• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.35 no.6
• /
• pp.471-484
• /
• 2017

GNSS PWV (Precipitable Water Vapor) is recognized as an important factor for weather forecasts of typhoons and heavy rainfall. Domestic and foreign research have been published that improve weather forecasts using GNSS PWV as initial input data to NWP (Numerical Weather Prediction) model. For rainfall-related weather forecasts, PWV should be provided in real time or NRT (Near-Real Time) and the accuracy and integrity should be maintained. In this paper, the development process of NRT GNSS PWV system using PPP (Precise Point Positioning). To this end, we optimized the variables related to tropospheric delay estimation of PPP. For the analysis of the PPP NRT PWV system, we compared the PWV precision of RP (Relative Positioning) and PPP. As a result, the accuracy of PPP was lower than that of RP, but good results were obtained in the PWV data integrity. Future research is needed to improve the precision of PWV in the PPP method.

• Journal of Astronomy and Space Sciences
• /
• v.23 no.4
• /
• pp.373-384
• /
• 2006

The Bevis' mean temperature equation (MTE) is generally used in estimating Precipitable Water Vapor (PWV) based on GPS measurements. Because the equation was derived from Worth American meteorological data, however, it may induce errors in PWV if the equation is applied to Korea which has different climate conditions. In this study, we developed a new MTE using local meteorological data. We compared PWVs from the new equation with those from the Bevis and two other local equations. The PWV differences from the four equations increase as a function of surface temperatures at the observation site, reaching up to $1{\sim}3mm$.

• Journal of Positioning, Navigation, and Timing
• /
• v.5 no.2
• /
• pp.75-85
• /
• 2016

The performance of up-to-date mapping functions and various mean temperature equations were analyzed to derive optimal mapping function and mean temperature equation when GNSS precipitable water vapor (PWV) was investigated in the Korean Peninsula. Bernese GNSS Software 5.2, which can perform high precision GNSS data processing, was used for accurate analysis, and zenith total delay (ZTD) required to calculate PWV was estimated via the Precise Point Positioning (PPP) method. GNSS, radiosonde, and meteorological data from 2009 to 2014 were acquired from Sokcho Observatory and used. ZTDs estimated by applying the global mapping function (GMF) and Vienna mapping function 1 (VMF1) were compared with each other in order to evaluate the performance of the mapping functions. To assess the performance of mean temperature equations, GNSS PWV was calculated by using six mean temperature equations and a difference with radiosonde PWV was investigated. Conclusively, accuracy of data processing was improved more when using VMF1 than using GMF. A mean temperature equation proposed by Wu (2003) had the smallest difference with that in the radiosonde in the analysis including all seasons. In summer, a mean temperature equation proposed by Song & Grejner-Brzezinska (2009) had the closest results with that of radiosonde. In winter, a mean temperature equation proposed by Song (2009) showed the closest results with that of radiosonde.

• Spatial Information Research
• /
• v.18 no.4
• /
• pp.33-41
• /
• 2010

Approximately 100 permanent GPS stations are currently operational in Korea. However, only 10 sites have their own weather sensors connected directly to the GPS receiver. Thus. calculation of meteorological data through interpolation of AWS data are needed to determine precipitable water vapors at a specific GPS station without a meteorological sensor. This study analyzed the accuracy of two meteorological data interpolation methods called reverse sea level correction and kriging. As a result, the root-mean square-error of reverse sea level correction were seven times more accurate in pressure and twice more accurate in temperature than the kriging method. For the analysis of PWV accuracy, we calculated GPS PWV during the summer season in :2008 by using GPS observation data and interpolated meteorological data by reverse sea level correction. And, we compared GPS PWV s based on interpolated meteorological data with those from radiosonde observations and GPS PWV s based on onsite GPS meteorological sensor measurements. As a result, the accuracy of GPS PWV s from our interpolated meteorological data was within the required operational accuracy of 3mm.

• Journal of Astronomy and Space Sciences
• /
• v.28 no.4
• /
• pp.291-298
• /
• 2011

The atmospheric infrared sounder (AIRS) sensor loaded on the Aqua satellite observes the global vertical structure of atmosphere and enables verification of the water vapor distribution over the entire area of South Korea. In this study, we performed a comparative analysis of the accuracy of the total precipitable water (TPW) provided as the AIRS level 2 standard retrieval product by Jet Propulsion Laboratory (JPL) over the South Korean area using the global positioning system (GPS) TPW data. The analysis TPW for the period of one year in 2008 showed that the accuracy of the data produced by the combination of the Advanced Microwave Sounding Unit sensor with the AIRS sensor to correct the effect of clouds (AIRS-X) was higher than that of the AIRS IR-only data (AIRS-I). The annual means of the root mean square error with reference to the GPS data were 5.2 kg/$m^2$ and 4.3 kg/$m^2$ for AIRS-I and AIRS-X, respectively. The accuracy of AIRS-X was higher in summer than in winter while measurement values of AIRS-I and AIRS-X were lower than those of GPS TPW to some extent.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2005.05b
• /
• pp.9-18
• /
• 2005

In order to systematically and visually understand well-known but qualitative and rotatively complicated relationships between synoptic fields in the BAIU season and heavy rainfall events in Japan, these synoptic fields were classified using the Self-Organizing Map (SOM) algorithm. This algorithm can convert complex nonlinear features into simple two-dimensional relationships, and was followed by the application of the clustering techniques of the U-matrix and the K-means. It was assumed that the meteorological field patterns be simply expressed by the spatial distribution of wind components at the 850 hPa level and Precipitable Water (PW) in the southwestern area including Kyushu in Japan. Consequently, the synoptic fields could be divided into eight kinds of patterns (clusters). One of the clusters has the notable spatial feature represented by high PW accompanied by strong wind components known as Low-Level Jet (LLJ). The features of this cluster indicate a typical meteorological field pattern that frequently causes disastrous heavy rainfall in Kyushu in the rainy season. From these results, the SOM technique may be an effective tool for the classification of complicated non-linear synoptic fields.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2023.05a
• /
• pp.113-113
• /
• 2023

기후변화로 인한 온도 상승이 대기 중 수분량을 증가시키면서 극한 강우가 전 세계적으로 빈번하게 발생하고 있으며, 이에 따라 대규모 홍수 피해가 지속적으로 초래되고 있다. 본 연구에서는 이러한 현상에 대응하기 위한 노력으로, 대기 연직 기둥 내 총 수분량을 나타내는 총가강수량(Total Precipitable Water, TPW)과 극한 강우사상(Extreme Precipitation, EP) 간의 연관성이 강우지속기간에 따라 어떻게 변하는지 분석하였다. 관측 및 재해석(reanalysis) 데이터를 활용하여 동시극한지수(Concurrent Extremes Index, CEI, 0~1, 1에 가까울수록 강한 연결)로 두 변수 간의 정량적 연결 강도를 살펴보았다. 분석 결과, CEI의 지속 시간에 따른 변동 경향성은 지역에 따라 상당한 차이를 보였다. 지중해와 중앙아시아와 같은 대부분의 중위도 지역에서는 강우의 지속 기간이 길어질수록 CEI 값이 급격히 감소하였다. 그러나 한반도를 포함한 동아시아 지역은 중위도임에도 불구하고 긴 지속 기간의 강우 사상에서도 높은 수준의 CEI 값을 유지하였다. 이러한 동아시아 지역의 경향성은 열대지역과 매우 유사하게 나타났으며, 이는 동아시아 지역의 극한 강우 증가가 기후변화의 직접적인 영향을 받을 수 있음을 시사한다.

• Korean Journal of Remote Sensing
• /
• v.24 no.4
• /
• pp.359-367
• /
• 2008

TRMM TMI data were used to investigate a relationship between physical parameters from microwave sensor and typhoon intensities from June to September, 2004. Several data such as 85GHz brightness temperature (TB), polarization corrected temperature (PCT), precipitable water, ice content, rain rate, and latent heat release retrieved from the TMI observation were correlated to the maximum wind speeds in the best-track database by RSMC-Tokyo. Correlation coefficient between TB and typhoon intensity was -0.2 - -0.4 with a maximum value in the 2.5 degree radius circle from the center of tropical cyclone. The value of correlation between in precipitable water, rain, latent heat, and typhoon intensity is in the range of 0.2-0.4. Correlation analysis with respect to storm intensity showed that maximum correlation is observed at 1.0-1.5 degree radius circle from the center of tropical cyclone in the initial stage of tropical cyclone, while maximum correlation is shown in 0.5 degree radius in typhoon stage. Correlation coefficient was used to produce regressed intensities and adopted for typhoon Rusa (2002) and Maemi (2003). Multiple regression with 85GHz TB and precipitable water was found to provide an improved typhoon intensity when taking into account the storm size. The results indicate that it may be possible to use TB and precipitable water from satellite observation as a predictor to estimate the intensity of a tropical cyclone.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.35 no.5
• /
• pp.423-430
• /
• 2017

Recently, data analysis research has been carried out using the deep learning technique in various fields such as image interpretation and/or classification. Various types of algorithms are being developed for many applications. In this paper, we propose a precipitation prediction algorithm based on deep learning with high accuracy in order to take care of the possible severe damage caused by climate change. Since the geographical and seasonal characteristics of Korea are clearly distinct, the meteorological factors have repetitive patterns in a time series. Since the LSTM (Long Short-Term Memory) is a powerful algorithm for consecutive data, it was used to predict precipitation in this study. For the numerical test, we calculated the PWV (Precipitable Water Vapor) based on the tropospheric delay of the GNSS (Global Navigation Satellite System) signals, and then applied the deep learning technique to the precipitation prediction. The GNSS data was processed by scientific software with the troposphere model of Saastamoinen and the Niell mapping function. The RMSE (Root Mean Squared Error) of the precipitation prediction based on LSTM performs better than that of ANN (Artificial Neural Network). By adding GNSS-based PWV as a feature, the over-fitting that is a latent problem of deep learning was prevented considerably as discussed in this study.

• Journal of Korean Society for Geospatial Information Science
• /
• v.21 no.4
• /
• pp.95-100
• /
• 2013

Several observation equipments are being used for determination of the water vapor content and precipitable water vapor (PWV) because the water vapor is highly variable temporally and spatially. In this study, we used GNSS systems such as GPS and GLONASS in standalone and combined modes to compute PWV and validated their accuracy with respect to the results of other water-vapor monitoring systems. The other systems used were radiosonde and microwave radiometer, and the comparisons were convenient because all three systems were collocated at the test site. The differences of PWW were in the range of 0.6-3.4 mm in the mean sense, and their standard deviations were 1.0-3.8 mm. The relatively large difference of GNSS compared with the other two systems were believed to be caused by the fact that the GNSS antenna used in this study was the kind for which the international standard of phase center variations (PCV) calibration is not available. We expect better accuracy of PWV determination and improved availability of it through integrated data processing of GPS/GLONASS when an appropriate antenna with PCV correction model is used.