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We conduct a case study of observed and simulated maximum daily 8-h average (MDA8) ozone (O 3 ) in three US cities for summers during 1996–2005. The purpose of this study is to evaluate the ability of a high resolution atmospheric chemistry model to reproduce observed relationships between meteorology and high or extreme O 3 . We employ regional coupled chemistry-transport model simulations to make three types of comparisons between simulated and observational data, comparing (1) tails of the O 3 response variable, (2) distributions of meteorological predictor variables, and (3) sensitivities of high and extreme O 3 to meteorological predictors. This last comparison is made using two methods: quantile regression, for the 0.95 quantile of O 3 , and tail dependence optimization, which is used to investigate even higher O 3 extremes. Across all three locations, we find substantial differences between simulations and observational data in both meteorology and meteorological sensitivities of high and extreme O 3 . [ABSTRACT FROM AUTHOR]

Summary Between 9th and 16th September 2013, northeast Colorado received some of its most extreme rainfall on record. The event affected 6 major rivers and their tributaries and 14 counties, breaking observed records for accumulations from sub-daily through to annual total. NOAA’s rainfall atlases indicated that this event had an anticipated return period of 1000 years. We use the rainfall that led to the 2013 Colorado floods as a case study in order to explore how a large event can affect the generalized extreme value (GEV) parameter estimates often used by designers and planners. We employ daily rainfall observations, with at least 30 years of data, from stations across Colorado’s Front Range of the Rocky Mountains to develop a spatial statistical model for annual maximum daily rainfall. We produce estimates of relatively rare events such as the 1% Annual Exceedance Probability (AEP) level and of extremely rare events such as the return period associated with Boulder’s 2013 observation. To explore sensitivity, we compare estimates including and excluding data from 2013, and both using only individual station data and our model which borrows strength across multiple stations. We compute the uncertainty associated with all of our estimates, and find large uncertainties associated with extremely rare events. Our statistical model is a spatial hierarchical model and we employ a two-stage approach for inference which can be implemented by practitioners. Additionally, the spatial model allows us to interpolate spatially and estimate the GEV parameters at unobserved locations. A further development of the model makes use of an alternatively defined space in terms of elevation and a climate variable, rather than geographical space defined by longitude and latitude, which seems to better account for orographic effects. In addition to producing AEP level and return period estimates to the annual maximum data, we investigate sensitivity to the choice of block length. We find point estimates indicate the tail to be much heavier when a longer block length is used, but the uncertainty associated with this parameter is such that one cannot say the difference is significant. To describe the spatial extent of severe storms, we also investigate the amount of data dependence between station locations. We find evidence in the record for storms with large spatial extent, although an extremal dependence parameter estimate indicates that this dependence is relatively weak. [ABSTRACT FROM AUTHOR]

Monoclonal antibody production in glycoengineered Pichia pastoris was optimized for high cell density fed-batch fermentations. Results show that antibody productivity was 1.72-fold higher when maintaining oxygen-limited conditions compared to methanol-limited conditions during the protein production phase of the bioprocess. Under oxygen-limited conditions with a 30 mmol L −1 h −1 of oxygen uptake rate, an optimum methanol dosage level of 10 gL −1 was established by comparing antibody titers under different methanol dosages during protein production. Real-time on-line NIR monitoring of glycerol concentration, methanol concentration, and relative cell density provided valuable data for process characterization and optimization. Real-time profiles of glycerol concentration and relative cell density documented run-to-run consistency during the batch and fed-batch phases by analyses of the glycerol consumption and cell growth kinetics. The volumetric methanol consumption rate was characterized for the induction process following the end of the glycerol fed-batch phase. Under oxygen-limited conditions, the volumetric methanol consumption rate increased over a 60-h period and this increase was independent of the methanol concentration over a range of 2.5–30 gL −1 when the oxygen utilization rate was maintained at 30 mmol L −1 h −1 . Utility of the on-line NIR monitor is demonstrated over the one-year period of this investigation by accurately tracking biomass, glycerol concentration, and methanol concentration without user required recalibration as well as by documenting abnormal events during high cell density bioprocesses. [ABSTRACT FROM AUTHOR]