scholarly journals Mudança no Regime Temporal da Temperatura do Ar e Precipitação Pluviométrica na Região de Rio Largo, Alagoas (Change in the Temporal Regime of Air Temperature and Rainfall in the Region of Rio Largo, Alagoas, Brazil)

2014 ◽  
Vol 7 (4) ◽  
pp. 662
Author(s):  
Henderson Silva Wanderley ◽  
André Luiz de Carvalho ◽  
Ronabson Cardoso Fernandes ◽  
José Leonaldo de Souza
Keyword(s):  
Time Series ◽  
Air Temperature ◽  
Change Point ◽  
Statistical Tests ◽  
Linear Regression Analysis ◽  
Homogeneity Test ◽  
Significance Level ◽  
Standard Normal Homogeneity Test ◽  
Standard Normal ◽  
History Of

Compreender como as alterações no clima têm modificado a temperatura do ar e a precipitação pluvial de uma região é essencial, sobretudo para regiões como o Nordeste brasileiro, que apresentam vasto histórico de secas e altas temperaturas. No entanto, estudos com esse fim são escassos ou até mesmo inexistentes para essa região. Deste modo, objetivou-se identificar mudanças ocorridas no regime temporal da temperatura diurna e noturna e na precipitação na região de Rio Largo, Alagoas. Para isto, utilizaram-se dados de temperatura diurna (máxima) e noturna (mínima) compreendidos entre 1973 e 2002, e de precipitação dispostos entre 1973 e 2008. As séries temporais foram submetidas ao teste estatístico SNHT (Standard Normal Homogeneity Test) para identificar possíveis pontos de mudança na média. A análise de regressão linear simples foi utilizada para identificar alterações nas séries temporais, testada por meio do teste t de Student, adotando-se nível de significância estatística de 0,05%, para ambos os testes estatísticos. A análise mostrou que as temperaturas demostraram pontos de mudanças significativos, no entanto, foi observada uma defasagem de quase dez anos entre os pontos. A tendência identificada entre as temperaturas foram opostas entre si, sendo de aumento para a temperatura diurna e de redução para a noturna. A precipitação demostrou tendência de redução, no entanto, não apresentou mudança estatística significativa.  ABSTRACTUnderstanding how changes in climate have changed air temperature and rainfall in a region is essential, especially for regions such as the Brazilian Northeast, which have long history of drought and high temperatures. However, studies for this purpose are scarce or even nonexistent for this region. Thus, this study aimed to identify changes in the temporal regime of daytime and nighttime temperature and rainfall in the region of Rio Largo, Alagoas, Brazil. For this, it was used data of daytime temperature (maximum) and night (minimum) ranging from 1973 to 2002, and rainfall arranged between 1973 and 2008. Time series were submitted to SNHT (Standard Normal Homogeneity Test) statistical test to identify possible change point in average. A simple linear regression analysis was used to identify changes in time series, tested using the Student t test, adopting a significance level of 0.05%, for both statistical tests. The analysis showed that temperatures demonstrated significant change points, however, there was a gap of almost ten years between the points. The trend identified among the temperatures was opposed to each other, with increasing daytime temperature and reduction of nighttime temperature. Rainfall demonstrated trend of reducing, however, showed no statistically significant change.Keywords: daytime and nighttime temperature, SNHT, trend, change point. 

scholarly journals Statistical analysis of temperature regime change on the example of Sokobanja basin in eastern Serbia

2015 ◽  
Vol 19 (suppl. 2) ◽  
pp. 323-330
Author(s):  
Aleksandar Radivojevic ◽  
Natasa Martic-Bursac ◽  
Milena Gocic ◽  
Ivan Filipovic ◽  
Mila Pavlovic ◽  
...  
Keyword(s):  
Air Temperature ◽  
Statistical Tests ◽  
Local Level ◽  
Homogeneity Test ◽  
Von Neumann ◽  
Early 21St Century ◽  
Scientific Disciplines ◽  
Standard Normal Homogeneity Test ◽  
Standard Normal ◽  
Almost All

The changes and oscillations in air temperature during the second half of the 20th and in the early 21st century, have become one of the major concerns of almost all scientific disciplines. Such changes are noticeable both at the local and global level. The objective of this paper is to point out that the changes of this climate element can also be detected at the local level. The research underlying this paper is, in terms of territory, limited to the Sokobanja basin in Eastern Serbia. The analysis relies on the data on air temperature for the period 1946-2012 taken from the Meteorological weather station located in Sokobanja. The obtained data were processed in line with the recommendations of the World Meteorological Organization (WMO). The evidenced statistically significant changes in air temperature were examined using the following statistical tests: Pettit test, Standard Normal Homogeneity test, Buishand range test, and von Neumann test.

scholarly journals A Novel Approach for the Detection of Inhomogeneities Affecting Climate Time Series

2012 ◽  
Vol 51 (2) ◽  
pp. 317-326 ◽  
Author(s):  
Andrea Toreti ◽  
Franz G. Kuglitsch ◽  
Elena Xoplaki ◽  
Jürg Luterbacher
Keyword(s):  
Time Series ◽  
Markov Models ◽  
Winter Precipitation ◽  
Homogeneity Test ◽  
Temperature And Precipitation ◽  
Novel Approach ◽  
Standard Normal Homogeneity Test ◽  
Standard Normal ◽  
Set Up ◽  
Climate Time Series

AbstractSudden changes caused by nonclimatic factors (inhomogeneities) usually affect instrumental time series of climate variables. To perform robust climate analyses based on observations, a proper identification of such changes is necessary. Here, an approach (named the “GAHMDI” method, after its components and purpose) that is based on a genetic algorithm and hidden Markov models is proposed for detection of inhomogeneities caused by changes in the mean and variance. Simulated series and a case study (winter precipitation from a weather station located in Milan, Italy) are set up to compare GAHMDI with existing methodologies and to highlight its features. For the identification of a single changepoint, GAHMDI performs similarly to other methods (e.g., standard normal homogeneity test). However, for the identification of multiple inhomogeneities and changes in variance, GAHMDI returns better results than three widespread methods by avoiding overdetection. For future applications and research in the homogenization of climate datasets (temperature and precipitation) the use of GAHMDI is encouraged, preferably in combination with another detection procedure (e.g., the method of Caussinus and Mestre) when metadata are not available. Since GAHMDI is developed in the generic context of time series segmentation, it can be applied to series of generic variables—for instance, those related to economics, biology, and informatics.

scholarly journals Penalized Maximal t Test for Detecting Undocumented Mean Change in Climate Data Series

2007 ◽  
Vol 46 (6) ◽  
pp. 916-931 ◽  
Author(s):  
Xiaolan L. Wang ◽  
Qiuzi H. Wen ◽  
Yuehua Wu
Keyword(s):  
Time Series ◽  
Practical Importance ◽  
T Test ◽  
Data Series ◽  
Homogeneity Test ◽  
Climate Data ◽  
Series Length ◽  
Standard Normal Homogeneity Test ◽  
Higher Power ◽  
Standard Normal

Abstract In this paper, a penalized maximal t test (PMT) is proposed for detecting undocumented mean shifts in climate data series. PMT takes the relative position of each candidate changepoint into account, to diminish the effect of unequal sample sizes on the power of detection. Monte Carlo simulation studies are conducted to evaluate the performance of PMT, in comparison with the most popularly used method, the standard normal homogeneity test (SNHT). An application of the two methods to atmospheric pressure series recorded at a Canadian site is also presented. It is shown that the false-alarm rate of PMT is very close to the specified level of significance and is evenly distributed across all candidate changepoints, whereas that of SNHT can be up to 10 times the specified level for points near the ends of series and much lower for the middle points. In comparison with SNHT, therefore, PMT has higher power for detecting all changepoints that are not too close to the ends of series and lower power for detecting changepoints that are near the ends of series. On average, however, PMT has significantly higher power of detection. The smaller the shift magnitude Δ is relative to the noise standard deviation σ, the greater is the improvement of PMT over SNHT. The improvement in hit rate can be as much as 14%–25% for detecting small shifts (Δ < σ) regardless of time series length and up to 5% for detecting medium shifts (Δ = σ–1.5σ) in time series of length N < 100. For all detectable shift sizes, the largest improvement is always obtained when N < 100, which is of great practical importance, because most annual climate data series are of length N < 100.

A note on the use of the standard normal homogeneity test to detect inhomogeneities in climatic time series

2011 ◽  
Vol 31 (4) ◽  
pp. 630-632 ◽  
Author(s):  
A. Toreti ◽  
F. G. Kuglitsch ◽  
E. Xoplaki ◽  
P. M. Della-Marta ◽  
E. Aguilar ◽  
...  
Keyword(s):  
Time Series ◽  
Homogeneity Test ◽  
Standard Normal Homogeneity Test ◽  
Standard Normal ◽  
Climatic Time Series

scholarly journals The Ås Temperature Series in Southern Norway–Homogeneity Testing And Climate Analysis

2014 ◽  
Vol 7 (1) ◽  
pp. 7-26 ◽  
Author(s):  
Herdis M. Gjelten ◽  
Øyvind Nordli ◽  
Arne A. Grimenes ◽  
Elin Lundstad
Keyword(s):  
Time Series ◽  
Temperature Series ◽  
Homogeneity Test ◽  
Linear Temperature ◽  
Homogeneity Testing ◽  
Standard Normal Homogeneity Test ◽  
Standard Normal ◽  
Southern Norway ◽  
Climate Analysis

Abstract Homogeneity is important when analyzing climatic long-term time series. This is to ensure that the variability in the time series is not affected by changes such as station relocations, instrumentation changes and changes in the surroundings. The subject of this study is a long-term temperature series from the Norwegian University of Life Sciences at Ås in Southern Norway, located in a rural area about 30 km south of Oslo. Different methods for calculation of monthly mean temperature were studied and new monthly means were calculated before the homogeneity testing was performed. The statistical method used for the testing was the Standard Normal Homogeneity Test (SNHT) by Hans Alexandersson. Five breaks caused by relocations and changes in instrumentation were identified. The seasonal adjustments of the breaks lay between -0.4°C and +0.5°C. Comparison with two other homogenized temperature series in the Oslo fjord region showed similar linear trends, which suggests that the long-term linear temperature trends in the Oslo fjord region are not much affected by spatial climate variation.

scholarly journals Inhomogeneity detection in the rainfall series for the Mae Klong River Basin, Thailand

2021 ◽  
Vol 11 (9) ◽  
Author(s):  
Alamgir Khalil
Keyword(s):  
River Basin ◽  
Curve Analysis ◽  
Rainfall Series ◽  
Rainfall Data ◽  
Homogeneity Test ◽  
Significance Level ◽  
Von Neumann ◽  
Standard Normal Homogeneity Test ◽  
Double Mass ◽  
Climate Studies

AbstractAn accurate and complete rainfall record is prerequisite for climate studies. The purpose of this research study was to evaluate the homogeneity of the rainfall series for the Mae Klong River Basin in Thailand. Monthly rainfall data of eight stations in the Mae Klong River Basin for the period 1971–2015 were used. The double mass curve analysis was used to check the consistency of rainfall data, whereas the absolute homogeneity was assessed using the Pettitt test, standard normal homogeneity test, Buishand test, and von Neumann test at a 5% significance level. The results of these tests were qualitatively classified as ‘useful’, ‘doubtful’, and ‘suspect’ according to the null hypothesis. Results of the monthly time series indicated the rainfall data as ‘useful’ for 75% of the stations, while two stations’ data were classified as ‘doubtful’ (Stn130221) and ‘suspect’ (Stn376401). On an annual scale, seven out of eight stations data were classified as ‘useful,’ while one station (Stn376401) data were classified as ‘suspect’. Double mass curve analysis technique was used for the adjustment of inhomogeneous data. The results of this study can help provide reliable rainfall data for climate studies in the basin.

The Application of the Smirnov Test in the Homogeneity Test for Non-Normal Distribution Temperature Series

2012 ◽  
Vol 516-517 ◽  
pp. 530-535
Author(s):  
Xin Jie Deng ◽  
Yang Sheng You ◽  
Yan Ying Chen ◽  
Xue Mei Yang
Keyword(s):  
Normal Distribution ◽  
Classical Method ◽  
Temperature Series ◽  
Homogeneity Test ◽  
Test Results ◽  
Standard Normal Homogeneity Test ◽  
Standard Normal ◽  
Better Than ◽  
Smirnov Test

The homogeneity test is the first stage to revise the climate records. Its accuracy will directly affect the follow-up work. The classic method SNHT (Standard Normal Homogeneity Test) can only be applied in climatic sequences obey normal distribution, but lots of non-normality climate sequences need to be examined. In this paper, the Smirnov Test was introduced to test the homogeneity of the temperature series, which is a classical method for distribution test, and it can apply for the temperature sequences obey any distribution. The homogeneity test results by testing Chongqing Municipality's temperature sequences show that: the Smirnov Test is better than SNHT

Detection of Rainfall and Runoff Trends of the Adda River in Lecco (1845-2014) at Different Time Scales

10.29007/gbqh ◽  
2018 ◽  
Author(s):  
Roberto Ranzi ◽  
Massimo Tomirotti ◽  
Michele Brunetti ◽  
Alice Crespi ◽  
Maurizio Maugeri
Keyword(s):  
Time Series ◽  
Time Scales ◽  
Statistical Tests ◽  
Annual Runoff ◽  
Water Levels ◽  
Cycle Period ◽  
Catchment Scale ◽  
Significance Level ◽  
Runoff Series ◽  
Different Time Scales

A recovery of ancient records of the Como Lake water levels at the Fortilizio in Lecco hydrometric station enabled the reconstruction of a time series of daily water level and runoff from the Como Lake spanning the 1845-2014 period. In parallel, the monthly areal precipitation at the Adda river catchment scale was estimated for the same 170 years- long period. This time series, which is one of the longest available for Italian riverbasins will support analyses of the reasons of changes in the runoff regime in response to climatic and anthropogenic changes. A comparison of the two series applying the Mann- Kendall, Spearman and Theil-Sen trend tests, shows a decline, in the long term, of runoff and a more significant one of precipitation. Because some changes in the operation at the outlet of the Como Lake occurred after 1946 and also in the storage capacity of the upstream reservoirs the time series was splitted in two periods, before and after 1946. The results of the statistical tests for both precipitation and runoff in three time periods are consistent, but only for the time series of annual runoff the decline is statistically significant with 5% significance level. To analyse if changes occurred at different time scales the wavelet transform was applied to the daily runoff series. Finally the Fourier power spectrum of the the daily runoff data shows a signal of higher energy corresponding to a period between 11 and 13 years, close to the sunspots cycle period, and its significance is under investigation.

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