Wyniki wyszukiwania - BazTech

Ograniczanie wyników

Powiadomienia systemowe

Sesja wygasła!
Sesja wygasła!
Sesja wygasła!
Sesja wygasła!

Znaleziono wyników: 2

Liczba wyników na stronie

Wyniki wyszukiwania

Wyszukiwano:
w słowach kluczowych: Sen’s slope estimator

Sortuj według:

Ogranicz wyniki do:

Trend analysis and SARIMA forecasting of mean maximum and mean minimum monthly temperature for the state of Kerala, India

Kabbilawsh P., Sathish Kumar D., Chithra N. R.

Acta Geophysica

2020

Vol. 68, no. 4

1161--1174

The development of temperature forecasting models for the state of Kerala using Seasonal Autoregressive Integrated Moving Average (SARIMA) method is presented in this article. Mean maximum and mean minimum monthly temperature data, for a period of 47 years, from seven stations, are studied and applied to develop the model. It is expected that the time-series datasets of temperature to display seasonality (and hence non-stationary), and a possible trend (due to the fact that the data spans 5 decades). Hence, the key step in the development of the models is the determination of the non-stationarity of the temperature time-series, and the transformation of the non-stationary time-series into a stationary time-series. This is carried out using the Seasonal and Trend decomposition using Loess technique and Kwiatkowski–Phillips–Schmidt–Shin test. Before carrying out this process, several preliminary tests are conducted for (1) fnding and flling the missing values, (2) studying the characteristics of the data, and (3) investigating the presence of the trend and seasonality. The non-stationary temperature time-series are transformed to stationary temperature time-series, by one seasonal diferencing and one frstorder diferencing. This information, along with the original time-series, is further utilized to develop the models using the SARIMA method. The parsimonious and best-ft SARIMA models are developed for each of the fourteen variables. The study revealed that SARIMA(2, 1, 1)(1, 1, 1)12 as the ideal forecasting model for eight out of the fourteen time-series datasets.

On the long-term variability of temperature trends and changes in surface air temperature in Kolkata Weather Observatory, West Bengal, India

Khan A., Chatterjee S., Bisai D.

Meteorology Hydrology and Water Management. Research and Operational Applications

2015

Vol. 3, Iss. 2

9--16

The impact of climate change on annual air temperature has received a great deal of attention from climatologists worldwide. Many studies have been conducted to illustrate that changes in temperature are becoming evident on a global scale. Air temperature, one of the most important components of climate parameters, has been widely measured as a starting point towards the apprehension of climate change and variability. The main objective of this study is to analyse the temporal variability of mean monthly temperature for the period of 1941 to 2010 (70 years). To detect the magnitude of trend in mean monthly temperature time series, we have used non-parametric test methods such as The Mann-Kendall test, often combined with the Theil-Sen’s robust estimate of linear trend. Whatever test is used, the user should understand the underlying assumptions of both the technique used to generate the estimates of a trend and the statistical methods used for testing. The results of this analysis reveal that four months – January, February, March and December – indicate a decreasing trend in average temperature, while the remaining eight months have an increasing trend. The magnitude of Mann-Kendall trend statistic Zc for this declining temperature and the magnitude of slope for the months of January, February and December are confirmed at the high significance levels of α = 0.001, 0.01 and 0.1 respectively. Though, the overall trend is positive for monthly as well as seasonally efficient time series.