Development of Flood Forecasting System Using Statistical and ANN Techniques in the Downstream Catchment of Mahanadi Basin, India

The floods in river Mahanadi delta are due to either dam release of Hirakud or due to contribution of intercepted catchment between Hirakud dam and delta. It is seen from post-Hirakud periods (1958) that out of 19 floods 14 are due to intercepted catchment contribution. The existing flood forecasting systems are mostly for upstream catchment, forecasting the inflow to reservoir, whereas the downstream catchment is devoid of a sound flood forecasting system. Therefore, in this study an attempt has been made to develop a workable forecasting system for downstream catchment. Instead of taking the flow time series concurrent flood peaks of 12 years of base and forecasting stations with its corresponding travel time are considered for analysis. Both statistical method and ANN based approach are considered for finding the peak to reach at delta head with its corresponding travel time. The travel time has been finalized adopting clustering techniques, there by differentiating high, medium and low peaks. The method is simple and it does not take into consideration the rainfall and other factors in the intercepted catchment. A comparison between both methods are tested and it is found that the ANN methods are better beyond the calibration range over statistical method and the efficiency of either methods reduces as the prediction reach is extended. However, it is able to give the peak discharge at delta head before 24 hour to 37 hour for high to low peaks.

APPLICATION OF STATISTICAL INTERPRETATION TECHNIQUES WITH NWP PRODUCTS FOR OBJECTIVE FORECASTING OF TROPICAL CYCLONE MOTION

Statistical study is first performed of the efficiency of the technique of statistical interpretation using the products of NWP. The result shows that the application of the technique has improved the predictabilily of predictors in objective forecasting of tropical cyclone motion, increased the forecasting skill of models and extended the valid period of forecast. Then a discussion is made of some technical problems in the application in the motion forecasting, suggesting: a large sample of data and perfect forecast method be employed in constructing objective forecast models for tropical cyclone motion, predictors be included that are so finely built that they reflect all synoptic features and physical quantity fields and NWP products be used and corrected that are available at multiple times. It is one of the effective ways to improve the skill and stability of the forecast by composite use of outcomes from various forecasting models.

Forecasting and Decision-Making of Systematic Theories for Engineering Geology in Environmental Geoscience

The paper discusses the problems of engineering geology in environmental geoscience from several aspects. For natural sciences and social sciences, it deduces essential theory from logistic cycle model, logic mapping and Verhulst model. It had been discovered that these aspects are equal. However, these were the studies of normal effects. We must establish mathematical model to check from contrary course for gray forecasting and decision-making and answer several questions satisfactorily.

Improving the Seasonal Forecast of Summer Precipitation in China Using a Dynamical-Statistical Approach

A dynamical-statistical post-processing approach is applied to seasonal precipitation forecasts in China during the summer.The data are ensemble-mean seasonal forecasts in summer (June August) from four atmospheric general circulation models (GCMs) in the second phase of the Canadian Historical Forecasting Project (HFP2) from 1969 to 2001.This dynamical-statistical approach is designed based on the relationship between the 500 geopotential height (Z500) forecast and the observed sea surface temperature (SST) to calibrate the precipitation forecasts.The results show that the post-processing can improve summer precipitation forecasts for many areas in China.Further examination shows that this post-processing approach is very effective in reducing the model-dependent part of the errors,which are associated with GCMs.The possible mechanisms behind the forecast's improvements are investigated.

Inferential Statistics and Machine Learning Models for Short-TermWind Power Forecasting

The inherent randomness,intermittence and volatility of wind power generation compromise the quality of the wind power system,resulting in uncertainty in the system’s optimal scheduling.As a result,it’s critical to improve power quality and assure real-time power grid scheduling and grid-connected wind farm operation.Inferred statistics are utilized in this research to infer general features based on the selected information,confirming that there are differences between two forecasting categories:Forecast Category 1(0-11 h ahead)and Forecast Category 2(12-23 h ahead).In z-tests,the null hypothesis provides the corresponding quantitative findings.To verify the final performance of the prediction findings,five benchmark methodologies are used:Persistence model,LMNN(Multilayer Perceptron with LMlearningmethods),NARX(Nonlinear autoregressive exogenous neural networkmodel),LMRNN(RNNs with LM training methods)and LSTM(Long short-term memory neural network).Experiments using a real dataset show that the LSTM network has the highest forecasting accuracy when compared to other benchmark approaches including persistence model,LMNN,NARX network,and LMRNN,and the 23-steps forecasting accuracy has improved by 19.61%.

Validation of a Statistical Forecast Model for Zonda Wind in West Argentina Based on the Vertical Atmospheric Structure

Zonda is a strong, warm, very dry wind associated with adiabatic compression upon descending the eastern slopes of the Andes Cordillera in western-central Argentina. This research seeks, first, to validate the skill of a statistical forecast of zonda based on the behavior of the vertical structure of the atmosphere and, second, to describe the climatology of the vertical profile leeward of the Andes. The forecast was built for May-August 1974/1983, and was verified against a series of cases recorded in the Mendoza Aero and San Juan Aero weather stations for May-August 2005/2014. It made use of the Stepwise Discriminant Analysis (SDA) and rawinsonde data from Mendoza Aero as predictors, with the following input variables: surface pressure, temperature, dew point, and the zonal and meridional components of the wind on surface and of the fixed levels up to 200 hPa. The variables selected as predictors by the SDA were: surface pressure, dew point depression at 850 hPa, meridional wind component at 850 hPa, and zonal wind component at 400 hPa. Climatology of the vertical profile of the atmosphere leeward of the Andes was built from daily rawinsonde data from Mendoza Aero for May-August 1974/1983. Zonda markedly influences the atmospheric structure leeward of the Andes in western-central Argentina. Its maximum impact occurs at 850 to 800 hPa, with significant heating and decrease of humidity. Validation of the prediction program considered deterministic and probabilistic forecasts. Contingency tables show that probability of zonda occurrence in the plains is generally overestimated, and false alarm cases are far more frequent than surprise events. The main contribution of this paper is precisely the validation of the prediction model, which ensures forecasters one more tool to improve zonda forecasting;this, in turn, will aid decision-makers when taking steps to ameliorate zonda wind impact.

A Hybrid Model Evaluation Based on PCA Regression Schemes Applied to Seasonal Precipitation Forecast

Possible changes in the structure and seasonal variability of the subtropical ridge may lead to changes in the rainfall’s variability modes over Caribbean region. This generates additional difficulties around water resource planning, therefore, obtaining seasonal prediction models that allow these variations to be characterized in detail, it’s a concern, specially for island states. This research proposes the construction of statistical-dynamic models based on PCA regression methods. It is used as predictand the monthly precipitation accumulated, while the predictors (6) are extracted from the ECMWF-SEAS5 ensemble mean forecasts with a lag of one month with respect to the target month. In the construction of the models, two sequential training schemes are evaluated, obtaining that only the shorter preserves the seasonal characteristics of the predictand. The evaluation metrics used, where cell-point and dichotomous methodologies are combined, suggest that the predictors related to sea surface temperatures do not adequately represent the seasonal variability of the predictand, however, others such as the temperature at 850 hPa and the Outgoing Longwave Radiation are represented with a good approximation regardless of the model chosen. In this sense, the models built with the nearest neighbor methodology were the most efficient. Using the individual models with the best results, an ensemble is built that allows improving the individual skill of the models selected as members by correcting the underestimation of precipitation in the dynamic model during the wet season, although problems of overestimation persist for thresholds lower than 50 mm.

Statistical Downscaling for Multi-Model Ensemble Prediction of Summer Monsoon Rainfall in the Asia-Pacific Region Using Geopotential Height Field

The 21-yr ensemble predictions of model precipitation and circulation in the East Asian and western North Pacific （Asia-Pacific） summer monsoon region （0°-50°N, 100° 150°E） were evaluated in nine different AGCM, used in the Asia-Pacific Economic Cooperation Climate Center （APCC） multi-model ensemble seasonal prediction system. The analysis indicates that the precipitation anomaly patterns of model ensemble predictions are substantially different from the observed counterparts in this region, but the summer monsoon circulations are reasonably predicted. For example, all models can well produce the interannual variability of the western North Pacific monsoon index （WNPMI） defined by 850 hPa winds, but they failed to predict the relationship between WNPMI and precipitation anomalies. The interannual variability of the 500 hPa geopotential height （GPH） can be well predicted by the models in contrast to precipitation anomalies. On the basis of such model performances and the relationship between the interannual variations of 500 hPa GPH and precipitation anomalies, we developed a statistical scheme used to downscale the summer monsoon precipitation anomaly on the basis of EOF and singular value decomposition （SVD）. In this scheme, the three leading EOF modes of 500 hPa GPH anomaly fields predicted by the models are firstly corrected by the linear regression between the principal components in each model and observation, respectively. Then, the corrected model GPH is chosen as the predictor to downscale the precipitation anomaly field, which is assembled by the forecasted expansion coefficients of model 500 hPa GPH and the three leading SVD modes of observed precipitation anomaly corresponding to the prediction of model 500 hPa GPH during a 19-year training period. The cross-validated forecasts suggest that this downscaling scheme may have a potential to improve the forecast skill of the precipitation anomaly in the South China Sea, western North Pacific and the East Asia Pacific regions, where the anomaly correlation coefficient （ACC） has been improved by 0.14, corresponding to the reduced RMSE of 10.4% in the conventional multi-model ensemble （MME） forecast.

A Survey of Wind Power Ramp Forecasting

At home and broad, more wind power is being installed in electricity markets, the influence brought by wind power become more important on power system stability, especially the fluctuation, the uncertainty in wind power production and multi-time scale of the wind. In order to forecast ramp events before the power system encountering failure, so that the operator can adopt some limited controlling strategy. This paper introduces the present status of the wind power ramp prediction at home and abroad. And it gives out four kinds of definitions of ramp events, which are used by many scholars, then provides various forecasting error algorithm. In the aspect of prediction models, it comes up with physical models and statistical models, and enumerates various examples of different models. Finally, it prospects the tendency of the model improvement about the wind power ramp events forecasting, which would be significant for ramp research.

Application of an Error Statistics Estimation Method to the PSAS Forecast Error Covariance Model

In atmospheric data assimilation systems, the forecast error covariance model is an important component. However, the paralneters required by a forecast error covariance model are difficult to obtain due to the absence of the truth. This study applies an error statistics estimation method to the Pfiysical-space Statistical Analysis System （PSAS） height-wind forecast error covariance model. This method consists of two components： the first component computes the error statistics by using the National Meteorological Center （NMC） method, which is a lagged-forecast difference approach, within the framework of the PSAS height-wind forecast error covariance model; the second obtains a calibration formula to rescale the error standard deviations provided by the NMC method. The calibration is against the error statistics estimated by using a maximum-likelihood estimation （MLE） with rawindsonde height observed-minus-forecast residuals. A complete set of formulas for estimating the error statistics and for the calibration is applied to a one-month-long dataset generated by a general circulation model of the Global Model and Assimilation Office （GMAO）, NASA. There is a clear constant relationship between the error statistics estimates of the NMC-method and MLE. The final product provides a full set of 6-hour error statistics required by the PSAS height-wind forecast error covariance model over the globe. The features of these error statistics are examined and discussed.

A GCM-Based Forecasting Model for the Landfall of Tropical Cyclones in China

A statistical dynamic model for forecasting Chinese landfall of tropical cyclones （CLTCs） was developed based on the empirical relationship between the observed CLTC variability and the hindcast atmospheric circulations from the Pusan National University coupled general circulation model （PNU-CGCM）.In the last 31 years,CLTCs have shown strong year-to-year variability,with a maximum frequency in 1994 and a minimum frequency in 1987.Such features were well forecasted by the model.A cross-validation test showed that the correlation between the observed index and the forecasted CLTC index was high,with a coefficient of 0.71.The relative error percentage （16.3%） and root-mean-square error （1.07） were low.Therefore the coupled model performs well in terms of forecasting CLTCs;the model has potential for dynamic forecasting of landfall of tropical cyclones.

A Probe for Consistency in CAPE and CINE During the Prevalence of Severe Thunderstorms:Statistical – Fuzzy Coupled Approach

Thunderstorms of pre-monsoon season (April – May) over Kolkata (22° 32’N, 88° 20’E), India are invariably accompanied with lightning flashes, high wind gusts, torrential rainfall, occasional hail and tornadoes which significantly affect the life and property on the ground and aviation aloft. The societal and economic impact due to such storms made accurate prediction of the weather phenomenon a serious concern for the meteorologists of India. The initiation of such storms requires sufficient moisture in lower troposphere, high surface temperature, conditional instability and a source of lift to initiate the convection. Convective available potential energy (CAPE) is a measure of the energy realized when conditional instability is released. It plays an important role in meso-scale convective systems. Convective inhibition energy (CINE) on the other hand acts as a possible barrier to the release of convection even in the presence of high value of CAPE. The main idea of the present study is to see whether a consistent quantitative range of CAPE and CINE can be identified for the prevalence of such thunderstorms that may aid in operational forecast. A statistical – fuzzy coupled method is implemented for the purpose. The result reveals that a definite range of CINE within 0 – 150 Jkg-1 is reasonably pertinent whereas no such range of CAPE depicts any consistency for the occurrence of severe thunderstorms over Kolkata. The measure of CINE mainly depends upon the altitude of the level of free convection (LFC), surface temperature (T) and surface mixing ratio (q). The box-and-whisker plot of LFC, T and q are drawn to select the most dependable parameter for the consistency of CINE in the prevalence of such thunderstorms. The skills of the parameters are evaluated through skill score analyses. The percentage error during validation with the observation of 2010 is estimated to be 0% for the range of CINE and 3.9% for CAPE.

Comparisons of Three-Dimensional Variational Data Assimilation and Model Output Statistics in Improving Atmospheric Chemistry Forecasts

Atmospheric chemistry models usually perform badly in forecasting wintertime air pollution because of their uncertainties. Generally, such uncertainties can be decreased effectively by techniques such as data assimilation（DA） and model output statistics（MOS）. However, the relative importance and combined effects of the two techniques have not been clarified. Here,a one-month air quality forecast with the Weather Research and Forecasting-Chemistry（WRF-Chem） model was carried out in a virtually operational setup focusing on Hebei Province, China. Meanwhile, three-dimensional variational（3 DVar） DA and MOS based on one-dimensional Kalman filtering were implemented separately and simultaneously to investigate their performance in improving the model forecast. Comparison with observations shows that the chemistry forecast with MOS outperforms that with 3 DVar DA, which could be seen in all the species tested over the whole 72 forecast hours. Combined use of both techniques does not guarantee a better forecast than MOS only, with the improvements and degradations being small and appearing rather randomly. Results indicate that the implementation of MOS is more suitable than 3 DVar DA in improving the operational forecasting ability of WRF-Chem.

A New Statistical Downscaling Scheme for Predicting Winter Precipitation in China

An effective statistical downscaling scheme was developed on the basis of singular value decomposition to predict boreal winter(December-January-February)precipitation over China.The variable geopotential height at 500 hPa(GH5)over East Asia,which was obtained from National Centers for Environmental Prediction’s Coupled Forecast System(NCEP CFS),was used as one predictor for the scheme.The preceding sea ice concentration(SIC)signal obtained from observed data over high latitudes of the Northern Hemisphere was chosen as an additional predictor.This downscaling scheme showed significantly improvement in predictability over the original CFS general circulation model(GCM)output in cross validation.The multi-year average spatial anomaly correlation coefficient increased from–0.03 to 0.31,and the downscaling temporal root-mean-square-error(RMSE)decreased significantly over that of the original CFS GCM for most China stations.Furthermore,large precipitation anomaly centers were reproduced with greater accuracy in the downscaling scheme than those in the original CFS GCM,and the anomaly correlation coefficient between the observation and downscaling results reached～0.6 in the winter of 2008.

AN IMPROVEMENT OF STATISTICAL PREDICTION OF TYPHOON TRACKS

In this paper, a statistical interpretation composite forecast model for typhoon track is set up by us-ing numerical forecast products and several forecast schemes. Tested in 1994 typhoon season, its forecastperformance is much better than that of a previous statistical forecast model. The test shows that it is aneffective method that sufficiently Anproves objective forecast of typhoon track using the numerical fore-cast output products obtained in forecast and adopting several schemes in composition.

The Research on and Application of the Multi-regression Technique in the Course of the Marketing Decision-making of Enterprises

The reformation of the economy system has led the f un ctional department and status of the enterprises into a variable state. Under th e condition of the market economy, the kernel of the enterprises’ functional dep artment has diverted to that of marketing decision-making, which face to market and meet with the need of consumption. Assuredly, the kernel of marketing decis ion-making is to prognosticate the future market demand of the production of en terprises accurately, so that it can ensure and realize the maximum of the enter prises’ profit increase. Using empirical research and the multi-regression technique, this paper ana lyzes the enterprises’ production demand forecast of the GMC (Global Management Challenge, held every year globally) and changes most of uncontrollable factors of demand forecast to the controllable ones of the enterprises. The method we us ed to forecast demand by using the multi-regression technique is as follows: 1. Look for the main factors which influence the demand of productions; 2. Establish the regression model; 3. Using the historical data, find the resolution of the correlative index an d do the prominent test; 4. Analyze and compare, regression, adjust parameter and optimize the regress ion model. Our method will make the forecast data closer to the actual prices of the future market requirement quantity in the production marketing decision-making of the enterprises and realize the optimizing combination and the working object w ith the minimum of the cost and the maximum of the profit. And it can ensure the realization of the equity maximum of the enterprises and increase the lifecycle of the production.

Wavelet Decomposition Impacts on Traditional Forecasting Time Series Models

This investigative study is focused on the impact of wavelet on traditional forecasting time-series models,which significantly shows the usage of wavelet algorithms.Wavelet Decomposition(WD)algorithm has been combined with various traditional forecasting time-series models,such as Least Square Support Vector Machine(LSSVM),Artificial Neural Network(ANN)and Multivariate Adaptive Regression Splines(MARS)and their effects are examined in terms of the statistical estimations.The WD has been used as a mathematical application in traditional forecast modelling to collect periodically measured parameters,which has yielded tremendous constructive outcomes.Further,it is observed that the wavelet combined models are classy compared to the various time series models in terms of performance basis.Therefore,combining wavelet forecasting models has yielded much better results.

Enhancing Air Quality Forecasts over Catalonia(Spain)Using Model Output Statistics

Model Output Statistics (MOS) is a well-known technique that allows improving outputs from numerical atmospheric models. In this contribution, we present the development of a MOS algorithm to improve air quality forecasts in Catalonia, a region in the northeast of Spain. These forecasts are obtained from an Eulerian coupled air quality modelling system developed by Meteosim. Nitrogen Dioxide (NO2), Particulate Matter (PM10) and Ozone (03) have been the pollutants considered and the methodology has been applied on statistical values of these pollutants according to regulatory levels. Four MOS algorithms have been developed, characterized by different approaches in relation with seasonal stratification and stratification according to the measurement stations considered. Algorithms have been compared among them in order to obtain a MOS that reduces the forecast uncertainties. Results obtained show that the best MOS designed increases the accuracy of NO2 maximum 1&#45h daily value forecast from 71% to 75%, from 68% to 81% in the case of daily values of PM10, and finally, the accuracy of O3 maximum 1-h daily value from 79% to 87%.

WavewatchⅢ模拟和统计方法在最大波高预报方面的评测分析

为了研究WavewatchⅢ(WWⅢ)海浪模型对最大波高的模拟能力及其与传统统计关系方法的差异,通过对两次台风浪过程的后报模拟和半年的业务化预报,分析了WWⅢ数值模拟的准确度及其与统计关系方法的精度差异。研究结果表明:WWⅢ数值模拟的最大波高(Hmax)的精度略低于有效波高(Hs),但也达到了24 h预报相对误差(H_(max)≥1 m)低于18%、相关系数高于0.94的水平,模拟精度可靠,可以用于业务化预报;与两种统计关系方法(H_(max)和H_(s)分别为1.42和1.52)计算的最大波高相比,数值模拟的精度总体与其相当,但在H_(max)和H_(s)比值大于1.65这种易出现危险的海况下,数值模拟具有更高的准确性,更适合应用于海浪预警报服务。

两种降水客观统计方法对ECMWF集合平均降水预报的订正研究

提升降水量级预报精度,有助于优化灾害预警与决策支持。选取2018年1月1日至2021年1月山东省逐12 h降水观测数据和欧洲中期天气预报中心(the European Centre for Medium-Range Weather Forecasting,ECMWF)的集合预报集合平均(Ensemble Prediction Ensemble Mean,EPEM)结果进行72 h内逐12 h降水量级预报统计订正,然后对比ECMWF集合平均降水预报插值的原始预报(EC_EPEM)、基于EC_EPEM的输出统计(Model Output Statistics,MOS)预报(EC_EPEM_MOS)、利用最优TS(Threat Score)评分订正(Optimal Threat Score,OTS)预报(EC_EPEM_OTS)的效果。结果表明:EC_EPEM_MOS在较小量级上表现最优,但在大量级上订正效果稍差,甚至略低于EC_EPEM;EC_EPEM_OTS仅在0.1、10 mm量级上低于EC_EPEM_MOS,其他量级均为最优,尤其在较大量级上订正效果更明显。在50、100 mm大量级上,EC_EPEM_OTS在12~72 h时效订正效果均最优,这是由于EC_EPEM_OTS在稍大量级上提高订正系数使得大量级降水漏报率减小,同时对大量级降水使用较小订正系数也减小了空报率。在较小量级降水中短期预报时效除了山东中部山区外EC_EPEM_MOS表现最佳,山区EC_EPEM_OTS最佳;中等以上量级、尤其较大量级降水,山东大部分地区EC_EPEM_OTS表现最佳。EC_EPEM_MOS订正预报有效地减小了EC_EPEM的空报问题。EC_EPEM_OTS的订正效果最佳,在大范围强降雨过程中与实况降雨分布更为接近,降水总体分布把握较好。