scholarly journals Incorporating the field border effect to reduce the predicted uncertainty of pollen dispersal model in Asia

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yuan-Chih Su ◽  
Cheng-Bin Lee ◽  
Tien-Joung Yiu ◽  
Bo-Jein Kuo
Keyword(s):  
Bayesian Method ◽  
Pollen Dispersal ◽  
Correlation Coefficients ◽  
Farming System ◽  
Cauchy Kernel ◽  
Good Prediction ◽  
Observation Model ◽  
Border Effect ◽  
Predictive Uncertainty ◽  
Dispersal Model

AbstractThe presence of the field border (FB), such as roadways or unplanted areas, between two fields is common in Asian farming system. This study evaluated the effect of the FB on the cross-pollination (CP) and predicted the CP rate in the field considering and not considering FB. Three experiments including 0, 6.75, and 7.5 m width of the FB respectively were conducted to investigate the effect of distance and the FB on the CP rate. The dispersal models combined kernel and observation model by calculating the parameter of observation model from the output of kernel. These models were employed to predict the CP rate at different distances. The Bayesian method was used to estimate parameters and provided a good prediction with uncertainty. The highest average CP rates in the field with and without FB were 74.29% and 36.12%, respectively. It was found that two dispersal models with the FB effect displayed a higher ability to predict average CP rates. The correlation coefficients between actual CP rates and CP rates predicted by the dispersal model combined zero-inflated Poisson observation model with compound exponential kernel and modified Cauchy kernel were 0.834 and 0.833, respectively. Furthermore, the predictive uncertainty was reducing using the dispersal models with the FB effect.

Sensitivity analysis to physical parameterizations schemes applied for wind forecasting

2020 ◽  
Author(s):  
Andrés Merino ◽  
Guillermo Mérida ◽  
Pablo Melcón ◽  
Laura López ◽  
José Luis Marcos ◽  
...  
Keyword(s):  
Surface Wind ◽  
Correlation Coefficients ◽  
Horizontal Resolution ◽  
Good Prediction ◽  
Excellent Correlation ◽  
Atmospheric Physics ◽  
Ensemble Techniques ◽  
Aerospace Technology ◽  
Low Levels ◽  
Different Levels

<p>The airborne research center called CIAR is placed in the airfield of Rozas (Lugo, Spain). It is a center for experimentation and development of new Unmanned Aerial Vehicles. Since you need to have a good planning of the flights of the prototypes, it is necessary to have a good prediction of the wind at different levels of height.</p><p>To obtain a reliable database for wind at different vertical levels, three types of instruments have been used: anemometers installed at 10 meters high to determine surface wind, a sodar for levels below 150 meters and a wind radar for those between 200 and 3000 m high above the CIAR level.</p><p>Concerning the mesoscale modelling: we have used the WRF with 48 sigma levels and horizontal resolution of up to 3 x 3 km. Therefore, we have applied multiphysics ensemble techniques. Five combinations of microphysics schemes (AEROSOL THOMPSON, MORRISON 2 MOMENTS, THOMPSON, GODDARD and WRF 2 MOMENTS), three of PBL (MYNN3, YSU and MYJ), and two of Surface (NOAA and RUC) have been selected.</p><p>Once the wind data databases were obtained, by means of the different instrumentation indicated above, it has been compared with each of the 20 WRF scenarios. To visualize the results, Taylor diagrams have been used for the different heights.</p><p>In summary, some conclusions have been found:</p><ol><li>It’s necessary distinguish between low levels and those of slightly higher heights. On the surface, the scenarios with the PBL parameterizations called YSU and MYNN3 show better results.</li> <li>It seems that the microphysics schemes settings have a less importance in wind forecast, which is consistent with the physical interpretation.</li> <li>Above 200 meter, the 20 scenarios behave more satisfactorily with excellent correlation coefficients and low standard deviations</li> </ol><p><strong>Acknowledgment</strong></p><p>Data support came from the Atmospheric Physics Group, IMA, University of León, Spain, and the National Institute of Aerospace Technology (INTA). This research was carried out in the framework of the SAFEFLIGHT project, financed by MINECO (CGL2016‐78702) and LE240P18 project (Junta de Castilla y León). We also thank R. Weigand for computer support to the research group.</p>

Track-Pattern-Based Model for Seasonal Prediction of Tropical Cyclone Activity in the Western North Pacific

2012 ◽  
Vol 25 (13) ◽  
pp. 4660-4678 ◽  
Author(s):  
Hyeong-Seog Kim ◽  
Chang-Hoi Ho ◽  
Joo-Hong Kim ◽  
Pao-Shin Chu
Keyword(s):  
Tropical Cyclone ◽  
North Pacific ◽  
Western North Pacific ◽  
Correlation Coefficients ◽  
Seasonal Prediction ◽  
Track Density ◽  
Good Prediction ◽  
Seasonal Characteristics ◽  
Novel Approach ◽  
Track Pattern

Abstract Skillful predictions of the seasonal tropical cyclone (TC) activity are important in mitigating the potential destruction from the TC approach/landfall in many coastal regions. In this study, a novel approach for the prediction of the seasonal TC activity over the western North Pacific is developed to provide useful probabilistic information on the seasonal characteristics of the TC tracks and vulnerable areas. The developed model, which is termed the “track-pattern-based model,” is characterized by two features: 1) a hybrid statistical–dynamical prediction of the seasonal activity of seven track patterns obtained by fuzzy c-means clustering of historical TC tracks and 2) a technique that enables researchers to construct a forecasting map of the spatial probability of the seasonal TC track density over the entire basin. The hybrid statistical–dynamical prediction for each pattern is based on the statistical relationship between the seasonal TC frequency of the pattern and the seasonal mean key predictors dynamically forecast by the National Centers for Environmental Prediction Climate Forecast System in May. The leave-one-out cross validation shows good prediction skill, with the correlation coefficients between the hindcasts and the observations ranging from 0.71 to 0.81. Using the predicted frequency and the climatological probability for each pattern, the authors obtain the forecasting map of the seasonal TC track density by combining the TC track densities of the seven patterns. The hindcasts of the basinwide seasonal TC track density exhibit good skill in reproducing the observed pattern. The El Niño–/La Niña–related years, in particular, tend to show a better skill than the neutral years.

scholarly journals Pollen productivity estimates strongly depend on assumed pollen dispersal II: Extending the ERV model

The Holocene ◽  
2021 ◽  
pp. 095968362110417
Author(s):  
Martin Theuerkauf ◽  
John Couwenberg
Keyword(s):  
Model Selection ◽  
Pollen Dispersal ◽  
Local Scale ◽  
Pollen Deposition ◽  
Data Sets ◽  
Pollen Data ◽  
Modern Pollen ◽  
Dispersal Model ◽  
Pollen Productivity ◽  
Fall Speed

Pollen productivity estimates (PPEs) are a key parameter for quantitative land-cover reconstructions from pollen data. PPEs are commonly estimated using modern pollen-vegetation data sets and the extended R-value (ERV) model. Prominent discrepancies in the existing studies question the reliability of the approach. We here propose an implementation of the ERV model in the R environment for statistical computing, which allows for simplified application and testing. Using simulated pollen-vegetation data sets, we explore sensitivity of ERV application to (1) number of sites, (2) vegetation structure, (3) basin size, (4) noise in the data, and (5) dispersal model selection. The simulations show that noise in the (pollen) data and dispersal model selection are critical factors in ERV application. Pollen count errors imply prominent PPE errors mainly for taxa with low counts, usually low pollen producers. Applied with an unsuited dispersal model, ERV tends to produce wrong PPEs for additional taxa. In a comparison of the still widely applied Prentice model and a Lagrangian stochastic model (LSM), errors are highest for taxa with high and low fall speed of pollen. The errors reflect the too high influence of fall speed in the Prentice model. ERV studies often use local scale pollen data from for example, moss polsters. Describing pollen dispersal on his local scale is particularly complex due to a range of disturbing factors, including differential release height. Considering the importance of the dispersal model in the approach, and the very large uncertainties in dispersal on short distance, we advise to carry out ERV studies with pollen data from open areas or basins that lack local pollen deposition of the taxa of interest.

scholarly journals Estimating Confidence Intervals around Relative Humidity Profiles from Satellite Observations: Application to the SAPHIR Sounder

2016 ◽  
Vol 33 (5) ◽  
pp. 1005-1022 ◽  
Author(s):  
Hélène Brogniez ◽  
Renaud Fallourd ◽  
Cécile Mallet ◽  
Ramsès Sivira ◽  
Christophe Dufour
Keyword(s):  
Relative Humidity ◽  
Confidence Intervals ◽  
Bayesian Method ◽  
Generalized Additive Model ◽  
Additive Model ◽  
Correlation Coefficients ◽  
Mean Square ◽  
Atmospheric Relative Humidity ◽  
Humidity Profiles

AbstractA novel scheme for the estimation of layer-averaged relative humidity (RH) profiles from spaceborne observations in the 183.31-GHz line is presented. Named atmospheric relative humidity profiles including analysis of confidence intervals (ARPIA), it provides for each vector of observations the parameters of the distribution of the RH instead of its expectation, as is usually done by the current methods. The profiles are composed of six layers distributed between 100 and 950 hPa. The approach combines the six channels of the Sondeur Atmosphérique du Profil d’Humidité Intertropical par Radiométrie (SAPHIR) instrument on board the Megha-Tropiques satellite and the generalized additive model for location, scale and shape (GAMLSS) method to infer the parametric distributions, assuming that they follow a Gaussian law. The knowledge of the conditional uncertainty is an asset in the evaluation using radiosounding profiles of RH with a dedicated Bayesian method. Taking the uncertainties into account in both the ARPIA estimates and the in situ measurements yields biases, root-mean-square, and correlation coefficients in the range of −0.56% to 9.79%, 1.58% to 13.32%, and 0.55 to 0.98, respectively, with the largest biases being obtained over the continent, in the midtropospheric layers.

scholarly journals Determination of the best model for estimation of genetic parameters on the Fars native chicken traits using Bayesian and REML methods

Genetika ◽  
2018 ◽  
Vol 50 (2) ◽  
pp. 431-447
Author(s):  
Ali Mohammadi ◽  
Yousef Naderi ◽  
Reza Nabavi ◽  
Fatemeh Jafari
Keyword(s):  
Body Weight ◽  
Bayesian Approach ◽  
Bayesian Method ◽  
Genetic Parameters ◽  
Genetic Parameter ◽  
Correlation Coefficients ◽  
Native Chicken ◽  
Egg Number ◽  
Estimation Of Genetic Parameters

This study was conducted to determine the best model for genetic parameter estimation on the Fars native chicken traits using Bayesian and REML Methods. Studied traits were body weight at first day (BW1), body weight at eighth weeks (BW8), body weight at 12th weeks (BW12), age at sexual maturity (ASM), egg number production (EGP) and mean egg weight during 28th ,30th and 32nd week ages (EGW) involving three generations 17, 18 and 19 during the years 2010 to 2012. Genetic parameters were estimated with REML method using WOMBAT software and with Bayesian approach using MTGSAM software. Based on AIC and DIC criteria, the most appropriate model was determined. Estimations of direct additive heritabilities for BW1, BW8, BW12, ASM, EGP and EGW by the best models using REML method were 0.31, 0.32, 0.29, 0.45, 0.24 and 0.22 and by Bayesian method were 0.36, 0.33, 0.30, 0.48, 0.26 and 0.25, respectively. The genetic correlation coefficients ranged from -0.709 between EGP and ASM to 0.844 between BW8 and BW12 (by Bayesian method) and ranged from -0.724 between ASM and EGP to 0.894 between BW12 and BW8 (by REML method). Generally, based on the employed criteria, the 1st and 2nd models can be suggested for analysis of body weight traits (BW1, BW8 and BW12), whereas for other traits (ASM, EGP and EGW), 1st, 5th, 4th and 6th models seems to be suitable for estimation of genetic parameters of the Fars Native fowls traits using Bayesian and REML Methods. The Bayesian approach recommended for estimation of genetic parameters on the Fars native chicken traits because this method used the prior distribution in the calculation process.

scholarly journals Performance behavior of prediction filters for respiratory motion compensation in radiotherapy

2017 ◽  
Vol 3 (2) ◽  
pp. 429-432
Author(s):  
Alexander Jöhl ◽  
Yannick Berdou ◽  
Matthias Guckenberger ◽  
Stephan Klöck ◽  
Mirko Meboldt ◽  
...  
Keyword(s):  
Time Delays ◽  
Tracking System ◽  
Correlation Coefficients ◽  
Support Vector ◽  
Linear Filter ◽  
Good Prediction ◽  
Local Regression ◽  
The Neural Network ◽  
Mitigation Methods ◽  
Performance Behavior

AbstractIntroduction: In radiotherapy, tumors may move due to the patient’s respiration, which decreases treatment accuracy. Some motion mitigation methods require measuring the tumor position during treatment. Current available sensors often suffer from time delays, which degrade the motion mitigation performance. However, the tumor motion is often periodic and continuous, which allows predicting the motion ahead. Method and Materials: A couch tracking system was simulated in MATLAB and five prediction filters selected from literature were implemented and tested on 51 respiration signals (median length: 103 s). The five filters were the linear filter (LF), the local regression (LOESS), the neural network (NN), the support vector regression (SVR), and the wavelet least mean squares (wLMS). The time delay to compensate was 320 ms. The normalized root mean square error (nRMSE) was calculated for all prediction filters and respiration signals. The correlation coefficients between the nRMSE of the prediction filters were computed. Results: The prediction filters were grouped into a low and a high nRMSE group. The low nRMSE group consisted of the LF, the NN, and the wLMS with a median nRMSE of 0.14, 0.15, and 0.14, respectively. The high nRMSE group consisted of the LOESS and the SVR with both a median nRMSE of 0.34. The correlations between the low nRMSE filters were above 0.87 and between the high nRMSE filters it was 0.64. Conclusion: The low nRMSE prediction filters not only have similar median nRMSEs but also similar nRMSEs for the same respiration signals as the high correlation shows. Therefore, good prediction filters perform similarly for identical respiration patterns, which might indicate a minimally achievable nRMSE for a given respiration pattern.

Selection of Copula to Generate Input Joint CDF for RBDO

2008 ◽  
Author(s):  
Yoojeong Noh ◽  
K. K. Choi ◽  
Liu Du
Keyword(s):  
Experimental Data ◽  
Bayesian Method ◽  
Correlation Coefficients ◽  
Cumulative Distribution ◽  
Limited Data ◽  
Paired Samples ◽  
Inverse Reliability Analysis ◽  
Standard Normal ◽  
Input Variables ◽  
The Right

For RBDO problems with correlated input variables, it is necessary to obtain the input joint distribution (CDF, cumulative distribution function). Then Rosenblatt transformation is used to transform the correlated input variables into the independent standard normal variables for the purpose of inverse reliability analysis. However, in practical industry RBDO problems, often only the marginal CDFs and paired samples are available from limited experimental data. In this paper, a copula, which is a link between a joint CDF and marginal CDFs, is proposed to generate an input joint CDF from these marginal CDFs and paired samples. To identify the right copula from limited data, Bayesian method is proposed to use in this paper. Using Bayesian method, the number of samples required to properly identify the right copula is investigated for different types of copulas and for different correlation coefficients. A real industry problem is used to show how a copula can be identified from the limited experimental data.

scholarly journals Pasting properties by near-infrared reflectance analysis of whole grain paddy rice samples

2015 ◽  
Vol 08 (06) ◽  
pp. 1550035 ◽  
Author(s):  
Pornarree Siriphollakul ◽  
Sirichai Kanlayanarat ◽  
Ronnarit Rittiron ◽  
Jaitip Wanitchang ◽  
Thongchai Suwonsichon ◽  
...  
Keyword(s):  
Near Infrared ◽  
Wavelength Region ◽  
Correlation Coefficients ◽  
Pasting Properties ◽  
Paddy Rice ◽  
Good Prediction ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance ◽  
Reflectance Analysis ◽  
Rice Samples

A rapid predictive method based on near-infrared reflectance spectroscopy (NIRS) of paddy rice was developed to measure the pasting properties of rice. The paddy rice samples were scanned by a near-infrared reflectance spectrometer in the wavelength region of 1400–2400 nm and preprocessed by mathematical pretreatments prior to pasting properties analysis using a rapid visco-analyzer (RVA). The results indicated that the developed models of setback (SB), peak viscosity (PV), breakdown (BD) and consistency (CS) provided good prediction results with relatively high correlation coefficients (0.81–0.96). In addition, the validity of the calibration models was statistically tested. Standard error of prediction (SEP) and bias were small enough without any significance at 95% confidence interval. Nonetheless, this study proved that the use of NIRS for predicting pasting properties was feasible in paddy rice and could be applied in commercial trade and research.

Reliability of the Minimum Masking Level as Outcome Variable in Tinnitus Clinical Research

2020 ◽  
Vol 29 (3) ◽  
pp. 429-435
Author(s):  
Patricia C. Mancini ◽  
Richard S. Tyler ◽  
Hyung Jin Jun ◽  
Tang-Chuan Wang ◽  
Helena Ji ◽  
...  
Keyword(s):  
Standard Deviation ◽  
High Frequency ◽  
Pearson Correlation ◽  
Correlation Coefficients ◽  
Outcome Variable ◽  
Reliable Estimate ◽  
Moderate Correlation ◽  
Trained Subjects ◽  
The Relationship ◽  
Minimum Intensity

Purpose The minimum masking level (MML) is the minimum intensity of a stimulus required to just totally mask the tinnitus. Treatments aimed at reducing the tinnitus itself should attempt to measure the magnitude of the tinnitus. The objective of this study was to evaluate the reliability of the MML. Method Sample consisted of 59 tinnitus patients who reported stable tinnitus. We obtained MML measures on two visits, separated by about 2–3 weeks. We used two noise types: speech-shaped noise and high-frequency emphasis noise. We also investigated the relationship between the MML and tinnitus loudness estimates and the Tinnitus Handicap Questionnaire (THQ). Results There were differences across the different noise types. The within-session standard deviation averaged across subjects varied between 1.3 and 1.8 dB. Across the two sessions, the Pearson correlation coefficients, range was r = .84. There was a weak relationship between the dB SL MML and loudness, and between the MML and the THQ. A moderate correlation ( r = .44) was found between the THQ and loudness estimates. Conclusions We conclude that the dB SL MML can be a reliable estimate of tinnitus magnitude, with expected standard deviations in trained subjects of about 1.5 dB. It appears that the dB SL MML and loudness estimates are not closely related.

Clausal Density Between Ages 4 and 9 Years for the Edmonton Narrative Norms Instrument: Reference Data and Psychometric Properties

2020 ◽  
pp. 1-15
Author(s):  
Ling-Yu Guo ◽  
Phyllis Schneider ◽  
William Harrison
Keyword(s):  
Diagnostic Accuracy ◽  
Psychometric Properties ◽  
Language Impairment ◽  
Reference Data ◽  
Reliability And Validity ◽  
Correlation Coefficients ◽  
Criterion Validity ◽  
Likelihood Ratios ◽  
Generation Task ◽  
Sensitivity Specificity

Purpose This study provided reference data and examined psychometric properties for clausal density (CD; i.e., number of clauses per utterance) in children between ages 4 and 9 years from the database of the Edmonton Narrative Norms Instrument (ENNI). Method Participants in the ENNI database included 300 children with typical language (TL) and 77 children with language impairment (LI) between the ages of 4;0 (years;months) and 9;11. Narrative samples were collected using a story generation task, in which children were asked to tell stories based on six picture sequences. CD was computed from the narrative samples. The split-half reliability, concurrent criterion validity, and diagnostic accuracy were evaluated for CD by age. Results CD scores increased significantly between ages 4 and 9 years in children with TL and those with LI. Children with TL produced higher CD scores than those with LI at each age level. In addition, the correlation coefficients for the split-half reliability and concurrent criterion validity of CD scores were all significant at each age level, with the magnitude ranging from small to large. The diagnostic accuracy of CD scores, as revealed by sensitivity, specificity, and likelihood ratios, was poor. Conclusions The finding on diagnostic accuracy did not support the use of CD for identifying children with LI between ages 4 and 9 years. However, given the attested reliability and validity for CD, reference data of CD from the ENNI database can be used for evaluating children's difficulties with complex syntax and monitoring their change over time. Supplemental Material https://doi.org/10.23641/asha.13172129

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