Global wave predictions produced at two U. S. forecasting centers, Fleet Numerical Meteorology and Oceanography Center and the National Centers for Environmental Prediction are evaluated with spectral buoy measurements. In this study, the fidelity of frequency-directional spectra predicted by WAM and WAVEWATCH III at the operational centers is examined with data from 3-meter discus and 6-meter nomad buoys operated by the National Data Buoy Center in the Atlantic and Pacific Oceans and Datawell Directional Waverider buoys deployed along the California coast by the Scripps Institution of Oceanography Coastal Data Information Program. Only buoys located in deep water are used in the comparisons. Model nowcasts of frequency spectra and mean wave directions are compared to buoy measurements over a six-month period from 1 October 2000 to 31 March 2001. At the Pacific buoy locations, individual swell events were identified in the spectra from the three models and the buoy data. Predicted and observed swell frequencies and arrival directions are compared as well as the total energy transported past the buoy over the duration of each individual event. At all buoy locations, predicted and observed wave energy fluxes integrated over fixed frequency ranges are compared. All three models yield reliable nowcasts of swell arrivals at the buoy locations. In most cases, the models under-predict the energy measured by the buoys. WAVEWATCH III better resolves low-frequency swells than WAM, possibly owing to a superior numerical scheme. Swell predictions at NCEP forced with AVN winds are more accurate than those at FNMOC forced with NOGAPS winds. / US Navy (USN) author

Thesis (M.S. in Meteorology and Physical Oceanography) Naval Postgraduate School, December 2001. / "December 2001". Thesis advisor(s): Herbers, Thomas H.C.; Wittmann, Paul A. Includes bibliographical references (p. 39). Also available online.

A study was conducted to quantify weather conditions at specific growth stages of Canadian western Spring wheat (Triticum aestivum) and relate those growing conditions to variations in wheat grade and quality characteristics and to develop pre-harvest prediction models for wheat quality using weather input data. Six Canadian western spring wheat genotypes were grown in five locations across the Canadian prairies during the 2003 and 2004 growing seasons. Intensive weather data was collected during the growing season at each location and used to calculate accumulated heat stress, useful heat, moisture demand, moisture supply, moisture use and moisture stress variables for numerous crop development stages. Grain samples were graded, milled and underwent an extensive analysis of flour, dough, and bread making quality. ANOVA indicated that genotype, environment and their interactions had significant effects on most quality parameters tested. Environmental contribution to wheat quality variance was considerably larger than the variance contribution of either genotype or GxE interaction. Using the weather and crop development stage information, significant regression equations with high regression coefficients were developed for most quality parameters using just a single independent weather variable. Multiple regression equations with even higher R2 values were developed using three complex weather variables, leading to the opportunity to predict wheat quality 2-5 weeks prior to harvest. Equally strong prediction models were developed utilizing basic weather variables which could be obtained from weather stations monitoring only daily maximum and minimum air temperature and precipitation. The development periods of planting to jointing and anthesis to soft dough were the stages most frequently exhibiting the highest correlation to wheat quality indicating weather needs to be monitored during the entire growing season to accurately predict quality. Grain quality forecast models were validated using 2005 weather and crop data. Prediction models developed from the 2003 and 2004 data required modification in order to accurately and consistently predict the grain properties in 2005. / February 2007

A study was conducted to quantify weather conditions at specific growth stages of Canadian western Spring wheat (Triticum aestivum) and relate those growing conditions to variations in wheat grade and quality characteristics and to develop pre-harvest prediction models for wheat quality using weather input data. Six Canadian western spring wheat genotypes were grown in five locations across the Canadian prairies during the 2003 and 2004 growing seasons. Intensive weather data was collected during the growing season at each location and used to calculate accumulated heat stress, useful heat, moisture demand, moisture supply, moisture use and moisture stress variables for numerous crop development stages. Grain samples were graded, milled and underwent an extensive analysis of flour, dough, and bread making quality. ANOVA indicated that genotype, environment and their interactions had significant effects on most quality parameters tested. Environmental contribution to wheat quality variance was considerably larger than the variance contribution of either genotype or GxE interaction. Using the weather and crop development stage information, significant regression equations with high regression coefficients were developed for most quality parameters using just a single independent weather variable. Multiple regression equations with even higher R2 values were developed using three complex weather variables, leading to the opportunity to predict wheat quality 2-5 weeks prior to harvest. Equally strong prediction models were developed utilizing basic weather variables which could be obtained from weather stations monitoring only daily maximum and minimum air temperature and precipitation. The development periods of planting to jointing and anthesis to soft dough were the stages most frequently exhibiting the highest correlation to wheat quality indicating weather needs to be monitored during the entire growing season to accurately predict quality. Grain quality forecast models were validated using 2005 weather and crop data. Prediction models developed from the 2003 and 2004 data required modification in order to accurately and consistently predict the grain properties in 2005.

No description available.

A study was conducted to quantify weather conditions at specific growth stages of Canadian western Spring wheat (Triticum aestivum) and relate those growing conditions to variations in wheat grade and quality characteristics and to develop pre-harvest prediction models for wheat quality using weather input data. Six Canadian western spring wheat genotypes were grown in five locations across the Canadian prairies during the 2003 and 2004 growing seasons. Intensive weather data was collected during the growing season at each location and used to calculate accumulated heat stress, useful heat, moisture demand, moisture supply, moisture use and moisture stress variables for numerous crop development stages. Grain samples were graded, milled and underwent an extensive analysis of flour, dough, and bread making quality. ANOVA indicated that genotype, environment and their interactions had significant effects on most quality parameters tested. Environmental contribution to wheat quality variance was considerably larger than the variance contribution of either genotype or GxE interaction. Using the weather and crop development stage information, significant regression equations with high regression coefficients were developed for most quality parameters using just a single independent weather variable. Multiple regression equations with even higher R2 values were developed using three complex weather variables, leading to the opportunity to predict wheat quality 2-5 weeks prior to harvest. Equally strong prediction models were developed utilizing basic weather variables which could be obtained from weather stations monitoring only daily maximum and minimum air temperature and precipitation. The development periods of planting to jointing and anthesis to soft dough were the stages most frequently exhibiting the highest correlation to wheat quality indicating weather needs to be monitored during the entire growing season to accurately predict quality. Grain quality forecast models were validated using 2005 weather and crop data. Prediction models developed from the 2003 and 2004 data required modification in order to accurately and consistently predict the grain properties in 2005.

This thesis proposes the methodology and guidelines for community-based macro-level CPM transferability to do road safety planning applications, with models developed in one spatial-temporal region being capable of used in a different spatial-temporal region. In doing this. the macro-level CPMs developed for the Greater Vancouver Regional District (GVRD) by Lovegrove and Sayed (2006, 2007) was used in a model transferability study. Using those models from GVRD and data from Central Okanagan Regional District (CORD), in the Province of British Columbia. Canada. a transferability test has been conducted that involved recalibration of the 1996 GVRD models to Kelowna, in 2003 context. The case study was carried out in three parts. First, macro-level CPMs for the City of Kelowna were developed using 2003 data following the research by GVRD CPM development and use. Next, the 1996 GVRD models were recalibrated to see whether they could yield reliable prediction of the safety estimates for Kelowna, in 2003 context. Finally, a comparison between the results of Kelowna’s own developed models and the transferred models was conducted to determine which models yielded better results. The results of the transferability study revealed that macro-level CPM transferability was possible and no more complicated than micro-level CPM transferability. To facilitate the development of reliable community-based, macro-level collision prediction models, it was recommended that CPMs be transferred rather than developed from scratch whenever and wherever communities lack sufficient data of adequate quality. Therefore, the transferability guidelines in this research, together with their application in the case studies, have been offered as a contribution towards model transferability to do road safety planning applications, with models developed in one spatial-temporal region being capable of used in a different spatial-temporal region.

This thesis proposes the methodology and guidelines for community-based macro-level CPM transferability to do road safety planning applications, with models developed in one spatial-temporal region being capable of used in a different spatial-temporal region. In doing this. the macro-level CPMs developed for the Greater Vancouver Regional District (GVRD) by Lovegrove and Sayed (2006, 2007) was used in a model transferability study. Using those models from GVRD and data from Central Okanagan Regional District (CORD), in the Province of British Columbia. Canada. a transferability test has been conducted that involved recalibration of the 1996 GVRD models to Kelowna, in 2003 context. The case study was carried out in three parts. First, macro-level CPMs for the City of Kelowna were developed using 2003 data following the research by GVRD CPM development and use. Next, the 1996 GVRD models were recalibrated to see whether they could yield reliable prediction of the safety estimates for Kelowna, in 2003 context. Finally, a comparison between the results of Kelowna’s own developed models and the transferred models was conducted to determine which models yielded better results. The results of the transferability study revealed that macro-level CPM transferability was possible and no more complicated than micro-level CPM transferability. To facilitate the development of reliable community-based, macro-level collision prediction models, it was recommended that CPMs be transferred rather than developed from scratch whenever and wherever communities lack sufficient data of adequate quality. Therefore, the transferability guidelines in this research, together with their application in the case studies, have been offered as a contribution towards model transferability to do road safety planning applications, with models developed in one spatial-temporal region being capable of used in a different spatial-temporal region.

This thesis proposes the methodology and guidelines for community-based macro-level CPM transferability to do road safety planning applications, with models developed in one spatial-temporal region being capable of used in a different spatial-temporal region. In doing this. the macro-level CPMs developed for the Greater Vancouver Regional District (GVRD) by Lovegrove and Sayed (2006, 2007) was used in a model transferability study. Using those models from GVRD and data from Central Okanagan Regional District (CORD), in the Province of British Columbia. Canada. a transferability test has been conducted that involved recalibration of the 1996 GVRD models to Kelowna, in 2003 context. The case study was carried out in three parts. First, macro-level CPMs for the City of Kelowna were developed using 2003 data following the research by GVRD CPM development and use. Next, the 1996 GVRD models were recalibrated to see whether they could yield reliable prediction of the safety estimates for Kelowna, in 2003 context. Finally, a comparison between the results of Kelowna’s own developed models and the transferred models was conducted to determine which models yielded better results. The results of the transferability study revealed that macro-level CPM transferability was possible and no more complicated than micro-level CPM transferability. To facilitate the development of reliable community-based, macro-level collision prediction models, it was recommended that CPMs be transferred rather than developed from scratch whenever and wherever communities lack sufficient data of adequate quality. Therefore, the transferability guidelines in this research, together with their application in the case studies, have been offered as a contribution towards model transferability to do road safety planning applications, with models developed in one spatial-temporal region being capable of used in a different spatial-temporal region. / Applied Science, Faculty of / Civil Engineering, Department of / Graduate

Indiana University-Purdue University Indianapolis (IUPUI) / Context. Prediction models for dementia are crucial for informing clinical decision making in older adults. Previous models have used genotype and age to obtain risk scores to determine risk of Alzheimer’s Disease, one of the most common forms of dementia (Desikan et al., 2017). However, previous prediction models do not account for the fact that the time to dementia onset is unknown, lying between the last negative and the first positive dementia diagnosis time (interval censoring). Instead, these models use time to diagnosis, which is greater than or equal to the true dementia onset time. Furthermore, these models do not account for the competing risk of death which is quite frequent among elder adults. Objectives. To develop a prediction model for dementia that accounts for interval censoring and the competing risk of death. To compare the predictions from this model with the predictions from a naïve analysis that ignores interval censoring and the competing risk of death. Methods. We apply the semiparametric sieve maximum likelihood (SML) approach to simultaneously model the cumulative incidence function (CIF) of dementia and death while accounting for interval censoring (Bakoyannis, Yu, & Yiannoutsos, 2017). The SML is implemented using the R package intccr. The CIF curves of dementia are compared for the SML and the naïve approach using a dataset from the Indianapolis Ibadan Dementia Project. Results. The CIF from the SML and the naïve approach illustrated that for healthier individuals at baseline, the naïve approach underestimated the incidence of dementia compared to the SML, as a result of interval censoring. Individuals with a poorer health condition at baseline have a CIF that appears to be overestimated in the naïve approach. This is due to older individuals with poor health conditions having an elevated risk of death. Conclusions. The SML method that accounts for the competing risk of death along with interval censoring should be used for fitting prediction/prognostic models of dementia to inform clinical decision making in older adults. Without controlling for the competing risk of death and interval censoring, the current models can provide invalid predictions of the CIF of dementia.