数值预报产品在夏季持续高温预报中的释用

利用 1999～ 2 0 0 2年 6～ 8月 96～ 192h日本数值预报产品 85 0hPa气温与吉林省的日平均气温、阶段性高温及极端最高气温 (长春市 )资料 ,通过线性分析 ,找出了日本数值预报产品的 85 0hPa气温与吉林省地面日平均气温、阶段性高温及极端最高气温的对应关系 :吉林省 6～ 8月日平均气温在 96～ 192h的 85 0hPa日本数值预报长春站日平均值上加 6 6～ 4 1℃ ,6～ 8月的极端最高气温在相应的日本数值预报产品上加 9 3～ 13 0℃。     

基于灰关联分析的GPS高程拟合加权平均函数模型权函数优选

基于灰色系统理论的灰关联分析方法,提出了权函数的优选方法,实例分析表明其具有较好的实用价值.     

Assessing the Comparability of Ammonium,Nitrate and Sulfate Concentrations Measured by Three Air Quality Monitoring Networks

Airborne fine particulate matter across the United States is monitored by different networks, the three prevalent ones presently being the Clean Air Status and Trend Network (CASTNet), the Interagency Monitoring of PROtected Visual Environment Network (IMPROVE) and the Speciation and Trend Network (STN). If combined, these three networks provide speciated fine particulate data at several hundred locations throughout the United States. Yet, differences in sampling protocols and samples handling may not allow their joint use. With these concerns in mind, the objective of this study is to assess the spatial and temporal comparability of the sulfate, nitrate and ammonium concentrations reported by each of these networks. One of the major differences between networks is the sampling frequency they adopted. While CASTNet measures pollution levels on seven-day integrated samples, STN and IMPROVE data pertain to 24-hour samples collected every three days. STN and IMPROVE data therefore exhibit considerably more short-term variability than their CASTNet counterpart. We show that, despite their apparent incongruity, averaging the data with a window size of four to six weeks is sufficient to remove the effects of differences in sampling frequency and duration and allow meaningful comparison of the signals reported by the three networks of concern. After averaging, all the sulfate and, to a lesser degree, ammonium concentrations reported are fairly similar. Nitrate concentrations, on the other hand, are still divergent. We speculate that this divergence originates from the different types of filters used to collect particulate nitrate. Finally, using a rotated principal component technique (RPCA), we determined the number and the geographical organization of the significant temporal modes of variation (clusters) detected by each network for the three pollutants of interest. For sulfate and ammonium, the clusters’ geographical boundaries established for each network and the modes of variations within each cluster seem to correspond. RPCA erformed on nitrate concentrations revealed that, for the CASTNet and IMPROVE networks, the modes of variation do not correspond to unified geographical regions but are found more sporadically. For STN, the clustered areas are unified and easily delineable. We conclude that the possibility of jointly using the data collected by CASTNet, IMPROVE and STN has to be weighed pollutant by pollutant. While sulfate and ammonium data show some potential for joint use, at this point, combining the nitrate data from these monitoring networks may not be a judicious choice.     

基于时序NDVI的昭觉植被覆盖度变化研究

归一化植被指数(NDVI)能精确地反映植被绿度、光合作用强度,在一定程度上反映着植被的演化信息,是评价生态环境状况的重要指标之一。本文利用Landsat TM数据分别对昭觉地区2009年和2014年的NDVI进行计算,并分别利用均值法、像元二分模型及NDVI差值植被指数对研究区域植被覆盖变化进行定量分析,结果表明:昭觉县NDVI均值上升了11.6%,植被覆盖度中极度改善的面积比例约占38%,昭觉县整体NDVI植被覆盖度显著提高,并对其变化原因进行简要分析,为生态环境建设提供决策依据。     

IEC的风机选型参数估算方法在中国的适用性研究

国际电工委员会编制的《风力发电机组设计要求》(IEC 61400-1)推荐了针对风电机组安全等级评估的极端风速和湍流强度特征值估算方法,因其简单便捷,在风电领域被广泛采用。利用全国风能资源专业观测网的193座测风塔观测数据,对IEC推荐的极端风速计算方法与我国规范推荐的基于极值I型概率分布方法进行比较,发现两种方法计算的193座塔70 m高度层50年一遇10 min平均最大风速,仅有7座测风塔较为一致,差异在±1%;IEC推荐方法的计算结果多数偏小,其中偏小10%以上的测风塔有121座,偏小30%以上的有44座测风塔,而偏大10%以上的只有9座测风塔;IEC方法计算的极值风速大幅度偏小的测风塔主要分布在台风影响的东南沿海地区,偏差较小的测风塔主要分布在西北和华北地形平缓区域,但同时偏大10%以上的测风塔也多分布在这一地区。以目前行业领域普遍采用的以15 m·s~(-1)风速的平均湍流强度作为风电机组选型指标,与严格按照规范,以15 m·s~(-1)风速段所有样本湍流强度的90%分位数处的值作为指标进行风电机组等级确定作对比,发现193座塔中有46座塔的选型是不安全的,甚至相差两个等级。     

中国地面气温统计降尺度预报方法研究

利用中国752个基本、基准地面气象观测站2000—2010年地面温度日值数据,采用具有自适应特征的Kalman滤波类型的递减平均统计降尺度技术,对中国地面温度进行精细化预报研究。分析该方案的降尺度效果,并与常用插值降尺度方法进行比较。结果表明:1)递减平均统计降尺度技术相比插值方法有较大的提高,显著减小东西部预报效果差异,1~3 d预报的均方根误差减小了1.4℃;2)该方案1~3 d预报的均方根误差为1.5℃,预报误差从东南地区(均方根误差为1.4℃)向西北地区(均方根误差为1.8℃)逐渐增大,并且预报效果夏季优于冬季。因此,递减平均统计降尺度技术对中国地面温度进行精细化预报是可行的。     

变形监测缺失数据序列灰色建模方法探讨

在测量工作中,由于气候环境、观测方法、观测仪器以及观测人员自身因素等多方面的原因,可能造成观测数据的丢失或者不完全。文中针对这类数据的处理,采用加权平均法和三次样条插值法对缺失数据进行修复,建立GM （1,1）模型,并与非等间隔预测模型进行对比。通过两组仿真数据和两组实测数据验证发现：对于呈指数增长的序列和高增长序列修复之后建模预测精度更高;三次样条插值法数据修复后GM （1,1）建模预测精度较加权平均法预测精度更高;对于低增长序列,直接采用非等间隔建模预测精度更高。     

Calculation method for thickness of discontinuous boundary layer of engineering pavement

The boundary layer is a buffer layer of water and heat exchange between the atmosphere and permafrost.Based on the atmospheric boundary layer and heat transfer theory,we established a method for determining the boundary layer thickness of engineering pavement(asphalt and sand pavement)in permafrost region.The boundary layer can be divided into the Boundary Layer Above Surface(BLAS)and the Boundary Layer Below Surface(BLBS).From in-situ monitoring data,the thickness of BLAS was determined through the laminar thickness,and the thickness of BLBS was determined through ground temperature,the heat conduction function,and the mean attenuation function(α).For asphalt pavement,the BLAS thickness varied between 2.90 and 4.31 mm and that of BLBS varied between 28.00 and 45.38 cm.For sand pavement,the BLAS thickness varied between 2.55 and 3.29 mm and that of BLBS varied between 15.00 and 46.44 cm.The thickness varied with freezing and thawing processes.The boundary layer calculation method described in this paper can provide a relatively stable boundary for temperature field analysis.     

An Innovative Bias-Correction Approach to CMA-GD Hourly QuantitativePrecipitation Forecasts

This paper proposes a simple and powerful optimal integration (OPI) method for improving hourly quantitative precipitation forecasts (QPFs, 0-24 h) of a single-model by integrating the benefits of different bias- corrected methods using the high-resolution CMA-GD model from the Guangzhou Institute of Tropical and Marine Meteorology of China Meteorological Administration (CMA). Three techniques are used to generate multi-method calibrated members for OPI: deep neural network (DNN), frequency-matching (FM), and optimal threat score (OTS). The results are as follows: (1) The QPF using DNN follows the basic physical patterns of CMA-GD. Despite providing superior improvements for clear-rainy and weak precipitation, DNN cannot improve the predictions for severe precipitation, while OTS can significantly strengthen these predictions. As a result, DNN and OTS are the optimal members to be incorporated into OPI. (2) Our new approach achieves state-of-the-art performances on a single model for all magnitudes of precipitation. Compared with the CMA-GD, OPI improves the TS by 2.5%, 5.4%, 7.8%, 8.3%, and 6.1% for QPFs from clear-rainy to rainstorms in the verification dataset. Moreover, OPI shows good stability in the test dataset. (3) It is also noted that the rainstorm pattern of OPI relies heavily on the original model and that OPI cannot correct for deviations in the location of severe precipitation. Therefore, improvements in predicting severe precipitation using this method should be further realized by improving the numerical model’s forecasting capability.     

东北地区冬半年积雪与气温对冻土的影响

利用东北地区121个气象站逐日冻土深度、积雪深度、平均气温、地表平均气温及降水量数据,分析了1964—2017年冬半年冻土的变化特征及气象要素对冻土的影响。结果表明：东北地区积雪深度、平均气温、地表平均气温与冻土深度相关系数较高,降水量相关性不大。20世纪60年代平均气温、地表平均气温及负积温最低,最大冻土深度为历年代最深;随着气候变暖,最大冻土深度以6.15 cm?（10a）^-1的速率显著减小。冬半年平均最大冻土深度为123 cm,呈显著纬向分布,自辽东半岛向大兴安岭北部递增;随纬度和海拔高度的增加,平均气温和地表平均气温降低,负积温增加,且由北向南地气温差增大。最大冻土深度全区有90%以上的站点减少,减少速率以0.1~10 cm?（10a）^-1为主。冻土持续时间随纬度升高而增加,月最大冻土深度和积雪深度最大值分别出现在3月和1月,最大冻土深度的增加要滞后于积雪深度的增加。由于积雪对地温的保温作用,积雪深度较浅时,冻土深度增加较明显,随着积雪深度的增加,冻土深度变化较小,积雪对冻土起到了保温的作用。对于高纬度地区站点,30 cm左右为积雪的保温界限值;对于沿海站点,积雪保温的界限值在5 cm左右;在相同地形下,冻土深度较浅区域积雪的保温值因海拔高度、气候特点而异。最大冻土深度对地表平均气温升温的响应更为显著,地表平均气温和平均气温每升高1 ℃,最大冻土深度将减小8.4 cm和10.6 cm,负积温每减少100 ℃?d,最大冻土深度减少4.9 cm。