• Korean Journal of Agricultural and Forest Meteorology
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• v.21 no.2
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• pp.85-96
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• 2019

Solar radiation is an important variable for estimation of energy balance and water cycle in natural and agricultural ecosystems. A deep neural network (DNN) model has been developed in order to estimate the daily global solar radiation. Temperature and precipitation, which would have wider availability from weather stations than other variables such as sunshine duration, were used as inputs to the DNN model. Five-fold cross-validation was applied to train and test the DNN models. Meteorological data at 15 weather stations were collected for a long term period, e.g., > 30 years in Korea. The DNN model obtained from the cross-validation had relatively small value of RMSE ($3.75MJ\;m^{-2}\;d^{-1}$) for estimates of the daily solar radiation at the weather station in Suwon. The DNN model explained about 68% of variation in observed solar radiation at the Suwon weather station. It was found that the measurements of solar radiation in 1985 and 1998 were considerably low for a small period of time compared with sunshine duration. This suggested that assessment of the quality for the observation data for solar radiation would be needed in further studies. When data for those years were excluded from the data analysis, the DNN model had slightly greater degree of agreement statistics. For example, the values of $R^2$ and RMSE were 0.72 and $3.55MJ\;m^{-2}\;d^{-1}$, respectively. Our results indicate that a DNN would be useful for the development a solar radiation estimation model using temperature and precipitation, which are usually available for downscaled scenario data for future climate conditions. Thus, such a DNN model would be useful for the impact assessment of climate change on crop production where solar radiation is used as a required input variable to a crop model.

• Atmosphere
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• v.15 no.1
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• pp.47-57
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• 2005

Extending the success of the Tropical Rainfall Measuring Mission (TRMM), the spaceborne measurement of precipitation by Global Precipitation Measurement (GPM) is initiated. The GPM consists of a core satellite which will have a dual-frequency precipitation radar (DPR) and a constellation of small satellites equipped with microwave radiometers. The GPM is inherently a global program. Responding to the GPM plan, many other nations are much interested in participating in the GPM team or simply utilizing GPM products aiming at the development of meteorological technology. Korea can fully function its role if Korea is selected as a CAL/VAL site for the GPM because Korea maintains a well-established dense rain gauge network (AWS), precipitation radars, and the Haenam super site for surface observation. In this feasibility study, the necessities of the GPM project in the context of academical and social backgrounds and associated international and domestic activities are investigated. And GPM-related core technologies and application areas are defined. As a result, it is found that GPM will represent a great opportunity for us because of its ability to provide not only much enhanced three-hourly global rain products but also very useful tools for the enhancement of weather forecasting capabilities, management of water resources, development and implementation of monitoring techniques for severe weather phenomena, agricultural managements and climate application. Furthermore, rain retrieval and CAL/VAL technologies obtained during the involvement in the international GPM project will serve as basic knowledges to run our own geostationary satellite program.

• Journal of Korea Water Resources Association
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• v.45 no.3
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• pp.275-290
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• 2012

In this study, we have developed an optimal time distribution model through extraction of peaks over threshold (POT) series. The median values for annual maximum rainfall dataset, which are obtained from the magnetic recording (MMR) and the automatic weather system(AWS) data at Seoul meteorological observatory, were used as the POT criteria. We also suggested the improved methodology for the time distribution of extreme rainfall compared to Huff method, which is widely used for time distributions of design rainfall. The Huff method did not consider changing in the shape of time distribution for each rainfall durations and rainfall criteria as total amount of rainfall for each rainfall events. This study have suggested an extracting methodology for rainfall events in each quartile based on interquartile range (IQR) matrix and selection for the mode quartile storm to determine the ranking cosidering weighting factors on minutely observation data. Finally, the optimal time distribution model in each rainfall duration was derived considering both data size and characteristics of distribution using kernel density function in extracted dimensionless unit rainfall hyetograph.

• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.2
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• pp.93-98
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• 2016

For accurate precipitation forecasts the choice of weather factors and prediction method is very important. Recently, machine learning has been widely used for forecasting precipitation, and artificial neural network, one of machine learning techniques, showed good performance. In this paper, we suggest a new method for forecasting precipitation using DBN, one of deep learning techniques. DBN has an advantage that initial weights are set by unsupervised learning, so this compensates for the defects of artificial neural networks. We used past precipitation, temperature, and the parameters of the sun and moon's motion as features for forecasting precipitation. The dataset consists of observation data which had been measured for 40 years from AWS in Seoul. Experiments were based on 8-fold cross validation. As a result of estimation, we got probabilities of test dataset, so threshold was used for the decision of precipitation. CSI and Bias were used for indicating the precision of precipitation. Our experimental results showed that DBN performed better than MLP.

• Atmosphere
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• v.26 no.4
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• pp.509-522
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• 2016

Heavy snowfall events have occurred frequently in the Yeongdong region but understanding of these events have trouble in lack of snowfall observation in this region because it is composed of complex topography like the "Taebaek mountains" and the "East sea". These problems can be solved by quantitative precipitation estimation technique using remote sensing such as radar, satellite, etc. Two radars which are able to cover over Yeondong region were installed at Gangneung (GNG) and Gwangdeoksan (GDK). This study uses radar and water equivalent of snow cover to investigate the characteristics of radar echoes and the $Z_e-R$ relations associated with the 10 Yeongdong heavy snowfall events during the last 5 years (2010~2014). It was found that the heights which the probability of detection (POD) of snow detection by GNG radar is more than 80% are 3,000 m and 1,500 m in convective cloud and stratiform cloud, respectively. The vertical gradient of radar reflectivity is less decreased in convective cloud than stratiform cloud. However, POD by GDK radar are lower than 80% at all layers because the majority of Yeondong observational stations are more than 100 km away from GDK radar site. Furthermore, we examined $Z_e-R$ relation from the 10 events using GNG radar and compared the "a" and "b" obtained from these examinations at Sokcho (SC) and Daegwallyeong (DG). These "a" and "b" are estimated from radar echo at 500 m (SC) and 1,500 m (DG). The values of "a" differ in their stations such as SC and DG are 30~116 and 6~39, respectively. But "b" is 0.4~1.7 irrespective of stations. Moreover, the value of "a" increased with surface air temperature. Therefore, quantitative precipitation estimation in heavy snowfall events by radar echo using fixed "a" and "b" is difficult because these values changed according to those precipitation characteristics.

• Journal of the Korean earth science society
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• v.31 no.4
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• pp.354-369
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• 2010

The synoptic and kinematic characteristics of a heavy rainfall that occurred in Gangneung region on 22 to 24 October 2006 were investigated using weather maps, infrared images, AWS observation data and NCEP global final analyses data. The total amount of rainfall observed in the region for the period was 316.5 mm, and the instanteneous maximum wind speed was $63.7m\;s^{-1}$. According to the analysis of weather maps, before the starting of the heavy rainfall, an extratropical low pressure system was developed in the middle region of the Korean Peninsula, and an inverted trough was formed in the northern region of the peninsula. In addition, a jet stream on the upper charts of 300 hPa was located over the Yellow Sea and the southern boundary of the peninsula. A cutoff low in the cyclonic shear side of the upper jet streak, which was linked to an anomaly of isentropic potential vorticity, was developed over the northwestern part of the peninsula. And there are analyzed potential vorticity and wind, time-height cross section of potential vorticity, vertical air motion, maximums of the divergence and convergence and vertical distribution of potential temperature in Gangneung region. The analyzed results of the synoptic conditions and kinematic processes strongly suggest that the tropopause folding made a significant role of initializing the heavy rainfall.

• Journal of the Korean earth science society
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• v.41 no.5
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• pp.459-468
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• 2020

In this study, the selection criteria for the occurrence of sea breezes in the Boseong area during the spring season (March-May) of 2016-2017 were prepared for the analysis of vertical weather characteristics. For this purpose, wind speed values were determined using the measured precipitation, cloud volume, wind direction, the difference between the ground and sea temperature, a wind Profiler at an altitude of 1 km, and numerical model data. The dates of the sea breezes in Boseong were classified according to the selection criteria, and the spatial and temporal characteristics of the sea breezes were identified by analyzing the time and altitude of the sea breeze and the size of the wind speed. Sea breezes occurred 23 out of 183 days (12%), and in Boseong, at least 1.2 out of 10 spring days exhibited sea breezes. Sea winds ranged from 1200 to 1800 LST, mainly from ground to 700 m altitude during the day. In addition, the maximum wind speed averaged 4.9 m s^-1, at an altitude of 40 m at 1600 LST, showing relatively lower values than those in a preceding study. This seems to be owing to the reduction in wind speed due to the complexity of the coastal terrain.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.4
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• pp.582-587
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• 2020

Recently, disasters caused by extreme weather and the damage caused by them are increasing worldwide. The interest in disasters, such as earthquakes, typhoons, and ground subsidence, is increasing in Korea. Korea has enacted a special law based on disaster management, and has built monitoring systems for individual facility units by building precision sensors and related systems to measure the displacement status of long bridges and high-rise composite buildings. On the other hand, the application of a real-time monitoring system is insufficient for slopes, open-pit mines, small and medium structures due to weather, measurement methods, cost, and constant monitoring difficulties. In this study, the displacement monitoring method using the total station was studied and the applicability was suggested through the experiment. Through the research, the concept and operation flow of a monitoring system that can measure the displacement of the terrain or the structure using the total station was presented. The monitoring system allows the user to select the location and operation method of the equipment so that the equipment can be installed according to the site situation, and set the number of observations, the period, and the observation range of the object. Using the experiment on the monitoring system, the station was monitored with precision within 5mm, and it was suggested that the displacement of the object can be monitored using the total station. Further research will be needed to assess the applicability of monitoring to real slopes and structures.

• Journal of the Korean earth science society
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• v.42 no.5
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• pp.514-523
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• 2021

Using the precipitation data observed at the Gochang Standard Weather Observatory (GSWO) during the winter seasons from 2014 to 2016, we analyzed the precipitation characteristics of the winter observation environment. For this study, we used four different types of precipitation gauges, i.e., No Shield (NS), Single Alter (SA), Double Fence Intercomparison Reference (DFIR), and Pit Gauge (PG). We analyzed the data from each to find differences in the accumulated precipitation, characteristics of the precipitation type, and the catch efficiency according to the wind speed based on the DFIR. We then classified these into three precipitation types, i.e., rain, mixed precipitation, and snow, according to temperature data from Gochang's Automated Synoptic Observing System (ASOS). We considered the DFIR to be the standard precipitation gauge for our analysis and the cumulative winter precipitation recorded by each other gauge compared to the DFIR data in the following order (from the most to least similar): SA, NS, and PG. As such, we find that the SA gauge is the most accurate when compared to the standard precipitation gauge used (DFIR), and the PG system is inappropriate for winter observations.

• Korean Journal of Agricultural and Forest Meteorology
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• v.20 no.4
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• pp.376-385
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• 2018

To estimate the hourly temperature distribution due to solar radiation during the day, on slope in complex terrain, an empirical formula was developed including the hourly deviation in the observed temperature following solar radiation deviation, at weather stations on the east-facing and west-facing slopes. The solar radiation effect was simulated using the empirical formula to estimate hourly temperature at 11 weather observation sites in mountainous agricultural areas, and the result was verified for the period from January 2015 to December 2017. When the estimated temperature was compared with the control, only considering temperature lapse rate, it was found that the tendency to underestimate the temperature from 9 am to 3 pm was reduced with the use of an empirical formula in the form of linear expression; consequently, the estimation error was reduced as well. However, for the time from 5 pm to 6 pm, the estimation error was smaller when a hyperbolic equation drawn from the deviation in solar radiation on the slope, which was calculated based on geometric conditions, was used instead of observed values. The reliability of estimating the daytime temperature at 3 pm was compared with existing estimation model proposed in other studies; the estimation error could be mitigated up to an ME (mean error) of $-0.28^{\circ}C$ and RMSE (root mean square error) of $1.29^{\circ}C$ compared to the estimation error in previous models (ME $-1.20^{\circ}C$, RMSE $2.01^{\circ}C$).