Study of statistically correcting model CMAQ-MOS for forecasting regional air quality

Based on analysis of the air pollution observational data at 8 observation sites in Beijing including outer suburbs during the period from September 2004 to March 2005, this paper reveals synchronal and in-phase characteristics in the spatial and temporal variation of air pollutants on a city-proper scale at deferent sites; describes seasonal differences of the pollutant emission influence between the heating and non-heating periods, also significantly local differences of the pollutant emission influence between the urban district and outer suburbs, i.e. the spatial and temporal distribution of air pollutant is closely related with that of the pollutant emission intensity. This study shows that due to complexity of the spatial and temporal distribution of pollution emission sources, the new generation Community Multi-scale Air Quality (CMAQ) model developed by the EPA of USA produced forecasts, as other models did, with a systematic error of significantly lower than observations, albeit the model has better capability than previous models had in predicting the spatial distribution and variation tendency of multi-sort pollutants. The reason might be that the CMAQ adopts average amount of pollutant emission inventory, so that the model is difficult to objectively and finely describe the distribution and variation of pollution emission sources intensity on different spatial and temporal scales in the areas, in which the pollution is to be forecast. In order to correct the systematic prediction error resulting from the average pollutant emission inventory in CMAQ, this study proposes a new way of combining dynamics and statistics and establishes a statistically correcting model CMAQ-MOS for forecasts of regional air quality by utilizing the relationship of CMAQ outputs with corresponding observations, and tests the forecast capability. The investigation of experiments presents that CMAQ-MOS reduces the systematic errors of CMAQ because of the uncertainty of pollution emission inventory and improves the forecast level of air quality. Also this work employed a way of combining point and area forecasting, i.e. taking the products of CMAQ for a center site to forecast air pollution for other sites in vicinity with the scheme of model products "reanalysis" and average over the "area".     

The diurnal and seasonal characteristics of urban heat island variation in Beijing city and surrounding areas and impact factors based on remote sensing satellite data

Based on the land surface temperature (LST), the land cover classification map,vegetation coverage, and surface evapotranspiration derived from EOS-MODIS satellite data, and by the use of GIS spatial analytic technique and multivariate statistical analysis method, the urban heat island (UHI) spatial distribution of the diurnal and seasonal variabilities and its driving forces are studied in Beijing city and surrounding areas in 2001. The relationships among UHI distribution and landcover categories, topographic factor, vegetation greenness, and surface evapotranspiration are analyzed. The results indicate that: (i) The significant UHI occur in Beijing city areas in the four seasons due to high heat capacity and multi-reflection of compression building, as well as with special topographic features of its three sides surrounded by mountains,especially in the summer. The UHI spatial distribution is corresponding with the urban geometry structure profile. The LST difference is approximately 4-6℃ between Beijing city and suburb areas, comparatively is 8- 10℃ between Beijing city area and outer suburb area in northwestern regions. (ii) The UHI distribution and intensity in daytime are different from nighttime in Beijing city area, the nighttime UHI is obvious. However, in the daytime, the significant UHI mainly appears in the summer, the autumn takes second place, and the UHI in the winter and the spring seem not obvious. The surface evapotranspiration in suburb areas is larger than that in urban areas in the summer, and high latent heat exchange is evident, which leads to LST difference between city area and suburb area. (iii) The reflection of surface landcover categories is sensitive to the UHI, the correlation between vegetation greenness and UHI shows obviously negative.The scatterplot shows that there is the negative correlation between NDVI and LST (R2 = 0.6481).The results demonstrate that the vegetation greenness is an important factor for reducing the UHI,and large-scale construction of greenbelts can considerably reduce the UHI effect.     

基于高通量测序的表层水体纤毛虫多样性评估方法比较与优化

基于扩增子的高通量测序技术广泛应用于微型生物多样性的研究, 不同的测序手段和数据分析流程, 影响着微型生物多样性与群落结构的分析结果。本研究以易于形态鉴定的砂壳纤毛虫作为研究对象, 比较DNA测序、RNA测序和形态学方法检获的多样性和物种组成等, 探究序列分析流程中关键步骤: 嵌合体处理、可操作分类单元分析方法选择、合并相似分类单元以及去除稀有类群等对多样性结果的影响。研究结果显示无论基于DNA还是RNA的分子手段与形态学方法检获的主要物种基本一致, 与DNA测序相比, RNA测序检获的物种数少, 但差异不显著。基于97%以上相似度聚类和单核苷酸变异所得群落结构相似, 无显著差异; 且所有分析方法都能在一定程度上反映出自然界中不同类群的相对丰度。相较于单核苷酸变异和其他相似度阈值, 99%相似度下聚类所得多样性更为接近形态学结果。去除嵌合体和稀有类群(去除阈值: DNA测序0.05%; RNA测序0.07%), 可明显改善分子多样性虚高的问题。本研究为纤毛虫等真核微生物的分子多样性研究提供了科学的数据分析流程, 对未来真核微生物多样性研究具有指导意义。     

基于YOLO算法的海洋中尺度涡三维结构构建

海洋中尺度涡是一种常见的中尺度海洋现象,研究海洋中尺度涡的分布及运动特性对航运、气候、军事等具有重要作用,海洋中尺度涡的识别是海洋学和计算机科学领域的一个热门研究课题。运用深度学习的方法和框架,对中尺度涡的二维识别和三维结构构建展开研究分析。首先,获取全球海洋再分析数据并进行流线可视化,构建涡旋流线数据集;其次,利用YOLO v5s卷积神经网络对涡旋流线数据集进行训练,并对南海区域中尺度涡进行有效检测。实验结果表明,YOLO v5s训练后得到最优模型经过测试,平均检测精度均值达到了86.10%;最后,根据涡旋检测结果,对检测出的同时刻不同深度的涡旋判断是否属于同一涡旋,确定后进行该涡旋的三维结构构建。     

能见度集合预报及后处理技术应用

基于污染物情况、环流系统和时空分布特征分析,利用神经网络对历史数据进行建模,生成了能见度集合预报产品。在2022年冬季的TS评分检验中,预报产品优于欧洲中期数值预报中心模式(ECMWF)的能见度预报产品。利用概率匹配、最优百分位和神经网络三种后处理方法生成后处理产品,这些产品的TS评分优于集合预报产品。预报输入的ECMWF模式2 m湿度与实况的偏差是误差的主要来源。利用集成方法对三种后处理产品进行集成,其TS评分结果在低能见度区间总体接近或略优于原始产品。生成的能见度集合预报后处理最优集成预报产品成功提高了对中期延伸期能见度天气的预测准确性。     

基于ROSE2.0的普洱地区CINRAD/CC雷达冰雹探测算法评估及参数本地化

为提高冰雹探测算法（Hail Detection Algorithm,HDA）产品的可用性,针对2015—2020年普洱地区监测到的22次冰雹个例,利用新一代雷达业务应用软件ROSE2.0对相关雷达基数据进行回放及产品分析,以命中率、虚警率、临界成功指数为指标对HDA算法在普洱地区的识别效果进行评估并给出本地化参数配置方案。结果表明:HDA算法在普洱地区命中率接近100%,但虚警现象非常普遍,使用强冰雹概率（Probability of Severe Hail,POSH）的预警效果优于任意大小冰雹概率（Probability of Hail,POH）,且冰雹尺寸越大POSH虚警的概率越低。进一步使用模拟测评法对POSH算法的适配参数进行分析,发现正确输入降雹日当天的0 ℃层和-20 ℃层高度能有效减少POSH的虚警率及提高临界成功指数;同时使算法预测的最大冰雹直径普遍偏大的情况得到控制,其中,中小冰雹直径偏离百分比减小76.07%,改善效果显著高于大冰雹。此外,增大反射率因子及POSH阈值能有效控制虚警,但也导致漏报次数快速增加,当阈值太大时命中率明显降低,为了保证较高的命中率和临界成功指数,选择Z=50 dBz或POSH=70%为阈值能明显改善HDA算法的识别效果。     

Characterization and Application of S-Band Polarimetric Radar and X-Band Phased Array Radar for a Tornadic Storm Event on June 16,2022

The X-band phased array radar offers faster scanning speed and higher spatial resolution compared to the S-band radar, making it capable of enhancing tornado monitoring and early warning capabilities. This study analyzed the char- acteristics and nowcasting signals of a tornado case that occurred on June 16, 2022 in the Guangzhou region. Our findings indicate that the violent contraction of rotation radius and the dramatic increase in rotation speed were important signal characteristics associated with tornado formation. The X-band phased array radar, with its high temporal and spatial resolution, provided an opportunity to capture early warning signals from polarimetric characteristics. The X-band phased array radar demonstrated noteworthy ability to identify apparent tornado vortex signature (TVS) features in a 10-minute lead time, surpassing the capabilities of the CINRAD/SA radar. Additionally, due to its higher scanning frequency, the X- band phased-array radar was capable of consistently identifying TVS with shorter intervals, enabling a more precise tracking of the tornado’s path. The application of professional radars, in this case, provides valuable insights for the monitoring of evolutions of severe local storms and even tornadoes and the issuance of early warning signals.     

人地复合生态系统韧性研究进展及其对海岸带生态系统韧性研究的启示

在全球气候变化与人类高强度开发的双重影响下,人地复合生态系统和受陆海系统交互影响的海岸带地区生态脆弱性更加明显。如何有效把握人地复合生态系统发展动向,合理规划与调控社会经济活动、促进人地关系协调发展一直是可持续发展领域研究的重点课题。韧性作为系统的内在特征,在防范化解灾害风险、维护区域生态安全中发挥着重要作用,是人地复合生态系统可持续发展能力的重要表现。然而,目前关于人地复合生态系统韧性的内涵、评估方法等方面尚未形成统一的范式,韧性的量化评估方法存在较大差异。通过梳理和归纳现有研究成果认为,韧性经历了从工程韧性、生态韧性到演进韧性的转变,其概念及内涵仍在不断拓展与深化;总结对比了基于过程形成性和状态总结性的韧性评估方法发现,将韧性作为过程与结果的综合评估研究仍较为缺乏;在抵抗扰动、维持稳定和协调发展的韧性理念下,生态系统的保护与修复等将是区域可持续发展应用中人地复合生态系统安全的重要保障。根据人地复合生态系统韧性研究进展的分析结果,结合海岸带生态系统韧性研究的现状及不足,认为未来需要在加强海岸带生态系统韧性内涵挖掘、完善海岸带生态系统韧性评价体系、提高海岸带生态系统韧性监测精度、促进学...     

预测有效波高的深度学习模型研究

研究基于RNN、LSTM、GRU深度学习模型,针对NOAA浮标数据集中的44013、44014、44017浮标的数据,通过斯皮尔曼相关性分析提高模型预测效果。实验结果表明,在进行相关性分析后,S-RNN、S-LSTM、 S-GRU的预测效果均比原始RNN、LSTM、GRU模型预测效果好。此外,提出一种基于LSTM的LSTM-Attention 波高预测模型,并进行相关实验,量化LSTM-Attention模型的预测效果,实验结果表明LSTM-Attention模型有更好的预测效果。为评估模型的泛化能力,研究还提出了一种采用邻近浮标数据进行学习,预测浮标缺失数据的方 法。实验结果表明,该方法的预测精度可以达到97.93%。本研究为海浪预测提供了新的方法和思路,也为未来深 度学习模型在海浪预测中的应用提供了参考。     

葛洲坝至胭脂坝长江干流段江豚活动规律

长江江豚是我国一级保护野生动物,也是长江生态系统的指示性物种,其种群数量恢复情况及活动规律一直备受关注。本文选择长江宜昌段江豚活动频繁的葛洲坝下游至胭脂坝江段为研究区域,于2021年6月-2022年5月采用定点目测、水上流动监测与无人机监测相结合的方法分区监测长江江豚的游泳行为,分析长江江豚的活动规律,构建了长江江豚出水头次的零膨胀泊松回归模型,识别影响长江江豚出水头次的关键因素,建立长江江豚出水头次与各因素间的相关关系。结果表明:(1)葛洲坝至胭脂坝长江干流段监测到最大江豚出水头次为19头次,长江江豚集群规模以2~3头最为常见,占比达58.1%;长江江豚主要表现出4种行为特征,各行为占比从大到小依次为:玩耍＞休息＞摄食＞抚幼。(2)长江江豚在葛洲坝下游近坝区(A区,葛洲坝至至喜长江大桥)出水头次最多,且在秋冬季节累计出水头次多于春夏季节,冬季累计出水头次最高,达252头次。(3)长江江豚出水头次与电站下泄流量呈显著负相关;浊度增大,长江江豚出水的概率减小。本文研究结果对长江江豚生态保护策略及长江十年禁渔效果评估具有重要参考价值。