scholarly journals The Use of Gridded Model Output Statistics (GMOS) in Energy Forecasting of a Solar Car

2020 ◽  
Author(s):  
Christiaan Oosthuizen ◽  
Barend Van Wyk ◽  
Yskandar Hamam ◽  
Dawood Desai ◽  
Yasser Alayli
Keyword(s):  
Model Output ◽  
Energy Forecasting ◽  
Model Output Statistics

scholarly journals The Use of Gridded Model Output Statistics (GMOS) in Energy Forecasting of a Solar Car

Energies ◽  
2020 ◽  
Vol 13 (8) ◽  
pp. 1984
Author(s):  
Christiaan Oosthuizen ◽  
Barend Van Wyk ◽  
Yskandar Hamam ◽  
Dawood Desai ◽  
Yasser Alayli
Keyword(s):  
Weather Forecasting ◽  
Forecast Error ◽  
Weather Prediction ◽  
Weather Conditions ◽  
Model Output ◽  
Energy Forecasting ◽  
Weather Forecasts ◽  
Improved Performance ◽  
Model Output Statistics ◽  
Auxiliary Services

For many years, primary weather forecasting services (Global Forecast System (GFS) and the European Centre for Medium-Range Weather Forecasts (ECMWF)) have been made available to the public through global Numerical Weather Prediction (NWP) models estimating a multitude of general weather variables in a variety of resolutions. Secondary services such as weather experts Meteomatics AG use data and improve the forecasts through various methods. They tailor for the specific needs of customers in the wind and solar power generation sector as well as data scientists, analysts, and meteorologists in all areas of business. These auxiliary services have improved performance and provide reliable data. However, this work extended these auxiliary services to so-called tertiary services in which the weather forecasts were further conditioned for the very niche application environment of mobile solar technology in solar car energy management. The Gridded Model Output Statistics (GMOS) Global Horizontal Irradiance (GHI) model developed in this work utilizes historical data from various ground station locations in South Africa to reduce the mean forecast error of the GHI component. An average Root Mean Square Error (RMSE) improvement of 11.28% was shown across all locations and weather conditions. It was also shown how the incorporation of the GMOS model could have increased the accuracy in regard to the State of Charge (SoC) energy simulation of a solar car during the Sasol Solar Challenge 2018 and the possible range benefits thereof.

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>

LQ-Warn – Entwicklung optimierter Vorhersagen verschiedener Luftqualitätsparameter

2021 ◽  
Author(s):  
Sabine Robrecht ◽  
Robert Osinski ◽  
Ute Dauert ◽  
Andreas Lambert ◽  
Stefan Gilge ◽  
...  
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Model Output ◽  
Mean Square ◽  
Post Processing ◽  
Eine Hohe ◽  
Erste Ergebnisse ◽  
Model Output Statistics ◽  
Monitoring Service

<p>Schlechte Luftqualität gefährdet die Gesundheit der Bevölkerung. Zur Information und zur Ergreifung kurzfristiger Maßnahmen zur Luftqualitätsverbesserung (z.B. Verkehrslenkung) ist eine möglichst genaue und – insbesondere in städtischen Gebieten – möglichst räumlich hochaufgelöste Luftqualitätsvorhersage notwendig. Numerische Luftqualitätsmodelle haben für diese Aufgabe in der Regel eine zu geringe räumliche Auflösung.</p> <p>Daher ist es Ziel des Projektes „LQ-Warn“ die Luftqualitätsvorhersage insbesondere im Hinblick auf die Überschreitung von Grenzwerten zu verbessern. Basierend auf den Modellergebnissen für Luftqualitätsparameter des Copernicus Atmospheric Monitoring Service (CAMS) werden zwei Ansätze verfolgt: Einerseits werden Vorhersagen mit dem regionalen chemischen Transportmodell „REM-CALGRID“ (RCG) unter Einbeziehung von CAMS-Ergebnissen und regionalen Emissionsdaten berechnet. Dabei kann eine hohe horizontale Auflösung von 2 km erzielt werden und Prognosen können für verschiedene Luftschadstoffe in stündlicher Auflösung mit bis zu 72 Stunden Vorlaufzeit erstellt werden, unter anderem für NO<sub>2</sub>, O<sub>3</sub>, PM<sub>10</sub> und PM<sub>2.5</sub>. Andererseits wird die statistische Post-Processing-Methode „Model Output Statistics“ (MOS) angewandt, um Punktvorhersagen für die Massenkonzentration der Spezies NO<sub>2</sub>, O<sub>3</sub>, PM<sub>10</sub> und PM<sub>2.5</sub> mit einer Vorlaufzeit von bis zu 96 Stunden zu berechnen. Dafür werden luftqualitätsbezogene Messungen, CAMS-Modellergebnisse und meteorologische Parameter aus dem numerischen Wettervorhersagemodell des ECMWF als Prädiktoren verwendet.</p> <p>Es werden erste Ergebnisse der mit den o.g. Ansätzen errechneten Vorhersagen präsentiert und die Vor- und Nachteile der jeweiligen Verfahren hervorgehoben. Durch die statistische Post-Processing-Methode MOS wird an den Vorhersagepunkten vor allem für die Massenkonzentration von O<sub>3 </sub>und NO<sub>2</sub> eine signifikante Verringerung des RMSE (Root Mean Square Error) im Vergleich zu den Vorhersagen des numerischen CAMS-Modells erreicht. Diese deutliche Verbesserung der Luftqualitätsvorhersage sinnvoll auf die Fläche auszudehnen ist jedoch noch eine Herausforderung. Im Gegensatz dazu zeigt die Vorhersage mit dem RCG-Modell eine geringere Verbesserung der Vorhersagegüte an einzelnen Vorhersagepunkten als der MOS-Ansatz. Stattdessen bietet das RCG-Modell zeitlich und räumlich konsistente Vorhersagen an allen Modellgitterpunkten. Kleinskalige Konzentrationsunterschiede können aufgrund der höheren Modellauflösung deutlich realistischer vorhergesagt werden als mit den CAMS-Vorhersagen. Ein weiterführendes Ziel des LQ-Warn-Projektes ist es die beiden Ansätze zu kombinieren, um die Vorteile beider nutzen zu können und eine präzise Luftqualitätsvorhersage flächendeckend für Deutschland bereitstellen zu können.</p>

scholarly journals An Evaluation of Model Output Statistics for Subseasonal Streamflow Forecasting in European Catchments

2019 ◽  
Vol 20 (7) ◽  
pp. 1399-1416
Author(s):  
Simon Schick ◽  
Ole Rössler ◽  
Rolf Weingartner
Keyword(s):  
Air Temperature ◽  
Land Surface ◽  
Linear Models ◽  
Surface Air Temperature ◽  
Grid Cell ◽  
Model Output ◽  
Streamflow Forecasting ◽  
Seasonal Forecasts ◽  
Total Runoff ◽  
Model Output Statistics

AbstractSubseasonal and seasonal forecasts of the atmosphere, oceans, sea ice, or land surfaces often rely on Earth system model (ESM) simulations. While the most recent generation of ESMs simulates runoff per land surface grid cell operationally, it does not typically simulate river streamflow directly. Here, we apply the model output statistics (MOS) method to the hindcast archive of the European Centre for Medium-Range Weather Forecasts (ECMWF). Linear models are tested that regress observed river streamflow on surface runoff, subsurface runoff, total runoff, precipitation, and surface air temperature simulated by ECMWF’s forecast systems S4 and SEAS5. In addition, the pool of candidate predictors contains observed precipitation and surface air temperature preceding the date of prediction. The experiment is conducted for 16 European catchments in the period 1981–2006 and focuses on monthly average streamflow at lead times of 0 and 20 days. The results show that skill against the streamflow climatology is frequently absent and varies considerably between predictor combinations, catchments, and seasons. Using streamflow persistence as a benchmark model further deteriorates skill. This is most pronounced for a catchment that features lakes, which extend to about 14% of the catchment area. On average, however, the predictor combinations using the ESM runoff simulations tend to perform best.

Probabilistic Forecasting of (Severe) Thunderstorms in the Netherlands Using Model Output Statistics

2005 ◽  
Vol 20 (2) ◽  
pp. 134-148 ◽  
Author(s):  
Maurice J. Schmeits ◽  
Kees J. Kok ◽  
Daan H. P. Vogelezang
Keyword(s):  
The Netherlands ◽  
Important Predictor ◽  
Convective Precipitation ◽  
Square Root ◽  
Model Output ◽  
Forecast System ◽  
Severe Thunderstorm ◽  
Severe Thunderstorms ◽  
Model Output Statistics ◽  
Ecmwf Model

Abstract The derivation and verification of logistic regression equations for the (conditional) probability of (severe) thunderstorms in the warm half-year (from mid-April to mid-October) in the Netherlands is described. For 12 regions of about 90 km × 80 km each, and for projections out to 48 h in advance (with 6-h periods), these equations have been derived using model output statistics (MOS). As a source for the predictands, lightning data from the Surveillance et d’Alerte Foudre par Interférométrie Radioélectrique (SAFIR) network have been used. The potential predictor dataset mainly consisted of the combined (postprocessed) output from two numerical weather prediction (NWP) models. It contained 15 traditional thunderstorm indices, computed from the High-Resolution Limited-Area Model (HIRLAM), and (postprocessed) output from the European Centre for Medium-Range Weather Forecasts (ECMWF) model. The most important predictor in the thunderstorm forecast system is the square root of the ECMWF 6-h convective precipitation sum, and the most important predictor in the severe thunderstorm forecast system is the HIRLAM Boyden index. The success of the square root of the ECMWF 6-h convective precipitation sum as a thunderstorm predictor indicates that there is a strong relation between the forecast convective precipitation by the ECMWF model and the occurrence of thunderstorms, at least in the Netherlands up to 3 days in advance. The overall verification results for the 0000, 0600, 1200, and 1800 UTC runs of the MOS (severe) thunderstorm forecast system are good, and, therefore, the system was made operational at the Royal Netherlands Meteorological Institute (KNMI) in April 2004.

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