scholarly journals A Spliced Gamma-Generalized Pareto Model for Short-Term Extreme Wind Speed Probabilistic Forecasting

2019 ◽  
Vol 24 (3) ◽  
pp. 517-534 ◽  
Author(s):  
Daniela Castro-Camilo ◽  
Raphaël Huser ◽  
Håvard Rue
Keyword(s):  
Wind Speed ◽  
Probabilistic Forecasting ◽  
Short Term ◽  
Generalized Pareto ◽  
Pareto Model ◽  
Extreme Wind ◽  
Extreme Wind Speed

scholarly journals Time Series Analysis on Incidence of Pulmonary Tuberculosis With Weather Factors During 2004-2017 in Guangdong Province, China

2021 ◽  
Author(s):  
Tianyu Qin ◽  
Yu Hao ◽  
Juan He
Keyword(s):  
Wind Speed ◽  
Atmospheric Pressure ◽  
Guangdong Province ◽  
Maximum Temperature ◽  
Maximum Pressure ◽  
Weather Factors ◽  
Sarima Model ◽  
Extreme Wind ◽  
Wind Speed Maximum ◽  
Extreme Wind Speed

Abstract Background: Although the occurrence of some infectious diseases including TB was found to be associated with specific weather factors, few studies have incorporated weather factors into the model to predict the incidence of tuberculosis (TB). We aimed to establish an accurate forecasting model using TB data in Guangdong Province, incorporating local weather factors.Methods: Data of sixteen meteorological variables (2003-2016) and the TB incidence data (2004-2016) of Guangdong were collected. Seasonal autoregressive integrated moving average (SARIMA) model was constructed based on the data. SARIMA model with weather factors as explanatory variables (SARIMAX) was performed to fit and predict TB incidence in 2017. Results: Maximum temperature, maximum daily rainfall, minimum relative humidity, mean vapor pressure, extreme wind speed, maximum atmospheric pressure, mean atmospheric pressure and illumination duration were significantly associated with log(TB incidence). After fitting the SARIMAX model, maximum pressure at lag 6 (β= -0.007, P < 0.05, 95% confidence interval (CI): -0.011, -0.002, mean square error (MSE): 0.279) was negatively associated with log(TB incidence), while extreme wind speed at lag 5 (β=0.009, P < 0.05, 95% CI: 0.005, 0.013, MSE: 0.143) was positively associated. SARIMAX (1, 1, 1) (0, 1, 1)12 with extreme wind speed at lag 5 was the best predictive model with lower Akaike information criterion (AIC) and MSE. The predicted monthly TB incidence all fall within the confidence intervals using this model. Conclusions: Weather factors have different effects on TB incidence in Guangdong. Incorporating meteorological factors into the model increased the accuracy of prediction.

Research on Probability Distribution of Extreme Wind Speed in Maize Growth Period

2012 ◽  
Vol 10 (1) ◽  
pp. 535-540 ◽  
Author(s):  
Chunqiao Mi ◽  
Dehai Zhu ◽  
Bernard A. Engel ◽  
Shaoming Li ◽  
Xiaodong Zhang ◽  
...  
Keyword(s):  
Wind Speed ◽  
Probability Distribution ◽  
Growth Period ◽  
Maize Growth ◽  
Extreme Wind ◽  
Extreme Wind Speed
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