Impact of Tropical Cyclones over the North Indian Ocean on Weather in China and Related Forecasting Techniques:A Review of Progress

Tropical cyclones(TCs)over the North Indian Ocean(NIO)are closely related to Asian summer monsoon activities and have a great impact on the precipitation in the Tibetan Plateau,southwestern China,and even the middle and lower reaches of the Yangtze River.In this paper,the research progress on the impacting mechanisms of NIO TCs on the weather in China and associated forecasting techniques is synthesized and reviewed,including characteristics of the NIO TC activity,its variability under climate change,related precipitation mechanism,and associated forecasting techniques.On this basis,the limitations and deficiencies in previous research on the physical mechanisms and forecasting techniques of NIO TCs affecting the weather in China are elucidated and the directions for future investigations are discussed.

A comprehensive review and analysis of solar forecasting techniques

In the last two decades,renewable energy has been paid immeasurable attention to toward the attainment of electricity requirements for domestic,industrial,and agriculture sectors.Solar forecasting plays a vital role in smooth operation,scheduling,and balancing of electricity production by standalone PV plants as well as grid interconnected solar PV plants.Numerous models and techniques have been developed in short,mid and long-term solar forecasting.This paper analyzes some of the potential solar forecasting models based on various methodologies discussed in literature,by mainly focusing on investigating the influence of meteorological variables,time horizon,climatic zone,pre-processing techniques,air pollution,and sample size on the complexity and accuracy of the model.To make the paper reader-friendly,it presents all-important parameters and findings of the models revealed from different studies in a tabular mode having the year of publication,time resolution,input parameters,forecasted parameters,error metrics,and performance.The literature studied showed that ANN-based models outperform the others due to their nonlinear complex problem-solving capabilities.Their accuracy can be further improved by hybridization of the two models or by performing pre-processing on the input data.Besides,it also discusses the diverse key constituents that affect the accuracy of a model.It has been observed that the proper selection of training and testing period along with the correlated dependent variables also enhances the accuracy of the model.

Evaluation of forecasting methods from selected stock market returns

Forecasting stock market returns is one of the most effective tools for risk management and portfolio diversification.There are several forecasting techniques in the literature for obtaining accurate forecasts for investment decision making.Numerous empirical studies have employed such methods to investigate the returns of different individual stock indices.However,there have been very few studies of groups of stock markets or indices.The findings of previous studies indicate that there is no single method that can be applied uniformly to all markets.In this context,this study aimed to examine the predictive performance of linear,nonlinear,artificial intelligence,frequency domain,and hybrid models to find an appropriate model to forecast the stock returns of developed,emerging,and frontier markets.We considered the daily stock market returns of selected indices from developed,emerging,and frontier markets for the period 2000–2018 to evaluate the predictive performance of the above models.The results showed that no single model out of the five models could be applied uniformly to all markets.However,traditional linear and nonlinear models outperformed artificial intelligence and frequency domain models in providing accurate forecasts.

Disease Law and Forecasting of the Needle Blight ofPinus sylvestris var. mongolica

Fixed quadrates were established in different stands. In continued six years, the occurring period, occurring amounts and the relation between epidemic disease and environmental factors were investigated according to spraying lawsof spores and accounting measures of disease ranking. The occuring peak period of the disease was from the last ten days ofMay to the second ten days of June. The epidemic period was from the last ten days of June to the second ten days of Julyand the initial decease period was from the last ten days of July to the beginning of September. The change of the diseasedepended on air temperature, relative humidity and precipitation. A multiple linear regression model was established usingcomputer, which can predict the disease index(Y) of 10 days later, with more than 95% reliability

Science and Prediction of Heavy Rainfall over China:Research Progress since the Reform and Opening-Up of New China

This paper reviews the major progress on development of the science and prediction of heavy rainfall over China since the beginning of the reform and opening-up of new China(roughly between 1980 and 2019).The progress of research on the physical mechanisms of heavy rainfall over China is summarized from three perspectives:1)the relevant synoptic weather systems,2)heavy rainfall in major sub-regions of China,and 3)heavy rainfall induced by typhoons.The development and application of forecasting techniques for heavy rainfall are summarized in terms of numerical weather prediction techniques and objective forecasting methods.Greatly aided by the rapid progress in meteorological observing technology and substantial improvement in electronic computing,studies of heavy rainfall in China have advanced to investigating the evolution of heavy-rain-producing storms and observational analysis of the cloud microphysical features.A deeper and more systematic understanding of the synoptic systems of importance to the production of heavy rainfall has also been developed.Operational forecast of heavy rainfall in China has changed from subjective weather event forecasts to a combination of both subjective and objective quantitative precipitation forecasts,and is now advancing toward probabilistic quantitative precipitation forecasts with the provision of forecast uncertainty information.