High-precision traffic sign detection plays an important role in enhancing road traffic safety, ensuring traffic smoothness, supporting the development of intelligent transport systems and promoting the standardization and normalization of traffic facilities. To overcome the limitations of traditional methods, this study proposes an enhanced YOLOv5 algorithm for complex road environments. First, the K-Means clustering algorithm is used to cluster and analyze the boundary boxes of traffic signs in the training dataset, obtaining anchor box sizes that are closer to the distribution of the dataset to improve detection accuracy. Then, a genetic algorithm was used to further optimize the initial anchor box, and a global search strategy was used to find the optimal anchor box configuration, further improving detection performance. In terms of model structure, a bidirectional feature pyramid network (Bi-FPN) is introduced, which effectively utilizes multi-scale feature information and enhances the adaptability of the model to traffic signs of different sizes through top-down and bottom-up feature fusion paths, as well as cross-scale connections. In addition, a global attention mechanism (GAM) was introduced to recalibrate the feature maps through channel attention and spatial attention dimensions, improving the robustness and detection accuracy of the model for complex environments. Finally, a loss function called Focal-EIoU was used to solve the problems of class imbalance and sample imbalance, which improved the stability and performance of the object detection model. Experiments on a Chinese traffic sign dataset demonstrate significant improvements, with an increase in mAP by 10.03%, precision by 4.7%, recall by 2.6% and F1-score by 3.48%, respectively. The results prove the validity of the proposed traffic sign recognition method, especially in complex road environments. This study provides new ideas and methods for the field of traffic sign detection, which has important theoretical significance and application value.

The data of polar-orbiting meteorological satellites and geostationary meteorological satellites from 2021 to 2022 were used, combined with land use types and administrative boundary vector data, and contextual inversion algorithms to adjust the key thresholds for fire point identification and monitor straw hot spots. UAVs and others tested the positioning accuracy, hot spot fire authenticity and hot spot classification results of satellite hot spot monitoring. The results showed that: satellite fire monitoring and hot spot classification were basically correct, and the positioning deviation was within 300m. The inversion method of satellite hot spot and the performance results are reliable. The high-incidence areas of straw hot spots in Hunan are located in the Dongting Lake Basin, Hengyang-Shaoyang-Loudi area, northern Yongzhou and eastern Chenzhou. The straw burning phenomenon was more serious in 2022 than that in 2021, and the high-density distribution range of straw hot spots was significantly expanded compared with 2021. There are two concentrated periods for straw burning: January to April and October to December. The number of straw hot spots in January, October and December accounts for 66.1% of the whole year. The daily distribution of straw hot spots is single peak. The number of straw hot spots began to increase rapidly at 11a.m and reached the peak at 14p.m. After that, the number of hot spots decreased rapidly, and the phenomenon of straw burning at night was significantly reduced.

China witnessed a warm and dry climate in 2023. The annual surface air temperature reached a new high of 10.71 degrees C, with the hottest autumn and the second hottest summer since 1961. Meanwhile, the annual precipitation was the second lowest since 2012, at 615.0 mm. Precipitation was less than normal from winter to summer, but more in autumn. Consistent with the annual condition, precipitation in the flood season from May to September was also the second lowest since 2012, which was 4.3% less than normal, with the anomalies in the central and eastern parts of China being higher in central areas and lower in the north and south. On the contrary, the West China Autumn Rain brought much more rainfall than normal, with an earlier start and later end. Although there was less annual precipitation in 2023, China suffered seriously from heavy precipitation events and floods. In particular, from the end of July to the beginning of August, a rare, extremely strong rainstorm caused by Typhoon Dussuri hit Beijing, Tianjin, and Hebei, causing an abrupt alteration from drought to flood conditions in North China. By contrast, Southwest China experienced continuous drought from the previous autumn to current spring. In early summer, North China and the Huanghuai region experienced the strongest high-temperature process since 1961. Nevertheless, there were more cold-air processes than normal impacting China, with the most severe of the year occurring in mid-January. Unexpectedly, in spring, there were more sand and dust occurrences in northern China.

China experienced a warm and dry climate in 2022. The average annual surface air temperature (SAT) was 10.51 degrees C, which was the second highest since 1961. The annual average rainfall was 606.1 mm, which was the lowest since 2012. The seasonal SAT broke the record in spring, summer, and autumn, while the SAT in winter was slightly cooler. More rainfall was observed in winter and spring, but less in summer and autumn. During the flood season from May to September, rainfall was 11.9% less than normal, which was the third lowest since 1961. The spatial distribution of rainfall anomalies exhibited a wet/dry pattern in the north/south of central and eastern China. The onset of the rainy season was generally earlier but with significant differences in rainfall. More rainfall was observed in the pre-flood season in South China and the rainy season in North China and Northeast China. In contrast, less rainfall occurred in the Mei-yu season in the middle and lower reaches of the Yangtze and Huaihe River valleys, the southwestern rainy season, and the autumn rainy season in West China. In 2022, China's drought and flood disasters were stark, and heat waves were strong. Severe droughts occurred along the Yangtze River valley during summer and autumn, while heavy rainfall and flooding struck South China in the pre-flood season and Northeast China in June-July. A historically strongest summer heat wave occurred in central and eastern China, while drastic cooling prevailed in most of China at the end of November. Landfalling typhoons were extremely less frequent.

基于环境、气象、探空、AERONET、云高仪和卫星遥感多数据源对北京春季一次重污染过程的热、动力机制和粒子微物理属性展开研究.结果表明:①多源数据协同观测显示本次污染起源于中国西部,影响范围广泛,对北京构成了达6级的重度污染,PM2.5、PM10质量浓度高达600 μg/m3和1 000 μg/m3.②MCD19A2 MAIAC AOD能很好地揭示本次污染起源、强度、输送和影响区域,MISR AOD显示本次污染粒子微物理属性具有大模态、非球形、非吸收型的特征.③CALIPSO显示,在1～4 km高度,退偏在0.1～0.5,色比在0.5～1.3均有分布,有"小退偏和大色比"现象.④AERONET数据显示,污染前、中、后AOD 550 nm值分别为0.9、2.0、0.2,AE 440-675 nm值分别为1.30、0.4、1.0,说明本次污染为PM2.5和PM10混合型污染.⑤探空显示本次污染大气层结非常稳定,各阶段有显著的热动力特征.污染爆发前(5月3日),上层大气潮湿且温度递减率小,低层大气干燥上升,构成对流空间.污染爆发期(5月4日),出现多层逆温,干燥大气下沉运动,受上层西风下层偏南风的动力配合.污染末期(5月5日),低层逆温消失,上层逆温层高度持续抬升,且空气干燥,深厚西风区风速持续加大,终结污染过程.⑥云高仪监测和EC模式结果表明5月4日大气稳定,边界层在空间上分布均匀,08:00大气边界层高度约1200 m,20:00约300 m.

This report is a summary of China's climate, as well as major weather and climate events, during 2021. In 2021, the mean temperature in China was 10.5 degrees C, which was 1.0 degrees C above normal (1981-2010 average) and broke the highest record since 1951. The annual rainfall in China was 672.1 mm, which was 6.7% above normal. Also, the annual rainfall in northern China was 40.2% above normal, which ranked second highest since 1961. The rainstorm intensity in the rainy season was strong and featured significant extremes, and disasters caused by rainstorms and flooding were more serious than the average in the past decade. In particular, the extremely strong rainstorm in Henan during July and autumn caused flooding in the middle and lower reaches of the Yellow River with severe consequences. Heatwaves occurred more frequently than normal, and their durations in southern China were longer than normal in summer and autumn. Phased drought was obvious, and caused serious impacts in South China. The number of generated and landfalling typhoons was lower than normal; however, Typhoon In-fa broke the record for the longest overland duration, held since 1949, and affected a wide area. Severe convective weather and extreme windy weather occurred frequently, causing serious impacts. The number of cold waves was more than normal, which caused wide-ranging extremely low temperatures in many places. Sandstorms appeared earlier than normal in 2021, and the number of strong dust storm processes was more than normal.

The health management of weather radar plays a key role in achieving timely and accurate weather forecasting. The current practice mainly exploits a fixed threshold prespecified for some monitoring parameters for fault detection. This causes abundant false alarms due to the evolving working environments, increasing complexity of the modern weather radar, and the ignorance of the dependencies among monitoring parameters. To address the above issues, we propose a deep learning-based health monitoring framework for weather radar. First, we develop a two-stage approach for problem formulation that address issues of fault scarcity and abundant false fault alarms in processing the databases of monitoring data, fault alarm record, and maintenance records. The temporal evolution of weather radar under healthy conditions is represented by a long short-term memory network (LSTM) model. As such, any anomaly can be identified according to the deviation between the LSTM-based prediction and the actual measurement. Then, construct a health indicator based on the portion of the occurrence of deviation beyond a user-specified threshold within a time window. The proposed framework is demonstrated by a real case study for the Chinese S-band weather radar (CINRAD-SA). The results validate the effectiveness of the proposed framework in providing early fault warnings.

Abstract [Objective] The land use of the Yellow River Basin(YRB) has a significant impact on the ecology and economy of the basin. It is extremely important to study the temporal and spatial change of land use in the YRB for the sustainable development of the basin. [Methods]Taking the YRB as the study area, the land use types in the basin were classified and evaluated based on remote sensing monitoring data of five years of land use. The temporal and spatial characteristics of land use in four time series units from 2000 to 2005, 2005 to 2010, 2010 to 2015, and 2015 to 2020 were analyzed by using geo-information Tupu method, and the internal factors of land use change were analyzed from the perspective of increase and decrease. [Results] The results show that from 2000 to 2020, the land type of the YRB is dominated by grassland, the growth trend of woodland is higher than that of grassland, the growth trend of built-up land is higher than that of water area, and the cultivated land and unused land show a decreasing trend; different time series units show similar land changes, with the strongest in 2000-2005. The Tupu units of "cultivated land →grassland", "grassland→cultivated land", "grassland→ unused land" and "unused land→ grassland" are the main changes in the four time series units ;In the rising trend Tupu, grassland is converted from a large number of cultivated land, and the increase of grassland has a tendency to occupy cultivated land. In the falling trend Tupu, a large number of grassland have been converted into cultivated land, and the increase of cultivated land is inclined to occupy grassland. All of them have the most obvious changes in Gansu, the southern part of Ningxia and the northern part of Shaanxi. From the perspective of spatial location, the two may be transformed into each other, and the distribution is relatively concentrated in the pilot areas of the policy of returning farmland to forests.[Conclusion] The analysis of temporal and spatial changes of land use in the YRB in the past 20 years provides a realistic case for the feedback role of land policy in society and environment, and lays a foundation for the harmonious development of human and ecology.

根据近60年华北地区236站逐日降水量资料和2021年夏季NCEP再分析资料,对2021年中国华北雨季的气候特征及其成因进行分析.结果表明,2021年华北雨季于7月12日开始,较常年偏早6 d;于9月9日结束,较常年偏晚22 d;平均雨量为276.4 mm,较常年偏多103.2％;雨季长度为59 d,为1961年以来的第2长.2020年8月至2021年4月的拉尼娜事件是2021年华北雨季偏强的重要外因,也是最重要的年际预测信号;另外,2020年冬季和2021年春季青藏高原积雪偏少是2021年我国北方降水偏多的另一年际预测信号.而造成2021年华北雨季偏强的直接原因则是大气环流异常,500 hPa东亚中高纬呈现"东高西低"的距平环流分布,贝加尔湖至我国长江下游的大槽非常有利于冷空气的南下,850 hPa我国长江以北地区受到异常气旋环流东北侧的偏南风距平控制,给北方地区带来良好的水汽输送.冷暖空气在北方地区交汇,造成华北东部等地降水偏多.

2021年7月17-23日,河南多地发生极端强降水,这次特大暴雨过程累计雨量大、强降水范围广、降水极端性强、短时强降水时段集中、持续时间长.河南"7·20"特大暴雨是东亚大气环流异常协同作用的直接结果,也是全球气候变暖背景下极端强降水事件频发的具体表现.近年来全球暴雨洪涝、高温热浪、干旱、台风等气象灾害频发强发,极端事件对经济社会发展造成的影响趋于严重,我国应对极端天气气候事件需坚持综合应对、科学应对、有效应对,不断提升应对极端灾害的能力.

In order to objectively identify the regional rainstorm and flood processes and quantitatively assess the intensity of regional rainstorm processes, based on the intensity-duration curve for extreme rainfall, the maximum precipitation intensity within 5 days is regarded as an index to define a flood-waterlogging event and the intensity of regional flood-waterlogging processes. Using the daily precipitation dataset at 502 meteorological stations in the middle and lower reaches of the Yangtze River from 1961 to 2019, the frequency, intensity and variation trend of the regional flood-waterlogging processes in the middle and lower reaches of the Yangtze River are analyzed. The results show that the frequency of the regional waterlogging processes in this region exhibits an increasing trend in recent 60 years. It is obvious that the regional flood-waterlogging processes occur more frequently in the last 20 years, and the proportion of the regional flood-waterlogging processes lasting for 5-9 days to all the waterlogging processes is two-thirds or above. The regional flood-waterlogging days display the spatial distribution of "more in the southern area and less in the northern area" and "decreasing in the northwestern area and increasing in the southeastern area". Moreover, the spatio-temporal variations of the annual precipitation and flood-waterlogging lead to a stronger difference of drought and flood between different areas in this region. The eastern area with more precipitation gets wetter, but the northern and western area with less precipitation gets drier.

利用国家气候中心实时和历史气象观测数据,对我国2020年汛期(3月28日至9月8日)气候特点进行了综合分析.分析结果显示,2020年汛期,我国气候特征总体雨多温高、涝重于旱,且气候异常突出、极端事件频发.一是我国多地出现阶段性的暴雨洪涝,覆盖范围广、极端性强、累计降雨量大;二是南方出现了1961年以来持续时间第2长的区域性高温天气过程;三是台风总体不活跃,但阶段性突出,出现了1949年以来首个7月无编号无登陆台风的情况;四是气象干旱总体偏轻.分析还显示,2019年秋季至2020年春季,赤道中东太平洋出现的弱厄尔尼诺事件,以及北印度洋海温的持续偏高导致西太平洋副热带高压持续偏强,是造成我国汛期气候异常的重要原因.

2020年10月至2021年2月上旬,受拉尼娜事件和大气环流异常的共同影响,我国江南南部、华南大部及西南的云南等地降水量较常年同期明显偏少,2020年11月上旬气象干旱开始露头并迅速发展,出现了较为严重的秋冬连旱.此次干旱过程影响范围广、干旱日数多、强度强,造成江南、华南及西南的云南等地湖库蓄水大幅减少,森林火险气象等级偏高.2021年2月8—11日,我国南方地区出现明显降水,江南、华南气象干旱解除,但西南的云南等地干旱仍然持续.从气候特征、干旱演变过程入手,分析了此次干旱过程的特点及气候成因,以期为从事气候影响评价和服务的科研人员提供相关信息,同时也为防旱抗旱工作提供参考依据.

基于气温、降水、土壤墒情以及历史干旱灾情等资料,从干旱时空分布特征、典型干旱过程诊断、不合理跃变分析以及与土壤墒情、干旱灾情的相关性等方面,分析标准化降水指数(SPI)、标准化降水蒸散指数(SPEI)、相对湿润度指数(MI)、气象干旱综合指数(MCI)在我国东北、西南和长江中下游地区的适用性.结果发现,四种指数对干旱的年际变化诊断基本一致,而对干旱空间分布的诊断,MCI与MI指数与实况更加吻合.针对典型干旱过程的逐日诊断,MCI指数对干旱过程的刻画效果最好,不合理跃变次数较SPI、SPEI和MI指数分别下降82.6％、73.8％和97.8％;各指数在长江中下游地区不合理跃变次数最少,其次为西南地区,东北地区相对较多.与土壤墒情的相关性方面,MCI指数最好,均通过99％的信度检验,较SPI、SPEI和MI指数分别提高9.2％、54.7％和68.8％;西南地区代表站与土壤墒情的相关性最好,其次为长江中下游地区,东北地区相对较差.与干旱受灾面积的相关性方面,MCI指数也是最好的,较SPI、SPEI和MI指数分别提高16.9％、37.1％和27.6％;各指数在东北地区与灾情的相关性优于长江中下游地区,西南地区总体较差.综合来看,MCI指数适用性最好,这与干旱指数的构造方法及其考虑的干旱影响因子、时间尺度、不同时段降水权重等因素密切相关.

Air temperature and precipitation are two important meteorological factors affecting the earth’s energy exchange and hydrological process. High quality temperature and precipitation forcing datasets are of great significance to agro-meteorology and disaster monitoring. In this study, the accuracy of air temperature and precipitation of the fifth generation of atmospheric reanalysis produced by the European Centre for Medium-Range Weather Forecasts (ERA5) and High-Resolution China Meteorological Administration Land Data Assimilation System (HRCLDAS) datasets are compared and evaluated from multiple spatial–temporal perspectives based on the ground meteorological station observations over major land areas of China in 2018. Concurrently, the applicability to the monitoring of high temperatures and rainstorms is also distinguished. The results show that (1) although both forcing datasets can capture the broad features of spatial distribution and seasonal variation in air temperature and precipitation, HRCLDAS shows more detailed features, especially in areas with complex underlying surfaces; (2) compared with the ground observations, it can be found that the air temperature and precipitation of HRCLDAS perform better than ERA5. The root-mean-square error (RMSE) of mean air temperature are 1.3 °C for HRCLDAS and 2.3 °C for ERA5, and the RMSE of precipitation are 2.4 mm for HRCLDAS and 5.4 mm for ERA5; (3) in the monitoring of important weather processes, the two forcing datasets can well reproduce the high temperature, rainstorm and heavy rainstorm events from June to August in 2018. HRCLDAS is more accurate in the area and magnitude of high temperature and rainstorm due to its high spatial and temporal resolution. The evaluation results can help researchers to understand the superiority and drawbacks of these two forcing datasets and select datasets reasonably in the study of climate change, agro-meteorological modeling, extreme weather research, hydrological processes and sustainable development.

基于2008-2017年全国自动气象观测站逐旬土壤相对湿度观测数据,综合评估中国气象局陆面数据同化系统(CMA Land Data Assimilation System,CLDAS)0～20 cm层融合土壤相对湿度产品在中国地区的适用性,评估表明CLDAS土壤相对湿度产品在中国东北、西北、江南大部及华南等地区存在较大系统性误差,总体上适用性较差.为消除CLDAS土壤相对湿度产品的系统性误差,采用回归订正法、7旬滑动平均订正法和临近加权前旬订正法对CLDAS 土壤相对湿度产品进行误差订正处理,对订正结果评估发现:订正处理后CLDAS土壤相对湿度产品与站点观测的相关性显著增加,系统偏差基本消除,适用性明显提高,3种订正方法中临近加权前旬订正法的订正效果最优.最后,采用经不同方法订正后的CLDAS土壤相对湿度产品对2017年5月东北—华北地区一次气象干旱个例进行重现,对比验证表明:相对其他两种订正方法,经临近加权前旬订正法处理后的CLDAS 土壤相对湿度产品能更为精准地重现2017年5月东北—华北地区气象干旱的落区和强度.

如今气象科普知识已被越来越多的公众所喜爱和接受,随着融媒体时代的到来,气象科普的传播渠道也越来越多样化,如何更好地利用新媒体和传统媒体的优势来开展气象科普工作已成为公众气象服务工作中的一项重要研究内容,本文就此进行了研究和探讨,对融媒体时代下如何做好气象科普工作具有一定的指导意义.

2020年注定是非同寻常的一年,当前新冠肺炎疫情仍在全球多国多地区肆虐,世界百年未有之大变局正在加速演进.这场跨越了冬春的新冠肺炎疫情防控阻击战硝烟在中国未熄时,1998年以来最严重的汛情又接踵而至.

本文试图从经典的大众传播学基本理论和模式入手,梳理现有气候变化知识产品的编制方式、主要传播途径和相关实践经验,以期为未来气候变化信息传播研究和实践提供参考.分析表明:气候变化知识传播过程应遵循信息传播的基本规律,并充分考虑气候变化知识自身所具有的复杂性和交叉性特点;在信息采集方面,应注重知识的科学性、系统性、准确性和权威性;在展示方式方面,应注重内容的可读性、趣味性、精练性和通俗性;在传播途径方面,除传统的大众媒介外,还需要注重利用新兴的互联网、社交媒体等平台,发挥人际关系网络作用;在国家经济社会发展的新形势下,需要进一步加强气候变化知识传播的理论、模式和实践等方面的创新性研究.