An Approach to Extract Effective Information of Monthly Dynamical Prediction-The Use of Ensemble Method

The approach of getting useful information of monthly dynamical prediction from ensemble forecasts is studied. The extended range ensemble forecasts (8 members, the initial perturbations of the lagged average forecast (LAF)(0000, 0600, 1200 and 1800 GMT in two consecutive days) of the 500 hPa height field with the global spectral model (T63L16) from January to May 1997 are provided by the National Climate Center of China. The relationship between the spread of ensemble measured by root–mean–square deviation of ensemble member from ensemble mean and forecast skill (the anomaly correlation or the root–mean–square distance between the ensemble mean forecast and the observation) is significant. The spread of ensemble can evaluate the useful forecast days N for the best estimate of 30 days mean. Thus, a weighted mean approach based on ensemble spread is put forward for monthly dynamical prediction. The anomaly correlation of the weighted monthly mean by the ensemble spread is higher than that of both the arithmetic mean and the linear weighted mean. Better results of the monthly mean circulation and anomaly are obtained from the ensemble spread weighted mean. Key words Monthly prediction - Ensemble method - Spread of ensemble Supported by the Excellent National State Key Laboratory Project (49823002), the National Key Project ‘Study on Chinese Short-Term Climate Forecast System’ (96-908-02) and IAP Innovation Foundation (8-1308).The data were provided through the National Climate Center of China. The authors wish to thank Ms. Chen Lijuan for her assistance.

ON THE PREDICTION OF TROPICAL CYCLONE LANDFALL AND INFLUENCE ON SOUTHERN CHINA USING MONTHLY OLR ANOMALIES FOR PRIME SUMMER

With the OLR data, the landfall and activity of tropical cyclones (TC) in southern China over a 20-year period (1975～1994) are studied. The result shows that the variation of the monthly anomalous OLR is somewhat teleconnected with the TC activity in southern China. The former is used to predict short-term climate for the latter over months with frequent or no TC influence. To some extent, the relationship between the TC activity in southern China and the monthly mean OLR anomalies is dependent on the climatological location of the subtropical high in northwestern Pacific region.

Monthly Mean Temperature Prediction Based on a Multi－level Mapping Model of Neural Network BP Type

In terms of 34-year monthly mean temperature series in 1946-1979,the multi-level maPPing model of neural netWork BP type was applied to calculate the system's fractual dimension Do=2'8,leading tO a three-level model of this type with ixj=3x2,k=l,and the 1980 monthly mean temperture predichon on a long-t6rm basis were prepared by steadily modifying the weighting coefficient,making for the correlation coefficient of 97% with the measurements.Furthermore,the weighhng parameter was modified for each month of 1980 by means of observations,therefore constrcuhng monthly mean temperature forecasts from January to December of the year,reaching the correlation of 99.9% with the measurements.Likewise,the resulting 1981 monthly predictions on a long-range basis with 1946-1980 corresponding records yielded the correlahon of 98% and the month-tO month forecasts of 99.4%.

Monthly prediction of tropical cyclone activity over the South China Sea using the FGOALS-f2 ensemble prediction system

The monthly prediction skill for tropical cyclone(TC)activity in the South China Sea(SCS)during the typhoon season(July to November)was evaluated using the FGOALS-f2 ensemble prediction system.Specifically,the prediction skill of the system at a 10-day lead time for monthly TC activity is given based on 35-year(1981–2015)hindcasts with 24 ensemble members.The results show that FGOALS-f2 can capture the climatology of TC track densities in each month,but there is a delay in the monthly southward movement in the area of high track densities of TCs.The temporal correlation coefficient of TC frequency fluctuates across the different months,among which the highest appears in October(0.59)and the lowest in August(0.30).The rank correlation coefficients of TC track densities are relatively higher(R>0.6)in July,September,and November,while those in August and October are relatively lower(R within 0.2 to 0.6).For real-time prediction of TCs in 2020(July to November),FGOALS-f2 demonstrates a skillful probabilistic prediction of TC genesis and movement.Besides,the system successfully forecasts the correct sign of monthly anomalies of TC frequency and accumulated cyclone energy for 2020(July to November)in the SCS.

Mapping soil organic matter in cultivated land based on multi-year composite images on monthly time scales

Rapid and accurate acquisition of soil organic matter(SOM)information in cultivated land is important for sustainable agricultural development and carbon balance management.This study proposed a novel approach to predict SOM with high accuracy using multiyear synthetic remote sensing variables on a monthly scale.We obtained 12 monthly synthetic Sentinel-2 images covering the study area from 2016 to 2021 through the Google Earth Engine(GEE)platform,and reflectance bands and vegetation indices were extracted from these composite images.Then the random forest(RF),support vector machine(SVM)and gradient boosting regression tree(GBRT)models were tested to investigate the difference in SOM prediction accuracy under different combinations of monthly synthetic variables.Results showed that firstly,all monthly synthetic spectral bands of Sentinel-2 showed a significant correlation with SOM(P<0.05)for the months of January,March,April,October,and November.Secondly,in terms of single-monthly composite variables,the prediction accuracy was relatively poor,with the highest R^(2)value of 0.36 being observed in January.When monthly synthetic environmental variables were grouped in accordance with the four quarters of the year,the first quarter and the fourth quarter showed good performance,and any combination of three quarters was similar in estimation accuracy.The overall best performance was observed when all monthly synthetic variables were incorporated into the models.Thirdly,among the three models compared,the RF model was consistently more accurate than the SVM and GBRT models,achieving an R^(2)value of 0.56.Except for band 12 in December,the importance of the remaining bands did not exhibit significant differences.This research offers a new attempt to map SOM with high accuracy and fine spatial resolution based on monthly synthetic Sentinel-2 images.

Reversal of monthly East Asian winter air temperature in 2020/21 and its predictability

In this study,the reversal of monthly East Asian winter air temperature(EAWT) in 2020/21 and its predictability were investigated.The reversal of monthly EAWT in 2020/21 was characterized by colder temperatures in early winter(December 2020 to mid-January 2021) and warmer temperatures in late winter(mid-January to February 2021).Results show that the reversal in the intensity of the Siberian high(SH) also occurred between early and late winter in 2020/21.In early winter,as the Barents-Laptev sea ice in the previous September(i.e., in2020) reached a minimum for the period 1981-2020,the SH was strengthaned via a reduction of the meridional gradient between the Arctic and East Asia.In late winter,as a sudden stratospheric warming occurred on 5 January 2021,the stratospheric polar vortex weakened,with the weakest center shifting to North America in January.Subsequently,the negative Arctic Oscillation-like structure shifted towards North America in the middle and lower troposphere,which weakened the SH in late winter.Furthermore,the predictability of the reversal in EAWT in 2020/21 was validated based on monthly and daily predictions from NCEP-CFSv2(National Centers for Environment Prediction-Climate Forecast System,version 2).The results showed that the model was unable to reproduce the monthly reversal of EAWT.However,it was able to forecast the reversal date(18 January 2021)of EAWT at lead times of 1-20 days on the daily scale.

DOWNSCALING FORECAST OF MONTHLY PRECIPITATION OVER GUANGXI BASED ON BP NEURAL NETWORK MODEL

Variables fields such as enstrophy, meridional-wind and zonal-wind variables are derived from monthly 500 hPa geopotential height anomalous fields. In this work, we select original predictors from monthly 500-hPa geopotential height anomalous fields and their variables in June of 1958 - 2001, and determine comprehensive predictors by conducting empirical orthogonal function (EOF) respectively with the original predictors. A downscaling forecast model based on the back propagation (BP) neural network is built by use of the comprehensive predictors to predict the monthly precipitation in June over Guangxi with the monthly dynamic extended range forecast products. For comparison, we also build another BP neural network model with the same predictands by using the former comprehensive predictors selected from 500-hPa geopotential height anomalous fields in May to December of 1957 - 2000 and January to April of 1958 - 2001. The two models are tested and results show that the precision of superposition of the downscaling model is better than that of the one based on former comprehensive predictors, but the prediction accuracy of the downscaling model depends on the output of monthly dynamic extended range forecast.

Monthly Electricity Consumption Forecast Based on Multi-Target Regression

Urban grid power forecasting is one of the important tasks of power system operators, which helps to analyze the development trend of the city. As the demand for electricity in various industries is affected by many factors, the data of relevant influencing factors are scarce, resulting in great deviations in the accuracy of prediction results. In order to improve the prediction results, this paper proposes a model based on Multi-Target Tree Regression to predict the monthly electricity consumption of different industrial structures. Due to few data characteristics of actual electricity consumption in Shanghai from 2013 to the first half of 2017. Thus, we collect data on GDP growth, weather conditions, and tourism season distribution in various industries in Shanghai, model and train the electricity consumption data of different industries in different months. The multi-target tree regression model was tested with actual values to verify the reliability of the model and predict the monthly electricity consumption of each industry in the second half of 2017. The experimental results show that the model can accurately predict the monthly electricity consumption of various industries.

Estimation of the Monthly Precipitation Predictability Limit in China Using the Nonlinear Local Lyapunov Exponent

By using the nonlinear local Lyapunov exponent and nonlinear error growth dynamics, the predictability limit of monthly precipitation is quantitatively estimated based on daily observations collected from approx- imately 500 stations in China for the period 1960-2012. As daily precipitation data are not continuous in space and time, a transformation is first applied and a monthly standardized precipitation index （SPI） with Gaussian distribution is constructed. The monthly SPI predictability limit （MSPL） is quantitatively calcu- lated for SPI dry, wet, and neutral phases. The results show that the annual mean MSPL varies regionally for both wet and dry phases： the MSPL in the wet （dry） phase is relatively higher （lower） in southern China than in other regions. Further, the pattern of the MSPL for the wet phase is almost opposite to that for the dry phase in both autumn and winter. The MSPL in the dry phase is higher in winter and lower in spring and autumn in southern China, while the MSPL values in the wet phase are higher in summer and winter than those in spring and autumn in southern China. The spatial distribution of the MSPL resembles that of the prediction skill of monthly precipitation from a dynamic extended-range forecast system.

2021年盛夏中国东部极端降水月际演变成因及可预测性

2021年7—8月中国东部雨带演变特征与气候平均季风北推进程存在显著差异。其中,7月降水正异常中心位于江淮-华北地区,8月则南移至华中地区。2021年中国东部降水异常偏多且存在月际差异主要与7(8)月西北太平洋副热带高压(西太副高)偏北偏东(偏南偏西)、东亚副热带西风急流偏北(偏南)以及南亚高压持续东伸相关联。进一步研究表明,热带对流的活跃位置和北大西洋的增暖加强是影响其降水中心南移的主要原因。2021年7月热带大气低频振荡(MJO)在海洋性大陆地区活跃对应其热带海洋性大陆对流异常偏强,激发北传的类太平洋-日本(PJ)型遥相关波列,使得西太副高偏北偏东,有利于西北太平洋水汽在江淮-华北地区辐合,导致其降水偏多。8月,新发展MJO在热带印度洋上空对流异常持续偏强,加强局地经向环流,使得中国35°N以南至西北太平洋地区出现异常下沉运动,有利于西太副高南移西伸。此外,2021年8月北大西洋海温(SST)异常偏暖激发对流层高层向东南传播的Rossby波,有利于南亚高压加强和东亚副热带西风急流加强南移。因此,8月降水中心南移至华中地区。CFSv2预测系统(6月起报)结果能预测7月江淮-华北大部分地区降水偏多,但预测的8月华中南部地区降水偏少与实况相反。这可能是由于模式能够较好再现7月海洋性大陆热带对流活动影响江淮-华北地区降水的过程,但不能预测2021年8月热带印度洋对流活动和北大西洋海温异常偏暖对华中地区降水的影响。

POTENTIAL PREDICTABILITY OF MONTHLY PRECIPITATION OVER CHINA

In this paper,based on the data at 70 stations selected evenly over China for 31 years from 1961—1991.three methods to estimate climatic noise have been discussed and then the climatic noise and potential predictability of monthly precipitation(January.July.April and October)have been examined.The estimating of climatic noise is based on the method of Madden and improved methods of Trenberth and Yamamoto et al.(1985).The potential predictability is approximated by the ratio of the estimated interannual variation to the natural variation.Generally.the climatic noise of monthly precipitation over China has obvious seasonal variation and it is greater in summer than in winter,a bit greater in autumn than in spring.In most areas,the climatic noise is prominently decreasing from south to north and from coast to inland.The potential predictability of monthly precipitation also has obvious seasonal and regional difference,but the potential predictability is greater in winter than in summer in most parts of China.Whereas the comparison of spring and autumn is not obvious.Comparing with the method of Madden,the estimated values of climatic noise based on the improved methods of Trenberth and Yamamoto et al.are relatively lower.

华南冬季异常冷月预测概念模型Ⅱ──大气环流、极冰、积雪等物理因子特征

统计分析了华南冬季异常冷月及其前期大气环流及外部强迫因子的变化特征。结果表明,异常冷月当月至其前期3月西太平洋副热带高压持续偏弱或位置偏东偏南。前一年东亚夏季风弱,当年冬季风强。前期7、8、9月和当年冬季亚洲极涡扩展。1月前期11月和12月青藏高原积雪日数偏少。2月前期11个月,特别是3～8月北极Ⅳ区极冰偏多,12月前期1～11月Ⅱ区极冰偏多,3～8月Ⅲ区极冰偏少。综合上述各因子以及500 hPa高度场和海温场前期特征,给出了华南冬季异常冷月预测概念模型。

胎儿卵圆孔相关参数对出生后卵圆孔早期闭合的预测价值

目的探讨胎儿卵圆孔相关指标对出生后卵圆孔早期闭合的预测价值。方法纳入100例出生后7 d内首次超声心动图检查提示卵圆孔未闭(PFO)的新生儿,根据出生后3月龄卵圆孔闭合情况分为闭合组38例、未闭合组62例。比较两组胎儿期卵圆孔相关指标,分析生后3月龄卵圆孔闭合的影响因素。评价相关指标对生后3月龄卵圆孔闭合的预测价值。结果未闭合组卵圆孔内径(FO-d)、卵圆孔瓣长度(FOV-L)、FO-d/房间隔总长度(AS-L)比值均大于闭合组,而卵圆孔收缩期峰值流速低于闭合组(均P<0.05)。FOV-L、FO-d/AS-L比值增大是出生后3月龄PFO的独立危险因素(均P<0.05)。FOV-L、FO-d/AS-L比值及两者联合预测生后3月龄卵圆孔闭合的曲线下面积分别为0.841、0.817、0.879;联合预测时的曲线下面积大于FO-d/AS-L比值(P<0.05)。结论胎儿期卵圆孔相关指标与PFO新生儿卵圆孔早期闭合密切相关,其中FOV-L和FO-d/AS-L比值可作为3月龄时卵圆孔闭合的预测指标。

利用地磁和天文因素进行地震三要素预测的研究

本文分析了华北地区地磁台站和流动磁测资料与近期河北及邻区中强地震的相关关系 ,发现了冀、京、津、渤地区近百年地震所显示的单月发震优势特征 ,结合地磁交汇点附近一般是未来可能发震的位置 。

ARIMA乘积季节模型在西藏自治区包虫病月发病人数中的预测应用

目的基于西藏自治区2013年8月至2018年7月包虫病的发病人数,采用X-12自回归移动平均(X-12-ARIMA)乘积模型拟合时间序列模型,对发病人数进行预测,为包虫病的防治提供科学的依据。方法采用X-12-ARIMA乘积季节模型对西藏自治区包虫病月度发病人数进行趋势分解,并自动选择ARIMA季节调整乘积模型,以赤池信息准则(AIC)和贝叶斯信息准则(BIC)值最小为最优模型选择标准。结果X-12选择的最优乘积季节模型为ARIMA(1,1,0)×(1,1,0)_(12);预测方程为,拟合模型结果显示2013至2018年包虫病的月发病人数不仅呈现季节性波动,而且呈现波动性下降的趋势。结论ARIMA(1,1,0)×(1,1,0)_(12)能够应用于西藏自治区包虫病发病人数的预测,西藏自治区包虫病的发病人数具有明显的季节性波动,并且呈现波动性下降趋势。

一月龄吮乳羔羊肠道菌群组成及其预测物质代谢功能的动态变化研究

羔羊出生至断奶时期消化系统形态结构和功能变化剧烈,但其肠道菌群组成的发育规律尚不清晰。本试验旨在研究一月龄湖羊羔羊肠道菌群的组成变化规律。随机选择出生后新生期(3日龄,d03)、哺乳期(10日龄,d10)、补饲期(15日龄,d15)和断奶前期(30日龄,d30)的无抗生素用药史的健康吮乳羔羊各10只,另选择在第43~45日龄之间一次性断奶的断奶后期(60日龄,d60)健康羔羊10只作为对照,采集直肠粪便样品进行16S rRNA扩增子测序,对一月龄肠道菌群组成结构与潜在功能的变化特点进行研究。结果发现,羔羊肠道菌群各分类水平OTU数量在一月龄内呈上升趋势,但极显著低于断奶后(P<0.01)。一月龄内羔羊肠道菌群α多样性呈先降后升特点,也显著低于断奶后(P<0.05)。3日龄和10日龄羔羊肠道菌群β多样性分别极显著独立分布,15日龄和30日龄极显著聚为一类(P<0.01),60日龄羔羊极显著独立分布(P<0.01)。一月龄内羔羊肠道菌群中拟杆菌门、螺旋体门、黏胶球形菌门、迷踪菌门、5-7N15属、CF231属、瘤胃球菌属、密螺旋体属和丁酸弧菌属丰度随日龄呈上升趋势,而厚壁菌门、拟杆菌属、乳酸菌属和韦荣球菌属随日龄呈下降趋势。3~15日龄羔羊肠道菌群的碳水化合物代谢显著高于断奶后(P<0.05),而氨基酸代谢、能量代谢显著低于断奶后(P<0.05)。本研究结果表明,一月龄羔羊肠道菌群组成结构和物质代谢功能呈显著动态变化,断奶后羔羊肠道菌群丰富度和多样性显著升高。15~30日龄左右羔羊肠道菌群组成结构及其预测的功能呈现“分水岭”样变化,该变化与引入补饲固体饲料的关系值得进一步研究。

时间序列模型在月平均住院日预测中的应用及评价

目的:对某综合医院的月平均住院日建立时间序列模型,利用指数平滑法和ARIMA模型对其进行模拟评价及短期预测。方法:在某综合医院统计报表中提取2014年1月—2017年12月的月平均住院日,其中2014年1月—2017年6月的数据用于创建时间序列,利用SPSS20.0进行统计分析,分别采用指数平滑法和ARIMA对创建的时间序列拟合模型,评价模型效果,并对2017年7月—2017年12月的平均住院日进行预测,比较实际值与预测值间的符合程度。结果:指数平滑法模型:平稳的R方为0.814,表明拟合程度较好。白噪声序列的Ljung-Box检验无统计学意义(Q18=18.730,P=0.226)。模型参数估计中平滑参数Alpha的估计值为0.200,且参数检验结果有统计学意义(T=2.106,P=0.042)。ARIMA模型:平稳的R方为0.361,Ljung-Box检验无统计学意义(Q18=15.215,P=0.580)。AR的参数检验有统计学意义(T=-4.652,P<0.001),为-0.654。两模型实际值与预测值间的相对误差绝对值均小于5%。结论:指数平滑模型比ARIMA拟合及预测效果更好,是某综合医院月平均住院日的首选预测模型,为医院管理决策提供科学依据。

灰色-马尔可夫链模型在股市预测中的应用

用GM(1,1)预测具有良好的精确性和规律性,但对于随机波动性较大的股市行业,它的预测精度比较低,而马尔可夫模型可以克服波动性较大的局限性,弥补灰色模型的不足,因此将两者结合起来对股市进行预测将能提高预测的精度。本文依据上交所20个月末收盘指数预测后四个月的月末收盘指数范围。

Study on the Rainfall Predication Equation Established by the Downscaling Technique

[Objective] The research aimed to study a kind of precipitation predication model which was established by using the multi-level mode circulation output field. [Method] The downscaling prediction method which was run in Anhui business was improved. The high related zone with the precipitation was found in the multi-level mode circulation field. Moreover, the optimal subset regression model was used to screen and assemble the forecast factors. The predication equation of monthly rainfall was formed. Finally, the actual and mode circulation fields during 2005-2009 were respectively set into the equation, and the prediction scores of two kinds of data schemes were contrasted. The monthly score was analyzed, and the feasibility of business operation was inspected. [Result] Compared with the traditional downscaling method, the data content of precipitation prediction model which was established by using the multi-level mode circulation output field was richer. Seen from the prediction effect, the average anomaly symbol consistence rate was 63%, and PS was 75 scores. It was not only higher than that of downscaling method of business operation, but also higher than the predicted score of business issue. In addition, the prediction effect of method on the typical flooding month was better. It showed that the method had the good prediction capability on the abnormal value. [Conclusion] The research provided the reference for enriching the downscaling technology scheme.

Method of Chaotic Prediction Based on Wavelet BP Network and Its Application

The advantage of artificial neural network and wavelet analysis are integrated through replacing the traditional S-shaped activation function with the wavelet function. One method of chaotic prediction based on wavelet BP network was put forward based on the reconstruction of state space. Training data construction and networks structure are determined by chaotic phase space, and nonlinear relationship of phase points was established by BP neural networks. As an example, the new method was applied on short term forecasting of monthly precipitation time series of Sanjiang Plain with chaotic characteristics. The results showed so higher precision of the method had that the theoretical evidence would be provided for applying the chaos theory to study the variable law of monthly precipitation.