scholarly journals Homogenization of Portuguese long-term temperature data series: Lisbon, Coimbra and Porto

2012 ◽  
Vol 4 (1) ◽  
pp. 187-213 ◽  
Author(s):  
A. L. Morozova ◽  
M. A. Valente
Keyword(s):  
Climate Variability ◽  
Volcanic Eruptions ◽  
Temperature Data ◽  
Temperature Series ◽  
Data Series ◽  
Homogeneity Test ◽  
Homogeneity Tests ◽  
Future Studies ◽  
Standard Normal Homogeneity Test

Abstract. Three long-term temperature data series measured in Portugal were studied to detect and correct non-climatic homogeneity breaks and are now available for future studies of climate variability. Series of monthly minimum (Tmin) and maximum (Tmax) temperatures measured in the three Portuguese meteorological stations of Lisbon (from 1856 to 2008), Coimbra (from 1865 to 2005) and Porto (from 1888 to 2001) were studied to detect and correct non-climatic breaks. These series, together with monthly series of average temperature (Taver) and temperature range (DTR) derived from them, were tested in order to detect breaks, using firstly metadata, secondly a visual analysis, and thirdly four widely used homogeneity tests: von Neumann ratio test, Buishand test, standard normal homogeneity test, and Pettitt test. The homogeneity tests were used in absolute (using temperature series themselves) and relative (using sea-surface temperature anomalies series obtained from HadISST2.0.0.0 close to the Portuguese coast or already corrected temperature series as reference series) modes. We considered the Tmin, Tmax and DTR series as most informative for the detection of breaks due to the fact that Tmin and Tmax could respond differently to changes in position of a thermometer or other changes in the instrument's environment; Taver series have been used mainly as control. The homogeneity tests showed strong inhomogeneity of the original data series, which could have both internal climatic and non-climatic origins. Breaks that were identified by the last three mentioned homogeneity tests were compared with available metadata containing data such as instrument changes, changes in station location and environment, observation procedures, etc. Significant breaks (significance 95% or more) that coincided with known dates of instrumental changes were corrected using standard procedures. It was also noted that some significant breaks, which could not be connected to known dates of any changes in the park of instruments or stations location and environment, were probably caused by large volcanic eruptions. The corrected series were again tested for homogeneity; the corrected series were considered free of non-climatic breaks when the tests of most of monthly series showed no significant (significance 95% or more) breaks that coincide with dates of known instrument changes. Corrected series are now available within the framework of ERA-CLIM FP7 project for future studies of climate variability (doi:10.1594/PANGAEA.785377).

scholarly journals Homogenization of Portuguese long-term temperature data series: Lisbon, Coimbra and Porto

2012 ◽  
Vol 5 (2) ◽  
pp. 521-584 ◽  
Author(s):  
A. L. Morozova ◽  
M. A. Valente
Keyword(s):  
Climate Variability ◽  
Volcanic Eruptions ◽  
Temperature Data ◽  
Temperature Series ◽  
Data Series ◽  
Homogeneity Test ◽  
Homogeneity Tests ◽  
Future Studies ◽  
Standard Normal Homogeneity Test

Abstract. Three long-term temperature data series measured in Portugal were studied to detect and correct non-climatic homogeneity breaks and are now available for future studies of climate variability. Series of monthly minimum (Tmin) and maximum (Tmax) temperatures measured in the three Portuguese meteorological stations of Lisbon (from 1856 to 2008), Coimbra (from 1865 to 2005) and Porto (from 1888 to 2001) were studied to detect and correct non-climatic homogeneity breaks. These series together with monthly series of average temperature (Taver) and temperature range (DTR) derived from them were tested in order to detect homogeneity breaks, using, firstly, metadata, secondly, a visual analysis and, thirdly, four widely used homogeneity tests: von Neumann ratio test, Buishand test, standard normal homogeneity test and Pettitt test. The homogeneity tests were used in absolute (using temperature series themselves) and relative (using sea-surface temperature anomalies series obtained from HadISST2 close to the Portuguese coast or already corrected temperature series as reference series) modes. We considered the Tmin, Tmax and DTR series as most informative for the detection of homogeneity breaks due to the fact that Tmin and Tmax could respond differently to changes in position of a thermometer or other changes in the instrument's environment; Taver series have been used, mainly, as control. The homogeneity tests show strong inhomogeneity of the original data series, which could have both internal climatic and non-climatic origins. Homogeneity breaks which have been identified by the last three mentioned homogeneity tests were compared with available metadata containing data, such as instrument changes, changes in station location and environment, observing procedures, etc. Significant homogeneity breaks (significance 95% or more) that coincide with known dates of instrumental changes have been corrected using standard procedures. It was also noted that some significant homogeneity breaks, which could not be connected to the known dates of any changes in the park of instruments or stations location and environment, could be caused by large volcanic eruptions. The corrected series were again tested for homogeneity: the corrected series were considered free of non-climatic breaks when the tests of most of monthly series showed no significant (significance 95% or more) homogeneity breaks that coincide with dates of known instrument changes. Corrected series are now available in the frame of ERA-CLIM FP7 project for future studies of climate variability (http://doi.pangaea.de/10.1594/PANGAEA.785377).

scholarly journals The Svalbard Airport Temperature Series

2010 ◽  
Vol 3 (1) ◽  
pp. 5-25 ◽  
Author(s):  
Øyvind Nordli
Keyword(s):  
Local Maximum ◽  
Thermal Conditions ◽  
Kendall Test ◽  
Temperature Series ◽  
Homogeneity Test ◽  
Svalbard Archipelago ◽  
Ww Ii ◽  
Standard Normal Homogeneity Test ◽  
Composite Series

Abstract In the Isfjorden region of Spitsbergen in the Svalbard archipelago, the air temperature has been observed continuously at different sites since 1911 (except for a break during WW II). The thermal conditions at these various sites turned out to be different so that nesting the many series together in one composite time series would produce an inhomogenous long-term series. By using the SNHT (Standard Normal Homogeneity Test) the differences between the sites were assessed and the series adjusted accordingly. This resulted in an homogenised, composite series mainly from Green Harbour (Finneset in Grønfjorden), Barentsburg (also in Grønfjorden), Longyearbyen and the current observation site at Svalbard Airport. A striking feature in the series is a pronounced, abrupt change from cold temperature in the 1910s to warmth in the 1930s, when temperature reached a local maximum. This event is called the early 20th century warming. Thereafter the temperature decreased to a local minimum in the 1960s before the start of another increase that still seems to be ongoing. For the whole series, statistically significant positive trends were detected by the Mann-Kendall test for annual and seasonal values (except for winter). Quite often the Norwegian Meteorological Institute receives queries about long-term temperature series from Svalbard. Hopefully, the Svalbard Airport composite series will fulfil this demand for data. It may be downloaded free of charge from the Institute’s home page: http://sharki.oslo.dnmi.no and should be used with reference to this article.

scholarly journals Evaluating the Quality of Spatial Data for the Analysis of Climate Variability in the Coastal Region of Nigeria

2021 ◽  
Vol 5 (1) ◽  
pp. 76-90
Author(s):  
A.I. Agbonaye ◽  
O.C. Izinyon
Keyword(s):  
Climate Change ◽  
Climate Variability ◽  
Spatial Data ◽  
Coastal Region ◽  
Climatic Research Unit ◽  
Data Series ◽  
Homogeneity Test ◽  
Climatic Data ◽  
Omnibus Test ◽  
Standard Normal Homogeneity Test

The lack of truly reliable data for climate change analyses and prediction presents challenges in climate modeling. Needed data are required to be hydrologically/statistically reliable to be useful for hydrological, meteorological, climate change, and estimation studies. Thus, data quality and homogeneity screening are preliminary analyses. In this study, the homogeneity of the climatic data used for analyses of climate variability was conducted in the coastal region of Nigeria. Climatic Research Unit (CRU 0.5× 0.5) gridded monthly climatic data for sixty years (1956- 2016) for nine states of the coastal region of Nigeria obtained from internet sources were validated with the Nigerian Meteorological Agency (NiMet) data to assure adequacy for use. The data were tested for normality using the Shapiro-Wilk (S-W) test, D’Agostino-Pearson omnibus test, and skewness test. Four homogeneity test methods were applied to 257 locations in the nine states of the coastal region of Nigeria and they include Pettit’s, Standard Normal Homogeneity Test (SNHT), Buishand’s and Von Neumann Ratio (VNR) tests. The results of the validity analysis indicated that the CRU data are very reliable and thus justified their use for the further analysis carried out in the study. Also, the results obtained indicated that CRU climatic data series were normally distributed and parametric methods could be used in further analysis of the data. Rainfall data homogeneity was detected for Bayelsa, Delta, Edo, Lagos, Ogun, and Ondo states and inhomogeneity for Akwa Ibom, Cross Rivers, and Rivers States. Also, temperature data inhomogeneity was detected for all the states in the study area.

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.

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

scholarly journals Assessing Inhomogeneities in Extreme Annual Rainfall Data Series by Multifractal Approach

Water ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 1030 ◽  
Author(s):  
Amanda García-Marín ◽  
Javier Estévez ◽  
Renato Morbidelli ◽  
Carla Saltalippi ◽  
José Ayuso-Muñoz ◽  
...  
Keyword(s):  
Central Italy ◽  
Extreme Rainfall ◽  
Break Point ◽  
Rainfall Data ◽  
Annual Rainfall ◽  
Data Series ◽  
Homogeneity Test ◽  
Umbria Region ◽  
Standard Normal Homogeneity Test ◽  
Break Points

Testing the homogeneity in extreme rainfall data series is an important step to be performed before applying the frequency analysis method to obtain quantile values. In this work, six homogeneity tests were applied in order to check the existence of break points in extreme annual 24-h rainfall data at eight stations located in the Umbria region (Central Italy). Two are parametric tests (the standard normal homogeneity test and Buishand test) whereas the other four are non-parametric (the Pettitt, Sequential Mann–Kendal, Mann–Whitney U, and Cumulative Sum tests). No break points were detected at four of the stations analyzed. Where inhomogeneities were found, the multifractal approach was applied in order to check if they were real or not by comparing the split and whole data series. The generalized fractal dimension functions Dq and the multifractal spectra f(α) were obtained, and their main parameters were used to decide whether or not a break point existed.

scholarly journals Historical Rainfall and Evapotranspiration Changes over Mpologoma Catchment in Uganda

2020 ◽  
Vol 2020 ◽  
pp. 1-19
Author(s):  
Ambrose Mubialiwo ◽  
Charles Onyutha ◽  
Adane Abebe
Keyword(s):  
Climate Change ◽  
Water Balance ◽  
Climate Variability ◽  
Climate Change Impacts ◽  
Positive Trend ◽  
Careful Planning ◽  
Future Studies ◽  
Nonparametric Approach ◽  
The Difference

Changes in the long-term (1948–2016) rainfall and evapotranspiration over Mpologoma catchment were analysed using gridded (0.25° × 0.25°) Princeton Global Forcing data. Trend and variability were assessed using a nonparametric approach based on the cumulative sum of the difference between exceedance and nonexceedance counts of data. Annual and March-May (MAM) rainfall displayed a positive trend (p<0.05), whereas October-December (OND) and June-September rainfall exhibited negative trends with p>0.05 and p<0.05, respectively. Positive subtrends in rainfall occurred in the 1950s and from the mid-2000s till 2016; however, negative subtrends existed between 1960 till around 2005. Seasonal evapotranspiration exhibited a positive trend (p>0.05). For the entire period (1948–2016), there was no negative subtrend in the OND and MAM evapotranspiration. Rainfall and evapotranspiration trends and oscillatory variation in subtrends over multidecadal time scales indicate the need for careful planning of predictive adaptation to the impacts of climate variability on environmental applications which depend on water balance in the Mpologoma catchment. It is recommended that future studies quantify possible contributions of human factors on the variability of rainfall and evapotranspiration. Furthermore, climate change impacts on rainfall and evapotranspiration across the study area should be investigated.

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 &lt; σ) regardless of time series length and up to 5% for detecting medium shifts (Δ = σ–1.5σ) in time series of length N &lt; 100. For all detectable shift sizes, the largest improvement is always obtained when N &lt; 100, which is of great practical importance, because most annual climate data series are of length N &lt; 100.

scholarly journals Homogeneity of the Temperature Data Series from ERA5 and MERRA2 and Temperature Trends

Atmosphere ◽  
2020 ◽  
Vol 11 (3) ◽  
pp. 235
Author(s):  
Michal Kozubek ◽  
Peter Krizan ◽  
Jan Lastovicka
Keyword(s):  
Vertical Distribution ◽  
Temporal Distribution ◽  
Weather Forecast ◽  
Data Series ◽  
Homogeneity Test ◽  
Medium Range Weather Forecast ◽  
Temperature Trends ◽  
Long Term Trends ◽  
Grid Points

The stratosphere and its dynamics are a very important part of atmospheric circulation. We need to analyze its climatology, as well as long-term trends. A long-term trend study needs homogenous datasets without significant artificial discontinuities. The analysis is based on the two newest released reanalyses, Modern Era-Retrospective Analysis (MERRA2) and European Center for Medium-Range Weather Forecast Reanalysis (ERA5). The aim of this study is to detect discontinuities in the temperature time series from the above reanalyses with the help of the Pettitt homogeneity test for pressure layers above 500 hPa up to 1 hPa in January and February, and show a comparison of temperature trends from the studied reanalyses and GPS radio occultation (GPS RO). We search for individual grid points where these discontinuities occur, and also for the years when they occur (geographical and temporal distribution). As expected, the study confirms better results for the Northern Hemisphere due to the denser data coverage. A high number of grid points with jumps on the Southern Hemisphere is found, especially at higher pressure levels (from 50 hPa). The spatial and vertical distribution of discontinuities is also presented. The vertical distribution reveals the reduction of the number of jumps around 10 hPa, especially for ERA5 reanalysis. The results show that ERA5 has significantly less jumps than MERRA2. We also study temperature trends from reanalyses and GPS RO and our analysis shows that the agreement between the reanalyses and observations are very good for the period 2006–2018.

scholarly journals Simulating rainfall IDF curve for flood warnings in the Ca Mau coastal area under the impacts of climate change

2020 ◽  
Vol 12 (5) ◽  
pp. 705-715
Author(s):  
Seung Kyu Lee ◽  
Truong An Dang
Keyword(s):  
Return Period ◽  
Extreme Rainfall ◽  
Rainfall Data ◽  
Data Series ◽  
Homogeneity Test ◽  
Content Type ◽  
Pettitt Test ◽  
Standard Normal Homogeneity Test ◽  
Intensity Duration Frequency ◽  
The Right

Purpose The purpose of this study is to evaluate the rainfall intensities and their limits for durations from 0.25 to 8 h with return periods from 2 to 100 years for Ca Mau City in Vietnam. Design/methodology/approach First, the quality of the historical rainfall data series in 44 years (1975–2018) at Ca Mau station was assessed using the standard normal homogeneity test and the Pettitt test. Second, the appraised rainfall data series are used to establish the rainfall intensity-duration-frequency curve for the study area. Findings Based on the findings, a two-year return period, the extreme rainfall intensities (ERIs) ranged from 9.1 mm/h for 8 h rainstorms to 91.2 mm/h for 0.25 h. At a 100-year return period, the ERIs ranged from 18.4 mm/h for 8 h rainstorms to 185.8 mm/h for 0.25 h. The results also show that the narrowest uncertainty level between the lower and upper limits recorded 1.6 mm at 8 h for the two-year return period while the widest range is at 42.5 mm at 0.25 h for the 100-year return period. In general, the possibility of high-intensity rainfall values compared to the extreme rainfall intensities is approximately 2.0% at the 100-year return period. Originality/value The results of the rainfall IDF curves can provide useful information for policymakers to make the right decisions in controlling and minimizing flooding in the study area.

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