scholarly journals Post-processing methods for calibrating the wind speed forecasts in central regions of Chile

2021 ◽  
Vol 53 ◽  
pp. 93-108
Author(s):  
Mailiu Díaz ◽  
Orietta Nicolis ◽  
Julio César Marín ◽  
Sándor Baran
Keyword(s):  
Wind Speed ◽  
Post Processing ◽  
Processing Methods ◽  
Central Regions

Incorporating the North Atlantic Oscillation into the post-processing of MOGREPS-G wind speed forecasts

2021 ◽  
Author(s):  
Sam Allen ◽  
Gavin Evans ◽  
Piers Buchanan ◽  
Frank Kwasniok
Keyword(s):  
Wind Speed ◽  
North Atlantic Oscillation ◽  
Mixture Model ◽  
North Atlantic ◽  
Ensemble Prediction ◽  
Post Processing ◽  
Processing Methods ◽  
The North ◽  
The North Atlantic ◽  
The North Atlantic Oscillation

<p>Changes in the North Atlantic Oscillation (NAO) heavily influence the weather across the UK and the rest of Europe. Due to an imperfect reconstruction of the polar jet stream and associated pressure systems, there is reason to believe that errors in numerical weather prediction models may also depend on the prevailing behaviour of the NAO. To address this, information regarding the NAO is incorporated into statistical post-processing methods through a regime-dependent mixture model, which is then applied to wind speed forecasts from the Met Office's global ensemble prediction system, MOGREPS-G. The mixture model offers substantial improvements upon conventional post-processing methods when the wind speed depends strongly on the NAO, but the additional complexity of the model can hinder forecast performance in other instances. A measure of regime-dependency is thus defined that can be used to differentiate between situations when the numerical model output is, and is not, expected to benefit from regime-dependent post-processing. Implementing the regime-dependent mixture model only when this measure exceeds a certain threshold is found to further improve predictive performance, while also producing more accurate forecasts of extreme wind speeds.</p>

scholarly journals Reducing errors of wind speed forecasts by an optimal combination of post-processing methods

2011 ◽  
Vol 20 (1) ◽  
pp. 32-40 ◽  
Author(s):  
Conor P. Sweeney ◽  
Peter Lynch ◽  
Paul Nolan
Keyword(s):  
Wind Speed ◽  
Optimal Combination ◽  
Post Processing ◽  
Processing Methods

scholarly journals Post-Processing and Surface Characterization of Additively Manufactured Stainless Steel 316L Lattice: Implications for BioMedical Use

Materials ◽  
2021 ◽  
Vol 14 (6) ◽  
pp. 1376
Author(s):  
Alex Quok An Teo ◽  
Lina Yan ◽  
Akshay Chaudhari ◽  
Gavin Kane O’Neill
Keyword(s):  
Surface Roughness ◽  
Stainless Steel ◽  
Surface Characterization ◽  
High Surface ◽  
Surface Characteristics ◽  
Post Processing ◽  
Stainless Steel 316L ◽  
Processing Methods ◽  
Particulate Debris

Additive manufacturing of stainless steel is becoming increasingly accessible, allowing for the customisation of structure and surface characteristics; there is little guidance for the post-processing of these metals. We carried out this study to ascertain the effects of various combinations of post-processing methods on the surface of an additively manufactured stainless steel 316L lattice. We also characterized the nature of residual surface particles found after these processes via energy-dispersive X-ray spectroscopy. Finally, we measured the surface roughness of the post-processing lattices via digital microscopy. The native lattices had a predictably high surface roughness from partially molten particles. Sandblasting effectively removed this but damaged the surface, introducing a peel-off layer, as well as leaving surface residue from the glass beads used. The addition of either abrasive polishing or electropolishing removed the peel-off layer but introduced other surface deficiencies making it more susceptible to corrosion. Finally, when electropolishing was performed after the above processes, there was a significant reduction in residual surface particles. The constitution of the particulate debris as well as the lattice surface roughness following each post-processing method varied, with potential implications for clinical use. The work provides a good base for future development of post-processing methods for additively manufactured stainless steel.

scholarly journals Bayesian Post-Processing Methods for Jitter Mitigation in Sampling

2011 ◽  
Vol 59 (5) ◽  
pp. 2112-2123 ◽  
Author(s):  
Daniel S. Weller ◽  
Vivek K Goyal
Keyword(s):  
Post Processing ◽  
Processing Methods

scholarly journals Wavelet-based Post-processing Methods for the Enhancement of Non-invasive Fetal ECG

2019 ◽  
Author(s):  
Giulia Baldazzi ◽  
Eleonora Sulas ◽  
Elisa Brungiu ◽  
Monica Urru ◽  
Roberto Tumbarello ◽  
...  
Keyword(s):  
Post Processing ◽  
Processing Methods ◽  
Non Invasive ◽  
Fetal Ecg

scholarly journals Generating wind power scenarios for probabilistic ramp event prediction using multivariate statistical post-processing

2018 ◽  
Author(s):  
Rochelle P. Worsnop ◽  
Michael Scheuerer ◽  
Thomas M. Hamill ◽  
Julie K. Lundquist
Keyword(s):  
Wind Speed ◽  
Wind Power ◽  
Wind Farm ◽  
Weather Prediction ◽  
Model Performance ◽  
The United States ◽  
Numerical Weather Prediction Model ◽  
Post Processing ◽  
Skill Scores ◽  
Ramp Events

Abstract. Wind power forecasting is gaining international significance as more regions promote policies to increase the use of renewable energy. Wind ramps, large variations in wind power production during a period of minutes to hours, challenge utilities and electrical balancing authorities. A sudden decrease in wind energy production must be balanced by other power generators to meet energy demands, while a sharp increase in unexpected production results in excess power that may not be used in the power grid, leading to a loss of potential profits. In this study, we compare different methods to generate probabilistic ramp forecasts from the High Resolution Rapid Refresh (HRRR) numerical weather prediction model with up to twelve hours of lead time at two tall-tower locations in the United States. We validate model performance using 21 months of 80-m wind speed observations from towers in Boulder, Colorado and near the Columbia River Gorge in eastern Oregon. We employ four statistical post-processing methods, three of which are not currently used in the literature for wind forecasting. These procedures correct biases in the model and generate short-term wind speed scenarios which are then converted to power scenarios. This probabilistic enhancement of HRRR point forecasts provides valuable uncertainty information of ramp events and improves the skill of predicting ramp events over the raw forecasts. We compute Brier skill scores for each method at predicting up- and down-ramps to determine which method provides the best prediction. We find that the Standard Schaake Shuffle method yields the highest skill at predicting ramp events for these data sets, especially for up-ramp events at the Oregon site. Increased skill for ramp prediction is limited at the Boulder, CO site using any of the multivariate methods, because of the poor initial forecasts in this area of complex terrain. These statistical methods can be implemented by wind farm operators to generate a range of possible wind speed and power scenarios to aid and optimize decisions before ramp events occur.

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