Forming of the products of non-hydrostatic modelling systems for short-range weather prediction COSMO-RuBy (Hydrometcentre of Russia) и WRF-ARW (Belhydromet)

2021 ◽  
pp. 6-29
Author(s):  
I.А. Rozinkina ◽  
◽  
G.S . Rivin ◽  
R.N. Burak ◽  
Е.D. Аstakhova ◽  
...  
Keyword(s):  
Machine Learning ◽  
Numerical Weather Prediction ◽  
Web Site ◽  
Short Range ◽  
Weather Prediction ◽  
Ensemble Forecasting ◽  
Grid Spacing ◽  
Numerical Weather ◽  
Prediction Systems

The paper considers the results of activities on the development of output products for the non-hydrostatic short-range numerical weather prediction systems: COSMO-RuBy with a grid spacing of 2.2 km at the Hydrometcentre of Russia and WRF-ARW with a grid spacing of 3 km in Belhydromet. The important results of the activities are the organization of the exchange of unified products between the countries and the development at the Hydrometcentre of Russia of two technologies for obtaining the unified products: the multi-model lagged ensemble system and the system for the complex correction based on machine learning of model results. A specialized web-site providing convenient work of forecasters with the COSMO-RuBy results and unified products was created at the Hydrometcentre of Russia based on the feedback from forecasters. The systems of common visualization and verification of COSMO-RuBy and WRF-ARW results are implemented in Belhydromet. Keywords: numerical weather prediction, ensemble forecasting, visualization, machine learning

scholarly journals Numerical Weather Prediction and Artificial Neural Network Coupling for Wind Energy Forecast

Energies ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 338
Author(s):  
Lorenzo Donadio ◽  
Jiannong Fang ◽  
Fernando Porté-Agel
Keyword(s):  
Machine Learning ◽  
Wind Energy ◽  
Wind Power ◽  
Numerical Weather Prediction ◽  
Wind Farm ◽  
Weather Prediction ◽  
Wind Power Forecasting ◽  
Numerical Weather ◽  
Artificial Neural ◽  
Power Forecasting

In the past two decades, wind energy has been under fast development worldwide. The dramatic increase of wind power penetration in electricity production has posed a big challenge to grid integration due to the high uncertainty of wind power. Accurate real-time forecasts of wind farm power outputs can help to mitigate the problem. Among the various techniques developed for wind power forecasting, the hybridization of numerical weather prediction (NWP) and machine learning (ML) techniques such as artificial neural networks (ANNs) are attracting many researchers world-wide nowadays, because it has the potential to yield more accurate forecasts. In this paper, two hybrid NWP and ANN models for wind power forecasting over a highly complex terrain are proposed. The developed models have a fine temporal resolution and a sufficiently large prediction horizon (>6 h ahead). Model 1 directly forecasts the energy production of each wind turbine. Model 2 forecasts first the wind speed, then converts it to the power using a fitted power curve. Effects of various modeling options (selection of inputs, network structures, etc.) on the model performance are investigated. Performances of different models are evaluated based on four normalized error measures. Statistical results of model predictions are presented with discussions. Python was utilized for task automation and machine learning. The end result is a fully working library for wind power predictions and a set of tools for running the models in forecast mode. It is shown that the proposed models are able to yield accurate wind farm power forecasts at a site with high terrain and flow complexities. Especially, for Model 2, the normalized Mean Absolute Error and Root Mean Squared Error are obtained as 8.76% and 13.03%, respectively, lower than the errors reported by other models in the same category.

scholarly journals Short-Range Numerical Weather Prediction of Extreme Precipitation Events Using Enhanced Surface Data Assimilation

Atmosphere ◽  
2019 ◽  
Vol 10 (10) ◽  
pp. 587
Author(s):  
Magnus Lindskog ◽  
Tomas Landelius
Keyword(s):  
Data Assimilation ◽  
Numerical Weather Prediction ◽  
Short Range ◽  
Weather Prediction ◽  
Heavy Precipitation ◽  
Numerical Weather ◽  
Extreme Precipitation Events ◽  
Surface Data ◽  
Enhanced Surface ◽  
Precipitation Events

A limited-area kilometre scale numerical weather prediction system is applied to evaluate the effect of refined surface data assimilation on short-range heavy precipitation forecasts. The refinements include a spatially dependent background error representation, use of a flow-dependent data assimilation technique, and use of data from a satellite-based scatterometer instrument. The effect of the enhancements on short-term prediction of intense precipitation events is confirmed through a number of case studies. Verification scores and subjective evaluation of one particular case points at a clear impact of the enhanced surface data assimilation on short-range heavy precipitation forecasts and suggest that it also tends to slightly improve them. Although this is not strictly statistically demonstrated, it is consistent with the expectation that a better surface state should improve rainfall forecasts.

scholarly journals Existence of multiple scales in uncertainty of numerical weather prediction

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Hyo-Jong Song
Keyword(s):  
Numerical Weather Prediction ◽  
Short Range ◽  
Multiple Scales ◽  
Forecast Error ◽  
Weather Prediction ◽  
Initial Condition ◽  
Forecast Uncertainty ◽  
Essential Information ◽  
Numerical Weather ◽  
Sample Correlation

Abstract Numerical weather prediction provides essential information of societal influence. Advances in the initial condition estimation have led to the improvement of the prediction skill. The process to produce the better initial condition (analysis) with the combination of short-range forecast and observation over the globe requires information about uncertainty of the forecast results to decide how much observation is reflected to the analysis and how far the observation information should be propagated. Forecast ensemble represents the error of the short-range forecast at the instance. The influence of observation propagating along with forecast ensemble correlation needs to be restricted by localized correlation function because of less reliability of sample correlation. So far, solitary radius of influence is usually used since there has not been an understanding about the realism of multiple scales in the forecast uncertainty. In this study, it is explicitly shown that multiple scales exist in short-range forecast error and any single-scale localization approach could not resolve this situation. A combination of Gaussian correlation functions of various scales is designed, which more weighs observation itself near the data point and makes ensemble perturbation, far from the observation position, more participate in decision of the analysis. Its outstanding performance supports the existence of multi-scale correlation in forecast uncertainty.

scholarly journals Crowd‐sourced observations for short‐range numerical weather prediction: Report from EWGLAM / SRNWP Meeting 2019

2021 ◽  
Author(s):  
Kasper S. Hintz ◽  
Conor McNicholas ◽  
Roger Randriamampianina ◽  
Hywel T. P. Williams ◽  
Bruce Macpherson ◽  
...  
Keyword(s):  
Numerical Weather Prediction ◽  
Short Range ◽  
Weather Prediction ◽  
Numerical Weather

Naissances du modèle et de la coopération Aladin

2021 ◽  
pp. 041
Author(s):  
András Horányi ◽  
Radmila Brožková
Keyword(s):  
Numerical Weather Prediction ◽  
Short Range ◽  
Weather Prediction ◽  
Early Years ◽  
Pivotal Role ◽  
Personal Account ◽  
Numerical Weather ◽  
The Creation

Jean-François Geleyn a joué un rôle central dans la création et le fonctionnement de la coopération Aladin sur la prévision numérique du temps (PNT). Le projet Aladin a non seulement développé des outils de prévision numérique du temps à court terme, qui pouvaient être utilisés pour la prévision numérique opérationnelle, mais a également instauré un lien durable entre ses participants. Dans cet article, nous rendons hommage à Jean-François avec notre récit historique et parfois personnel des premières années de la coopération. Nous reconnaissons et soulignons que Jean-François n'a pas seulement créé et façonné la coopération elle-même, mais qu'il a également influencé la carrière et la vie de beaucoup des scientifiques appartenant aux instituts participant à ce projet. Jean-François Geleyn had a pivotal role in the creation and running of the Aladin Numerical Weather Prediction (NWP) cooperation. The Aladin project not only developed short-range NWP tools, which could be used for operational numerical forecasting, but also instilled a long-lasting bond among its participants. In this article we pay tribute to Jean-François with our historical and sometimes personal account of the early years of the cooperation. We acknowledge and stress that Jean-François not only created and shaped the cooperation itself, but also influenced the career and life of many scientists from the participating institutes.

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