• Proceedings of the KSRS Conference
• /
• 2004.10a
• /
• pp.178-181
• /
• 2004

The Tropical Rainfall Measuring Mission (TRMM) is an US-Japan joint space mission to observe tropical and subtropical rainfall. This satellite is equipped with the world's first precipitation radar that operates at 13.8 GHz. We introduce the TRMM precipitation radar (PR) system, along with the PR data processing and analysis algorithms, and some observation results obtained by the TRMM PR. It is concluded that the TRMM PR can give quite useful rainfall data for the understanding of global climate changes, meteorology, climatology, atmospheric science, and also for the studies of satellite communication.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2022.05a
• /
• pp.143-143
• /
• 2022

Precipitation plays an essential role in water resources management and disaster prevention. Therefore, the understanding related to spatiotemporal characteristics of rainfall is necessary. Nowadays, highly accurate precipitation is mainly obtained from gauge observation systems. However, the density of gauge stations is a sparse and uneven distribution in mountainous areas. With the proliferation of technology, satellite-based precipitation sources are becoming increasingly common and can provide rainfall information in regions with complex topography. Nevertheless, satellite-based data is that it still remains uncertain. To overcome the above limitation, this study aims to take the strengthens of machine learning to generate a new reanalysis of precipitation data by fusion of multiple satellite precipitation products (SPPs) with gauge observation data. Several machine learning algorithms (i.e., Random Forest, Support Vector Regression, and Artificial Neural Network) have been adopted. To investigate the robustness of the new reanalysis product, observed data were collected to evaluate the accuracy of the products through Kling-Gupta efficiency (KGE), probability of detection (POD), false alarm rate (FAR), and critical success index (CSI). As a result, the new precipitation generated through the machine learning model showed higher accuracy than original satellite rainfall products, and its spatiotemporal variability was better reflected than others. Thus, reanalysis of satellite precipitation product based on machine learning can be useful source input data for hydrological simulations in ungauged river basins.

• Journal of Korea Water Resources Association
• /
• v.48 no.11
• /
• pp.937-944
• /
• 2015

This study proposed a novel technique, namely the Radar Polygon Method (RPM), for areal rainfall estimation based on radar precipitation data. The RPM algorithm has the following steps: 1. Determine a map of the similar rainfall occurrence of which each grid cell contains the binary information on whether the grid cell rainfall is similar to that of the observation gage; 2. Determine the similar rainfall probability map for each gage of which each grid cell contains the probability of having the rainfall similar to that of the observation gage; 3. Determine the governing territory of each gage by comparing the probability maps of the gages. RPM method was applied to the Anseong stream basin. Radar Polygons and Thiessen Polygons of the study area were similar to each other with the difference between the two being greater for the rain gage highly influenced by the orography. However, the weight factor between the two were similar with each other. The significance of this study is to pioneer a new application field of radar rainfall data that has been limited due to short observation period and low accuracy.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2009.05a
• /
• pp.2161-2166
• /
• 2009

It is important for 0-6 hour nowcasting to provide for a high-quality initial condition in a meso-scale atmospheric model by a data assimilation of several observation data. The polarimetric radar data is expected to be assimilated into the forecast model, because the radar has a possibility of measurements of the types, the shapes, and the size distributions of hydrometeors. In this paper, an impact on rainfall prediction of the data assimilation of hydrometeor types (i.e. raindrop, graupel, snowflake, etc.) is evaluated. The observed information of hydrometeor types is estimated using the fuzzy logic algorism. As an implementation, the cloud-resolving nonhydrostatic atmospheric model, CReSS, which has detail microphysical processes, is employed as a forecast model. The local ensemble transform Kalman filter, LETKF, is used as a data assimilation method, which uses an ensemble of short-term forecasts to estimate the flowdependent background error covariance required in data assimilation. A heavy rainfall event occurred in Okinawa in 2008 is chosen as an application. As a result, the rainfall prediction accuracy in the assimilation case of both hydrometeor types and the Doppler velocity and the radar echo is improved by a comparison of the no assimilation case. The effects on rainfall prediction of the assimilation of hydrometeor types appear in longer prediction lead time compared with the effects of the assimilation of radar echo only.

• Journal of Astronomy and Space Sciences
• /
• v.27 no.4
• /
• pp.367-375
• /
• 2010

In this study, we compared the precipitable water vapor (PWV) data derived from the radiosonde observation data at Sokcho Observatory and the PWV data at Sokcho Global Positioning System (GPS) Observatory provided by Korea Astronomy and Space Science Institute, from 0000 UTC, June 1, 2007 to 1200 UTC, May 31, 2009, and analyzed the radiosonde bias between the day and the night. In the scatter diagram of the daytime and nighttime radiosonde PWV data and the GPS PWV data, dry bias was found in the daytime radiosonde observation as known in the previous study. In addition, for all the rainfall events, the tendency that the wet bias of the radiosonde PWV increased as the GPS PWV decreased and the dry bias of the radiosonde PWV increased as the GPS PWV increased was significantly less distinctive in nighttime than in daytime. The quantitative analysis of the bias and error of the radiosonde PWV data showed that the mean bias decreased in the second year, regardless of nighttime or daytime rainfall, and the non-rainfall root mean square error (RMSE) was similar to that of the previous studies, while the rainfall RMSE was larger to a certain extent.

• Journal of Korea Water Resources Association
• /
• v.55 no.12
• /
• pp.1105-1114
• /
• 2022

A study on a method to overcome the limitations of the topographical and hydrological observation environment for estimating the QPE with high consistency with the ground rainfall by utilizing the spatiotemporal observation advantages of the rainfall radar for use in flood forecasting, and quantitative observations of localized rainfall due to these limiting conditions Uncertainty should be identified in terms of flood analysis. Against this background, in this study, 22 major heavy rain events in 2016 were analyzed for each of Mt. Biseul (BSL), Mt. Sobaek (SBS), Mt. Gari (GRS), Mt. Mohu (MHS), and Mt. Seodae (SDS) to determine the observation distance and altitude. The uncertainty of observation was quantified and an error map was derived. As a result of the analysis, it was found that, on average, the rainfall radar exceeded 10% up to 100 km and 30% over 150 km. Based on the average radar operating altitude angle, it was found that the error for the altitude was approximately 10% or less up to the second altitude angle, 20% at the third or higher altitude angle, and more than 50% at the fourth altitude angle or higher.

• Water Engineering Research
• /
• v.4 no.4
• /
• pp.187-192
• /
• 2003

The rainfall observation systems have largely been improved in Japan. The Japan Meteorological Agency, prefecture governments, and other administrative bodies have also increased the number of rain gauges thru out the country. The density of observatories is now one per several $\km^2$. Heavy rainfall information systems have been improved. Besides it, the Internet was popularized in the late 1990s, and has been used to transmit data of heavy rainfall. Internet accessible cellular phones have been popular in Japan since 1999. Such phones are expected to be useful in the field of disaster warning announcements, because they can automatically notify users bye-mail of pending disasters. The use of the Internet during natural disasters is groundbreaking in Japan today. However, in order to use disaster information effectively on Internet it is necessary to investigate how to use the information during the rainfall disaster. Therefore in our study we suggest methods on the effective construction and their use of information technology on Internet.

• Korean Journal of Remote Sensing
• /
• v.10 no.1
• /
• pp.1-14
• /
• 1994

The purpose of this study is to find the relationship between GMS image data and hourly observed rainfalls data. Heavy rainfall cases over South Korea on 10th September 1990 and on 29th July 1993 were selected for studying of the relationship between the image data and reinfalls. First, image data were converted to TBB(Temperature of Black Body) and albedo and then these values were extracted for the pixels closest to the surface observation station to correlate with the rainfall data. Horizontal distribution of TBB and albedo tells roughly rainfall regions. The correlation between rainfall and TBB is found to be very low in quantitative analysis. The weak relationship between the brighter albedo and the higher rainfall probability is observed. This study suggests that the TBB values are useful in classifying rain areas and for heavy rainfalls the albedo values are more useful than the TBB. Low linear correlation between the fields may be attributed to the neglect of cloud types in this study.

• Journal of Environmental Science International
• /
• v.13 no.7
• /
• pp.637-643
• /
• 2004

A new method of automatic recording raingauge is developed to measure rainfall with 0.01mm resolution. This use two different signals to measure rainfall more accurately compare than other raingauges. One is weight of the tipping bucket with rainfall amount and the other is pulse from tipping bucket reverse. New method applied 1 mm tipping bucket mechanism and install loadcell under tipping bucket mechanism for measuring rainfall weight. Loadcell measure weight of rainfall until 1 mm with 0.01 mm resolution and more than 1 mm than bucket reverse and pulse signal generate, after that loadcell measure weight again. The validation of new instrument was examined in the room 65 mm/hour rainfall rate total 53 mm range. There is below than 1 % error of absolute rainfall amount and 0.01 mm resolution. The field test of instrument was carried out by comparing its measured values with values recorded by weight type and standard type on June 1 2003 at Terrestrial Environmental Research Center at Tsukuba University in Tsukuba of Japan, when it has recorded total amount of 40.58 mm rainfall by standard raingauge and new raingauge recorded 41.032 mm. Same rainfall intensity pattern observed in field observation with weight type raingauge. Rainfall intensity between weight type and Lee-A type raingauge reached 0.9947 correlation in 3 minute average.

• Journal of the Korean Society of Hazard Mitigation
• /
• v.7 no.5
• /
• pp.89-97
• /
• 2007

This study proposed new simple methodologies for testing the stationarity of rainfall quantiles, and applied to the rainfall data at Seoul. The methodologies in this study are based on the analysis of frequency change of rainfall quantiles, different from previous studies like Ahn et al. (2001) who analyzed the change of rainfall quantiles themselves. The different types of methodologies are proposed in this study; one is to evaluate the occurrence frequency of rainfall with its return period more than the data length, and the other is to evaluate the effect of new observation on the highest rainfall data recorded. The application of these methodologies shows that the rainfall quantiles at Seoul have no significant proof leading their non-stationarity.