Homogenization of Temperature Series via Pairwise Comparisons

2009 ◽  
Vol 22 (7) ◽  
pp. 1700-1717 ◽  
Author(s):  
Matthew J. Menne ◽  
Claude N. Williams
Keyword(s):  
Pairwise Comparison ◽  
Temperature Data ◽  
Temperature Series ◽  
Reference Series ◽  
Monthly Temperature ◽  
History Information ◽  
Difference Series ◽  
Apparent Shift ◽  
Abrupt Shifts ◽  
Lower False Alarm Rate

Abstract An automated homogenization algorithm based on the pairwise comparison of monthly temperature series is described. The algorithm works by forming pairwise difference series between serial monthly temperature values from a network of observing stations. Each difference series is then evaluated for undocumented shifts, and the station series responsible for such breaks is identified automatically. The algorithm also makes use of station history information, when available, to improve the identification of artificial shifts in temperature data. In addition, an evaluation is carried out to distinguish trend inhomogeneities from abrupt shifts. When the magnitude of an apparent shift attributed to a particular station can be reliably estimated, an adjustment is made for the target series. The pairwise algorithm is shown to be robust and efficient at detecting undocumented step changes under a variety of simulated scenarios with step- and trend-type inhomogeneities. Moreover, the approach is shown to yield a lower false-alarm rate for undocumented changepoint detection relative to the more common use of a reference series. Results from the algorithm are used to assess evidence for trend inhomogeneities in U.S. monthly temperature data.

scholarly journals A Bayes Factor Model for Detecting Artificial Discontinuities via Pairwise Comparisons

2012 ◽  
Vol 25 (24) ◽  
pp. 8462-8474 ◽  
Author(s):  
Jun Zhang ◽  
Wei Zheng ◽  
Matthew J. Menne
Keyword(s):  
Large Scale ◽  
Bayes Factor ◽  
Factor Model ◽  
Pairwise Comparison ◽  
Temperature Series ◽  
Sensitivity Analyses ◽  
Temperature Record ◽  
Comparison Approach ◽  
Difference Series ◽  
Large Scale Simulations

Abstract In this paper, the authors present a Bayes factor model for detecting undocumented artificial discontinuities in a network of temperature series. First, they generate multiple difference series for each station with the pairwise comparison approach. Next, they treat the detection problem as a Bayesian model selection problem and use Bayes factors to calculate the posterior probabilities of the discontinuities and estimate their locations in time and space. The model can be applied to large climate networks and realistic temperature series with missing data. The effectiveness of the model is illustrated with two realistic large-scale simulations and four sensitivity analyses. Results from applying the algorithm to observed monthly temperature data from the conterminous United States are also briefly discussed in the context of what is currently known about the nature of biases in the U.S. surface temperature record.

scholarly journals A Method to Detect Inhomogeneities in Historical Dewpoint Temperature Series

2009 ◽  
Vol 48 (11) ◽  
pp. 2362-2376 ◽  
Author(s):  
Paula J. Brown ◽  
Arthur T. DeGaetano
Keyword(s):  
United States ◽  
Minimum Temperature ◽  
The United States ◽  
Temperature Data ◽  
Temperature Series ◽  
Daily Minimum Temperature ◽  
Reference Series ◽  
Sparse Network ◽  
Dewpoint Temperature ◽  
Time Period

Abstract Hourly dewpoint temperature data for the 1951–2006 period at 10 stations in the contiguous United States were investigated to determine if inhomogeneities in their records could be detected. At least three instrument changes are known to have occurred during this time period. The relatively sparse network of stations with dewpoint temperature data in the United States necessitated a nonconventional method to create a reference series. Utilizing nighttime occurrences of fog, clear/calm conditions, and precipitation as meteorological situations during which dewpoint temperatures and minimum temperatures are similar, three potential reference series based on daily minimum temperature were developed to test for inhomogeneities. Four stations with independent network neighbors recording hourly dewpoint data provided a direct validation of the effect of inhomogeneities on dewpoint temperatures. It was determined that fog conditions and the combined results from all three meteorologically based tests performed best when detecting documented inhomogeneities. However, a larger number of undocumented inhomogeneities, a feature common in most traditional inhomogeneity tests, were also detected that may or may not be valid.

Modeling monthly temperature data in Lisbon and Prague

2009 ◽  
Vol 20 (7) ◽  
pp. 835-852 ◽  
Author(s):  
Teresa Alpuim ◽  
Abdel El-Shaarawi
Keyword(s):  
Temperature Data ◽  
Monthly Temperature

Climatic comparisons with tree-ring data from montane forests: are the climatic data appropriate?

1988 ◽  
Vol 18 (4) ◽  
pp. 385-390 ◽  
Author(s):  
Kenneth D. Kimball ◽  
MaryBeth Keifer
Keyword(s):  
Tree Ring ◽  
Montane Forest ◽  
Temperature Data ◽  
Growth Patterns ◽  
Forest Growth ◽  
Precipitation Data ◽  
Montane Forests ◽  
Monthly Temperature ◽  
Tree Ring Data ◽  
Mount Washington

The appropriateness of relating spatially proximate (40-km radius) temperature and precipitation data from different elevations to montane forest growth patterns was investigated for Mount Washington, New Hampshire. Monthly mean temperature and total precipitation data (1933–1983) were correlated (p < 0.05) among all pairs of meteorological stations (280, 420, 610, 1915 m and regional averages) on or near Mount Washington. The unexplained variance (1 − r2) for precipitation comparisons between meteorological stations was greater relative to temperature. When correlated with the average tree-ring index chronology of 90 red spruce trees on Mount Washington (800–1200 m), the monthly temperature data yielded similar correlative patterns among the four meteorological stations. However, the monthly temperature data from the meteorological stations (610 and 1915 m) most proximate to the montane forest study site were correlated (p < 0.10) with the tree-ring indices for two to three times as many months as the temperature data from the lower elevations. There was no consistency in correlative results of tree-ring indices with monthly precipitation data among the four meteorological stations. However, precipitation measurements and Palmer drought indices are poor indicators of moisture availability in montane forests. We conclude that spatially proximate, low elevation temperature data can underestimate correlative relationships between temperature and montane tree-ring data in the northeastern United States.

Generation of low tehmperature series for Ising models with nearest and next nearest neighbor interactions

1976 ◽  
Vol 54 (16) ◽  
pp. 1646-1650 ◽  
Author(s):  
M. Plischke ◽  
C. F. S. Chan
Keyword(s):  
Low Temperature ◽  
Nearest Neighbor ◽  
Tricritical Point ◽  
Ising Models ◽  
Temperature Data ◽  
Temperature Series ◽  
Bcc Lattice ◽  
Special Cases ◽  
Ising Antiferromagnet ◽  
Ferromagnetic Interactions

We have generalized the code method of Sykes et al. and applied it to the Ising model with nearest and next nearest neighbor interactions. On the bcc lattice, we have obtained the first seven low temperature polynomials for arbitrary sign of the interactions. Special cases of this model are the Ising ferromagnet and the Ising antiferromagnet with next nearest neighbor ferromagnetic interactions. The latter system exhibits a tricritical point which we plan to study using our low temperature data and high temperature series to be obtained in the future.

scholarly journals Sarima Modeling of Monthly Temperature in the Northern part of Ghana

2021 ◽  
pp. 37-45
Author(s):  
Emmanuel Ayitey ◽  
Justice Kangah ◽  
Frank B. K. Twenefour
Keyword(s):  
Northern Region ◽  
Temperature Data ◽  
Information Criteria ◽  
Sarima Model ◽  
Monthly Temperature ◽  
Augmented Dickey Fuller ◽  
Best Fitting ◽  
Adf Test ◽  
Fitting Model ◽  
Sarima Models

The Sarima model is used in this study to forecast the monthly temperature in Ghana's northern region. The researchers used temperature data from January 1990 to December 2020. The temperature data was found to be stationary using the Augmented Dickey Fuller (ADF) test. The ACF and PACF plots proposed six SARIMA models: SARIMA (1,0,0) (1,0,0) (12), SARIMA (2,0,0) (1,0,0) (12), SARIMA (1,0,1) (1,0,0) (12), SARIMA (0,0,1) (1,0,0) (12), SARIMA (0,0,1) (0,0,1) (12), SARIMA (0,0,1) (0,0,1) (12). The best model was chosen based on the lowest Akaike Information Criteria (AICs) and Bayesian Information Criteria (BIC) values. The Ljung-Box data, among others, were used to determine the model's quality. All diagnostic tests are passed by the SARIMA (1,0,0) (1,0,0) (12) model. As a result, the SARIMA (1,0,0) (1,0,0) (12) is the best-fitting model for predicting monthly temperatures in Ghana's northern region.

scholarly journals A new approach to homogenize global sub-daily radiosonde temperature data from 1958 to 2018

2020 ◽  
pp. 1-64
Author(s):  
Chunlüe Zhou ◽  
Junhong Wang ◽  
Aiguo Dai ◽  
Peter W. Thorne
Keyword(s):  
Temperature Data ◽  
Radiosonde Data ◽  
Segment Length ◽  
Raw Data ◽  
New Approach ◽  
Daily Difference ◽  
Kolmogorov Smirnov ◽  
Difference Series ◽  
Natural Variations ◽  
Smirnov Test

AbstractThis study develops an innovative approach to homogenize discontinuities in both mean and variance in global sub-daily radiosonde temperature data from 1958-2018. First, temperature natural variations and changes are estimated using reanalyses and removed from the radiosonde data to construct monthly and daily difference series. A Penalized Maximal F test and an improved Kolmogorov-Smirnov test are then applied to the monthly and daily difference series to detect spurious shifts in the mean and variance, respectively. About 60% (40%) of the changepoints appear in the mean (variance), and ∼56% of them are confirmed by available metadata. The changepoints display a country-dependent pattern likely due to changes in national radiosonde networks. Mean segment length is 7.2 (14.6) years for the mean (variance)-based detection. A mean (quantile)-matching method using up to 5-years of data from two adjacent mean (variance)-based segments is used to adjust the earlier segments relative to the latest segment. The homogenized series is obtained by adding the two homogenized difference series back to the subtracted reference series. The homogenized data exhibit more spatially coherent trends and temporally consistent variations than the raw data, and lack the spurious tropospheric cooling over North China and Mongolia seen in several reanalyses and raw datasets. The homogenized data clearly show a warming maximum around 300hPa over 30oS-30oN, consistent with model simulations, in contrast to the raw data. The results suggest that spurious changes are numerous and significant in the radiosonde records and our method can greatly improve their homogeneity.

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