• Journal of The Korean Society of Agricultural Engineers
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• v.55 no.6
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• pp.87-99
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• 2013

For analysis of climate change effects on agriculture, precise agricultural meteorological data are needed to target period and site. In this study, agricultural meteorological data under new climate change scenario (RCP 8.5) are constructed from 2011 to 2099 in 111 agriculture major station suggested by Rural Development Administration (RDA). For verifying constructed data, comparison with field survey data in Suwon shows same trend in maximum temperature, minimum temperature, average temperature, and precipitation in 2011. Also comparison with normals of daily data in 2025, 2055, and 2085 shows reliability of constructed data. In analysis of constructed data, we can calculate sum of days over temperature and under temperature. Results effectively show the change of average temperature in each region and odd days of precipitation which means flood and dry days in target region.

• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 2002.10a
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• pp.5-8
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• 2002

Daily potential evapotranspiration was estimated using meteorological data which are observing regularly such as rainfall, temperature, humidity, wind speed, and duration of sunshine. Penman method is used practically in estimating evapotranspiration at present, and its regional coefficients were derived at 19 stations in the Korean Peninsular. Because meteorological data are observing at 77 stations under the Korea Meteorological Administration, the methodology of estimating evapotranspiration using meteorological data will be able to be applied in more regions than Penman method.

• Journal of Environmental Science International
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• v.21 no.4
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• pp.461-470
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• 2012

Agriculture is more influenced by environmental factors rather than other industries. Among the environmental factors, the meteorological conditions mainly impact the output of agricultural products. Hence, the purpose of this study is to analyze the impact of meteorological factors on the output of elemental agricultural products. As a first step, we obtained the data of the meteorological factors (i.e., precipitation, humidity, temperature, insolation, snowdrifts, wind velocity) and the output of the various agricultural products (i.e., grain, fruits and vegetables, root crops, green vegetables, seasoned vegetables, fruits, special crops) from the year 1990 to 2009 (20 years) of Seoul and the six metropolitan cities in Korea. Then, the analysis of the correlation between the agricultural product with the largest output and the meteorological factors of the place where the corresponding agricultural product is most produced, was carried out in order to determine the core meteorological factor that most impacts the output of agricultural product. The correlation analysis revealed that humidity, insolation and wind velocity have been the crucial meteorological factors to influence the output of the agricultural products. From the result, we can induce that the meteorological forecast information about the vital meteorological factors, i.e., humidity, insolation and wind velocity, facilitates the optimized cultivation plan to maximize the output of agricultural products.

• Korean Journal of Agricultural and Forest Meteorology
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• v.14 no.2
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• pp.53-62
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• 2012

Demand for high resolution weather data grows in the agriculture and forestry fields. Local Analysis and Prediction System (LAPS) can be used to analyze the local weather at high spatial and temporal resolution, utilizing the data from various sources including numerical weather prediction models, wind or temperature profilers, Automated Weather Station (AWS) networks, radars, and satellites. LAPS has been set to analyze weather elements such as air temperature, relative humidity, wind speed, and wind direction every hour at the spatial resolution of $100m{\times}100m$ for Gyeonggi-do on near real-time basis. The AWS data were revised by adding the agricultural field AWS data (33 stations) in addition to the KMA data. The analysis periods were from 1 to 31 August 2009 and from 15 to 21 February 2010. The comparison of the LAPS output showed the smaller errors when using the agricultural AWS observation data together with the KMA data as its input data than using only either the agricultural or KMA AWS data. The accuracy of the current system needs improvement by further optimization of analyzing options of the system. However, the system is highly applicable to various fields in agriculture and forestry because it can provide site specific data with reasonable time intervals.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.2
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• pp.99-107
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• 2023

Recently, the abnormal weather events and crop damages occurred frequently likely due to climate change. The importance of meteorological data in agricultural research is increasing. Researchers can download weather observation data by accessing the websites provided by the KMA (Korea Meteorological Administration) and the RDA (Rural Development Administration). However, there is a disadvantage that multiple inquiry work is required when a large amount of meteorological data needs to be received. It is inefficient for each researcher to store and manage the data needed for research on an independent local computer in order to avoid this work. In addition, even if all the data were downloaded, additional work is required to find and open several files for research. In this study, data collected by the KMA and RDA were uploaded to GitHub, a remote storage service, and a package was created that allows easy access to weather data using Python. Through this, we propose a method to increase the accessibility and usability of meteorological data for agricultural personnel by adopting a method that allows anyone to take data without an additional authentication process.

• Journal of The Korean Society of Agricultural Engineers
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• v.49 no.4
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• pp.3-12
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• 2007

The purpose of this paper is to analyze the relationship between meteorological factors and agricultural reservoir storage, and predict the reservoir storage by multiple regression equation selected by high correlated meteorological factors. Two agricultural reservoirs (Geumgwang and Gosam) located in the upsteam of Gongdo water level gauging station of Anseong-cheon watershed were selected. Monthly reservoir storage data and meteorological data in Suwon weather station of 21 years (1985-2005) were collected. Three cases of correlation (case 1: yearly mean, case 2: seasonal mean dividing a year into 3 periods, and case 3: lagging the reservoir storage from 1 month to 3 months under the condition of case 2) were examined using 8 meteorological factors (precipitation, mean/maximum/minimum temperature, relative humidity, sunshine hour, wind velocity and evaporation). From the correlation analysis, 4 high correlated meteorological factors were selected, and multiple regression was executed for each case. The determination coefficient ($R^{2}$) of predicted reservoir storage for case 1 showed 0.45 and 0.49 for Geumgwang and Gosam reservoir respectively. The predicted reservoir storage for case 2 showed the highest $R^{2}$ of 0.46 and 0.56 respectively in the period of April to June. The predicted reservoir storage for 1 month lag of case 3 showed the $R^{2}$ of 0.68 and 0.85 respectively for the period of April to June. The results showed that the status of agricultural reservoir storage could be expressed with couple of meteorological factors. The prediction enhanced when the storage data are divided into periods rather than yearly mean and especially from the beginning time of paddy irrigation (April) to high decrease of reservoir storage (June) before Jangma.

• Atmosphere
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• v.34 no.1
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• pp.69-81
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• 2024

In this paper, Annual flux variations in the Boseong Tall Tower (BTT) from 2016 to 2020 were analyzed using data from three levels (2.5 m, 60 m, and 300 m). BTT was installed in Boseong-gun, Jeollanam-do in February 2014 and continued to conduct energy exchange observations such as CO2, sensible heat, and latent heat using the eddy covariance method until March 2023. The BTT was located in a very flat and uniform paddy field, and flux observations were conducted at four levels: 2.5 m, 60 m, 140 m, and 300 m above ground. Surface energy balance was confirmed from observed data of net radiation flux, soil heat flux, sensible heat flux, and latent heat flux. Additionally, 2.5 m height surface fluxes, which are most influenced by agricultural land, were compared with data from Local Data Assimilation and Prediction System (LDAPS) of the Korea Meteorological Administration to evaluate the accuracy of LDAPS flux data. The correlation coefficient between LDAPS flux data and observed values was 0.95 or higher. Excluding summer latent heat flux data, there was a general tendency for LDAPS data to be higher than observed values. The footprint areas estimated below 60 m height mainly covered agricultural land, and flux observations at 2.5 m and 60 m heights showed typical agricultural characteristics. In contrast, the footprint estimated at 300 m height did not show agricultural characteristics, indicating that observations at this height encompassed a wide range, including mountains, sea, and roads. The analysis results of long-term flux observations can contribute to understanding the energy and carbon dioxide fluxes in agricultural fields. Furthermore, these results can be utilized as essential data for validating and improving numerical models related to such fluxes.

• Korean Journal of Agricultural and Forest Meteorology
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• v.22 no.3
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• pp.117-127
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• 2020

The Early Warning System for agrometeorological hazard of the Rural Development Administration (Korea) forecasts detailed weather for each farm based on the meteorological information provided by the Korea Meteorological Administration, and estimates the growth of crops and predicts a meteorological hazard that can occur during the growing period by using the estimated detailed meteorological information. For verification of early warning system, automated weather observation network was constructed in the study area. Moreover, a real-time web display system was built to deliver near real-time weather data collected from the observation network. The meteorological observation system collected diverse meteorological variables including temperature, humidity, solar radiation, rainfall, soil moisture, sunshine duration, wind velocity, and wind direction. These elements were collected every minute and transmitted to the server every ten minutes. The data display system is composed of three phases: the first phase builds a database of meteorological data collected from the meteorological observation system every minute; the second phase statistically analyzes the collected meteorological data at ten-minutes, one-hour, or one-day time step; and the third phase displays the collected and analyzed meteorological data on the web. The meteorological data collected in the database can be inquired through the webpage for all data points or one data point in the unit of one minute, ten minutes, one hour, or one day. Moreover, the data can be downloaded in CSV format.

• Journal of Environmental Science International
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• v.27 no.7
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• pp.499-507
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• 2018

Agricultural meteorological information is an important resource that affects farmers' income, food security, and agricultural conditions. Thus, such data are used in various fields that are responsible for planning, enforcing, and evaluating agricultural policies. The meteorological information obtained from automatic weather observation systems operated by rural development agencies contains missing values owing to temporary mechanical or communication deficiencies. It is known that missing values lead to reduction in the reliability and validity of the model. In this study, the hierarchical Bayesian spatio-temporal model suggests replacements for missing values because the meteorological information includes spatio-temporal correlation. The prior distribution is very important in the Bayesian approach. However, we found a problem where the spatial decay parameter was not converged through the trace plot. A suitable spatial decay parameter, estimated on the bias of root-mean-square error (RMSE), which was determined to be the difference between the predicted and observed values. The latitude, longitude, and altitude were considered as covariates. The estimated spatial decay parameters were 0.041 and 0.039, for the spatio-temporal model with latitude and longitude and for latitude, longitude, and altitude, respectively. The posterior distributions were stable after the spatial decay parameter was fixed. root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and bias were calculated for model validation. Finally, the missing values were generated using the independent Gaussian process model.

• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
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• 2001.06a
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• pp.43-48
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• 2001

Many applications in the areas of agricultural, hydrological and environmental resource management require data over very large areas and with a high imaging frequency - monitoring crop growth, water stress, seasonal wetland flooding and natural vegetation development. This precludes the use of fine resolution data (Landsat, Spot) on the grounds of cost, accessibility and low imaging frequency. Meteorological satellites have the potential to fill this need, given their very wide spatial coverage, and high repeat imaging. The Remote Sensing Unit (RSU) at the Zambia Meteorological Department routinely receives, processes and archives imagery from both Meteosat and NOAA AVHRR satellites. Here I wish to present some examples of applications of these data sets that arise from the RSU work - relationships between rainfall and vegetation development as assessed by satellite, derived information and seasonal patterns of flooding in the Barotse floodplain and the Kafue flats. I also wish to outline ways in which a more widespread use of this data by the Zambian institutions canbe achieved.