Bayesian regression model for seasonal forecast of precipitation over Korea

In this paper, we apply three different Bayesian methods to the seasonal forecasting of the precipitation in a region around Korea (32.5°N?C42.5°N, 122.5°E-132.5°E). We focus on the precipitation of summer season (June?CJuly?CAugust; JJA) for the period of 1979?C2007 using the precipitation produced by the Global Data Assimilation and Prediction System (GDAPS) as predictors. Through cross-validation, we demonstrate improvement for seasonal forecast of precipitation in terms of root mean squared error (RMSE) and linear error in probability space score (LEPS). The proposed methods yield RMSE of 1.09 and LEPS of 0.31 between the predicted and observed precipitations, while the prediction using GDAPS output only produces RMSE of 1.20 and LEPS of 0.33 for CPC Merged Analyzed Precipitation (CMAP) data. For station-measured precipitation data, the RMSE and LEPS of the proposed Bayesian methods are 0.53 and 0.29, while GDAPS output is 0.66 and 0.33, respectively. The methods seem to capture the spatial pattern of the observed precipitation. The Bayesian paradigm incorporates the model uncertainty as an integral part of modeling in a natural way. We provide a probabilistic forecast integrating model uncertainty.     

Prediction of East Asian summer precipitation via independent component analysis

A new statistical postprocessing method is proposed for seasonal climate prediction. The proposed method is based on a combination of independent component analysis (ICA) and canonical correlation analysis (CCA). Since the classical CCA cannot handle high-dimensional data wherein the number of variables is larger than the number of observations, ICA is pre-performed to reduce the dimension of the data. It is well known that empirical orthogonal function (EOF) analysis is a popular method for dimension reduction in the climatology community; however, loss of information occurs when the data is not Gaussian distributed. To extend the scope of distribution assumption and improve the prediction ability simultaneously, we propose the ICA-based method. This study focuses on the prediction of future precipitation for the boreal summer (June?CJuly?CAugust; JJA) through 29 years (1979?C2007) on East Asia region. Results of the proposed ICA-based method show an improvement in seasonal climate prediction in terms of correlation and root mean square error as compared with those of the GCM simulation and the EOF/CCA method.     

Improved multisite stochastic weather generation with applications to historical data in South Korea

In this paper, we propose an improved multisite weather generation with applications to the historical data in South Korea. The proposed method improve the algorithm of Wilks (1998, 1999) by automatically selecting an optimal model that represents precipitation amounts and by providing a procedure to obtain a symmetric positive definite estimate for the covariance matrix. The proposed method is computationally fast, and hence, it can be feasible to handle a massive data. We apply the proposed method to the precipitation and temperature data collected 170 stations in South Korea for the period 1976–2005 which are given by the Korea Meteorological Administration (KMA). Results of the proposed method demonstrate the promising performance in terms of spatial correlation and long-term variation as compared with those of the multisite method of Wilks (1998) and the single-site weather generator.     

Spatio-temporal analysis of particulate matter extremes in Seoul: use of multiscale approach

This paper considers a problem of analyzing temporal and spatial structure of particulate matter (PM) data with emphasizing high-level \(\text {PM}_{10}\). The proposed method is based on a combination of a generalized extreme value (GEV) distribution and a multiscale concept from scaling property theory used in hydrology. In this study, we use hourly \(\text {PM}_{10}\) data observed for 5 years on 25 stations located in Seoul metropolitan area, Korea. For our analysis, we calculate monthly maximum values for various duration times and area coverages at each station, and show that their distribution follows a GEV distribution. In addition, we identify that the GEV parameters of \(\text {PM}_{10}\) maxima hold a new scaling property, termed ‘piecewise linear scaling property’ for certain duration times. By using this property, we construct a 12-month return level map of hourly \(\text {PM}_{10}\) data at any arbitrary d-hour duration. Furthermore, we extend our study to understand spatio-temporal multiscale structure of \(\text {PM}_{10}\) extremes over different temporal and spatial scales.     

Cloning and expression of alpha class glutathione S-transferase gene from the small hermaphroditic fish Rivulus marmoratus (Cyprinodontiformes, Rivulidae)

In order to assess its potential as a biomarker of aquatic pollution, an alpha class glutathione S-transferase gene (GSTalpha gene) was cloned from the small hermaphroditic fish Rivulus marmoratus. The R. marmoratus GSTalpha gene spanned 1.3 kb, consisting of 6 exons encoding 221 amino acid residues. It showed high similarity to zebrafish GST. We named this R. marmoratus GSTalpha gene as rm-GSTalpha. The cDNA of the rm-GSTalpha gene was also investigated for its phylogeny, tissue-specific and chemical-induced expression. Rm-GSTalpha was subcloned into a 6 x His-tagged pCRT7 TOPO TA expression vector to produce the recombinant 6 x His-tagged rm-GST protein. This will be used in future to raise an rm-GSTalpha antibody for use in the study of phase II metabolism involved in detoxification. We also exposed R. marmoratus to 300 microg/l of 4-nonylphenol in water, and found approximately 4-fold induction of R. marmoratus GSTalpha mRNA in the treated animals. In this paper, we discuss the characteristics of the R. marmoratus GSTalpha gene as well as its potential use in relation to environmental pollution.     

Cloning and expression of alpha class glutathione S-transferase gene from the small hermaphroditic fish Rivulus marmoratus (Cyprinodontiformes, Rivulidae)

In order to assess its potential as a biomarker of aquatic pollution, an alpha class glutathione S-transferase gene (GSTα gene) was cloned from the small hermaphroditic fish Rivulus marmoratus. The R. marmoratus GSTα gene spanned 1.3 kb, consisting of 6 exons encoding 221 amino acid residues. It showed high similarity to zebrafish GST. We named this R. marmoratus GSTα gene as rm-GSTα. The cDNA of the rm-GSTα gene was also investigated for its phylogeny, tissue-specific and chemical-induced expression. Rm-GSTα was subcloned into a 6 × His-tagged pCR^®T7 TOPO TA expression vector to produce the recombinant 6 × His-tagged rm-GST protein. This will be used in future to raise an rm-GSTα antibody for use in the study of phase II metabolism involved in detoxification. We also exposed R. marmoratus to 300 μg/l of 4-nonylphenol in water, and found approximately 4-fold induction of R. marmoratus GSTα mRNA in the treated animals. In this paper, we discuss the characteristics of the R. marmoratus GSTα gene as well as its potential use in relation to environmental pollution.