Some Features of Surface Air Temperature: A Statistical Viewpoint

Dutta, Partha Nath; Karlo, T.; Dutta, Pallabi

doi:10.13189/eer.2017.050506

Cited by 7 publications

(3 citation statements)

References 10 publications

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“…Cluster 5, cluster 6, cluster 3, cluster 1, and cluster 4 have lower RMSE and MAE values than cluster 2, cluster 6, cluster 3, cluster 1, and cluster 4. In comparison to the other clusters, this shows that QM provides reliable results for cluster 5 [48].…”

Section: Resultsmentioning

confidence: 73%

Projections of Cardiovascular Disease Mortality in Peninsular Malaysia Using Statistical Downscaling Based on Cluster Approach

Esa¹,

Halim²,

Ali³

2021

eer

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Projecting the mortality of cardiovascular disease in future is crucial in preparing the mitigation strategies. The purpose of this research is to estimate number of deaths of the cardiovascular disease in Peninsular Malaysia based on future temperature projections using the cluster approach. Ward's method is used to identify the number of clusters of 45 meteorological stations by calculating the shortest distance between the two coordinates of the stations. The output of global climate model (GCM) is incredibly useful for the projection of future temperature, but the large bias in the observational datasets may lead to inaccurate projection. To tackle the bias, a good fitted model for temperature series is important in order to ensure that the mean and variability of the observed series are well captured. It is important to estimate the parameters for each cluster precisely. Furthermore, a good fitted model for temperature series is also crucial in order to ensure that the mean and variability of the observations are well captured. Thus, this study proposed the appropriate statistical distribution for the temperature series to be associated in the bias correction method (BCM) using the quantile mapping (QM) technique to reduce the biases between observations and historical GCM temperature data series. Next, Ward's method is applied to determine the optimal number of clusters for Peninsular Malaysia. The results have shown that the proposed model is able to reduce the temperature series biases between the GCM and the observations. Six clusters throughout Peninsular Malaysia have been selected based on Ward's method. The projection number of deaths of cardiovascular disease under is estimated to increase between 2006 and 2100 in all clusters across Peninsular Malaysia, based on the temperature projections.

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Section: Resultsmentioning

confidence: 73%

Projections of Cardiovascular Disease Mortality in Peninsular Malaysia Using Statistical Downscaling Based on Cluster Approach

Esa¹,

Halim²,

Ali³

2021

eer

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show abstract

“…The monthly behavior of T ave,m , T max,m and T min,m showed that the values were higher in April and May and lower in winter. This is probably due to the minimization of topographic factors during the winter months, the short duration of the day, and the lowest angle at which the sun's rays strike the earth's surface (Dutta et al, 2017). In the agricultural area, the inter-monthly changes of T ave,m , T min,m , T SD,m and T Ra,m , were more significant than in the forest and the urban area.…”

Section: Monthly Air Temperaturementioning

confidence: 93%

Air temperature perturbation in La Malinche volcano area, Tlaxcala, Mexican Highland

Muñoz Nava,

Chamizo Checa,

Suárez Sánchez

et al. 2024

Geofis Int

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Evaluating air temperature perturbation is important to know the anthropic activity's effect on the environmental system. The study case was La Malinche volcano, concerning the urban, agricultural, and forest environments. The air temperature data (average, maximum, minimum, standard deviation and range), was analyzed by prin- cipal components analysis (PCA), and the Kruskal-Wallis (K-W) test. Data analyses were made on a diurnal (warming and cooling rates), daily and monthly basis. The K-W test showed that warming and cooling rates are significantly different between the agricultural, urban, and forest zones, despite the north and south sides of La Malinche volcano had significant differences. The PCA indicated more perturbation concerning the cooling rates of air temperature among the environments than the warming rates. The average, maximum, and minimum air temperature of the urban environment and the standard deviation and range of the agricultural environment were the highest. The minimum air temperature changes more than the maximum in the volcano's urban, agricultural, and forest south side. The K-W test showed that the environmental conditions differed sig- nificantly based on average and maximum. The daily air temperature on the north side of La Malinche Volcano was very different from the south side. The PCA with average, maximum, minimum, standard deviation, and range showed that the environments are modified. The average monthly air temperature in the agricultural and forest areas was lower than average. Minimum air temperature increase was more accentuated in urban areas than in agriculture and forest areas and increased more than maximum.

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“…On the other hand, research by Hashim (1) detected that urbanization, industrialization and house development contributed to the increasing of temperature. Next, study by Dutta et al (28) reveals that location of observation also is one of the factor that can influence the temperature.…”

Section: Factors Influence Temperature Time Seriesmentioning

confidence: 99%

Chaos Theory Modelling for Temperature Time Series at Malaysian High Population Area during Dry Season

Hamid¹,

Adenan²,

Wahid³

et al. 2021

eer

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The aim of this study is to model the temperature time series at Malaysian high population area during dry season through chaos theory. The selected high population area is Shah Alam located in Selangor state of Malaysia. Chaos theory modelling is categorized into two parts namely analysis and prediction. Analysis by the phase space plot showed that the nature of the observed temperature time series is chaos. Hence, the time series is predicted via the chaotic model. Results from the chaotic model showed that the temperature time series is well predicted with Pearson correlation coefficient near to 1. The result is compared with the traditional method of autoregressive linear model. Based on the computed values of average absolute error, root mean squared error and Pearson correlation coefficient, the chaotic model is found better in predicting temperature time series at Shah Alam area during dry season. This indicates that the chaos theory is applicable for temperature time series at Malaysian high population area. This finding is expected to facilitate stakeholders such as Malaysian Meteorological Department and Department of Environment Malaysia in managing temperature and climate change problem.

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Some Features of Surface Air Temperature: A Statistical Viewpoint

Cited by 7 publications

References 10 publications

Projections of Cardiovascular Disease Mortality in Peninsular Malaysia Using Statistical Downscaling Based on Cluster Approach

Projections of Cardiovascular Disease Mortality in Peninsular Malaysia Using Statistical Downscaling Based on Cluster Approach

Air temperature perturbation in La Malinche volcano area, Tlaxcala, Mexican Highland

Chaos Theory Modelling for Temperature Time Series at Malaysian High Population Area during Dry Season

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