基于PC-CCA方法的气象场资料插补试验

引入主成分典型相关分析(PC-CCA)方法建立缺测气象要素场序列的插补模式,对区域性气象场序列（以长江流域月气温距平场为例）各种时空缺测分布型态作插补试验。结果表明,当插补场站网分布型取包含子场在内的混合站网时,建模样本量达25年就可有最优插补精度,且性能稳定,效果优良。尤其当距平符号单一且大尺度分量占优势时,插补精度随缺测场变化很小。     

气象场序列几种插补方案的对比试验

采用基于 Ｅ Ｏ Ｆ Ｓ的主分量回归（ Ｐ Ｃ Ｒ）、 Ｅ Ｏ Ｆ Ｓ迭代法（ Ｉ Ｅ Ｏ Ｆ）和基于主分量典型相关的典型变量回归（ Ｃ Ｖ Ｒ）３ 种不同的统计插补计算方案,对同一区域同一种气象要素序列进行缺测资料的插补试验。结果表明,各种方案插补精度都与参数选择有关,无论缺测站点空间分布类型如何,当缺测点数小于 ６０ ％ 时,３ 种方案均有较好效果,以 Ｃ Ｖ Ｒ 最佳,且随缺测年数增长, Ｃ Ｖ Ｒ 优势更显著。     

气候资料缺测插补方法的对比研究

采用均生函数正交筛选（ＭＧＦ）和一维车贝雪夫多项式展开（ＣＰ）进行了年降水量各种缺测情况下资料的插补试验，并计算各种统计量，结果表明：无论何种缺测ＭＧＦ法拟合或插补精度一般高于ＣＰ法，尤其对连续多年缺测和序列一开始便连续缺测更为明显；同一种方法１ａ缺测拟会精度高于多年缺测，连续４—５ａ缺测但在序列中处于不同的位置拟合结果差别不大，但多段同时缺测拟合精度低于一段缺测.     

20世纪全球表面温度场序列的插补试验

利用基于主分量典型相关分析的典型变量回归 (CVR)插补模式 ,在综合分析Jones等 5°× 5°格点温度资料覆盖率的演变情况、缺测场与基本场温度距平相关结构、稳定性的基础上 ,确定合理的插补方案 ,对其陆面格点温度场进行插补延长试验 ,得到了 1 90 0～ 1 998年连续、均一的全球月平均气温场序列。独立样本检验表明插补效果优良 ,总缺测场误差方差与原序列方差之比低于 0 .40。插补前后全球及纬向平均序列的演变特征基本一致 ,原 Jones序列的线性增温率较重建序列高0 .1 1℃ /(1 0 0年 ) ,可能与原 Jones场序列空间分布的不均一性有关     

一种基于SVD的迭代方法及其用于气候资料场的插补试验

提出一种基于SVD的迭代对气象场序列缺测记录插补延长的技术方法,对长江流域20个测站1月份气温做插补试验,平均均方误差为0.25,插补精度明显优于迭代EOF,插补效果良好且性能稳定;而且插补站数所占比例越小效果越好.此研究表明,基于SVD迭代的插补方法是一种非常有效的插补途径.     

西安观测站长序列气温资料缺测记录插补和非均一性检验

以西安观测站1971—2013年日平均气温、最高气温和最低气温序列为研究对象,利用标准序列法和多元线性回归法进行插补实验,计算插补值与实测值的平均误差、平均绝对误差、均方根误差和插补值与实测值误差在0.5℃以内的样本比例,对比分析两种插值方法的相对优劣。结果表明：多元线性回归法插补得到的气温序列效果好于标准序列法,并且气候趋势特征与实际观测值序列更具一致性。采用t检验法、惩罚最大T检验（PMTT）、惩罚最大F检验（PMFT）对西安站1951—2020年平均气温序列的均一性进行检验。依据台站历史沿革数据进行的t检验,在6次台站历史沿革变化中,只有2次造成了年平均气温和年平均最高气温序列间断,分别由观测时次增加和仪器换型导致;年平均最低气温有4次出现间断,分别由台站站址迁移、观测时次增加、仪器换型、缺测值插补造成。PMTT和PMFT检测中发现的4次间断点因无元数据支持,认为属于合理间断点,这2种方法均未检测出因缺测值插补引起的间断点,一定程度上说明采用多元线性回归法对缺测值插补得到的西安站1951—2020年气温序列相对合理,气温序列的均一性较好。     

利用车贝雪夫多项式进行资料缺测插补的研究

使用一维车贝雪夫多项式展开进行历史年降水量和月平均气温各种缺测情况下资料的插补试验，在迭代计算过程中，还对理想初值的两种临界迭代次数选取方案和迭代终值法进行了大量的试验。结果表明，一般情况下迭代终值计算精度较高，旱涝年则理想初值拟合结果更好一些；一年缺测插补精度高于连续多年缺测；双向插补计算结果优于单独使用顺序或逆序插补结果。     

近百年来我国降水量的变化

从相邻地理区域内降水距平分布特征的相关性出发,通过降水量距平场经验正交函数展开,建立了我国42站月降水量插补模式,并得到了各站1881—1981年间连续、均一的降水量序列。对年、季降水量距平场的经验正交函数分解表明,不同符号的降水量距平带状交替出现,是我国年、季降水量距平分布的基本型式。42站年、季降水量的主成分,一般都没有明显的长期趋势,方差谱分析得出的统计显著周期分量,有集中出现在周期为35年、4.7年和2年左右波段中的倾向。统计分析表明,4.7年左右的周期分量,可能与南方涛动有关。除了降水量变化的周期性以外,在太阳黑子11年周期中,平均距平程度也有显著的变化,在太阳黑子活动11年周期极大值年附近,旱、涝出现机会显著增加。     

新疆春季降水量与印度洋1月海温关系的初步研究

采用奇异值分解（SVD）法对1970~1994年共25年的新疆31个站春季降水量距平场与印度洋（40°S－8°N,22°~118°E）1月海温距平场进行相关分析及反查和验证,发现新疆春季降水量距平场的某些分布型与印度洋1月海温距平场的某些分布型具有较好的对应关系。     

北半球月平均环流异常与我国夏季月降水量异常之间关系的时空综合EOF分析

本文利用一种时空综合的经验正交函数（ＥＯＦ）方法探讨了我国夏季降水场与同期及前期北半球５００ｈＰａ环流场之间的关系。这种方法能够同时给出降水场的距平分布及其对应的同期和前期环流场距平分布型。分析了前三个特征向量场的空间结构，得到了三种不同降水异常分布型及其与之相应的同期和前期５００ｈＰａ环流异常型。结果发现，前期（冬季）５００ｈＰａ高度距平场在中高纬度地区有着同夏季几乎完全相反的分布特征，在这一特征下，我国东部夏季降水场有较一致的距平分布，此外，当前期冬季５００ｈＰａ出现ｗｐ型环流异常时，我国长江流域夏季降水会出现显著异常。     

Possible Impact of Spatial and Temporal Non-Uniformity in Land Surface Temperature Data on Trend Estimation

The present work investigates possible impact of the non-uniformity in observed land surface temperature on trend estimation, based on Climatic Research Unit (CRU) Temperature Version 4 (CRUTEM4) monthly temperature datasets from 1900 to 2012. The CRU land temperature data exhibit remarkable non-uniformity in spatial and temporal features. The data are characterized by an uneven spatial distribution of missing records and station density, and display a significant increase of available sites around 1950. Considering the impact of missing data, the trends seem to be more stable and reliable when estimated based on data with < 40% missing percent, compared to the data with above 40% missing percent. Mean absolute error (MAE) between data with < 40% missing percent and global data is only 0.011°C (0.014°C) for 1900–50 (1951–2012). The associated trend estimated by reliable data is 0.087°C decade^–1 (0.186°C decade^–1) for 1900–50 (1951–2012), almost the same as the trend of the global data. However, due to non-uniform spatial distribution of missing data, the global signal seems mainly coming from the regions with good data coverage, especially for the period 1900–50. This is also confirmed by an extreme test conducted with the records in the United States and Africa. In addition, the influences of spatial and temporal non-uniform features in observation data on trend estimation are significant for the areas with poor data coverage, such as Africa, while insignificant for the countries with good data coverage, such as the United States.     

Dynamical and Microphysical Retrieval from Simulated Doppler Radar Observations Using the 4DVAR Assimilation Technique

Based on a cloud model and the four-dimensional variational (4DVAR) data assimilation method developed by Sun and Crook (1997), simulated experiments of dynamical and microphysical retrieval from Doppler radar data were performed. The 4DVAR data assimilation technique was applied to a cloud scale model with a warm rain parameterization scheme. The 3D wind, thermodynamical, and microphysical fields were determined by minimizing a cost function, defined by the difference between both radar observed radial velocities and reflectivities and their model predictions. The adjoint of the numerical model was used to provide the gradient of the cost function with respect to the control variables. Experiments have demonstrated that the 4DVAR assimilation method is able to retrieve the detailed structure of wind, thermodynamics, and microphysics by using either dual-Doppler or single-Doppler information. The quality of retrieval depends strongly on the magnitude of constraint with respect to the variables. Retrieving the temperature field, cloud water and water vapor is more difficult than the recovery of the wind field and rainwater. Accurate thermodynamic retrieval requires a longer assimilation period. The inclusion of a background term, even mean fields from a single sounding, helped reduce the retrieval errors. Less accurate velocity fields were obtained when single-Doppler data were used. It was found that the retrieved velocity is sensitive to the location of the retrieval domain relative to the radars while the other fields have very little changes. Two radar volumetric scans are generally adequate for providing the evolution, although the use of additional volumes improves the retrieval. As the amount of the observations decreases, the performance of the retrieval is degraded. However, the missing observations can be compensated by adding a background term to the cost function. The technique is robust to random errors in radial velocity and calibration errors in reflectivity. The boundary conditions from the dual-Doppler synthesized winds are sufficient for the retrieval. When the retrieval is mainly controlled by the observations in the regions away from the boundaries, the simple boundary conditions from velocity azimuth display (VAD) analysis are also available. The microphysical retrieval is sensitive to model errors.     

Analysis and estimation of the effects of missing values on the calculation of monthly temperature indices

Long and complete climatic data series are a fundamental resource for scientific research on climate change. Data quality is important, and missing value or data gap management is a key process that must be dealt with carefully to produce reliable datasets. Although a large variety of techniques are available for gap-filling, a widespread strategy is to consider a dataset reliable if the rate of missing data is below a given threshold. However this strategy varies from study to study. The aim of this paper is to analyze the impact of missing daily values on the estimation of monthly average temperature indices. The relationship between the error of the estimate and the presence of random or consecutive missing values, as well as data series autocorrelation is also analyzed. A theoretical, a linear and a nonlinear model to estimate the maximum error at the 95 % confidence interval are tested on data series provided by national and worldwide networks of stations. Consecutive missing values have an important effect on error estimation due to autocorrelation of temperature data series. On our dataset, the mean and standard deviation of the error for five consecutive missing values (0.27?±?0.05 °C) on a normalized daily series (σ?=?1) was higher than for five random missing values (0.14?±?0.006 °C). A nonlinear model taking into account the number of consecutive missing values is able to estimate the error and its performance is less affected by the presence of consecutive missing values than the other proposed models.     

Doppler Radar Data Assimilation with a Local SVD-En3DVar Method

An observation localization scheme is introduced into an ensemble-based three-dimensional variational (3DVar) assimilation method based on the singular value decomposition technique (SVD-En3DVar) to improve assimilation skill. A point-by-point analysis technique is adopted in which the weight of each observation decreases with increasing distance between the analysis point and the observation point. A set of numerical experiments, in which simulated Doppler radar data are assimilated into the Weather Research and Forecasting (WRF) model, is designed to test the scheme. The results are compared with those obtained using the original global and local patch schemes in SVD-En3DVar, neither of which includes this type of observation localization. The observation localization scheme not only eliminates spurious analysis increments in areas of missing data, but also avoids the discontinuous analysis fields that arise from the local patch scheme. The new scheme provides better analysis fields and a more reasonable short-range rainfall forecast than the original schemes. Additional forecast experiments that assimilate real data from 10 radars indicate that the short-term precipitation forecast skill can be improved by assimilating radar data and the observation localization scheme provides a better forecast than the other two schemes.     

Reconstructing missing hourly real-time precipitation data using a novel intermittent sliding window period technique for automatic weather station data

Precipitation is the most discontinuous atmospheric parameter because of its temporal and spatial variability. Precipitation observations at automatic weather stations (AWSs) show different patterns over different time periods. This paper aims to reconstruct missing data by finding the time periods when precipitation patterns are similar, with a method called the intermittent sliding window period (ISWP) technique—a novel approach to reconstructing the majority of non-continuous missing real-time precipitation data. The ISWP technique is applied to a 1-yr precipitation dataset (January 2015 to January 2016), with a temporal resolution of 1 h, collected at 11 AWSs run by the Indian Meteorological Department in the capital region of Delhi. The acquired dataset has missing precipitation data amounting to 13.66%, of which 90.6% are reconstructed successfully. Furthermore, some traditional estimation algorithms are applied to the reconstructed dataset to estimate the remaining missing values on an hourly basis. The results show that the interpolation of the reconstructed dataset using the ISWP technique exhibits high quality compared with interpolation of the raw dataset. By adopting the ISWP technique, the root-mean-square errors (RMSEs) in the estimation of missing rainfall data—based on the arithmetic mean, multiple linear regression, linear regression, and moving average methods—are reduced by 4.2%, 55.47%, 19.44%, and 9.64%, respectively. However, adopting the ISWP technique with the inverse distance weighted method increases the RMSE by 0.07%, due to the fact that the reconstructed data add a more diverse relation to its neighboring AWSs.     

基于矩阵补全的气象数据推测

传统的气象数据推测大多基于插值方法,而此方法需要近邻台站的完整观测数据,这在很大程度上限制了插值方法的应用。为此,本文提出了一种基于矩阵补全的气象数据推测方法,该方法根据气象数据的近似低秩性来推测缺失数据。首先,选取我国662个气象台站2004—2013年的逐日平均温度和日照时数两种气象要素作为研究对象,通过矩阵奇异值的累积贡献率来检验数据集的近似低秩性。然后设计了两组试验,第1组试验考虑了不同采样概率下各年份的数据推测,第2组试验随机选取某些台站,考虑所选台站数据连续缺测时的推测。最后,使用矩阵补全方法推测缺失数据,采用10a的平均误差作为评价指标。试验结果表明:矩阵补全方法能很好地推测缺失数据,且具有一定的鲁棒性。     

初值形成方法对降水数值预报的影响

北欧有限区域模式ＨＩＲＬＡＭ被应用于中国的暴雨个例以探讨初值形成方法对有限区域模式定量降水数值预报的影响,对两种初值形成方案进行了对比,一种是由ＨＩＲＬＡＭ自己的数值同化系统提供初值,另一种是直接内插ＥＣＭＷＦ全球模式的相应分析场,与这两种方案对应的数值试验分别是控制试验（ＣＯＮＬ）和对比试验（ＣＯＭＰ）,将ＣＯＮＬ和ＣＯＭＰ的降水预报与观测值比较,结果表明：（１）当为ＣＯＭＰ提供初值的ＥＣＭＷＦ