• Proceedings of the Korea Water Resources Association Conference
• /
• 2009.05a
• /
• pp.112-115
• /
• 2009

The research of climate change impact in hydrometeorology often relies on climate change information. In this paper, neural networks models such as support vector machine neural networks model (SVM-NNM) and multilayer perceptron neural networks model (MLP-NNM) are proposed statistical downscaling of the monthly precipitation. The input nodes of neural networks models consist of the atmospheric meteorology and the atmospheric pressure data for 2 grid points including $127.5^{\circ}E/35^{\circ}N$ and $125^{\circ}E/35^{\circ}N$, which produced the best results from the previous study. The output node of neural networks models consist of the monthly precipitation data for Seoul station. For the performances of the neural networks models, they are composed of training and test performances, respectively. From this research, we evaluate the impact of SVM-NNM and MLP-NNM performances for the downscaling of the monthly precipitation data. We should, therefore, construct the credible monthly precipitation data for Seoul station using statistical downscaling method. The proposed methods can be applied to future climate prediction/projection using the various climate change scenarios such as GCMs and RCMs.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2023.05a
• /
• pp.159-159
• /
• 2023

The Amazon River basin is one of the largest basins in the world, and its ecosystem is vital for biodiversity, hydrology, and climate regulation. Thus, understanding the hydrometeorological process is essential to the maintenance of the Amazon River basin. However, it is still tricky to monitor the Amazon River basin because of its size and the low density of the monitoring gauge network. To solve those issues, remote sensing products have been largely used. Yet, those products have some limitations. Therefore, this study aims to do bias corrections to improve the accuracy of Satellite Precipitation Products (SPPs) in the Amazon River basin. We use 331 rainfall stations for the observed data and two daily satellite precipitation gridded datasets (CHIRPS, TRMM). Due to the limitation of the observed data, the period of analysis was set from 1st January 1990 to 31st December 2010. The observed data were interpolated to have the same resolution as the SPPs data using the IDW method. For bias correction, we use convolution neural networks (CNN) combined with an autoencoder architecture (ConvAE). To evaluate the bias correction performance, we used some statistical indicators such as NSE, RMSE, and MAD. Hence, those results can increase the quality of precipitation data in the Amazon River basin, improving its monitoring and management.

• 한국방재학회:학술대회논문집
• /
• 2011.02a
• /
• pp.172-172
• /
• 2011

The interaction between land surface and atmosphere is essentially affected by hydrometeorological variables including soil moisture. Accurate estimation of soil moisture at spatial and temporal scales is crucial to better understand its roles to the weather systems. The KLDAS(Korea Land Data Assimilation System) is a regional, specifically Korea peninsula land surface information systems. As other prior land data assimilation systems, this can provide initial soil field information which can be used in atmospheric simulations. For this study, as an enabling high-resolution tool, weather research and forecasting(WRF-ARW) model is applied to produce precipitation data using GFS(Global Forecast System) with GFS embedded and KLDAS soil moisture information as initialization data. WRF-ARW generates precipitation data for a specific region using different parameters in physics options. The produced precipitation data will be employed for simulations of Hydrological Models such as HEC(Hydrologic Engineering Center) - HMS(Hydrologic Modeling System) as predefined input data for selected regional water responses. The purpose of this study is to show the impact of a hydrometeorological variable such as soil moisture in KLDAS on hydrological consequences in Korea peninsula. The study region, Chongmi River Basin, is located in the center of Korea Peninsular. This has 60.8Km river length and 17.01% slope. This region mostly consists of farming field however the chosen study area placed in mountainous area. The length of river basin perimeter is 185Km and the average width of river is 9.53 meter with 676 meter highest elevation in this region. We have four different observation locations : Sulsung, Taepyung, Samjook, and Sangkeug observatoriesn, This watershed is selected as a tentative research location and continuously studied for getting hydrological effects from land surface information. Simulations for a real regional storm case(June 17~ June 25, 2006) are executed. WRF-ARW for this case study used WSM6 as a micro physics, Kain-Fritcsch Scheme for cumulus scheme, and YSU scheme for planetary boundary layer. The results of WRF simulations generate excellent precipitation data in terms of peak precipitation and date, and the pattern of daily precipitation for four locations. For Sankeug observatory, WRF overestimated precipitation approximately 100 mm/day on July 17, 2006. Taepyung and Samjook display that WRF produced either with KLDAS or with GFS embedded initial soil moisture data higher precipitation amounts compared to observation. Results and discussions in detail on accuracy of prediction using formerly mentioned manners are going to be presented in 2011 Annual Conference of the Korean Society of Hazard Mitigation.

• Journal of Korea Water Resources Association
• /
• v.41 no.3
• /
• pp.265-276
• /
• 2008

The observed data of enough period need for design of hydrological works. But, most hydrological data aren't enough. Therefore in this paper, hourly precipitation generated by nonhomogeneous Markov chain model using variable Kernel density function. First, the Kernel estimator is used to estimate the transition probabilities. Second, wet hours are decided by transition probabilities and random numbers. Third, the amount of precipitation of each hours is calculated by the Kernel density function that estimated from observed data. At the results, observed precipitation data and generated precipitation data have similar statistic. Also, rainfall mass curve is derived by calculated transition probabilities for generation of hourly precipitation.

• Journal of Korean Society for Atmospheric Environment
• /
• v.18 no.E2
• /
• pp.97-106
• /
• 2002

The purpose of this study was to quantitatively estimate the washout removal efficiencies of criteria air pollutants such as SO$_2$, TSP, PM10, CO, NO$_2$, and O$_3$corresponding to the amounts and durations of precipitation. The removal patters by washout were studied with air pollutants data and the corresponding precipitation data in Seoul, Korea during the periods of 1990 to 1999. In addition, washout patterns were classified into four seasons and four time Bones, i.e., night, morning, afternoon, and evening. In this study, natures of air pollutants by sequential precipitation were also intensively studied by examining the linear relationships between removal efficiencies and the amounts and durations of precipitation for each pollutant. The results of this study showed that SO$_2$, TSP, and O$_3$were rapidly removed by initial precipitation; however, NO$_2$was slowly removed 2-hour after precipitation. Both CO and PM10 were weakly removed by washout and their removal patters showed to be irregular.

• Atmosphere
• /
• v.16 no.3
• /
• pp.235-246
• /
• 2006

Remote sensing techniques using satellites or the scanning weather radars depend mostly on the presence of clouds or precipitation, and leave the extensive regions of clear air unobserved. But wind profilers provide the most direct measurements of mesoscale vertical air motion in the troposphere, even in the context of heavy precipitation. In this paper, the precipitation events during the Changma period was classified into 4 precipitation types - stratiform, mixed stratiform/ convective, deep convective, and shallow convective. The parameters for the classification of analysis are the vertical structure of reflectivity, Doppler velocity, and spectral width measured with the wind profiler at Haenam for a three-year period (2003-2005). In addition, the synoptic fields and total amount of precipitation were analyzed using the Global Final Analyses (FNL) data and the Global Precipitation Climatology Project (GPCP) data. During the Changma period, the results show that the stratiform type was dominant under the moist-neutral atmosphere in 2003, whereas the deep convective type was under the moist unstable condition in 2004. The stratiform type was no less popular than the deep convective type among four seasons because the moist neutral layer was formed by the convergence between the upper-level jet and the low-level jet, and by the moisture transport along the western rim of the North Pacific subtropical anticyclone.

• Journal of Environmental Science International
• /
• v.23 no.4
• /
• pp.595-603
• /
• 2014

Jeju Island, a volcanic island, is the region that shows the biggest rainfall and has a big elevation-specific deviation of precipitation, but Jeju Island River Maintenance Plan doesn't reflect the characteristics of Jeju Island as it only calculates probable precipitation from four weather stations with elevation less than 100m. Therefore, this study uses AWS observational data in four Jeju Island weather stations and other regions to calculate location-specific probable precipitation, review the elevation-probable precipitation correlation in southern and northern regions, and create a probable precipitation map for all regions of Jeju Island, in order to produce better outcomes. This study is expected to be the most basic data to establish a safe Jeju island from flood disaster in preparation for the future climate changes and widely used for Jejudo Basin Dimension Planning, River Maintenance Plan, Pre-Disaster Impact Review, etc.

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

• Journal of Environmental Science International
• /
• v.15 no.1
• /
• pp.9-19
• /
• 2006

The purpose of this research is development of radar data assimilation observed at Jindo S-band radar The accurate observational data assimilation system is one of the important factors to meteorological numerical prediction of the region scale. Diagnostic analysis system LAPS(Local Analysis and Prediction System) developed by US FSL(Forecast Systems Laboratory) is adopted assimilation system of the Honam district forecasting system. The LAPS system was adjusted in calculation environment in the Honam district. And the improvement in the predictability by the application of the LAPS system was confirmed by the experiment applied to Honam district local severe rain case of generating 22 July 2003. The results are as follows: 1) Precipitation amounts of Gwangju is strong associated with the strong in lower level from analysis of aerological data. This indicated the circulation field especially, 850hPa layer, acts important role to precipitation in Homan area. 2) Wind in coastal area tends to be stronger than inland area and radar data show the strong wind in conversions zone around front. 3) Radar data assimilation make the precipitation area be extended and maximum amount of precipitation be smaller. 4) In respect to contribution rate of different height wind field on precipitation variation, radar data assimilation of upper level is smaller than that of lower level.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.40 no.3
• /
• pp.265-271
• /
• 2020

Automotive radar that is one of the most important equipment in high-tech vehicles, is commonly used to detect the speed and range of objects such as cars. In this paper, in addition to objects detection, a method of retrieving precipitation information using the automotive radar data is proposed. The proposed method is based on the fact that the degree of attenuation of the returned radar signal differs depending on the precipitation intensity and the assumption that the distribution of precipitation is constant in short spatial and temporal observation. The purpose of this paper is to assesses the possibility of retrieving precipitation information using a vehicle radar. To verify the feasibility of the proposed method during actual driving, a method of estimating precipitation information for each time segment of various precipitation events was applied. From the results of driving field experiments, it was found that the proposed method is suitable for estimating precipitation information in various rainfall types.