Intergovernmental Panel on Climate Change (IPCC) provides various prospects of future climate change under the Representative Concentration Pathways (RCP) scenarios using General Circulation Models (GCMs) of Coupled Model Intercomparison Project (CMIP). This paper describes a modified application of Ensemble Bayesian Model Averaging (EBMA) to produce daily mean temperature ensembles using 19 GCMs provided by CMIP. We proposed two types of approach: (1) monthly weighting scheme for a whole area (EBMA.v1) and (2) monthly weighting for each grid point (EBMA.v2), which can take into account the spatially heterogeneous pattern of GCM. For the training period of 1979- 2005 for East Asia, 9,855 sets of daily temperature ensembles (27 years × 365 days) were produced and compared to the ERA-Interim reanalysis data of European Centre for Medium-Range Weather Forecasts (ECMWF), which showed better validation statistics than the general mean and median ensembles. In particular, EBMA.v2 outperformed EBMA.v1 by diminishing the large errors of inland areas where the surface heterogeneity is larger than the ocean. The EBMA.v2 was able to handle the problem of spatial variability by employing monthly and spatially varying weighting scheme. We finally produced daily mean temperature ensembles for the period of 2006-2100 by using the EBMA.v2 under the RCP 6.0 scenario, which are going to be provided on the web.

The COMS (Communication, Ocean and Meteorological Satellite) has been used in numerical weather prediction and meteorological monitoring over East Asia and Oceania since it has been launched in 2010. For more active utilization in climate research, the COMS level 3 products should be available in appropriate spatial and temporal resolutions. We compared different methods to generate monthly sea surface temperature (SST) products from the COMS time-series data. We employed three techniques for aggregating the time series, which are arithmetic mean, timeslot average, and moving average, and also used mean ensemble of them. Each level 3 dataset around South Korea was compared with monthly SST product from the Moderate Resolution Imaging Spectroradiometer (MODIS) of Aqua satellite during April 2011-March 2014. The timeslot average showed better root mean squared difference (RMSD) during the initial operational period of the COMS, when the retrieved values could be somewhat unstable. Daytime aggregations were derived more accurately by using the arithmetic mean or moving average, and the accuracy of nighttime aggregation was improved by the mean ensemble. Also, the timeslot average presented reasonable results particularly for coastlines where the standard deviation and missing value ratio were greater than normal. Because an optimal aggregation technique was variable depending on spatial and temporal conditions, we should be careful in selecting appropriate method for generation of the COMS level 3 products according to research objectives.

This paper described the estimation of corn and soybeans yields of four states in the US Midwest using time-series satellite imagery and climate dataset between 2001 and 2012. We first constructed a database for (1) satellite imagery acquired from Terra MODIS (Moderate Resolution Imaging Spectroradiometer) including NDVI (Normalized Di°erence Vegetation Index), EVI (Enhanced Vegetation Index), LAI (Leaf Area Index), FPAR (Fraction of Photosynthetically Active Radiation), and GPP (Gross Primary Productivity), (2) climate dataset created by PRISM (Parameter-Elevation Regressions on Independent Slopes Model) such as precipitation and mean temperature, and (3) US yield statistics of corn and soybeans. ˜en we built OLS (Ordinary Least Squares) regression models for corn and soybeans yields between 2001 and 2010 after correlation analyses and multicollinearity tests. These regression models were used in estimating the yields of 2011 and 2012. Comparisons with the US yield statistics showed the RMSEs (Root Mean Squared Errors) of 0.892 ton/ha and 1.095 ton/ha for corn yields in 2011 and 2012 respectively, and those of 0.320 ton/ha and 0.391 ton/ha for soybeans yields. ˜is result can be thought of as a good agreement with the in-situ statistics because the RMSEs were approximately 10% of the usual yields: 9 ton/ha for corn and 3 ton/ha for soybeans. Our approach presented a possibility for extending to more advanced statistical modeling of crop yields using satellite imagery and climate dataset.

In this study, we produced satellite-based drought indices such as NDVI(normalized difference vegetation index), NDWI(normalized difference water index), and NDDI(normalized difference drought index) and extracted time-series principal components from the drought indices using EOF(empirical orthogonal function) method to examine their relationships with climatic variables. We found that the first principal components of the drought indices for Sonid Right Banner, Inner Mongolia were dominant in cold seasons and were closely related to low temperature, little rainfall, and high surface albedo. The satellite-based drought indices and their EOF analyses can be utilized for the studies of cold-season drought and warm-season drought as well.

Wildfires are recently increasing in frequencies and intensities worldwide. Hence, reliable and continuous monitoring of sudden occurrences of wildfire is demanded, and geostationary meteorological satellites are an alternative to detection of wildfire in large areas. We currently have two geostationary meteorological satellites for the Korean Peninsula: the Korean COMS(Communication, Ocean and Meteorological Satellite) and the Japanese MTSAT(Multifunctional Transport Satellite). However, neither of them provides satellite products for wildfire detection although the MODIS(Moderate Resolution Imaging Spectroradiometer) on polar-orbiting satellites has been operated for wildfire detection for a decade. In this study, we applied the MODIS algorithm for wildfire detection to the COMS and the MTSAT in order to evaluate the detection performances for South Korea. Both satellites were successful in detection of big fires, but the COMS was better in detecting small fires because of its higher saturation temperature of 350 K approx. at 4-μm band. The comparison results will be informative for an emergency plan of COMS and for the preparation of next-generation geostationary meteorological satellite.

The CMDPS(COMS Meteorological Data Processing System) provides 16 meteorological products by processing satellite data obtained from 1 visible and 4 infrared channels. It is expected that the COMS(Communication, Ocean and Meteorological Satellite) products will contribute to numerical weather prediction and climate change monitoring. For more active utilization of the products, we first need a scheme for the quality management based on outlier detection. This paper describes a method for detecting spatial and temporal outliers from the COMS products using Moran scatterplot and wavelet transform. The applicability of our method was made sure through the tests for seven products such as sea surface temperature, land surface temperature, aerosol optical depth, total precipitable water, upper tropospheric humidity, cloud top height, and cloud top temperature. Their quality was estimated very good in terms of our spatio-temporal statistical approach. For future work, we need more comprehensive study on the outlier detection, particularity focusing on the threshold setting in accordance with physical characteristics of each product.