• 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.

• Korean Chemical Engineering Research
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• v.43 no.2
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• pp.286-293
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• 2005

An interpretation on the solid circulation characteristics in a fluidized-bed process has been carried out as a first step to simulate the dry entrained-bed absorption and bubbling-bed regeneration system for $CO_2$ removal from flue gas. A particle population balance has been developed to determine the solid flow rates and particle size distributions in the process. Effects of principal process parameters have been discussed in a laboratory scale process (absorption column: 25 mm i.d., 6 m in height; regeneration column: 0.1 m i.d., 1.2 m in height). The particle size distributions in absorption and regeneration columns were nearly the same. As gas velocity or static bed height in the absorption column increased, soild circulation rate and feed rate of fresh sorbent increased, however, mean particle diameter decreased in the absorption column. As cut diameter of the cyclone of the absorption column increased, solid circulation rate decreased, whereas feed rate of fresh sorbent and mean particle diameter in the absorption column increased. As attrition coefficient of sorbent particle increased, solid circulation rate and feed rate of fresh sorbent increased but mean particle diameter in the absorption column decreased.