Articles | Volume 24, issue 10
https://doi.org/10.5194/angeo-24-2451-2006
© Author(s) 2006. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/angeo-24-2451-2006
© Author(s) 2006. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
20 Oct 2006
Applications of Kalman filters based on non-linear functions to numerical weather predictions
G. Galanis
University of Athens, School of Physics, Division of Applied Physics, Atmospheric Modelling and Weather Forecasting Group, University Campus, Bldg. PHYS-V, 15784 Athens, Greece
Naval Academy of Greece, Section of Mathematics, Xatzikyriakion, Piraeus 185 39, Greece
P. Louka
University of Athens, School of Physics, Division of Applied Physics, Atmospheric Modelling and Weather Forecasting Group, University Campus, Bldg. PHYS-V, 15784 Athens, Greece
Hellenic National Meteorological Service, El. Venizelou 14, Hellinikon 167 77, Athens, Greece
P. Katsafados
University of Athens, School of Physics, Division of Applied Physics, Atmospheric Modelling and Weather Forecasting Group, University Campus, Bldg. PHYS-V, 15784 Athens, Greece
I. Pytharoulis
University of Athens, School of Physics, Division of Applied Physics, Atmospheric Modelling and Weather Forecasting Group, University Campus, Bldg. PHYS-V, 15784 Athens, Greece
Section of Meteorology and Climatology, Department of Geology, Aristotle University of Thessaloniki, University Campus, 54124 Thessaloniki, Greece.
G. Kallos
University of Athens, School of Physics, Division of Applied Physics, Atmospheric Modelling and Weather Forecasting Group, University Campus, Bldg. PHYS-V, 15784 Athens, Greece
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78 citations as recorded by crossref.
- Bias correction of global irradiance modelled with weather and research forecasting model over Paraguay A. Rincón et al. https://doi.org/10.1016/j.solener.2018.05.061
- Short-term wind speed prediction using an unscented Kalman filter based state-space support vector regression approach K. Chen & J. Yu https://doi.org/10.1016/j.apenergy.2013.08.025
- Lagrangian Drifter to Identify Ocean Eddy Characteristics P. Chu & C. Fan https://doi.org/10.3390/cli7120137
- An Autoregressive-Based Kalman Filter Approach for Daily PM2.5 Concentration Forecasting in Beijing, China X. Zhang et al. https://doi.org/10.1089/big.2022.0082
- Power Generation Prediction of an Open Cycle Gas Turbine Using Kalman Filter C. Manasis et al. https://doi.org/10.3390/en13246692
- A Fully Conserved Minimal Adjustment Scheme with (T, S) Coherency for Stabilization of Hydrographic Profiles X. Wang et al. https://doi.org/10.1175/JTECH-D-12-00025.1
- Short-term wind speed forecasting by spectral analysis from long-term observations with missing values H. Akçay & T. Filik https://doi.org/10.1016/j.apenergy.2017.01.063
- A Conserved Minimal Adjustment Scheme for Stabilization of Hydrographic Profiles P. Chu & C. Fan https://doi.org/10.1175/2010JTECHO742.1
- Local temperature forecasts based on statistical post‐processing of numerical weather prediction data E. Alerskans & E. Kaas https://doi.org/10.1002/met.2006
- Post-processing of Solar Irradiance Forecasts from WRF Model at Reunion Island M. Diagne et al. https://doi.org/10.1016/j.egypro.2014.10.127
- A hybrid Bayesian Kalman filter and applications to numerical wind speed modeling G. Galanis et al. https://doi.org/10.1016/j.jweia.2017.04.007
- A Kalman Filter Scheme for the Optimization of Low-Cost Gas Sensor Measurements I. Christakis et al. https://doi.org/10.3390/electronics13010025
- Solar and photovoltaic forecasting through post‐processing of the Global Environmental Multiscale numerical weather prediction model S. Pelland et al. https://doi.org/10.1002/pip.1180
- A new methodology for the extension of the impact of data assimilation on ocean wave prediction G. Galanis et al. https://doi.org/10.1007/s10236-009-0191-8
- Wind gust forecasting by post-processing the WRF model outputs using ANN M. Mohammadi et al. https://doi.org/10.1016/j.dynatmoce.2023.101425
- The application of model predictive control on stock portfolio optimization with prediction based on Geometric Brownian Motion-Kalman Filter W. Syaifudin & E. Putri https://doi.org/10.3934/jimo.2021119
- Data-Driven Adaptive Safe Path-Planning for Helicopter in Urban Wind Fields Y. Liu et al. https://doi.org/10.1007/s42405-026-01200-z
- Kalman Filter bank post-processor methodology for the Weather Research and Forecasting Model wind speed grid model output correction A. Baró Pérez et al. https://doi.org/10.1080/14786451.2018.1432615
- A methodology for optimizing probabilistic wind power forecasting C. Stathopoulos et al. https://doi.org/10.5194/adgeo-45-289-2018
- Comparison of two new short-term wind-power forecasting systems I. Ramirez-Rosado et al. https://doi.org/10.1016/j.renene.2008.11.014
- Temperature Prediction using Kalman Filters in Connection N. Assimakis et al. https://doi.org/10.37394/23201.2025.24.22
- Decision support system for forest fire protection in the Euro-Mediterranean region K. Kalabokidis et al. https://doi.org/10.1007/s10342-011-0534-0
- Wind speed and wind energy forecast through Kalman filtering of Numerical Weather Prediction model output F. Cassola & M. Burlando https://doi.org/10.1016/j.apenergy.2012.03.054
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- A new methodology for using buoy measurements in sea wave data assimilation G. Emmanouil et al. https://doi.org/10.1007/s10236-010-0328-9
- Model aggregation using optimal transport and applications in wind speed forecasting G. Papayiannis et al. https://doi.org/10.1002/env.2531
- Forecasting Geomagnetic activity (Dst Index) using the ensemble kalman filter B. Nilam & S. Ram https://doi.org/10.1093/mnras/stac099
- Wind power prediction based on numerical and statistical models C. Stathopoulos et al. https://doi.org/10.1016/j.jweia.2012.09.004
- <i>H</i><sub>∞</sub> Filtering for Bias Correction in Post-Processing of Numerical Weather Prediction J. LIM & H. PARK https://doi.org/10.2151/jmsj.2019-041
- Combined Kalman Filter and Universal Kriging to Improve Storm Wind Speed Predictions for the Northeastern United States A. Samalot et al. https://doi.org/10.1175/WAF-D-18-0068.1
- Novel stochastic methods to predict short-term solar radiation and photovoltaic power J. Dong et al. https://doi.org/10.1016/j.renene.2019.05.073
- Forecasting Algorithm Based on Temperature Error Prediction Using Kalman Filter for Management System Development N. Bogdanovs et al. https://doi.org/10.2478/lpts-2021-0038
- An application of the splitting-up method for the computation of a neural network representation for the solution for the filtering equations D. Crisan et al. https://doi.org/10.1007/s40072-022-00260-y
- An Integrated Energy Simulation Model for Buildings N. Kampelis et al. https://doi.org/10.3390/en13051170
- A review and discussion of decomposition-based hybrid models for wind energy forecasting applications Z. Qian et al. https://doi.org/10.1016/j.apenergy.2018.10.080
- Hybrid numerical models for wind speed forecasting M. Brabec et al. https://doi.org/10.1016/j.jastp.2021.105669
- System bias correction of short-term hub-height wind forecasts using the Kalman filter J. Xu et al. https://doi.org/10.1186/s41601-021-00214-x
- An intercomparison study between RAMS and CRES-Flow-NS models and evaluation with wind tunnel experimental data: Toward improving atmospheric modeling for wind resource assessment N. Barranger et al. https://doi.org/10.1016/j.jweia.2015.04.003
- Wave height characteristics in the Mediterranean Sea by means of numerical modeling, satellite data, statistical and geometrical techniques G. Galanis et al. https://doi.org/10.1007/s11001-011-9142-0
- Probability distribution function of the upper equatorial Pacific current speeds P. Chu https://doi.org/10.1029/2008GL033669
- Performance evaluation and accuracy enhancement of a day-ahead wind power forecasting system in China P. Zhao et al. https://doi.org/10.1016/j.renene.2011.11.051
- Parameter identification of fluid field based on CFD reduced-order model and 3D-Var data assimilation C. Dai et al. https://doi.org/10.1016/j.anucene.2025.111459
- Solar GHI Ensemble Prediction Based on a Meteorological Model and Method Kalman Filter Y. Liu & U. Rathnayake https://doi.org/10.1155/2022/1523198
- Numerical modeling and analysis of the effect of complex Greek topography on tornadogenesis I. Matsangouras et al. https://doi.org/10.5194/nhess-14-1905-2014
- Forecasting the High Penetration of Wind Power on Multiple Scales Using Multi-to-Multi Mapping J. Yan et al. https://doi.org/10.1109/TPWRS.2017.2787667
- Accuracy Progressive Calculation of Lagrangian Trajectories from a Gridded Velocity Field P. Chu & C. Fan https://doi.org/10.1175/JTECH-D-13-00204.1
- Simplified Method to Derive the Kalman Filter Covariance Matrices to Predict Wind Speeds from a NWP Model C. Lynch et al. https://doi.org/10.1016/j.egypro.2014.12.431
- Statistical Characteristics of the Global Surface Current Speeds Obtained From Satellite Altimetry and Scatterometer Data P. Chu https://doi.org/10.1109/JSTARS.2009.2014474
- A Meteorological–Statistic Model for Short-Term Wind Power Forecasting J. Lima et al. https://doi.org/10.1007/s40313-017-0329-8
- Operational atmospheric and wave modelling in the California’s coastline and offshore area with applications to wave energy monitoring and assessment G. Galanis et al. https://doi.org/10.1080/1755876X.2017.1349640
- Daily wind speed forecasting through hybrid KF-ANN model based on ARIMA O. Shukur & M. Lee https://doi.org/10.1016/j.renene.2014.11.084
- Development of a High-Resolution Wind Forecast System Based on the WRF Model and a Hybrid Kalman-Bayesian Filter C. Otero-Casal et al. https://doi.org/10.3390/en12163050
- The future climatic variabilities in the Mano River union, its implications on socio-economic development M. Solomon et al. https://doi.org/10.5937/gp26-36677
- Very Short-Term Generating Power Forecasting for Wind Power Generators Based on Time Series Analysis A. Yona et al. https://doi.org/10.4236/sgre.2013.42022
- Kalman Filter and Its Application in Data Assimilation B. Wang et al. https://doi.org/10.3390/atmos14081319
- Combination of statistical Kalman filters and data assimilation for improving ocean waves analysis and forecasting G. Emmanouil et al. https://doi.org/10.1016/j.ocemod.2012.09.004
- Estimation-free Prediction Algorithms N. Assimakis et al. https://doi.org/10.37394/23203.2023.18.59
- Wave height characteristics in the north Atlantic ocean: a new approach based on statistical and geometrical techniques G. Galanis et al. https://doi.org/10.1007/s00477-011-0540-2
- Solar forecasting with hourly updated numerical weather prediction G. Zhang et al. https://doi.org/10.1016/j.rser.2021.111768
- Numerical weather prediction wind correction methods and its impact on computational fluid dynamics based wind power forecasting Y. Liu et al. https://doi.org/10.1063/1.4950972
- Ocean spectral data assimilation without background error covariance matrix P. Chu et al. https://doi.org/10.1007/s10236-016-0971-x
- A transformer neural network for predicting near‐surface temperature E. Alerskans et al. https://doi.org/10.1002/met.2098
- Solar and wind power generation forecasts using elastic net in time-varying forecast combinations D. Nikodinoska et al. https://doi.org/10.1016/j.apenergy.2021.117983
- DEMI: Dynamic High-Resolution Imaging of the Earth’s Upper Atmosphere J. Urco et al. https://doi.org/10.1109/TGRS.2026.3675447
- Improvements in wind speed forecasts for wind power prediction purposes using Kalman filtering P. Louka et al. https://doi.org/10.1016/j.jweia.2008.03.013
- A new Kalman filter based on Information Geometry techniques for optimizing numerical environmental simulations G. Galanis et al. https://doi.org/10.1007/s00477-016-1332-5
- Extreme wind events in a complex maritime environment: Ways of quantification P. Patlakas et al. https://doi.org/10.1016/j.jweia.2015.11.006
- Post-processing of solar irradiance forecasts from WRF model at Reunion Island M. Diagne et al. https://doi.org/10.1016/j.solener.2014.03.016
- Adaptive Kalman Filtering for Postprocessing Ensemble Numerical Weather Predictions A. Pelosi et al. https://doi.org/10.1175/MWR-D-17-0084.1
- Enhancing Robustness of Variational Data Assimilation in Chaotic Systems: An α-4DVar Framework with Rényi Entropy and α-Generalized Gaussian Distributions Y. Luo et al. https://doi.org/10.3390/e27070763
- Wind gust estimation by combining a numerical weather prediction model and statistical post-processing P. Patlakas et al. https://doi.org/10.1016/j.egypro.2017.08.179
- Steepest Ascent Low/Non‐Low‐Frequency Ratio in Empirical Mode Decomposition to Separate Deterministic and Stochastic Velocities From a Single Lagrangian Drifter P. Chu https://doi.org/10.1002/2017JC013500
- Nonlinear Autoregressive Neural Network and Extended Kalman Filters for Prediction of Financial Time Series G. Benrhmach et al. https://doi.org/10.1155/2020/5057801
- An Adaptive Kalman Filtering Approach to Sensing and Predicting Air Quality Index Values J. Chen et al. https://doi.org/10.1109/ACCESS.2019.2963416
- Optimal Spectral Decomposition (OSD) for Ocean Data Assimilation P. Chu et al. https://doi.org/10.1175/JTECH-D-14-00079.1
- EALSTM-QR: Interval wind-power prediction model based on numerical weather prediction and deep learning X. Peng et al. https://doi.org/10.1016/j.energy.2020.119692
- Optimization of Low Cost Gas Sensor Measurements Using Kalman Filter Approach N. ΠΑΠΑΔΑΚΗΣ et al. https://doi.org/10.2139/ssrn.4143287
- A Hybrid Extended Kalman Filter Based on Parametrized ANNs for the Improvement of the Forecasts of Numerical Weather and Wave Prediction Models A. Donas et al. https://doi.org/10.3390/atmos15070828
Latest update: 22 Jul 2026
