• Atmosphere
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• v.30 no.4
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• pp.319-333
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• 2020

Aircraft observations constitute one of the major sources of temperature observations which provide three-dimensional information. But it is well known that the aircraft temperature data have warm bias against sonde observation data, and therefore, the correction of aircraft temperature bias is important to improve the model performance. In this study, the algorithm of the bias correction modified from operational KMA (Korea Meteorological Administration) global model is adopted in the preprocessing of aircraft observations, and the effect of the bias correction of aircraft temperature is investigated by conducting the two experiments. The assimilation with the bias correction showed better consistency in the analysis-forecast cycle in terms of the differences between observations (radiosonde and GPSRO (Global Positioning System Radio Occultation)) and 6h forecast. This resulted in an improved forecasting skill level of the mid-level temperature and geopotential height in terms of the root-mean-square error. It was noted that the benefits of the correction of aircraft temperature bias was the upper-level temperature in the midlatitudes, and this affected various parameters (winds, geopotential height) via the model dynamics.

• Structural Engineering and Mechanics
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• v.80 no.6
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• pp.711-726
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• 2021

This paper presents a multiple parameters-based recursive methodology for the reliability quantification of civil structures subjected to meteorological disasters. Recognizing the challenge associated with characterizing at a single stroke all the meteorological disasters that may hit a structure during its service life, the proposed methodology by contrast features a multiparameter recursive mechanism to describe the meteorological demand of the structure. The benefit of the arrangements is that the essentially inevitable deviation of the practically observed meteorological data from those in the existing model can be mitigated in an adaptive manner. In particular, the implications of potential climate change to the relevant reliability of civil structures are allowed for. The application of the formulated methodology of recursive reliability quantification is illustrated by first considering the reliability quantification of a linear shear frame against simulated strong wind loads. A parametric study is engaged in this application to examine the effect of some hyperparameters in the configured hierarchical model. Further, the application is extended to a nonlinear hysteretic shear frame involving some field-observed cyclone data, and the incompleteness of the relevant structural diagnosis data that may arise in reality is taken into account. Also investigated is another application scenario where the reliability of a building envelope is assessed under hailstone impacts, and the emphasis is to demonstrate the recursive incorporation of newly obtained meteorological data.

• Smart Structures and Systems
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• v.32 no.2
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• pp.83-99
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• 2023

The temperature prediction approaches of three important locations in an operational longitudinal slab track-bridge structure by using three typical neural network methods based on the field measuring platform of four meteorological factors and internal temperature. The measurement experiment of four meteorological factors (e.g., ambient temperature, solar radiation, wind speed, and humidity) temperature in the three locations of the longitudinal slab and base plate of three important locations (e.g., mid-span, beam end, and Wide-Narrow Joint) were conducted, and then their characteristics were analyzed, respectively. Furthermore, temperature prediction effects of three locations under five various meteorological conditions are tested by using three neural network methods, respectively, including the Artificial Neural Network (ANN), the Long Short-Term Memory (LSTM), and the Convolutional Neural Network (CNN). More importantly, the predicted effects of solar radiation in four meteorological factors could be identified with three indicators (e.g., Root Means Square Error, Mean Absolute Error, Correlation Coefficient of R2). In addition, the LSTM method shows the best performance, while the CNN method has the best prediction effect by only considering a single meteorological factor.

• Journal of Environmental Science International
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• v.32 no.12
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• pp.899-914
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• 2023

Various seeding materials for cloud seeding are being used, and sodium chloride powder is one of them, which is commonly used. This study analyzed the experimental results of multi-aircraft cloud seeding in connection with Republic of Korea Air Force (CN235) and KMA/NIMS(Korea Meteorological Administration/National Institute of Meteorological Sciences) Atmospheric Research Aircraft. Powdered sodium chloride was used in CN235 for the first time in South Korea. The analysis of the cloud particle size distributions and radar reflectivity before and after cloud seeding showed that the growth efficiency of powdery seeding material in the cloud is slightly higher than that of hygroscopic flare composition in the distribution of number concentrations by cloud aerosol particle diameter (10 ~ 1000 ㎛). Considering the radar reflectivity, precipitation, and numerical model simulation, the enhanced precipitation due to cloud seeding was calculated to be a maximum of 3.7 mm for 6 hours. The simulated seeding effect area was about 3,695 km², which corresponds to 13,634,550 tons of water. In the precipitation component analysis, as a direct verification method, the ion equivalent concentrations (Na⁺, Cl^-, Ca²⁺) of the seeding material at the Bukgangneung site were found to be about 1000 times higher than those of other non-affected areas between about 1 and 2 hours after seeding. This study suggests the possibility of continuous multi-aircraft cloud seeding experiments to accumulate and increase the amount of precipitation enhancement.

• Journal of the Korean Data and Information Science Society
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• v.14 no.4
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• pp.773-787
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• 2003

Principal component analysis, factor analysis and cluster analysis have been performed to analyze the relationship between air pollutants and meteorological variables measured in 1999 in Seoul. In principal analysis, the first principal has been shown the contrast effect between $O_3$ and the other pollutants, the second principal has been shown the contrast effect between CO, $SO_2$, $NO_2$ and $O_3$, PM10, TSP. In factor analysis, the first factor has been found as PM10, TSP, $NO_2$ concentrations which are related with suspended particulates. As a result of cluster analysis, three clusters respectively have represented different air pollution levels, seasonal characteristics of air pollutants and meteorological situations.

• Journal of Korean Society for Atmospheric Environment
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• v.15 no.3
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• pp.257-266
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• 1999

In order to analyze the effect of meteorological elements on ozone concentration, we carried out cross-correlation of the elements with ozone concentraton, and time series analysis on them. As a result, it revealed that temperature, wind speed and humidity are not independent variables with ozone concentrations, and also, solar radiation and mixing height are the major elements that affect them. We developed models for ozone with solar radiation and mixing height as dependent variables to verify the effect of major meteorological elements. The predicted ozone concentration has strong correlation coefficients, So, We could conclude that we can predict ozone concentreation only with solar raidation and mixing height as dependents.

• Journal of Environmental Science International
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• v.27 no.11
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• pp.1141-1154
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• 2018

Numerical experiments were carried out to investigate the effect of data assimilation of observational data on weather and PM (particulate matter) prediction. Observational data applied to numerical experiment are aircraft observation, satellite observation, upper level observation, and AWS (automatic weather system) data. In the case of grid nudging, the prediction performance of the meteorological field is largely improved compared with the case without data assimilations because the overall pressure distribution can be changed. So grid nudging effect can be significant when synoptic weather pattern strongly affects Korean Peninsula. Predictability of meteorological factors can be expected to improve through a number of observational data assimilation, but data assimilation by single data often occurred to be less predictive than without data assimilation. Variation of air pressure due to observation nudging with high prediction efficiency can improve prediction accuracy of whole model domain. However, in areas with complex terrain such as the eastern part of the Korean peninsula, the improvement due to grid nudging were only limited. In such cases, it would be more effective to aggregate assimilated data.

• Atmosphere
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• v.23 no.2
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• pp.187-204
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• 2013

This study analyzed the synoptic distribution and vertical structure about four cases of precipitation occurrences using NCEP/NCAR reanalysis data and upper level data of winter intensive observation to be performed by National Institute of Meteorological Research at Bukgangneung, Incheon, Boseong during 63days from 4 JAN to 6 MAR in 2012, and Observing System Experiment (OSE) using 3DVAR-WRF system was conducted to examine the precipitation predictability of upper level data at western and southern coastal regions. The synoptic characteristics of selected precipitation occurrences were investigated as causes for 1) rainfall events with effect of moisture convergence owing to low pressure passing through south sea on 19 JAN, 2) snowfall events due to moisture inflowing from yellow sea with propagation of Siberian high pressure after low pressure passage over middle northern region on 31 JAN, 3) rainfall event with effect of weak pressure trough in west low and east high pressure system on 25 FEB, 4) rainfall event due to moisture inflow according to low pressures over Bohai bay and south eastern sea on 5 MAR. However, it is identified that vertical structure of atmosphere had different characteristics with heavy rainfall system in summer. Firstly, depth of convection was narrow due to absence of moisture convergence and strong ascending air current in middle layer. Secondly, warm air advection by veering wind with height only existed in low layer. Thirdly, unstable layer was limited in the narrow depth due to low surface temperature although it formed, and also values of instability indices were not high. Fourthly, total water vapor amounts containing into atmosphere was small due to low temperature distribution so that precipitable water vapor could be little amounts. As result of OSE conducting with upper level data of Incheon and Boseong station, 12 hours accumulated precipitation distributions of control experiment and experiments with additional upper level data were similar with ones of observation data at 610 stations. Although Equitable Threat Scores (ETS) were different according to cases and thresholds, it was verified positive influence of upper level data for precipitation predictability as resulting with high improvement rates of 33.3% in experiment with upper level data of Incheon (INC_EXP), 85.7% in experiment with upper level data of Boseong (BOS_EXP), and 142.9% in experiment with upper level data of both Incheon and Boseong (INC_BOS_EXP) about accumulated precipitation more than 5 mm / 12 hours on 31 January 2012.

• Journal of Korean Society of Forest Science
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• v.99 no.2
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• pp.197-203
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• 2010

The data of average temperature, average relative humidity, precipitation and average wind speed were collected from 674 meteorological stations in China. A specific procedure that processes original data into a new data format needed in forest fire danger rating forecast system of China was introduced systematically, and the feasibility of this method was validated in this paper. In addition, a set of meteorological data processing software was constructed by the secondary development of GIS in order to realize automation of processing data for the system. Results showed that the approach preformed well in handling temperature, average relative humidity and average wind speed, and the processing effect of precipitation was acceptable. Moreover, the automated procedure could be achieved by GIS and the working efficiency was about 3 times as much as that of manual handling. The informationization level of processing meteorological data was greatly enhanced.

• Proceedings of the Korea Air Pollution Research Association Conference
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• 2003.11a
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• pp.512-513
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• 2003

Human activities have become a major factor that significantly changes the global environment. Mankind has increasingly used land, water, minerals and other natural resources since the beginning of industrialization, and future growth in the population and economy is thought to further enhance the impact upon the Earth. The global climate, biogeochemical process and natural ecosystems are closely linked with one another, and changes in any one of these systems may effect the others, which could result in consequences detrimental to humans and other living organisms on the Earth.(omitted)