Quantification and Forecasting of Cumulonimbus (Cb) Clouds Direction, Nebulosity and Occurrence With Autoregression Using 2018-2020 Dataset From Yaounde-nsimalen

This works reports the quantification and forecasting of Cumulonimbus (Cb) clouds direction, nebulosity and occurrence with auto regression using 2018-2020 dataset from Yaoundé –Nsimalen of Cameroon. Data collected for October 2018-2020 consisted of 2232 hourly observations. Codes were written to automatically align, multi-find and replace data points in excel to facilitate treating big datasets. The approach included quantification of direction generating time series from data and determination of model orders using the correlogram. The coefficients of the SARIMA model were determined using Yule-Walker equations in matrix form, the Augmented Dickey Fuller test (ADF) was used to check for stationarity assumption, Portmanteau test to check for white noise in residuals and Shapiro-Wilk test to check normality assumptions. After writing several algorithms to test different models, an Autoregressive Neural Network (ANN) was fitted and used to predict the parameters over window of 2 weeks. Autocorrelation Function (ACF) shows no correlation between residuals, with p ≤ 0.05, fitting the stationarity assumption. Average performance is 80%. A stationary wavelike occurrence of the direction has been observed, with East as the most frequented sector. Forecast of Cb parameters is important in effective air traffic management, creating situational awareness and could serve as reference for future research. The method of decomposition could be made applicable in future research to quantify/forecast cloud directions.

Identification of Dynamical Systems Through the Structure of Auto-regression With Exogenous Variable by Decreasing Gradient and Least Squares

In this article was made the identification of dynamic systems of first and second order more common in electronics such as low and high pass filters of the first order, pass-band filter and direct current motor through the structure of auto-regression with exogenous variable. The proposed dynamical systems are initially modeled by a continuous-time transfer function using physical laws. Subsequently, a step entry signal was applied and the data for the identification process was recorded in discrete time. The estimation of parameters was performed with the method of decreasing gradient and least squares. It was obtained as a result that the least squares method could not find a model for the first-order high-pass filter, but the decreasing grade method allowed to model all the proposed systems.

Forecasting maximum and minimum temperature over airports with special relevance to aviation in flight planning

Forecasting of maximum temperature and minimum temperature for aviation and non-aviation purpose has been attempted through auto regression and by employing the method of adaptive filtering and Kalman filtering during the hot weather season (March to May) over Madras. The filtering techniques have been outlined and the results are compared with the method of climatology and persistence. The Kalman filter using the model output of adaptive filtering. forecasts well the day-to-day variability of maximum and minimum temperature during hot weather season over Madras with an efficiency close to 90%. As the model performs reasonably well over Madras. a coastal station. the same has been tried over Trichy (300 km southwest of Madras), an inland airport station in Tamilnadu to ascertain its efficacy. The efficiency is better than 90% in predicting maximum and minimum temperature within an accuracy of 2°C).

A method for forecasting visibility at Hindon

At present the approach to forecasting visibility is synoptic and personal experience of the weather forecaster. The month of December typically a winter month, is associated with poor visibility. Aviators require visibility forecast in terms of a definite quantitative value at a specific place in specific time frame. Therefore, in this study an attempt is made to develop a suitable model for forecasting visibility in December at a place Hindon near Delhi in a quantitative manner. In the development process of forecasting visibility, different approaches such as auto-regression, multiple regression, climatology and persistence have been attempted. The models are developed using seven years (1984-90) data of December. The model is evaluated with the independent data sets from the recent years 1994-95. It is found that climatology-persistence method provides better results as compared to the multiple regression and auto-regression methods. The developed model provided positive skill scores as high as 70% on development as well as independent data sets.

Regional Differential Decomposition and Formation Mechanism of Dynamic Carbon Emission Efficiency of China’s Logistics Industry

Among China’s five major industries, the logistics industry is the only one in which carbon emission intensity is continuing to increase, so it is of great importance in developing a low-carbon economy for China. Thus, some scholars have learned about carbon emission efficiency (CEE) in logistic industry recently; however, few of them have considered the inner structure, regional differentiation, or dynamic items of CEE. To fill this gap, we first calculate the dynamic carbon emission efficiency of China’s logistics industry (CEELI) (2001–2017) using the three-stage DEA-Malmquist model, and then using the Dagum Gini coefficient method, the Kernel Density Estimation (KDE), and the panel vector auto-regression (PVAR) model to analyze regional differential decomposition and their formation mechanism. The results indicate that the dynamic CEELI is ‘inefficient’ overall; it shows a decreasing trend, and the decline of dynamic efficiency mainly comes from technical backwardness rather than efficiency decline. Moreover, the domestic differences are gradually narrowing; the Gini inequality between regions and the density of trans-variation between regions are the main reasons for the gap between different regions and different periods.

The determinants of demand for tourism in Turkey: Does terror-threat matter? A Markov Regime Switching-VAR approach

This paper investigates the impacts of potential determinants of demand for tourism in Turkey through Markov Regime Switching-Vector Auto Regression (MS-VAR) estimations from 1999 to 2017 on monthly data. The determinants are income level, exchange rates and the threat of terror incidences. The terror variable, following the Global Terrorism Index (GTI) 2017 report, is calculated for Turkey by the author. This research has conducted two separate MS-VAR models to observe the relevant parameters’ signs of the demand for tourism function. Both MS-VAR models revealed that income level and exchange rates have positive influences on tourism while the terror threat has a negative impact on tourism in Turkey. Terror adversely affects the demand for tourism in the short-term in which terror has occurred in the nearest past (i.e., a month ago). The MS-VAR models also yield that a similar negative impact of terror on tourism activities does not appear over the longer periods.

Education Budget Through Central or Local Government Spending: Which Is More Effective in Improving the Quality of Human Life?

Regional autonomy demands a division of authority between the Center and the regions, which in turn has an impact on budgeting policies. On the one hand, central government spending is oriented towards equity, but on the other hand, the regions understand very well their respective characteristics. The government's budget is always results-oriented, so this research can later be used as a benchmark in planning budgeting. In terms of spending on Education in Indonesia, the budget is channeled through central government spending and local government spending. This research is structured to see between the Central Government or Local Government, more significant in accelerating human quality (IPM) in Indonesia. This study uses Vector Auto Regression with Bayesian Vector Auto Regression model specifications to determine the effect between the variables studied. The variables used in this study are the Central Government Expenditure budget, Regional Government Expenditure on Education through Transfers from the Center to the Regions, Adjusted Per Capita Expenditure, and the Human Development Index from 2007 – 2020. The estimation results show a tendency for local government spending to be more able to increase Human Development Index compared to the Education budget through central government spending. This finding indicates that in the end, the results of decentralization, one of which is the delegation of authority for local government spending, can accelerate the human development index higher than the expenditure issued by the central government.