The wind profile radar data observed from the western mountainous and eastern plain areas of Chengdu from 1 Jan to 31 Dec 2022 are used to analyse the regional differences in the vertical structure and daily variation trend of the boundary layer wind field. The results indicate that: (1) There are regional differences in the prevailing wind direction of the lower boundary layer in Chengdu, with north-north-east winds prevailing in the western mountains area and northeast winds prevailing in the eastern plains, and without seasonal difference. While there is no regional difference in the prevailing wind direction between the east and west regions in the middle and upper boundary layer, both occur alternately with northeast and southwest winds that are consistent with the direction of the western mountain range. (2) The variation trend of the average wind speed profile in the boundary layer between the eastern and western areas of Chengdu is consistent, and the horizontal wind speed in the eastern plain area of the same detection height layer is greater than that in the western mountains area. (3) The local mountain-valley breeze is significant in the lower boundary layer of the western mountains area; the valley breeze with southeast winds prevails during the day; and the mountain breeze with northwest winds prevails at night. (4) There are no regional differences in the diurnal variation characteristics of horizontal wind speed at the same detection height layer, and it is a “single peak and single valley” type, with a peak during the day and a valley at night. (5) The differences in the wind field characteristics of the lower boundary layer between the east and west regions of Chengdu are mainly caused by the local complex terrain of the western mountains area.
NO2 is the common precursor of the secondary conversion of PM2.5 and O3. Understanding its change characteristics and influencing factors is of great significance for the collaborative treatment of PM2.5 and O3. Based on the VLF/LF three-dimensional lightning location monitoring system, SNPP/VIIRS satellite fire point data, NO2 column density in Sentinel-5P NRTI NO2 data products and other data, using various statistical methods, selecting February to April with high NO2 density and June to August with frequent lightning activities, this paper compares and analyses the influence of lightning activities on NO2 density in region A (96.5°-102°E, 20.5°-24°N) with high biomass burning in southwest Yunnan and its surrounding areas and region B (102°-104°E, 24°-26°N) with high human activities in central Yunnan. The results show that: (1) There are obvious differences in the spatial and temporal distribution of the number of lightning and NO2 column density in region A and region B. The NO2 column density outside area A is higher than that in China, but the distribution of lightning times is the opposite. The NO2 column density in region B decreases from Kunming to the surrounding area, and the number of lightning is less and more. In the dry season (November to April of the next year), the concentration of NO2 density is higher, and the rainy season (May to October) is lower, and the number of lightning is opposite. (2) From February to April, the NO2 column density in region A and region B has a significant positive spatial correlation with the number of fire points and anthropogenic CO2 emissions, respectively, but a significant negative correlation with the number of lightning. (3) Lightning activity is mostly accompanied by obvious rainfall (R≥1 mm). When the lightning activity is weak, the wet deposition effect of rainfall on the ground NO2 density is obvious, and the wet deposition effect of rain falling from the stronger lightning activity cannot completely offset the contribution of lightning to the increase of ground NO2 density. (4) The change of surface NO2 density during the first lightning from June to August is more regular than that from February to April, which shows that the ground NO2 density increases hourly in the first 6 hours and decreases slowly hourly in the last 3 hours. (5) The ground NO2 density on the lightning days in the two regions is generally higher than those on the days without lightning. Changes in biomass combustion intensity, rainfall intensity and planetary boundary layer height have a significant impact on the ground NO2 density.
To enhance the forecasting capability for daily extreme wind speeds, particularly for winds exceeding force 8, this paper uses the “past 3 h gust” wind speed forecast output from the European Centre for Medium-Range Weather Forecasts (ECMWF) model as the primary input factor. Additionally, the paper addresses the extremely uneven sample distribution in the daily extreme wind speed series (samples with wind force above level 8 constitute a very small proportion of the total sample, while samples with wind force below level 5 constitute the vast majority). Moreover, the ECMWF model’s “past 3 h gust” wind speed forecast tends to overestimate low-level winds and underestimate high-level winds. Therefore, the paper leverages nearly five years of surface observations and ECMWF model “past 3 h gust” forecast data to develop a Tabnet-based daily extreme wind classification correction forecast model. The model’s input design includes previous observations, geographic information of the stations, ECMWF forecast fields, and previous forecast error terms. In the evaluation of an independent sample over one and a half years, the new correction forecast model reduces the mean absolute error (MAE) by 45.2% and the root mean square error (RMSE) by 25.7% compared to the interpolated ECMWF model. Furthermore, for wind force levels 1-5 and above 8-9, the new correction forecast model significantly improves the forecasting accuracy compared to the method using interpolated ECMWF forecast fields, demonstrating the feasibility of this forecasting approach.The model is constructed with a focus on overcoming the inherent limitations of the ECMWF model’s wind speed forecasts. By incorporating comprehensive input factors such as historical observation data, the geographical context of observation stations, and systematic forecast error corrections, the model aims to provide a more accurate prediction of extreme wind events. The primary challenge addressed by the model is the skewed distribution of wind force levels in the dataset, where extreme wind events are underrepresented. The innovative use of the Tabnet algorithm allows for a sophisticated analysis and adjustment of the forecast data, thus ensuring higher accuracy in predicting both low and high wind force levels. The independent validation over an extensive period highlights the robustness of the model. The significant reduction in MAE and RMSE underscores the model’s enhanced performance. Specifically, the accuracy improvements for the critical wind force levels 1-5 and 8-9 plus indicate the model’s practical applicability in real-world scenarios. This advancement is crucial for sectors reliant on precise wind forecasts, such as maritime operations, aviation, and disaster preparedness. The results clearly suggest that integrating historical data and addressing the ECMWF model’s biases can lead to substantial improvements in extreme wind speed forecasting. In conclusion, the development of the Tabnet-based correction forecast model represents a significant step forward in meteorological forecasting. By effectively addressing the biases and limitations of existing models, this new approach offers a more reliable tool for predicting extreme wind events.
There are many ships and ports in Shanghai coastal zones, where disastrous weather occurs frequently. Meteorological disasters often threaten the safety of people’s lives and properties along the coast and in the ports. In the past, the meteorological warnings for Shanghai coastal zones are mainly based on those issued for Yangshan Port by Shanghai Marine Meteorological Centre (SMMC), which are called “unified warnings”. However, there are obvious differences in the time and intensity of meteorological disasters in each region, and the unified warnings cannot meet the needs of the production and operation of the shipping and ports. In 2020, Shanghai coastal zones were divided into five sub-zones, where the meteorological forecast and warnings were carried out separately from July 2020. Based on hourly observational data of the representative stations in Shanghai coastal zones and warning signal data from 2016 to 2022, the gale events are selected to analyse the statistical characteristics of temporal and spatial distribution and evaluate the forecast quality and the economic benefits of the zonal warnings. The results show that: (1) The farther away from the coastline, the more gale days, the higher the wind speed and the longer the duration; the higher the wind speed during the process, the more obvious the difference of wind scale. In particular, the wind scale caused by typhoons can range up to 5 levels. (2) Compared with the “unified warning”, the missing alarm rate (MAR) of gale warnings in each sea area has been reduced significantly, by up to 5%, the false alarm rate (FAR) for the western part of Yangtze River estuary is reduced by more than 8% and the TS score is significantly improved by more than 10%. (3) The advance time of gale warnings has been reduced by more than 3 hours, the maintaining duration has been shortened by more than 16 hours at most, which can reduce the loss of nearly 18 million RMB and improve the production efficiency of the coastal zones of Shanghai greatly. The results of this paper show that refined marine meteorological forecasts and early warnings provide a safety guarantee for marine transportation and port production operations, resulting in significant social and economic benefits and the enhancement of the comprehensive guarantee level of marine meteorological services in Shanghai. In the next step, we will continue to research and develop more refined objective forecast methods adapted to this business, then build a regional shared operational system platform and extend it to the Yangtze River Delta region, so as to promote the high-quality development of shipping meteorological integration in the Yangtze River Delta region.
The increasing frequency, intensity and scope of extreme heat events due to climate change, which is mainly characterised by significant warming, is one of the current key climate stressors for sustainable development in terms of socio-economics, ecological balance and agricultural production in Jiangxi Province. High-temperature dangerousness evaluation is the basic work of high-temperature disaster risk assessment. However, in Jiangxi Province, the current research on high-temperature hazards mainly focuses on the analysis of trends and spatial distribution patterns, and few studies are conducted to reveal the risk of high-temperature occurrence through disaster risk theory. In this paper, based on the daily maximum temperature data of 79 meteorological stations in Jiangxi Province from 1961 to 2022, the trends of three disaster-inducing factors (the number of high-temperature days, the extreme maximum temperature and the high-temperature intensity) and their values under four return periods (1 in 5 years, 1 in 10 years, 1 in 20 years, and 1 in 50 years, respectively) are analysed using the least square method and the Kernel density estimation method, respectively. Then, through K-mean cluster analysis, the dangerousness distribution of each disaster-causing factor is obtained and a comprehensive high-temperature dangerousness map is produced. Finally, according to the disaster risk theory, the agricultural heat risk is assessed by the product of high-temperature dangerousness, agricultural exposure (quantified by land use cover) and agricultural fragility (quantified by gross domestic product kilometre gridded data). The results show that: (1) The overall trend of the number of high-temperature days, extreme maximum temperature and high-temperature intensity in Jiangxi Province during 1961-2022 shows an increasing trend, but the trend has a phased character, with a decreasing trend before 1997. (2) The dangerousness of each disaster-inducing factor is relatively high, with the proportion of high-risk areas in the province ranging from 41.7% to 61.4%. (3) The comprehensive dangerousness shows a spatial distribution pattern of low in the north and low in the centre, and the high-risk areas are mainly concentrated in the eastern part of Shangrao and most parts of Ji’an. (4) Agricultural medium-high risk zones are consistent with the spatial distribution of the dangerousness map. However, due to the uneven distribution of agricultural fragility, the low-risk zone is more surrounded by cities, and is mainly concentrated in southern Ganzhou, most of Xinyu, north-central Nanchang, and eastern Jiujiang. This paper can provide some reference for the comprehensive risk assessment of meteorological disasters.
This study uses litchi from Conghua and Zengcheng regions in Guangzhou as insurance targets, and combines the meteorological disasters throughout the entire growth period of litchi to design litchi meteorological index insurance products and verify insurance payout rates. Based on the litchi yield data and historical meteorological data, litchi yield reduction rate models are structured. Based on the correlation between meteorological factors and yield reduction rate, key meteorological indicators that affect litchi yield are screened. Further, the distribution of litchi meteorological index is fitted, the insurance pure premium rate and the insurance amount per unit area for different meteorological index grades which trigger compensation are determined. The meteorological index is set to 2 to 4 levels; different weather index levels of the amount of compensation are determined; and the rationality of compensation rates at different time scales is verified. In this study, the time scale of litchi yield and planting area data is from 2001 to 2020, while the time scale of meteorological data is from 1959 to 2020. The results show that the precipitation index at the flowering stage of litchi in Conghua district, the low-temperature index at the heading stage, and the precipitation index at the ripening stage of litchi in Zengcheng district are the key meteorological indicators that lead to the reduction of litchi yield in the two regions mentioned above, respectively. When the cumulative precipitation in Conghua district reaches 350 mm at the flowering stage of litchi, the minimum temperature is 3.3 ℃ at the heading stage of litchi, and the cumulative daily rainfall of ≥100 mm at the ripening stage of litchi lasts 2 days in Zengcheng district, a claim is triggered. The premium amounts corresponding to different levels of three meteorological indices are between 448-2582 yuan/hm2, 522-2567 yuan/hm2, 1403-3284 yuan/hm2, respectively. The compensation ratios for different meteorological indices and levels are between 7%-10%, 7%-10%, and 10%-12%, respectively. The average payout rates of Conghua district and Zengcheng district at three times in 10, 20 and 30 years are 77.0%, 69.6% and 63.7% respectively, which is consistent with the requirement of 65%-75% of the insurance company’s claim rate. The designed insurance product of Litchi Meteorological Index can provide a reference for a new round of policy agricultural insurance in Guangzhou, and provide technical support for litchi farmers to disperse and transfer the risk of meteorological disasters.
Based on the observation data from the coastal island stations in Beibu Gulf and the fifth generation ECMWF reanalysis (ERA5) data, the probability of occurrence of mixed-type (that is, high-pressure rear type to southwest inverted trough type) sea fog processes in Beibu Gulf is analyzed. Furthermore, the typical mixed-type sea fog process from 15 March to 23 March 2016 is selected for in-depth analysis. The results show the following: (1) Compared with other types of sea fog processes, the probability of occurrence of the mixed-type sea fog processes was the highest during 2015-2020, with a cumulative number of 15 times (58 days), accounting for 34.9% (39.5%). (2) The typical mixed-type sea fog process from 15 March to 23 March 2016 was characterised by long duration and wide range. In the early stage, the Beibu Gulf was influenced by the easterly flow behind the high pressure entering the sea. In the later period, the plateau trough developed and moved eastward, which guided the warm and humid south-western airflow to expand inland, and the easterly airflow gradually turned to the southerly airflow at the edge of the south-west inverted trough in Beibu Gulf. (3) In terms of thermal conditions, the strong warm and humid transport of the low-level jet stream increased the specific humidity over the Beibu Gulf, and then the water vapour saturated layer extended from the near-surface layer to 800 hPa on the one hand. It also contributed to the establishment of the temperature inversion layer on the other hand. And the inversion layer cooperated with the high humidity region below to reach a stable state. As for the dynamical conditions, the wind shear in the vertical direction maintained a certain intensity, which was favourable to the turbulent mixing in the boundary layer. The cooperation of the wind shear with the baroclinic atmosphere was the configuration that favoured the maintenance of a stable layer in the low-layer. In addition, the weak upward motion below 800 hPa was conducive to water vapour uplift and condensation in the low-layer, which promoted the development and maintenance of the sea fog. (4) During the occurrence of the typical mixed-type sea fog, the air temperature was mostly higher than the sea surface temperature in each sea area. And the difference between the air temperature and sea surface temperature in the early stage (late stage) was between 0 and 1.5 ℃ (1.5 and 2.5 ℃).
Compared to inland areas, meteorological stations in the island regions appear scarce and unevenly distributed, which leads to noteworthy uncertainty in detailed characterisation of various meteorological elements. For the Zhoushan Islands, located in Southeast China, there exist many islands and islets, and the local terrain is quite complex. Therefore, different interpolation strategies usually generate diverse gridded results, which largely influence the reliability and accuracy of operational climate monitoring and diagnosing. Under the background of climate change, the Zhoushan region is frequently invaded by cold waves in recent years, so how to scientifically choose an interpolation scheme to reasonably represent spatial distribution characteristics of temperature becomes an important issue in local climate operations. To solve this problem, based on the index of root mean square error (RMSE), the interpolation effect of Ordinary Kriging (OK), Inverse Distance Weighting (IDW), and ANUSPLIN (ANU) are comparatively analysed for 8 cold wave processes influencing Zhoushan during 2014-2021. Two subdivided indices, i.e., temporal RMSE (TRMSE) and spatial RMSE (SRMSE) are further designed to evaluate the interpolation results on temporal and spatial dimensions respectively. Eleven stations are randomly selected from the total 53 meteorological observational stations to test the interpolation results of OK, IDW, and ANU for the minimum temperature, reduction of daily minimum temperature and daily-mean temperature in the 8 processes. It can be found that the bias in the ANU case is higher than that in the OK and IDW cases. To explain such a phenomenon, 3 interpolation experiments with dense surrounding stations, sparse surrounding stations, and specific distribution of examining stations (all the examining stations are not distributed in the main island of Zhoushan) are further designed. The results demonstrate that the performance of the ANU strategy is closely linked to the spread situation of peripheral stations. When the surrounding stations are concentrated, the interpolation bias of ANU is usually smaller than that of OK and IDW. However, if the surrounding stations appear sparse, the bias of ANU exhibits much larger. In the scenario of dense peripheral stations, regardless of the examining sites distributed over the main island or not, the ANU solution can always get the optimal interpolation results, which implies that the impact of topography on the performance of ANU in temperature interpolation is of less importance. Also, the influence of horizontal resolution for interpolation seems secondary. When the horizontal resolution for three interpolation schemes falls down to 1 km×1 km from 30 m×30 m, the change of RMSE is generally less than 0.1 ℃ for most circumstances, so the impact of interpolation resolution can be neglected.
Prolonged and widespread heavy rainfall events can significantly impact hydrological conditions, leading to devastating floods. This study focuses on individual cases of flood-waterlogging rainfall events in the middle and lower reaches of the Yangtze River since the 1960s. We employed a combination of SAN (simulated annealing and diversified randomization) clustering method and perturbed ensemble analog method to investigate the key circulation patterns associated with these flood-waterlogging rainfall events and quantify their contributions to heavy rainfall. Results indicate that these flood-waterlogging rainfall events in the middle and lower reaches of the Yangtze River typically occur during the Meiyu season. Averaged over all events, the daily peak precipitation intensity reaches the level of a rainstorm. The 500 hPa circulation patterns associated with extreme rainfall were categorized into four classes: East Asian dipole mode (Class 1), East Asian Sandwich mode (Class 2), South Branch Trough (Class 3), and High-Latitude Double Block (Class 4). The key circulation features of each class are distributed in the Western Pacific Subtropical High, South Branch Trough, mid-latitude westerly trough, and high-latitude blocking activity areas, contributing to 40% to 70% of flood-waterlogging rainfall events. The Western Pacific Subtropical High and South Branch Trough contribute relatively consistently in all four categories, accounting for approximately 30% and 15%, respectively. However, the influence of mid-high latitude systems is less stable, with the Northeast Asian circulation anomaly contributing to an average of nearly 20% to Classes 1, 3, and 4, and the Baikal Lake blocking anomaly making a weak contribution to Class 2. The mid-latitude westerly trough anomaly contributes to approximately 20% of Class 4 events. The significant anomaly regions of low-frequency circulation at 10-30 days and 30-60 days in the four classes are generally consistent with the observed circulation anomaly key regions. Low-frequency circulation in the Western Pacific Subtropical High region has a positive contribution to all events, ranging from about 20% to 70%. Among these, the 10-30 day low-frequency activity also has a notable impact on Class 1 and 2 events. The 30-60 day low-frequency circulation in the South Branch Trough region contributes 27% and 16% to Classes 3 and 4 events, respectively. The key high-latitude low-frequency circulation relating to the flood-waterlogging rainfall events located in Lake Baikal and the Okhotsk Sea (Class 1), the Ural Mountains and the westerly trough region (Class 2), the cold vortex region in Northeast China (Class 3), with their contribution varying among different classes (12%-31%). The findings of this study on key circulation patterns and their quantitative contributions provide valuable insights for a deeper understanding of the formation and prediction of flood-waterlogging rainfall events.
Effective data cleaning methods can improve the quality of wind turbine measurement data. The quality of wind turbine data plays a very important role in wind resource assessment, wind power accurate prediction, and performance diagnosis of wind turbines. There are many uncertainties in the data collection and monitoring systems of different wind turbines for fault diagnosis, which result in uneven quality of wind measurement data for wind turbines. This paper proposes a new method for identifying the probability interval of wind power. This method uses the characteristic changes between wind speed and power to clean and correct the effective data of wind turbine measurement data. It can effectively improve the utilisation rate of wind turbine data. This paper selects wind turbine data from a wind farm in the northern part of Ulanqab, Inner Mongolia Autonomous Region from 2020 to 2022. By sequentially subjecting the data to rationality and validity tests, wind power interval checks, and finally, cleaning and correcting abnormal data, which are carried out by utilising the correlation of the turbine. The final results indicate that: by using the wind power interval method, it is difficult to distinguish abnormal wind speeds. This method can improve data quality and enhance the accuracy of wind speed and power. According to statistics, the data integrity has been significantly improved from 68.7%-92.5% to 90.1%-92.7%. Above all, the data integrity has been significantly improved. This method achieves mutual calibration between wind speed and power through the wind power probability interval recognition method. It provides fundamental support data for predicting and regulating the power generation of wind farms. It provides guidance and a basis for more refined meteorological service products for power and other related sectors.
Based on conventional meteorological observation data, ERA5 reanalysis data and numerical model data, the mesoscale characteristics, circulation background, physical quantity environment and predictability in different stages of typhoon Chaba rainstorm at northern Hubei are analyzed. The results indicate that: (1) This process occurred under the circulation background of the typhoon moving northward and the combination of the westerly trough. The 700-925 hPa strong southerly jet on the east side of typhoon Chaba, combined with the upper-level typhoon circulation, provided the dynamic lifting conditions and water vapour supply conditions for the heavy precipitation. According to the influence of the system, this process could be divided into two precipitation stages: spiral cloud belt and low pressure main body. Both stages had obvious characteristics of convective warm cloud precipitation, and the hourly rain was strong. However, the precipitation in the spiral cloud belt was short in duration and dispersed in scope, while the precipitation in the low pressure main body was long in duration, wide in scope and large in cumulative rainfall. (2) The primary factor contributing to the disparity in rain mass evolution between the two stages lay in the fact that the spiral cloud belt stage exhibited robust convective instability and potential energy, while vertical motion and water vapour convergence at mid-to-low levels were relatively weak. Consequently, convective triggering was more dispersed and short-lived, primarily driven by cold outflows generated by ambient wind fields and cold precipitation areas. Conversely, during the main stage of the low pressure main body, there was also strong convective instability. The ascending movement, water vapour transport and convergence formed by the convergence of the lower level and the divergence of the upper level were significantly enhanced compared with the spiral cloud belt stage. At the same time, the cold air from the north invaded into the energy front, providing favourable thermal, dynamic and water vapour conditions for a large range of strong convection for a long time. Additionally, topographic blocking effects induced backward propagation of mesoscale convective systems (MCS), which together with synoptic-scale systems gave rise to a “train effect”. (3) In this process, the short-term predictability of heavy rain was high, but the predictability of precipitation extremes and strong central falling areas was low. In comparison, the global model had more advantages for the precipitation forecast of the low pressure main body stage, among which ECMWF yielded the best results, while the mesoscale model had more advantages for the precipitation forecast of the spiral cloud belt stage.
The optimal interpolation method is widely used in meteorological applications worldwide. However, due to the different precipitation climate characteristics and the spatial distribution of gauges, the implementation of the optimal interpolation method involves many empirical models and key parameters. There is still some uncertainty on how to derive a set of optimised parameters for the application of the optimal interpolation method in a local region. This study analyses and optimises the key parameters of the optimal interpolation method for the calibration of weather radar quantitative precipitation estimation using radar and gauge observations from May to September 2020 in Qingdao, Shandong. The search radius of gauges from a grid point is determined by analysing the spatial distribution of all gauges relative to the analysis point. The optimal number of gauges used to calibrate the precipitation estimation of a grid point is determined by analysing the change of relative analysis error with respect to the number of gauges. Eight groups of sensitivity experiments with different correlation functions are compared by random sampling and cross-validation to find the best set of parameters for the Qingdao radar. The verification of calibration results produced by the best parameters shows that the calibration significantly improves the accuracy of the quantitative precipitation estimation. The median values of MAE, RMAE, and BIAS are 1.5 mm, 1.0, and 0.03 mm respectively, and the CORR is higher than 0.9. Comparing the quantitative precipitation estimation at different levels of precipitation before and after calibration produced by the best parameters shows that the MAE and RMAE of light rain are reduced by 90%, and the CORR is about 0.87. The MAE and RMAE in moderate to heavy rain are decreased by 89%, and the CORR is higher than 0.9. The MAE and RMAE of rainstorm are decreased by more than 83.9%, and the CORR is about 0.77. Based on the verification of a widespread rainfall case on 26 August 2020, the intensity and spatial distribution of the quantitative precipitation estimation after calibration are closer to the gauge observations. The original quantitative precipitation estimation is relatively smooth and lacks small-scale variations. The calibrated results can reflect the characteristics of the local change that is consistent with the observation of gauges. The results of this paper suggest that the optimal interpolation method with optimised local parameters can significantly improve the accuracy of the quantitative precipitation estimation, which has important application value for rainstorm warning and flood disaster prevention.
新一代天气雷达的故障识别和判定一直是雷达运维面临的技术挑战,部分严重故障的滞后响应会导致雷达相应部件的损坏加剧.本文在结合人工智能技术和雷达数据特点的基础上,提出了基于自适应阈值的SA雷达发射机系统故障预警和判定方法,建立了基于重构模型的发射机故障在线监测的算法框架.该算法框架能够在雷达发射机故障发生早期识别异常并给出预警.采用实际运行记录进行计算分析,并验证了该方法在实际运行中有效可用.
青藏高原积雪监测在地球辐射平衡、全球气候变化和生态环境等方面有重要作用,对气候预测、雪灾预测等具有重要意义.FY-4(风云4号)卫星数据具有高时空分辨率的优势,基于FY-4A(风云4号A星)构建积雪监测方法与模型,不仅拓展了静止卫星应用领域,也丰富了积雪监测应用的手段.FY-4的高时间分辨率为积雪监测的研究提供了分钟级数据,对积雪与云的变化掌握的更为细致,但用于积雪监测的波段,因分辨率不高容易导致错判与漏判.本文基于2020年小时级野外地面雪深观测数据、风云3号D星积雪覆盖产品(FY-3D_SNC)数据,构建了基于归一化积雪指数(Normalized Difference Snow Index,NDSI)的FY-4A卫星积雪判识方法,提出了雪深监测模型与等级划分指标.结果表明:NDSI≥0.20是青藏高原地区FY-4A卫星积雪判识的适用阈值,无论有云或无云条件,其漏判率均低于8.0％.地面站点验证结果表明,积雪判识准确率达83.33％以上.空间范围内直接剔除云区后,积雪判识经混淆矩阵验证准确率在82.48％以上.因此,FY-4A卫星在青藏高原地区具有积雪监测的能力.虽然FY-4A卫星对超过10 cm以上雪深不具备区分能力,但可以较好地识别10 cm以下浅雪雪深,相关系数达到0.745,通过了0.001显著性水平检验.据此建立的FY-4A卫星0～10 cm雪深等级指标,总体分级精度达到87.50％.FY-4A卫星雪深反演方法在青藏高原地区对0～10 cm浅雪雪深有较好的估算能力.
为科学评价雷电防护措施的防护能力,分析国内外多种评价方法的优劣,结合雷电防护措施的特点提出基于改进层次分析法的模糊综合评价.依据雷电防护相关技术标准和规范,综合考虑雷电环境、承载体特征、自然因素的影响,建立雷电防护能力评价指标体系.利用改进层次分析法计算各级指标的权重,采用模糊综合评价法确定雷电防护能力评价等级.运用此方法对采取多种雷电防护措施的油气集输站进行雷电防护能力评价,评价等级与实际情况和有关文献案例相符,表明该方法适用于雷电防护能力的评价.
为达到整合省级气象服务数据资源、统一气象服务出口的目标,推动气象数据产品在防灾减灾和公共服务中的高效应用,基于Spring Cloud云架构,采用软件插件(Plugin)、WEB服务(WebService)等技术研发了湖南气象服务平台.本文详细介绍了此平台的分层模型和功能结构,以及所采用的多源气象数据融合处理、基于微服务的气象数据并行处理、基于可缩放矢量图形(Scalable Vector Graphics,SVG)的气象数据矢量化和数据安全交互等关键技术方法.此平台通过天擎获取气象数据产品,统一加工处理后封装为数据插件、图形插件和行业插件,通过标准接口提供观测资料、预报预警、卫星云图、雷达拼图等产品,基于气象数字对象标识符(Meteorological Digital Ob-ject Identifier,MOID)保障气象数据产品服务过程的安全性.此平台的气象服务插件已应用于湖南省应急管理厅、自然资源厅、水利厅等单位的业务系统.应用结果表明:此平台的插件服务模式适应C/S客户端、WEB网站、移动APP等不同类型的气象服务需求,实现了气象数据服务的统一管理、高效加工和规范应用,提升了省级气象服务的能力.
针对2021年7月20日发生于河南省郑州市的极端暴雨事件,利用数值模式对此次暴雨过程中的地形影响问题进行了数值试验分析.结果发现,CMA-MESO(GRAPES-MESO 3 km)模式能够较好地模拟此次极端降水过程.地形对降水具有显著影响,地形高度降低时,降水中心强度减弱,位置偏北;地形高度增加时,降水中心强度增加,位置偏南.其主要影响机制为:①太行山与郑州市西侧山体抬升作用使郑州市西部出现较强的上升运动中心;太行山南端阻挡作用使东南暖湿气流北支一部分气流向西偏转,与越过伏牛山的偏南气流及东南气流南支汇合,使郑州市上空维持大尺度水汽辐合,进而产生极端暴雨.②当地形高度增加时,东南气流北支的部分气流遇太行山阻挡转为偏东北气流,气流辐合区强度增强,郑州市上空水汽含量明显增加;而东南气流南支受伏牛山阻挡抬升作用影响在郑州市西南侧也产生了强上升气流,在郑州市西北侧与西南侧形成两个强降水中心.
受限于地理环境、通信条件及人员保障能力,青藏高原科学考察数据无法通过现有气象通信系统进行收集和传输,这一直是困扰着气象科研工作者的难题.本文依托电信运营商的公有云资源建立科考数据汇集与传输平台,在西藏墨脱地区开展了大量科考观测数据的收集和传输工作.该平台采用高并发数据获取和分布式并行处理框架,实现在单台服务器上数据收集、处理、分发、监控等多个应用的持续有序工作,并结合科考仪器特点对数据进行有针对性的监控和归档服务.同时,还设计实现了轻量化的数据上传客户端,解决布设于野外的科考观测设备的数据采集问题.该平台的设计可应用于其他地区的科考观测数据收集和传输工作中.
针对双权重标准差法对探空温度质量控制中阈值参数不确定的问题,提出了以正态波形指标偏度和峰度为判定依据的阈值Z参数优化算法.利用2017年福建3部探空温度资料和ERA5模式再分析温度数据,对探空温度增量数据进行不同数据集质控前后对比分析总结出:峰偏值CKS随阈值Z的分布曲线表明阈值Z偏大则会造成质控不完全,阈值Z偏小则会造成过度质控;以峰偏值CKS所确定的最优阈值Z双权重标准差法比固定阈值的更符合模式同化系统正态分布要求,为探空温度数据在模式同化的应用提供了更好的质控方法.在质控前后温度增量分布特征上,温度观测增量总是以某一固定增量值在整个气压高度上剔除离群值,其中57.15％的离群点主要分布在0～100 hPa范围内,42.85％的离群点均匀分布在100～1000 hPa范围内,其中0～100 hPa范围内温度增量绝对值大于10 ℃的异常点是由于温度传感器受太阳辐射等因素影响超出了正常探测误差范围,这些异常点可以在质控前提前剔除,进一步提升探空温度数据质量.
城市热岛效应是人类活动对大气系统影响的最主要体现之一.本文利用(Space and Time Multiscale Analy-sis System,STMAS)时空多尺度分析系统,融合了地面自动站、雷达、卫星等多源高时空分辨率观测资料,建立了城市热岛三维数据集.并在此基础上统计了2021年北京夏季的城市热岛强度变化特征,选取其中一次超强城市热岛个例(2021年6月11-12日)详细分析了其三维精细化结构特征.结果表明:①本个例中,夜间郊区近地面迅速降温,形成逆温层;而城市近地面降温缓慢,使得近地面城郊温差不断增大.②本次超强城市热岛三维温度场暖心结构在地面和990 hPa以下低空等压面清晰可见,风场距平呈现气旋性环流特征并在低空从郊区向城区辐合,引起可到达中高空的上升运动,说明城市热岛效应有增强垂直环流的作用.
