Browsing by Author "Liu, Gin-Rong"

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An improved genetic programming to SSM/I estimation typhoon precipitation over ocean
(2011-07-30)
Chen, Li
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Yeh, Keh-Chia
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Wei, Hsiao-Ping
;
Liu, Gin-Rong
;
土木工程學系
This article proposes an improved multi-run genetic programming (GP) and applies it to estimate the typhoon rainfall over ocean using multi-variable meteorological satellite data. GP is a well-known evolutionary programming and data mining method used to automatically discover the complex relationships among nonlinear systems. The main advantage of GP is to optimize appropriate types of function and their associated coefficients simultaneously. However, the searching efficiency of traditional GP can be decreased by the complex structure of parse tree to represent the multiple input variables. This study processed an improvement to enhance escape ability from local optimums during the optimization procedure. We continuously run GP several times by replacing the terminal nodes at the next run with the best solution at the current run. The current method improves GP, obtaining a highly nonlinear meaningful equation to estimate the rainfall. In the case study, this improved GP (IGP) described above combined with special sensor microwave imager (SSM/I) seven channels was employed. These results are then verified with the data from four offshore rainfall stations located on islands around Taiwan. The results show that the IGP generates sophisticated and accurate multi-variable equation through two runs. The performance of IGP outperforms the traditional multiple linear regression, back-propagated network (BPN) and three empirical equations. Because the extremely high values of precipitation rate are quite few and the number of zero values (no rain) is very large, the underestimations of heavy rainfall are obvious. A simple genetic algorithm was therefore used to search for the optimal threshold value of SSM/I channels, detecting the data of no rain. The IGP with two runs, used to construct an appropriate mathematical function to estimate the precipitation, can obtain more favourable results from estimating extremely high values. Copyright. (C) 2011 John Wiley & Sons, Ltd.
An overview of regional experiments on biomass burning aerosols and related pollutants in Southeast Asia: From BASE-ASIA and the Dongsha Experiment to 7-SEAS
(2013-10-01)
Lin, Neng-Huei
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Tsay, Si-Chee
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Maring, Hal B.
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Yen, Ming-Cheng
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Sheu, Guey-Rong
By modulating the Earth-atmosphere energy, hydrological and biogeochemical cycles, and affecting regional-to-global weather and climate, biomass burning is recognized as one of the major factors affecting the global carbon cycle. However, few comprehensive and wide-ranging experiments have been conducted to characterize biomass-burning pollutants in Southeast Asia (SEA) or assess their regional impact on meteorology, the hydrological cycle, the radiative budget, or climate change. Recently, BASE-ASIA (Biomass-burning Aerosols in South-East Asia: Smoke Impact Assessment) and the 7-SEAS (7-South-East Asian Studies)/Dongsha Experiment were conducted during the spring seasons of 2006 and 2010 in northern SEA, respectively, to characterize the chemical, physical, and radiative properties of biomass-burning emissions near the source regions, and assess their effects. This paper provides an overview of results from these two campaigns and related studies collected in this special issue, entitled "Observation, modeling and impact studies of biomass burning and pollution in the SE Asian Environment". This volume includes 28 papers, which provide a synopsis of the experiments, regional weather/climate, chemical characterization of biomass-burning aerosols and related pollutants in source and sink regions, the spatial distribution of air toxics (atmospheric mercury and dioxins) in source and remote areas, a characterization of aerosol physical, optical, and radiative properties, as well as modeling and impact studies. These studies, taken together, provide the first relatively complete dataset of aerosol chemistry and physical observations conducted in the source/sink region in the northern SEA, with particular emphasis on the marine boundary layer and lower free troposphere (LFT). The data, analysis and modeling included in these papers advance our present knowledge of source characterization of biomass-burning pollutants near the source regions as well as the physical and chemical processes along transport pathways. In addition, we raise key questions to be addressed by a coming deployment during springtime 2013 in northern SEA, named 7-SEAS/BASELInE (Biomass-burning Aerosols & Stratocumulus Environment: Lifecycles and Interactions Experiment). This campaign will include a synergistic approach for further exploring many key atmospheric processes (e.g., complex aerosol-cloud interactions) and impacts of biomass burning on the surface-atmosphere energy budgets during the lifecycles of biomass-burning emissions. (c) 2013 The Authors. Published by Elsevier Ltd. All rights reserved.
Combining satellite data for estimation of rainfall for analysis of a watershed scale
(2011)
Wei, Hsiao-Ping
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Yeh, Keh-Chia
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Liu, Gin-Rong
;
Chao, Chun-Chieh
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土木工程學系
The microwave/infrared rainfall algorithm (MIRA) method combines microwave and infrared channels for their respective contributions to the achievement of higher rainfall-correlated and spatial resolution data, in order to acquire appropriate rainfall data for hydrological models. In this study, estimates of rainfall rate using the MIRA method are affected by the time lag between the data acquisition of the microwave and infrared channels. The root-mean-square error (RMSE) for comparing the rainfall rate retrieved by the MIRA method (5-minute time lag between microwave and infrared data) to the ground rainfall rate in Taiwan is 12.23 mm hour(-1) with a correlation coefficient of 0.58. In addition, by comparing rainfall estimated by the TMI-2A12 and the MIRA method for three typhoons, the rainfall estimated by the latter method is more significantly correlated with the ground-observed rainfall.
Estimating Typhoon Rainfall over Sea from SSM/I Satellite Data Using a Back-propagated Network
(2010-01-01)
Chine, Wen-Huan
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Wei, Hsiao-Ping
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Yeh, Keh-Chia
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Chen, Li
;
Liu, Gin-Rong
This paper proposes a back-propagated network (BPN) and applies it to predict the rainfall using meteorological satellite data. In the case study, the back-propagated network (BPN) combining with SGji-Dao SM/I microwave frequency channels was employed to establish a suitable method for estimating rainfall at sea surface during typhoon periods. These results are then verified with the data from four rainfall stations located at Peng-Jia-Yu, Don-, Lan-Yu, and Green Island. From the results, the back-propagated network (BPN) outperforms the traditional multiple linear regression and empirical equations with lower estimating errors for predicting the sea-surface rainfall during typhoon periods.