Yaeji Lim

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–42.5°N, 122.5°E-132.5°E). We focus on the precipitation of summer season (June–July–August; JJA) for the period of 1979–2007 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.

Independent component regression for seasonal climate prediction: an efficient way to improve multimodel ensembles

The main goal of this study is to improve the seasonal climate prediction of multimodel ensembles. The conventional principal component regression has been used to build a statistical relation between observations and multimodel ensembles. It predicts future climate values when there are a large number of variables, which is a typical issue in climate research field. However, principal component analysis which is prerequired to perform principal component regression assumes that information of the data should be retained by the second moment. This condition would be stringent to climate data. In this paper, we present a new prediction method that is efficient to adapt to non-Gaussian and high-dimensional data. The proposed method is based on a combination of independent component analysis and regularized regression approach. The main benefits of the proposed method are as follows. (1) It explains a statistical relationship between multimodel ensembles and observations, when either one is not normally distributed; and (2) it is capable of evaluating the contribution of climate models for prediction by selecting some specific models that are appropriate. The superiority of the proposed method is demonstrated by the prediction of future precipitation in boreal summer (June-July-August; JJA) for 20 years (1983–2002) on both global and regional scales.

A Data-Adaptive Principal Component Analysis: Use of Composite Asymmetric Huber Function

This article considers a new type of principal component analysis (PCA) that adaptively reflects the information of data. The ordinary PCA is useful for dimension reduction and identifying important features of multivariate data. However, it uses the second moment of data only, and consequently, it is not efficient for analyzing real observations in the case that these are skewed or asymmetric data. To extend the scope of PCA to non-Gaussian distributed data that cannot be well represented by the second moment, a new approach for PCA is proposed. The core of the methodology is to use a composite asymmetric Huber function defined as a weighted linear combination of modified Huber loss functions, which replaces the conventional square loss function. A practical algorithm to implement the data-adaptive PCA is discussed. Results from numerical studies including simulation study and real data analysis demonstrate the promising empirical properties of the proposed approach. Supplementary materials for this article are available online.

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–July–August; JJA) through 29 years (1979–2007) 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.

Climate Prediction by a Hybrid Method with Emphasizing Future Precipitation Change of East Asia

A canonical correlation analysis(CCA)-based method is proposed for prediction of future climate change which combines information from ensembles of atmosphere-ocean general circulation models(AOGCMs) and observed climate values. This paper focuses on predictions of future climate on a regional scale which are of potential economic values. The proposed method is obtained by coupling the classical CCA with empirical orthogonal functions(EOF) for dimension reduction. Furthermore, we generate a distribution of climate responses, so that extreme events as well as a general feature such as long tails and unimodality can be revealed through the distribution. Results from real data examples demonstrate the promising empirical properties of the proposed approaches.

Particulate Matter Prediction using Quantile Boosting

Concerning the national health, it is important to develop an accurate prediction method of atmospheric particulate matter (PM) because being exposed to such ne dust can trigger not only respiratory diseases as well as dermatoses, ophthalmopathies and cardiovascular diseases. The National Institute of Environmental Research (NIER) employs a decision tree to predict bad weather days with a high PM concentration. However, the decision tree method (even with the inherent unstableness) cannot be a suitable model to predict bad weather days which represent only 4% of the entire data. In this paper, while presenting the inaccuracy and inappropriateness of the method used by the NIER, we present the utility of a new prediction model which adopts boosting with quantile loss functions. We evaluate the performance of the new method over various -value’s and justify the proposed method through comparison.