<p>That air pollutants of different kind has impact on our climate can hardly have</p><p>avoided anyone during the past years environment explosion in the media. But how</p><p>is it actually with the reverse, i. e. the climate's impact on air pollutants? The aim</p><p>with our degree thesis is to try to find out if different weather factors, statistically</p><p>seen, promotes atmospheric pollutants and particles in the air and vice versa.</p><p>We got access to data on air pollutants and weather factors that was registered</p><p>during year 2007 at Femmanhusets air pollutant and weather station in Gothenburg.</p><p>In order to do a statistical investigation of these data, and to be able to analyze if it</p><p>occurs relations of various kind, we used the statistics program SPSS. In SPSS we</p><p>worked with correlation, regression and multiple linear regressions in order to get</p><p>our statistical relations between atmospheric pollutants and weather conditions.</p><p>Our results showed that ozone is the atmospheric pollutant that is most weather</p><p>sensitive and ozone has also proven to have the strongest connections to the weather</p><p>factors. Also nitrogen monoxide, nitrogen dioxide and carbon monoxide are</p><p>considerably influenced by the weather, however not in equally big extent as ozone.</p><p>Sulphur dioxide and PM10 is on the other hand influenced in a low extent and it is in</p><p>these cases difficult to say if the weather actually has any influence at all. Wind speed</p><p>was, apart from sulphur dioxide and PM10, the weather factor that were strongest</p><p>correlated to the remaining atmospheric pollutants. The relative humidity and the air</p><p>pressure were the two factors that overall gave the weakest connections to the</p><p>pollutants. As a conclusion, we can say that our results can be seen as an indication</p><p>to the public about how sensitive people should minimize their time outdoors in the</p><p>inner city on cold and calm days. It is when these weather conditions occur that the</p><p>highest concentrations of atmospheric pollutants, statistically seen, can be found.</p><p>However, this does not apply for ozone. For ozone, they should instead be aware on</p><p>sunny and windy days as it, statistically seen, is days where high concentrations of</p><p>ozone can be found in the air.</p>

Thesis (M.S.)--Oregon State University, 2007. / Printout. Includes bibliographical references (leaves 87-88). Also available on the World Wide Web.

Stochastic weather generators create multiple series of synthetic daily weather (precipitation, maximum temperature, etc.), and ideally these series will have statistical properties similar to those of the input historical data. The synthetic output has many applications and for example, can be used in sectors such as agriculture and hydrology. This work used a ?hybrid? weather generator which consists of a parametric Markov chain for generating precipitation occurrence and a nonparametric k-nearest neighbor method for generating values of maximum temperature, minimum temperature, and precipitation. The hybrid weather generator was implemented and validated for use at 11 different locations in the Southeastern United States. A total of 36 graphic diagnostics were used to assess the model?s performance. These diagnostics revealed that the weather generator successfully created synthetic series with most statistical properties of the historical data including extreme wet and dry spell lengths and days of first and last freeze. Climate forecasts are typically provided for seasons or months. Alternatively, process models used for risk assessment often operate at daily time scales. If climate forecasts were incorporated into the daily weather input for process models, stakeholders could then use these models to assess possible impacts on their sector of interest due to anticipated changes in climate conditions. In this work, an ?ad hoc? resampling approach was developed to create sets of daily synthetic weather series consistent with seasonal climate forecasts in the Southeastern United States. In this approach, the output of the hybrid weather generator was resampled based on forecasts in two different formats: the commonly used tercile format and a probability distribution function. This resampling approach successfully created sets of synthetic series which reflected different forecast scenarios (i.e. wetter or drier conditions). Distributions of quarterly total precipitation from the resampled synthetic series were found to be shifted with respect to the corresponding historical distributions, and in some cases, the occurrence and intensity statistics of precipitation in the new weather series had changed with respect to the historical values.

Thesis (Ph. D.)--Florida State University, 2004. / Advisor: Dr. Xiaolei Zou, Florida State University, College of Arts and Sciences, Dept. of Meteorology. Title and description from dissertation home page (June 18, 2004). Includes bibliographical references.

Thesis (Ph. D.)--University of Texas at Austin, 2003. / Vita. Includes bibliographical references. Available also from UMI Company.

Thesis (M. Sc.(Meteorology)) -- University of Pretoria, 2000. / Includes summary. Includes bibliographical references

Thesis (M.S. in Meteorology)--Naval Postgraduate School, June 2004. / Thesis advisor(s): Roger T. Williams. Includes bibliographical references (p. 91-95). Also available online.

Thesis (Ph. D.)--School of Electrical and Computer Engineering, Georgia Institute of Technology, 2004. Directed by Linda M. Wills. / Vita. Includes bibliographical references (leaves 169-175).

Thesis (M.S. in Meteorology and Physical Oceanography)--Naval Postgraduate School, September 1990. / Thesis Advisor(s): Wash, Carlyle H. Second Reader: Nuss, Wendell A. "September 1990." Description based on title screen viewed on December 17, 2009. DTIC Descriptor(s): Weather forecasting, satellite meteorology, uncertainty, polarization, ships, coastal regions, light, rates, theses, radar, regression analysis, precipitation, solutions(general), rain, winter, rainfall intensity, storms, equations, cyclones, channels, corrections, temperate regions, cyclogenesis, algorithms, temperature. DTIC Identifier(s): Rainfall intensity, erica project, ssm/i(special sensor microwave/images). Author(s) subject terms: Microwave, ERICA, SSM/I, precipitation forecasting, rain. Includes bibliographical references (p. 81-82). Also available in print.

That air pollutants of different kind has impact on our climate can hardly have avoided anyone during the past years environment explosion in the media. But how is it actually with the reverse, i. e. the climate's impact on air pollutants? The aim with our degree thesis is to try to find out if different weather factors, statistically seen, promotes atmospheric pollutants and particles in the air and vice versa. We got access to data on air pollutants and weather factors that was registered during year 2007 at Femmanhusets air pollutant and weather station in Gothenburg. In order to do a statistical investigation of these data, and to be able to analyze if it occurs relations of various kind, we used the statistics program SPSS. In SPSS we worked with correlation, regression and multiple linear regressions in order to get our statistical relations between atmospheric pollutants and weather conditions. Our results showed that ozone is the atmospheric pollutant that is most weather sensitive and ozone has also proven to have the strongest connections to the weather factors. Also nitrogen monoxide, nitrogen dioxide and carbon monoxide are considerably influenced by the weather, however not in equally big extent as ozone. Sulphur dioxide and PM10 is on the other hand influenced in a low extent and it is in these cases difficult to say if the weather actually has any influence at all. Wind speed was, apart from sulphur dioxide and PM10, the weather factor that were strongest correlated to the remaining atmospheric pollutants. The relative humidity and the air pressure were the two factors that overall gave the weakest connections to the pollutants. As a conclusion, we can say that our results can be seen as an indication to the public about how sensitive people should minimize their time outdoors in the inner city on cold and calm days. It is when these weather conditions occur that the highest concentrations of atmospheric pollutants, statistically seen, can be found. However, this does not apply for ozone. For ozone, they should instead be aware on sunny and windy days as it, statistically seen, is days where high concentrations of ozone can be found in the air.