• Journal of Sensor Science and Technology
• /
• v.32 no.3
• /
• pp.164-168
• /
• 2023

Accurate and reliable weather forecasts for temperature, relative humidity, and precipitation using advanced transformer models and IoT are essential in various fields related to global climate change. We propose a novel weather prediction device that integrates state-of-the-art transformer models and IoT techniques to improve prediction accuracy and real-time processing. The proposed system demonstrated high reliability and performance, offering valuable insights for industries and sectors that rely on accurate weather information, including agriculture, transportation, and emergency response planning. The integration of transformer models with the IoT signifies a substantial advancement in weather and climate modeling.

• Journal of The Korean Society of Grassland and Forage Science
• /
• v.41 no.1
• /
• pp.47-55
• /
• 2021

The objective of this study was to access the effect of climate and soil factors on alfalfa dry matter yield (DMY) by the contribution through constructing the yield prediction model in a general linear model considering climate and soil physical variables. The processes of constructing the yield prediction model for alfalfa was performed in sequence of data collection of alfalfa yield, meteorological and soil, preparation, statistical analysis, and model construction. The alfalfa yield prediction model used a multiple regression analysis to select the climate variables which are quantitative data and a general linear model considering the selected climate variables and soil physical variables which are qualitative data. As a result, the growth degree days(GDD) and growing days(GD), and the clay content(CC) were selected as the climate and soil physical variables that affect alfalfa DMY, respectively. The contributions of climate and soil factors affecting alfalfa DMY were 32% (GDD, 21%, GD 11%) and 63%, respectively. Therefore, this study indicates that the soil factor more contributes to alfalfa DMY than climate factor. However, for examming the correct contribution, the factors such as other climate and soil factors, and the cultivation technology factors which were not treated in this study should be considered as a factor in the model for future study.

• Atmosphere
• /
• v.33 no.2
• /
• pp.155-171
• /
• 2023

This paper aims to examine from various perspectives how domestic research studies and projects related to climate change have been conducted to mark the 60th anniversary of the Korean Meteorological Society (KMS). The 『50-year History of the Korean Meteorological Society』, published more than a decade ago, has never dealt with the history of development of individual fields of meteorology such as climate change. Therefore, it is of significance to look at the history of research activities and studies achieved by KMS members in the area of climate change over the past 60 years. The research on climate change in KMS is classified by era from the beginning to the latest and the contents are examined by major research projects at that time. During the past 60 years, climatological research in KMS has been mainly focused on general climate, synoptic climate, and applied climate (urban climate) until the 2000s. However, since the 1990s, climate change has become an important area for climate research. The 2000s are the beginning era of climate change research, since the major projects and researches for climate change has begun in the period. The 2010s can be a time when climate change prediction and monitoring are expanded and refined to meet the rapidly increasing demands for climate information from a wide range of areas. We concluded that the development of the research capabilities of the society over the past 60 years, in particular in the past two decades, in the field of climate change research is remarkable.

• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
• /
• 2017.11a
• /
• pp.34-34
• /
• 2017

• Atmosphere
• /
• v.32 no.4
• /
• pp.367-379
• /
• 2022

In this paper, the evaluation of the performance of Korea Meteorological Administratio (KMA) Global Seasonal forecasting system version 6 (GloSea6) is presented by assessing the effects of larger ensemble size and carrying out the test using different initial conditions for hindcast in sub-seasonal to seasonal scales. The number of ensemble members increases from 3 to 7. The Ratio of Predictable Components (RPC) approaches the appropriate signal magnitude with increase of ensemble size. The improvement of annual variability is shown for all basic variables mainly in mid-high latitude. Over the East Asia region, there are enhancements especially in 500 hPa geopotential height and 850 hPa wind fields. It reveals possibility to improve the performance of East Asian monsoon. Also, the reliability tends to become better as the ensemble size increases in summer than winter. To assess the effects of using different initial conditions, the area-mean values of normalized bias and correlation coefficients are compared for each basic variable for hindcast according to the four initial dates. The results have better performance when the initial date closest to the forecasting time is used in summer. On the seasonal scale, it is better to use four initial dates, where the maximum size of the ensemble increases to 672, mainly in winter. As the use of larger ensemble size, therefore, it is most efficient to use two initial dates for 60-days prediction and four initial dates for 6-months prediction, similar to the current Time-Lagged ensemble method.

• Journal of Environmental Impact Assessment
• /
• v.21 no.1
• /
• pp.105-117
• /
• 2012

Future climate according to land-use change was simulated by regional climate model. The goal of study was to predict the distribution of meteorological elements using the Weather Research & Forecasting Model (WRF). The KME (Korea Ministry of Environment) medium-category land-use classification was used as dominant vegetation types. Meteorological modeling requires higher and more sophisticated land-use and initialization data. The WRF model simulations with HyTAG land-use indicated certain change in potential vegetation distribution in the future (2086-2088). Compared to the past (1986-1988) distribution, coniferous forest area was decreased in metropolitan and areas with complex terrain. The research shows a possibility to simulate regional climate with high resolution. As a result, the future climate was predicted to $4.5^{\circ}$ which was $0.5^{\circ}$ higher than prediction by Meteorological Administration. To improve future prediction of regional area, regional climate model with HyTAG as well as high resolution initial values such as urban growth and CO2 flux simulation would be desirable.

• Atmosphere
• /
• v.21 no.1
• /
• pp.17-33
• /
• 2011

A dynamical downscaling system for seasonal forecast has been constructed based on a regional climate model, and its predictability was investigated for 10 years' wintertime (December-January-February; DJF) climatology in East Asia. Initial and lateral boundary conditions were obtained from the operational seasonal forecasting data, which are realtime output of the Global Data Assimilation and Prediction System (GDAPS) at Korea Meteorological Administration (KMA). Sea surface temperature was also obtained from the operational forecasts, i.e., KMA El-Nino and Global Sea Surface Temperature Forecast System. In order to determine the better configuration of the regional climate model for East Asian regions, two sensitivity experiments were carried out for one winter season (97/98 DJF): One is for the topography blending and the other is for the cumulus parameterization scheme. After determining the proper configuration, the predictability of the regional forecasting system was validated with respect to 850 hPa temperature and precipitation. The results showed that mean fields error and other verification statistics were generally decreased compared to GDAPS, most evident in 500 hPa geopotential heights. These improved simulation affected season prediction, and then HSS was better 36% and 11% about 850 hPa temperature and precipitation, respectively.

• Journal of Korea Water Resources Association
• /
• v.49 no.5
• /
• pp.361-372
• /
• 2016

Reliable long-term dam inflow prediction is necessary for efficient multi-purpose dam operation in changing climate. Since 2000s the teleconnection between global climate indices (e.g., ENSO) and local hydroclimate regimes have been widely recognized throughout the world. To date many hydrologists focus on predicting future hydrologic conditions using lag teleconnection between streamflow and climate indices. This study investigated the utility of teleconneciton for predicting dam inflow with 1-month lead time at Andong dam basin. To this end 40 global climate indices from NOAA were employed to identify potential predictors of dam inflow, areal averaged precipitation, temperature of Andong dam basin. This study compared three different approaches; 1) dam inflow prediction using SWAT model based on teleconneciton-based precipitation and temperature forecast (SWAT-Forecasted), 2) dam inflow prediction using teleconneciton between dam inflow and climate indices (CIR-Forecasted), and 3) dam inflow prediction based on the rank of current observation in the historical dam inflow (Rank-Observed). Our results demonstrated that CIR-Forecasted showed better predictability than the other approaches, except in December. This is because uncertainties attributed to temporal downscaling from monthly to daily for precipitation and temperature forecasts and hydrologic modeling using SWAT can be ignored from dam inflow forecast through CIR-Forecasted approach. This study indicates that 1-month lead dam inflow forecast based on teleconneciton could provide useful information on Andong dam operation.

• Journal of The Korean Society of Agricultural Engineers
• /
• v.64 no.5
• /
• pp.27-39
• /
• 2022

The smart farm is recognized as a solution for future farmers having positive effects on the sustainability of the poultry industry. Intelligent microclimate control can be a key technology for broiler production which is extremely vulnerable to abnormal indoor air temperatures. Furthermore, better control of indoor microclimate can be achieved by accurate prediction of indoor air temperature. This study developed predictive models for internal air temperature in a mechanically-ventilated broiler house based on the data measured during three rearing periods, which were different in seasonal climate and ventilation operation. Three machine learning models and a mechanistic model based on thermal energy balance were used for the prediction. The results indicated that the all models gave good predictions for 1-minute future air temperature showing the coefficient of determination greater than 0.99 and the root-mean-square-error smaller than 0.306℃. However, for 1-hour future air temperature, only the mechanistic model showed good accuracy with the coefficient of determination of 0.934 and the root-mean-square-error of 0.841℃. Since the mechanistic model was based on the mathematical descriptions of the heat transfer processes that occurred in the broiler house, it showed better prediction performances compared to the black-box machine learning models. Therefore, it was proven to be useful for intelligent microclimate control which would be developed in future studies.

• Atmosphere
• /
• v.23 no.2
• /
• pp.131-141
• /
• 2013

Many studies have related the recent variations of wildfire regime such as the increasing number of occurrances, their patterns and timing changes, and the severity of their extreme cases with global warming. However, there are only a few numbers of wildfire studies to assess how the future wildfire regime will change in the interactions between land and atmosphere with climate change especially over East Asia. This study was performed to estimate the future changing aspect of wildfire danger with global warming, using Haines Index (HI). Calculated from atmospheric instability and dryness, HI is the potential of an existing fire to become a dangerous wildfire. Using the Weather Research and Forecasting (WRF) model, two separated 5-year simulations of current (1995~1999) and far future (2095~2099) were performed and analyzed. Community Climate System Model 3 (CCSM3) model outputs were utilized for the model inputs for the past and future over East Asia; future prediction was driven under the IPCC A1B scenario. The results indicate changes of the wildfire danger regime, showing overall decreasing the wildfire danger in the future but intensified regional deviations between north and south. The overall changes of the wildfire regime seems to stem from atmospheric dryness which is sensitive to soil moisture variation. In some locations, the future wildfire danger overall decreases in summer but increases in winter or fall when the actual fire occurrence are generally peaked especially in South China.