Four-dimensional variational data assimilation for estimation of the atmospheric chemical state from the tropopause to the lower mesosphere

A four-dimensional variational data assimilation system for stratospheric trace gas observations has been developed and operated. The system offers the flexibility to make use of data from different instruments and it was designed (1) to enforce chemical consistency by constraining the analyses to a state of the art stratospheric model,(2) to provide realistic estimates of anisotropic and inhomogeneous background error covariances, and (3) to be sufficiently efficient for application in near real time. The assimilating model has been assembled from scratch to allow for a couple of novel features: The meteorological fields are computed online by the global forecast model GME of German Weather Service, leading to an improved numerical representation of wind fields compared to traditional offline chemistry-transport models, which use spatially and temporally interpolated meteorological analyses. A number of 155 photolysis, gas phase, and heterogeneous reaction of 41 stratospheric trace gases is considered by the chemistry module. Since spatial correlations between background errors evolve according to the atmospheric flow, a flow dependent formulation of the background error covariance matrix has been devised by means of a diffusion approach. It can be shown that this measure considerably improves the analysis quality particularly in regions where large gradients of potential vorticity prevail. The governing equations are discretised on an icosahedral grid, as this significantly reduces the computational cost. Therefore, it is possible to operate the model with a relatively fine spatial resolution without violating the near real time constraint. A comprehensive set of case studies has been conducted in order to test and evaluate the new system. Trace gas profiles derived from measurements of the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) have been assimilated. Comparison with independent (not assimilated) control data sets and statistical evaluation demonstrates an excellent performance of the new assimilation system

A Dynamic Analysis of the Market for Wide-Bodied Commercial Aircraft

This paper develops a multi-agent dynamic model of the commercial aircraft industry and then uses that model to analyze industry pricing, industry performance, and optimal industry policy. In the model, firms are differentiated in their products and cost structure, and entry, exit, prices, and quantity sold are endogenously determined in dynamic equilibrium. Re ecting the focus of the paper, demand and supply are modeled structurally, while investment is modeled in reduced form. The model utilizes a cost model of commercial aircraft production developed and estimated in a previous paper (Benkard (2000)), and a discrete choice model of commercial aircraft demand to determine static profits. I find that many unusual aspects of the aircraft data, such as high concentration and pricing below the level of static marginal cost, are explained by this model. The model also replicates the stochastic evolution of the industry well. Many of these properties could not be explained with a static model. These results provide support for the structural dynamic modeling approach in general. I also find that the unconstrained Markov perfect equilibrium is quite efficient from a social perspective, providing only 9% less welfare on average than a social planner would obtain, but that the Markov perfect equilibrium shifts a substantial amount of welfare from consumers to producers. Finally, I provide simulation evidence that an anti-trust policy in the form of a concentration restriction would be welfare reducing with high probability.

An investigation into coordinate measuring machine task specific measurement uncertainty and automated conformance assessment of airfoil leading edge profiles

The growing demand for ever more greener aero engines has led to ever more challenging designs and higher quality products. An investigation into Coordinate Measuring Machine measurement uncertainty using physical measurements and virtual simulations revealed that there were several factors that can affect the measurement uncertainty of a specific task. Measurement uncertainty can be affected by temperature, form error and measurement strategy as well as Coordinate Measuring Machine specification. Furthermore the sensitivity of circular features size and position varied, when applying different substitute geometry algorithms was demonstrated. The Least Squares Circle algorithm was found to be more stable when compared with the Maximum Inscribed Circle and the Minimum Circumscribed Circle. In all experiments it was found that the standard deviation when applying Least Squares Circle was of smaller magnitude but similar trends when compared with Maximum Inscribed Circle and the Minimum Circumscribed Circle. A Virtual Coordinate Measuring Machine was evaluated by simulating physical measurement scenarios of different artefacts and different features. The results revealed good correlation between physical measurements uncertainty results and the virtual simulations. A novel methodology for the automated assessment of leading edge airfoil profiles was developed by extracting the curvature of airfoil leading edge, and the method lead to a patent where undesirable features such as flats or rapid changes in curvature could be identified and sentenced. A software package named Blade Inspect was developed in conjunction with Aachen (Fraunhoufer) University for the automated assessment and integrated with a shop floor execution system in a pre-production facility. The software used a curvature tolerancing method to sentence the leading edge profiles which aimed at removing the subjectivity associated with the manual vision inspection method. Initial trials in the pre-production facility showed that the software could sentence 200 profiles in 5 minutes successfully. This resulted in a significant improvement over the current manual visual inspection method which required 3 hours to assess the same number of leading edge profiles

Estimating central blood pressure from aortic flow: development and assessment of algorithms

Central blood pressure (cBP) is a highly prognostic cardiovascular (CV) risk factor whose accurate, invasive assessment is costly and carries risks to patients. We developed and assessed novel algorithms for estimating cBP from noninvasive aortic hemodynamic data and a peripheral blood pressure measurement. These algorithms were created using three blood flow models: the two- and three-element Windkessel (0-D) models and a one-dimensional (1-D) model of the thoracic aorta. We tested new and existing methods for estimating CV parameters (left ventricular ejection time, outflow BP, arterial resistance and compliance, pulse wave velocity, and characteristic impedance) required for the cBP algorithms, using virtual (simulated) subjects (n = 19,646) for which reference CV parameters were known exactly. We then tested the cBP algorithms using virtual subjects (n = 4,064), for which reference cBP were available free of measurement error, and clinical datasets containing invasive (n = 10) and noninvasive (n = 171) reference cBP waves across a wide range of CV conditions. The 1-D algorithm outperformed the 0-D algorithms when the aortic vascular geometry was available, achieving central systolic blood pressure (cSBP) errors ≤ 2.1 ± 9.7 mmHg and root-mean-square errors (RMSEs) ≤ 6.4 ± 2.8 mmHg against invasive reference cBP waves (n = 10). When the aortic geometry was unavailable, the three-element 0-D algorithm achieved cSBP errors ≤ 6.0 ± 4.7 mmHg and RMSEs ≤ 5.9 ± 2.4 mmHg against noninvasive reference cBP waves (n = 171), outperforming the two-element 0-D algorithm. All CV parameters were estimated with mean percentage errors ≤ 8.2%, except for the aortic characteristic impedance (≤13.4%), which affected the three-element 0-D algorithm’s performance. The freely available algorithms developed in this work enable fast and accurate calculation of the cBP wave and CV parameters in datasets containing noninvasive ultrasound or magnetic resonance imaging data. NEW & NOTEWORTHY First, our proposed methods for CV parameter estimation and a comprehensive set of methods from the literature were tested using in silico and clinical datasets. Second, optimized algorithms for estimating cBP from aortic flow were developed and tested for a wide range of cBP morphologies, including catheter cBP data. Third, a dataset of simulated cBP waves was created using a three-element Windkessel model. Fourth, the Windkessel model dataset and optimized algorithms are freely available.This work was supported by a PhD Fellowship awarded by the King’s College London and Imperial College London EPSRC Centre for Doctoral Training in Medical Imaging [EP/L015226/1], the British Heart Foundation (BHF) [PG/15/104/31913], and the Wellcome EPSRC Centre for Medical Engineering at King’s College London [WT 203148/Z/16/Z]. The authors acknowledge financial support from the Department of Health through the National Institute for Health Research (NIHR) Cardiovascular MedTech Co-operative at Guy’s and St Thomas’ NHS Foundation Trust (GSTT)

Estimating central blood pressure from aortic flow: development and assessment of algorithms

Central blood pressure (cBP) is a highly prognostic cardiovascular (CV) risk factor whose accurate, invasive assessment is costly and carries risks to patients. We developed and assessed novel algorithms for estimating cBP from noninvasive aortic hemodynamic data and a peripheral blood pressure measurement. These algorithms were created using three blood flow models: the two- and three-element Windkessel (0-D) models and a one-dimensional (1-D) model of the thoracic aorta. We tested new and existing methods for estimating CV parameters (left ventricular ejection time, outflow BP, arterial resistance and compliance, pulse wave velocity, and characteristic impedance) required for the cBP algorithms, using virtual (simulated) subjects (n = 19,646) for which reference CV parameters were known exactly. We then tested the cBP algorithms using virtual subjects (n = 4,064), for which reference cBP were available free of measurement error, and clinical datasets containing invasive (n = 10) and noninvasive (n = 171) reference cBP waves across a wide range of CV conditions. The 1-D algorithm outperformed the 0-D algorithms when the aortic vascular geometry was available, achieving central systolic blood pressure (cSBP) errors ≤ 2.1 ± 9.7 mmHg and root-mean-square errors (RMSEs) ≤ 6.4 ± 2.8 mmHg against invasive reference cBP waves (n = 10). When the aortic geometry was unavailable, the three-element 0-D algorithm achieved cSBP errors ≤ 6.0 ± 4.7 mmHg and RMSEs ≤ 5.9 ± 2.4 mmHg against noninvasive reference cBP waves (n = 171), outperforming the two-element 0-D algorithm. All CV parameters were estimated with mean percentage errors ≤ 8.2%, except for the aortic characteristic impedance (≤13.4%), which affected the three-element 0-D algorithm’s performance. The freely available algorithms developed in this work enable fast and accurate calculation of the cBP wave and CV parameters in datasets containing noninvasive ultrasound or magnetic resonance imaging data