scholarly journals Sensitivity analysis of methane emissions derived from SCIAMACHY observations through inverse modelling

2005 ◽  
Vol 5 (5) ◽  
pp. 9405-9445 ◽  
Author(s):  
J. F. Meirink ◽  
H. J. Eskes ◽  
A. P. H. Goede
Keyword(s):  
Inverse Modelling ◽  
Limiting Factor ◽  
Atmospheric Methane ◽  
Simulation Experiments ◽  
Cloud Parameters ◽  
Global Emission ◽  
Spatial Coverage ◽  
Methane Source ◽  
The Impact ◽  
Modelling Approach

Abstract. Satellite observations of trace gases in the atmosphere offer a promising method for global verification of emissions and improvement of global emission inventories. Here, an inverse modelling approach based on four-dimensional variational (4D-var) data assimilation is presented and applied to synthetic measurements of atmospheric methane. In this approach emissions and initial concentrations are optimised simultaneously, thus allowing inversions to be carried out on time scales of weeks to months, short compared with the lifetime of methane. Observing System Simulation Experiments (OSSEs) have been performed to demonstrate the feasibility of the method and to investigate the utility of SCIAMACHY observations for methane source estimation. The impact of a number of parameters on the error in the methane emission field retrieved has been analysed. These parameters include the measurement error, the error introduced by the presence of clouds, and the spatial resolution of the emission field. It is shown that 4D-var is an efficient method to deal with large amounts of satellite data and to retrieve emissions at high resolution. Some important conclusions regarding the SCIAMACHY measurements can be drawn: (i) the observations at their estimated precision of 1.5 to 2% will contribute considerably to uncertainty reduction in monthly, subcontinental (~500 km) methane source strengths; (ii) it is essential to take partly cloudy pixels into account in order to achieve sufficient spatial coverage; and (iii) the uncertainty in measured cloud parameters may at some point become the limiting factor, rather than the uncertainty in measured methane.

scholarly journals Sensitivity analysis of methane emissions derived from SCIAMACHY observations through inverse modelling

2006 ◽  
Vol 6 (5) ◽  
pp. 1275-1292 ◽  
Author(s):  
J. F. Meirink ◽  
H. J. Eskes ◽  
A. P. H. Goede
Keyword(s):  
Methane Emission ◽  
Measurement Errors ◽  
Inverse Modelling ◽  
Limiting Factor ◽  
Atmospheric Methane ◽  
Cloud Parameters ◽  
Global Emission ◽  
Spatial Coverage ◽  
Methane Source ◽  
The Impact

Abstract. Satellite observations of trace gases in the atmosphere offer a promising method for global verification of emissions and improvement of global emission inventories. Here, an inverse modelling approach based on four-dimensional variational (4D-var) data assimilation is presented and applied to synthetic measurements of atmospheric methane. In this approach, emissions and initial concentrations are optimised simultaneously, thus allowing inversions to be carried out on time scales of weeks to months, short compared with the lifetime of methane. Observing System Simulation Experiments (OSSEs) have been performed to demonstrate the feasibility of the method and to investigate the utility of SCIAMACHY observations for methane source estimation. The impact of a number of parameters on the error in the methane emission field retrieved has been analysed. These parameters include the measurement error, the error introduced by the presence of clouds, and the spatial resolution of the emission field. It is shown that 4D-var is an efficient method to deal with large amounts of satellite data and to retrieve emissions at high resolution. Some important conclusions regarding the SCIAMACHY measurements can be drawn. (i) The observations at their estimated precision of 1.5 to 2% will contribute considerably to uncertainty reduction in monthly, subcontinental (~500 km) methane source strengths. (ii) Systematic measurement errors well below 1% have a dramatic impact on the quality of the derived emission fields. Hence, every effort should be made to identify and remove such systematic errors. (iii) It is essential to take partly cloudy pixels into account in order to achieve sufficient spatial coverage. (iv) The uncertainty in measured cloud parameters may at some point become the limiting factor for methane emission retrieval, rather than the uncertainty in measured methane itself.

scholarly journals Time Granularity Impact on Propagation of Disruptions in a System-of-Systems Simulation of Infrastructure and Business Networks

2021 ◽  
Vol 18 (8) ◽  
pp. 3922
Author(s):  
Mateusz Iwo Dubaniowski ◽  
Hans Rudolf Heinimann
Keyword(s):  
Recovery Time ◽  
System Of Systems ◽  
Future Research ◽  
Business Networks ◽  
Simulation Experiments ◽  
Huge Impact ◽  
High Level ◽  
Systems Simulation ◽  
Different Characteristics ◽  
The Impact

A system-of-systems (SoS) approach is often used for simulating disruptions to business and infrastructure system networks allowing for integration of several models into one simulation. However, the integration is frequently challenging as each system is designed individually with different characteristics, such as time granularity. Understanding the impact of time granularity on propagation of disruptions between businesses and infrastructure systems and finding the appropriate granularity for the SoS simulation remain as major challenges. To tackle these, we explore how time granularity, recovery time, and disruption size affect the propagation of disruptions between constituent systems of an SoS simulation. To address this issue, we developed a high level architecture (HLA) simulation of three networks and performed a series of simulation experiments. Our results revealed that time granularity and especially recovery time have huge impact on propagation of disruptions. Consequently, we developed a model for selecting an appropriate time granularity for an SoS simulation based on expected recovery time. Our simulation experiments show that time granularity should be less than 1.13 of expected recovery time. We identified some areas for future research centered around extending the experimental factors space.

scholarly journals Selecting pH cut-offs for the safe verification of nasogastric feeding tube placement: a decision analytical modelling approach

BMJ Open ◽  
2017 ◽  
Vol 7 (11) ◽  
pp. e018128 ◽  
Author(s):  
Melody Zhifang Ni ◽  
Jeremy R Huddy ◽  
Oliver H Priest ◽  
Sisse Olsen ◽  
Lawrence D Phillips ◽  
...  
Keyword(s):  
Care Pathway ◽  
Feeding Tube ◽  
Tube Placement ◽  
X Rays ◽  
Clinical Environment ◽  
Nasogastric Feeding ◽  
Relative Safety ◽  
Checking Procedure ◽  
The Impact ◽  
Modelling Approach

ObjectivesThe existing British National Patient Safety Agency (NPSA) safety guideline recommends testing the pH of nasogastric (NG) tube aspirates. Feeding is considered safe if a pH of 5.5 or lower has been observed; otherwise chest X-rays are recommended. Our previous research found that at 5.5, the pH test lacks sensitivity towards oesophageal placements, a major risk identified by feeding experts. The aim of this research is to use a decision analytic modelling approach to systematically assess the safety of the pH test under cut-offs 1–9.Materials and methodsWe mapped out the care pathway according to the existing safety guideline where the pH test is used as a first-line test, followed by chest x-rays. Decision outcomes were scored on a 0–100 scale in terms of safety. Sensitivities and specificities of the pH test at each cut-off were extracted from our previous research. Aggregating outcome scores and probabilities resulted in weighted scores which enabled an analysis of the relative safety of the checking procedure under various pH cut-offs.ResultsThe pH test was the safest under cut-off 5 when there was ≥30% of NG tube misplacements. Under cut-off 5, respiratory feeding was excluded; oesophageal feeding was kept to a minimum to balance the need of chest X-rays for patients with a pH higher than 5. Routine chest X-rays were less safe than the pH test while to feed all without safety checks was the most risky.DiscussionThe safety of the current checking procedure is sensitive to the choice of pH cut-offs, the impact of feeding delays, the accuracy of the pH in the oesophagus, as well as the extent of tube misplacements.ConclusionsThe pH test with 5 as the cut-off was the safest overall. It is important to understand the local clinical environment so that appropriate choice of pH cut-offs can be made to maximise safety and to minimise the use of chest X-rays.Trial registration numberISRCTN11170249; Pre-results.

The Min-Max Load Criteria as a Measure of Machining Fixture Performance

1994 ◽  
Vol 116 (4) ◽  
pp. 500-507 ◽  
Author(s):  
E. C. DeMeter
Keyword(s):  
Convex Hull ◽  
Linear Model ◽  
Model Validation ◽  
Contact Region ◽  
Extreme Points ◽  
Simulation Experiments ◽  
Machining Operations ◽  
The Impact ◽  
External Loads ◽  
The Continuum

Spherical-tipped locators and clamps are often used for the restraint of castings during machining. For structurally rigid castings, contact region deformation and micro-slippage are the predominant modes of workpiece displacement. In turn contact region deformation and micro-slippage are heavily influenced by contact region loading. This paper presents a linear model for predicting the impact of locator and clamp placement on workpiece displacement throughout a series of machining operations. It illustrates how the continuum of external loads exerted on a workpiece during machining can be bounded within a convex hull, and how the extreme points of this hull are used within the model. Finally it describes the simulation experiments which were used for model validation.

scholarly journals Extending methane profiles from aircraft into the stratosphere for satellite total column validation using the ECMWF C-IFS and TOMCAT/SLIMCAT 3-D model

2017 ◽  
Vol 17 (11) ◽  
pp. 6663-6678 ◽  
Author(s):  
Shreeya Verma ◽  
Julia Marshall ◽  
Mark Parrington ◽  
Anna Agustí-Panareda ◽  
Sebastien Massart ◽  
...  
Keyword(s):  
Greenhouse Gases ◽  
Transport Model ◽  
A Priori ◽  
Atmospheric Composition ◽  
Random Errors ◽  
Chemical Transport Model ◽  
Spatial Coverage ◽  
Forecasting System ◽  
The Impact ◽  
Total Column

Abstract. Airborne observations of greenhouse gases are a very useful reference for validation of satellite-based column-averaged dry air mole fraction data. However, since the aircraft data are available only up to about 9–13 km altitude, these profiles do not fully represent the depth of the atmosphere observed by satellites and therefore need to be extended synthetically into the stratosphere. In the near future, observations of CO2 and CH4 made from passenger aircraft are expected to be available through the In-Service Aircraft for a Global Observing System (IAGOS) project. In this study, we analyse three different data sources that are available for the stratospheric extension of aircraft profiles by comparing the error introduced by each of them into the total column and provide recommendations regarding the best approach. First, we analyse CH4 fields from two different models of atmospheric composition – the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecasting System for Composition (C-IFS) and the TOMCAT/SLIMCAT 3-D chemical transport model. Secondly, we consider scenarios that simulate the effect of using CH4 climatologies such as those based on balloons or satellite limb soundings. Thirdly, we assess the impact of using a priori profiles used in the satellite retrievals for the stratospheric part of the total column. We find that the models considered in this study have a better estimation of the stratospheric CH4 as compared to the climatology-based data and the satellite a priori profiles. Both the C-IFS and TOMCAT models have a bias of about −9 ppb at the locations where tropospheric vertical profiles will be measured by IAGOS. The C-IFS model, however, has a lower random error (6.5 ppb) than TOMCAT (12.8 ppb). These values are well within the minimum desired accuracy and precision of satellite total column XCH4 retrievals (10 and 34 ppb, respectively). In comparison, the a priori profile from the University of Leicester Greenhouse Gases Observing Satellite (GOSAT) Proxy XCH4 retrieval and climatology-based data introduce larger random errors in the total column, being limited in spatial coverage and temporal variability. Furthermore, we find that the bias in the models varies with latitude and season. Therefore, applying appropriate bias correction to the model fields before using them for profile extension is expected to further decrease the error contributed by the stratospheric part of the profile to the total column.

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