Application of AIRS Soundings to Afternoon Convection Forecasting and Nowcasting at Airports

In Taiwan, the frequency of afternoon convection increases in summer (July and August), and the peak hour of afternoon convection occurs at 1500–1600 local solar time (LST). Afternoon convection events are forecasted based on the atmospheric stability index, as computed from the 0800 LST radiosonde data. However, the temporal and spatial resolution and forecast precision are not satisfactory. This study used the observation data of Aqua satellite overpass near Taiwan around 1–3 h before the occurrence of afternoon convection. Its advantages are that it improves the prediction accuracy and increases the data coverage area, which means that more airports can use results of this research, especially those without radiosondes. In order to determine the availability of Atmospheric Infrared Sounder (AIRS) in Taiwan, 2010–2016 AIRS and radiosonde-sounding data were used to determine the accuracy of AIRS. This study also used 2017–2018 AIRS data to establish K index (KI) and total precipitable water (TPW) thresholds for the occurrence of afternoon convection of four airports in Taiwan. Finally, the KI and TPW were calculated using the independent AIRS atmospheric sounding (2019–2020) to forecast the occurrence of afternoon convection at each airport. The average predictive accuracy rate of the four airports is 84%. Case studies at Hualien Airport show the average predictive accuracy rate of this study is 81.8%, which is 9.1% higher than that of the traditional sounding forecast (72.7%) during the same period. Research results show that using AIRS data to predict afternoon convection in this study could not only increase data coverage area but also improve the accuracy of the prediction effectively.

Image recognition based on deep learning in Haemonchus contortus motility assays

Poor efficacy of some anthelmintics and rising concerns about the widespread drug resistance have highlighted the need for new drug discovery. The parasitic nematode Haemonchus contortus is an important model organism widely used for studies of drug resistance and drug screening with the current gold standard being the motility assay. We applied a deep learning approach Mask R-CNN for analysing motility videos and compared it to other commonly used algorithms with different levels of complexity, namely Wiggle Index and Wide Field-of-View Nematode Tracking Platform. Mask R-CNN consistently outperformed the other algorithms in terms of the forecast precision across the videos containing varying rates of motile worms with a mean absolute error of 5.6%. Using Mask R-CNN for motility assays confirmed the common problem of algorithms that use Non-Maximum Suppression in detecting overlapping objects, which negatively impacted the overall precision. The use of intersect over union (IoU) as a measure of the classification of motile / non-motile instances had an overall accuracy of 89%. In comparison to the existing methods evaluated here, Mask R-CNN performed better and we can anticipate that this method will broaden the number of possible approaches to video analysis of worm motility. IoU has shown promise as a good metric for evaluating motility of individual worms.

Precision of determination of thawing depth of the frozen rocks

An important quantity determining the choice of technical solutions in design of both surface and underground structures in the permafrost area is the thawing depth of the rocks. To obtain simple analytical relations to determine the thawing depth over time, a simple assumption is used: that the initial temperature of the rocks is equal to the melting temperature of ice. The aim of the present work was the assessment of impact of this assumption on the forecast precision. For a quantitative assessment, a simple typical formula recommended by construction norms was used. Functional dependence of the density of the rocks and their heat capacity on the fraction of ice content was considered in the formulas. A rock consisting of a combination of quartz sand and ice was used as an example.Multiple variant calculations were done according to the formulas and their results presented in the form of charts. It was shown that the relative error in determination of thawing depth depends solely on the Stefan criterion and is independent of the thawing duration, thermal conductivity coefficient of the thawing rocks and the air temperature during the thawing. A relation was obtained which allows to quickly assess at which initial values (temperature and ice content of the frozen rocks) it is possible to use the formulas obtained from the simplified calculation models with the assumption that the temperature of the rocks is equal to the melting temperature of ice.

Improved of Forecasting Sea Surface Temperature based on Hybrid ARIMA and Support Vector Machines Models

Forecasting is a very effortful task owing to its features which simultaneously contain linear and nonlinear patterns. The Autoregressive Integrated Moving Average (ARIMA) model has been one the most widely utilized linear model in time series forecasting. Unfortunately, the ARIMA model cannot effortlessly handle nonlinear patterns alone. Thus, Support Vector Machine (SVM) model is introduced to solve nonlinear behavior in the datasets with high variance and uncertainty. The purposes of this study are twofold. First, to propose a hybrid ARIMA models using SVM. Secondly, to test the effectiveness of the proposed hybrid model using sea surface temperature (SST) data. Our investigation is based on two well-known real datasets, i.e., SST (modis) and in-situ SST (hycom). Statistical measurement such as MAE, MAPE, MSE, and RMSE are carried out to investigate the efficacy of the proposed models as compared to the previous ARIMA and SVMs models. The empirical results reveal that the proposed models produce lesser MAE, MAPE, MSE, and RMSE values in comparison to the single ARIMA and SVMs models. In additional, ARIMA-SVM are much better than compared to the existing models since the forecasting values are closer to the actual value. Therefore, we conclude that the presented models can be used to generate superior predicting values in time series forecasting with a way higher forecast precision.

Comparison between two generalized Nash models with a same non-zero initial condition

Initial condition can impact the forecast precision especially in a real-time forecasting stage. The discrete linear cascade model (DLCM) and the generalized Nash model (GNM), though expressed in different ways, are both the generalization of the Nash cascade model considering the initial condition. This paper investigates the relationship and difference between DLCM and GNM both mathematically and experimentally. Mathematically, the main difference lies in the way to estimate the initial storage state. In the DLCM, the initial state is estimated and not unique, while that in the GNM is observed and unique. Hence, the GNM is the exact solution of the Nash cascade model, while the DLCM is an approximate solution and it can be transformed to the GNM when the initial storage state is calculated by the approach suggested in the GNM. As a discrete solution, the DLCM can be directly applied to the practical discrete streamflow data system. However, the numerical calculation approach such as the finite difference method is often used to make the GNM practically applicable. At last, a test example obtained by the solution of the Saint-Venant equations is used to illustrate this difference. The results show that the GNM provides a unique solution while the DLCM has multiple solutions, whose forecast precision depends upon the estimate accuracy of the current state.

A new Xin'anjiang and Sacramento combined rainfall-runoff model and its application

The Xin'anjiang model and the Sacramento model are two widely used short-term watershed rainfall-runoff forecasting models, each with their own unique model structure, strengths, weaknesses and applicability. This paper introduces a weight factor to integrate the two models into a combined model, and uses the cyclic coordinate method to calibrate the weight factor and the parameters of the two models to explore the possibility of the complementarity between the two models. With application to the Yuxiakou watershed in Qingjiang River, it is verified that the cyclic coordinate method, although simple, can converge rapidly to a satisfactory calibration accuracy, mostly after two iterations. Also, the results in case studies show that the forecast accuracy of the new combined rainfall-runoff model can improve the forecast precision by 4.3% in a testing period, better in runoff process fitting than the Xin'anjiang model that performs better than the Sacramento model. HIGHLIGHT This paper introduces a weight factor to integrate the two models into a combined model, and uses the cyclic coordinate method to calibrate the weight factor. it is verified that the cyclic coordinate method can converge fast to a satisfactory calibration accuracy. The results show that the forecast accuracy of the new combined rainfall-runoff model can improve the forecast precision.

ABC and HML-methods application for determination and optimization of stock for electrical equipment accessory parts

THE PURPOSE. To describe the use of ABC and HML-methods for predicting the volume of emergency stock for main electrical equipment accessory parts. To describe the content of the methods, consider the problems and complexity of use. To propose an algorithm constructing a new method for forming an emergency stock based on the statistical data of an electric power company. METHODS. Various practical tasks can arise in electric power company. We have determined the most effective method for solving them using a numerical experiment. The highest efficiency of the ABC method is shown. The results of statistical processing will help to improve forecast precision using the ABC-method. RESULTS. The complex of statistical data and ABC-analysis showed high efficiency in short-term forecasting of electrical equipment components emergency stocks. Authors found that HML-analysis provides less precision in predicting the requirement of equipment. CONCLUSION. The ABC method is the most promising for implementation the specific task. The use of the method helps to accurately predict emergency stocks of company electrical equipment. The HML-method does not allow to precision planing the amount of required equipment.

ABC and HML-methods application for determination and optimization of stock for electrical equipment accessory parts

THE PURPOSE. To describe the use of ABC and HML-methods for predicting the volume of emergency stock for main electrical equipment accessory parts. To describe the content of the methods, consider the problems and complexity of use. To propose an algorithm constructing a new method for forming an emergency stock based on the statistical data of an electric power company. METHODS. Various practical tasks can arise in electric power company. We have determined the most effective method for solving them using a numerical experiment. The highest efficiency of the ABC method is shown. The results of statistical processing will help to improve forecast precision using the ABC-method. RESULTS. The complex of statistical data and ABC-analysis showed high efficiency in short-term forecasting of electrical equipment components emergency stocks. Authors found that HML-analysis provides less precision in predicting the requirement of equipment. CONCLUSION. The ABC method is the most promising for implementation the specific task. The use of the method helps to accurately predict emergency stocks of company electrical equipment. The HML-method does not allow to precision planing the amount of required equipment.

The information content of managerial insider trading: evidence from analyst forecasts

PurposeThis paper examines the effect of managerial insider trading on analyst forecast accuracy, dispersion and bias. Specifically, the authors test whether insider-trading information is positively associated with the precision of earnings forecasts. In addition, this relationship between Regulation Fair Disclosure (FD) and the Galleon insider trading case is examined.Design/methodology/approachPooled ordinary least squares (Pooled OLS) rregressions with year-fixed effects, firm-fixed effects, and firm-level clustered standard errors are used. Our proxies for forecast precision are regressed on alternative measures of insider trading activities and a vector of control variables.FindingsInsider-trading information is positively associated with the precision of earnings forecasts. Analysts provide better forecast accuracy, less forecast dispersion and lower forecast bias among firms with insider trading in the six months leading to the forecast issues. In addition, bullish (bearish) insider trades are associated with increased (decreased) forecast bias. Insider trading information complements analysts' independent opinion and increases the precision of their forecast.Practical implicationsRegulators may pursue rules that promote the rapid disclosure of managerial insider trades, particularly given the increasing availability of Internet tools. Securities regulators may attempt to increase transparency and enhance the reporting procedures of corporate insiders, for example, using Internet sources with direct release to the public to ensure more timely information dissemination.Originality/valueThe authors document a positive association between earnings forecast precision and managerial insider trading up to six months prior to the forecast issue. This relationship is stronger after the Securities and Exchange Commission (SEC) prohibited the selective disclosure of material nonpublic information through Regulation FD. In addition, the association between insider trading and forecast accuracy has weakened after the Galleon insider trading case.