AbstractWeather forecasting is complex and not always accurate, moreover, it is generally defined by its very nature as a process that has to deal with uncertainties. In a previous work, a new weather prediction scheme was presented, which uses evolutionary computing methods, particularly, Genetic Algorithms in order to find the most timely ‘optimal’ values of model closure parameters that appear in physical parametrization schemes which are coupled with numerical weather prediction (NWP) models. Currently, these parameters are specified manually. Our hypothesis is that the NWP model forecast skill is sensitive to the specified parameter values. And thus, by finding ‘optimal’ values of these parameters, we aim to enhance prediction quality. In t...
Analog methods (AMs) are statistical downscaling methods often used for precipitation prediction in ...
The weather is a chaotic system. Small errors in the initial conditions of a forecast grow rapidly a...
Data-driven modeling based on machine learning (ML) is showing enormous potential for weather foreca...
Weather forecasting is complex and not always accurate, moreover, it is generally defined by its ver...
AbstractWeather forecasting is complex and not always accurate, moreover, it is generally defined by ...
AbstractThe need for reliable predictions in environmental modelling is well-known. Particularly, th...
The need for reliable predictions in environmental modelling is well-known. Particularly, the predic...
Analog methods (AMs) allow for the prediction of local meteorological variables of interest(predicta...
Analogue methods (AMs) rely on the hypothesis that similar situations, in terms of atmospheric circu...
Weather systems use extremely complex combinations of mathematical tools for anal-ysis and forecasti...
Title from first page of PDF file (viewed September 9, 2010) ; Includes bibliographical references (...
International audienceIn this study, optimal parameter estimations are performed for both physical a...
In meteorology, the small changes in the initial condition of the atmosphere will lead to big change...
Algorithmic numerical weather prediction (NWP) skill optimization has been tested using the Integrat...
The Analogue Method (AM) aims at forecasting a local meteorological variable of interest (the predic...
Analog methods (AMs) are statistical downscaling methods often used for precipitation prediction in ...
The weather is a chaotic system. Small errors in the initial conditions of a forecast grow rapidly a...
Data-driven modeling based on machine learning (ML) is showing enormous potential for weather foreca...
Weather forecasting is complex and not always accurate, moreover, it is generally defined by its ver...
AbstractWeather forecasting is complex and not always accurate, moreover, it is generally defined by ...
AbstractThe need for reliable predictions in environmental modelling is well-known. Particularly, th...
The need for reliable predictions in environmental modelling is well-known. Particularly, the predic...
Analog methods (AMs) allow for the prediction of local meteorological variables of interest(predicta...
Analogue methods (AMs) rely on the hypothesis that similar situations, in terms of atmospheric circu...
Weather systems use extremely complex combinations of mathematical tools for anal-ysis and forecasti...
Title from first page of PDF file (viewed September 9, 2010) ; Includes bibliographical references (...
International audienceIn this study, optimal parameter estimations are performed for both physical a...
In meteorology, the small changes in the initial condition of the atmosphere will lead to big change...
Algorithmic numerical weather prediction (NWP) skill optimization has been tested using the Integrat...
The Analogue Method (AM) aims at forecasting a local meteorological variable of interest (the predic...
Analog methods (AMs) are statistical downscaling methods often used for precipitation prediction in ...
The weather is a chaotic system. Small errors in the initial conditions of a forecast grow rapidly a...
Data-driven modeling based on machine learning (ML) is showing enormous potential for weather foreca...