observation data are compared with the FY-4B satellite’s L2-level surface shortwave radiation product, which has a 15-minute resolution. Based on this, a Random Forest (RF) bias correction model is constructed using solar geometric parameters (solar zenith angle, azimuth angle), geographic information (longitude, latitude, elevation), and time-series features as input variables. A “leave-one-out” cross-validation strategy is employed, where data from four stations are used to train the model, and the independent Jiuquan station is used as a test set for validation. The results show that: (1) The FY-4B radiation product captures the diurnal variation of surface radiation well, showing a high correlation with ground observations. The correlation coefficients (R) for individual stations range from 0.90 to 0.94 (overall R = 0.92). However, the satellite retrieval exhibits a significant systematic negative bias, with an overall Mean Bias Error (MBE) of -35.5 W/m2. Spatially, the Jiuquan station shows the largest deviation (MBE = -75.9 W/m2, RMES=145.4 W/m2), while Xifeng has the smallest. Temporally, errors fluctuate seasonally, characterised as “most negative in autumn, smallest in summer, and intermediate in spring and winter.” (2) After implementing the Random Forest correction on the four training stations, data quality improves significantly. The R for training stations increases to 0.96-0.97, the Root Mean Square Error (RMES) drops sharply from the original 102-114 W/m2 to 64-82 W/m2, and the absolute value of MBE converges to 0-3 W/m2, essentially eliminating systematic bias. (3) Validation based on the independent Jiuquan station demonstrates that the correction model possesses good generalisation capability. Post-correction, the R at Jiuquan rises from 0.91 to 0.93, the RMES decreases from 145.4 W/m2 to 107.5 W/m2 (a reduction of 26%), and the MBE improves from -75.9 W/m2 to -28.6 W/m2. Notably, the model effectively corrects the significant underestimation observed in the original product during high-irradiance periods. The FY-4B satellite shortwave radiation product demonstrates high retrieval consistency over the Gansu Plateau and holds potential for operational application, despite existing systematic underestimation. The introduction of a Random Forest model that accounts for solar geometry and geographic features effectively corrects the satellite product’s systematic errors and significantly reduces the RMES. The corrected data offers substantially improved accuracy, providing more credible radiation input parameters for refined solar resource surveys and PV power forecasting in Gansu and surrounding plateau regions.
Abstract: With the growing demand for clean energy, accurate solar radiation data are crucial for the assessment and forecasting of solar energy resources in Hubei Province. As ground observation stations are sparse, reanalysis datasets like ERA5 are widely used. However, ERA5 products often exhibit systematic biases due to the influence of complex terrain, clouds, and aerosols. This study evaluates the applicability of ERA5 surface downward shortwave radiation products in Hubei and improves their accuracy using a machine learning approach. Hourly downward shortwave radiation data from six ground observation stations (Wuhan, Xiaogan, Jingzhou, Suizhou, Nanzhang, and Yichang) in Hubei Province are used as the ground truth. The study period covers two full years (2021-2022). The 2022 data are used to train a Random Forest (RF) correction model, while the 2021 data are used for cross-validation to test the model’s generalisation ability. The RF model integrates the original ERA5 radiation data with five spatiotemporal features: longitude, latitude, altitude, day of year (DOY), and solar altitude angle. The model parameters are optimised using 5-fold cross-validation, with 1500 decision trees and a maximum depth of 30. The results show that: (1) The evaluation of the original ERA5 product reveals a strong linear correlation with observations (Correlation Coefficient, R=0.78-0.83). However, significant negative biases are observed, with Mean Error (ME) ranging from -22.08 to -4.81 W/m2. The errors show distinct spatial and temporal patterns: stations in plain areas perform better than those in hilly/mountainous areas; biases are irradiance-dependent (overestimation in low irradiance intervals <200 W/m2 and underestimation in high irradiance intervals >800 W/m2) and seasonal (significant overestimation in winter and slight underestimation in summer). (2) The self-evaluation based on 2022 data shows that the RF model significantly improves data quality. After correction, R increases to 0.92-0.93, the Root Mean Square Error (RMSE) decreases by over 32% (from 157-176 W/m2 to 104-112 W/m2), and the Mean Relative Error (MRE) drops from 90.88%-159.49% to 66.98%-77.76%. (3) The cross-validation using independent data from 2021 confirms the model’s robustness. The corrected data show improved R (0.81-0.87) and an RMSE improvement rate of 15.40%-18.13%. The annual solar exposure increases by 1.3%-1.7% compared to the raw data, correcting the systematic underestimation of total annual resources. The ERA5 solar radiation product has good correlation but systematic biases in Hubei. The proposed Random Forest correction model effectively mitigates these biases, especially the seasonal and irradiance-dependent errors. This study provides a reliable correction method and high-quality data support for the refined utilisation of solar energy resources in Hubei Province.
Abstract: Using conventional ground, sounding observation data and ERA5 reanalysis data, the phase characteristics of precipitation during the rainy and snowy weather process in the Beijing area on 6 November 2021, are analysed. We draw the following conclusions compared with the event on 12 January, 2023: In the starting stage of precipitation, cooling resulting from rainfall evaporation led to rapid transformation of rain and snow phases. Evaporative cooling caused by the rainfall stage played a decisive role in near-surface cooling, while the contribution of temperature advection to rain-snow phase transition was more important as the humidity increased. At the beginning of rainfall, the relative humidity was low, evaporation was significant during the rainfall stage, and relative humidity rapidly increased, leading to a sudden drop in temperature of more than 2 ℃/h. This caused the boundary layer temperature to drop rapidly, thus enabling the rapid conversion of rain and snow phases. Model prediction of the situation field was accurate; however, the ability to predict the cooling effect caused by evaporation at the initial stage of rainfall was poor. The predicted temperature decreased more slowly than observed, resulting in a delayed transition time of the rain and snow phase.
Abstract: Based on hourly precipitation data from 19 national observation stations and 551 regional automatic stations over Hainan Island during 2019-2023, as well as ERA5 reanalysis data, this paper statistically analyses the spatiotemporal distribution characteristics, precipitation properties, physical parameters, and circulation patterns of easterly flow rainstorms during the pre-flood season (March-May) and post-flood season (September-November) in Hainan. The results indicate that: (1) Easterly flow rainstorms are mainly concentrated on the windward slopes of Wuzhi Mountain, especially from Qionghai and Wanning to Qiongzhong, exhibiting a distinct east-heavy and west-light spatial pattern. These rainstorms exhibit a bimodal temporal distribution, with higher frequency and intensity during the post-flood season compared to the pre-flood season. (2) Both pre-flood and post-flood rainstorms comprise convective and stratiform precipitation, but the post-flood season is characterised by more prominent convective and extreme events, with the longest duration reaching nearly 120 hours, 3.2 times that of the pre-flood season. (3) The pre-flood season is marked by significant vertical wind shear, which favours strong dynamic lifting, while the post-flood season features consistent easterly flow throughout the troposphere, higher CAPE values, and greater wind speed dispersion, all conducive to sustained moisture transport. (4) During both periods, cold air moves southward and a low-pressure centre develops in central Vietnam, resulting in a north-high and south-low pressure pattern that promotes the development of easterly (jet) flow over the northern South China Sea. However, differences in the position of the pressure gradient, the intensity of the subtropical high, and the strength of the easterly flow result in distinct rainfall characteristics between the two periods.
Abstract: To scientifically project the future climate change trends in Hulun Buir and support regional ecological protection and adaptive development, this study adopts monthly temperature and precipitation data from 11 CMIP6 models, covering historical simulation data from 1961 to 2014 and future projection data from 2026 to 2100 under four SSP scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5). Combined with the ERA5-Land reanalysis data and in-situ observation data from 16 national meteorological stations in Hulun Buir, methods including Delta downscaling, Taylor diagram screening, weighted multi-model ensemble (MME), and linear trend analysis are applied to conduct model optimisation and analysis of climate change characteristics. The main results are as follows: (1) From 1961 to 2025, the temperature in Hulun Buir shows an extremely significant warming trend, with a climate tendency rate of 0.34-0.51 ℃ per decade, and the warming is particularly prominent in spring and winter; the annual precipitation presents a slight decreasing trend, with a significant reduction only observed in partial areas. (2) The optimised weighted MME model achieves a significantly improved simulation performance. For temperature simulation, the correlation coefficient reaches 0.989, and the centred root mean square error is reduced to 2.48 ℃; for precipitation simulation, the correlation coefficient reaches 0.744, and the centred root mean square error is reduced to 31.5 mm, outperforming all single CMIP6 models in simulation effect. (3) From 2026 to 2100, the temperature in Hulun Buir shows an extremely significant upward trend under all four scenarios, with the warming amplitude increasing with the rise of radiative forcing intensity, and the highest climate tendency rate reaching 0.786 ℃ per decade under the SSP5-8.5 scenario. Under high-forcing scenarios, warming accelerates sharply after 2050, with a significantly higher winter warming amplitude than summer, a feature closely related to the “Arctic amplification effect”. (4) The increase in precipitation is insignificant under the low-forcing SSP1-2.6 scenario, but turns significant to extremely significant under medium and high-forcing scenarios, with the highest precipitation tendency rate reaching 12.110 mm per decade under the SSP5-8.5 scenario. Under high-forcing scenarios, the water vapour accumulation capacity is enhanced and the scope of humid areas is expanded, leading to an overall “warm-wet” climate trend in Hulun Buir. While the warm-wet trend under high-forcing scenarios improves regional hydrothermal conditions and extends the plant growing season, exerting a positive impact on agricultural and animal husbandry production, it also aggravates permafrost degradation, pest and rodent damage, as well as flood and drought disasters, increasing the uncertainty of regional ecological security. The results of this study provide a reliable scientific basis for the construction of ecological security barriers and the formulation of climate change adaptation strategies in Hulun Buir.
Abstract: This study investigates a complex cryogenic freezing rain and snow event that occurred over the Yunnan-Guizhou Plateau in late January 2024, using multi-source datasets including upper-air soundings, surface observations, FY2G satellite imagery, Doppler radar data, and ERA5 reanalysis. The event is divided into two distinct stages based on synoptic system evolution and precipitation characteristics. In the first stage (21-22 January), a trough-ridge pattern at 500 hPa facilitated strong cold advection southward. A 700 hPa shear line pushed southward, coupled with the westward advancement of the surface Kunming quasi-stationary front, which led to widespread snowfall east of 103°E. The second stage (23-28 January) was characterised by a weakening cold advection, strengthening southwesterly flow at 700 hPa, and an eastward retreat of the quasi-stationary front. This period featured sporadic light rain, snow, and freezing rain, with localised moderate rain in western Yunnan. Heavy precipitation was closely associated with the co-existence of a deep moist layer and strong vertical upward motion. Synoptic-scale dynamic forcing, including the south branch trough, low-level shear line, and frontal lifting, played a decisive role in initiating and sustaining vertical motion. Although FY2G imagery effectively captured moisture band development, accurate precipitation forecasting required integrated analysis of both dynamic and moisture-related parameters. Frontogenesis analysis revealed that the horizontal divergence term (F3) dominated the frontal intensification. The diabatic heating term (F1), though weak in magnitude, exhibited a distinct diurnal cycle-frontogenesis at night and frontolysis during daytime, which explained the frequent nocturnal precipitation during the stationary phase of the front over eastern Yunnan. Frontogenesis enhanced atmospheric baroclinicity, triggering a secondary circulation through the solenoid effect, which further strengthened pre-frontal ascent and provided key dynamic support for precipitation. Precipitation phase transitions were governed by thermal structure evolution. Snow occurred when strong cold advection penetrated up to 600 hPa, surface temperatures fell below 1 ℃, and the inversion layer dissipated. Freezing rain required persistent surface temperatures below 0 ℃, coupled with a mid-level warm tongue (above -4 ℃) and a pronounced inversion layer. The event exhibited a transition from a “warm-rain” mechanism (fully sub-freezing column) to an “ice-phase” mechanism (a melting layer presented), with the radar-observed bright band at the 0 ℃ level serving as a reliable indicator for the latter. Analysis of moist potential vorticity indicates that convective instability dominated the pre-frontal region. During snowfall, both convective and symmetric instabilities were present near the front, while only symmetric instability below 700 hPa was identified during freezing rain periods. Vertical motion intensity during freezing rain was significantly weaker than during snowfall. This study provides a systematic analysis of the dynamic and thermodynamic mechanisms governing complex precipitation phases over the Yunnan-Guizhou Plateau, offering valuable insights for forecasting similar winter weather events. Future work incorporates higher-resolution data and numerical modelling to further elucidate microphysical processes and local topographic effects.
Abstract: Based on multi-source datasets including conventional surface observations, wind profiler radar, millimetre-wave cloud radar, and ERA5 reanalysis data, this study systematically analyses the characteristics and formation mechanisms of rain and fog during a persistent fog event in the central and southern regions of Hebei Province from November 11 to 15, 2024. Meanwhile, a comparative analysis between precipitation fog and radiation fog is conducted from multiple dimensions, and a longitudinal study is carried out on three typical precipitation fog events that occur in the central and southern regions of Hebei Province in recent years to enhance the generalisability of the research findings. The results show that the evaporation of continuous light precipitation served as the dominant mechanism for precipitation fog formation. Specifically, the eastward movement of an upper-level shallow trough induced significant warm and moist advection in the study area, which provided sufficient water vapour for the subsequent fog formation. Meanwhile, the lower troposphere was jointly regulated by a warm ridge and a weak cold high-pressure system, forming a relatively stable atmospheric stratification, which, combined with weak dynamic forcing, contributed to the development of a deep, vertically distributed saturated moisture layer in the lower atmosphere. During the precipitation periods, the intrusion of low-level cold air caused raindrop condensation, thus forming precipitation fog. In the intermittent periods between rainfall episodes, a stable near-surface inversion layer persisted, effectively maintaining high humidity in the lower troposphere and providing favourable conditions for the prolonged duration of the fog event. In eastern Hebei Province, especially around Cangzhou, the advection of warm and moist air over a relatively cold underlying surface caused the precipitation fog to gradually evolve into advection fog, resulting in a continuous and significant deterioration in surface visibility. Comparative analysis revealed significant differences between precipitation fog and radiation fog in key meteorological elements, such as temperature variation, inversion structure, and wind direction. Further analysis demonstrates that temperature inversion was not a necessary condition for precipitation fog formation. In contrast, pre-existing environmental pollution promoted its formation, and the weaker the dynamic conditions, the lower the visibility. Additionally, it was emphasised that the impact of precipitation fog on visibility degradation constituted a major challenge in operational meteorological forecasting. Therefore, to improve the accuracy of precipitation fog prediction, it is recommended that forecasters conduct a comprehensive analysis of multiple factors, including large-scale circulation background, vertical humidity stratification characteristics, surface wind field patterns, and environmental aerosol concentration conditions.
Abstract: To deeply understand the microphysical characteristics of precipitation in the complex terrain of the Hengduan Mountains, the typical dry basin,Xiangyun basin at the southern edge of the Hengduan Mountains, is selected as the research object. Based on the observation data of the DSG1 precipitation weather phenomenon instrument and the SL3-1 tipping-bucket rain gauge from the Xiangyun National Basic Meteorological Observing Station from 2017 to 2024, the raindrop size distribution characteristics of this basin under different precipitation types and different precipitation intensities are analysed, and the relationship between the shape factor (μ) and the slope factor (Λ), the reflectivity factor (Z) and the rainfall intensity (R), and the mass-weighted average diameter (Dm) and the standardised intercept parameter (lgNw) are discussed. The results show that: (1) The variation trends of rainfall measured by the two instruments on the inter-annual scale are basically similar, but the precipitation weather phenomenon instrument displays a significant underestimation, with its multi-year average annual cumulative rainfall amount being 19.9% lower than that of the tipping-bucket rain gauge. (2) The microphysical characteristics of convective cloud precipitation differ significantly from those of stratiform clouds and mixed clouds precipitation. The average particle number concentration and average particle diameter of convective cloud precipitation are larger, and the corresponding rainfall intensity and water content are also higher. The rainfall intensity of stratiform cloud and mixed cloud precipitation is generally lower than 5.0 mm/h, while convective cloud precipitation mainly occurs above 10.0 mm/h. Although convective cloud precipitation samples account for only 5.1% of the total samples, their cumulative precipitation contribution rate is as high as 40.4%. (3) The average measured raindrop spectra of convective clouds, stratiform clouds, and mixed clouds, as well as the Gamma fitted spectra, all exhibit typical single-peak variations. The peak diameters of the measured spectra range from 0.5 to 0.6 mm. The spectral widths decrease successively in the order of convective clouds, stratiform clouds, and mixed clouds. (4) Compared with the relationship of Z-R obtained by using the least squares method for fitting, when using the classical relationship of Z-R for quantitative precipitation estimation, it closely approximates the measured values under convective cloud precipitation conditions, while for other types of precipitation, it shows an underestimation. (5) Both stratiform cloud and convective cloud precipitation show a decrease in lgNw with increasing Dm, with the data points concentrated mainly in the smaller Dm range. The raindrop spectrum of convective cloud precipitation displays characteristics closer to those of marine convective clusters, featuring smaller Dm and larger lgNw.
Abstract: Zhengzhou, located in the hinterland of Henan Province, is prone to short-duration heavy rainfall, but research on its precipitation clouds is scarce, especially studies using new remote sensing equipment. To clarify the macro-microphysical characteristics of summer precipitation clouds in Zhengzhou, this study analyses observational data from Ka-band millimetre-wave cloud radar and precipitation disdrometer (2023-2024 flood seasons) and hourly precipitation data from national meteorological stations (2022-2024 flood seasons). Precipitation is classified into six grades by hourly intensity R: light rain (0.1<R≤2 mm·h-1), moderate rain (2<R≤5 mm·h-1), heavy rain (5<R≤10 mm·h-1), rainstorm (10<R≤20 mm·h-1), heavy rainstorm (20<R≤40 mm·h-1), and extreme heavy rainstorm (R>40 mm·h-1). After quality control, 15773 valid cloud radar samples (covering 375 precipitation hours) are obtained. Cloud top heights are identified via continuous cloud signals, normalised frequency-height diagrams (NCFAD) are used to analyse vertical characteristics of reflectivity factor (Z), and a typical 2023 precipitation event is selected for microphysical analysis combining radar power spectrum and raindrop size distribution (DSD). Results show significant diurnal variation in summer precipitation: precipitation frequency exhibits a bimodal distribution (15:00-19:00 and 01:00-04:00), with accumulated precipitation concentrated at 14:00-20:00. Light/moderate rain occurs in 17:00-21:00, heavy rain in 04:00-07:00 and 13:00-16:00, and rainstorms in 17:00-21:00. Solar radiation regulates cloud top heights, which is 8-9 km from 23:00 to 10:00 and rises to 10-12 km from 10:00 to 22:00. Below 5 km, Z values of moderate to extreme heavy rain are similar and much higher than light rain; above 5 km, Z of rainstorms is low (about 0 dBz) due to signal attenuation. The zero-degree layer is around 5 km, where heavy rain particles show obvious coalescence growth with increased mass-weighted mean diameter (Dm). Moderate rain (Dm: 1.16-1.36 mm) transforms into heavy rain via coalescence, with Dm increasing to 1.38-1.64 mm and normalised intercept parameter (NW) decreasing, indicating heavy rain intensity is dominated by raindrop size growth. Moderate rain shows mixed precipitation characteristics, while heavy rain is convective. This study fills the gap in Zhengzhou’s precipitation cloud research, provides an observational basis for regional precipitation mechanisms, and the identified diurnal variation laws and key microphysical processes help optimise forecast parameters and improve short-duration heavy rainfall early warning accuracy.
Abstract: This study aims to understand the microphysical characteristics of warm-sector heavy rainfall in the Sichuan Basin and to improve the accuracy of radar quantitative precipitation estimation (QPE) in the region. Using sounding data from 2019 to 2022, it selects 15 cases of warm-sector and 16 cases of cold heavy rainfall affecting the basin. The raindrop size distributions of the two types of heavy rainfall are compared, and a radar QPE formula applicable to warm-sector events is developed and tested against actual radar cases. The results indicate that, with a narrower droplet size distribution, warm-sector heavy rainfall exhibits a more rapid decrease in number concentration with particle size. For all samples (convective cloud samples and stratiform cloud samples), the number concentration of raindrops in warm-sector heavy rainfall is higher for diameters below 1.125 mm, 2.125 mm, and 1.1875 mm. Under stratiform precipitation, the total particle number concentration (Nt), intercept parameter (Nw), Gamma shape parameter (μ), and slope parameter (λ) of warm-sector heavy rainfall are greater than those of cold heavy rainfall, while the rainfall intensity (R), radar reflectivity factor (Z), liquid water content (W), and mass-weighted mean diameter (Dm) are smaller. Under convective precipitation, except for Dm, all other physical quantities and spectral parameters of warm-sector heavy rainfall exceed those of cold heavy rainfall. Regarding the accuracy of radar precipitation estimation for warm-sector heavy rainfall, validation using actual radar cases demonstrates that all four fitted QPE formulas outperform the formulas currently used in operational applications. Among them, Rnew(ZH) performs the best, followed by Rnew(KDP) and Rnew(ZH, ZDR), while Rnew(KDP,ZDR) performs the worst. The findings provide a basis for improving the short-term monitoring and early warning capabilities for warm-sector heavy rainfall in Sichuan.
Abstract: To comprehensively reveal the intricate influence of multiple underlying surface factors on the spatiotemporal characteristics of lightning disasters in Jiangxi Province, a region known for its significant vulnerability to severe convective weather, this study conducts an in-depth quantitative analysis. It utilises a robust, multi-source dataset comprising a 20-year (2001-2020) georeferenced record of lightning disaster incidents, a high-resolution Digital Elevation Model (DEM) to derive key topographic parameters, and satellite-based remote sensing imagery for land use classification. A sophisticated analytical framework is employed, integrating the powerful spatial processing capabilities of Geographic Information Systems (ArcGIS) with rigorous mathematical statistics and correlation methods to systematically dissect the relationships between environmental variables and disaster patterns. The results of this investigation indicate several critical findings. Firstly, there are highly significant variations in the distribution of lightning disaster activity types and their temporal occurrence. Disasters impacting individuals engaged in agricultural labour are found to be the most prevalent category, accounting for a substantial 53.3% of all events, which underscores the heightened occupational risk in rural areas. The temporal distribution is also highly concentrated, with the vast majority of incidents occurring during the summer (58.7%) and spring (33.1%) thunderstorm seasons, and a pronounced diurnal peak is consistently observed in the late afternoon between 15:00 and 16:00 local time, coinciding with peak atmospheric instability. Secondly, a pronounced topographic effect is identified as a primary modulator of disaster location. A remarkable 74.2% of all events are concentrated in low-lying hilly areas with elevations between 30 and 200 metres, and 82.5% occur on gentle slopes of less than 10 degrees. The frequency of disaster events decreases sharply and predictably with increasing altitude, a relationship that is accurately modelled by a power-law function exhibiting an exceptionally high goodness-of-fit. Furthermore, topographic aspect plays a crucial role, with south-facing slopes experiencing the highest frequency of events and the greatest relative risk, followed by east-facing slopes. Seasonally, south and southeast-facing slopes account for more lightning disaster events and fatalities in summer, whereas southeast and northwest-facing slopes record the most injuries. Finally, the risk of lightning disasters varies significantly across different land use types, with urbanised areas (47.6%) and cultivated land (28.8%) being identified as distinct high-risk zones. In conclusion, terrain and land use types are key factors influencing the spatial distribution disparities of lightning disasters. They hold significant implications for lightning disaster prevention and mitigation efforts in Jiangxi Province, such as lightning disaster risk zoning and the enhancement of lightning disaster defence capabilities.
Abstract: Using the meteorological observation data of Yuepuhu in Xinjiang from 1981 to 2019, combined with the growth and development of Flos Lonicerae, the relationship between the climatic conditions and the growth of Flos Lonicerae in Yuepuhu are analyzed. According to the ecological characteristics of Flos Lonicerae, the meteorological conditions of Flos Lonicerae cultivation in Yuepuhu are systematically analyzed, and the results show that the average temperature of each phenological stage of Flos Lonicerae in Yuepuhu show an obvious increasing trend; the number of sunshine hours has an obvious increasing trend; and the water source is sufficient. These are conducive to the normal growth and development of Flos Lonicerae. As the temperature rises and the number of sunshine hours increases, the planting time has been advanced from the previous mid March to early March; the planting area has expanded year by year, from tens of hectares in 2016 to 345 hm2 in 2019; and the planting mode has been adjusted from the plain cropping to inter cropping method. In the inter planting mode, the varieties are unified with Beihua No.1. The number of consecutive high temperature days of ≥38 ℃ during the growth and development of Flos Lonicerae, especially in ≥40 ℃ high temperature weather, the short term heavy precipitation weather, windy and sandy weather and other meteorological conditions have certain influence on the quality and yield of Flos Lonicerae. Exploration of the favorable climatic conditions for the development of the Flos Lonicerae planting industry in Yuepuhu provides a scientific basis for the construction of the Yuepuhu Flos Lonicerae industrial base, as well as the meteorological guarantee for the increase of income of flower farmers.
Abstract: Clouds are an important part of the earth system, which can affect the radiation balance of the earth atmosphere system by affecting atmospheric radiation transmission. At present, the information obtained from three dimensional cloud observation has certain limitations, so it is necessary to obtain more accurate three dimensional cloud information by using multi source observation data merging analysis. Based on the successive correction method, 〖JP2〗the Three Dimensional Cloud Merge Analysis Operation System (3DCloudA V1.0) integrates multi source data such as numerical forecast products, geostationary meteorological satellite observation, meteorological radar observation to produce the real time 0.05°/h three dimensional cloud merging analysis product covering China and its surrounding areas (0°-60°N, 70°-140°E), which is distributed to the national and provincial meteorological departments through the China Telecommunication System. The modular system framework is considered in the operation system design and construction process, and the fault tolerant functions such as EC Flow scheduling process real time monitoring and automatic restarting are developed, which effectively improves the stability and reliability of the operation system. Evaluations show that through merging multi source observation data, the three dimensional cloud merge analysis product can describe cloud the top, inside and bottom information more accurately.〖JP〗
Abstract: In order to improve the weather forecast quality over the low latitude plateau regions, the wind data retrieved with VAD (Velocity Azimuth Display) method are assimilated to the WRF (Weather Research and Forecasting) model by WRF 3DVar (3 Dimensional Variational Data Assimilation System). With different assimilation schemes, a torrential rain event occurred in Yunnan Province from 00:00UTC 30 June 2009 to 00:00UTC 1 July 2009 is numerically simulated and comparatively analyzed. The results indicate that the initial wind fields of the WRF model are markedly improved by assimilating the retrieved wind data. The WRF 3DVar can availably introduce the information of the retrieved wind to the initial conditions of the regional numerical model. The assimilation of the retrieved wind data helps enhance the wind convergence and vapor transportation over the rainy area. Furthermore, the assimilation help improve quantitative precipitation forecasts. The quantitative test of the 18 hour rainfall forecast shows that forecasts are more accurate, less pretermissions, and more rational pertinence for over 250 mm precipitation in the assimilation experimentations. The higher the assimilation frequency and the longer the assimilation time is, the more obvious the influence of data assimilation on the initial fields and forecast fields of the regional model is. But long assimilation time may increase the speed of synoptic systems and the overestimate rainfall, and so the suitable selection of frequency and time is crucial in numerical experimentations.
Abstract: Through investigating the lightning disasters of ancient buildings, the distribution of ancient buildings being stricken by lightning are analyzed. It is found that animal finials and prominent parts of the like, old trees, towers and kiosks, service facilities and other parts of ancient buildings are vulnerable to lightning strikes. It is found that once an ancient building is stricken by lightning, it is probable to be stricken again by lightning. The reasons for that ancient buildings are stricken and caught fire by lightning are analyzed, and the proportions of casualties caused by ancient building lightning disasters are calculated. It is concluded that the reasons for ancient buildings stricken by lightning includes the appropriate location and structure of ancient buildings, tree triggering, internal environment changes, and water infiltration because of disrepair and other factors vulnerable to lightning.
Abstract: Data quality assessment is an important part in model operation application. In this paper, the soil moisture observation data and China Meteorological Administration Land Data Assimilation System (CLDAS) data are used to establish the online CLDAS data quality assessment system through the MySQL database and the Web technology like html, JavaScript, HighChart， etc. The assessment analysis between the simulated soil moisture and the observed soil moisture at any of stations and provinces, times and different soil layers is implemented in the form of correlation coefficient, root mean square error, relative deviation, and mean deviation. Diagrams such as time series and scatter are visually displayed to compare the observation and simulated data in the system. The statistical indexes can be calculated immediately using JavaScript language in the Web platform. The assessment results and the comparison diagrams can be showed through the internet Web page, and the real time monitoring of the model product data quality can be achieved.
Abstract: Due to the influence of the curve Earth, the fixed detection mode of the CINRAD/SA weather radar uses the minimum elevation angle of 0.5 °, so the blind area is relatively big, and the detection capability for low level precipitation echoes is limited. On the basis of experiments, the calculation formulas of the minimum height applicable when CINRAD/SA detects with positive and negative elevation angles are devised. Then the minimum detecting heights of CINRAD/SA at different distances with different elevation angles (0.5°, 0°, -0.3°,-0.5°) are calculated. Through analyzing characteristics of radar products detected under different elevation angles, some suggestions on CINRAD/SA about using negative elevation angles are presented.
Abstract: With the intensive observation data and NCEP/NCAR reanalyzed data, an unusual heavy fog process occurred over the east central China from 25 to 27 December in 2006 is analyzed in aspects of the large scale synoptic condition and dynamic and thermodynamic mechanisms. It was shown that the fog occurred while the near ground wind velocity varied from 0.3 to 2.9 m/s and the dense fog occurred while the wind velocity varied from 0.3 to 2.4 m/s and the visibility was within 15 meters when velocity was from 0.8 to 1.1 m/s. Although vapor condition was bad and rainfall didn’t occur within a few days before the heavy fog, the continuous vapor transportation of the southwestern air current before a trough offered plentiful vapor for the fog. The results also show that the stable stratification gradually established before the fog.At first, the instable stratification built at higher levels after sunrise, subsequently passed downward to lower levels, and then the inversion layer destroyed and the fog dispersed and cleared off. The results indicate that the visibility changed rapidly and violently before the first stage of the severe heavy fog but it did not before the second stage.
Abstract: In order to reveal the development mechanisms of heavy snowfall in Hebei Province,two heave snow processes on 14 to 16 March 2003 and 20 to 21 February 2004 are selected. A contrast analysis of their meteorological characteristics is made from aspect of synoptic situation and physical mechanism by means of numerical diagnosis with the NCEP reanalysis data and conventional observation data. The results show that the combination of south and north troughs with identical phase around 110°E at 500 hPa, the allocation of the surface pressure field with high in north and low in south, and the appearance of the ground inverted trough in the Hetao area of NW China, as well as the thermodynamic conditions with warm temperature tongue and warm advection in the lower troposphere, are the favorable large scale background for the formation of heavy snowfall. There are three important paths of water vapor in the two snow events: from southwest in front of the 500 hPa trough, from east at low level, and from low level jet. From the cross sections of vorticity, divergence, vertical velocity and vertical helicity, it is found that the vertical distribution of convergence at low level but divergence at upper level and ascending motion in the whole troposphere benefit the forming and maintaining of heavy snowfall, and the distribution of positive vorticity (vertical helicity) in the whole troposphere is most favorable. It is also suggested the temperature descending to below 0 ℃ at both 850 hPa and 925 hPa, meanwhile below 1 ℃ in the surface, is favorable to snowing. The results can be used as reference in the forecasting heavy snowfall.
Abstract: A whole province range thunderstorm occurred in Zhejiang Province on 26 June 2009, and the occurrence frequency of cloud to ground lightning in this thunderstorm is the highest since the establishment of the lightning position system in 2006. By means of the observation data from the lightning position system, the intensive rainfall observation system, and Doppler radar, the characteristics of the cloud to ground lightning process are analyzed. The results indicate that lightning strokes were mainly negative; in the lightning echo image, negative strokes were mostly distributed in the area of 25 dBz to 55 dBz, and positive strokes were usually in the area of 25 dBz to 35 dBz; lightning strokes occurred mostly on the side of echo development or advancement, distributed around the area with maximum gradients, and there seldom appeared lightning strokes around a strong echo center; the frequency of cloud to ground lightning was correlated closely with the accumulated precipitation of the whole province during the thunderstorm. The peak value of precipitation lagged more than 0.5 hour behind the peak value of the frequency of cloud to ground lightning, and the accumulated precipitation of the whole province occurred 1 to 2 hours behind the peak value of the frequency of cloud to ground lightning. Therefore, the cloud to ground lightning data can be used as a basis in short range severe precipitation forecasting.
Abstract: Soil moisture is a key variable in water and energy exchanges in land atmosphere interface. The passive microwave remote sensing is the most potent technology to retrieve soil moisture. A brief introduction is made to microwave theory, and a general review of soil moisture retrieval algorithms is given. Three typical cases are illustrated based on the different microwave sensors by comparing various algorithms, which correspond to the three parameter AMSR based retrieval developed by Njoku and Li, the two parameter SMMR based retrieval developed by Owe et al. and the two parameter SSM/I based retrieval developed by Wen et al. The insufficiency and potentials in the researches on soil moisture are discussed.
Abstract: In order to expand the space of meteorological business, integrate multiple fields of monitoring, and promote the development of the meteorological industry towards efficiency, convenience, and intensification, the Guizhou Province Meteorological Comprehensive Monitoring System APP is developed using mainstream mobile apps as carriers, based on the Springboot+Vue+Mybatis Plus development framework, and using multi-platform compatible development (uni-app), real-time capture of change data (FlinkCDC), and an efficient packaging framework (Mybatis-Plus) among other technical means. The article provides a detailed introduction to the framework structure and functional design adopted by the APP as an independent monitoring system, as well as the big data development technology and its business advantages involved. At the technical level, the system utilises uni-app development technology to make the APP client more compatible and can simultaneously adapt to various application platforms such as iOS, Android, Web, and various mini-programs; using Mybatis-Plus as the database driver framework to improve code reusability and reduce database performance overhead; by using FlinkCDC as a data processing and incremental synchronisation tool, resource waste caused by full data synchronisation can be avoided, simultaneously serving as a one-way synchronisation tool to enhance the security of meteorological data. At the framework level, in order to avoid security risks caused by network mixing, the system introduces a Demilitarised Zone (DMZ) to isolate the internal and external network data environments. The internal network department is responsible for collecting and storing meteorological data from various formats such as databases, static files, API interfaces, logs, etc. Then, it will be synchronised unidirectionally with the external network environment through FlinkCDC. The external network interacts with the mobile APP by receiving data pushed by FlinkCDC. The software is aimed at meteorological users at all levels of province, city, county, and station. Through preliminary research and analysis, four functional modules have been developed for different users, including regional automatic stations, weather radar stations, network connectivity, and interface service status. This provides convenience for meteorological data monitoring and equipment maintenance, and improves the timeliness of response. The system has been put into use throughout the province since 2022. The application results show that the APP adapts to multiple mobile system platforms such as Android and iOS, and has a friendly interface, simple operation, and stable operation. Since its application, the timeliness of meteorological data has improved, enriching the monitoring business methods of Guizhou Province, meeting the user needs at all levels, and playing a positive role in the development of the meteorological industry.
Abstract: Under the common influence of factors including complex terrain, subtropical high pressure, and monsoon weather, the wind field in the alpine canyon areas of is complex and changeable, and it is easy to form the “narrow pipe effect”, which leads to disastrous gales that have a great impact on the construction and operation of large-scale projects. In this paper, based on Fluent, a fluid dynamics computing software, a standard turbulence model and PISO algorithm are used to study the variation of wind velocity field near the dam during dam construction and the influence of dam construction on the wind velocity field, taking the level 7 north wind in Baihetan Hydropower Station as a typical calculation condition. The research results show that the blocking effect of the dam body makes the wind velocity field at the top of the dam generate flow separation and wind field uplift, and a low wind velocity zone forms below the dam elevation. When the dam elevation is 650 m and 750 m, the wind speed within the cable platform is about 15 m/s to 16 m/s, and the channel length of the significant influence area by the wind speed vertical distribution downstream of the dam is 4.4 Ht and 4.5 Ht (Ht being the dam height). The significant influence heights of the wind velocity field at the top of the dam are 2.0 Ht and 3.0 Ht respectively. When the dam is filled to the normal water level of 825 m, the channel length of the significant influence area by the wind field downstream of the dam is 8.0 times the dam height (2.3 km), and the maximum influence channel length is 30.4 times the dam height (8.8 km). The influence height of the dam top reaches about 1500 m height, which is 3.5 times the dam height.
Abstract: Flash heavy rain and the resulting low visibility make it difficult for pilots to visually assess the runway clearly, severely impacting the take-off and landing of aircraft, thereby posing a threat to aviation operational safety. Moreover, the flight delays and diversions caused by this also result in significant losses for airlines and negatively affect socioeconomic benefits. Therefore, conducting comprehensive studies on flash heavy rain is crucial for ensuring aviation safety and enhancing flight punctuality. A thorough analysis of sufficiently detailed observational data is beneficial for clarifying the dynamic mechanisms of convective organisation and enhancement. On July 15, 2022, Xiamen Airport experienced a rare flash heavy rain event triggered by a weak background gust front. During this period, the precipitation intensity peaked at 2.5 mm per minute, and runway visibility rapidly decreased to 600 m, which is relatively uncommon at Xiamen Airport. To analyse this flash heavy rain event, this study utilises minute rainfall data from both ends of the runway, conventional observational data, densified automatic weather station data, ERA5 reanalysis, and S-band dual-polarisation and X-band dual-polarisation phased array radar data of Xiamen. The results of the study indicate that this event occurred under weak weather-scale forcing, where the gust front triggered uplift by intersecting and merging with the surface convergence line during propagation. In an environment characterised by negative large values of pseudo-equivalent potential temperature (θse500-850 hPa) and a warm and humid lower atmosphere, new convection was stimulated, resulting in the rare flash heavy rain at Xiamen Airport. During heavy rain, strong water vapour convergence appeared in the boundary layer at 1000 hPa. Minute rainfall on the runway showed an inverse correlation with visibility, but this correlation weakened when the minute rainfall exceeded 1.6 mm, and the visibility minimum lagged behind the rainfall peak by 7 minutes. Observational analysis reveals that the cyclonic shear of radial velocity was consistent with the trend of minute rainfall change. The peak minute rainfall at both ends of the runway corresponded to the peak cyclonic shear at a certain height layer, indicating a good correspondence between the two. When there was cyclonic shear in the radial velocity at heights of 2-5 km, rainfall significantly intensified. When the shear intensity at two height layers exceeded 2×10-3s-1, minute rainfall could reach approximately 2 mm (equivalent to an hourly rainfall of 120 mm), which emerged as a characteristic feature of this flash heavy rain event.
Abstract: By introducing the relief shading method, which is often used in making the topographic maps, into the visualisation of numerical weather forecast data, this article presents the achievement in three-dimensional drawing of meteorological variables, such as air pressure and geopotential height. Based on the principle and implementation of hill shading, which uses the relationship between the illumination angle, the direction, the slope, and the orientation of the terrain to calculate the brightness value of luminous flux, the relief shading method makes use of the brightness value to display the three-dimensional sense of meteorological model data. At the same height, the steeper the terrain, the darker (brighter) the shaded (sunny) side; under the same slope, the higher the terrain, the darker (brighter) the shaded (sunny) side, which is consistent with the real-life visual effect. The colouring method of the shaded relief map is to use the brightness value (V) in the HSV colour space which is calculated on each grid point, combined with hue (H) and saturation (S) to obtain a complete HSV colour scheme. Through the conversion from the HSV colour space to the RGB colour space, the latter colour space is used for drawing a coloured shaded relief map for meteorological model data. In the shaded relief map, the high-pressure centre in the weather system is often shown as a raised peak, and the low pressure is shown as a depressed valley; a large pressure gradient can be seen as a steep slope, while a small pressure gradient can be seen as a gentle slope. Compared with the traditional isoline and colour filling analysis, it is found that the shaded relief map can help to identify high-low weather systems by concave-convex shapes and reflects the gradient changes of weather systems through the steepness of slope, thus intuitively representing the three-dimensional distribution of atmospheric circulation. In addition, the shaded relief map has the ability to visualise model data in pixel level details, identify early eddy current disturbances in small gradients, and reveal equivalent terrain effects, which helps the meteorologists better interpret the model data and provides the references for the improvements of data process functions in numerical models. Furthermore, the relief shading method is suitable for using the synthetic animations to showcase the fluid characteristics of atmospheric motion, which is conducive to popularising the concept of various weather systems, such as the high, the low, the trough and ridge, and their evolution to the public.
Abstract: Aiming at the problems of data quality degradation caused by multi-channel scanning-type loads on geostationary orbit remote sensing satellites in the process of imaging, transmission and storage, i.e., the influence of texture distortion and edge blurring in the meteorological remote sensing feature recognition images on the analysis of meteorological remote sensing images, this study proposes an improved BM3D noise reduction algorithm. The algorithm combines Morlet wavelet decomposition theory (with good symmetry and its decay characteristics follow the exponential law, it is able to match the mutation signals in the meteorological remote sensing images, thus realising signal denoising) and BM3D denoising principle (a non-local filtering algorithm that includes two parts: block matching and 3D collaborative filtering. Block matching involves grouping image blocks similar to a given reference block and composing them into a 3D array). Firstly, the image decomposes using wavelet transform to get four components. Secondly, the meteorological remote sensing image decomposes into three levels with a total of ten components. Finally, each component denoises using a separate BM3D filter, and the output image of the 10 components reconstructs. The output reconstructed image views as an estimate of the desired image, capable of suppressing meteorological remote sensing image noise and preserving edge detail. Compared with the traditional BM3D denoising algorithm, the improved BM3D algorithm is able to reduce the computation by about one-fifth. The eight meteorological remote sensing images process by equalising the grayscale and adding additive Gaussian white noise with mean 0 and standard deviation σ and random impulse noise. The median filter (suitable for removing isolated noise such as pepper noise), mean filter (suitable for removing noise from images), NL-Bayes (suitable for smoothing images and preserving image details), BM3D algorithm and the improved BM3D algorithm also compare to process the images respectively, and based on the results of peak signal-to-noise ratio (according to the definition of peak signal-to-noise ratio, it considers as the main metric to evaluate the quality of an image and utilises to measure the degree of realism of an image, with higher values indicating better denoising effects) of the meteorological remote sensing images, it finds that the average PSNR gain of the algorithms proposed in this study is in the range of 0.39 dB to 4.45 dB. The above experimental results of meteorological remote sensing images indicate that the improved BM3D algorithm works better, especially in the mixed noise denoising of Gaussian white noise and impulse noise.
Abstract: Based on the hourly and daily precipitation data of 61 national meteorological stations from 1961 to 2020 and 998 regional automatic meteorological observation stations from the beginning of the establishment to 2020 in Liaoning Province, we analyse the main causing factors of rainstorm and flood disaster, calculate the environmental indicators of rainstorm and flood disaster, and complete the hazard assessment of rainstorm and flood disaster in Liaoning Province. The results show that the high-risk area of rainstorms and floods is mainly located in Dandong. The high population risk areas of rainstorm and flood disaster are mainly located in Shenyang and Dalian urban areas. The high economic risk areas of rainstorm and flood disaster are mainly located in Dalian and Panjin urban areas. The high-risk areas of rice and maize are mainly located in Jinzhou, Panjin, and Dandong. The disaster risk of the rainstorm process on 28-29 July 2022 is pre-assessed using the intelligent grid forecast data of Liaoning Province. It is found that the high hazardous areas are mainly distributed in Chaoyang, Huludao, and the central part of Liaoning. The population and economic high-risk areas caused by the rainstorm disaster are mainly located in the western and central areas. The high-risk areas of rice and maize caused by rainstorm disaster are mainly located in Shenyang, Tieling, and the north of Chaoyang. It is estimated that the population affected in the high-risk area is about 4.49 million, the economic loss is about 14.32 million yuan. The affected rice area is about 10,280 hectares, the maize area is about 17,798 hectares. Through the post-disaster effect test, it is found that the pre-assessment model is effective and can be used in the actual rainstorm and flood disaster risk assessment business.
Abstract: In order to further strengthen the application of satellite-to-ground lightning, the spatial-temporal distribution characteristics and spatial-temporal matching features are comparatively analysed in Zhejiang Province based on lightning data from FengYun (FY)-4A Lightning Mapping Imager (LMI) and Advanced Direction and Time-of-arrival Detecting (ADTD)-2C three-dimensional lightning location system from June to August in 2020. In addition, by combining reflectivity of Doppler radar mosaics and cloud top brightness temperature from FY-4A Advanced Geosynchronous Radiation Imager (AGRI), the spatial and temporal evolution patterns of lightning data from two observation systems are analysed during a thunderstorm process in Zhejiang Province on 15 July 2020. The results show that from June to August in 2020, the number of LMIG detected by LMI was 8483, while the number of lightning detected by the ADTD-2C three-dimensional lightning location system was 376932. The ratio of the two sets of data was approximately 1∶44.43. The monthly and spatial distributions of lightning detected by these two systems were generally consistent, while diurnal variation of which were different. Specifically, diurnal variation of LMIG presented two peaks, and diurnal variation of three-dimensional lightning showed only one peak. Besides, when the time matching window was larger than 1.8 seconds, and the latitude and longitude matching window was larger than 0.5°, the matching rate gradually tended to be stable. Furthermore, the height of three-dimensional lightning matched with LMIG was mainly concentrated below 16 km, and the lightning intensity of which was mainly concentrated below 50 kA. During the thunderstorm weather in Zhejiang Province in the afternoon on 15 July 2020, the ratio of LMIG to three-dimensional lightning was approximately 1∶25.44. The time of the first LMIG and its peak time were both later than the time of the first three-dimensional lightning and its peak time. What’s more, the lightning data observed by the two systems corresponded well with the development process of the thunderstorm. When the thunderstorm was at the developing stage, the number of lightning data detected by the two systems was both gradually increasing, and when the thunderstorm was at the mature stage, the number of lightning data detected by the two systems was both maintaining a relatively high value, and when the thunderstorms were at the dissipation stage, the number of lightning data detected by the two systems was both decreasing rapidly. When it came to the spatial distribution of the lightning, both of the two datasets corresponded well with the spatial distribution of low cloud top brightness temperature.
Abstract: In order to apply the Hail Detection Algorithm (HDA) related products more extensively and correctly, for the 22 hail cases monitored in Pu’er area from 2015 to 2020, the new Radar Operational Software Engineering (ROSE2.0) is used to replay radar-based data and analyse the relevant products. The recognition effect of the HDA algorithm in the Pu’er area is evaluated with the probability of detection (POD), false alarm rate (FAR), and critical success index (CSI), and a localised parameter configuration scheme is provided after that, which is useful to improve the local hail warning ability. The results show that although the POD of the HDA algorithm in Pu’er area is close to 100%, there are also many ordinary storm cells that are identified as hail cells mistakenly. The number of false alarms is very huge, and the low CSI cannot meet the requirement of the weather forecasting operation. The warning effect of using Probability of Severe Hail (POSH) is better than that of Probability of Hail (POH) for any size of hail, and the larger the size of hail, the lower the probability of false alarm of POSH. Further analysis of the adaptation parameters of the POSH algorithm by a simulation test method shows that the height of the 0 ℃ and -20 ℃ layers has a significant impact on the recognition ability of POSH, the original default value is significantly lower in Pu’er area, correctly inputting the height of 0 ℃ and -20 ℃ layers on the day of hail can effectively reduce the FAR and improve the CSI of POSH; at the same time, it can control the situation that the maximum hail diameter predicted by the algorithm is generally too large, and the maximum expected hail size (MEHS) is closer to the observation value; the deviation percentage of small and medium-sized hail diameter decreases by 76.07%, with a significantly higher improvement effect than large hail, but the diameter prediction error of MEHS for large hail is smaller. In addition, increasing the reflectivity factor and POSH threshold can effectively control FAR, but it also leads to a rapid increase in the number of missed alarms. When the threshold is too large, the POD significantly decreases. In order to achieve a higher POD and CSI, selecting Z=50 dBz or POSH=70% as the threshold can improve the recognition effect of the HDA algorithm. Setting the optimal threshold of multiple parameters at the same time can effectively improve the recognition ability of the HDA algorithm in Pu’er.
Abstract: Based on the ensemble forecast data derived from European Centre for Medium-range Weather Forecasts (ECMWF) ensemble forecast system and observation data derived from automatic observation stations in Zhejiang region, the Bayesian Model Averaging (BMA) method is used to calibrate the probabilistic forecasts of precipitation during the super long Meiyu season in 2020. In this paper, we verify the raw ensemble probabilistic forecast and BMA calibrated probabilistic forecast from 1 June to 15 July, 2020, by Mean Absolute Error (MAE), Continuous Ranked Probability Score (CRPS), Brier Score (BS), Talagrand, Probability Integral Transform (PIT) histogram, and attribute diagram. The verification results before and after calibration are compared. The analysis results are listed as follows. (1) In 8 different training periods (10 days to 80 days), 50 days correspond to smaller MAE and CRPS score values. So we set 50 days as the optimal BMA training period for ECMWF ensemble forecast calibration in the Meiyu season in Zhejiang Province. After BMA calibration in the optimal training period, the spread of ensemble forecast increases and the forecast error decreases. Analysing from the quantitative verification indicators, BMA can effectively calibrate the overall precipitation in the test stage, but it cannot calibrate the daily precipitation in the test stage. (2) For forecasting of different threshold precipitation, BMA has different calibration performance. For the thresholds of 0.1 mm, 10.0 mm, and 25.0 mm, BMA has a significant calibration effect. After BMA calibration, the CRPS of precipitation probabilistic forecast for these three thresholds (0.1 mm, 10.0 mm, and 25.0 mm) decreases by 25.92%, 19.29%, and 4.76%, respectively. However, the calibration effect of BMA weakens with the increase of precipitation threshold. For the events with total precipitation exceeding 50.0 mm, the BMA calibration effect is not as significant as that of the smaller threshold. In addition, BMA can effectively improve the forecast skills of 0.1 mm, 10.0 mm and 25.0 mm threshold precipitation and make the forecast probability more closely match the observation. (3) In the case of heavy rain, the high probability range of the raw ensemble probabilistic forecast is always wider than that of the observation. BMA has the ability to slightly calibrate the raw ensemble forecast probability. After BMA calibration, the high probability range of precipitation forecast at each threshold effectively reduces the deviation. The empty message information and the probability of empty message events also reduce after calibration. So BMA can make the calibrated high probability range of precipitation forecast more consistent with the observed range. But unfortunately, BMA cannot adjust the spatial distribution of precipitation forecast probability.
Abstract: In order to achieve the goal of independent and controllable key core technologies for Meteo by 2025, the Meteo Big Data Cloud Platform (referred to as Tianqing) establishes a simulation environment based on Hygon X86 CPU and Kylin OS. However, in the operation of simulation platforms, it finds that the docker scheduling performance of data processing and assembly line subsystems based on Kubernetes is poor, which cannot meet the timeliness requirements of user integration algorithms. In response to this issue, this article adopts a comparative analysis method, selecting servers based on three types of CPU and three types of operating systems from the simulation environment and business environment for Tianqing as the research objects. A series of combined comparative test cases are designed. It finds that the kernel is the key factor affecting docker scheduling performance. Further analysis is conducted on the impact of operating system kernel settings on real-time and throughput, as well as the suitable business scenarios. Finally, a method for adjusting the Kylin OS kernel is provided. By adjusting the kernel settings, the docker scheduling performance significantly improves, meeting the timeliness requirements of the data processing system and laying the foundation for achieving self-supporting of the key core technology of Tianqing.
Abstract: CINRAD/SA, China Next Generation Weather Radar,was produced by the Beijing METSATAR Radar Co., Ltd, based on the NEXRAD WSR-88D technology.Its software system was modified to provide the new RHI/PPI scan mode because NEXRAD WSR-88D provides only the volume scan mode. The design and realization of the RHI/PPI scan mode on the CINRAD/SA are described.
Abstract: An analysis is made of the annual, seasonal, and monthly variation characteristics of sunshine duration in recent 50 years and its relationship with total cloudiness, trying to detect the variation of sunshine duration in Chengdu by the abrupt climate change theory. The results indicate that in recent 50 years, the sunshine duration decreased with a tendency of 69.41 hours per ten years; the interannual variation amplitude was obviously greater; and the difference between the sunshine durations in 1963 and in 1989 is up to 662.8 hours. There is obvious seasonal difference in sunshine duration, with bigger decreasing amplitude in summer and winter than those in spring and autumn and a tendency of -29.77 and -20.17, -9.91 and -9.56 hours per ten years, respectively. The decreasing tendency is obviously greater in August and less in April. The annual variation of sunshine duration is consistent with sunshine percentage. The sudden change occurred around 1978, with the annual sunshine duration decreased rapidly.
Abstract: In order to develop and utilize reasonably climate resources and offer a scientific basis for the sub-area management of livestock production over grasslands, an analysis was made of the Inner Mongolia grassland climate characteristics and effects of climate on the growth of pasture grass, the distribution of domestic animal breeds and the soil environment. It is found that some isolines of climatic moisture are almost superposed with the boundaries of soil, which indicates that the formation of soil zones is closely related to climatic conditions, and climate and soil environment are main influence factors for pasture types and the ecosystem. Based on the climatic moisture, in combination with the distribution characteristics of soil over Inner Mongolia, a regionalization was carried out of grassland ecological types, which is not only rational, but also stable. It is pointed out that the climatic warming and the resulting changes in recent years improved, to some extent, the productivity of the grasslands, but not changed the ecotype in Inner Mongolia.
Abstract: An observational experiment was conducted on the impact of air temperature and humidity variation on soil resistivity and earthing resistance with different structures by selecting three typical soil conditions to set three vari structure lightning protecting earthing bodies in Ningxia for one year. By means of comparative and regression analysis, the impacts of different soil conditions on soil resistivity at different temperatures and humidity in different seasons, and the variation characteristics and regularities of the lightning protecting earthing bodies with different structures are studied, and accordingly the optimal requirements for the layout and structure of lightning protecting earthing bodies are presented.
Abstract: An introduction of the main reanalysis data of NCEP, ECMWF, JMA and the preliminary comparison among them are given from the following aspects: (1) assimilation systems, including the assimilation module and method; (2) the data used in the reanalysis; and (3) the methods of quality control and bias correction. The main assimilation methods of all reanalysis datasets include the 3D variational method, 4D variational method, and optimum interpolation. The dominating differences of these reanalysis datasets are data types and the resolution of modules. In addition, the advantages and deficiencies of these reanalysis datasets are given by empirical analysis. It is helpful for selecting the correct reanalysis dataset. The advances in reanalysis in China ars introduced simply and some problems on the improvement of the reanalysis in China are discussed.
