Ishihara and Yamaguchi 12 obtained extreme wind speeds over complex terrain with the typhoon simulation method and measure-correlate-predict method. Later, Vickery and Twisdale 10 obtained typhoon extreme wind speeds under different return periods along the typhoon-prone coastline of the United States by incorporating its developed typhoon model and filling model into typhoon simulation method. But different typhoon models developed by themselves were used in these studies. 6 also predicted extreme wind speeds with the typhoon simulation method. This method was first proposed by Russell 9 and applied to the prediction of extreme wind speeds in Texas coast. Finally, extreme wind speed analysis is used to obtain the extreme wind speeds under different return periods in the specific areas. Then, a series of typhoon key parameters are generated by Monte Carlo simulation method, and these typhoon key parameters are substituted into the typhoon model for a series of typhoon simulation. Within this method, historical typhoon wind data is first used directly or indirectly to determine the probability distributions of typhoon key parameters. The typhoon simulation method has been developed gradually and widely used to predict typhoon extreme wind speed 5, 6, 7, 8, 9, 10. In past years, many researchers have carried out a series of studies on the prediction of typhoon extreme wind speed. Thus, to ensure the safety of wind -sensitive structures in these areas, it is essential to determine extreme wind speeds of structures for a given return period which can provide support for the structure design and safety assessment 3, 4. The southeast coastal region of China is the typhoon-prone region of the Northwest Pacific area 2. Typhoon is the most often extreme weather on earth and gives a massive threat to the safety of wind-sensitive structures, such as tall buildings, long-span bridges and other unique buildings 1. Compared with the traditional typhoon simulation method, the improved typhoon simulation method has higher accuracy in predicting the typhoon extreme wind speed in Hong Kong, increasing by about 8% and 11% respectively at 200 m height and gradient height. The results show that the improved typhoon simulation method can generate the correlations among all typhoon key parameters satisfactorily. The results show that the correlation coefficients among typhoon key parameters can be maintained satisfactorily with this improved typhoon simulation method. To validate this method, two aspects of analysis is carried out, including correlation analysis among typhoon key parameters and prediction of extreme wind speeds under various return periods. Then, this method is applied to the prediction of extreme wind speeds under various return periods in Hong Kong. In this paper, the improved typhoon simulation method is first given a detailed introduction. A comparison below shows how each of three looks like in the 2-dimension data space.In order to further improve the prediction accuracy of typhoon simulation method for extreme wind speed in typhoon prone areas, an improved typhoon simulation method is proposed by introducing the Latin hypercube sampling method into the traditional typhoon simulation method. On the other hand, LHS covers the data space more evenly in a way similar to the Quasi Random, such as Sobol Sequence. LHS is similar to the Uniform Random in the sense that the Uniform Random number is drawn within each equal-space interval. For the N-dimension LHS with N > 1, we just need to independently repeat the 1-dimension LHS for N times and then randomly combine these sequences into a list of N-tuples. We first partition the whole data space into 10 equal intervals and then randomly select a data point from each interval. Let’s assume that we’d like to perform LHS for 10 data points in the 1-dimension data space. Latin Hypercube Sampling (LHS) is another interesting way to generate near-random sequences with a very simple idea. In my previous post, I’ve shown the difference between the uniform pseudo random and the quasi random number generators in the hyper-parameter optimization of machine learning.
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