• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.135-135
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• 2021

In water resources management, rainfall prediction with high accuracy is still one of controversial issues particularly in countries facing heavy rainfall during wet seasons in the monsoon climate. The aim of this study is to develop an artificial neural network (ANN) for predicting future six months of rainfall data (from April to September 2020) from daily meteorological data (from 1971 to 2019) such as rainfall, temperature, wind speed, and humidity at Seoul, Korea. After normalizing these data, they were trained by using a multilayer perceptron (MLP) as a class of the feedforward ANN with 15,000 neurons. The results show that the proposed method can analyze the relation between meteorological datasets properly and predict rainfall data for future six months in 2020, with an overall accuracy over almost 70% and a root mean square error of 0.0098. This study demonstrates the possibility and potential of MLP's applications to predict future daily rainfall patterns, essential for managing flood risks and protecting water resources.

• Atmosphere
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• v.16 no.3
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• pp.215-223
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• 2006

Among observational, local-environmental, and large-scale factors causing significant changes in climate records, the site relocations and the replacement of the instruments are well-known nonclimatic factors for the analysis of climatic trends, climatic variability, and for the detection of anthropogenic climate change such as heat-island effect and global warming. Using dataset that were contaminated by these nonclimatic factors can affect seriously the assessment of climatic trends and variability, and the detection of the climatic change signal. In this paper, the inhomogeneities, which have been caused by relocation of the observation site, in the climate data of Korea Meteorological Administration (KMA) were examined using two-phase regression model. The observations of pan evaporation and wind speed are more sensitive to site relocations than those of other meteorological elements, such as daily mean, maximum and minimum temperatures, with regardless to region.

• Proceedings of the Korea Water Resources Association Conference
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• 2002.05b
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• pp.1167-1172
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• 2002

• Journal of Institute of Convergence Technology
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• v.9 no.1
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• pp.31-36
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• 2019

After 30 years of KKP model analysis and extended 30 years of accuracy analysis, the unique correlation and various problems between measured horizontal surface insolation and measured weather data are found in this paper. The KKP model's 10yrs daily total horizontal surface insolation forecasting was averaged about 97.7% on average, and the forecasting accuracy at peak times per day was about 92.1%, which is highly applicable regardless of location and weather conditions nationwide. The daily total solar radiation forecasting accuracy of the modified KKP cloud model was 98.9%, similar to the KKP model, and 93.0% of the forecasting accuracy at the peak time per day. And the results of evaluating the accuracy of calculation for 30 years of KKP model were cloud model 107.6% and cloud model 95.1%. During the accuracy analysis evaluation, this study found that inaccuracies in measurement data of cloud cover should be clearly assessed by the Meteorological Administration.

• Proceedings of the KSRS Conference
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• 2008.10a
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• pp.313-316
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• 2008

Although the positioning accuracy of the Global Positioning System (GPS) has been studied extensively and used widely, it is still limited due to errors from sources such as the ionospheric effect, orbital uncertainty, antenna phase center variation, signal multipath and tropospheric influence. This investigation addresses the tropospheric effect on GPS height determination. Data obtained from GPS receivers and co-located surface meteorological instruments in 2003 are adopted in this study. The Ministry of the Interior (MOl), Taiwan, established these GPS receivers as continuous operating reference stations. Two different approaches, parameter estimation and external correction, are utilized to correct the zenith tropospheric delay (ZTD) by applying the surface meteorological measurements (SMM) data. Yet, incorrect pressure measurement leads to very poor accuracy. The GPS height can be affected by a few meters, and the root-mean-square (rms) of the daily solution ranges from a few millimeters to centimeters, no matter what the approach adopted. The effect is least obvious when using SMM data for the parameter estimation approach, but the constant corrections of the GPS height occur more often at higher altitudes. As for the external correction approach, the Saastamoinen model with SMM data makes the repeatability of the GPS height maintained at few centimeters, while the rms of the daily solution displays an improvement of about 2-3 mm.

• Journal of Environmental Science International
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• v.8 no.3
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• pp.287-292
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• 1999

This study was performed to investigate the characteristics of nocturnal maxiumu ozone occurrence and the meteorological relevance using to hourly ozone data and meteorological data for 1995~1996 in Pusan coastal area. Kwangbokdong showed the highest occurrence of nocturnal maximum ozone as 36.9%, and Deokcheondong showed the lowest occurrence(9.2%) for research period in Pusan. The occurrence rates of nocturnal maximum ozone concentration were decreased toward land area. The low maximum temperature, high minimum temperature, low diurnal range, high relative humidity, high wind speed, high could amount, low sunshine and low radiation were closely related to the main meteorological characteristics occuring the nocturnal maximum concnetration of ozone. It was shown that normal daily variation of ozone concentration by strong photochemical reaction at the before day of nocturnal maximum ozone. The concnetration of nocturnal maximum ozone were occured by entrainment of ozone from the upper layer of developed mixing layer. There are no ozone sources near the ground at night, so that the nighttime ozone should be entrained from the upper layer by forced convection.

• Journal of Astronomy and Space Sciences
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• v.37 no.3
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• pp.165-170
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• 2020

Muons and neutrons are representative secondary particles that are generated by interactions between primary cosmic ray particles (mostly protons) and the nuclei of atmospheric gas compounds. Previous studies reported that muons experience seasonal variations because of the meteorological effects of temperature. The intensity of neutrons has a typical modulation with various periods and reasons, such as diurnal and solar variation or transient events. This paper reports that cosmic ray particles, which were observed by neutron monitors, have seasonal variations using the daily data at the Oulu neutron monitor. To eliminate the effects of solar activity across time, the daily data were normalized by two different transformations: transformations with respect to the grand mean and yearly mean. The data after transformation with respect to the yearly mean showed more statistical stability and clear seasonal variations. On the other hand, it is difficult to determine if the seasonal variation results from terrestrial effects, such as meteorological factors, or extraterrestrial effects, such as the position of the Earth in its orbit of revolution.

• Korean Journal of Remote Sensing
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• v.19 no.5
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• pp.341-349
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• 2003

The so-called B-method, a simplified surface energy budget, permits calculation of daily actual evapotranspiration (ET) using remotely sensed data, such as NOAA-AVHRR. Even if the use of satellite data allows estimation of the albedo and surface temperature, this model requires meteorological data measured at ground-level to obtain the other inputs. In addition, a difficulty may be occurred by the difference of temporal scales between the net radiation in daily scale and instantaneous measurement at midday of the surface and air temperatures because the data covered whole day are necessary to obtain accumulated daily net radiation. In order to solve these problems, this study attempted a modification of B-method through an extension of hourly ET value calculated using a complete instantaneous inputs. The estimation of the daily apparent ET from newly proposed system showed a root mean square error of 0.26 mm/day as compared the output obtained from the classical model. It is evident that this may offer more rapid estimation and reduced data volume.

• Magazine of the Korean Society of Agricultural Engineers
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• v.30 no.1
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• pp.50-62
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• 1988

This study refers to the development of a hydrologic model simulating daily inflow and release rates for irrigation reservoirs. A daily - based model is needed for adequate operation of an irrigation reservoir sufficing the water demand for paddy fields which is closely related to meteorological conditions. Inflow rates to a reservoir need to be accurately described, which may be simulated using a hydrologic model from daily rainfall data. And the objective of this paper is to develop, test, and apply a hydrologic model for daily runoff simmulation. A well - known tank model was selected and modified to simulate daily inflow rates. The model parameters were calibrated using observed runoff data from twelve watersheds, Relationships between the parameters and the watershed characteristics were derived by a multiple regression analysis. The simulation results were in agreement with the data. The inflow model was found to simulate low flow conditions more accurately than high flow conditions, which may be adequate for water resources utilization.

• Korean Journal of Agricultural and Forest Meteorology
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• v.6 no.4
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• pp.242-249
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• 2004

This study was conducted to develop a forest fire occurrence model using meteorological characteristics for the practical purpose of forecasting forest fire danger. Forest fire in South Korea is highly influenced by humidity, wind speed, and temperature. To effectively forecast forest fire occurrence, we need to develop a forest fire danger rating model using weather factors associated with forest fire. Forest fore occurrence patterns were investigated statistically to develop a forest fire danger rating index using time series weather data sets collected from 8 meteorological observation centers. The data sets were for 5 years from 1997 through 2001. Development of the forest fire occurrence probability model used a logistic regression function with forest fire occurrence data and meteorological variables. An eight-province probability model by was developed. The meteorological variables that emerged as affective to forest fire occurrence are effective humidity, wind speed, and temperature. A forest fire occurrence danger rating index of through 10 was developed as a function of daily weather index (DWI).