Experimental Design of a Prescribed Burn Instrumentation

Observational data collected during experiments, such as the planned Fire and Smoke Model Evaluation Experiment (FASMEE), are critical for evaluating and transitioning coupled fire-atmosphere models like WRF-SFIRE and WRF-SFIRE-CHEM into operational use. Historical meteorological data, representing typical weather conditions for the anticipated burn locations and times, have been processed to initialize and run a set of simulations representing the planned experimental burns. Based on an analysis of these numerical simulations, this paper provides recommendations on the experimental setup such as size and duration of the burns, and optimal sensor placement. New techniques are developed to initialize coupled fire-atmosphere simulations with weather conditions typical of the planned burn locations and times. The variation and sensitivity analysis of the simulation design to model parameters performed by repeated Latin Hypercube Sampling is used to assess the locations of the sensors. The simulations provide the locations for the measurements that maximize the expected variation of the sensor outputs with varying the model parameters.

Download Full-text

Modeling bronchial circulation with application to soluble gas exchange: description and sensitivity analysis

Journal of Applied Physiology ◽

10.1152/jappl.1998.84.6.2070 ◽

1998 ◽

Vol 84 (6) ◽

pp. 2070-2088 ◽

Cited By ~ 25

Author(s):

Thien D. Bui ◽

Donald Dabdub ◽

Steven C. George

Keyword(s):

Sensitivity Analysis ◽

Gas Exchange ◽

Pulmonary Circulation ◽

Blood Flow Rate ◽

Latin Hypercube Sampling ◽

Inert Gases ◽

Phase Iii ◽

Model Parameters ◽

Bronchial Circulation ◽

Wide Range

The steady-state exchange of inert gases across an in situ canine trachea has recently been shown to be limited equally by diffusion and perfusion over a wide range (0.01–350) of blood solubilities (βblood; ml ⋅ ml−1 ⋅ atm−1). Hence, we hypothesize that the exchange of ethanol (βblood = 1,756 at 37°C) in the airways depends on the blood flow rate from the bronchial circulation. To test this hypothesis, the dynamics of the bronchial circulation were incorporated into an existing model that describes the simultaneous exchange of heat, water, and a soluble gas in the airways. A detailed sensitivity analysis of key model parameters was performed by using the method of Latin hypercube sampling. The model accurately predicted a previously reported experimental exhalation profile of ethanol ( R 2= 0.991) as well as the end-exhalation airstream temperature (34.6°C). The model predicts that 27, 29, and 44% of exhaled ethanol in a single exhalation are derived from the tissues of the mucosa and submucosa, the bronchial circulation, and the tissue exterior to the submucosa (which would include the pulmonary circulation), respectively. Although the concentration of ethanol in the bronchial capillary decreased during inspiration, the three key model outputs (end-exhaled ethanol concentration, the slope of phase III, and end-exhaled temperature) were all statistically insensitive ( P > 0.05) to the parameters describing the bronchial circulation. In contrast, the model outputs were all sensitive ( P < 0.05) to the thickness of tissue separating the core body conditions from the bronchial smooth muscle. We conclude that both the bronchial circulation and the pulmonary circulation impact soluble gas exchange when the entire conducting airway tree is considered.

Download Full-text

A Sensitivity Analysis of Two Mesoscale Models: COAMPS and WRF

Monthly Weather Review ◽

10.1175/mwr-d-19-0271.1 ◽

2020 ◽

Vol 148 (7) ◽

pp. 2997-3014

Author(s):

Caren Marzban ◽

Robert Tardif ◽

Scott Sandgathe

Keyword(s):

Sensitivity Analysis ◽

Spatial Structure ◽

Latin Hypercube Sampling ◽

Forecast Model ◽

The United States ◽

Model Parameters ◽

Analysis Methodology ◽

Trigger Function ◽

Multivariate Multiple Regression

Abstract A sensitivity analysis methodology recently developed by the authors is applied to COAMPS and WRF. The method involves varying model parameters according to Latin Hypercube Sampling, and developing multivariate multiple regression models that map the model parameters to forecasts over a spatial domain. The regression coefficients and p values testing whether the coefficients are zero serve as measures of sensitivity of forecasts with respect to model parameters. Nine model parameters are selected from COAMPS and WRF, and their impact is examined on nine forecast quantities (water vapor, convective and gridscale precipitation, and air temperature and wind speed at three altitudes). Although the conclusions depend on the model parameters and specific forecast quantities, it is shown that sensitivity to model parameters is often accompanied by nontrivial spatial structure, which itself depends on the underlying forecast model (i.e., COAMPS vs WRF). One specific difference between these models is in their sensitivity with respect to a parameter that controls temperature increments in the Kain–Fritsch trigger function; whereas this parameter has a distinct spatial structure in COAMPS, that structure is completely absent in WRF. The differences between COAMPS and WRF also extend to the quality of the statistical models used to assess sensitivity; specifically, the differences are largest over the waters off the southeastern coast of the United States. The implication of these findings is twofold: not only is the spatial structure of sensitivities different between COAMPS and WRF, the underlying relationship between the model parameters and the forecasts is also different between the two models.

Download Full-text

Optimality-based non-Redfield plankton–ecosystem model (OPEM v1.1) in UVic-ESCM 2.9 – Part 2: Sensitivity analysis and model calibration

Geoscientific Model Development ◽

10.5194/gmd-13-4691-2020 ◽

2020 ◽

Vol 13 (10) ◽

pp. 4691-4712

Author(s):

Chia-Te Chien ◽

Markus Pahlow ◽

Markus Schartau ◽

Andreas Oschlies

Keyword(s):

Sensitivity Analysis ◽

Climate Model ◽

Driving Forces ◽

Ingestion Rate ◽

Latin Hypercube Sampling ◽

Ecosystem Model ◽

Model Parameters ◽

Model Assessment ◽

Model Solutions ◽

Elemental Stoichiometry

Abstract. We analyse 400 perturbed-parameter simulations for two configurations of an optimality-based plankton–ecosystem model (OPEM), implemented in the University of Victoria Earth System Climate Model (UVic-ESCM), using a Latin hypercube sampling method for setting up the parameter ensemble. A likelihood-based metric is introduced for model assessment and selection of the model solutions closest to observed distributions of NO3-, PO43-, O2, and surface chlorophyll a concentrations. The simulations closest to the data with respect to our metric exhibit very low rates of global N2 fixation and denitrification, indicating that in order to achieve rates consistent with independent estimates, additional constraints have to be applied in the calibration process. For identifying the reference parameter sets, we therefore also consider the model's ability to represent current estimates of water-column denitrification. We employ our ensemble of model solutions in a sensitivity analysis to gain insights into the importance and role of individual model parameters as well as correlations between various biogeochemical processes and tracers, such as POC export and the NO3- inventory. Global O2 varies by a factor of 2 and NO3- by more than a factor of 6 among all simulations. Remineralisation rate is the most important parameter for O2, which is also affected by the subsistence N quota of ordinary phytoplankton (Q0,phyN) and zooplankton maximum specific ingestion rate. Q0,phyN is revealed as a major determinant of the oceanic NO3- pool. This indicates that unravelling the driving forces of variations in phytoplankton physiology and elemental stoichiometry, which are tightly linked via Q0,phyN, is a prerequisite for understanding the marine nitrogen inventory.

Download Full-text

Groundwater pollution risk using a modified Latin hypercube sampling

Journal of Hydroinformatics ◽

10.2166/hydro.2006.018b ◽

2006 ◽

Vol 8 (3) ◽

pp. 223-234 ◽

Cited By ~ 3

Author(s):

Husam Baalousha

Keyword(s):

Sensitivity Analysis ◽

Groundwater Pollution ◽

Probabilistic Models ◽

Latin Hypercube Sampling ◽

Model Parameters ◽

Latin Hypercube ◽

Deterministic Models ◽

Estimation Errors ◽

Mean Estimation ◽

Longitudinal Dispersivity

Characterisation of groundwater modelling involves significant uncertainty because of estimation errors of these models and other different sources of uncertainty. Deterministic models do not account for uncertainties in model parameters, and thus lead to doubtful output. The main alternatives for deterministic models are the probabilistic models and perturbation methods such as Monte Carlo Simulation (MCS). Unfortunately, these methods have many drawbacks when applied in risk analysis of groundwater pollution. In this paper, a modified Latin Hypercube Sampling method is presented and used for risk, uncertainty, and sensitivity analysis of groundwater pollution. The obtained results were compared with other sampling methods. Results of the proposed method have shown that it can predict the groundwater contamination risk for all values of probability better than other methods, maintaining the accuracy of mean estimation. Sensitivity analysis results reveal that the contaminant concentration is more sensitive to longitudinal dispersivity than to velocity.

Download Full-text

Optimality-Based Non-Redfield Plankton-Ecosystem Model (OPEMv1.0) in the UVic-ESCM 2.9. Part II: Sensitivity Analysis and Model Calibration

10.5194/gmd-2019-324 ◽

2020 ◽

Author(s):

Chia-Te Chien ◽

Markus Pahlow ◽

Markus Schartau ◽

Andreas Oschlies

Keyword(s):

Sensitivity Analysis ◽

Climate Model ◽

Driving Forces ◽

Ingestion Rate ◽

Latin Hypercube Sampling ◽

Ecosystem Model ◽

Model Parameters ◽

Model Assessment ◽

Model Solutions ◽

Elemental Stoichiometry

Abstract. We analyse 400 perturbed-parameter simulations for two configurations of an optimality-based plankton-ecosystem model (OPEM), implemented in the University of Victoria Earth-System Climate Model (UVic-ESCM), using a Latin-Hypercube sampling method for setting up the parameter ensemble. A likelihood-based metric is introduced for model assessment and selection of the model solutions closest to observed distributions of NO3−, PO43−, O2, and surface chlorophyll a concentrations. According to our metric the optimal model solutions comprise low rates of global N2 fixation and denitrification. These two rate estimates turned out to be poorly constrained by the data. For identifying the “best” model solutions we therefore also consider the model’s ability to represent current estimates of water-column denitrification. We employ our ensemble of model solutions in a sensitivity analysis to gain insights into the importance and role of individual model parameters as well as correlations between various biogeochemical processes and tracers, such as POC export and the NO3− inventory. Global O2 varies by a factor of two and NO3− by more than a factor of six among all simulations. Remineralisation rate is the most important parameter for O2, which is also affected by the subsistence N quota of ordinary phytoplankton (QN0,phy) and zooplankton maximum specific ingestion rate. QN0,phy is revealed as a major determinant of the oceanic NO3− pool. This indicates that unraveling the driving forces of variations in phytoplankton physiology and elemental stoichiometry, which are tightly linked via QN0,phy, is a prerequisite for understanding the marine nitrogen inventory.

Download Full-text

Global sensitivity analysis of the BSM2 dynamic influent disturbance scenario generator

Water Science & Technology ◽

10.2166/wst.2012.089 ◽

2012 ◽

Vol 65 (11) ◽

pp. 1912-1922 ◽

Cited By ~ 2

Author(s):

Xavier Flores-Alsina ◽

Krist V. Gernaey ◽

Ulf Jeppsson

Keyword(s):

Sensitivity Analysis ◽

Flow Rate ◽

Global Sensitivity Analysis ◽

Weather Conditions ◽

Model Parameters ◽

Rain Event ◽

Global Sensitivity ◽

Yearly Average ◽

Catchment Size ◽

Wet Weather

This paper presents the results of a global sensitivity analysis (GSA) of a phenomenological model that generates dynamic wastewater treatment plant (WWTP) influent disturbance scenarios. This influent model is part of the Benchmark Simulation Model (BSM) family and creates realistic dry/wet weather files describing diurnal, weekend and seasonal variations through the combination of different generic model blocks, i.e. households, industry, rainfall and infiltration. The GSA is carried out by combining Monte Carlo simulations and standardized regression coefficients (SRC). Cluster analysis is then applied, classifying the influence of the model parameters into strong, medium and weak. The results show that the method is able to decompose the variance of the model predictions (R2> 0.9) satisfactorily, thus identifying the model parameters with strongest impact on several flow rate descriptors calculated at different time resolutions. Catchment size (PE) and the production of wastewater per person equivalent (QperPE) are two parameters that strongly influence the yearly average dry weather flow rate and its variability. Wet weather conditions are mainly affected by three parameters: (1) the probability of occurrence of a rain event (Llrain); (2) the catchment size, incorporated in the model as a parameter representing the conversion from mm rain · day−1 to m3 · day−1 (Qpermm); and, (3) the quantity of rain falling on permeable areas (aH). The case study also shows that in both dry and wet weather conditions the SRC ranking changes when the time scale of the analysis is modified, thus demonstrating the potential to identify the effect of the model parameters on the fast/medium/slow dynamics of the flow rate. The paper ends with a discussion on the interpretation of GSA results and of the advantages of using synthetic dynamic flow rate data for WWTP influent scenario generation. This section also includes general suggestions on how to use the proposed methodology to any influent generator to adapt the created time series to a modeller's demands.

Download Full-text

Sensitivity analysis of infectious disease models: methods, advances and their application

Journal of The Royal Society Interface ◽

10.1098/rsif.2012.1018 ◽

2013 ◽

Vol 10 (86) ◽

pp. 20121018 ◽

Cited By ~ 79

Author(s):

Jianyong Wu ◽

Radhika Dhingra ◽

Manoj Gambhir ◽

Justin V. Remais

Keyword(s):

Infectious Disease ◽

Sensitivity Analysis ◽

Model Performance ◽

Rank Correlation ◽

Quantitative Relationship ◽

Latin Hypercube Sampling ◽

Transmission Model ◽

Model Parameters ◽

State Variables ◽

Heat Map

Sensitivity analysis (SA) can aid in identifying influential model parameters and optimizing model structure, yet infectious disease modelling has yet to adopt advanced SA techniques that are capable of providing considerable insights over traditional methods. We investigate five global SA methods—scatter plots, the Morris and Sobol’ methods, Latin hypercube sampling-partial rank correlation coefficient and the sensitivity heat map method—and detail their relative merits and pitfalls when applied to a microparasite (cholera) and macroparasite (schistosomaisis) transmission model. The methods investigated yielded similar results with respect to identifying influential parameters, but offered specific insights that vary by method. The classical methods differed in their ability to provide information on the quantitative relationship between parameters and model output, particularly over time. The heat map approach provides information about the group sensitivity of all model state variables, and the parameter sensitivity spectrum obtained using this method reveals the sensitivity of all state variables to each parameter over the course of the simulation period, especially valuable for expressing the dynamic sensitivity of a microparasite epidemic model to its parameters. A summary comparison is presented to aid infectious disease modellers in selecting appropriate methods, with the goal of improving model performance and design.

Download Full-text

An Optimal Control Perspective on Weather and Climate Modification

Mathematics ◽

10.3390/math9040305 ◽

2021 ◽

Vol 9 (4) ◽

pp. 305

Author(s):

Sergei Soldatenko ◽

Rafael Yusupov

Keyword(s):

Optimal Control ◽

Sensitivity Analysis ◽

Dynamical Systems ◽

Weather Conditions ◽

Model Parameters ◽

Weather Modification ◽

Atmospheric Sciences ◽

Atmospheric Processes ◽

Weather And Climate ◽

Earth’S Climate

Intentionally altering natural atmospheric processes using various techniques and technologies for changing weather patterns is one of the appropriate human responses to climate change and can be considered a rather drastic adaptation measure. A fundamental understanding of the human ability to modify weather conditions requires collaborative research in various scientific fields, including, but not limited to, atmospheric sciences and different branches of mathematics. This article being theoretical and methodological in nature, generalizes and, to some extent, summarizes our previous and current research in the field of climate and weather modification and control. By analyzing the deliberate change in weather and climate from an optimal control and dynamical systems perspective, we get the ability to consider the modification of natural atmospheric processes as a dynamic optimization problem with an emphasis on the optimal control problem. Within this conceptual and unified theoretical framework for developing and synthesizing an optimal control for natural weather phenomena, the atmospheric process in question represents a closed-loop dynamical system described by an appropriate mathematical model or, in other words, by a set of differential equations. In this context, the human control actions can be described by variations of the model parameters selected on the basis of sensitivity analysis as control variables. Application of the proposed approach to the problem of weather and climate modification is illustrated using a low-order conceptual model of the Earth’s climate system. For the sake of convenient interpretation, we provide some weather and climate basics, as well as we give a brief glance at control theory and sensitivity analysis of dynamical systems.

Download Full-text

Modelling Optimal Control of Cholera in Communities Linked by Migration

Computational and Mathematical Methods in Medicine ◽

10.1155/2015/898264 ◽

2015 ◽

Vol 2015 ◽

pp. 1-12 ◽

Cited By ~ 6

Author(s):

J. B. H. Njagarah ◽

F. Nyabadza

Keyword(s):

Mathematical Model ◽

Optimal Control ◽

Sensitivity Analysis ◽

Control Theory ◽

Optimal Control Theory ◽

Temporal Relationship ◽

Latin Hypercube Sampling ◽

Living Conditions ◽

Model Parameters ◽

Reproduction Numbers

A mathematical model for the dynamics of cholera transmission with permissible controls between two connected communities is developed and analysed. The dynamics of the disease in the adjacent communities are assumed to be similar, with the main differences only reflected in the transmission and disease related parameters. This assumption is based on the fact that adjacent communities often have different living conditions and movement is inclined toward the community with better living conditions. Community specific reproduction numbers are given assuming movement of those susceptible, infected, and recovered, between communities. We carry out sensitivity analysis of the model parameters using the Latin Hypercube Sampling scheme to ascertain the degree of effect the parameters and controls have on progression of the infection. Using principles from optimal control theory, a temporal relationship between the distribution of controls and severity of the infection is ascertained. Our results indicate that implementation of controls such as proper hygiene, sanitation, and vaccination across both affected communities is likely to annihilate the infection within half the time it would take through self-limitation. In addition, although an infection may still break out in the presence of controls, it may be up to 8 times less devastating when compared with the case when no controls are in place.

Download Full-text

Sensitivity analysis of timber-concrete composite structures

Facta universitatis - series Architecture and Civil Engineering ◽

10.2298/fuace170712010v ◽

2018 ◽

Vol 16 (1) ◽

pp. 125-134

Author(s):

Nikola Velimirovic ◽

Dragoslav Stojic

Keyword(s):

Sensitivity Analysis ◽

Composite Structures ◽

Rank Order ◽

Equivalent Stress ◽

Latin Hypercube Sampling ◽

Simulation Method ◽

Design Parameters ◽

Model Parameters ◽

Design And Optimization ◽

Stochastic Sensitivity Analysis

The sensitivity analysis could be defined as a study of how the variability of the output parameter of the considered model can be distributed to its sources, actually, on the variability of the various input model parameters. It helps to identify the most important design parameters of a particular structure and to focus on them during the design and optimization process. This paper is focused on the application of stochastic sensitivity analysis of maximum equivalent stress and maximum mid-span deflection of timber-concrete composite beam. All input parameters were considered to be random variables. Latin Hypercube Sampling numerical simulation method was employed. The estimation of the sensitivity was derived from Spearman rank-order correlation coefficient.

Download Full-text