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ABSTRACT Seven air quality basins are classified in Taiwan according to landforms¡Bweather conditions and atmosphere diffusion. The data showed that the percentages of fractional number of days that PSI is greater than 100 (unhealthful) are 11.4%, 10.5% and 15.3% in three parts of Southern Taiwan (including Kaohsiung City, Kaohsiung County and Pintung County) in the years of 1999, 2000and 2001. For Kao-Ping area alone, the percentages of unhealthful days are, 12.1% , 10.7%and 8.2% in recent three years. The air pollutants in ambient air in Kao-Ping area are particulate matter (PM10) and ozone (O3). Particulate matter (PM10) is a primary pollutant and derivative aerosol, the mainly were factory stack¡Bmobile vehicles¡Bconstruction site and emission the surface of the earth. O3 is a secondary pollutant that is generated by its precursors such as hydrocarbons (HC) and nitrogen oxides (NOx) through complex photo-chemical reactions. Accordingly, attention has been shifted to the source and control of ozone in recently years in southern part of Taiwan, instead of PM10 alone. In order to improve the air quality, the deteriorated reason and pollution condition must be understood. The purpose of this proposal is thus to identify the O3 trend variation and weather pattern and mixing height. This proposal contains two parts: Collect the meteorological data (including pressure¡Btemperature¡Bwind velocity¡Bwind direction¡Bhumidity and sunshine time) and air quality data. Then we analyze the relationship between meteorological factors and air quality. Analyze the high O3 episodes based on the characteristics of hourly¡Bmonthly and seasonally data and to investigate the effects of mixing height and Ventilation index on air quality in Kao-Ping area.

ABSTRACT The relationships between high ozone events and synoptic weather patterns were investigated based on the meteorological and air-quality data at four monitoring stations in Kaohsiung city during 1997 to 2004 in this study. The PCA (principal component analysis) and correlation studies show that ozone concentration exhibit positive correlations with the ambient temperature and sunshine duration, while negative correlation with the cloud cover. The frequency of high ozone events was highest under the weather patterns of northeast seasonal wind (B1 type), high-pressure system with recirculation (B3 type), high-pressure system with offshore type (B2 type), and in the warm core of approaching front (B4 type), most frequently in autumn, winter, and spring, and least frequently in summer. The above four weather patterns are primarily dominated by the high-pressure system from Mainland China, with relatively cold and dry weather and little rain in Taiwan. Since the high-pressure center is usually located north or northeast of Taiwan during autumn, winter, and spring such that the leeside areas of Central Mountain Ranges in the western coast of Taiwan are prone to high pollution events, particularly when the wind speed is low (< 4m/s) and duration of sunshine is long (> 5.5 hr). Or sometimes the meso-scale wind system, for example, the sea-land breeze or the combined flow caused by the detouring flow from the Henchun Peninsula and northeast wind tends to raise the pollutant concentrations. Keywords¡GHigh ozone event, Synoptic weather pattern, Principal component analysis.

Abstract This study aims to investigate the influence of micrometeorology on atmospheric visibility in Taipei Basin by carrying on routine and intensive visibility observation, analyzing particle concentrations and size distribution, monitoring atmospheric optical coefficients of particles, and collecting meteorological conditions (wind direction, wind speed, temperature and humidity). Based on the information of the broad view of the meteorological parameters from 1998 to 2005, the air pollution episode in Taipei Basin mainly occurred during the winter periods accompanied with the weather patterns of high pressure outflow style I, high pressure outflow style II or circus-sluice of high pressure outflow. Based on the collected visibility data from Taipei of Central Weather Bureau for the past 21 years (1983~2004), it shows the visibility increasing each year. The visibility seasonal variation was also observed higher than that during winter. The mean visibilities from Tamsui, Songshan, and Sindian directions are 10.54 km , 9.72 km and 8.62 km, respectively. In general, the visibility from the Tamsui direction is slightly higher than those the from two directions, This result shows visibility is influenced not only the pollution factor and meteorological factor, but affected by the local topography. Our study revealed that the visibility data leads on the Tamsui, Songshan obserrative directions is better. Two intensive sampling campaign were conducted during August 15~22 of 2004 and January 15~22 of 2005 on the roof of Taipei County Government, respectively. The results showed mass concentration of particulate matter in winter is higher than that in summer. A bi-mode size distribution of aerosol particles was also observed in the Taipei basin. Two peaks of aerodynamic diameter of PM were observed at 0.56~1.0 &#x00B5;m and 3.2~5.6 &#x00B5;m, respectively. In addition, extinction coefficient is always high during winter. Mean scattering coefficient is about 80~108 Mm-1 while the mean absorption coefficient is around 9~35 Mm-1, during the 1st intensive sampling period. Mean scattering coefficient is about 101~119 Mm-1 while the mean absorption coefficient is around 16~64 Mm-1, during the 2nd intensive sampling period. Our study also shows that visibility has a higher correlation with fine particles(R=0.70~0.96) than that with coarse particles(R=0.045~0.629). Hance, visibility is affected mainly by fine particles. In general, the intensive observation of visibility also shows that in summer the visibility is higher than that in winter. In addition, the analysis from MM5 shows that the visibility for the Tamsui direction is usually influenced by the prevailing wine direction, It is because of that northwestern from sea brings lots of water spray into the basin. This spray water will increase the particle diameter and cause the increasing of extinction coefficient. It will also impair the visibility.

<p>A global climate change may have large impacts on water resources on regional and global scales. General circulation models (GCMs) are the most used tools to evaluate climate-change scenarios on a global scale. They are, however, insufficiently describing the effects at the local scale. This thesis evaluates different approaches of statistical downscaling of precipitation from large-scale circulation variables, both concerning the method performance and the optimum choice of predictor variables. </p><p>The analogue downscaling method (AM) was found to work well as “benchmark” method in comparison to more complicated methods. AM was implemented using principal component analysis (PCA) and Teweles-Wobus Scores (TWS). Statistical properties of daily and monthly precipitation on a catchment in south-central Sweden, as well as daily precipitation in three catchments in China were acceptably downscaled.</p><p>A regression method conditioning a weather generator (SDSM) as well as a fuzzy-rule based circulation-pattern classification method conditioning a stochastical precipitation model (MOFRBC) gave good results when applied on Swedish and Chinese catchments. Statistical downscaling with MOFRBC from GMC (HADAM3P) output improved the statistical properties as well as the intra-annual variation of precipitation.</p><p>The studies show that temporal and areal settings of the predictor are important factors concerning the success of precipitation modelling. The MOFRCB and SDSM are generally performing better than the AM, and the best choice of method is depending on the purpose of the study. MOFRBC applied on output from a GCM future scenario indicates that the large-scale circulation will not be significantly affected. Adding humidity flux as predictor indicated an increased intensity both in extreme events and daily amounts in central and northern Sweden.</p>

A global climate change may have large impacts on water resources on regional and global scales. General circulation models (GCMs) are the most used tools to evaluate climate-change scenarios on a global scale. They are, however, insufficiently describing the effects at the local scale. This thesis evaluates different approaches of statistical downscaling of precipitation from large-scale circulation variables, both concerning the method performance and the optimum choice of predictor variables. The analogue downscaling method (AM) was found to work well as “benchmark” method in comparison to more complicated methods. AM was implemented using principal component analysis (PCA) and Teweles-Wobus Scores (TWS). Statistical properties of daily and monthly precipitation on a catchment in south-central Sweden, as well as daily precipitation in three catchments in China were acceptably downscaled. A regression method conditioning a weather generator (SDSM) as well as a fuzzy-rule based circulation-pattern classification method conditioning a stochastical precipitation model (MOFRBC) gave good results when applied on Swedish and Chinese catchments. Statistical downscaling with MOFRBC from GMC (HADAM3P) output improved the statistical properties as well as the intra-annual variation of precipitation. The studies show that temporal and areal settings of the predictor are important factors concerning the success of precipitation modelling. The MOFRCB and SDSM are generally performing better than the AM, and the best choice of method is depending on the purpose of the study. MOFRBC applied on output from a GCM future scenario indicates that the large-scale circulation will not be significantly affected. Adding humidity flux as predictor indicated an increased intensity both in extreme events and daily amounts in central and northern Sweden.

Submitted by Maria Cristina (library@lncc.br) on 2015-09-25T19:05:07Z No. of bitstreams: 1 thesis.pdf: 3598969 bytes, checksum: 03cf8e5a078613d707c68e89e449d6d3 (MD5) / Approved for entry into archive by Maria Cristina (library@lncc.br) on 2015-09-25T19:05:20Z (GMT) No. of bitstreams: 1 thesis.pdf: 3598969 bytes, checksum: 03cf8e5a078613d707c68e89e449d6d3 (MD5) / Made available in DSpace on 2015-09-25T19:05:31Z (GMT). No. of bitstreams: 1 thesis.pdf: 3598969 bytes, checksum: 03cf8e5a078613d707c68e89e449d6d3 (MD5) Previous issue date: 2015-05-27 / Conselho Nacional de Desenvolvimento Cientifico e Tecnológico / In this thesis, the evolutionary computation algorithm known as Genetic Programming has been explored as an alternative tool for improving the ensemble forecast of rainfall amount. The efficiency of Genetic Programming to deal with the problem of ensemble forecast of rainfall amount was confirmed on three artificial experiments. The work continued with the application of the evolutionary algorithms on some real-world data sets over south, southeast and central parts of Brazil during the period from October to February of 2008 to 2013. According to the results, Genetic Programming obtained a higher performance relative to two traditional statistical methods, reaching mean errors 27-49% lower than simple mean and the MASTER Super Model Ensemble System. In addition, the results revealed that the evolutionary algorithms outperformed the best individual forecasts, achieving an improvement of 30%. On the other hand, the evolutionary algorithms had a performance similar to the Bayesian Model Averaging technique, but the former are methods far more versatile. In general, the real and artificial experiments showed the potential of Genetic Programming and suggest that further research on the improvement of the technique is needed. / Na presente tese de doutorado, o algoritmo da computação evolutiva conhecido por Programação Genética foi explorado como ferramenta alternativa para o aperfeiçoamento da previsão quantitativa de chuva por conjunto. A aplicabilidade da Programação Genética no problema de previsão quantitativa de chuva por conjunto foi confirmada em três experimentos controlados. O trabalho seguiu com a aplicação dos algoritmos evolutivos sobre algumas bases de dados reais referentes a localidades situadas no sul, sudeste e parte do centro-oeste do Brasil durante o período de outubro a fevereiro de 2008-2013. Os resultados evidenciaram a superioridade da Programação Genética frente aos métodos estatísticos tradicionais: média simples e MASTER Super Model Ensemble System, com erros médios da ordem de 27-49% menores. Ademais, a previsão por conjunto via algoritmos evolutivos ofereceu previsões consideravelmente mais acuradas que as melhores previsões obtidas individualmente, chegando a uma melhora de 30%. Por outro lado, os algoritmos evolutivos apresentaram desempenho equivalente à técnica Bayesian Model Averaging, mas os primeiros são métodos bem mais versáteis. De maneira geral, os experimentos baseados em dados reais e artificiais revelaram a potencialidade da Programação Genética, e encorajam o seu aprimoramento para o problema de previsão quantitativa de chuva por conjunto.