ARMA model development and analysis for global temperature uncertainty
Temperature uncertainty models for land and sea surfaces can be developed based on statistical methods. In this paper, we developed a novel time-series temperature uncertainty model, which is the autoregressive moving average (ARMA) (1,1) model. The model was developed for an observed annual mean te...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-04-01
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| Series: | Frontiers in Astronomy and Space Sciences |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fspas.2023.1098345/full |
| _version_ | 1797852910788280320 |
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| author | Mahmud Hasan Gauree Wathodkar Mathias Muia |
| author_facet | Mahmud Hasan Gauree Wathodkar Mathias Muia |
| author_sort | Mahmud Hasan |
| collection | DOAJ |
| description | Temperature uncertainty models for land and sea surfaces can be developed based on statistical methods. In this paper, we developed a novel time-series temperature uncertainty model, which is the autoregressive moving average (ARMA) (1,1) model. The model was developed for an observed annual mean temperature anomaly X(t), which is a combination of a true (latent) global anomaly Y(t) for a year (t) and normal variable w(t). The uncertainty is taken as the variance of w(t), which was divided into land surface temperature (LST) uncertainty, sea surface temperature (SST) uncertainty, and the corresponding source of uncertainty. The ARMA model was analyzed and compared with autoregressive (AR) and autoregressive integrated moving average (ARIMA) for the data obtained from the NASA Goddard Institute for Space Studies Surface Temperature (GISTEMP) Analysis. The statistical analysis of the autocorrelation function (ACF), partial autocorrelation function (PACF), normal quantile–quantile (normal Q-Q) plot, density of the residuals, and variance of normal variable w(t) shows that ARMA (1,1) fits better than AR (1) and ARIMA (1, d, 1) for d = 1, 2. |
| first_indexed | 2024-04-09T19:41:59Z |
| format | Article |
| id | doaj.art-53aae02e934f4386b95a8c89787ab651 |
| institution | Directory Open Access Journal |
| issn | 2296-987X |
| language | English |
| last_indexed | 2024-04-09T19:41:59Z |
| publishDate | 2023-04-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Astronomy and Space Sciences |
| spelling | doaj.art-53aae02e934f4386b95a8c89787ab6512023-04-04T05:08:07ZengFrontiers Media S.A.Frontiers in Astronomy and Space Sciences2296-987X2023-04-011010.3389/fspas.2023.10983451098345ARMA model development and analysis for global temperature uncertaintyMahmud HasanGauree WathodkarMathias MuiaTemperature uncertainty models for land and sea surfaces can be developed based on statistical methods. In this paper, we developed a novel time-series temperature uncertainty model, which is the autoregressive moving average (ARMA) (1,1) model. The model was developed for an observed annual mean temperature anomaly X(t), which is a combination of a true (latent) global anomaly Y(t) for a year (t) and normal variable w(t). The uncertainty is taken as the variance of w(t), which was divided into land surface temperature (LST) uncertainty, sea surface temperature (SST) uncertainty, and the corresponding source of uncertainty. The ARMA model was analyzed and compared with autoregressive (AR) and autoregressive integrated moving average (ARIMA) for the data obtained from the NASA Goddard Institute for Space Studies Surface Temperature (GISTEMP) Analysis. The statistical analysis of the autocorrelation function (ACF), partial autocorrelation function (PACF), normal quantile–quantile (normal Q-Q) plot, density of the residuals, and variance of normal variable w(t) shows that ARMA (1,1) fits better than AR (1) and ARIMA (1, d, 1) for d = 1, 2.https://www.frontiersin.org/articles/10.3389/fspas.2023.1098345/fullARMAARGISTEMPLSTSSTACF |
| spellingShingle | Mahmud Hasan Gauree Wathodkar Mathias Muia ARMA model development and analysis for global temperature uncertainty Frontiers in Astronomy and Space Sciences ARMA AR GISTEMP LST SST ACF |
| title | ARMA model development and analysis for global temperature uncertainty |
| title_full | ARMA model development and analysis for global temperature uncertainty |
| title_fullStr | ARMA model development and analysis for global temperature uncertainty |
| title_full_unstemmed | ARMA model development and analysis for global temperature uncertainty |
| title_short | ARMA model development and analysis for global temperature uncertainty |
| title_sort | arma model development and analysis for global temperature uncertainty |
| topic | ARMA AR GISTEMP LST SST ACF |
| url | https://www.frontiersin.org/articles/10.3389/fspas.2023.1098345/full |
| work_keys_str_mv | AT mahmudhasan armamodeldevelopmentandanalysisforglobaltemperatureuncertainty AT gaureewathodkar armamodeldevelopmentandanalysisforglobaltemperatureuncertainty AT mathiasmuia armamodeldevelopmentandanalysisforglobaltemperatureuncertainty |