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Williams 2009 ). As such, a changepoint analysis of a climate series is often a worthy initial exploratory endeavor. Statistical methods to detect changepoints have rapidly evolved over the last few decades. These include methods to detect a single shift in the series’ mean ( Chernoff and Zacks 1964 ), in its variance ( Hsu 1977 ), or in a general linear regression model ( Quandt 1958 ; Robbins et al. 2016 ). In the climate literature, changepoint detection has most often been used to detect mean
Williams 2009 ). As such, a changepoint analysis of a climate series is often a worthy initial exploratory endeavor. Statistical methods to detect changepoints have rapidly evolved over the last few decades. These include methods to detect a single shift in the series’ mean ( Chernoff and Zacks 1964 ), in its variance ( Hsu 1977 ), or in a general linear regression model ( Quandt 1958 ; Robbins et al. 2016 ). In the climate literature, changepoint detection has most often been used to detect mean
Gaussian series and in the parameter of an exponential distribution. In pursuit of performing change-point analysis of radiosonde temperature series observed at four layers of the earth’s atmosphere, we first develop in this paper the large sample distribution of the change-point MLE when a change has occurred in both the mean vector and the covariance matrix of a multivariate Gaussian series. Then, we extend the Bayesian conjugate posterior derived by Perreault et al. (2000c) for change in the
Gaussian series and in the parameter of an exponential distribution. In pursuit of performing change-point analysis of radiosonde temperature series observed at four layers of the earth’s atmosphere, we first develop in this paper the large sample distribution of the change-point MLE when a change has occurred in both the mean vector and the covariance matrix of a multivariate Gaussian series. Then, we extend the Bayesian conjugate posterior derived by Perreault et al. (2000c) for change in the
studies have been conducted regarding the change point of extreme precipitation, especially on subdaily-scale precipitation in recent years. The goal of this study was to investigate the existence of a change point in the area-averaged annual maximum precipitation ( A3MP ) for six accumulated time periods (1, 3, 6, 12, 24, and 48 h) using Bayesian changepoint analysis during a 30-year period (1976–2005) over South Korea. When we consider the fact that the majority of disasters related to heavy
studies have been conducted regarding the change point of extreme precipitation, especially on subdaily-scale precipitation in recent years. The goal of this study was to investigate the existence of a change point in the area-averaged annual maximum precipitation ( A3MP ) for six accumulated time periods (1, 3, 6, 12, 24, and 48 h) using Bayesian changepoint analysis during a 30-year period (1976–2005) over South Korea. When we consider the fact that the majority of disasters related to heavy
: Bayesian changepoint analysis of tropical cyclone activity: The central North Pacific case. J. Climate , 17 , 4893 – 4901 . Chu , P-S. , X. Zhao , M. Grubbs , and Y. Ruan , 2009 : Extreme rainfall events in the Hawaiian Islands. J. Appl. Meteor. Climatol. , 48 , 502 – 516 . Deser , C. A. , S. Philips , and J. W. Hurrell , 2004 : Pacific interdecadal climate variability: Linkages between the tropics and the North Pacific during boreal winter since 1900. J. Climate , 17
: Bayesian changepoint analysis of tropical cyclone activity: The central North Pacific case. J. Climate , 17 , 4893 – 4901 . Chu , P-S. , X. Zhao , M. Grubbs , and Y. Ruan , 2009 : Extreme rainfall events in the Hawaiian Islands. J. Appl. Meteor. Climatol. , 48 , 502 – 516 . Deser , C. A. , S. Philips , and J. W. Hurrell , 2004 : Pacific interdecadal climate variability: Linkages between the tropics and the North Pacific during boreal winter since 1900. J. Climate , 17
analysis ( Potter 1981 ; Vincent 1998 ; Caussinus and Mestre 2004 ; Menne and Williams 2005 , 2009 ; Lu and Lund 2007 ). The changepoint locations and mean shift sizes need to be estimated to make accurate inferences from the data; in fact, Lund et al. (2001) show that changepoint information is the single most important data feature to account for when reliably estimating a temperature trend at a fixed U.S. station. Once the changepoint times are identified, most other statistical inference
analysis ( Potter 1981 ; Vincent 1998 ; Caussinus and Mestre 2004 ; Menne and Williams 2005 , 2009 ; Lu and Lund 2007 ). The changepoint locations and mean shift sizes need to be estimated to make accurate inferences from the data; in fact, Lund et al. (2001) show that changepoint information is the single most important data feature to account for when reliably estimating a temperature trend at a fixed U.S. station. Once the changepoint times are identified, most other statistical inference
our examples—what caused the changepoints is immaterial in this discussion. Toward this, most homogenizations aim to remove artificial changepoint features from the record (e.g., station moves); changepoints reflecting “true fluctuations” (e.g., natural variability) should be retained in the series. This can be done by subtracting a reference series from a nearby location from the target series to be homogenized before analysis. The target minus reference subtraction eliminates naturally occurring
our examples—what caused the changepoints is immaterial in this discussion. Toward this, most homogenizations aim to remove artificial changepoint features from the record (e.g., station moves); changepoints reflecting “true fluctuations” (e.g., natural variability) should be retained in the series. This can be done by subtracting a reference series from a nearby location from the target series to be homogenized before analysis. The target minus reference subtraction eliminates naturally occurring
necessarily induces a true shift in the series. This said, time changes reported in the metadata record seem more likely to be true changepoint times. The typical objective of a changepoint analysis is to identify how many changepoints a series has and where they occur. The appropriate statistical methods to handle documented and undocumented changepoint times radically differ ( Lund and Reeves 2002 ). To appreciate the issue, consider a record of n = 100 annualized temperatures X 1 , …, X n and
necessarily induces a true shift in the series. This said, time changes reported in the metadata record seem more likely to be true changepoint times. The typical objective of a changepoint analysis is to identify how many changepoints a series has and where they occur. The appropriate statistical methods to handle documented and undocumented changepoint times radically differ ( Lund and Reeves 2002 ). To appreciate the issue, consider a record of n = 100 annualized temperatures X 1 , …, X n and
. The dataset used in this study is described in section 2 , whereas the method and an example of the inhomogeneity test and adjustment are presented in section 3 . The results of changepoints and their causes and the trend analysis, including the national average SSR series for China, are shown and discussed in section 4 . Finally, conclusions of this study are presented in section 5 . 2. Data Monthly SSR and SSD data and the related metadata at Chinese routine weather stations were released by
. The dataset used in this study is described in section 2 , whereas the method and an example of the inhomogeneity test and adjustment are presented in section 3 . The results of changepoints and their causes and the trend analysis, including the national average SSR series for China, are shown and discussed in section 4 . Finally, conclusions of this study are presented in section 5 . 2. Data Monthly SSR and SSD data and the related metadata at Chinese routine weather stations were released by
merged list Merged_p1Cs+p0Cs. The analysis includes defining initial groups of nearby changepoints (including finding the most likely one to represent the group), as well as replacing a type-1 changepoint with its nearest type-0 changepoint if they are no more than 5 months apart. For more details, readers are referred to the first four steps in SM1. Second, we obtained a list of not insignificant changepoints by eliminating, one by one, all changepoints in the list Merged_p1Cs+p0Cs that are not
merged list Merged_p1Cs+p0Cs. The analysis includes defining initial groups of nearby changepoints (including finding the most likely one to represent the group), as well as replacing a type-1 changepoint with its nearest type-0 changepoint if they are no more than 5 months apart. For more details, readers are referred to the first four steps in SM1. Second, we obtained a list of not insignificant changepoints by eliminating, one by one, all changepoints in the list Merged_p1Cs+p0Cs that are not
from Jacksonville. To obtain changepoints in the monthly average series, a Gaussian MDL analysis akin to that in Lu et al. (2010) was used—a reference series was again constructed from the 40 most correlated neighbors. Table 2 lists our findings. The metadata identify station location or temperature gauge changes in 1895, 1927, 1962, and 1974. There is no metadata record after 1986. The MDL analysis of the monthly averaged series estimates changepoints in 1900, 1931, 1941, 1961, and 1970
from Jacksonville. To obtain changepoints in the monthly average series, a Gaussian MDL analysis akin to that in Lu et al. (2010) was used—a reference series was again constructed from the 40 most correlated neighbors. Table 2 lists our findings. The metadata identify station location or temperature gauge changes in 1895, 1927, 1962, and 1974. There is no metadata record after 1986. The MDL analysis of the monthly averaged series estimates changepoints in 1900, 1931, 1941, 1961, and 1970
