基于MOS的杭州秋冬季空气污染预报和霾诊断

利用由数值预报模式WRF和辨识理论实时迭代统计方法RTIM组成的MOS方法对杭州市2013年2~3月和11~12月期间的空气污染物日平均浓度做预报,预报值与实测值之间相关系数都超过0.75 ,PM2.5、PM10、SO2、NO2、CO 24h平均浓度和O3 8h平均浓度分类预报临界成功指数(CSI)分别为89%、87%、100%、93%、100%和100%,命中率(POD)分别为93%、95%、100%、100%、100%和100%.分析表明,研究期间杭州地区气溶胶以细颗粒为主.根据PM2.5浓度、相对湿度及能见度预报值做霾日分类预报,临界成功指数为89%,命中率为93%.说明该MOS系统对污染物浓度及霾天气预报性能良好,可以为业务化预报提供参考.     

PM_(2.5)和PM_(2.5~10)资料同化及在南京青奥会期间的应用试验

建立了面向PM_(2.5)和PM10观测资料的三维变分同化系统,并在南京地区青奥会期间进行了同化和预报试验.同化系统的控制变量为PM_(2.5)和PM_(2.5~10)(PM10中扣除PM_(2.5)后剩余部分),利用南京地区2014年8月的WRF-Chem模拟结果,估计了PM_(2.5)和PM_(2.5~10)的背景误差协方差,发现在水平和垂直方向上PM_(2.5)的相关系数随距离的衰减均小于PM_(2.5~10),这可能与PM_(2.5)粒径小、生命史长,在大气中传播地更远有关.利用南京及周边区域的134个监测站PM_(2.5)和PM_(10)逐时观测资料,对青奥会期间(2014年8月16~28日)进行滚动同化和预报试验,并利用模式最内层观测资料进行检验分析,结果表明同化对初始场有显著改进,PM_(2.5)和PM10的相关系数均提高53%以上,均方根误差降低55%以上,平均偏差则降低了90%左右;同化试验对其后的预报场也有明显改进,正效应可以持续到20h以后,模式对PM10的预报效果好于PM_(2.5).     

四川盆地一次污染过程的WRF模式参数化方案最优配置

精准的气象场是空气质量模型的基础,本文对中尺度气象模式WRF(The Weather Research and Forecasting)中微物理过程方案、陆面过程方案、行星边界层方案以及积云参数化方案进行组合,设计了24组参数化方案,对2014年5月初四川盆地内一次空气重污染过程中的气象场进行了模拟,将模拟输出的10 m风速、2 m温度、2 m相对湿度、水汽混合比廓线及位温廓线与研究区内14个气象站及1个探空站的实测数据进行对比.结果表明,Mellor-Yamada-Janjic(MYJ)边界层方案能更好的模拟出盆地内风速的变化趋势,而Yonsei University(YSU)边界层方案模拟的2 m相对湿度效果更优.第16组参数化方案(由WRF Single-Moment 3-class(WSM3)方案,热扩散(SLAB)方案、Mellor-Yamada-Janjic(MYJ)方案及Grell-Devenyi(GD)方案组合)能够较好地模拟盆地近地面风场的水平分布及风速的日变化规律,模拟的温度日变化规律更贴合实际情况,同时能够模拟出边界层内水汽混合比及位温的垂直分布特征,对边界层内逆温层和混合层的模拟也更加贴近实际,尽管该方案对2m相对湿度的模拟并不是最好,但基本能够模拟出四川盆地气象要素的变化特征,因此认为第16组参数化方案(WSM3,SLAB,MYJ及GD)适用于模拟此次重污染过程的气象场.     

基于WRF-NAQPMS的一次沙尘天气预报评估

为提高沙尘天气的预报准确率,利用ECWMF再分析资料和近地面PM₁₀小时质量浓度监测数据,评估WRFNAQPMS模式对2021年3月15—21日甘肃强沙尘过程的预报能力。结果表明,WRF-NAQPMS能够在一定程度上模拟此次污染过程:WRF对“3· 15”天气系统的模拟与实况整体趋势较为一致,随着预报时效延长,气象模拟场移动偏快,导致沙尘预报场发展偏快、沙尘二次传输影响下游时间提前;近地面风向的局地偏差是导致甘肃中东部地区沙尘浓度出现预报误差的主要气象因素。NAQPMS模式对PM₁₀小时质量浓度的模拟随着预报时效增加和离沙源地距离的增大,预报误差逐步增大:在河西地区,沙尘影响时段和起沙浓度的模拟值均接近监测值,其中嘉峪关、酒泉、张掖的PM₁₀小时质量浓度模拟值与监测值相关系数r>0.8;中部地区城市的沙尘影响时段预报略有偏差,且模拟值低于监测值;受复杂下垫面和气象场预报误差影响,省内其他地区沙尘预报结果参考性较低。     

珠三角土地覆被资料优选及在WRF模式中的初步应用

针对WRF模式中珠三角地区土地覆被资料不准确的现况,从国内外主要的土地覆被产品中筛选与统计年鉴资料相符的产品,与模式内置的静态资料进行对比试验以评估土地覆被资料对模拟结果的影响.试验模拟的时段设为2010—2011年1、4、7、10月.结果表明:①WRF模式内置的MODIS资料在珠三角地区对于建成区高估超过3倍,而主要土地覆被遥感产品中GLC2009资料最为接近统计年鉴和调查结果值;②GLC2009土地覆被数据相比模式内置的MODIS资料,对珠三角区域地表温度、风速、相对湿度的模拟有一定改善,2 m地表气温与观测的平均偏差从0.32℃降至0.08℃;2 m水汽压平均偏差从0.31 hPa降至0.28 hPa;10 m风速平均偏差从0.59 m·s-1降至0.38 m·s-1,其中10 m风速对于土地覆被变化最为敏感;③从空间分布上看,GLC2009资料相对内置的MODIS资料,其对整个模拟区域内的温度、湿度模拟结果有所改善,并且对珠三角城市外围区域风速模拟结果的改善明显;④模拟结果的变化是由于土地覆被类型及其比例的变化直接改变了模式中地表反照率、粗糙度、植被覆盖率、植被气孔阻抗等参数的取值引起的.     

Case study of dust event sources from the Gobi and Taklamakan deserts: An investigation of the horizontal evolution and topographical effect using numerical modeling and remote sensing

A severe dust event occurred from April 23 to April 27, 2014, in East Asia. A state-of-the-art online atmospheric chemistry model, WRF/Chem, was combined with a dust model, GOCART, to better understand the entire process of this event. The natural color images and aerosol optical depth (AOD) over the dust source region are derived from datasets of moderate resolution imaging spectroradiometer (MODIS) loaded on a NASA Aqua satellite to trace the dust variation and to verify the model results. Several meteorological conditions, such as pressure, temperature, wind vectors and relative humidity, are used to analyze meteorological dynamic. The results suggest that the dust emission occurred only on April 23 and 24, although this event lasted for 5 days. The Gobi Desert was the main source for this event, and the Taklamakan Desert played no important role. This study also suggested that the landform of the source region could remarkably interfere with a dust event. The Tarim Basin has a topographical effect as a “dust reservoir” and can store unsettled dust, which can be released again as a second source, making a dust event longer and heavier.     

Identifying stream temperature variation by coupling meteorological,hydrological, and water temperature models

In this study, we demonstrate a physically based semi-Lagrangian water temperature model known as the River Basin Model (RBM) coupled with the Variable Infiltration Capacity (VIC) hydrological model and Weather Research & Forecasting Model in the Mississippi River Basin (MRB). The results of this coupling compare favorably with observed water temperature data available from six river gages located in the MRB. Further sensitivity analysis indicates that the mean water temperatures may increase by 1.3, 1.5, and 1.8°C in northern, central, and southern MRB zones under a hypothetical uniform air temperature increase of 3.0°C. If air temperatures increase uniformly by 6.0°C in this scenario, then water temperatures are projected to increase by 3.3, 3.5, and 4.0°C. Lastly, downscaled air temperatures from a global climate model are used to drive the coupled VIC and RBM model from 2020 to 2099. Average stream temperatures from 2020 to 2099 increase by 1.0 to 8.0°C above 1950 to 2010 average water temperatures, with non-uniform increases along the river. In some portions of the MRB, stream temperatures could increase above survival thresholds for several native fish species, which are critical components of the stream ecosystem. In addition, increased water temperatures interact with nutrient loadings from sources throughout the MRB, which is expected to exacerbate harmful algal blooms and dead zones in the Gulf of Mexico.     

Spatiotemporal Evaluation of Simulated Evapotranspiration and Streamflow over Texas Using the WRF‐Hydro‐RAPID Modeling Framework

This study assesses a large‐scale hydrologic modeling framework (WRF‐Hydro‐RAPID) in terms of its high‐resolution simulation of evapotranspiration (ET) and streamflow over Texas (drainage area: 464,135 km²). The reference observations used include eight‐day ET data from MODIS and FLUXNET, and daily river discharge data from 271 U.S. Geological Survey gauges located across a climate gradient. A recursive digital filter is applied to decompose the river discharge into surface runoff and base flow for comparison with the model counterparts. While the routing component of the model is pre‐calibrated, the land component is uncalibrated. Results show the model performance for ET and runoff is aridity‐dependent. ET is better predicted in a wet year than in a dry year. Streamflow is better predicted in wet regions with the highest efficiency ~0.7. In comparison, streamflow is most poorly predicted in dry regions with a large positive bias. Modeled ET bias is more strongly correlated with the base flow bias than surface runoff bias. These results complement previous evaluations by incorporating more spatial details. They also help identify potential processes for future model improvements. Indeed, improving the dry region streamflow simulation would require synergistic enhancements of ET, soil moisture and groundwater parameterizations in the current model configuration. Our assessments are important preliminary steps towards accurate large‐scale hydrologic forecasts.     

基于机器学习算法的精细化流场模拟研究

基于决策树和随机森林两种机器学习算法,以长沙市国控站气象观测数据和WRF模式模拟得到的风场数据构建数据集,对WRF预报长沙市城区风场数据进行优化.同时,利用树模型特征选择法,筛选对近地面风场数据有重要影响的气象要素,将其作为两种机器学习算法的输入,并利用网格搜索法进行调参建模.最后,将训练结果与地面观测数据进行对比评估...     

基于WRF模式的云南省干旱发生风险的预测分析

云南省作为西部大开发的典型省份之一,有着独特的自然环境和资源禀赋,但同时全省地形复杂多变且处于气候带的脆弱区,气候变率相当大.本研究在分析云南省近30年的土地利用/土地覆被变化的前提下,采用中尺度天气研究与预报Weather Researchand Forecasting (WRF)模式系统,对云南省未来40年干旱发生风险进行了预测分析.研究首先对WRF模式系统适用性进行了验证,重点分析了气温和降水两个主要干旱影响因子的未来时空变异特征.然后根据WRF模式系统的输出结果,选取了系列气象干旱指标因子,并采用综合气象干旱指数计算方法在空间和时间上对干旱发生风险进行了预测.从研究结果来看,加强对现用森林资源的保护,执行长期有效的生态工程建设,可以很大程度上减缓未来干旱发生频率.同时云南省当前“城市上山”等政策实施中也应考虑到建设用地扩张的不可逆转性,避免城市盲目上山.研究指出,未来区域发展规划应该对人为土地利用进行长期且合理有效部署,减少政策的不确定性,合理的区域发展规划是应对干旱风险的有效举措.