scholarly journals Model-Inspired Predictors for Model Output Statistics (MOS)*

2007 ◽  
Vol 135 (10) ◽  
pp. 3496-3505 ◽  
Author(s):  
Piet Termonia ◽  
Alex Deckmyn
Keyword(s):  
Linear Regression ◽  
Thermal Radiation ◽  
Degrees Of Freedom ◽  
Selection Procedure ◽  
Heat Fluxes ◽  
Scale Model ◽  
Model Output ◽  
Model Equations ◽  
Model Output Statistics ◽  
Temporal Accumulation

Abstract This article addresses the problem of the choice of the predictors for the multiple linear regression in model output statistics. Rather than devising a selection procedure directly aimed at the minimization of the final scores, it is examined whether taking the model equations as a guidance may render the process more rational. To this end a notion of constant fractional errors is introduced. Experimental evidence is provided that they are approximately present in the model and that their impact is sufficiently linear to be corrected by a linear regression. Of particular interest are the forcing terms in the coupling of the physics parameterization to the dynamics of the model. Because such parameterizations are estimates of subgrid processes, they are expected to represent degrees of freedom that are independent of the resolved-scale model variables. To illustrate the value of this approach, it is shown that the temporal accumulation of sensible and latent heat fluxes and net solar and thermal radiation utilized as predictors add a statistically significant improvement to the 2-m temperature scores.

scholarly journals Spread Calibration of Ensemble MOS Forecasts

2013 ◽  
Vol 141 (7) ◽  
pp. 2467-2482 ◽  
Author(s):  
Bruce A. Veenhuis
Keyword(s):  
Linear Regression ◽  
Kernel Density ◽  
Ensemble Member ◽  
Regression Equations ◽  
Model Output ◽  
The North ◽  
Probabilistic Forecasts ◽  
Ensemble Mean ◽  
Systematic Biases ◽  
Model Output Statistics

Abstract Ensemble forecasting systems often contain systematic biases and spread deficiencies that can be corrected by statistical postprocessing. This study presents an improvement to an ensemble statistical postprocessing technique, called ensemble kernel density model output statistics (EKDMOS). EKDMOS uses model output statistics (MOS) equations and spread–skill relationships to generate calibrated probabilistic forecasts. The MOS equations are multiple linear regression equations developed by relating observations to ensemble mean-based predictors. The spread–skill relationships are one-term linear regression equations that predict the expected accuracy of the ensemble mean given the ensemble spread. To generate an EKDMOS forecast, the MOS equations are applied to each ensemble member. Kernel density fitting is used to create a probability density function (PDF) from the ensemble MOS forecasts. The PDF spread is adjusted to match the spread predicted by the spread–skill relationship, producing a calibrated forecast. The improved EKDMOS technique was used to produce probabilistic 2-m temperature forecasts from the North American Ensemble Forecast System (NAEFS) over the period 1 October 2007–31 March 2010. The results were compared with an earlier spread adjustment technique, as well as forecasts generated by rank sorting the bias-corrected ensemble members. Compared to the other techniques, the new EKDMOS forecasts were more reliable, had a better calibrated spread–error relationship, and showed increased day-to-day spread variability.

scholarly journals Design and Calibration of a Hall Effect System for Measurement of Six-Degree-of-Freedom Motion within a Stacked Column

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3740
Author(s):  
Olafur Oddbjornsson ◽  
Panos Kloukinas ◽  
Tansu Gokce ◽  
Kate Bourne ◽  
Tony Horseman ◽  
...  
Keyword(s):  
Hall Effect ◽  
Degrees Of Freedom ◽  
Reactor Core ◽  
Degree Of Freedom ◽  
Life Extension ◽  
Scale Model ◽  
Design Development ◽  
Seismic Safety ◽  
Six Degrees Of Freedom ◽  
Six Degree Of Freedom

This paper presents the design, development and evaluation of a unique non-contact instrumentation system that can accurately measure the interface displacement between two rigid components in six degrees of freedom. The system was developed to allow measurement of the relative displacements between interfaces within a stacked column of brick-like components, with an accuracy of 0.05 mm and 0.1 degrees. The columns comprised up to 14 components, with each component being a scale model of a graphite brick within an Advanced Gas-cooled Reactor core. A set of 585 of these columns makes up the Multi Layer Array, which was designed to investigate the response of the reactor core to seismic inputs, with excitation levels up to 1 g from 0 to 100 Hz. The nature of the application required a compact and robust design capable of accurately recording fully coupled motion in all six degrees of freedom during dynamic testing. The novel design implemented 12 Hall effect sensors with a calibration procedure based on system identification techniques. The measurement uncertainty was ±0.050 mm for displacement and ±0.052 degrees for rotation, and the system can tolerate loss of data from two sensors with the uncertainly increasing to only 0.061 mm in translation and 0.088 degrees in rotation. The system has been deployed in a research programme that has enabled EDF to present seismic safety cases to the Office for Nuclear Regulation, resulting in life extension approvals for several reactors. The measurement system developed could be readily applied to other situations where the imposed level of stress at the interface causes negligible material strain, and accurate non-contact six-degree-of-freedom interface measurement is required.

Pengaruh Minat terhadap Prestasi Belajar Siswa di Madrasah Ibtidaiyah

2021 ◽  
Vol 20 (1) ◽  
pp. 58-79
Author(s):  
Yusi Srihartini
Keyword(s):  
Student Achievement ◽  
Linear Regression ◽  
Degrees Of Freedom ◽  
Regression Equation ◽  
P Value ◽  
Simple Linear Regression ◽  
Learning Achievement ◽  
Significance Level ◽  
Anova Table ◽  
Learning Interest

The Influence of Learning Interest on Student Achievement at MI Mathla'ul Anwar 05 Cigudeg, Bogor Regency, a survey research with a functional associative quantitative approach. The research aims to: 1) find out the data on the level of interest in learning at MI Mathlaul Anwar 05 Cigudeg, Bogor Regency; 2) find out data on student achievement at MI Mathlaul Anwar 05 Cigudeg, Bogor Regency; 3) determine the effect of interest in learning on student achievement at MI Mathlaul Anwar 05 Cigudeg, Bogor Regency. Research data were collected using questionnaires and documentation techniques. The research instrument used a questionnaire for interest in learning and for student achievement using the general score for Indonesian language subjects. The data analysis technique used a simple linear regression test and the interpretation of the data was carried out in a deductive narrative. The results showed that there was a significant correlation between variable X (interest in learning) and variable Y (student learning achievement) of 0.618. The significance level obtained is 0.000 which is smaller than = 0.050 (one-tailed test), so Ho: = 0 is rejected. Thus, because the significance of 0.000 is smaller than = 0.050 (one-tailed test or one-tailed test), then Ho : = 0 is rejected. This means that there is a significant relationship between interest in learning and student achievement at MI Mafthaul Anwar 05 Cigudeg, Bogor Regency. The form of a simple linear regression equation is obtained = 53.686 + 0.275X. Based on the value of R square, the regression equation can be explained that 38.2% of the variance of student achievement at MI Mafthaul Anwar 05 Cigudeg Bogor Regency can be explained by changes in learning interest. Regression (functional relationship) variable interest in learning with learning achievement statistically with a value of F = 29.635 significant at degrees of freedom k = 1 and n - k - 1 = 48, and P-value = 0.000 which is smaller than = 0.05. Thus, testing the hypothesis Ho:β1=0 against H1: 1≠0 based on the ANOVA table, Ho is rejected because P-value = 0.000 which is smaller than = 0.050. This means that there is a significant effect of interest in learning on student achievement at MI Mafthaul Anwar 05 Cigudeg, Bogor Regency.

scholarly journals APPLICATION OF ABLATION MATERIALSFOR HEAT PROTECTION OF CABINS OF FIRE-FIGHTING MACHINES

2020 ◽  
Vol 9 (4) ◽  
pp. 134-141
Author(s):  
Vladimir Kotenko ◽  
Vladimir Abrazumov ◽  
Mihail Ermochenkov
Keyword(s):  
Thermal Radiation ◽  
Forest Fire ◽  
Forest Fires ◽  
Thermal Protection ◽  
Protective Coatings ◽  
Heat Exposure ◽  
Heat Fluxes ◽  
Main Requirement ◽  
Low Efficiency ◽  
Intense Heat

Forest fires are accompanied by the release of a huge amount of heat, and the temperature at the edge of a forest fire, where firefighting equipment usually operates, reaches 300-700 °C. Fire engines are exposed to intense heat to extinguish forest fires. The main requirement for the design of such machines is the availability of rational thermal protection. Studies of various methods of thermal protection of cabins have showed the possibility of lowering the temperature on the inner surface of the cabin, but these methods show low efficiency. Protection of cabs from thermal radiation is not provided in the new developments of forest fire machines. It is proposed to use pre-preg coatings to protect cabins of forest fire engines. They are successfully used in spacecraft designs. Recent technologies for the production of such materials, developed recently, have significantly reduced the cost of production of these materials. It expands the possibilities of their application for other equipment subjected to intense heat exposure. The calculations have showed that the heat-protective coatings of the cabins made of pre-pregs quickly warm up to acceptable temperatures. However the use of water reserves in the tank of the car to cool the inside of the cabs provides high protection efficiency even at the limiting values of heat fluxes that occur in the fireplace. At the same time, water is not consumed; it is heated, circulating between the tank and the heat exchanger. The proposed method of protecting cabs of fire machines from thermal radiation is original one. It is a subject of further development.

scholarly journals PENENTUAN DOMAIN SPASIAL NWP DALAM PEMBANGUNAN MODEL OUTPUT STATISTICS

2013 ◽  
Vol 14 (3) ◽  
Author(s):  
Urip Haryoko ◽  
Hidayat Pawitan ◽  
Edvin Aldrian ◽  
Aji Hamim Wigena
Keyword(s):  
Model Output ◽  
Model Output Statistics

scholarly journals Deep Learning for Climate Model Output Statistics

2021 ◽  
Author(s):  
Michael Steininger ◽  
Daniel Abel ◽  
Katrin Ziegler ◽  
Anna Krause ◽  
Heiko Paeth ◽  
...  
Keyword(s):  
Climate Change ◽  
Machine Learning ◽  
Deep Learning ◽  
Climate Models ◽  
Climate Model ◽  
Model Output ◽  
Climate Change Effects ◽  
Climate Model Output ◽  
Improved Performance ◽  
Model Output Statistics

<p>Climate models are an important tool for the assessment of prospective climate change effects but they suffer from systematic and representation errors, especially for precipitation. Model output statistics (MOS) reduce these errors by fitting the model output to observational data with machine learning. In this work, we explore the feasibility and potential of deep learning with convolutional neural networks (CNNs) for MOS. We propose the CNN architecture ConvMOS specifically designed for reducing errors in climate model outputs and apply it to the climate model REMO. Our results show a considerable reduction of errors and mostly improved performance compared to three commonly used MOS approaches.</p>

scholarly journals Comparing Linear Regression and Regression Trees for Spatial Modelling of Soil Reaction in Dobrovăţ Basin (Eastern Romania)

2011 ◽  
Vol 68 (1) ◽  
Author(s):  
Cristian Valeriu PATRICHE ◽  
Radu Gabriel PÎRNĂU ◽  
Bogdan ROŞCA
Keyword(s):  
Linear Regression ◽  
Digital Elevation Model ◽  
Spatial Models ◽  
Selection Procedure ◽  
Regression Trees ◽  
Training Sample ◽  
Soil Reaction ◽  
Soil Profiles ◽  
Digital Elevation ◽  
Elevation Model

Our study compares the performances of two statistical methods, namely multiple linear regression and classification and regression trees, for deriving spatial models of soil reaction in the surface horizon. The applications were carried out within a 186 km2 hydrographic basin situated in eastern Romania. Statistical models were computed from a sample of 235 soil profiles, scattered in the eastern half of the basin. An independent sample of 237 expeditionary pH measurements was used to validate the results within the interpolation area, whereas an independent sample of 50 soil profiles was used to validate the results within the extrapolation area (the western half of the basin). The predictors included geomorphometrical parameters, derived from a 10x10 m digital elevation model, X and Y coordinates of soil profiles and the main soil types for the regression trees approach. The stepwise selection procedure indicated Y coordinate, digital elevation model, wetness index and surface ratio as the best predictors for soil reaction. The correlation between observed and predicted pH values for the training sample suggests a much higher quality of the regression trees spatial model. However, the validation using the two independent samples points out the instability of this model and recommends the regression model more reliable.

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