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Pao-Shin Chu

Abstract

An analysis of composite, seasonal rainfall anomalies in Hawaii shows that deficient rainfall tends to occur frequently in winter and spring of the year following an El Niño. The reliability of the El Niño composite has been tested using a Monte Carlo simulation technique. Upper-air circulation patterns during the recent three El Niño events are discussed in relation to drought winters in Hawaii. The more eastward elongated subtropical jet stream in the North Pacific and the thermally induced local Hadley circulation in the central North Pacific, characteristics of El Niño winters, are unfavorable for rainfall in Hawaii.

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Pao-Shin Chu

Abstract

Tropical cyclone frequency in the central North Pacific (CNP) from 1966 to 2000 has exhibited decadal-scale variability. A statistical changepoint analysis reveals objectively that the shifts occur in 1982 and 1995, with fewer cyclones during the 1966–81 and 1995–2000 epochs and more during the 1982–94 epoch. A bootstrap resampling method is then applied to determine the frequency distribution of the mean annual cyclones for the 1966–81 and 1982–94 epochs, as well as to infer the confidence intervals of the observed mean and variance of cyclones for each epoch.

Large-scale environmental conditions conducive to cyclone incidences during the peak hurricane season (July–September) for the inactive (1966–81) and active (1982–94) epochs are investigated. A nonparametric Mann–Whitney test is used to investigate whether the differences in location between the two epochs are significant. In contrast to the first epoch, warmer sea surface temperatures, lower sea level pressure, stronger low-level anomalous cyclonic vorticity, reduced vertical wind shear, and increased total precipitable water covered a large domain of the tropical North Pacific in the second epoch. These changes in environmental conditions favor more cyclone incidences for the second epoch. Many of the aforementioned changes were already established prior to the peak season. In addition, atmospheric steering flows have changed remarkably in October and November so that tropical cyclones in the eastern North Pacific have a better chance to enter the CNP, and cyclones formed in the CNP are more likely to be steered through the western Hawaiian Islands in the second epoch.

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Pao-Shin Chu

Abstract

The large-scale atmospheric circulation of the Brazil-Atlantic sector is studied in relation to extreme rainfall anomalies in two large regions of Northeast Brazil (Nordeste). Long-term rainfall series, aerological records of stations in South America, and ship observations over the tropical Atlantic form the data base for this study.

Departure patterns of meteorological elements over the Atlantic are investigated for composites of extremely dry and wet years in the southern and northern Nordeste. Southern Nordeste's peak rainy season is around November/December. The wet years in the southern Nordeste are marked by negative pressure departures over the South Atlantic, weak onshore southeast trades and anomalously cold waters along the south Brazil coast. These features appear to be related to Southern Hemispheric frontal systems. During the dry years, departure patterns are nearly reversed to those of the wet years.Northern Nordeste receives its maximum rainfall in March/April. Rainfall variations are modulated by the latitudinal displacement of the baric trough and confluence axis over the equatorial Atlantic and concomitant sea surface temperature anomalies. Case studies of recent extreme years indicate the possible existence of a local meridional circulation. The more northerly position of the convergence band over the Atlantic, the anomalously cold waters to the south of the equator and the subsidence in the southern portion of the thermally-induced meridional circulation cell over the Nordeste, characteristic of drought years, are all unfavorable for rainfall in Northeast Brazil.

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Pao-Shin Chu and Jianxin Wang

Abstract

Tropical cyclones in the vicinity of Hawaii have resulted in great property damage. An estimate of the return periods of tropical cyclone intensities is of particular interest to governments, public interest groups, and private sectors.

A dimensionless quantity called relative intensity (RI) is used to combine all available information about the tropical cyclone characteristics at different places and times. To make a satisfactory estimate of the return periods of tropical cyclone intensities, a large number of RIs are simulated by the Monte Carlo method based on the extreme value distribution. The return periods of RIs and the corresponding maximum wind speeds associated with tropical cyclones are then estimated by combining the information about the intensities and occurrences. Results show that the return periods of maximum wind speeds equal to or greater than 125, 110, 100, 80, 64, 50, and 34 kt are estimated to be 137, 59, 33, 12, 6.6, 4, and 3. 2 years, respectively.

The Monte Carlo method is also used to estimate the confidence intervals of the return periods of tropical cyclone intensities. The sensitivity test is conducted by removing the portion of the data prior to satellite observations. For maximum wind speeds less than 80 kt, estimates of return periods from the shorter dataset (1970–95) are almost identical to those when the complete duration time series are used (1949–95).

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Pao-Shin Chu and Huaiqun Chen

Abstract

Hawaii rainfall has exhibited both interannual and interdecadal variations. On the interannual time scale, Hawaii tends to be dry during most El Niño events, but low rainfall also occurred in the absence of El Niño. On the interdecadal time scale, Hawaii rainfall is negatively and significantly correlated with the Pacific decadal oscillation (PDO) signal; an epoch of low rainfall persists from the mid-1970s to 2001, which is preceded by an epoch of high rainfall lasting for nearly 28 yr.

Difference patterns in winter [November–December–January–February–March (NDJFM)] rainfall are investigated for composites of extremely dry and wet winters during the dry and wet epochs, respectively. These patterns (i.e., DRY minus WET) are then compared to the difference in constructive match conditions of El Niño and PDO (i.e., El Niño/+PDO minus La Niña/−PDO). Relative to the El Niño/PDO stage, the magnitude of dryness during the rainfall-based stage is enhanced. The corresponding large-scale atmospheric circulation composites are studied. Similar patterns are revealed between these two stages. However, anomalously stronger and deeper sinking motions over Hawaii are revealed in the height–latitude section of the rainfall-based analysis compared to the El Niño/PDO stage. Moreover, an anomalous zonal circulation cell is well established over the subtropical North Pacific with a pronounced descending branch over Hawaii in the rainfall-based stage. The band of anomalous surface westerlies to the north of Hawaii, and the deep sinking motion as well as the anomalously vertically integrated moisture flux divergence over Hawaii are all unfavorable for rainfall in Hawaii.

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Pao-Shin Chu and Xin Zhao

Abstract

Bayesian analysis is applied to detect change points in the time series of annual tropical cyclone counts over the central North Pacific. Specifically, a hierarchical Bayesian approach involving three layers—data, parameter, and hypothesis—is formulated to demonstrate the posterior probability of the shifts throughout the time from 1966 to 2002. For the data layer, a Poisson process with gamma distributed intensity is presumed. For the hypothesis layer, a “no change in the intensity” hypothesis and a “single change in the intensity” hypothesis are considered. Results indicate that there is a great likelihood of a change point on tropical cyclone rates around 1982, which is consistent with earlier work based on a simple log-linear regression model. A Bayesian approach also provides a means for predicting decadal tropical cyclone variations. A higher number of tropical cyclones is predicted in the next decade when the possibility of the change point in the early 1980s is taken into account.

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Xin Zhao and Pao-Shin Chu

Abstract

A Bayesian framework is developed to detect multiple abrupt shifts in a time series of the annual major hurricanes counts. The hurricane counts are modeled by a Poisson process where the Poisson intensity (i.e., hurricane rate) is codified by a gamma distribution. Here, a triple hypothesis space concerning the annual hurricane rate is considered: “a no change in the rate,” “a single change in the rate,” and “a double change in the rate.” A hierarchical Bayesian approach involving three layers—data, parameter, and hypothesis—is formulated to demonstrate the posterior probability of each possible hypothesis and its relevant model parameters through a Markov chain Monte Carlo (MCMC) method.

Based on sampling from an estimated informative prior for the Poisson rate parameters and the posterior distribution of hypotheses, two simulated examples are illustrated to show the effectiveness of the proposed method. Subsequently, the methodology is applied to the time series of major hurricane counts over the eastern North Pacific (ENP). Results indicate that the hurricane activity over ENP has very likely undergone a decadal variation with two changepoints occurring around 1982 and 1999 with three epochs characterized by the inactive 1972–81 epoch, the active 1982–98 epoch, and the inactive 1999–2003 epoch. The Bayesian method also provides a means for predicting decadal major hurricane variations. A lower number of major hurricanes are predicted for the next decade given the recent inactive period of hurricane activity.

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Xin Zhao and Pao-Shin Chu

Abstract

A hierarchical Bayesian framework is developed to identify multiple abrupt regime shifts in an extreme event series. Specifically, extreme events are modeled as a Poisson process with a gamma-distributed rate. Multiple candidate hypotheses are considered, under each of which there presumably exist a certain number of abrupt shifts of the rate. A Bayesian network involving three layers—data, parameter, and hypothesis—is formulated. A reversible jump Markov chain Monte Carlo (RJMCMC) algorithm is developed to calculate posterior probability for each hypothesis as well its associated within-hypothesis parameters. Based on the proposed RJMCMC algorithm, a simulated example is designed to illustrate the effectiveness of the method. Subsequently, the algorithm is applied to three real, rare event time series: the annual typhoon counts over the western North Pacific (WNP), the annual extreme heavy rainfall event counts at the Honolulu airport, and the annual heat wave frequency in the Chicago area. Results indicate that the typhoon activity over the WNP is very likely to have undergone a decadal variation, with two change points occurring around 1972 and 1989 characterized by the active 1960–71 epoch, the inactive 1972–88 epoch, and the moderately active 1989–2006 epoch. For the extreme rainfall case, only one shift around 1970 is found and heavy rainfall frequency has remained stationary since then. There is no evidence that the rate of the annual heat wave counts in the Chicago area has had any abrupt change during the past 50 years.

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Pao-Shin Chu and Xin Zhao

Abstract

In this study, a Poisson generalized linear regression model cast in the Bayesian framework is applied to forecast the tropical cyclone (TC) activity in the central North Pacific (CNP) in the peak hurricane season (July–September) using large-scale environmental variables available up to the antecedent May and June. Specifically, five predictor variables are considered: sea surface temperatures, sea level pressures, vertical wind shear, relative vorticity, and precipitable water. The Pearson correlation between the seasonal TC frequency and each of the five potential predictors over the eastern and central North Pacific is computed. The critical region for which the local correlation is statistically significant at the 99% confidence level is determined. To keep the predictor selection process robust, a simple average of the predictor variable over the critical region is then computed. With a noninformative prior assumption for the model parameters, a Bayesian inference for this model is derived in detail. A Gibbs sampler based on the Markov chain Monte Carlo (MCMC) method is designed to integrate the desired posterior predictive distribution. The proposed hierarchical model is physically based and yields a probabilistic prediction for seasonal TC frequency, which would better facilitate decision making. A cross-validation procedure was applied to predict the seasonal TC counts within the period of 1966–2003 and satisfactory results were obtained.

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Pao-Shin Chu and Jianxin Wang

Abstract

Tropical cyclones in the vicinity of Hawaii are rare. However, when they occurred, they caused enormous property damage. The authors have examined historical records (1949–95) of cyclones and classified them into El Niño and non–El Niño batches. A bootstrap resampling method is used to simulate sampling distributions of the annual mean number of tropical cyclones for the above two batches individually. The statistical characteristics for the non–El Niño batch are very different from the El Niño batch.

A two-sample permutation procedure is then applied to conduct statistical tests. Results from the hypothesis testing indicate that the difference in the annual mean number of cyclones between El Niño and non–El Niño batches is statistically significant at the 5% level. Therefore, one may say with statistical confidence that the mean number of cyclones in the vicinity of Hawaii during an El Niño year is higher than that during a non–El Niño year. Likewise, the difference in variances between El Niño and non–El Niño batches is also significant. Cyclone tracks passing Hawaii during the El Niño batch appear to be different from those of the non–El Niño composite. A change in large-scale dynamic and thermodynamic environments is believed to be conducive to the increased cyclone incidence in the vicinity of Hawaii during an El Niño year.

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