北半球月平均环流异常演变的相似韵律现象

本文用严格的统计方法和较长时间序列的观测资料对北半球月平均环流相似性的演变做了比较全面细致的分析。结果表明:两个不同年份的月平均距平场在某个起始月相似后相似性随之变差,但过了约半年左右变得又相似。这就是所谓的长期天气异常演变过程中半年左右的相似韵律现象。     

韵律与长期天气预报

本文讨论了对长期天气预报比较重要的韵律问题,共有三部分内容:(1)分析广大台站在相关普查中发现的隔季相关现象,相关间隔自2个月到11个月不等,其中大约半年左右的比较常见,并且地理分布区域性强,大洋上最明显,指出这是韵律现象。 (2)月平均环流与海温的相似性分析表明,北半球环流与海温都存在着隔季相似性。例如,春或夏海温距平相似时,大约6个月之后又相似。而秋冬春三季大气环流的相似容易造成夏季大气环流的相似。指出这是大气环流与海洋的韵律活动的表现。 (3)讨论了两种可能的韵律形成过程,一种是北太平洋西风漂流区夏季海温通过韵律关系影响半年后的冬季海温,从而影响大气环流和天气。另一种是冬季低纬东太平洋海温与大气环流相互作用,通过韵律关系影响半年之后夏季低纬太平洋的环流,从而影响西太平洋副高及我国天气。     

长江中下游地区冬夏干湿韵律特征分析

通过分析中国160站1952—2013年的月平均降水观测资料,揭示了长江中下游地区冬季和夏季降水间存在显著的韵律现象,即当该地区冬季降水异常偏少(偏多)时,次年夏季降水也趋于异常偏少(偏多),这里称之为干(湿)韵律现象。对干、湿韵律年大气环流背景的分析结果显示,干韵律年和湿韵律年对应的环流形势基本相反:在干(湿)韵律年冬季,东亚地区500 hPa位势高度距平呈现西高东低(东高西低)的分布型,中国南方东部主要受偏北(南)风异常控制,这不利(有利)于低纬度暖湿气流向长江中下游地区输送,导致该地区冬季降水异常偏少(多);在次年夏季,西北太平洋副热带高压异常偏弱(强),不利(有利)于西南暖湿气流向中国东部地区输送,使得长江中下游地区夏季降水也异常偏少(多)。研究进一步指出,长江中下游地区的冬夏干、湿韵律现象与东亚冬夏季风活动的强度密切相关。干、湿韵律现象多在东亚冬夏季风强度变化一致的情况下出现:冬、夏季风一致偏强时多导致干韵律现象,而一致偏弱时易导致湿韵律现象。     

中国月平均降水场的时空相关特征

本文通过对我国1951～1984年月平均降水资料的分析,给出了我国东部降水的空间分布具有三种类型,一种类型是长江流域多雨,华南和华北及东北少雨;另一种类型与此型相反;第三种类型为正常型,降水距平百分率全场都很小。并指出了长江中下游地区降水时间特征具有明显的半年韵律现象,冬季1月和12月的降水可以作为6～8月降水的信息期。同时还表明在1951～1984年中9个ElNino年的下一年6～8月汛期降水具有不完全相同的类型。     

一种计算区域内气象要素平均值的方法

采用面积分的原理,提出了一种计算区域内气象要素平均值的新方法,通过对湖南全省温度、降水的平均值计算分析,对计算误差进行了检验。结果表明,采用该计算方法可以减少因气象台站分布不均匀对平均值的影响,计算得到的平均值更能反映实际情况。     

利用水平面太阳直接辐射日曝辐量反演大气气溶胶光学厚度的方法研究

本文发展了一个从宽带水平面太阳直接辐射日曝辐量 (总辐射与散射辐射日曝辐量之差) 反演光谱大气气溶胶光学厚度的方法, 包括建立一个 “等效” 的瞬时太阳天顶角模型, 并提出了一个基于气溶胶标高的云影响甄别方法。对该反演方法的数值模拟和误差分析表明: “等效” 瞬时太阳天顶角模型的不稳定性引起的光学厚度反演误差平均为3.66％; 光学厚度日变化对一段较长时间的平均光学厚度的影响不显著; 订正造成的散射辐射误差≤20％时, 光学厚度平均偏差≤4％。通过与AERONET产品的比对验证表明: 本文发展的光学厚度反演方法和云影响甄别方法都是有效的; 晴空反演的0.75 μm光学厚度与AERONET的相关系数超过0.95, 平均误差约0.02; 云甄别方法计算的季节和年平均光学厚度与AERONET具有较好的一致性。     

基于Mask R-CNN的遥感影像土地分割与轮廓提取

传统的模式识别难以对土地遥感影像一次性精确统计分析.在精确分割出土地种类的前提下,本文提出了一种基于骨干网络为ResNet-101-RPN的Mask R-CNN的遥感影像土地分割与轮廓提取方法.该方法包括以下步骤：数据获取、图像去雾、遥感影像土地统计分析、土地分割和轮廓获取.在一个具有挑战性的卫星地图瓦片数据集上对所提出的方法进行训练和测试.实验结果表明,该方法以0.907的均值平均精度（mAP）和31.33像素的均值平均距离误差（mADE）获得了令人满意的不同种类土地分割和轮廓提取结果.     

Compilation method for 1 km grid data of monthly mean air temperature for quantitative assessments of climate change impacts

A new method is proposed to compile 1 km grid data of monthly mean air temperature by dynamically downscaling general circulation model (GCM) data with a regional climate model (RCM). The downscaling method used is a technique referred to as the pseudoglobal warming method to reduce GCM bias. For the grid data, RCM data were corrected with data from an existing meteorological network. The correction model for the RCM bias was developed by stepwise multiple regression analysis using the difference in the monthly mean air temperatures between the observation and RCM output as a dependent variable and the geographical factors as independent variables. Our method corrected the RCM bias from 1.69°C to 0.58°C for the month of August in the 1990s (1990–1999).     

Prediction of East Asian summer precipitation via independent component analysis

A new statistical postprocessing method is proposed for seasonal climate prediction. The proposed method is based on a combination of independent component analysis (ICA) and canonical correlation analysis (CCA). Since the classical CCA cannot handle high-dimensional data wherein the number of variables is larger than the number of observations, ICA is pre-performed to reduce the dimension of the data. It is well known that empirical orthogonal function (EOF) analysis is a popular method for dimension reduction in the climatology community; however, loss of information occurs when the data is not Gaussian distributed. To extend the scope of distribution assumption and improve the prediction ability simultaneously, we propose the ICA-based method. This study focuses on the prediction of future precipitation for the boreal summer (June?CJuly?CAugust; JJA) through 29 years (1979?C2007) on East Asia region. Results of the proposed ICA-based method show an improvement in seasonal climate prediction in terms of correlation and root mean square error as compared with those of the GCM simulation and the EOF/CCA method.     

Estimating the Random Error in Eddy-Covariance Based Fluxes and Other Turbulence Statistics: The Filtering Method

A spatially local decomposition of turbulent fluxes based on properties of spatial filters is used to develop a new method of estimating random error in turbulent moments of any order. The proposed error estimation method does not require an estimate of the integral time scale, which can be highly sensitive to the method used to calculate it. The error estimation method is validated using synthetic flux data with a known ensemble mean and intercompared with existing methods using data from the Advection Horizontal Array Turbulence Study (AHATS). Typical errors for a 27.3-min block of data collected at a height of 8 m are found to be approximately 10% for the heat flux and 7–15% for variances. The error in the momentum flux increases rapidly with increasing atmospheric instability, reaching values of 40% or greater for unstable conditions. A new method based on filtering is also proposed to estimate integral time scales of turbulent quantities.     

Estimation of the Friction Velocity in Stably Stratified Boundary-Layer Flows Over Hills

A method is suggested for the calculation of the friction velocity for stable turbulent boundary-layer flow over hills. The method is tested using a continuous upstream mean velocity profile compatible with the propagation of gravity waves, and is incorporated into the linear model of Hunt, Leibovich and Richards with the modification proposed by Hunt, Richards and Brighton to include the effects of stability, and the reformulated solution of Weng for the near-surface region. Those theoretical results are compared with results from simulations using a non-hydrostatic microscale-mesoscale two-dimensional numerical model, and with field observations for different values of stability. These comparisons show a considerable improvement in the behaviour of the theoretical model when the friction velocity is calculated using the method proposed here, leading to a consistent variation of the boundary-layer structure with stability, and better agreement with observational and numerical data.     

基于深度学习的气象要素预测方法

针对气象预测内容繁多且影响因素多样的问题,提出了一种基于长短时记忆（LSTM）的气象预测方法。方法能够对繁杂的气象数据进行自动预处理,提取相应的特征信息。通过神经网络的前向训练、长短时记忆反馈学习,经过多隐藏层地自主训练,对能见度、温度、露点、风速、风向以及压力气象信息实现准确预测。通过实验以及与经典机器学习预测方法的比较,验证了本文方法在气象预测中的有效性,进一步提升了气象预测的准确性,各项预测值的均方检验误差平均值为0.35。      

ENSO预测的目标观测敏感区在热带太平洋海温的多模式集合预报中的应用

本文将ENSO预测的目标观测敏感区与多模式集合预报方法相结合, 提出了一种能够有效提高预报技巧且又具有较小计算成本的多模式集合预报方法。该方法在目标观测敏感区内采用模式不等权的多模式超级集合预报方法(SUP), 而在其他区域采用相对简单的等权的多模式消除偏差集合平均方法(BREM)。利用CMIP5中15个气候系统模式的工业革命前参照试验(pi-Control)数据, 针对热带太平洋海温的长期演变开展了理想预报试验。将新集合预报方法与现有的多模式集合预报方法进行了比较。结果表明, 在所考察的预报期内(即1～20年), 新集合预报方法与整个热带太平洋区域使用SUP方法具有相当的预报技巧, 但前者的计算成本明显小于后者, 计算时间仅为后者的1/4。可见, 新方法是一个具有较高预报技巧且计算成本较小的多模式集合预报方法。同时, 其较高的预报技巧强调了热带太平洋SST预测对ENSO目标观测敏感区内的模式误差也是极端敏感的, 也正因如此, 多模式集合预报方法才能够有效过滤模式误差的影响, 具有较高的预报技巧。     

A new method to reconstruct low-frequency climatic variability from documentary sources: application to winter rainfall series in Andalusia (Southern Spain) from 1501 to 2000

In this work a simple approach to reconstruct climatic variables from documentary data is proposed. This approach may be especially useful when an overlapping period between documentary and instrumental data is not available. On the other hand, the method avoids use artificial statistical procedures to rescale the reconstructed series and overcome the problem of the loss of variance. The methodology allows reconstruct changes in the mean value and standard deviation of the climate variables. It is based on accounting the number of extreme events in past, and inferring mean value and standard deviation using the assumption of normal distribution for the climatic variables. The application of the method is only possible if a sufficient number of events is recorded in the data base. It is useful to reconstruct changes in the long-time scale, using at least decadal periods as time units. The method is applied to winter rainfall series corresponding to 30-year periods in Andalusia (southern Spain), obtaining results comparable with those of previous analysis.     

Statistical downscaling of precipitation using local regression and high accuracy surface modeling method

Downscaling precipitation is required in local scale climate impact studies. In this paper, a statistical downscaling scheme was presented with a combination of geographically weighted regression (GWR) model and a recently developed method, high accuracy surface modeling method (HASM). This proposed method was compared with another downscaling method using the Coupled Model Intercomparison Project Phase 5 (CMIP5) database and ground-based data from 732 stations across China for the period 1976–2005. The residual which was produced by GWR was modified by comparing different interpolators including HASM, Kriging, inverse distance weighted method (IDW), and Spline. The spatial downscaling from 1° to 1-km grids for period 1976–2005 and future scenarios was achieved by using the proposed downscaling method. The prediction accuracy was assessed at two separate validation sites throughout China and Jiangxi Province on both annual and seasonal scales, with the root mean square error (RMSE), mean relative error (MRE), and mean absolute error (MAE). The results indicate that the developed model in this study outperforms the method that builds transfer function using the gauge values. There is a large improvement in the results when using a residual correction with meteorological station observations. In comparison with other three classical interpolators, HASM shows better performance in modifying the residual produced by local regression method. The success of the developed technique lies in the effective use of the datasets and the modification process of the residual by using HASM. The results from the future climate scenarios show that precipitation exhibits overall increasing trend from T1 (2011–2040) to T2 (2041–2070) and T2 to T3 (2071–2100) in RCP2.6, RCP4.5, and RCP8.5 emission scenarios. The most significant increase occurs in RCP8.5 from T2 to T3, while the lowest increase is found in RCP2.6 from T2 to T3, increased by 47.11 and 2.12 mm, respectively.

     

近60a来全球河川径流量变化趋势及影响因素分析

基于全球2 894个流域的河川径流量、归一化植被指数（Normalized Differential Vegetation Index,NDVI）以及气温和降水量长期数据,发现年内月均径流量（MQ）呈上升、下降以及不显著变化趋势的流域比例分别为9.1%、12.4%和78.5%,年内最小月径流量（LQ）呈3种变化趋势的流域比例分别为24.1%、11.7%和64.2%,年内最大月径流量（HQ）呈3种变化趋势的流域比例分别为6.4%、13.6%和79.9%。在区域（大陆和纬度带）和全球尺度上,没有出现LQ下降和HQ上升变化趋势的现象,MQ下降与HQ下降联系在一起,MQ上升与LQ上升联系在一起,表明区域和全球尺度上水资源量及年内分配在过去60多年里是改善的。进一步分析发现,气候变化是导致水资源量和年内分配变化的主要因素。但与年内月极端径流量（LQ或HQ）变化不同的是,发生年内月极端径流量的月份大多发生了显著的改变,这个改变可能对社会和自然生态系统以及依靠自然节律的生产生活方式产生深远影响。     

Nonlinear dynamics of meteorological variables: multifractality and chaotic invariants in daily records from Pastaza, Ecuador

Weather represents the daily state of the atmosphere. It is usually considered as a chaotic nonlinear dynamical system. The objectives of the present study were (1) to investigate multifractal meteorological trends and rhythms at the Amazonian area of Ecuador and (2) to estimate some nonlinear invariants for describing the meteorological dynamics. Six meteorological variables were considered in the study. Datasets were collected on a daily basis from January 1st 2001 to January 1st 2005 (1,460 observations). Based on a new multifractal method, we found interesting fractal rhythms and trends of antipersistence patterns (Fractal Dimension >1.5). Nonlinear time series analyses rendered Lyapunov exponent spectra containing more than one positive Lyapunov exponent in some cases. This sort of hyperchaotic structures could explain, to some extent, larger fractal dimension values as the Kaplan–Yorke dimension was also in most cases larger than two. The maximum prediction time ranged from ξ?=?1.69 days (approximately 41 h) for E/P ratio to ξ?=?14.71 days for evaporation. Nonlinear dynamics analyses could be combined with multifractal studies for describing the time evolution of meteorological variables.     

一个制作长期天气预报的前后相关图解方法

本文提出一个利用相关散布图制作长期天气预报的方法,试验结果指出准确率甚高.     

500hPa月平均距平场演变的宏观描述

近年来,采用数值方法进行长期预报和气候异常的研究有了很大的进展,但是这些研究多是沿用了中短期预报的概念和方法,由于长期天气系统是一个强迫耗散的非线性系统,而“耗散系统最终将趋向维数比原始相空间低的极限集合——吸引子,许多自由度在演变过程中成为‘无关变量’,最终剩下支撑起吸引子的少数自由度”。如果不考虑系统的极其复杂的暂态行为,我们自然可以把这些‘无关变量’去掉,从而把高维动力系统简化成低维动力系统。这个解释H.Haken称做随动原理。本文的目的旨在应用上述原理探讨长期天气异常演变的宏观描述方法。我们希望这里提出的概念和方法将有助于建立有效的长期数值预报模式。     

禹州小麦吸浆虫发生程度与气象因素的关系

小麦吸浆虫的发生程度与温度、湿度、日照密切相关。适宜的气温、湿度及日照条件以及2005年土壤中虫源基数较高,是2006年禹州吸浆虫发生严重的主要原因。依据温度条件,建立了吸浆虫发生期预测方程;依据温湿条件,建立了发生量预测方法。此外,还提出了麦播期、孕穗期(蛹期)和抽穗期(成虫期)的防治方法。