Initial Errors Growth in Chaotic Low-Dimensional Weather Prediction Model

2013 ◽  
pp. 333-342 ◽  
Author(s):  
Hynek Bednar ◽  
Ales Raidl ◽  
Jiri Miksovsky
Keyword(s):  
Prediction Model ◽  
Weather Prediction ◽  
Initial Errors ◽  
Low Dimensional

EALSTM-QR: Interval wind-power prediction model based on numerical weather prediction and deep learning

Energy ◽  
2020 ◽  
pp. 119692
Author(s):  
Xiaosheng Peng ◽  
Hongyu Wang ◽  
Jianxun Lang ◽  
Wenze Li ◽  
Qiyou Xu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Prediction Model ◽  
Wind Power ◽  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
Power Prediction ◽  
Numerical Weather ◽  
Model Based ◽  
Wind Power Prediction

scholarly journals Time Evolution of Initial Errors in Lorenz’s 05 Chaotic Model

2015 ◽  
Vol 2015 ◽  
pp. 1-9
Author(s):  
Hynek Bednář ◽  
Aleš Raidl ◽  
Jiří Mikšovský
Keyword(s):  
Weather Prediction ◽  
Prediction Method ◽  
Atmospheric Model ◽  
Ensemble Prediction ◽  
Asymptotic Value ◽  
Time Limits ◽  
Initial Errors ◽  
Asymptotic Values ◽  
Chaotic Model ◽  
Almost All

Initial errors in weather prediction grow in time and, as they become larger, their growth slows down and then stops at an asymptotic value. Time of reaching this saturation point represents the limit of predictability. This paper studies the asymptotic values and time limits in a chaotic atmospheric model for five initial errors, using ensemble prediction method (model’s data) as well as error approximation by quadratic and logarithmic hypothesis and their modifications. We show that modified hypotheses approximate the model’s time limits better, but not without serious disadvantages. We demonstrate how hypotheses can be further improved to achieve better match of time limits with the model. We also show that quadratic hypothesis approximates the model’s asymptotic value best and that, after improvement, it also approximates the model’s time limits better for almost all initial errors and time lengths.

scholarly journals Parameterization of mires in a numerical weather prediction model

2014 ◽  
Vol 50 (11) ◽  
pp. 8982-8996 ◽  
Author(s):  
Alla Yurova ◽  
Mikhail Tolstykh ◽  
Mats Nilsson ◽  
Andrey Sirin
Keyword(s):  
Prediction Model ◽  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
Numerical Weather Prediction Model ◽  
Numerical Weather
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