风廓线雷达回波信号强度定标方法

风廓线雷达返回信号功率定标通常通过返回信号信噪比和系统噪声功率的估算得到。该方法存在着噪声电平的确定、外部噪声等不确定性因素,影响了定标的精度。采用信号源分别对接收机和信号处理器进行定量测试,进而对雷达系统进行定标,是另一种可行的办法,该文利用这种方法对CFL-03风廓线雷达进行了定标,并利用该雷达在东莞2009年7月和8月探测资料与广州S波段天气雷达和地面雨量计资料进行比较。结果表明:用该定标方法得到的回波强度与天气雷达回波强度和地面雨量计资料估算的回波强度基本一致,平均标准差在1 dB左右,表明这种定标方法是可行的。     

线性化物理过程对GRAPES 4DVAR同化的影响

线性化物理过程能够改善四维变分同化中极小化收敛的稳定性和增加极小化过程中对大气物理过程和动力更加精确的描述,它是四维变分同化中非常重要的一部分。通过在GRAPES全球模式中研究线性化物理过程,尤其是两个湿线性化物理过程,改善切线性模式预报精度,来提高GRAPES全球四维变分同化的分析和预报效果。线性化物理过程的开发首先需要简化原非线性化物理过程中的强非线性项,然后对线性化物理过程进行规约化,以抑制切线性扰动的异常增长。目前GRAEPS全球模式中的线性化物理过程主要包括次网格尺度地形参数化、垂直扩散、积云深对流和大尺度凝结。线性化物理过程预报精度的检验方法是通过选择合适大小的初始扰动（同化分析增量）,来比较非线性模式和切线性模式中的扰动演化的纬向平均误差。然后以绝热版本的切线性模式为基础,通过冬、夏两个个例试验来分别检验4个线性化物理过程的12 h预报效果。试验结果表明,通过添加次网格地形参数化和垂直扩散两个干线性化物理过程方案,可以有效抑制住绝热版本切线性模式低层扰动的异常增长,大幅度改善切线性模式预报效果。通过添加积云深对流和大尺度凝结两个湿线性化物理过程,可以在热带区域和中、高纬度地区提高切线性模式中湿变量和温度变量的近似精度,提高GRAPES全球四维变分同化的分析和预报效果。      

Simulation and Regionalization of Daily Global Solar Radiation: A Case Study in Quebec,Canada

Global solar radiation (GSR) is essential for agricultural and plant growth modelling, air and water heating analyses, and solar electric power systems. However, GSR gauging stations are scarce compared with stations for monitoring common meteorological variables such as air temperature and relative humidity. In this study, one power function, three linear regression, and three non-linear models based on an artificial neural network (ANN) are developed to extend short records of daily GSR for meteorological stations where predictors (i.e., temperature and/or relative humidity) are available. The seven models are then applied to 19 meteorological stations located across the province of Quebec (Canada). On average, the root-mean-square errors (RMSEs) for ANN-based models are 0.33–0.54?MJ?m^?2?d^?1 smaller than those for the power function and linear regression models for the same input variables, indicating that the non-linear ANN-based models are more efficient in simulating daily GSR. Regionalization potential of the seven models is also evaluated for ungauged stations where predictors are available. The power function and the three linear regression models are tested by interpolating spatially correlated at-site coefficients using universal kriging or by applying a leave-one-out calibration procedure for spatially uncorrelated at-site coefficients. Regional ANN-based models are also developed by training the model based on the leave-one-out procedure. The RMSEs for regional ANN models are 0.08–0.46?MJ?m^?2?d^?1 smaller than for other models using the same input conditions. However, the regional ANN-based models are more sensitive to new station input values compared with the other models. Maps of interpolated coefficients and regional equations of the power function and the linear regression models are provided for direct application to the study area.     

交叉定标产生的NOAA卫星长期大气温度观测资料

介绍一套用于气候变化研究的NOAA卫星微波观测资料, 资料为全球5 d平均, 2.5°×2.5°经纬网格, 3个深层大气(对流中层、对流层顶以及平流层下层)的温度分布。该文简介了资料产生的方法, 包括交叉定标、角度订正、网格化以及网格误差修正等, 给出了使用这套资料分析得到的1987—2006年间不同大气层的温度变化趋势, 并提供了获取这套资料的方法。     

GFS模式地形高度偏差对地面2m气温预报的影响

利用2016年1月1日—12月31日全球预报系统(GFS,Global Forecasting System)1～5 d的2 m气温预报资料,以及同期中国地面气象站2 m气温观测资料,研究模式地形高度偏差对地面2 m气温预报的影响。结果表明,较大模式地形高度偏差可严重影响2 m气温模式预报性能,导致较大预报误差。随着模式预报时效延长,2 m气温预报均方根误差也略有增加。比较模式地形高度偏差和预报时效对于模式预报性能的影响,发现模式地形高度偏差对于模式预报效果的影响更加显著。两种地形订正方案,即不做温度垂直订正的线性回归以及对温度进行垂直订正的线性回归都能显著减小2 m气温模式预报的误差,后者的订正效果更好。     

风云三号A星微波湿度计主探测通道辐射特性

微波湿度计装载在2008年5月27日发射升空的我国新一代极轨气象卫星风云三号A星上,完成全天候大气湿度信息的被动微波遥感探测。主探测通道中心频点位于183.31 GHz,设置有3个双边带探测通道。为保证风云三号微波湿度计在轨定量应用,研制过程中完成了地面真空试验。该文对风云三号A星微波湿度计正样产品真空试验数据进行了定量分析,数据分析结果表明:风云三号A星微波湿度计正样产品主探测频点183.31 GHz附近的3个探测通道等效噪声温差小于等于0.91 K;定标准确度优于1.05 K;真空试验过程中微波湿度计辐射定标结果稳定。风云三号A星微波湿度计发射前辐射定标特性分析结果,为仪器在轨定量应用奠定了基础。     

Non-Linear Numerical Model Predictions of Flow Over an Isolated Hill of Moderate Slope

Non-linear model simulations of atmospheric boundary-layer flow over the hill called Blashaval have been compared with observations and linear model predictions. Previous studies have shown that linear models can give good predictions of wind speed at the summit and on the upwind slopes of Blashaval. The non-linear model provided wind speed predictions of similar accuracy when compared with the mean observed values at these locations.The published experimental data showed that on the lee-slope the wind speeds at 8m were reduced to approximately 10% of their upstream value at the same height. This was associated with an 180° change in wind direction compared with the upstream flow, suggesting that flow separation had occurred. The non-linear model predictions of lee-slope wind speed, when used with high-resolution topography data, were significantly better than linear model predictions. However, the non-linear model predicted lee-slope wind speeds that were still stronger than observed. The non-linear model simulated flow separation more readily with a 1 1/2-order turbulence closure than with a first-order, mixing-length closure. The configuration of the non-linear model that gave best agreement with observations predicted an 8m lee-slope wind speed that was around 50% of the upstream value.     

气象因素对淮北市电力负荷的影响及其预测研究

利用2012—2016年安徽省淮北市逐日用电负荷和气象要素数据,采用相关分析、回归分析、曲线拟合等统计方法,分析用电负荷的季节变化、周末/节假日效应变化规律,提炼主要气象影响因子并分析温度对负荷的1℃效应和负荷对最高温度的敏感性,构建趋势负荷和趋势方程,介绍将周末/节假日效应应用于气象负荷提取和不同预测模型的建立,采用趋势法建立逐日负荷预测的多元回归方程和曲线拟合方程,并针对趋势法的弱点提出2日增量法,建立相应的预测模型,其中2日增量法预测模型的历史拟合率和2017年试报应用均达到96%—97%,比趋势法高2%—3%,比目前的考核要求高4%—5%,提高了负荷预测精度。     

基于径向基函数神经网络的探空仪湿度传感器曲线拟合

针对数字式探空仪上采用的XGH-02型高分子碳膜湿敏电阻湿度感应元件,采用正规化径向基函数(RBF)神经网络模型对其进行曲线拟合,与传统的曲线拟合效果相比较,寻求一种更加准确的湿度传感器标定和误差的校准模型。用训练样本和检验样本对建立的RBF模型分别进行训练和检验结果表明,建立的RBF模型有效提高了湿敏电阻的准确度,其测量的最大误差为2.0298%(RH),明显好于采用现用公式的测量准确度。     

利用FY-1D极轨气象卫星分裂窗区通道计算陆表温度

根据理论和经验上已证明的地表温度与AVHRR窗区通道4、5的亮度温度存在线性或非线性关系, 通过对2818条全球晴空大气廓线做不同比辐射率地表的FY-1D窗区通道4、5辐射率的模拟计算, 推导出FY-1D极轨气象卫星的红外通道4、5亮温与地表温度的二次回归关系式。同时详细介绍了由这一回归关系式和FY-1D高分辨率图像传输 (HRPT) 遥测数据计算陆表温度的方法, 最后给出陆表温度计算结果的精度:用中国地面气象台站的0 cm地温观测数据与相同时刻的分辨率为0.01°×0.01°经纬度的卫星陆表温度相对比, 两者非常吻合, 绝大部分台站|ΔT|＜3.0 K。     

Non-linear climate feedback analysis in an atmospheric general circulation model

 A method is described for evaluating the ‘partial derivatives’ of globally averaged top-of-atmosphere (TOA) radiation changes with respect to basic climate model physical parameters. This method is used to analyse feedbacks in the Australian Bureau of Meteorology Research Centre general circulation model. The parameters considered are surface temperature, water vapour, lapse rate and cloud cover. The climate forcing which produces the changes is a globally uniform sea surface temperature (SST) perturbation. The first and second order differentials of model parameters with respect to the forcing (i.e. SST changes) are estimated from quadratic least square fitting. Except for total cloud cover, variables are found to be strong functions of global SST. Strongly non-linear variations of lapse rate and high cloud amount and height appear to relate to the non-linear response in penetrative convection. Globally averaged TOA radiation differentials with respect to model parameters are also evaluated. With the exception of total cloud contributions, a high correlation is generally found to exist, on the global mean level, between TOA radiation and the respective parameter perturbations. The largest non-linear terms contributing to radiative changes are those due to lapse rate and high cloud. The contributions of linear and non-linear terms to the overall radiative response from a 4 K SST perturbation are assessed. Significant non-linear responses are found to be associated with lapse rate, water vapour and cloud changes. Although the exact magnitude of these responses is likely to be a function of the particular model as well as the imposed SST perturbation pattern, the present experiments flag these as processes which cannot properly be understood from linear theory in the evaluation of climate change sensitivity. Received: 16 January 1997/Accepted: 9 May 1997     

PTB220气压传感器模拟量调整方法

通过对PTB220气压传感器工作原理的分析与研究,利用超级终端技术、Keithley 2000数字多用表、线性方程,实现了超差气压传感器模拟量的调整,并解决了数字量调整影响模拟量输出的问题。从而避免了自动站采集器通道误差影响校准精度,保证自动站气压观测数据的准确性,进一步完善了超差气压传感器调整方法。     

基于单CCD尺寸检测的摄像机标定方法

为提高机械零件尺寸检测的精度,针对Tsai两步标定法中初始参数不精确的问题,提出了基于直线投影约束的三步标定方法.该方法首先利用直线投影约束条件求解出图像中心点和畸变参数的初始值,然后结合径向排列约束条件求解超定方程组得出全部外部参数和余下内部参数值,最后对全局参数进行非线性优化.为验证提出方法的效果,通过建立基于图像模板的世界坐标系实现,即首先利用图像像素坐标求取摄像机坐标,再将摄像机坐标进行归一化,利用外参数矩阵就可以求解出图像点的世界坐标.结果表明,该方法可以有效提高摄像机标定的精度,像素误差可达0.126 5像素点,单目视觉尺寸检测中任意两点距离误差小于0.41%.     

Quantifying global climate feedbacks, responses and forcing under abrupt and gradual CO2 forcing

Experiments with abrupt CO₂ forcing allow the diagnosis of the response of global mean temperature and precipitation in terms of fast temperature independent adjustments and slow, linear temperature-dependent feedbacks. Here we compare responses, feedbacks and forcings in experiments performed as part of version 5 of the coupled model inter-comparison project (CMIP5). The experiments facilitate, for the first time, a comparison of fully coupled atmosphere-ocean general circulation models (GCM’s) under both linearly increasing and abrupt radiative forcing. In the case of a 1 % per year compounded increase in CO₂ concentration, we find that the non-linear evolution of surface air temperature in time, when combined with the linear evolution of the radiative balance at the top of the atmosphere, results in a feedback parameter and effective climate sensitivity having an offset compared to values computed from abrupt 4× CO₂ forcing experiments. The linear evolution of the radiative balance at the top of the atmosphere also contributes to an offset between the global mean precipitation response predicted in the 1 % experiment using linear theory and that diagnosed from the experiments themselves, and a potential error between the adjusted radiative forcing and that produced using a standard linear formula. The non-linear evolution of temperature and precipitation responses are also evident in the RCP8.5 scenario and have implications for understanding, quantifying and emulating the global response of the CMIP5 climate GCMs.     

Evaluation of Linear and Non-Linear Downscaling Methods in Terms of Daily Variability and Climate Indices: Surface Temperature in Southern Ontario and Quebec,Canada

We downscaled atmospheric reanalysis data using linear regression and Bayesian neural network (BNN) ensembles to obtain daily maximum and minimum temperatures at ten weather stations in southern Quebec and Ontario, Canada. Performance of the linear and non-linear downscaling models was evaluated using four different sets of predictors, not only in terms of their ability to reproduce the magnitude of day-to-day variability (i.e., “weather,” mean absolute error between the daily values of the predictand(s) and the downscaled data) but also in terms of their ability to reproduce longer time scale variability (i.e., “climate,” indices of agreement between the predictand's observed annual climate indices and the corresponding downscaled values). The climate indices used were the 90th percentile of the daily maximum temperature, 10th percentile of the daily minimum temperature, number of frost days, heat wave duration, growing season length, and intra-annual temperature range.

Our results show that the non-linear models usually outperform their linear counterparts in the magnitude of daily variability and, to a greater extent, in annual climate variability. In particular, the best model simulating weather and climate was a BNN ensemble using stepwise selection from 20 reanalysis predictors, followed by a BNN ensemble using the three leading principal components from the aforementioned predictors. Finally, we showed that, on average, the first three indices presented higher skills than the growing season length, number of frost days, and the heat wave duration.     

Detecting human influence on climate using neural networks based Granger causality

In this note we observe that a problem of linear approach to Granger causality testing between CO₂ and global temperature is that such tests can have low power. The probability to reject the null hypothesis of non-causality when it is false is low. Regarding non-linear Granger causality, based on multi-layer feed-forward neural network, the analysis provides evidence of significant unidirectional Granger causality from CO₂ to global temperature.     

L520型中子仪的测试标定

采用改进后的Ｌ５２０型中子仪及多项式回归方法标定不同层次的土壤湿度。测定结果表明,改进型中子仪较ＬＮＷ－５０Ｃ仪器,及改直线标定为曲线标定后精度大大提高,结果令人满意,有一定的推广价值。     

基于GNSS的MODIS大气可降水量校正模型

MODIS大气可降水量（PWV）空间分辨率高但易受云雨等环境因素影响,精度不高.GNSS PWV 空间分辨率较低但具有全天候、不受天气影响、精度高的优点.研究表明两者存在显著的线性相关性,结合两者的优点,基于GNSS PWV校正MODIS PWV可获取大面积高精度的PWV.针对传统的线性回归校正模型没有考虑云、气溶胶等的影响使两种数据线性相关性变差的问题,本文在传统的线性校正模型上增加了使用年积日的非线性周期项的方法来构建校正模型.利用2017—2019年香港地区GNSS对流层延迟与MODIS近红外数据,使用频谱分析线性残差项,结果表明残差具有显著的年周期.对比传统模型,本文模型的平均绝对误差、平均相对误差、均方根误差和拟合度都有明显的改善,表明本文模型可行有效且精度较高.     

青藏高原春夏季对流层温度异常特征

利用NCEP/NCAR再分析资料,在揭示青藏高原对流层中上层(500~200hPa)温度变化特征的基础上,通过比较与同纬度地区对流层中上层温度的差异,从温度纬向偏差角度定义了一个高原热力指数(TDI),并分析了该指数在春夏季的多时间尺度变化特征。结果表明:1由春到夏,亚洲对流层中上层的暖中心经历了从西太平洋西进到大陆,并逐渐发展控制整个东亚地区,之后东退的过程。春季扰动温度暖中心由我国华南地区逐渐西移至高原南部,中心强度逐渐增大,夏季扰动中心稳定在青藏高原南部;2TDI的年变化曲线呈现出明显的单峰型特征,表明高原的热力作用从4月开始明显增强,并在7月达到最大,9月后又迅速减弱;3各月TDI的最高值、最低值和平均值均表现出夏季大冬季小的特征,夏季TDI变幅明显小于其他季节;4TDI具有明显的年际变化,但春(夏)季该指数存在一定(明显)的月际差异,且无明显的线性变化趋势。     

Active and passive remote sensing of precipitating storms during CaPE. Part II: Intercomparison of precipitation retrievals over land from AMPR radiometer and CP-2 radar

Summary One of the recent campaigns devoted to precipitation studies using both active and passive microwave remote sensing systems was the Convection and Precipitation/Electrification Experiment (CaPE), which took place in central Florida during the summer of 1991. During CaPE, the airborne Advanced Microwave Precipitation Radiometer (AMPR), having four channels at 10.7, 19.35, 37.1 and 85.5 GHz and the National Center for Atmospheric Research CP-2 multiparameter radar at S-band (3 GHz) and X-band (10 GHz) were operated simultaneously. In this paper, we compare estimated hydrometeor liquid/ice water contents and surface rainrates, both retrieved from the AMPR radiometer and CP-2 radar measurements, for a case study consisting of a heavy precipitating storm over land near Cape Canaveral on August 12, 1991. The multi-frequency radiometer-based retrieval scheme uses a cloud-precipitation dataset generated from a cloud model and extended by a physically-constrained Monte Carlo procedure, along with a discrete-ordinate radiative transfer model and a principal component statistical technique to help formulate non-linear regression equations for the sought-after hydrometeor quantities. By applying linear discriminant analysis, the algorithm is used to estimate column integrated liquid/ice water contents, as well as the vertical profiles of these quantities to within a specified accuracy. Rainfall rates are estimated either by non-linear regression or by a suitable fallout model. The analysis has confined itself to along-track nadir-looking AMPR measuremets to avoid complications with variable polarization mixing and geometric distortion for off-nadir observations. Considering the different model assumptions used in the two types of retrieval algorithms and the diverse geophysical information content within the two types of measurements, substantial agreement between the radar- and radiometer-derived retrievals has been achieved for the columnar liquid/ice water contents and rainrates.With 19 FiguresThe National Center for Atmospheric Research (NCAR) is sponsored by the National Science Foundation.