Retrieval of water vapor profiles with radio occultation measurements using an artificial neural network

A new method applying an artificial neural network （ANN） to retrieve water vapor profiles in the troposphere is presented. In this paper, a fully-connected, three-layer network based on the backpropagation algorithm is constructed. Month, latitude, altitude and bending angle are chosen as the input vectors and water vapor pressure as the output vector. There are 130 groups of occultation measurements from June to November 2002 in the dataset. Seventy pairs of bending angles and water vapor pressure profiles are used to train the ANN, and the sixty remaining pairs of profiles are applied to the validation of the retrieval. By comparing the retrieved profiles with the corresponding ones from the Information System and Data Center of the Challenging Mini-Satellite Payload for Geoscientific Research and Application （CHAMP-ISDC）, it can be concluded that the ANN is relatively convenient and accurate. Its results can be provided as the first guess for the iterative methods or the non-linear optimal estimation inverse method.     

RBF神经网络在雷达定量估测降水中的应用研究

利用重庆气象局CINRAD/SA气象雷达降水回波资料和相应地区的地面雨量站资料,基于径向基函数神经网络,建立雷达定量估测降水模型,将其用于地面降水估测。作为比较,同时以变分法得到的Z-R关系式估测所得降水。经二者对比试验结果表明:建立的雷达定量估测降水模型的估测精度和稳定性要明显优于Z-R关系式,能较好地反映降雨的真实情况。     

从不稳定能量角度对条件不稳定相关问题的讨论

大多数深厚的局地对流是在条件不稳定的层结中发生的,这就有必要考虑较厚气层对由底部上升气块所可能产生的总影响。从能量角度讨论了条件不稳定问题中的不稳定能量和对流有效位能,并利用观测资料、结合实例分析了单站地面湿静力能与条件不稳定中不稳定能量的关系。主要结论如下:（1）从能量观点,对对流有效位能的两种定义进行了讨论,认为对流有效位能定义为正、负不稳定能量之差时,其与动能相联系的表达式中包含了低层抬升速度,可以更全面地用于强对流天气预报,尤其是中国南方暖季的强对流预报。（2）气块从底层上升时,无论经历的是干绝热还是湿绝热过程,湿静力能守恒,所以总能量的变化就是动能的变化,等于外界合力对气块做功的大小,不存在湿静力能向动能的转换。（3）从本质上来说,单站低层湿静力能的时序变化用于对流预报的意义在于预示着正、负不稳定能量的改变,从而影响不稳定能量的值。这可以弥补由于高空探测时间分辨率较粗,不能计算逐时正、负不稳定能量的不足。      

人工神经网络在月降水量预测业务中的研究和应用综述

月降水量的年际变化具有显著的非线性变化特征,预测难度大,历来是重大气象灾害预测的重点难点问题.BP(back propagation)神经网络在月降水量预测业务中的研究和应用中,取得了较好的成果,其中应用较广泛的是PCA-BP神经网络模型、遗传算法优化神经网络、RBF神经网络预测模型、小波神经网络模型、粒子群-神经网络...     

Retrieving atmospheric temperature profiles from AMSU-A data with neural networks

Back propagation neural networks are used to retrieve atmospheric temperature profiles from NOAA-16 Advanced Microwave Sounding Unit-A (AMSU-A) measurements over East Asia. The collocated radiosonde observation and AMSU-A data over land in 2002-2003 are used to train the network, and the data over land in 2004 are used to test the network. A comparison with the multi-linear regression method shows that the neural network retrieval method can significantly improve the results in all weather conditions. When an offset of 0.5 K or a noise level of ±0.2 K is added to all channels simultaneously, the increase in the overall root mean square (RMS) error is less than 0.1 K. Furthermore, an experiment is conducted to investigate the effects of the window channels on the retrieval. The results indicate that the brightness temperatures of window channels can provide significantly useful information on the temperature retrieval near the surface. Additionally, the RMS errors of the profiles retrieved with the trained neural network are compared with the errors from the International Advanced TOVS (ATOVS) Processing Package (IAPP). It is shown that the network-based algorithm can provide much better results in the experiment region and comparable results in other regions. It is also noted that the network can yield remarkably better results than IAPP at the low levels and at about the 250-hPa level in summer skies over ocean. Finally, the network-based retrieval algorithm developed herein is applied in retrieving the temperature anomalies of Typhoon Rananim from AMSU-A data.     

基于神经网络的太阳黑子面积平滑月均值预测

黑子面积数是表征太阳活动的重要物理量,准确预测黑子面积能为太阳活动研究、空间天气业务等提供重要参考依据。本文提出一种基于BP神经网络的黑子面积平滑月均值预测方法,利用第20个太阳周之前的数据对网络进行训练,建立预测模型。对第21个太阳周至今的数据进行预测试验,并考虑不同训练步长、预测步长对模型精度的影响。结果表明,该模型能准确逐月预测黑子面积,采用不同训练步长时相对误差均不超过5％,进行更长时间的预测,相对误差会逐渐增大。     

基于Elman动态神经网络的降雨—径流模拟研究

采用Elman动态神经网络对沂沭河流域上游临沂子流域日径流量进行模拟。为了更好地检验该网络估测径流的精度,同时采用陆面水文过程模型TOPX进行对比分析。确定性线系数、相关系数、平均相对误差和平均相对均方根误差四个统计指数及流域径流过程。结果表明,Elman动态神经网络能够对日径流量进行较好的模拟,较好地捕捉洪峰流量和出现时间,为降雨径流模拟提供了一种有效可靠的方法。     

基于Elman动态神经网络的降雨一径流模拟研究

采用Elman动态神经网络对沂沭河流域上游临沂子流域口径流量进行模拟。为了更好地检验该网络估测径流的精度,同时采用陆面水文过程模型TOPX进行对比分析。确定性线系数、相关系数、平均相对误差和平均相对均方根误差四个统计指数及流域径流过程。结果表明,Elman动态神经网络能够对日径流量进行较好的模拟,较好地捕捉洪峰流量和出现时间,为降雨径流模拟提供了一种有效可靠的方法。     

Estimating sunshine duration from other climatic data by artificial neural network for ET0 estimation in an arid environment

Sunshine duration data are desirable for calculating daily solar radiation (R _s) and subsequent reference evapotranspiration (ET₀) using the Penman–Monteith (PM) method. In the absence of measured R _s data, the Ångström equation has been recommended by the Food and Agriculture Organization (FAO) of the United Nations. This equation requires actual sunshine duration that is not commonly observed at many weather stations. This paper examines the potential for the use of artificial neural networks (ANNs) to estimate sunshine duration based on air temperature and humidity data under arid environment. This is important because these data are commonly available parameters. The impact of the estimated sunshine duration on estimation of R _s and ET₀ was also conducted. The four weather stations selected for this study are located in Sistan and Baluchestan Province (southeast of Iran). The study demonstrated that modelling of sunshine duration through the use of ANN technique made acceptable estimates. Models were compared using the determination coefficient (R ²), the root mean square error (RMSE) and the mean bias error (MBE). Average R ², RMSE and MBE for the comparison between measured and estimated sunshine duration were calculated resulting 0.81, 6.3 % and 0.1 %, respectively. Our analyses also demonstrate that the difference between the measured and estimated sunshine duration has less effect on the estimated R _s and ET₀ by using Ångström and FAO-PM equations, respectively.     

基于神经网络方法的不确定非线性微分-代数子系统的鲁棒反推镇定控制

对于指数1且关联可测的不确定非线性微分-代数子系统,将反推方法和神经网络相结合,研究了其鲁棒渐近镇定控制问题.基于反推方法来构造镇定控制器,利用3层的神经网络来逼近每一步控制器构造过程中的不确定项.提出一种新的自适应算法对神经网络权值进行在线调节,并适当选取每一步虚拟控制器的参数,最终得到的控制器使得闭环系统是渐近稳定的.     

人工智能技术发展概述

近年来,以深度学习为核心的人工智能技术,取得了一系列重大突破.本文将就人工智能的产业化热潮,主要研究流派及发展历史,以深度学习为核心的成功应用,以及存在的一些问题和今后的可能研究方向做一个介绍.     

多因子方法在雷达降水量估测中的应用

使用合肥雷达站2007年7月和广州雷达站2008年5-10月的雷达以及雨量计资料提出了使用雷达反射率因子、水平梯度和垂直积分液态水含量测量降水量的方法(简称多因子方法).此方法在人工神经网络构架之上隐含地实现了在降水类型识别基础上的降水量测量,并与使用单一Z-R关系测量的降水量进行比较.结果表明:多因子方法和使用Z=300R1.4测量的降水量相比,前者的计算结果与雨量计观测值相比具有较高的相关系数和较低的均方根误差,即前者测量降水量的精度高于后者.     

非对称双环神经网络系统的稳定性和Hopf分岔

环型结构在神经网络中普遍存在,目前对环型神经动力学分岔研究大多数局限于单环情形.值得注意的是,神经网络由成千上万个神经元耦合而成,这些复杂的神经元网络结构不可能只由一个环形结构来准确表述,因此研究具多环拓扑的神经网络模型更具实际意义.本文提出了一种非对称双环神经元网络模型,选择单环的时滞和为分岔参数,分析了双环模型的稳定性和Hopf分岔.最后给出数值仿真对结论进行了验证.     

Comparison of artificial neural network and multivariate linear regression methods for estimation of daily soil temperature in an arid region

Soil temperature (T _S) strongly influences a wide range of biotic and abiotic processes. As an alternative to direct measurement, indirect determination of T _S from meteorological parameters has been the focus of attention of environmental researchers. The main purpose of this study was to estimate daily T _S at six depths (5, 10, 20, 30, 50 and 100?cm) by using a multilayer perceptron (MLP) artificial neural network (ANN) model and a multivariate linear regression (MLR) method in an arid region of Iran. Mean daily meteorological parameters including air temperature (T _a), solar radiation (R _S), relative humidity (RH) and precipitation (P) were used as input data to the ANN and MLR models. The model results of the MLR model were compared to those of ANN. The accuracy of the predictions was evaluated by the correlation coefficient (r), the root mean-square error (RMSE) and the mean absolute error (MAE) between the measured and predicted T _S values. The results showed that the ANN method forecasts were superior to the corresponding values obtained by the MLR model. The regression analysis indicated that T _a, RH, R _S and P were reasonably correlated with T _S at various depths, but the most effective parameters influencing T _S at different depths were T _a and RH.     

A Short-Term Climate Prediction Model Based on a Modular Fuzzy Neural Network

In terms of the modular fuzzy neural network (MFNN) combining fuzzy c-mean (FCM) cluster and single-layer neural network, a short-term climate prediction model is developed. It is found from modeling results that the MFNN model for short-term climate prediction has advantages of simple structure, no hidden layer and stable network parameters because of the assembling of sound functions of the selfadaptive learning, association and fuzzy information processing of fuzzy mathematics and neural network methods. The case computational results of Guangxi flood season (JJA) rainfall show that the mean absolute error (MAE) and mean relative error (MRE) of the prediction during 1998 2002 are 68.8 mm and 9.78%, and in comparison with the regression method, under the conditions of the same predictors and period they are 97.8 mm and 12.28% respectively. Furthermore, it is also found from the stability analysis of the modular model that the change of the prediction results of independent samples with training times in the stably convergent interval of the model is less than 1.3 mm. The obvious oscillation phenomenon of prediction results with training times, such as in the common back-propagation neural network (BPNN) model, does not occur, indicating a better practical application potential of the MFNN model.     

基于BP神经网络的浙北夏季降尺度降水预报方法的应用

利用NCEP提供的全球空间分辨率为2.5°×2.5°、2007—2012年6—8月日平均500 h Pa高度场再分析格点资料和浙北地区158个站点观测资料,研究了不同大气环流型下局地降水与大尺度降水场之间的关系,以4种不同环流型下的预报对象和预报因子分别采用BP神经网络方法对观测资料进行逼近,得到4种空间降尺度的预报模型,分析对比4种预报模型158站逐日的降水量的预报。结果表明:神经网络模型的隐层节点数为2时,对降水的拟合效果最好;对降水的极值拟合效果中,环流分型中NW型和C型的效果优于SW型和SE型;从4种分型下的误差空间分布来看,浙北地区沿海的宁波、舟山一带的误差小于浙北其他区域;把雨量分等级后进行预测,发现模型对暴雨的预测能力最好。     

人工神经网络算法在红外高光谱资料反演大气温度廓线中的应用

基于红外高光谱大气探测器AIRS实况观测资料,尝试用人工神经网络算法来实现晴空时大气的温度垂直廓线反演,重点将该算法与目前已经发展比较成熟的特征向量统计反演算法进行比较。结果表明,两种算法在计算时间效率和反演精度上相当。鉴于人工神经网络算法的统计物理本质,误差反向传播BP神经网络可以求解非线性问题,在优化训练样本和继续调试网络种类和网络参数的基础上,有望能进一步提高反演精度。     

A hybrid model for short-term rainstorm forecasting based on a back-propagation neural network and synoptic diagnosis

暴雨是我国最重要的自然灾害之一.大量的研究表明,暴雨的频率和强度在全球变暖的背景下正在逐年增强.但是如何成功的预测短期暴雨,特别是发生在复杂地形下的暴雨,仍然是一个巨大的挑战.本项研究采用BP神经网络和天气学诊断相结合的方法,探索了一种四川盆地西部复杂地形下的暴雨预报模型.该模型有效改善了喇叭口地形下,受低层偏东风影响的暴雨预报准确性.机器学习与天气学理论的结合,提升了模型的物理基础和预测成功率,同时该方法也为发展具有本地特征的暴雨预报客观工具,提供了一定的参考价值.     

遗传算法优化的BP神经网络在地面温度多模式集成预报的应用研究

基于TIGGE资料集中的ECMWF、CMA和JMA的数值预报产品,利用加权集成、回归集成和消除偏差集成等线性集成方式与遗传算法优化的BP神经网络(GABP)集成,对我国大部开展地面2 m温度在24 h、48 h和72 h预报时效的多模式集成预报试验。通过对2013年1—6月的预报检验,结果表明:GABP集成预报效果有较大提升,均方误差明显小于各单一模式预报。GABP集成的误差分布在新疆和华北均方误差较大,但是在预报效果改进上GABP集成在西部地区相对单一模式的误差减小更加明显。在进行几种多模式集成方式时,GABP集成相比线性方法预报结果更加精准。对于天气过程个例的预报,GABP集成预报出预报量的变化趋势,预报效果优于单一模式和线性集成预报。无论是较长时间段还是短时间的天气过程,在改进预报效果上GABP集成都起到了最佳的作用。     

中国东部夏季雨型的人工神经网络集合预测

在BP(back propagation)人工神经网络方法的基础上,考虑到历史资料的个体差异及其年代际变化会影响到样本均值,由此使得中国东部夏季雨型模拟和预测效果产生差异,故引入交叉检验及集合预报思想,以改进人工神经网络独立预报方法.在利用不同历史样本资料建立人工神经网络模型,并进行交叉检验的同时,对预测年的雨型进行预测,可获得预测年的多次预测结果.该方法在中国东部夏季四类雨型的试验预报中表现出较好效果.