• Proceedings of the Korea Water Resources Association Conference
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• 2009.05a
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• pp.125-128
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• 2009

The research of climate change impact in hydrometeorology often relies on climate change information. In this paper, neural networks models such as generalized regression neural networks model (GRNNM) and multilayer perceptron neural networks model (MLP-NNM) are proposed statistical downscaling of the daily precipitation. The input nodes of neural networks models consist of the atmospheric meteorology and the atmospheric pressure data for 4 grid points including $127.5^{\circ}E/37.5^{\circ}N$, $127.5^{\circ}E/35^{\circ}N$, $125^{\circ}E/37.5^{\circ}N$ and $125^{\circ}E/35^{\circ}N$, respectively. The output node of neural networks models consist of the daily precipitation data for Seoul station. For the performances of the neural networks models, they are composed of training and test performances, respectively. From this research, we evaluate the impact of GRNNM and MLP-NNM performances for the downscaling of the daily precipitation data. We should, therefore, construct the credible daily precipitation data for Seoul station using statistical downscaling method. The proposed methods can be applied to future climate prediction/projection using the various climate change scenarios such as GCMs and RCMs.

• Korean Journal of Agricultural and Forest Meteorology
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• v.3 no.1
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• pp.55-70
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• 2001

It was unusual crop weather for 1998 and 1999 compared with normal in Korea. The consecutive days of the optimum ripening period for rice plant that had daily mean temperature 21~23$^{\circ}C$ for 40 days after flowering, increased with long anomalies in 1998~99. The air temperature during ripening period was much higher than the optimum temperature and lower sunshine hour than norm in the local adaptability tests of newly developed rice lines during those years. In response of rice cultivation to warming and cloudy weather during crop season, the yield shall be decreased. Most scientists agree that the rate of heating is accelerating and temperature change could become increasingly disruptive. Weather patterns should also become more erratic. Agrometeorologists could be analyzed yearly variations of temperature, sunshine hour and rainfall pattern focused on transient agroclimate change for last a decade. Rice agronomists could be established taking advantage of real time agricultural meteorology information system for fertilization, irrigation, pest control and harvest. Also they could be analyzed the characteristics of flowering response of the recommended and newly bred rice cultivars for suitable cropping plan such as cultural patterns and sowing or transplanting date. Rice breeders should be deeply considered introducing the characteristics of basic vegetative type of flowering response like Togil rices as prospective rice cultivars corresponding to global warming because of the rices needed higher temperature at ripening stage than japonica rices, photoperiod-sensitive and thermo-sensitive ecotypes.

• Korean Journal of Agricultural and Forest Meteorology
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• v.17 no.3
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• pp.236-247
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• 2015

Domestic agricultural and forest management suffers disturbances arising from rapid economic, social and environmental changes including climate change. Sustainable management has emerged as a key to overcoming these challenges. From the domestic and international viewpoint, we have identified mainly three (i.e. indicator, accounting, and ecological) approaches to sustainable management of agricultural and forest ecosystems. Compared to persistent investment in indicator and accounting approaches, we find the paucity of the domestic efforts in ecological approach. The latter approach can be facilitated based on the long-term meteorological and flux data including the ecosystem-level energy, matter and information flows, which have been monitored and managed by Korea Meteorological Administration, Rural Development Administration and Korea Forest Service. In order to keep up with vigorous international efforts toward sustainable ecosystem management, more interdisciplinary, multidisciplinary and transdisciplinary collaborations among diverse domestic sectors and institutes are essential.

• Journal of Environmental Impact Assessment
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• v.24 no.2
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• pp.134-153
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• 2015

When making urban planning, it is important to understand climate effect caused by urban structural changes. Seoul city applies UPIS(Urban Plan Information System) which provides information on urban planning scenario. Technology for analyzing climate effect resulted from urban planning needs to developed by linking urban planning scenario provided by UPIS and climate analysis model, CAS(Climate Analysis Seoul). CAS develops for analyzing urban climate conditions to provide realistic information considering local air temperature and wind flows. Quantitative analyses conducted by CAS for the production, transportation, and stagnation of cold air, wind flow and thermal conditions by incorporating GIS analysis on land cover and elevation and meteorological analysis from MetPhoMod(Meteorology and atmospheric Photochemistry Meso-scale model). In order to reflect land cover and elevation of the latest information, CAS used to highly accurate raster data (1m) sourced from LiDAR survey and KOMPSAT-2(KOrea Multi-Purpose SATellite) satellite image(4m). For more realistic representation of land surface characteristic, DSM(Digital Surface Model) and DTM(Digital Terrain Model) data used as an input data for CFD(Computational Fluid Dynamics) model. Eight inflow directions considered to investigate the change of flow pattern, wind speed according to reconstruction and change of thermal environment by connecting green area formation. Also, MetPhoMod in CAS data used to consider realistic weather condition. The result show that wind corridors change due to reconstruction. As a whole surface temperature around target area decreases due to connecting green area formation. CFD model coupled with CAS is possible to evaluate the wind corridor and heat environment before/after reconstruction and connecting green area formation. In This study, analysis of climate impact before and after created the green area, which is part of 'Connecting green network across the north and south in Seoul' plan, one of the '2020 Seoul master plan'.

• Korean Journal of Agricultural and Forest Meteorology
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• v.6 no.2
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• pp.71-84
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• 2004

There is a growing demand for agrometeorological information that end-users can use and not just interesting information. lo achieve this, each region/community needs to develop and provide localized climate and weather information for growers. Additionally, provide tools to help local users interpret climate forecasts issued by the National Weather Service in the country. Real time information should be provided for farmers, including some basic data. An ideal agrometeorological information system includes several components: an efficient data measuring and collection system; a modern telecommunication system; a standard data management processing and analysis system; and an advanced technological information dissemination system. While it is conventional wisdom that, Internet is and will play a major role in the delivery and dissemination of agrometeorological information, there are large gaps between the "information rich" and the "information poor" countries. Rural communities represent the "last mile of connectivity". For some time to come, TV broadcast, radio, phone, newspaper and fax will be used in many countries for communication. The differences in achieving this among countries arise from the human and financial resources available to implement this information and the methods of information dissemination. These differences must be considered in designing any information dissemination system. Experience shows that easy across to information more tailored to user needs would substantially increase use of climate information. Opportunities remain unexplored for applications of geographical information systems and remote sensing in agro meteorology.e sensing in agro meteorology.

• Korean Journal of Agricultural and Forest Meteorology
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• v.6 no.2
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• pp.127-139
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• 2004

Agricultural meteorology has advanced during the last 100 years from a descriptive to a quantitative science using physical and biological principles. The agricultural community is becoming more aware that using climate and weather information will improve their profitability and this will no doubt increase the demand for agrometeorological services. Hence it is timely that the needs and perspectives for agrometeorology in the 21$^{21}$ Century are grouped under two major headings: agrometeorological services for agricultural production and agrometeorological support systems for such services. Emphasis must be placed on the components of such support systems comprising of data, research, policies and training/education/extension. As Monteith (2000) mentioned, food supplies ultimately depend upon the skill with which farmers ran exploit the potential of good weather and minimize the impact of bad weather. Recent developments in instrumentation, data management systems, climate prediction, crop modelling, dissemination of agrometeorological information etc., provide agrometeorologists the tools necessary help the farmers improve such skills. The future for operational applications of agricultural meteorology appears bright and such applications could contribute substantially to promote sustainable agriculture and alleviate poverty.

• Korean Journal of Agricultural and Forest Meteorology
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• v.21 no.4
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• pp.347-357
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• 2019

The usefulness of processing and analysis systems of GIS-based agricultural climate data is affected by the reliability and availability of computing infrastructures such as cloud, on-premises, and hybrid. Cloud technology has grown in popularity. However, various reference cases accumulated over the years of operational experiences point out important features that make on-premises technology compatible with cloud technology. Both cloud and on-premises technologies have their advantages and disadvantages in terms of operational time and cost, reliability, and security depending on cases of applications. In this study, we have described characteristics of four general computing platforms including cloud, on-premises with hardware-level virtualization, on-premises with operating system-level virtualization and hybrid environments, and compared them in terms of advantages and disadvantages when a huge amount of GIS-based agricultural climate data were stored and processed to provide public services of agro-meteorological and climate information at high spatial and temporal resolutions. It was found that migrating high-resolution agricultural climate data to public cloud would not be reasonable due to high cost for storing a large amount data that may be of no use in the future. Therefore, we recommended hybrid systems that the on-premises and the cloud environments are combined for data storage and backup systems that incur a major cost, and data analysis, processing and presentation that need operational flexibility, respectively.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.4
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• pp.368-375
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• 2023

In this study, we examined the error characteristic and bias correction method for one-month temperature forecast data produced through joint development between the Rural Development Administration and the H ong Kong University of Science and Technology. For this purpose, hindcast data from 2013 to 2021, weather observation data, and various environmental information were collected and error characteristics under various environmental conditions were analyzed. In the case of maximum and minimum temperatures, the higher the elevation and latitude, the larger the forecast error. On average, the RMSE of the forecast data corrected by the linear regression model and the XGBoost decreased by 0.203, 0.438 (maximum temperature) and 0.069, 0.390 (minimum temperature), respectively, compared to the uncorrected forecast data. Overall, XGBoost showed better error improvement than the linear regression model. Through this study, it was found that errors in prediction data are affected by topographical conditions, and that machine learning methods such as XGBoost can effectively improve errors by considering various environmental factors.

• Korean Journal of Agricultural and Forest Meteorology
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• v.18 no.3
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• pp.109-119
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• 2016

This study was conducted to analyze the effect of yearly changes in growing degree days (GDD) on the potential distribution and growth of Quercus mongolica in Korea. Annual tree-ring growth data of Quercus mongolica collected by the $5^{th}$ National Forest Inventory were first organized to identify the range of current distribution for the species. Yearly GDD was calculated based on daily mean temperature data from 1951 to 2010 for counties with current distribution of Q. monglica. When tree-ring growth data were analyzed through cluster analysis based on similarity of climatic conditions, seven clusters were identified. Yearly GDD based on daily mean temperature data of each county were calculated for each of the cluster to predict the change of potential distribution. Temperature effect indices were estimated to predict the effect of GDD on the growth patterns. In addition, RCP 4.5 and RCP 8.5 of climate change scenarios were adopted to estimate yearly GDD and temperature effect indices from 2011 to 2100. The results indicate that the areas with low latitude and elevation exceed the upper threshold of GDD for the species due to the increase of mean temperature with climate change. It was also predicted that the steep increase of temperature will have negative influences on tree-ring growth, and will move the potential distribution of the species to areas with higher latitude or higher elevation, especially after the year of 2050. The results of this study are expected to provide valuable information necessary for estimating local growth characteristics and for predicting changes in the potential distribution of Q. mongolica caused by climate change.

• Asian Journal of Atmospheric Environment
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• v.8 no.3
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• pp.140-153
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• 2014

The extreme heat watch and warnings (EHWW) which is constructed as a part of the climate change adaptation took effect in the summer of 2008, but active response actions failed to be taken because of low perception among citizens. Therefore, a survey investigation targeting citizens residing in Busan and the Gyeongnam province was conducted in order to know the perception regarding EHWW issued by the Korea Meteorological Administration, to identify the main media through which information is acquired, and to propose an improvement measures which may enhance the usefulness and the degree of satisfaction of weather information. The results are as follows; The perception regarding EHWW was not very high as it remained at 59.8% in terms of percentile. Although the statistical significance was not fulfilled in the categories of gender or occupation, significant differences did exist among age groups. The main medium through which citizens acquired information regarding EHWW was the television, which was followed in order by the internet, acquaintances, short message service (SMS), radio, newspapers, the 131 weather hotline, and other media. The usefulness of EHWW was somewhat high (67.2%), and female students were found to utilize the information to a higher degree than male students. The statistics on the level of satisfaction regarding the weather information (65.4%) revealed that most respondents were satisfied. Housewives, professional, and the elder age groups exhibited great satisfaction, leading to the conclusions that the level of perception and interest regarding to the special weather reports (SWR) have an impact on satisfaction of SWR.