scholarly journals Spatial and Seasonal Variations and Inter-Relationship in Fitted Model Parameters for Rainfall Totals across Australia at Various Timescales

Climate ◽  
2019 ◽  
Vol 7 (1) ◽  
pp. 4 ◽  
Author(s):  
Md Masud Hasan ◽  
Barry F. W. Croke ◽  
Fazlul Karim
Keyword(s):  
Probabilistic Models ◽  
Dispersion Model ◽  
Synthetic Data ◽  
Daily Rainfall ◽  
Seasonal Rainfall ◽  
Model Parameters ◽  
Exponential Dispersion Model ◽  
Gamma Distributions ◽  
Using Data ◽  
Combined Data

Probabilistic models are useful tools in understanding rainfall characteristics, generating synthetic data and predicting future events. This study describes the results from an analysis on comparing the probabilistic nature of daily, monthly and seasonal rainfall totals using data from 1327 rainfall stations across Australia. The main objective of this research is to develop a relationship between parameters obtained from models fitted to daily, monthly and seasonal rainfall totals. The study also examined the possibility of estimating the parameters for daily data using fitted parameters to monthly rainfall. Three distributions within the Exponential Dispersion Model (EDM) family (Normal, Gamma and Poisson-Gamma) were found to be optimal for modelling the daily, monthly and seasonal rainfall total. Within the EDM family, Poisson-Gamma distributions were found optimal in most cases, whereas the normal distribution was rarely optimal except for the stations from the wet region. Results showed large differences between regional and seasonal ϕ-index values (dispersion parameter), indicating the necessity of fitting separate models for each season. However, strong correlations were found between the parameters of combined data and those derived from individual seasons (0.70–0.81). This indicates the possibility of estimating parameters of individual season from the parameters of combined data. Such relationship has also been noticed for the parameters obtained through monthly and daily models. Findings of this research could be useful in understanding the probabilistic features of daily, monthly and seasonal rainfall and generating daily rainfall from monthly data for rainfall stations elsewhere.

3D modeling and inversion of VLF and VLF-R electromagnetic data

Geophysics ◽  
2012 ◽  
Vol 77 (4) ◽  
pp. WB219-WB231 ◽  
Author(s):  
P. Kaikkonen ◽  
S. P. Sharma ◽  
S. Mittal
Keyword(s):  
Field Data ◽  
Three Dimensional ◽  
Synthetic Data ◽  
Low Frequency ◽  
Reliability And Validity ◽  
Physical Parameters ◽  
Model Parameters ◽  
Data Inversion ◽  
Inversion Procedure ◽  
Using Data

Three-dimensional linearized nonlinear electromagnetic inversion is developed for revealing the subsurface conductivity structure using isolated very low frequency (VLF) and VLF-resistivity anomalies due to conductors that may be arbitrarily directed towards the measuring profiles and the VLF transmitter. We described the 3D model using a set of variables in terms of geometric and physical parameters. These model parameters were then optimized (parametric inversion) to obtain their best estimates to fit the observations. Two VLF transmitters, i.e., the [Formula: see text], [Formula: see text] (“E”) and the [Formula: see text], [Formula: see text] (“H”) polarizations, respectively, can be considered jointly in inversion. After inverting several noise-free and noisy synthetic data, the results revealed that the estimated model parameters and the functionality of the approach were very good and reliable. The inversion procedure also worked well for the field data. The reliability and validity of the results after the field data inversion have been checked using data from a shear zone associated with uranium mineralization.

scholarly journals ESTIMATE OF THE INTENSITY-DURATION-FREQUENCY PARAMETERS OF INTENSE RAINFALL FOR THE STATE OF ALAGOAS, BRAZIL

2020 ◽  
Vol 28 ◽  
pp. 314-325
Author(s):  
João Batista Lopes da Silva ◽  
Nicole Lopes Bento ◽  
Gabriel Soares Lopes Gomes ◽  
Alcinei Ribeiro Campos ◽  
Danilo Paulúcio da Silva
Keyword(s):  
Return Period ◽  
Probabilistic Models ◽  
Daily Rainfall ◽  
The State ◽  
Data Series ◽  
Intense Rainfall ◽  
Maximum Rainfall ◽  
Rain Gauges ◽  
Intensity Duration Frequency ◽  
Using Data

The study of the rainfall characteristics is of fundamental importance since the frequency of floods has increased in several parts of Brazil due to anthropic impacts of climatic changes. Thus, this study aimed to determine the parameters of the intense rainfall equation (K, a, b, c) for 52 municipalities in the State of Alagoas using data from 164 rain gauges ta available from the National Water Agency (ANA). The data series were subjected to consistency analysis and further desegregation of maximum daily rainfall to durations of the 5; 10; 15; 20; 25; 30; 60; 360; 480; 600; 720 and 1,440 minutes and return period of 5; 10; 25; 50 and 100 years according to different probabilistic models. The adjustment of the parameters was carried out by means of non-linear regression, with R² greater than 0.949 for all the stations, considering for this purpose one station per municipality, totaling 51 municipalities of study. It was obtained that the maximum rainfall intensity predicted increases with the increase in the return period and decreases with the increase of the duration of the rain. The greater intensities were detected in the mesoregion of Eastern Alagoano and the lowest intensities in the mesoregion of Sertão Alagoano.

scholarly journals A flexible framework for sequential estimation of model parameters in computational hemodynamics

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Christopher J. Arthurs ◽  
Nan Xiao ◽  
Philippe Moireau ◽  
Tobias Schaeffter ◽  
C. Alberto Figueroa
Keyword(s):  
Unscented Kalman Filter ◽  
Three Dimensional ◽  
Synthetic Data ◽  
Sequential Estimation ◽  
Wall Material ◽  
Patient Specific ◽  
Model Parameters ◽  
Computational Framework ◽  
Lumped Parameter ◽  
Estimation Of Model Parameters

AbstractA major challenge in constructing three dimensional patient specific hemodynamic models is the calibration of model parameters to match patient data on flow, pressure, wall motion, etc. acquired in the clinic. Current workflows are manual and time-consuming. This work presents a flexible computational framework for model parameter estimation in cardiovascular flows that relies on the following fundamental contributions. (i) A Reduced-Order Unscented Kalman Filter (ROUKF) model for data assimilation for wall material and simple lumped parameter network (LPN) boundary condition model parameters. (ii) A constrained least squares augmentation (ROUKF-CLS) for more complex LPNs. (iii) A “Netlist” implementation, supporting easy filtering of parameters in such complex LPNs. The ROUKF algorithm is demonstrated using non-invasive patient-specific data on anatomy, flow and pressure from a healthy volunteer. The ROUKF-CLS algorithm is demonstrated using synthetic data on a coronary LPN. The methods described in this paper have been implemented as part of the CRIMSON hemodynamics software package.

scholarly journals Mechanisms Associated with Large Daily Rainfall Events in Northeast Brazil

2011 ◽  
Vol 24 (2) ◽  
pp. 376-396 ◽  
Author(s):  
Brant Liebmann ◽  
George N. Kiladis ◽  
Dave Allured ◽  
Carolina S. Vera ◽  
Charles Jones ◽  
...  
Keyword(s):  
Daily Rainfall ◽  
Annual Rainfall ◽  
Northeast Brazil ◽  
Kelvin Wave ◽  
Rainfall Events ◽  
Independent Events ◽  
Precipitation Anomalies ◽  
Wave Trains ◽  
Using Data ◽  
Forcing Mechanisms

Abstract The mechanisms resulting in large daily rainfall events in Northeast Brazil are analyzed using data filtering to exclude periods longer than 30 days. Composites of circulation fields that include all independent events do not reveal any obvious forcing mechanisms as multiple patterns contribute to Northeast Brazil precipitation variability. To isolate coherent patterns, subsets of events are selected based on anomalies that precede the Northeast Brazil precipitation events at different locations. The results indicate that at 10°S, 40°W, the area of lowest annual rainfall in Brazil, precipitation occurs mainly in association with trailing midlatitude synoptic wave trains originating in either hemisphere. Closer to the equator at 5°S, 37.5°W, an additional convection precursor is found to the west, with a spatial structure consistent with that of a Kelvin wave. Although these two sites are located within only several hundred kilometers of each other and the midlatitude patterns that induce precipitation appear to be quite similar, the dates on which large precipitation anomalies occur at each location are almost entirely independent, pointing to separate forcing mechanisms.

Predicting the longitudinal modulus of elasticity of Sitka spruce from cellulose orientation and abundance

Holzforschung ◽  
2010 ◽  
Vol 64 (4) ◽  
Author(s):  
J. Paul McLean ◽  
Robert Evans ◽  
John R. Moore
Keyword(s):  
Bulk Density ◽  
Modulus Of Elasticity ◽  
Microfibril Angle ◽  
Sitka Spruce ◽  
Picea Sitchensis ◽  
Model Parameters ◽  
Bending Tests ◽  
Wood Stiffness ◽  
Using Data ◽  
Longitudinal Modulus

Abstract Sitka spruce (Picea sitchensis) is the most widely planted commercial tree species in the United Kingdom and Ireland. Because of the increasing use of this species for construction, the ability to predict wood stiffness is becoming more important. In this paper, a number of models are developed using data on cellulose abundance and orientation obtained from the SilviScan-3 system to predict the longitudinal modulus of elasticity (MOE) of small defect-free specimens. Longitudinal MOE was obtained from both bending tests and a sonic resonance technique. Overall, stronger relationships were found between the various measures of cellulose abundance and orientation and the dynamic MOE obtained from the sonic resonance measurements, rather than with the static MOE obtained from bending tests. There was only a moderate relationship between wood bulk density and dynamic MOE (R2=0.423), but this relationship was improved when density was divided by microfibril angle (R2=0.760). The best model for predicting both static and dynamic MOE involved the product of bulk density and the coefficient of variation in the azimuthal intensity profile (R2=0.725 and 0.862, respectively). The model parameters obtained for Sitka spruce differed from those obtained in earlier studies on Pinus radiata and Eucalyptus delegatensis, indicating that the model might require recalibration before it can be applied to different species.

scholarly journals Investigations Concerning the Residence Time Distribution of Twin-Screw-Extrusion Processes as Indicator for Inherent Mixing

Pharmaceutics ◽  
2018 ◽  
Vol 10 (4) ◽  
pp. 207 ◽  
Author(s):  
Jens Wesholowski ◽  
Andreas Berghaus ◽  
Markus Thommes
Keyword(s):  
Residence Time ◽  
Mass Flow ◽  
Time Distribution ◽  
Residence Time Distribution ◽  
Dispersion Model ◽  
Model Parameters ◽  
Twin Screw Extrusion ◽  
Characteristic Model ◽  
Screw Extrusion ◽  
Twin Screw

Over recent years Twin-Screw-Extrusion (TSE) has been established as a platform technology for pharmaceutical manufacturing. Compared to other continuous operation, one of the major benefits of this method is the combination of several unit operations within one apparatus. Several of these are linked to the Residence Time Distribution (RTD), which is typically expressed by the residence time density function. One relevant aspect for pharmaceutical processes is the mixing capacity, which is represented by the width of this distribution. In the frame of this study the influence of the mass flow, the temperature and the screw-barrel clearance were investigated for a constant barrel load (specific feed load, SFL). While the total mass flow as well as the external screw diameter affected the mixing performance, the barrel temperature had no influence for the investigated range. The determined results were additionally evaluated with respect to a fit to the Twin-Dispersion-Model (TDM). This model is based on the superimposition of two mixing functions. The correlations between varied process parameters and the obtained characteristic model parameters proved this general physical view on extrusion.

scholarly journals THE FULL TAILS GAMMA DISTRIBUTION APPLIED TO MODEL EXTREME VALUES

2017 ◽  
Vol 47 (3) ◽  
pp. 895-917 ◽  
Author(s):  
Joan del Castillo ◽  
Jalila Daoudi ◽  
Isabel Serra
Keyword(s):  
Extreme Value Theory ◽  
Pareto Distribution ◽  
Extreme Values ◽  
Dispersion Model ◽  
Value Theory ◽  
Quantitative Modelling ◽  
Exponential Distributions ◽  
Exponential Dispersion Model ◽  
Aggregate Loss ◽  
Very High

AbstractIn this paper, we introduce the simplest exponential dispersion model containing the Pareto and exponential distributions. In this way, we obtain distributions with support (0, ∞) that in a long interval are equivalent to the Pareto distribution; however, for very high values, decrease like the exponential. This model is useful for solving relevant problems that arise in the practical use of extreme value theory. The results are applied to two real examples, the first of these on the analysis of aggregate loss distributions associated to the quantitative modelling of operational risk. The second example shows that the new model improves adjustments to the destructive power of hurricanes, which are among the major causes of insurance losses worldwide.

scholarly journals Stochastic daily rainfall generation on tropical islands with complex topography

2021 ◽  
Author(s):  
Lionel Benoit ◽  
Lydie Sichoix ◽  
Alison D. Nugent ◽  
Matthew P. Lucas ◽  
Thomas W. Giambelluca
Keyword(s):  
Probabilistic Models ◽  
Conceptual Models ◽  
Rainfall Variability ◽  
Daily Rainfall ◽  
French Polynesia ◽  
High Elevation ◽  
Time Behavior ◽  
Tropical Islands ◽  
Orographic Effects ◽  
Spatial Gradients

Abstract. Stochastic rainfall generators are probabilistic models of rainfall space-time behavior. During parameterization and calibration, they allow the identification and quantification of the main modes of rainfall variability. Hence, stochastic rainfall models can be regarded as probabilistic conceptual models of rainfall dynamics. As with most conceptual models in Earth Sciences, the performance of stochastic rainfall models strongly relies on their adequacy in representing the rain process at hand. On tropical islands with high elevation topography, orographic rain enhancement challenges most existing stochastic models because it creates localized rains with strong spatial gradients, which break down the stationarity of rain statistics. To allow for stochastic rainfall modeling on tropical islands, despite non-stationarity, we propose a new stochastic daily rainfall generator specifically for areas with significant orographic effects. Our model relies on a preliminary classification of daily rain patterns into rain types based on rainfall space and intensity statistics, and sheds new light on rainfall variability at the island scale. Within each rain type, the spatial distribution of rainfall through the island is modeled following a meta-Gaussian approach combining empirical spatial copulas and a Gamma transform function, which allows us to generate realistic daily rain fields. When applied to the stochastic simulation of rainfall on the islands of O‘ahu (Hawai‘i, United States of America) and Tahiti (French Polynesia) in the tropical Pacific, the proposed model demonstrates good skills in jointly simulating site specific and island scale rain statistics. Hence, it provides a new tool for stochastic impact studies in tropical islands, in particular for watershed water resources management and downscaling of future precipitation projections.

scholarly journals Bias and error in modelling thermochronometric data: resolving a potential increase in Plio-Pleistocene erosion rate

2021 ◽  
Vol 9 (5) ◽  
pp. 1153-1221
Author(s):  
Sean D. Willett ◽  
Frédéric Herman ◽  
Matthew Fox ◽  
Nadja Stalder ◽  
Todd A. Ehlers ◽  
...  
Keyword(s):  
Error Analysis ◽  
Inverse Method ◽  
Synthetic Data ◽  
Systematic Bias ◽  
Exhumation Rate ◽  
Erosion Rates ◽  
The Difference ◽  
Path Lengths ◽  
Using Data ◽  
Exhumation History

Abstract. Thermochronometry provides one of few methods to quantify rock exhumation rate and history, including potential changes in exhumation rate. Thermochronometric ages can resolve rates, accelerations, and complex histories by exploiting different closure temperatures and path lengths using data distributed in elevation. We investigate how the resolution of an exhumation history is determined by the distribution of ages and their closure temperatures through an error analysis of the exhumation history problem. We define the sources of error, defined in terms of resolution, model error and methodological bias in the inverse method used by Herman et al. (2013) which combines data with different closure temperatures and elevations. The error analysis provides a series of tests addressing the various types of bias, including addressing criticism that there is a tendency of thermochronometric data to produce a false inference of faster erosion rates towards the present day because of a spatial correlation bias. Tests based on synthetic data demonstrate that the inverse method used by Herman et al. (2013) has no methodological or model bias towards increasing erosion rates. We do find significant resolution errors with sparse data, but these errors are not systematic, tending rather to leave inferred erosion rates at or near a Bayesian prior. To explain the difference in conclusions between our analysis and that of other work, we examine other approaches and find that previously published model tests contained an error in the geotherm calculation, resulting in an incorrect age prediction. Our reanalysis and interpretation show that the original results of Herman et al. (2013) are correctly calculated and presented, with no evidence for a systematic bias.

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