scholarly journals Precipitation Downscaling with Gibbs Sampling: An Improved Method for Producing Realistic, Weather-Dependent, and Anisotropic Fields

2020 ◽  
Vol 21 (11) ◽  
pp. 2487-2505 ◽  
Author(s):  
Joseph Bellier ◽  
Michael Scheuerer ◽  
Thomas M. Hamill
Keyword(s):  
Spatial Variability ◽  
Gibbs Sampling ◽  
Hydrological Modeling ◽  
Regression Equations ◽  
Perfect Model ◽  
Extensive Test ◽  
Gibbs Sampling Algorithm ◽  
The Mean ◽  
Anisotropic Fields ◽  
Human Eyes

AbstractDownscaling precipitation fields is a necessary step in a number of applications, especially in hydrological modeling where the meteorological forcings are frequently available at too coarse resolution. In this article, we review the Gibbs sampling disaggregation model (GSDM), a stochastic downscaling technique originally proposed by Gagnon et al. The method is capable of introducing realistic, weather-dependent, and possibly anisotropic fine-scale details, while preserving the mean rain rate over the coarse-scale pixels. The main developments compared to the former version are (i) an adapted Gibbs sampling algorithm that enforces the downscaled fields to have a similar texture to that of the analysis fields, (ii) an extensive test of various meteorological predictors for controlling specific aspects of the texture such as the anisotropy and the spatial variability, and (iii) a review of the regression equations used in the model for defining the conditional distributions. A perfect-model experiment is conducted over a domain in the southeastern United States. The metrics used for verification are based on the concept of gridded, stratified variogram, which is introduced as an effective way of reproducing the abilities of human eyes for detecting differences in the field texture. Results indicate that the best overall performances are obtained with the most sophisticated, predictor-based GSDM variant. The 600-hPa wind is found to be the best year-round predictor for controlling the anisotropy. For the spatial variability, kinematic predictors such as wind shear are found to be best during the convective periods, while instability indices are more informative elsewhere.

scholarly journals Comparison of corneal irregular astigmatism by the type of corneal regular astigmatism

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yuta Ueno ◽  
Risa Nomura ◽  
Takahiro Hiraoka ◽  
Katsuhito Kinoshita ◽  
Mutsuko Ohara ◽  
...  
Keyword(s):  
Higher Order ◽  
Stepwise Multiple Regression ◽  
Group Differences ◽  
Corneal Surface ◽  
Fourier Harmonic ◽  
Normal Human ◽  
The Mean ◽  
Human Eyes ◽  
Topography Data ◽  
Irregular Astigmatism

AbstractWe investigated the relation between corneal regular and irregular astigmatism in normal human eyes. In 951 eyes of 951 patients, corneal irregular astigmatism, such as asymmetry and higher-order irregularity components, was calculated using the Fourier harmonic analysis of corneal topography data within the central 3-mm zone of the anterior corneal surface. The eyes were classified by the type of corneal regular astigmatism into four groups; minimum (< 0.75 diopters), with-the-rule (WTR), against-the-rule (ATR), and oblique astigmatism. The mean age was significantly different among the four groups (P < 0.001); patients with WTR astigmatism were the youngest, followed by those with minimum, oblique, and ATR astigmatism. Significant inter-group differences were found among the four groups in asymmetry (P = 0.005) and higher-order irregularity components (P < 0.001); the largest was in eyes with oblique astigmatism, followed by ATR, WTR, and minimum astigmatism. The stepwise multiple regression analysis revealed that corneal regular astigmatism pattern significantly influenced the amount of corneal irregular astigmatism after controlling for confounding factors (P < 0.001). Corneal irregular astigmatism, such as asymmetry and higher order irregularity components, was the largest in eyes with oblique astigmatism, followed by those with ATR, WTR, and minimum astigmatism, even after adjustment for age of subjects.

How landscape and climate affect the spatial variability of the Italian rainfall extremes? Some initial clues based on I2-RED

2021 ◽  
Author(s):  
Paola Mazzoglio ◽  
Ilaria Butera ◽  
Pierluigi Claps
Keyword(s):  
Spatial Variability ◽  
Linear Regression Analysis ◽  
Alpine Region ◽  
Annual Rainfall ◽  
Mean Values ◽  
Rainfall Extremes ◽  
Geomorphological Parameters ◽  
The Mean ◽  
The Relationship ◽  
Italian Territory

&lt;p&gt;The intensity and the spatial distribution of precipitation depths are known to be highly dependent on relief and geomorphological parameters. Complex environments like mountainous regions are prone to intense and frequent precipitation events, especially if located near the coastline. Although the link between the mean annual rainfall and geomorphological parameters has received substantial attention, few literature studies investigate the relationship between the sub-daily maximum annual rainfall depth and geographical or morphological landscape features.&lt;br&gt;In this study, the mean of the rainfall extremes in Italy, recently revised in the so-called I&lt;sup&gt;2&lt;/sup&gt;-RED dataset, are investigated in their spatial variability in comparison with some landscape and also some broad climatic characteristics. The database includes all sub-daily rainfall extremes recorded in Italy from 1916 until 2019 and this analysis considers their mean values (from 1 to 24 hours) in stations with at least 10 years of records, involving more than 3700 stations.&lt;br&gt;The geo-morpho-climatic factors considered range from latitude, longitude and minimum distance from the coastline on the geographic side, to elevation, slope, openness and obstruction morphological indices, and also include an often-neglected robust climatological information, as the local mean annual rainfall.&lt;br&gt;Obtained results highlight that the relationship between the annual maximum rainfall depths and the hydro-geomorphological parameters is not univocal over the entire Italian territory and over different time intervals. Considering the whole of Italy, the highest correlation is reached between the mean values of the 24-hours records and the mean annual precipitation (correlation coefficient greater than 0.75). This predominance remains also in sub-areas of the Italian territory (i.e., the Alpine region, the Apennines or the coastal areas) but correlation decreases as the time interval decreases, except for the Alpine region (0.73 for the 1-hour maximum). The other geomorphological parameters seem to act in conjunction, making it difficult to evaluate, with a simple linear regression analysis, their impact. As an example, the absolute value of the correlation coefficient between the elevation and the 1-hour extremes is greater than 0.35 for the Italian and the Alpine regions, while for the 24-hours interval it is greater than 0.35 over the coastal areas.&lt;br&gt;To further investigate the spatial variability of the relationship between rainfall and elevation, a spatial linear regression analysis has been undertaken. Local linear relationships have been fitted in circles centered on any of the 0.5-km size pixels in Italy, with 1 to 30 km radius and at least 5 stations included. Results indicate the need of more comprehensive terrain analysis to better understand the causes of local increasing or decreasing relations, poorly described in the available literature.&lt;/p&gt;

Spatial dynamics of soil moisture and temperature in a black spruce boreal chronosequence

2006 ◽  
Vol 36 (11) ◽  
pp. 2794-2802 ◽  
Author(s):  
Ben Bond-Lamberty ◽  
Karen M Brown ◽  
Carol Goranson ◽  
Stith T Gower
Keyword(s):  
Soil Moisture ◽  
Spatial Variability ◽  
Sampling Design ◽  
Black Spruce ◽  
Structural Characteristics ◽  
Spatial Dynamics ◽  
Spherical Model ◽  
Local Scale ◽  
Spatial Dependencies ◽  
The Mean

This study analyzed the spatial dependencies of soil moisture and temperature in a six-stand chronosequence of boreal black spruce (Picea mariana (Mill.) BSP) stands. Spatial variability of soil temperature (TSOIL) was evaluated twice during the growing season using four transects in each stand, employing a cyclic sampling design with measurements spaced 2–92 m apart. Soil moisture (θg) was measured on one occasion. A spherical model was used to analyze the geostatistical correlation structure; θg and TSOIL at the 7- and 21-year-old stands did not exhibit stable ranges or sills. The fits with stable ranges and sills modeled the spatial patterns in the older stands reasonably well, although unexplained variability was high. Calculated ranges varied from 3 to 150 m for these stands, lengths probably related to structural characteristics influential in local-scale energy transfer. Transect-to-transect variability was significant and typically 5%–15% of the mean for TSOIL and 10%–70% for θg. TSOIL and θg were negatively correlated for most stands and depths, with TSOIL dropping 0.5–0.9 °C for every 1% rise in θg. The results reported here provide initial data to assess the spatial variability of TSOIL and θg in a variety of boreal forest stand ages.

scholarly journals GENETIC PARAMETERS FOR GROWTH TRAITS OF SANTA INES SHEEP USING GIBBS SAMPLING

2015 ◽  
Vol 28 (4) ◽  
pp. 211-216 ◽  
Author(s):  
LEANDRO TEIXEIRA BARBOSA ◽  
GLEICIANNY DE BRITO SANTOS ◽  
EVANDRO NEVES MUNIZ ◽  
HYMERSON COSTA AZEVEDO ◽  
JAILSON LARA FAGUNDES
Keyword(s):  
Gibbs Sampling ◽  
Genetic Parameters ◽  
Mixed Model ◽  
Growth Traits ◽  
Meat Production ◽  
Gibbs Sampling Algorithm ◽  
Santa Inês ◽  
Variance Parameters ◽  
Additive Genetic Effects ◽  
Maternal Heritability

ABSTRACT: This study sought to estimate (co)variance and genetic parameters for birth weight (BWT) and weaning weight (WWT) in Santa Ines sheep. A total of 2,111 records were obtained from EMBRAPA/CPATC experimental herds, dating from the years 1998 to 2008. (Co)variance parameters were obtained through a two-trait analysis with the Gibbs sampling algorithm using the MTGSAM program. The mixed model included the environmental effects of sex, contemporary group and type of birth, in addition to residual, direct and maternal additive effects. Mean estimates of direct heritability for BWT and WWT were 0.25 and 0.09, respectively. Mean estimates of maternal heritability were 0.34 for BWT and 0.24 for WWT. The genetic correlation between BWT and WWT was 0.14. The results suggest that breeding Santa Ines sheep for meat production must take into consideration direct and maternal additive genetic effects.

scholarly journals Probabilistic Bearing Capacity of a Pavement Resting on Fibre Reinforced Embankment Considering Soil Spatial Variability

2021 ◽  
Vol 7 ◽  
Author(s):  
Kouseya Choudhuri ◽  
Debarghya Chakraborty
Keyword(s):  
Spatial Variability ◽  
Soil Properties ◽  
Bearing Capacity ◽  
Coefficient Of Variation ◽  
Friction Angle ◽  
Horizontal Scale ◽  
Vertical Scale ◽  
Scale Of Fluctuation ◽  
The Mean ◽  
The Finite Difference Method

This paper intends to examine the influence of spatial variability of soil properties on the probabilistic bearing capacity of a pavement located on the crest of a fibre reinforced embankment. An anisotropic random field, in combination with the finite difference method, is used to carry out the probabilistic analyses. The cohesion and internal friction angle of the soil are assumed to be lognormally distributed. The Monte Carlo simulations are carried out to obtain the mean and coefficient of variation of the pavement bearing capacity. The mean bearing capacity of the pavement is found to decrease with the increase in horizontal scale of fluctuation for a constant vertical scale of fluctuation; whereas, the coefficient of variation of the bearing capacity increases with the increase in horizontal scale of fluctuation. However, both the mean and coefficient of variation of bearing capacity of the pavement are observed to be increasing with the increase in vertical scale of fluctuation for a constant horizontal scale of fluctuation. Apart from the different scales of fluctuation, the effects of out of the plane length of the embankment and randomness in soil properties on the probabilistic bearing capacity are also investigated in the present study.

scholarly journals Generalized predictive model of estimation of inhibition of muscarinic receptors M1-M5

2020 ◽  
Vol 3 (3) ◽  
pp. e00129
Author(s):  
A.V. Mikurova ◽  
V.S. Skvortsov ◽  
V.V. Grigoryev
Keyword(s):  
Predictive Model ◽  
Muscarinic Receptors ◽  
Three Dimensional ◽  
Dynamics Simulation ◽  
Dimensional Structure ◽  
Regression Equations ◽  
Energy Characteristics ◽  
Value Prediction ◽  
M3 Receptor ◽  
The Mean

A general predictive model for assessing the inhibition constant (K<sub>i</sub>) value of human acetylcholine muscarinic receptors M1-M5 by potential ligands has been constructed. We used information on the three-dimensional structure of human M1, M2, M4, and M5 receptors, as well as a model of the M3 receptor constructed according to homology based on the structure of the rat M3 receptor. A set of complexes of known inhibitors with the target receptor constructed by means of molecular docking, was selected using an additional option: the coincidence of the spatial position of 4 pharmacophore points of a tested inhibitor and tiotropium, for which the position in the crystal structure was known. For five types of M receptors 199 complexes with known K<sub>i</sub> values were selected. Based on the data obtained during molecular dynamics simulation of these complexes by means of the MM-PBSA/MM-GBSA methods, their energy characteristics were calculated. They were used as independent variables in linear regression equations for pK<sub>i</sub> value prediction. The R<sup>2</sup> prediction for the generalized equation was 0.7, and the mean prediction error was 0.55 logarithmic units with a range for pK<sub>i</sub>=4.7.

scholarly journals Prediksi Tinggi Badan Ditentukan dari Panjang Rentang Lengan dan Panjang Telapak Kaki Pada Mongoloid Feminina Khususnya Etnis Jawa

2018 ◽  
Vol 2 (2) ◽  
Author(s):  
Nur Mujaddidah Mochtar
Keyword(s):  
Clinical Medicine ◽  
Pearson Correlation ◽  
Body Height ◽  
Regression Equation ◽  
The Body ◽  
Regression Equations ◽  
Foot Length ◽  
Arm Span ◽  
Knee Height ◽  
The Mean

Background: There are various circumstances where measurements are not actually possible, replacement parameters can be used to estimate body height. Many characteristics of body height measurement and how to measure it. These include anthropometric measurements that can be used for the identification of medicolegal-forensic processes. Body height in clinical medicine and in the field of scientific research can be easily estimated using various anthropometric parameters such as arm span, knee height, foot length and foot breadth, and others. The arm span and foot length has proved to be one of the most reliable predictors. This study was conducted to estimate of body height from arm span and foot length using the regression equation and to determine the correlation between the body height and arm span and foot length.Methods: This study was conducted at Universitas Muhammadiyah Surabaya with 182 Javanese female students. Stature, arm span and foot length measured directly using anthropometric technique and measuring tape. The data obtained were then analyzed with SPSS version 16. The regression equation was derived for the estimate of body height and the relationship between stature, arm span and foot length determined by the Pearson correlation.               Results: We found that the mean body height of Javanese women was 1534,45 ± 47,623  mm, mean of arm span 1543,25 ± 60,468 mm and the mean of foot length 226,14 ± 9,586 mm. The correlation between stature and arm span was positive and significant (r = 0,715  , p <0,05). The correlation between stature and foot length was positive and significant (r = 0,726 , p <0,05). The correlation between stature and arm span and foot length was positive and significant (r = 0,798, p <0,05).               Conclusion: Body height correlates well with the arm span and foot length so that it can be used as a reliable marker for high estimates using regression equations.

scholarly journals Ensemble long-term soil moisture forecast using hydrological modeling

RBRH ◽  
2020 ◽  
Vol 25 ◽  
Author(s):  
Bibiana Rodrigues Colossi ◽  
Carlos Eduardo Morelli Tucci
Keyword(s):  
Soil Moisture ◽  
Hydrological Modeling ◽  
Hydrological Model ◽  
Computing Time ◽  
Ensemble Member ◽  
Lead Times ◽  
Hydrologic Simulation ◽  
The Mean ◽  
Group B

ABSTRACT Long-term soil moisture forecasting allows for better planning in sectors as agriculture. However, there are still few studies dedicated to estimate soil moisture for long lead times, which reflects the difficulties associated with this topic. An approach that could help improving these forecasts performance is to use ensemble predictions. In this study, a soil moisture forecast for lead times of one, three and six months in the Ijuí River Basin (Brazil) was developed using ensemble precipitation forecasts and hydrologic simulation. All ensemble members from three climatologic models were used to run the MGB hydrological model, generating 207 soil moisture forecasts, organized in groups: (A) for each model, the most frequent soil moisture interval predicted among the forecasts made with each ensemble member, (B) using each model’s mean precipitation, (C) considering a super-ensemble, and (D) the mean soil moisture interval predicted among group B forecasts. The results show that long-term soil moisture based on precipitation forecasts can be useful for identifying periods drier or wetter than the average for the studied region. Nevertheless, estimation of exact soil moisture values remains limited. Forecasts groups B and D performed similarly to groups A and C, and require less data management and computing time.

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