• Atmosphere
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• v.30 no.4
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• pp.335-346
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• 2020

Three free parameters included in a cumulus parameterization are optimized by using micro-genetic algorithm for three precipitation cases occurred in the Korea Peninsula during the summer season in order to reduce biases in a regional model associated with the uncertainties of the parameters and thus to improve the predictability of precipitation. The first parameter is the one that determines the threshold in convective trigger condition. The second parameter is the one that determines boundary layer forcing in convective closure. Finally, the third parameter is the one used in calculating conversion parameter determining the fraction of condensate converted to convective precipitation. Optimized parameters reduce the occurrence of convections by suppressing the trigger of convection. The reduced convection occurrence decreases light precipitation but increases heavy precipitation. The sensitivity experiments are conducted to examine the effects of the optimized parameters on the predictability of precipitation. The predictability of precipitation is the best when the three optimized parameters are applied to the parameterization at the same time. The first parameter most dominantly affects the predictability of precipitation. Short-range forecasts for July 2018 are also conducted to statistically assess the precipitation predictability. It is found that the predictability of precipitation is consistently improved with the optimized parameters.

• Journal of Climate Change Research
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• v.8 no.2
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• pp.109-123
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• 2017

The effects of recent climate change on hydrological systems could affect the Winter Olympic Games (WOG) because the event is dependent on suitable snow and ice conditions to support elite-level competitions. We investigate the long-term variability and change in winter total precipitation (P), snowfall water equivalent (SFE), and ratios of SFE to P during the period 1973/74~2015/16 in Gangwon province. The climatological percentages of SFE relative to winter total precipitation were 71%, 28%, and 44% in Daegwallyeong, Chuncheon, and Gangneung, respectively. The winter total P, SFE, and SFE/P has decreased (but not significantly), although significant increases of winter maximum and minimum temperature were detected at a 95% confidence level. Notably, a significant negative trend of SFE/P at Daegwallyeong in February, the month of the WOG, was attributable to a larger decrease in SFE related to the increases in maximum and minimum temperature. Winter wet-day minimum temperatures were warmer than climatological minimum temperatures averaged over the study period. The 20-year return values of daily maximum P and SFE decreased in Yongdong area. Since the SFE/P decrease with increasing temperature, the probability of rainfall rather than snowfall can increase if global warming continues.

• Proceedings of the Korea Water Resources Association Conference
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• 2012.05a
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• pp.200-204
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• 2012

In engineering hydrology, an estimation of precipitation loss is one of the most important issues for successful modeling to forecast flooding or evaluate water resources for both surface and subsurface flows in a watershed. An accurate estimation of precipitation loss is required for successful implementation of rainfall-runoff models. Precipitation loss or hydrological abstraction may be defined as the portion of the precipitation that does not contribute to the direct runoff. It may consist of several loss elements or abstractions of precipitation such as infiltration, depression storage, evaporation or evapotranspiration, and interception. A composite loss rate model that combines four loss rates over time is derived as a lumped form of a continuous time function for a storm event. The composite loss rate model developed is an exponential model similar to Horton's infiltration model, but its parameters have different meanings. In this model, the initial loss rate is related to antecedent precipitation amounts prior to a storm event, and the decay factor of the loss rate is a composite decay of four losses.

• Metals and materials international
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• v.24 no.6
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• pp.1193-1201
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• 2018

Dilatometry is a useful technique to obtain experimental data concerning transformation. In this paper, a dilation conversional model was established to calculate carbides fraction in AISI H13 hot-work tool steel based on the measured length changes. After carbides precipitation, the alloy contents in the matrix changed. In the usual models, the content of carbon atoms after precipitation is considered as the only element that affects the lattice constant and the content of the alloy elements such as Cr, Mo, Mn, V are often ignored. In the model introduced in this paper, the alloying elements (Cr, Mo, Mn, V) changes caused by carbides precipitation are incorporated. The carbides were identified using scanning electron microscope and transmission electron microscope. The relationship between lattice constant of carbides and temperature are measured by high-temperature X-ray diffraction. The results indicate that the carbides observed in all specimens cooled at different rates are V-rich MC and Cr-rich $M_{23}C_6$, and most of them are V-rich MC, only very few are Cr-rich $M_{23}C_6$. The model including the effects of substitutional alloying elements shows a good improvement on carbides fraction predictions. In addition, lower cooling rate advances the carbides precipitation for AISI H13 specimens. The results between experiments and mathematical model agree well.

• Journal of the Korean Data and Information Science Society
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• v.23 no.6
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• pp.1309-1317
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• 2012

Stochastic weather generators are commonly used to simulate time series of daily weather, especially precipitation amount. Recently, a generalized linear model (GLM) has been proposed as a convenient approach to fitting these weather generators. In this paper, a stochastic weather generator is considered to model the time series of daily precipitation at Seoul in South Korea. As a covariate, global temperature is introduced to relate long-term temporal scale predictor to short-term temporal predictands. One of the limitations of stochastic weather generators is a marked tendency to underestimate the observed interannual variance of monthly, seasonal, or annual total precipitation. To reduce this phenomenon, we incorporate time series of seasonal total precipitation in the GLM weather generator as covariates. It is veri ed that the addition of these covariates does not distort the performance of the weather generator in other respects.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.120-120
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• 2020

Spatial precipitation data is one of the essential components in modeling hydrological problems. The estimation of these data has achieved significant achievements own to the recent advances in remote sensing technology. However, there are still gaps between the satellite-derived rainfall data and observed data due to the significant dependence of rainfall on spatial and temporal characteristics. An effective approach based on the Convolutional Neural Network (CNN) model to correct the satellite-derived rainfall data is proposed in this study. The Mekong River basin, one of the largest river system in the world, was selected as a case study. The two gridded precipitation data sets with a spatial resolution of 0.25 degrees used in the CNN model are APHRODITE (Asian Precipitation - Highly-Resolved Observational Data Integration Towards Evaluation) and PERSIANN-CDR (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks). In particular, PERSIANN-CDR data is exploited as satellite-based precipitation data and APHRODITE data is considered as observed rainfall data. In addition to developing a CNN model to correct the satellite-based rain data, another statistical method based on standard deviations for precipitation bias correction was also mentioned in this study. Estimated results indicate that the CNN model illustrates better performance both in spatial and temporal correlation when compared to the standard deviation method. The finding of this study indicated that the CNN model could produce reliable estimates for the gridded precipitation bias correction problem.

• The Korean Journal of Applied Statistics
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• v.22 no.2
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• pp.425-433
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• 2009

Spatial models suitable for describing the evolving random fields in climate and environmental systems have been developed by many researchers. In general, rainfall in South Korea is highly variable in intensity and amount across space. This study characterizes the monthly and regional variation of rainfall fields using the spatial modeling. The main objective of this research is spatial prediction with the Bayesian hierarchical modeling (kriging) in order to further our understanding of water resources over space. We use the Bayesian approach in order to estimate the parameters and produce more reliable prediction. The Bayesian kriging also provides a promising solution for analyzing and predicting rainfall data.

• Journal of Korean Society for Atmospheric Environment
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• v.28 no.6
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• pp.615-631
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• 2012

This study provides an evaluation for capability of Integrated Climate and Air quality Modeling System (ICAMS) on future regional scale climate projection. Temperature and precipitation are compared between ground-level observation data and results of regional models (MM5) for the past 30 years over the Korean peninsula. The ICAMS successfully simulates the local-scale spatial/seasonal variation of the temperature and precipitation. The probability distribution of simulated daily mean and minimum temperature agree well with the observed patterns and trends, although mean temperature shows a little cold bias about $1^{\circ}C$ compared to observations. It seems that a systematic cold bias is mostly due to an underestimation of maximum temperature. In the case of precipitation, the rainfall in winter and light rainfall are remarkably simulated well, but summer precipitation is underestimated in the heavy rainfall phenomena of exceeding 20 mm/day. The ICAMS shows a tendency to overestimate the number of washout days about 7%. Those results of this study indicate that the performance of ICAMS is reasonable regarding to air quality predication over the Korean peninsula.

• Proceedings of the Korea Water Resources Association Conference
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• 2010.05a
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• pp.268-272
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• 2010

The research presented here represents a collaborative effort with the SFWMD on developing scenarios for future climate for the SFWMD area. The project focuses on developing methodology for simulating precipitation representing both natural quasi-oscillatory modes of variability in these climate variables and also the secular trends projected by the IPCC scenarios that are publicly available. This study specifically provides the results for precipitation modeling. The starting point for the modeling was the work of Tebaldi et al that is considered one of the benchmarks for bias correction and model combination in this context. This model was extended in the framework of a Hierarchical Bayesian Model (HBM) to formally and simultaneously consider biases between the models and observations over the historical period and trends in the observations and models out to the end of the 21st century in line with the different ensemble model simulations from the IPCC scenarios. The low frequency variability is modeled using the previously developed Wavelet Autoregressive Model (WARM), with a correction to preserve the variance associated with the full series from the HBM projections. The assumption here is that there is no useful information in the IPCC models as to the change in the low frequency variability of the regional, seasonal precipitation. This assumption is based on a preliminary analysis of these models historical and future output. Thus, preserving the low frequency structure from the historical series into the future emerges as a pragmatic goal. We find that there are significant biases between the observations and the base case scenarios for precipitation. The biases vary across models, and are shrunk using posterior maximum likelihood to allow some models to depart from the central tendency while allowing others to cluster and reduce biases by averaging. The projected changes in the future precipitation are small compared to the bias between model base run and observations and also relative to the inter-annual and decadal variability in the precipitation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.5
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• pp.1690-1707
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• 2021

Precipitation prediction during flood season has been a key task of climate prediction for a long time. This type of prediction is linked with the national economy and people's livelihood, and is also one of the difficult problems in climatology. At present, there are some precipitation forecast models for the flood season, but there are also some deviations from these models, which makes it difficult to forecast accurately. In this paper, based on the measured precipitation data from the flood season from 1993 to 2019 and the precipitation return data of CWRF, ANN cycle modeling and a weighted integration method is used to correct the CWRF used in today's operational systems. The MAE and TCC of the precipitation forecast in the flood season are used to check the prediction performance of the proposed algorithm model. The results demonstrate a good correction effect for the proposed algorithm. In particular, the MAE error of the new algorithm is reduced by about 50%, while the time correlation TCC is improved by about 40%. Therefore, both the generalization of the correction results and the prediction performance are improved.