A Hybrid Extended Kalman Filter Based on Parametrized ANNs for the Improvement of the Forecasts of Numerical Weather and Wave Prediction Models
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| Publicado en: | Atmosphere vol. 15, no. 7 (2024), p. 828 |
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| Otros Autores: | , , |
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MDPI AG
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| Acceso en línea: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 022 | |a 2073-4433 | ||
| 024 | 7 | |a 10.3390/atmos15070828 |2 doi | |
| 035 | |a 3084736413 | ||
| 045 | 2 | |b d20240101 |b d20241231 | |
| 084 | |a 231428 |2 nlm | ||
| 100 | 1 | |a Donas, Athanasios |u microSENSES Laboratory, Department of Electrical and Electronics Engineering, Ancient Olive Grove Campus, University of West Attica, 250, Thivon Ave., Egaleo, 12241 Athens, Greece; <email>adonas@uniwa.gr</email>; Mathematical Modeling and Applications Laboratory, Hellenic Naval Academy, Hatzikiriakion, 18539 Piraeus, Greece; <email>ggalanis@hna.gr</email> | |
| 245 | 1 | |a A Hybrid Extended Kalman Filter Based on Parametrized ANNs for the Improvement of the Forecasts of Numerical Weather and Wave Prediction Models | |
| 260 | |b MDPI AG |c 2024 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a A hybrid optimization filter for weather and wave numerical models is proposed and tested in this study. Parametrized Artificial Neural Networks are utilized in conjunction with Extended Kalman Filters to provide a novel postprocess strategy for 10 m wind speed, 2 m air temperature, and significant wave height simulations. The innovation of the developed model is the implementation of Feedforward Neural Networks and Radial Basis Function Neural Networks as estimators of an exogenous parameter that adjusts the covariance matrices of the Extended Kalman Filter process. This hybrid system is evaluated through a time window process leading to promising results, thus enabling a decrease in systematic errors alongside the restriction of the error variability and the corresponding forecast uncertainty. The obtained results showed that the average reduction of the systematic error exceeded 75%, while the corresponding nonsystematic part of that error decreased by 35%. | |
| 651 | 4 | |a Greece | |
| 651 | 4 | |a Mediterranean Sea | |
| 653 | |a Wind speed | ||
| 653 | |a Kalman filters | ||
| 653 | |a Topography | ||
| 653 | |a Artificial neural networks | ||
| 653 | |a Weather | ||
| 653 | |a Neural networks | ||
| 653 | |a Air temperature | ||
| 653 | |a Wave predicting | ||
| 653 | |a Weather forecasting | ||
| 653 | |a Parameter uncertainty | ||
| 653 | |a Boundary conditions | ||
| 653 | |a Prediction models | ||
| 653 | |a Case studies | ||
| 653 | |a Covariance matrix | ||
| 653 | |a Systematic errors | ||
| 653 | |a Simulation | ||
| 653 | |a Parameterization | ||
| 653 | |a Parameter estimation | ||
| 653 | |a Radial basis function | ||
| 653 | |a Numerical models | ||
| 653 | |a Mathematical models | ||
| 653 | |a Electromagnetic wave filters | ||
| 653 | |a Wave height | ||
| 653 | |a Land use | ||
| 653 | |a Error reduction | ||
| 653 | |a Significant wave height | ||
| 653 | |a Algorithms | ||
| 653 | |a Hybrid systems | ||
| 653 | |a Extended Kalman filter | ||
| 700 | 1 | |a Galanis, George |u Mathematical Modeling and Applications Laboratory, Hellenic Naval Academy, Hatzikiriakion, 18539 Piraeus, Greece; <email>ggalanis@hna.gr</email> | |
| 700 | 1 | |a Pytharoulis, Ioannis |u Department of Meteorology and Climatology, School of Geology, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece; <email>pyth@geo.auth.gr</email> | |
| 700 | 1 | |a Famelis, Ioannis Th |u microSENSES Laboratory, Department of Electrical and Electronics Engineering, Ancient Olive Grove Campus, University of West Attica, 250, Thivon Ave., Egaleo, 12241 Athens, Greece; <email>adonas@uniwa.gr</email> | |
| 773 | 0 | |t Atmosphere |g vol. 15, no. 7 (2024), p. 828 | |
| 786 | 0 | |d ProQuest |t Publicly Available Content Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3084736413/abstract/embedded/75I98GEZK8WCJMPQ?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3084736413/fulltextwithgraphics/embedded/75I98GEZK8WCJMPQ?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3084736413/fulltextPDF/embedded/75I98GEZK8WCJMPQ?source=fedsrch |