scholarly journals Characterization of an Instrument Model for Exoplanet Transit Spectrum Estimation through Wide-scale Analysis on HST Data

2021 ◽  
Vol 163 (1) ◽  
pp. 22
Author(s):  
Noah Huber-Feely ◽  
Mark R. Swain ◽  
Gael Roudier ◽  
Raissa Estrela
Keyword(s):  
Light Curve ◽  
Model Performance ◽  
Distribution Functions ◽  
Model Parameters ◽  
Spectrum Estimation ◽  
Parent Distribution ◽  
Wide Scale ◽  
And Performance ◽  
In Transit

Abstract Instrument models (IMs) enable the reduction of systematic error in transit spectroscopy light-curve data, but, since the model formulation can influence the estimation of science model parameters, characterization of the instrument model effects is crucial to the interpretation of the reduced data. We analyze a simple instrument model and assess its validity and performance across Hubble WFC3 and STIS instruments. Over a large, n = 63, sample of observed targets, a Markov chain Monte Carlo sampler computes the parent distribution of each instrument model parameter. Possible parent distribution functions are then fit and tested against the empirical IM distribution. Correlation and other analyses are then performed to find IM relationships. The model is shown to perform well across the two instruments and three filters analyzed and, further, the Student’s t distribution is shown to closely fit the empirical parent distribution of IM parameters and the Gaussian distribution is shown to poorly model the observed distribution. This parent distribution can be used in the MCMC prior fitting and demonstrates IM consistency for wide-scale atmospheric analysis using this model. Finally, we propose a simple metric based on light-curve residuals to determine model performance, and we demonstrate its ability to determine whether a derived spectrum under this IM is high quality and robust.

Applicability of Different Hydrological Model Concepts on Small Catchments: Case Study of Bükkös Creek, Hungary

2014 ◽  
Vol 10 (1) ◽  
pp. 77-90 ◽  
Author(s):  
Péter Torma ◽  
Borbála Széles ◽  
Géza Hajnal
Keyword(s):  
Model Comparison ◽  
Hydrological Model ◽  
Model Performance ◽  
Digital Terrain Model ◽  
Model Parameters ◽  
Lumped Model ◽  
Terrain Model ◽  
Distributed Approach ◽  
Model Setup ◽  
And Performance

Abstract This study aims to test and compare the applicability and performance of two different hydrological model concepts on a small Hungarian watershed. The lumped model of HEC-HMS and the semi-distributed TOPMODEL have been implemented to predict streamflow of Bükkös Creek. Models were calibrated against the highest flood event recorded in the basin in May, 2010. Validation was done in an extended interval when smaller floods were observed. Acceptable results can be achieved with the semi-distributed approach. Model comparison is made by means of sensitivity analysis of model parameters. For TOPMODEL the effect of spatial resolution of the digital terrain model while for HMS the complexity of the model setup was further explored. The results were quantified with model performance indices.

scholarly journals COMET: a Lagrangian transport model for greenhouse gas emission estimation – forward model technique and performance for methane

2006 ◽  
Vol 6 (5) ◽  
pp. 8727-8779 ◽  
Author(s):  
A. T. Vermeulen ◽  
G. Pieterse ◽  
A. Hensen ◽  
W. C. M. van den Bulk ◽  
J. W. Erisman
Keyword(s):  
Greenhouse Gas ◽  
Greenhouse Gas Emission ◽  
Transport Model ◽  
Model Performance ◽  
Model Parameters ◽  
Atmospheric Concentration ◽  
Absolute Size ◽  
Lagrangian Transport ◽  
Emission Estimation ◽  
And Performance

Abstract. The Lagrangian transport model COMET has been developed to evaluate emission estimates based on atmospheric concentration observations. This paper describes the model and its application in modelling the methane concentrations at the European stations Cabauw and Macehead. The COMET model captures in most cases both synoptic and diurnal variations of the concentrations as a function of time and in absolute size quite well. The explained variability by COMET of the mixed layer concentration for Cabauw varies from 50% to 84%; for all hourly observations in 2002 the explained variability is 71% with a RMSE of 112 ppb. The explained variability for Macehead is 48%. The most important model parameters were tested for their influence on model performance, but in general the model is not very sensitive to variations within acceptable limits. For a regionally and locally polluted continental site the COMET model shows only a small bias and a moderate random error, and therefore is considered to capture the influence of the sources on the concentration variations quite well. It is therefore concluded that inverse methods and more specifically the COMET model is suitable to be applied in deriving independent estimates of greenhouse gas emissions using Source-Receptor relationships.

scholarly journals Noise and Breakdown Characterization of SPAD Detectors with Time-Gated Photon-Counting Operation

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5287
Author(s):  
Hiwa Mahmoudi ◽  
Michael Hofbauer ◽  
Bernhard Goll ◽  
Horst Zimmermann
Keyword(s):  
Breakdown Voltage ◽  
Single Photon ◽  
Photon Counting ◽  
Low Noise ◽  
Dark Count ◽  
Fully Integrated ◽  
Trade Offs ◽  
And Performance ◽  
Noise Models

Being ready-to-detect over a certain portion of time makes the time-gated single-photon avalanche diode (SPAD) an attractive candidate for low-noise photon-counting applications. A careful SPAD noise and performance characterization, however, is critical to avoid time-consuming experimental optimization and redesign iterations for such applications. Here, we present an extensive empirical study of the breakdown voltage, as well as the dark-count and afterpulsing noise mechanisms for a fully integrated time-gated SPAD detector in 0.35-μm CMOS based on experimental data acquired in a dark condition. An “effective” SPAD breakdown voltage is introduced to enable efficient characterization and modeling of the dark-count and afterpulsing probabilities with respect to the excess bias voltage and the gating duration time. The presented breakdown and noise models will allow for accurate modeling and optimization of SPAD-based detector designs, where the SPAD noise can impose severe trade-offs with speed and sensitivity as is shown via an example.

scholarly journals Sensitivity and Performance Analyses of the Distributed Hydrology–Soil–Vegetation Model Using Geomorphons for Landform Mapping

Water ◽  
2021 ◽  
Vol 13 (15) ◽  
pp. 2032
Author(s):  
Pâmela A. Melo ◽  
Lívia A. Alvarenga ◽  
Javier Tomasella ◽  
Carlos R. Mello ◽  
Minella A. Martins ◽  
...  
Keyword(s):  
Complex Terrain ◽  
Overland Flow ◽  
Hydrological Model ◽  
Model Performance ◽  
Soil Parameters ◽  
Vegetation Model ◽  
Landform Classification ◽  
Physically Based ◽  
And Performance ◽  
Streamflow Simulation

Landform classification is important for representing soil physical properties varying continuously across the landscape and for understanding many hydrological processes in watersheds. Considering it, this study aims to use a geomorphology map (Geomorphons) as an input to a physically based hydrological model (Distributed Hydrology Soil Vegetation Model (DHSVM)) in a mountainous headwater watershed. A sensitivity analysis of five soil parameters was evaluated for streamflow simulation in each Geomorphons feature. As infiltration and saturation excess overland flow are important mechanisms for streamflow generation in complex terrain watersheds, the model’s input soil parameters were most sensitive in the “slope”, “hollow”, and “valley” features. Thus, the simulated streamflow was compared with observed data for calibration and validation. The model performance was satisfactory and equivalent to previous simulations in the same watershed using pedological survey and moisture zone maps. Therefore, the results from this study indicate that a geomorphologically based map is applicable and representative for spatially distributing hydrological parameters in the DHSVM.

scholarly journals Synthesis and Characterization of Novel Bio-Chiral Dopants Obtained from Bio-Betulin Produced by a Fermentation Process

Crystals ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 785
Author(s):  
Veridiana G. Guimarães ◽  
Anastasiia Svanidze ◽  
Tianyi Guo ◽  
Pawan Nepal ◽  
Robert J. Twieg ◽  
...  
Keyword(s):  
Liquid Crystals ◽  
Natural Product ◽  
Fermentation Process ◽  
Synthesis And Characterization ◽  
Cholesteric Liquid Crystals ◽  
And Performance ◽  
Chiral Dopants

Cholesteric liquid crystals are frequently produced by the addition of chiral dopants to achiral nematic hosts. We report here the synthesis and performance of chiral dopants obtained from bio-betulin produced by a fermentation process. An important aspect of this work is to point out that the fermentation process used to obtain the starting materials is much easier and cheaper when carried out in large volumes than isolating it from the natural product. The performance of the dopants obtained from bio-betulin is indistinguishable from those obtained from commercially available synthetic betulin.

scholarly journals Industrial Plume Properties Retrieved by Optimal Estimation Using Combined Hyperspectral and Sentinel-2 Data

2021 ◽  
Vol 13 (10) ◽  
pp. 1865
Author(s):  
Gabriel Calassou ◽  
Pierre-Yves Foucher ◽  
Jean-François Léon
Keyword(s):  
Matched Filter ◽  
Estimation Method ◽  
Optimal Estimation ◽  
Model Parameters ◽  
Surface Reflectance ◽  
Sequential Method ◽  
Modal Radius ◽  
Sentinel 2

Stack emissions from the industrial sector are a subject of concern for air quality. However, the characterization of the stack emission plume properties from in situ observations remains a challenging task. This paper focuses on the characterization of the aerosol properties of a steel plant stack plume through the use of hyperspectral (HS) airborne remote sensing imagery. We propose a new method, based on the combination of HS airborne acquisition and surface reflectance imagery derived from the Sentinel-2 Multi-Spectral Instrument (MSI). The proposed method detects the plume footprint and estimates the surface reflectance under the plume, the aerosol optical thickness (AOT), and the modal radius of the plume. Hyperspectral surface reflectances are estimated using the coupled non-negative matrix factorization (CNMF) method combining HS and MSI data. The CNMF reduces the error associated with estimating the surface reflectance below the plume, particularly for heterogeneous classes. The AOT and modal radius are retrieved using an optimal estimation method (OEM), based on the forward model and allowing for uncertainties in the observations and in the model parameters. The a priori state vector is provided by a sequential method using the root mean square error (RMSE) metric, which outperforms the previously used cluster tuned matched filter (CTMF). The OEM degrees of freedom are then analysed, in order to refine the mask plume and to enhance the quality of the retrieval. The retrieved mean radii of aerosol particles in the plume is 0.125 μμm, with an uncertainty of 0.05 μμm. These results are close to the ultra-fine mode (modal radius around 0.1 μμm) observed from in situ measurements within metallurgical plant plumes from previous studies. The retrieved AOT values vary between 0.07 (near the source point) and 0.01, with uncertainties of 0.005 for the darkest surfaces and above 0.010 for the brightest surfaces.

scholarly journals Enhanced Neural Network Model for Worldwide Estimation of Weighted Mean Temperature

2021 ◽  
Vol 13 (12) ◽  
pp. 2405
Author(s):  
Fengyang Long ◽  
Chengfa Gao ◽  
Yuxiang Yan ◽  
Jinling Wang
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Model Performance ◽  
Global Scale ◽  
Radiosonde Data ◽  
Model Parameters ◽  
Measured Temperature ◽  
Weighted Mean ◽  
Weighted Mean Temperature ◽  
Application Scope

Precise modeling of weighted mean temperature (Tm) is critical for realizing real-time conversion from zenith wet delay (ZWD) to precipitation water vapor (PWV) in Global Navigation Satellite System (GNSS) meteorology applications. The empirical Tm models developed by neural network techniques have been proved to have better performances on the global scale; they also have fewer model parameters and are thus easy to operate. This paper aims to further deepen the research of Tm modeling with the neural network, and expand the application scope of Tm models and provide global users with more solutions for the real-time acquisition of Tm. An enhanced neural network Tm model (ENNTm) has been developed with the radiosonde data distributed globally. Compared with other empirical models, the ENNTm has some advanced features in both model design and model performance, Firstly, the data for modeling cover the whole troposphere rather than just near the Earth’s surface; secondly, the ensemble learning was employed to weaken the impact of sample disturbance on model performance and elaborate data preprocessing, including up-sampling and down-sampling, which was adopted to achieve better model performance on the global scale; furthermore, the ENNTm was designed to meet the requirements of three different application conditions by providing three sets of model parameters, i.e., Tm estimating without measured meteorological elements, Tm estimating with only measured temperature and Tm estimating with both measured temperature and water vapor pressure. The validation work is carried out by using the radiosonde data of global distribution, and results show that the ENNTm has better performance compared with other competing models from different perspectives under the same application conditions, the proposed model expanded the application scope of Tm estimation and provided the global users with more choices in the applications of real-time GNSS-PWV retrival.

Design, Installation, and Performance Characterization of a Laboratory-Scale Solar Thermal System for Experiments in Solar Energy Utilization

2012 ◽  
Author(s):  
Stephanie Drozek ◽  
Christopher Damm ◽  
Ryan Enot ◽  
Andrew Hjortland ◽  
Brandon Jackson ◽  
...  
Keyword(s):  
Renewable Energy ◽  
Solar Energy ◽  
Laboratory Scale ◽  
Energy Utilization ◽  
Solar Thermal ◽  
Thermal System ◽  
Performance Characterization ◽  
And Performance ◽  
Solar Thermal System

The purpose of this paper is to describe the implementation of a laboratory-scale solar thermal system for the Renewable Energy Systems Laboratory at the Milwaukee School of Engineering (MSOE). The system development began as a student senior design project where students designed and fabricated a laboratory-scale solar thermal system to complement an existing commercial solar energy system on campus. The solar thermal system is designed specifically for educating engineers. This laboratory equipment, including a solar light simulator, allows for variation of operating parameters to investigate their impact on system performance. The equipment will be utilized in two courses: Applied Thermodynamics, and Renewable Energy Utilization. During the solar thermal laboratories performed in these courses, students conduct experiments based on the American Society of Heating, Refrigeration and Air-Conditioning Engineers (ASHRAE) 93-2010 standard for testing and performance characterization of solar thermal systems. Their measurements are then used to quantify energy output, efficiency and losses of the system and subsystem components.

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