An Online Model Correction Method Based on an Inverse Problem:Part I—Model Error Estimation by Iteration

Errors inevitably exist in numerical weather prediction （NWP） due to imperfect numeric and physical parameterizations. To eliminate these errors, by considering NWP as an inverse problem, an unknown term in the prediction equations can be estimated inversely by using the past data, which are presumed to represent the imperfection of the NWP model （model error, denoted as ME）. In this first paper of a two-part series, an iteration method for obtaining the MEs in past intervals is presented, and the results from testing its convergence in idealized experiments are reported. Moreover, two batches of iteration tests were applied in the global forecast system of the Global and Regional Assimilation and Prediction System （GRAPES-GFS） for July-August 2009 and January-February 2010. The datasets associated with the initial conditions and sea surface temperature （SST） were both based on NCEP （National Centers for Environmental Prediction） FNL （final） data. The results showed that 6th h forecast errors were reduced to 10% of their original value after a 20-step iteration. Then, off-line forecast error corrections were estimated linearly based on the 2-month mean MEs and compared with forecast errors. The estimated error corrections agreed well with the forecast errors, but the linear growth rate of the estimation was steeper than the forecast error. The advantage of this iteration method is that the MEs can provide the foundation for online correction. A larger proportion of the forecast errors can be expected to be canceled out by properly introducing the model error correction into GRAPES-GFS.

Correction of CMPAS Precipitation Products over Complex Terrain Areas with Machine Learning Models

Machine learning models were used to improve the accuracy of China Meteorological Administration Multisource Precipitation Analysis System(CMPAS)in complex terrain areas by combining rain gauge precipitation with topographic factors like altitude,slope,slope direction,slope variability,surface roughness,and meteorological factors like temperature and wind speed.The results of the correction demonstrated that the ensemble learning method has a considerably corrective effect and the three methods(Random Forest,AdaBoost,and Bagging)adopted in the study had similar results.The mean bias between CMPAS and 85%of automatic weather stations has dropped by more than 30%.The plateau region displays the largest accuracy increase,the winter season shows the greatest error reduction,and decreasing precipitation improves the correction outcome.Additionally,the heavy precipitation process’precision has improved to some degree.For individual stations,the revised CMPAS error fluctuation range is significantly reduced.

Research on the Relationship between Income and Consumption of the Urban Residents in Hunan Province on the Basis of Error Correction Model

By using error correction model, I conduct co-integration analysis on the research of the relationship between the per capita practical consumption and per capita practical disposable income of urban residents in Hunan Province from 1978 to 2009. The results show that there is a co-integration relationship between the per capita practical consumption and the practical per capita disposable income of urban residents, and based on these, the corresponding error correction model is established. Finally, corresponding countermeasures and suggestions are put forward as follows: broaden the income channel of urban residents; create goods consuming environment; perfect socialist security system.

基于VMD-Transformer-ECM模型的空气中有害气体浓度预测

空气中有害气体的浓度序列具有较强的复杂性、非线性及波动性,为气体浓度的准确预测带来了很大挑战。针对该问题,提出了一种基于变分模态分解(VMD)和误差补偿(ECM)的Transformer预测模型(VMD-Transformer-ECM)。首先通过VMD将气体浓度时间序列分解成不同频率的本征模态函数(IMF),以降低预测模型输入的复杂性和非平稳性;然后通过Transformer模型对分解所得的各模态分量进行预测,并对预测结果进行重构以得到初步预测值;最后通过ECM模型对误差序列进行预测,并使用误差预测值来补偿初步预测值,以进一步提高模型的预测精度。在不同数据集中对所提模型进行了验证,结果显示,与其他模型相比,VMD-Transformer-ECM模型对CO_(2)及其他有害气体浓度预测的平均绝对百分比误差(MAPE)和均方根误差(RMSE)最小,决定系数(R^(2))最大,其中在预测步长为3 h时,本模型对CO_(2)浓度预测的MAPE为4.38%,RMSE为35.44×10^(-6),R^(2)为0.94,表明所提模型的预测精度较高,预测性能较好。

An Online Model Correction Method Based on an Inverse Problem:PartⅡ——Systematic Model Error Correction

An online systematic error correction is presented and examined as a technique to improve the accuracy of real-time numerical weather prediction, based on the dataset of model errors （MEs） in past intervals. Given the analyses, the ME in each interval （6 h） between two analyses can be iteratively obtained by introducing an unknown tendency term into the prediction equation, shown in Part I of this two-paper series. In this part, after analyzing the 5-year （2001-2005） GRAPES- GFS （Global Forecast System of the Global and Regional Assimilation and Prediction System） error patterns and evolution, a systematic model error correction is given based on the least-squares approach by firstly using the past MEs. To test the correction, we applied the approach in GRAPES-GFS for July 2009 and January 2010. The datasets associated with the initial condition and SST used in this study were based on NCEP （National Centers for Environmental Prediction） FNL （final） data. The results indicated that the Northern Hemispheric systematically underestimated equator-to-pole geopotential gradient and westerly wind of GRAPES-GFS were largely enhanced, and the biases of temperature and wind in the tropics were strongly reduced. Therefore, the correction results in a more skillful forecast with lower mean bias and root-mean-square error and higher anomaly correlation coefficient.

Research on the Large Precision Instrument Error Correction Model under the Perspectives of Stability and Robustness

In this paper, we conduct research on the large precision instrument error correction model under the perspectives of stability androbustness. It is one of the effective methods to improve the instruments accuracy using error correction technology, but at present, a lot of errorcorrection is limited to the system error modifi cation, only a small number of the instruments to an error in the dynamic error correction timely,device on the instrument precision sensors, apparently complicate the instrument structure. To fully system error correction that will affect theprecision of instrument mainly random error. Instrument is the main task of error correction is to use a certain method to compensate separableinstruments each component part of a deterministic system error, so the key problems of error correction as is the requirement of equipmentstructure stability is good, with this to ensure that the instrument error of the uncertainty, so that the fundamental fl aw. Under this basis, this paperproposes the novel countermeasure of the issues that is innovative.

基于VECM模型的房地产价格成分实证研究——以北上广深四城市为例

本文基于2008年至2023年我国四个一线城市的面板数据,运用向量误差修正模型(VECM)对理论框架进行实证检验,研究发现:长期房价的大部分动态趋势可以用狭义货币供应量、抵押贷款利率和租金收入来解释;北京、广州和深圳的房地产市场由经济和政府基本面主导,其房价更多依托于基本价值和周期性成分,上海的房地产市场则由其它成分所主导,投资属性较强;结合房地产市场未来发展趋势,“房住不炒”的政策定调仍有理论依据和现实必要,政府应支持实体产业经济发展,引导资金注入实体经济,而非房地产市场。

Analogue correction method of errors and its application to numerical weather prediction

In this paper, an analogue correction method of errors （ACE） based on a complicated atmospheric model is further developed and applied to numerical weather prediction （NWP）. The analysis shows that the ACE can effectively reduce model errors by combining the statistical analogue method with the dynamical model together in order that the information of plenty of historical data is utilized in the current complicated NWP model, Furthermore, in the ACE, the differences of the similarities between different historical analogues and the current initial state are considered as the weights for estimating model errors. The results of daily, decad and monthly prediction experiments on a complicated T63 atmospheric model show that the performance of the ACE by correcting model errors based on the estimation of the errors of 4 historical analogue predictions is not only better than that of the scheme of only introducing the correction of the errors of every single analogue prediction, but is also better than that of the T63 model.

Improved algorithm of atmospheric refraction error in Longley-Rice channel model

Longley-Rice channel model modifies the atmospheric refraction by the equivalent earth radius method, which is simple calculation but is not accurate. As it only uses the horizontal difference, but does not make use of the vertical section information, it does not agree with the actual propagation path. The atmospheric refraction error correction method of the Longley-Rice channel model has been improved. The improved method makes use of the vertical section information sufficiently and maps the distance between the receiver and transmitter to the radio wave propagation distance, It can exactly reflect the infection of propagation distance for the radio wave propagation loss. It is predicted to be more close to the experimental results by simulation in comparison with the measured data. The effectiveness of improved methods is proved by simulation.

基于PECM模型的长江流域经济社会发展与水资源利用动态关系分析

为评估长江流域经济社会综合发展水平和水资源综合利用水平以及两者间的内在关系,采用熵值法、面板协整理论和误差修正模型对2000—2017年间长江流域经济社会发展和水资源利用的面板Granger因果关系进行检验。结果表明:长江流域上中下游3个区域的经济社会发展和水资源利用之间存在长期协整关系;在长期关系中,上游地区经济社会发展与水资源利用之间为双向因果关系,中游地区为经济社会发展到水资源利用的单向因果关系,下游地区为水资源利用到经济社会发展的单向因果关系,上游地区经济社会发展和水资源利用具有更高的关联度;在短期关系中,仅下游地区存在经济社会发展到水资源利用的单向因果关系。基于二者之间存在不同的因果关系,各省市在制定经济发展规划时应因地制宜地制定水资源政策,以期实现经济社会与水资源的协调发展。

Relationship Between Agricultural Credits and Agricultural Economy Based on Error Correct Model in Heilongjiang Province

Heilongjiang is a large agriculture province.Problems of agriculture,rural areas and farmers are urgent to be solved.The development of agriculture needs the support of agricultural credits,because finance is the center of agriculture economy.However,the low comparative advantage in agriculture and pursuit of the capital interests which aggravate the conflicts of supply and demand of agricultural funds.Lacking of fund is the main factor that constrains the development of agricultural economy.In order to analyze the economic effect of agricultural credits on agricultural economy,an error correction model was set up to research the relationship between them,which based on the least square methods.Through the study of the contribution from agricultural credits to total value of agricultural out-put,the empirical evidence for developing the rural financial vigorously was provided,in order to promote the agricultura leconomic development.

Local Influence on the Error-Correction Variable in a Cointegrated System

The concept of cointegration describes an equilibrium relationship among a set of time-varying variables, and the cointegrated relationship can be represented through an error-correction model (ECM). The error-correction variable, which represents the short-run discrepancy from the equilibrium state in a cointegrated system, plays an important role in the ECM. It is natural to ask how the error-correction mechanism works, or equivalently, how the short-run discrepancy affects the development of the cointegrated system? This paper examines the effect or local influence on the error-correction variable in an error-correction model. Following the argument of the second-order approach to local influence suggested by reference [5], we develop a diagnostic statistic to examine the local influence on the estimation of the parameter associated with the error-correction variable in an ECM. An empirical example is presented to illustrate the application of the proposed diagnostic. We find that the short-run discre pancy may have strong influence on the estimation of the parameter associated with the error-correction model. It is the error-correction variable that the short-run discrepancies can be incorporated through the error-correction mechanism.

A Robust Conformer-Based Speech Recognition Model for Mandarin Air Traffic Control

This study aims to address the deviation in downstream tasks caused by inaccurate recognition results when applying Automatic Speech Recognition(ASR)technology in the Air Traffic Control(ATC)field.This paper presents a novel cascaded model architecture,namely Conformer-CTC/Attention-T5(CCAT),to build a highly accurate and robust ATC speech recognition model.To tackle the challenges posed by noise and fast speech rate in ATC,the Conformer model is employed to extract robust and discriminative speech representations from raw waveforms.On the decoding side,the Attention mechanism is integrated to facilitate precise alignment between input features and output characters.The Text-To-Text Transfer Transformer(T5)language model is also introduced to handle particular pronunciations and code-mixing issues,providing more accurate and concise textual output for downstream tasks.To enhance the model’s robustness,transfer learning and data augmentation techniques are utilized in the training strategy.The model’s performance is optimized by performing hyperparameter tunings,such as adjusting the number of attention heads,encoder layers,and the weights of the loss function.The experimental results demonstrate the significant contributions of data augmentation,hyperparameter tuning,and error correction models to the overall model performance.On the Our ATC Corpus dataset,the proposed model achieves a Character Error Rate(CER)of 3.44%,representing a 3.64%improvement compared to the baseline model.Moreover,the effectiveness of the proposed model is validated on two publicly available datasets.On the AISHELL-1 dataset,the CCAT model achieves a CER of 3.42%,showcasing a 1.23%improvement over the baseline model.Similarly,on the LibriSpeech dataset,the CCAT model achieves a Word Error Rate(WER)of 5.27%,demonstrating a performance improvement of 7.67%compared to the baseline model.Additionally,this paper proposes an evaluation criterion for assessing the robustness of ATC speech recognition systems.In robustness evaluation experiments based on this criterion,the proposed model demonstrates a performance improvement of 22%compared to the baseline model.

基于改进BVAR模型和MS-VECM模型的能源消费分析

针对向量自回归模型(VAR)的高维估计问题,结合贝叶斯理论提出了一种融合正态-逆Wishart共轭先验分布的估计方法。在该估计方法中,所提出的模型引入Metropolis-Hastings(MH)算法,从以往数据集中确定先验分布超参数,并通过设定与模型尺寸相关的收缩系数从而进行估计。与传统VAR模型相比,基于贝叶斯理论的估计方法可在保留相关样本信息的同时控制过度拟合,具有较好的稳健性和有效性。此外,在改进的BVAR模型基础上,结合区制转移技术与误差修正模型提出了MS-BVECM模型,该模型能够有效分析经济周期内各变量之间长期与短期均衡状态变化,当短期内经济变量受到波动而与长期均衡状态发生偏离时,误差修正模型机制会使其逐渐重新回到长期均衡状态,以保证模型的稳健性。最后,以重庆市为例,利用所提模型对其能源消费、产业结构升级和经济增长的动态关系进行了分析与预测并提供了可行建议。

一种光波大气折射率剖面模型构建方法

根据经典光波折射率计算公式和ITU-R建议书中水汽密度经验公式,给出了一种新的光波大气折射率计算公式;借鉴Hopfield折射率静力项剖面模型和ITU-R建议书中水汽密度剖面模型,给出了一种基于历史气象探空数据构建光波大气折射率剖面模型的方法。以青岛地区为例,通过对1986—1995年历史气象探空数据的处理并结合参考标准大气,建立了适合当地的光波大气折射率剖面模型;统计剖面模型预测折射率剖面与实测折射率剖面的均方根误差,结果表明:构建的剖面模型具有较好的预测精度,这对光学外测设备的折光修正数据处理具有很好的参考价值。

基于改进JRD及误差修正的轴承剩余寿命预测方法

目前,风电机组齿轮箱性能发生初始退化时难以识别,现有退化指标易出现剧烈波动、单调性较差,且无法准确预测齿轮箱关键部件如轴承的剩余使用寿命(RUL),针对该问题,提出了一种基于改进杰森-瑞丽散度(JRD)及误差修正的双指数模型轴承RUL预测方法。首先,提取了振动信号样本的多域特征指标,利用高斯混合模型(GMM)与指数型权重JRD,得到了样本的后验概率分布向量,再经归一化处理得到置信值(CV);然后,对轴承从初始健康状态退化至当前检查时刻的CV值进行了相空间重构,提取了CV序列的动力学特征,并将其作为相关向量机(RVM)的训练集,获得了支撑整个退化轨迹的相关向量;最后,利用双指数模型拟合了相关向量,外推趋势至失效门限以计算RUL,并引入了差分整合移动平均自回归模型(ARIMA),对拟合相关向量产生的拟合误差进行了预测,以修正预测的结果。实验结果表明:改进后的退化指标单调性指标提高14.3%;且在不同工况、不同时刻下,经误差修正后的轴承的RUL预测结果较未修正之前有明显提高。研究结果表明:该预测方法可为风电机组齿轮箱重要部件的预测性维护提供参考。

基于VAR与VECM模型的数字经济对就业的影响研究

数字经济迅速发展,推动全球经济,对就业的影响备受瞩目。数字经济对就业的影响包括消极的替代效应和积极的创造效应,但这两种效应的共同影响仍有争议。使用协整分析和误差修正模型,分析数字经济对就业的综合影响和直接影响,发现我国数字经济对就业率的贡献率更大,已经成为主要的影响因素,同时文章验证了数字经济和就业的因果关系。最后,综合实证结果分析了本研究的不足和改进之处。

平原圩区复合下垫面水文水动力耦合模型

平原圩区地势低洼,下垫面类型多样,产汇流过程较为复杂,水文过程模拟和预报十分困难。为解决平原圩区洪水预报难题,建立考虑水田、旱地、林地、城市区与水域等多种下垫面类型的产汇流模型,利用MIKE 11 HD水动力模型模拟河道汇流过程,提出面向平原圩区复合下垫面条件的水文水动力耦合模型;并采用BP神经网络进行河道水位预报误差校正,以提高模型精度。选择广州市南沙蕉门河排涝片为研究区,检验耦合模型的水位预报精度,并以2023年“9·7深圳特大暴雨”为移置场景输入,模拟不同排涝措施对河道水位的影响。结果表明:模型能够较好地模拟研究区场次洪水的河道水位过程,率定期和验证期的平均Nash效率系数分别为0.86和0.91,10场洪水中有8场的最高水位模拟误差小于0.05 m;采用BP神经网络校正后所有场次洪水的Nash效率系数均大于0.9,满足洪水预报的精度要求。研究区面临“9·7深圳特大暴雨”场景时存在内涝风险,需提升圩内蓄洪排涝能力。

基于MPC的光电热联合系统建模与控制优化

为解决我国北方地区冬季采暖产生的能源消耗与环境污染问题,改善光资源丰富、较丰富地区光能利用率不足的现状,充分利用谷电和日照优势,针对太阳能利用与建筑采暖相结合的分布式能源系统性问题,提出一种基于TRNSYS动态建模与数值建模相结合的光电热联合供暖系统。综合考虑小范围内供暖温度的时滞性以及系统各设备的出力情况,联合Matlab搭建模型预测控制器(MPC),提出一种基于MPC的误差实时校正优化控制策略。分析表明:采用MPC的控制优化,在热负荷跟踪方面,最大误差降低4.16%,平均误差降低2.79%;在室内温度控制方面,最大偏差降低1.2℃,平均偏差降低0.2℃;在太阳能利用占比方面,太阳辐射强度趋近于800 W/m^(2)时,太阳能利用占比差距达最大8.9%。分析结果说明该系统可以更快速、更准确地跟踪建筑热负荷波动,并且有效抑制室内温度波动,提高清洁能源的利用率。

《医林改错》脏腑理论模型的构建与运用

王清任一生的医学贡献凝聚于一册薄薄的《医林改错》,其构建的脏腑理论模型具有生理学与病理学的双重含义,具体可分为“气血运行模型”和“水谷运化模型”。前者侧重于病理探讨,生理描述亦多,可细分为“气运模块”与“血运模块”;后者常见于生理阐释,病理描述甚少,可细分为“水运模块”与“谷运模块”。“气运模块”的生理功能以存储和运行元气、津液为主,其病理变化以津液失常和气虚为主;“血运模块”的生理功能以存储和运行血液为主,其病理变化以血瘀为主;“水运模块”的生理功能主以运行水液,“谷运模块”的生理功能主以运化谷物,二者的病理变化均较少涉及。