Abstract
In this article, we analyze the real meteorological data recorded by Wenzhou Meteorological Bureau from 1951 to 1997. The data has not been used elsewhere and is available at Meteorological Station Wenzhou (ID: CHM00058659) at https://geographic.org/global_weather/china. We perform the time series volatility analysis including ARMA, ARIMA, ARCH-LM, PARCH, SARMA and Morlet wavelet analysis and use the Mann-Kendall (M-K) test to analyze both the trend and mutation defined by statistics sequence. In addition, a Morete wavelet time-frequency model is established to show that both the precipitation and temperature have a very important 12-month cycle and the precipitation is also very unstable. We then employ the STL, coif1 decompositions and NAR model to capture both the volatility and Heteroscedasticity in the data. In addition, the performance of the fitted model has been proven to be satisfactory on actual climate data with the small Mean Square Error (MSE), Root-Mean-Squarred Error (RMSE), and coefficient of determination. Finally, monthly average temperature is added as an exogenous (covariate) variable and a nonlinear autoregressive exogenous model is employed to improve the performance of the model. Our results show that the performance of NARX model is more accurate and stable with better mean square error.
Highlights
Climate change is an important environmental, social and economic issue
We proposed using a nonlinear autoregressive exogenous (NARX) neural network model to convert the input into two neurons so that TAVG can be added in the model
We investigate the characteristics of cyclical and variables of climate change based on the real datasets of monthly precipitation and temperature recorded in East China Wenzhou meteorological station
Summary
Climate change is an important environmental, social and economic issue. It threatens the achievement of Millennium development goals aimed at poverty and hunger reduction, health improvement and environmental sustainability. Significant improvements in information recovery and processing analysis techniques have led to longer temperature records based on more accurate estimates and better uncertainty assessments (Hansen et al, 2010) Due to their importance, many forecast models for weather forecasting have been developed. 1-2 show the time series of featuring variables, namely PRCP, TAVG, TMAX and TMIN, in the scale of Month. In order to make the comparisons of featuring variables of TAVG and PRCP in time series analysis, we select the top 10 years of values in July and last 10 years of values in January from TAVG, while for PRCP, we select the June data of the past 10 years and the January data of the last 10 years to facilitate the model processing.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
