• Korean Journal of Remote Sensing
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• v.36 no.5_3
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• pp.1037-1051
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• 2020

The accurate monitoring and forecasting of the intensity of tropical cyclones (TCs) are able to effectively reduce the overall costs of disaster management. In this study, we proposed a multi-task learning (MTL) based deep learning model for real-time TC intensity estimation and forecasting with the lead time of 6-12 hours following the event, based on the fusion of geostationary satellite images and numerical forecast model output. A total of 142 TCs which developed in the Northwest Pacific from 2011 to 2016 were used in this study. The Communications system, the Ocean and Meteorological Satellite (COMS) Meteorological Imager (MI) data were used to extract the images of typhoons, and the Climate Forecast System version 2 (CFSv2) provided by the National Center of Environmental Prediction (NCEP) was employed to extract air and ocean forecasting data. This study suggested two schemes with different input variables to the MTL models. Scheme 1 used only satellite-based input data while scheme 2 used both satellite images and numerical forecast modeling. As a result of real-time TC intensity estimation, Both schemes exhibited similar performance. For TC intensity forecasting with the lead time of 6 and 12 hours, scheme 2 improved the performance by 13% and 16%, respectively, in terms of the root mean squared error (RMSE) when compared to scheme 1. Relative root mean squared errors(rRMSE) for most intensity levels were lessthan 30%. The lower mean absolute error (MAE) and RMSE were found for the lower intensity levels of TCs. In the test results of the typhoon HALONG in 2014, scheme 1 tended to overestimate the intensity by about 20 kts at the early development stage. Scheme 2 slightly reduced the error, resulting in an overestimation by about 5 kts. The MTL models reduced the computational cost about 300% when compared to the single-tasking model, which suggested the feasibility of the rapid production of TC intensity forecasts.

• Journal of Soil and Groundwater Environment
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• v.14 no.3
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• pp.1-13
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• 2009

Alkalinity and total carbon contents were measured by acid neutralizing titration (ANT), back titration (BT), gravitational weighing (GW), non-dispersive infrared-total carbon (NDIR-TC) methods for assessing precision and accuracy of alkalinity and total carbon concentration in $CO_2$-rich water. Artificial $CO_2$-rich water(ACW: pH 6.3, alkalinity 68.8 meq/L, $HCO_3^-$ 2,235 mg/L) was used for comparing the measurements. When alkalinity measured in 0 hr, percent errors of all measurement were 0~12% and coefficient of variation were less than 4%. As the result of post-hoc analysis after repeated measure analysis of variance (RM-AMOVA), the differences between the pair of methods were not significant (within confidence level of 95%), which indicates that the alkalinity measured by any method could be accurate and precise when it measured just in time of sampling. In addition, alkalinity measured by ANT and NDIR-TC were not change after 24 and 48 hours open to atmosphere, which can be explained by conservative nature of alkalinity although $CO_2$ degas from ACW. On the other hand, alkalinity measured by BT and GW increased after 24 and 48 hours open to atmosphere, which was caused by relatively high concentration of measured total carbon and increasing pH. The comparison between geochemical modeling of $CO_2$ degassing and observed data showed that pH of observed ACW was higher than calculated pH. This can be happen when degassed $CO_2$ does not come out from the solution and/or exist in solution as $CO_{2(g)}$ bubble. In that case, $CO_{2(g)}$ bubble doesn't affect the pH and alkalinity. Thus alkalinity measured by ANT and NDIR-TC could not detect the $CO_2$ bubble although measured alkalinity was similar to the calculated alkalinity. Moreover, total carbon measured by ANT and NDIR-TC could be underestimated. Consequently, it is necessary to compare the alkalinity and total carbon data from various kind of methods and interpret very carefully. This study provide technical information of measurement of dissolve $CO_2$ from $CO_2$-rich water which could be natural analogue of geologic sequestration of $CO_2$.

• Korean Journal of Remote Sensing
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• v.37 no.5_1
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• pp.1135-1147
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• 2021

In order to geometrically correct high-resolution satellite imagery, the sensor modeling process that restores the geometric relationship between the satellite sensor and the ground surface at the image acquisition time is required. In general, high-resolution satellites provide RPC (Rational Polynomial Coefficient) information, but the vendor-provided RPC includes geometric distortion caused by the position and orientation of the satellite sensor. GCP (Ground Control Point) is generally used to correct the RPC errors. The representative method of acquiring GCP is field survey to obtain accurate ground coordinates. However, it is difficult to find the GCP in the satellite image due to the quality of the image, land cover change, relief displacement, etc. By using image maps acquired from various sensors as reference data, it is possible to automate the collection of GCP through the image matching algorithm. In this study, the RPC of KOMPSAT-3A satellite image was corrected through the extracted matching point using the UAV (Unmanned Aerial Vehichle) imagery. We propose a pre-porocessing method for the extraction of matching points between the UAV imagery and KOMPSAT-3A satellite image. To this end, the characteristics of matching points extracted by independently applying the SURF (Speeded-Up Robust Features) and the phase correlation, which are representative feature-based matching method and area-based matching method, respectively, were compared. The RPC adjustment parameters were calculated using the matching points extracted through each algorithm. In order to verify the performance and usability of the proposed method, it was compared with the GCP-based RPC correction result. The GCP-based method showed an improvement of correction accuracy by 2.14 pixels for the sample and 5.43 pixelsfor the line compared to the vendor-provided RPC. In the proposed method using SURF and phase correlation methods, the accuracy of sample was improved by 0.83 pixels and 1.49 pixels, and that of line wasimproved by 4.81 pixels and 5.19 pixels, respectively, compared to the vendor-provided RPC. Through the experimental results, the proposed method using the UAV imagery presented the possibility as an alternative to the GCP-based method for the RPC correction.