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。     

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。     

Effect of the Initial Vortex Structure on Intensity Change During Eyewall Replacement Cycle of Tropical Cyclones: A Numerical Study

This study investigates the effect of the initial tropical cyclone (TC) vortex structure on the intensity change during the eyewall replacement cycle (ERC) of TCs based on two idealized simulations using the Weather Research and Forecasting (WRF) model. Results show that an initially smaller TC with weaker outer winds experienced a much more drastic intensity change during the ERC than an initially larger TC with stronger outer winds. It is found that an initially larger TC vortex with stronger outer winds favored the development of more active spiral rainbands outside the outer eyewall, which slowed down the contraction and intensification of the outer eyewall and thus prolonged the duration of the concentric eyewall and slow intensity evolution. In contrast, the initially smaller TC with weaker outer winds corresponded to higher inertial stability in the inner core and weaker inertial stability but stronger filamentation outside the outer eyewall. These led to stronger boundary layer inflow, stronger updraft and convection in the outer eyewall, and suppressed convective activity outside the outer eyewall. These resulted in the rapid weakening during the formation of the outer eyewall, followed by a rapid re-intensification of the TC during the ERC. Our study demonstrates that accurate in- itialization of the TC structure in numerical models is crucial for predicting changes in TC intensity during the ERC. Additionally, monitoring the activity of spiral rainbands outside the outer eyewall can help to improve short-term intensity forecasts for TCs experiencing ERCs.     

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.     

近1800年以来内蒙古达里湖浮游植物群落演变及驱动因素

湖泊是地质历史上区域生态环境演变的重要载体,其沉积物中包含了丰富的环境演变信息。达里湖是典型的草原内陆封闭型湖泊,位于东亚夏季风的北部边缘,地理位置关键。本研究基于在达里湖采集的约238 cm沉积物岩芯 (GDL-1),通过²¹⁰Pb和¹³⁷Cs (岩芯上部20 cm)、AMS¹⁴C测年,以及沉积物样品中甾醇等生物标志物含量分析,重建了近1800年以来达里湖硅藻、蓝藻等典型藻类含量的演化历史,并结合总氮(TN)、总磷(TP)、盐度(Sr/Ba)和温度(T)等环境代用指标,分析藻类群落的主要影响因素。结果显示达里湖典型藻类总量平均为2.03 ng/g(最高6.69 ng/g,最小0.53 ng/g),其中蓝藻占比平均为60%,绿藻和硅藻占比平均为20%;环境因子对硅藻、蓝藻和典型藻类生物量的解释率分别为47.7%、55.20%和48.10%,T、TN和Sr/Ba是影响浮游植物群落的主要影响因素;硅藻占比与温度呈显著负相关,小冰期硅藻占比最高,中世纪暖期占比最低;在达里湖的高盐环境下,湖泊营养盐浓度对藻类的影响受到限制,成为藻类等生长的限制因素;Sr/Ba小于0.9时,硅藻含量与典型藻类总量随着Sr/Ba值的升高而增加,Sr/Ba大于0.9时,典型藻类总量与Sr/Ba值呈负相关关系,而硅藻则表现出对盐度具有一定的耐受性;整体上,近1800年以来,区域或全球性气候事件通过改变达里湖盐度、营养元素浓度和温度来影响典型藻类群落结构。在暖期阶段,蒸发作用增强等导致的湖泊盐度增加成为达里湖典型藻类群落结构演变的主要影响因素;在冷期,营养元素浓度和温度的降低成为湖泊水体浮游植物生物量的主要影响因素。     

广西钦江河口枯季水动力特征及其对冷空气的响应

河口作为陆海相互作用的关键带,是河流入海泥沙及污染物的主要归宿地。钦江作为广西第二大河流,在茅尾海资源开发利用和生态环境保护与修复中发挥着重要作用。为进一步认识钦江河口地区的沉积动力过程,2021年10月27日至12月8日在此展开水动力和水体环境要素的连续观测。现场观测与分析结果表明,观测期间钦江河口潮汐类型为正规全日潮,浅水分潮显著,平均潮差为3.07 m,具有落潮优势;潮流以全日分潮流为主导,平均流速为0.12 m/s,运动形式主要为往复流;余流流向主要为西南方向。钦江河口潮汐和潮流性质较外湾和茅尾海中部海域全日潮特征更显著。枯季期间河口表层沉积物随潮汐运动表现为侵蚀-沉降交替的变化规律,垂向上侵蚀通量大于沉降通量,水平方向上悬沙净向海输运。冷空气过境带来的降雨使得钦江河口水体盐度降低、浊度增大,悬沙浓度及输沙量增加,同时冷空气南下时北风增强引起钦江河口减水效应并使潮差略有增加,水动力强度增大,增加表层沉积物的活动性,从而引起底部沉积物再悬浮强度和频率增加。     

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。