scholarly journals A Nonparametric Ensemble Postprocessing Approach for Short-Range Visibility Predictions in Data-Sparse Areas

2018 ◽  
Vol 33 (3) ◽  
pp. 835-855 ◽  
Author(s):  
William R. Ryerson ◽  
Joshua P. Hacker
Keyword(s):  
Air Force ◽  
Short Range ◽  
Predictive Power ◽  
Cross Validation ◽  
Physical Mechanism ◽  
Cloud Water ◽  
Weather Research And Forecasting ◽  
Forecast System ◽  
Clear Cases ◽  
Observational Record

Abstract This work develops and tests the viability of obtaining skillful short-range (<20 h) visibility predictions using statistical postprocessing of a 4-km, 10-member Weather Research and Forecasting (WRF) ensemble configured to closely match the U.S. Air Force Mesoscale Ensemble Forecast System. The raw WRF predictions produce excessive forecasts of zero cloud water, which is simultaneously predicted by all ensemble members in 62% of observed fog cases, leading to zero ensemble dispersion and no skill in these cases. Adding dispersion to the clear cases by making upward adjustments to cloud water predictions from individual members not predicting fog on their own provides the best chance to increase the resolution and reliability of the ensemble. The technique leverages traits of a joint parameter space in the predictions and is generally most effective when the space is defined with a moisture parameter and a low-level stability parameter. Cross-validation shows that the method adds significant overnight skill to predictions in valley and coastal regions compared to the raw WRF forecasts, with modest skill increases after sunrise. Postprocessing does not improve the highly skillful raw WRF predictions at the mountain test sites. Since the framework addresses only systematic WRF deficiencies and identifies parameter pairs with a clear, non-site-specific physical mechanism of predictive power, it has geographical transferability with less need for recalibration or observational record compared to other statistical postprocessing approaches.

Simple correlations for calculating NHTPB enthalpy of formation through molecular structures

2019 ◽  
Vol 15 (1) ◽  
pp. 258-264 ◽  
Author(s):  
Hamid Reza Ghaieni ◽  
Saeed Tavangar ◽  
Mohammad Moein Ebrahimzadeh Qhomi
Keyword(s):  
Enthalpy Of Formation ◽  
Design Methodology ◽  
Predictive Power ◽  
Cross Validation ◽  
Molecular Structures ◽  
Mean Square ◽  
Mean Square Deviation ◽  
Simple Correlation ◽  
Content Type ◽  
Regression Methods

Purpose The purpose of this paper is to present simple correlation for calculating nitrated hydroxyl-terminated polybutadiene (NHTPB) enthalpy of formation. Design/methodology/approach It uses multiple linear regression methods. Findings The proposed correlation has determination coefficient 0.96. The correlation has root mean square deviation and the average absolute deviations values 53.4 and 46.1 respectively. Originality/value The predictive power of correlation is checked by cross-validation method (R2=0.96, Q L O O 2 = 0.96 ).

Cross-validation of the Air Force Cycle Ergometer Test with Maximal Treadmill Testing

2004 ◽  
Vol 36 (Supplement) ◽  
pp. S118
Author(s):  
Shannon Crumpton ◽  
Henry Williford ◽  
Michele Scharff-Olson ◽  
Shawn OʼMailia ◽  
Elese Woollen ◽  
...  
Keyword(s):  
Air Force ◽  
Cross Validation ◽  
Cycle Ergometer ◽  
Cycle Ergometer Test ◽  
Treadmill Testing ◽  
Ergometer Test

Cross-validation of the Air Force Cycle Ergometer Test with Maximal Treadmill Testing

2004 ◽  
Vol 36 (Supplement) ◽  
pp. S118
Author(s):  
Shannon Crumpton ◽  
Henry Williford ◽  
Michele Scharff-Olson ◽  
Shawn O??Mailia ◽  
Elese Woollen ◽  
...  
Keyword(s):  
Air Force ◽  
Cross Validation ◽  
Cycle Ergometer ◽  
Cycle Ergometer Test ◽  
Treadmill Testing ◽  
Ergometer Test

scholarly journals Short-Range Numerical Weather Prediction Using Time-Lagged Ensembles

2007 ◽  
Vol 22 (3) ◽  
pp. 580-595 ◽  
Author(s):  
Chungu Lu ◽  
Huiling Yuan ◽  
Barry E. Schwartz ◽  
Stanley G. Benjamin
Keyword(s):  
Short Range ◽  
Weather Forecasting ◽  
Weather Prediction ◽  
Cycle Model ◽  
Forecast System ◽  
Multilinear Regression ◽  
Probabilistic Forecasts ◽  
Regression Approach ◽  
Ensemble Mean ◽  
Time Lagged

Abstract A time-lagged ensemble forecast system is developed using a set of hourly initialized Rapid Update Cycle model deterministic forecasts. Both the ensemble-mean and probabilistic forecasts from this time-lagged ensemble system present a promising improvement in the very short-range weather forecasting of 1–3 h, which may be useful for aviation weather prediction and nowcasting applications. Two approaches have been studied to combine deterministic forecasts with different initialization cycles as the ensemble members. The first method uses a set of equally weighted time-lagged forecasts and produces a forecast by taking the ensemble mean. The second method adopts a multilinear regression approach to select a set of weights for different time-lagged forecasts. It is shown that although both methods improve short-range forecasts, the unequally weighted method provides the best results for all forecast variables at all levels. The time-lagged ensembles also provide a sample of statistics, which can be used to construct probabilistic forecasts.

scholarly journals Evaluation of a Mesoscale Short-Range Ensemble Forecast System over the Northeast United States

2007 ◽  
Vol 22 (1) ◽  
pp. 36-55 ◽  
Author(s):  
Matthew S. Jones ◽  
Brian A. Colle ◽  
Jeffrey S. Tongue
Keyword(s):  
United States ◽  
Wind Speed ◽  
Sea Level ◽  
Short Range ◽  
Warm Season ◽  
Ensemble Forecast ◽  
Cool Season ◽  
Forecast System ◽  
Sea Level Pressure ◽  
Level Pressure

Abstract A short-range ensemble forecast system was constructed over the northeast United States down to 12-km grid spacing using 18 members from the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5). The ensemble consisted of 12 physics members with varying planetary boundary layer schemes and convective parameterizations as well as seven different initial conditions (ICs) [five National Centers for Environmental Prediction (NCEP) Eta-bred members at 2100 UTC and the 0000 UTC NCEP Global Forecast System (GFS) and Eta runs]. The full 18-member ensemble (ALL) was verified at the surface for the warm (May–September 2003) and cool (October 2003–March 2004) seasons. A randomly chosen subset of seven physics (PHS) members at each forecast hour was used to quantitatively compare with the seven IC members. During the warm season, the PHS ensemble predictions for surface temperature and wind speed had more skill than the IC ensemble and a control (shared PHS and IC member) run initialized 12 h later (CTL12). During the cool and warm seasons, a 14-day running-mean bias calibration applied to the ALL ensemble (ALLBC) added 10%–30% more skill for temperature, wind speed, and sea level pressure, with the ALLBC far outperforming the CTL12. For the 24-h precipitation, the PHS ensemble had comparable probabilistic skill to the IC ensemble during the warm season, while the IC subensemble was more skillful during the cool season. All ensemble members had large diurnal surface biases, with ensemble variance approximating ensemble uncertainty only for wind direction. Selection of ICs was also important, because during the cool season the NCEP-bred members introduced large errors into the IC ensemble for sea level pressure, while none of the subensembles (PHS, IC, or ALL) outperformed the GFS–MM5 for sea level pressure.

scholarly journals PREDIKSI DEBIT SUNGAI BENGAWAN SOLO MENGGUNAKAN NUMERICAL WEATHER MODEL GLOBAL FORECAST SYSTEM DAN INTEGRATED FLOOD ANALYSIS SYSTEM (Prediction of discharge in Bengawan Solo River using Numerical Weather Model Global Forecast System and Integrated Flood Analysis System)

2021 ◽  
Vol 5 (1) ◽  
pp. 41-50
Author(s):  
Deffi Munadiyat Putri ◽  
◽  
Aries Kristianto ◽  
Keyword(s):  
Weather Research And Forecasting ◽  
Global Forecast System ◽  
Forecast System ◽  
Numerical Weather ◽  
Numerical Weather Model ◽  
Weather Model ◽  
Simulation Based ◽  
Discharge Simulation ◽  
Flood Analysis ◽  
Analysis System

Flood is one of the most common hydro-meteorological disasters. Bengawan Solo is one of the watersheds in Indonesia that also hit by this disaster. This study discusses the flood disaster in the Bengawan Solo area in early March 2019. The purpose of this study is to conduct a discharge simulation using numerical weather model Global Forecast System (GFS) data through Integrated Flood Analysis System (IFAS) so it is possible to predict discharge in the future. There are three types of numerical weather model GFS data that have been downscale using weather research and forecasting model which differentiated based on spin-up time. The numerical weather model product is then used as rainfall data input for IFAS simulation. Based on the analysis, the flood discharge simulation using an 84-hour spin-up time has a satisfactory performance in describing the change in discharge with respect to time. This happens because numerical weather models will be better at quantifying processes that occur on a meso scale with spatial scale of 10 to 1000 km. The result of this research shows that it is possible to predict river discharge up to 84 hours before the disaster so this is can support the mitigation process for hydrometeorological disasters.

scholarly journals A COMPARISON OF THE PREDICTIVE POWERS OF TENURE CHOICES BETWEEN PROPERTY OWNERSHIP AND RENTING

2019 ◽  
Vol 23 (2) ◽  
pp. 130-141 ◽  
Author(s):  
Chun-Chang Lee ◽  
Chih-Min Liang ◽  
Yang-Tung Liu
Keyword(s):  
Logistic Regression ◽  
Discriminant Analysis ◽  
Performance Prediction ◽  
Predictive Power ◽  
Cross Validation ◽  
Binary Logistic Regression ◽  
Analysis Model ◽  
Linear Modeling ◽  
Binary Logistic Regression Model ◽  
Hit Rate

This paper compares the predictive powers of hierarchical generalized linear modeling (HGLM), logistic regression, and discriminant analysis with regard to tenure choices between buying property and renting property by sampling the residents of the Greater Taipei area. The results imply that the hit rate and other indicators included in HGLM have better predictive power with regard to tenure choices than the binary logistic regression model and the discriminant analysis model. That is, using HGLM to process nested data can increase prediction accuracy regarding household tenure choices. Furthermore, cross-validation is performed to analyze hit rate stability. The hit rate sequencing from this cross-validation is found to be consistent with the HGLM results, implying that the comparison of the three models in terms of hit rate performance prediction in this study is stable and reliable.

scholarly journals Altered Glycosylation of Human Alpha-1-Acid Glycoprotein as a Biomarker for Malignant Melanoma

Molecules ◽  
2021 ◽  
Vol 26 (19) ◽  
pp. 6003
Author(s):  
Dávid Virág ◽  
Tibor Kremmer ◽  
Kende Lőrincz ◽  
Norbert Kiss ◽  
Antal Jobbágy ◽  
...  
Keyword(s):  
Malignant Melanoma ◽  
Predictive Power ◽  
Cross Validation ◽  
Comparative Assessment ◽  
Healthy Individuals ◽  
Glycosylation Pattern ◽  
Linear Discriminant ◽  
Acid Glycoprotein ◽  
Derivatives Of ◽  
Anthranilic Acid Derivatives

A high-resolution HILIC-MS/MS method was developed to analyze anthranilic acid derivatives of N-glycans released from human serum alpha-1-acid glycoprotein (AGP). The method was applied to samples obtained from 18 patients suffering from high-risk malignant melanoma as well as 19 healthy individuals. It enabled the identification of 102 glycan isomers separating isomers that differ only in sialic acid linkage (α-2,3, α-2,6) or in fucose positions (core, antenna). Comparative assessment of the samples revealed that upregulation of certain fucosylated glycans and downregulation of their nonfucosylated counterparts occurred in cancer patients. An increased ratio of isomers with more α-2,6-linked sialic acids was also observed. Linear discriminant analysis (LDA) combining 10 variables with the highest discriminatory power was employed to categorize the samples based on their glycosylation pattern. The performance of the method was tested by cross-validation, resulting in an overall classification success rate of 96.7%. The approach presented here is significantly superior to serological marker S100B protein in terms of sensitivity and negative predictive power in the population studied. Therefore, it may effectively support the diagnosis of malignant melanoma as a biomarker.

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