• Journal of Environmental Science International
• /
• v.23 no.10
• /
• pp.1719-1729
• /
• 2014

This study is carried out in order to bridge the gap to understand the relationships between South Asian and East Asian monsoon systems by comparing the summer (June-September) precipitation of Nepal and South Korea. Summer monsoon precipitation data from Nepal and South Korea during 30 years (1981-2010) are used in this research to investigate the association. NCEP/NCAR reanalysis data are also used to see the nature of large scale phenomena. Statistical applications are used to analyze these data. The analyzed results show that summer monsoon precipitation is higher over Nepal ($1513.98{\pm}159.29mm\;y^{-1}$) than that of South Korea ($907.80{\pm}204.71mm\;y^{-1}$) and the wettest period in both the countries is July. However, the coefficient of variation shows that amplitude of interannual variation of summer monsoon over South Korea (22.55%) is larger in comparison to that of Nepal (10.52%). Summer monsoon precipitation of Nepal is found to be significantly correlated to that of South Korea with a correlation coefficient of 0.52 (99% confidence level). Large-scale circulations are studied to further investigate the relationship between the two countries. wind and specific humidity at 850 hPa show a strong westerly from Arabian Sea to BOB and from BOB, wind moves towards Nepal in a northwestward direction during the positive rainfall years. In case of East Asia, strong northward displacement of wind can be observed from Pacific to South Korea and strong anticyclone over the northwestern Pacific Ocean. However, during the negative rainfall years, in the South Asian region we can find weak westerly from the Arabian Sea to BOB, wind is blowing in a southerly direction from Nepal and Bangladesh to BOB.

• Korean Journal of Remote Sensing
• /
• v.34 no.6_2
• /
• pp.1261-1272
• /
• 2018

It is important to measure the Arctic surface air temperature because it plays a key-role in the exchange of energy between the ocean, sea ice, and the atmosphere. Although in-situ observations provide accurate measurements of air temperature, they are spatially limited to show the distribution of Arctic surface air temperature. In this study, we proposed machine learning-based models to estimate the Arctic surface air temperature in summer based on buoy data and Advanced Microwave Scanning Radiometer 2 (AMSR2)satellite data. Two machine learning approaches-random forest (RF) and support vector machine (SVM)-were used to estimate the air temperature twice a day according to AMSR2 observation time. Both RF and SVM showed $R^2$ of 0.84-0.88 and RMSE of $1.31-1.53^{\circ}C$. The results were compared to the surface air temperature and spatial distribution of the ERA-Interim reanalysis data from the European Center for Medium-Range Weather Forecasts (ECMWF). They tended to underestimate the Barents Sea, the Kara Sea, and the Baffin Bay region where no IABP buoy observations exist. This study showed both possibility and limitations of the empirical estimation of Arctic surface temperature using AMSR2 data.

• Korean Journal of Remote Sensing
• /
• v.37 no.2
• /
• pp.337-357
• /
• 2021

Because the growth of paddy rice is affected by meteorological factors, the selection of appropriate meteorological variables is essential to build a rice yield prediction model. This paper examines the suitability of multiple meteorological datasets for the rice yield modeling in South Korea, 1996-2019, and a hindcast experiment for rice yield using a machine learning method by considering the nonlinear relationships between meteorological variables and the rice yield. In addition to the ASOS in-situ observations, we used CRU-JRA ver. 2.1 and ERA5 reanalysis. From the multiple meteorological datasets, we extracted the four common variables (air temperature, relative humidity, solar radiation, and precipitation) and analyzed the characteristics of each data and the associations with rice yields. CRU-JRA ver. 2.1 showed an overall agreement with the other datasets. While relative humidity had a rare relationship with rice yields, solar radiation showed a somewhat high correlation with rice yields. Using the air temperature, solar radiation, and precipitation of July, August, and September, we built a random forest model for the hindcast experiments of rice yields. The model with CRU-JRA ver. 2.1 showed the best performance with a correlation coefficient of 0.772. The solar radiation in the prediction model had the most significant importance among the variables, which is in accordance with the generic agricultural knowledge. This paper has an implication for selecting from multiple meteorological datasets for rice yield modeling.