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.

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.

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.

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.

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.

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.

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。

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。

ECMWF模式地面气温预报的四种误差订正方法的比较研究

采用均方根误差对欧洲中期天气预报中心(ECWMF)确定性预报模式2007年1月至2010年12月的地面气温预报结果进行评估,并分别利用一元线性回归、多元线性回归、单时效消除偏差和多时效消除偏差平均的订正方法,对ECMWF模式地面气温预报结果进行订正。结果表明,4种订正方法都能有效地减小地面气温多个时效预报的误差,改进幅度约为1℃。在短期预报中仅考虑最新预报结果的一元线性回归订正方法要优于考虑多个预报结果的多元集成预报订正方法。在中期预报中考虑多个预报结果的多元集成预报订正方法更优,更稳定。在模式预报误差较大的情况下,多时效集成的订正方法能更稳定地减小误差。

基于VECM模型的经济增长与环境污染和能源消耗关系研究

根据中国1978-2007年人均CO2排放量、人均用油当量与人均GDP的统计数据,通过构建向量误差修正模型,应用Johansen协整检验和Granger因果关系检验方法,考察了中国经济增长与环境污染和能源消耗的关系。结果表明:中国经济增长与环境污染和能源消耗存在长期稳定的协整关系,而且经济增长与能源消耗之间存在着长期双向因果关系,能源消耗是环境污染强单向因果关系。方差分解表明:短期内经济增长与能源消耗主要受自身波动影响较大,长期中经济增长与能源消耗间相互影响越来越明显;另外,能源消耗波动始终是环境污染的主要原因,而且影响越来越突出。

产业结构、城镇化与我国经济增长关系的ECM模型研究

当前我国处于经济增长质量转变的关键时期,产业结构优化、城市化发展都对我国经济增长起到关键的决定作用。本文以我国1978～2011年的经济数据为研究对象,通过构建ECM模型说明三者之间的关系,并通过实证检验结果对三者更好的促进三者发展提出相关建议。

基于VECM的汽柴油价格不对称性分析

原油作为成品油的主要原材料,原油价格的变化会引起成品油价格的相应变化,从成品油价格关于原油价格变化的反应可以了解成品油定价的合理性。本文利用一个不对称的向量误差修正模型(Vector Error CorrectionModel,VECM),检验了我国汽柴油关于原油成本变化不对称性问题,检验结果表明,我国汽柴油对原油成本上涨的反应快,但持续的时间短;对原油成本下降的反应慢,但持续的时间长。研究结果对我国石油定价机制的改革和企业实施油价风险管理有参考价值。

基于ECM的区域科技成果转化绩效实证分析

搜集省际面板数据,运用主成分方法测度各地区科技成果转化绩效,建立误差修正模型探究影响科技成果转化绩效的因素。研究表明,科技集聚对科技成果转化绩效具有长期和短期正向显著影响;R&D经费对科技成果转化绩效有长期和短期负向显著影响;R&D人员投入、教育环境、基础设施水平对科技成果转化绩效有长期显著影响;制度因素对科技成果转化绩效影响不显著。

基于VECM-PT-IS模型的我国三大股指期货价格发现功能对比研究

为比较沪深300、上证50与中证500股指期货的价格发现能力强弱程度,选取了高频数据进行实证研究,首先,运用协整分析与因果检验方法,建立向量误差修正模型,比较三个股指期货与现货市场之间的价格动态调整关系;其次,运用公共因子模型中的永久短暂模型和信息份额模型计算三大指数期货市场对新信息的融入比例;最后运用脉冲响应分析和方差分解,分析各自短期内的动态反应过程和长期中的价格波动贡献度。研究发现:三个股指期货与现货之间均具有双向引导关系;中证500和沪深300股指期货的价格发现功能较强;上证50股指期货市场的价格发现功能相对其他两个市场而言较弱,并针对此差异给出相应的解释,提出了完善价格发现功能的对策建议。

国债对民间消费效率的计量分析--基于协整和ECM模型的检验

采用1981年～2007年数据,用协整和误差修正模型分析了我国国债对民间消费的效率,分析表明:无论是从长期还是短期看,国债的存量和流量都促进了我国的民间消费;我国国债符合"凯恩斯主义"理论,"李嘉图主义"关于国债理论在中国不成立。

辽宁地区ECMWF模式气温预报检验及误差订正研究

利用2016—2018年ECMWF细网格模式12—36 h内2 m温度预报产品,选取辽宁地区65个城镇站点观测资料,评估预报产品在不同季节的预报准确率,并按季节分析固定误差订正方法和最优滑动周期订正方法对提高准确率的作用。结果表明:ECMWF模式预报产品对辽宁地区气温预报的准确率表现为,ECMWF模式最高气温冬季预报最优(城镇站点预报准确率为81.5%),最低气温夏季预报最好(城镇站点预报准确率为84.3%);采用最优滑动周期订正后,2016—2018年辽宁地区的最高气温和最低气温准确率较ECMWF模式自身分别提高了19.7%和20.5%,最低气温的预报准确率提高程度优于最高气温;在整个空间分布中,ECMWF模式对辽宁中部平原地区最高(低)气温预报准确率高于东、西部地区,辽宁东北部和西南部以及东南部的长白山余脉影响区域准确率明显低于其他区域。同时,在各季中,最高气温和夏季最低气温的订正预报能力优于其他季节;在地面晴、雨两种特征下,对辽宁地区24 h气温预报进行订正检验表明,该检验结果对辽宁地区最高(低)气温订正有一定补充作用,尤其是冬季降水出现时,最高气温预报补充订正效果最为显著。

中国工业环境规制政策的技术创新效应——基于面板协整和PVECM模型的实证分析

基于价值链视角,将中国工业的技术创新过程划分为技术开发和技术转化两个阶段,以2004—2010年我国工业37个细分行业面板数据为对象,采用面板协整、面板误差修正模型等方法,对中国工业环境规制政策的技术创新效应进行了实证检验。研究结果显示:中国工业环境规制政策与技术开发能力、技术转化能力均存在长期协整关系,虽然环境规制政策的实施在一定程度上促进了技术开发能力和技术转化能力的提升,但这种提升作用具有倒"U"型规律特征,即环境规制政策的实施对中国工业技术创新能力的提升有"度"的限制,当环境规制政策强度超过一定水平时,反而不利于工业技术创新能力提升;相对技术转化阶段而言,中国工业技术开发阶段的创新活动对环境规制政策的适应能力更强;环境规制政策对技术开发能力、技术转化能力的短期波动均有正向影响,二者之间的长期均衡具有显著的短期调节效应。