scholarly journals A new Lagrangian-based short-term prediction methodology for high-frequency (HF) radar currents

Ocean Science ◽  
2021 ◽  
Vol 17 (3) ◽  
pp. 755-768
Author(s):  
Lohitzune Solabarrieta ◽  
Ismael Hernández-Carrasco ◽  
Anna Rubio ◽  
Michael Campbell ◽  
Ganix Esnaola ◽  
...  
Keyword(s):  
Real Time ◽  
High Frequency ◽  
Skill Score ◽  
Hf Radar ◽  
Surface Currents ◽  
Short Term ◽  
High Frequency Radar ◽  
Term Prediction ◽  
Short Term Prediction ◽  
Temporal Horizon

Abstract. The use of high-frequency radar (HFR) data is increasing worldwide for different applications in the field of operational oceanography and data assimilation, as it provides real-time coastal surface currents at high temporal and spatial resolution. In this work, a Lagrangian-based, empirical, real-time, short-term prediction (L-STP) system is presented in order to provide short-term forecasts of up to 48 h of ocean currents. The method is based on finding historical analogs of Lagrangian trajectories obtained from HFR surface currents. Then, assuming that the present state will follow the same temporal evolution as the historical analog, we perform the forecast. The method is applied to two HFR systems covering two areas with different dynamical characteristics: the southeast Bay of Biscay and the central Red Sea. A comparison of the L-STP methodology with predictions based on persistence and reference fields is performed in order to quantify the error introduced by this approach. Furthermore, a sensitivity analysis has been conducted to determine the limit of applicability of the methodology regarding the temporal horizon of Lagrangian prediction. A real-time skill score has been developed using the results of this analysis, which allows for the identification of periods when the short-term prediction performance is more likely to be low, and persistence can be used as a better predictor for the future currents.

scholarly journals A new Lagrandian based short term prediction methodology for HF radar currents

2020 ◽  
Author(s):  
Lohitzune Solabarrieta ◽  
Ismael Hernandez-Carrasco ◽  
Anna Rubio ◽  
Alejandro Orfila ◽  
Michael Campbell ◽  
...  
Keyword(s):  
Real Time ◽  
Red Sea ◽  
Skill Score ◽  
Hf Radar ◽  
Surface Currents ◽  
Bay Of Biscay ◽  
Short Term ◽  
High Frequency Radar ◽  
Term Prediction ◽  
Short Term Prediction

Abstract. The use of High Frequency Radar (HFR) data is increasing worldwide for operational oceanography and data assimilation, as it provides real-time coastal surface currents at high temporal and spatial resolution. In this work, a Lagrangian based empirical real-time, Short-Term Prediction (L-STP) system is presented in order to provide short term forecasts of up to 48 hours of ocean currents from HFR data. The method is based on the finding of historical gridded analogues of Lagrangian trajectories obtained from HFR surface currents. Then, assuming that the present state will follow the same temporal evolution as did the historical analogue, we obtain a short-term prediction of the surface currents. The method is applied to two HFR systems covering two areas with different dynamical characteristics: the southeast Bay of Biscay and the central Red Sea. The L-STP improves on previous prediction systems implemented for the SE Bay of Biscay and provides good results for the Red Sea study area. A comparison of the L-STP methodology with predictions based on persistence and reference fields has been performed in order to quantify the error introduced by this Lagrangian approach. Furthermore, a temporal sensitivity analysis has been addressed to determine the limit of applicability of the methodology regarding the temporal horizon of Lagrangian prediction. A real-time skill-score has been developed using the results of this analysis which allows to identify periods when the short-term prediction performance is more likely to be low and persistence can be used as a better predictor for the future currents.

scholarly journals Variational interpolation of high-frequency radar surface currents using DIVAnd

2021 ◽  
Vol 71 (3) ◽  
pp. 293-308
Author(s):  
Alexander Barth ◽  
Charles Troupin ◽  
Emma Reyes ◽  
Aida Alvera-Azcárate ◽  
Jean-Marie Beckers ◽  
...  
Keyword(s):  
Pressure Gradient ◽  
Coriolis Force ◽  
Surface Pressure ◽  
High Frequency ◽  
Cross Validation ◽  
Hf Radar ◽  
Validation Dataset ◽  
Surface Currents ◽  
Dynamic Constraints ◽  
High Frequency Radar

AbstractDIVAnd (Data-Interpolating Variational Analysis, in n-dimensions) is a tool to interpolate observations on a regular grid using the variational inverse method. We have extended DIVAnd to include additional dynamic constraints relevant to surface currents, including imposing a zero normal velocity at the coastline, imposing a low horizontal divergence of the surface currents, temporal coherence and simplified dynamics based on the Coriolis force, and the possibility of including a surface pressure gradient. The impact of these constraints is evaluated by cross-validation using the HF (high-frequency) radar surface current observations in the Ibiza Channel from the Balearic Islands Coastal Ocean Observing and Forecasting System (SOCIB). A small fraction of the radial current observations are set aside to validate the velocity reconstruction. The remaining radial currents from the two radar sites are combined to derive total surface currents using DIVAnd and then compared to the cross-validation dataset and to drifter observations. The benefit of the dynamic constraints is shown relative to a variational interpolation without these dynamical constraints. The best results were obtained using the Coriolis force and the surface pressure gradient as a constraint which are able to improve the reconstruction from the Open-boundary Modal Analysis, a quite commonly used method to interpolate HF radar observations, once multiple time instances are considered together.

scholarly journals Ultra Short-term Prediction of Pole Coordinates via Combination of Empirical Mode Decomposition and Neural Networks

2016 ◽  
Vol 51 (4) ◽  
pp. 149-161
Author(s):  
Yu Lei ◽  
Danning Zhao ◽  
Hongbing Cai
Keyword(s):  
Neural Networks ◽  
Empirical Mode Decomposition ◽  
High Frequency ◽  
Short Term ◽  
Pass Filter ◽  
Term Prediction ◽  
Mode Decomposition ◽  
Short Period ◽  
The Future ◽  
Short Term Prediction

Abstract It was shown in the previous study that the increase of pole coordinates prediction error for about 100 days in the future is mostly caused by irregular short period oscillations. In this paper, the ultra short-term prediction of pole coordinates is studied for 10 days in the future by means of combination of empirical mode decomposition (EMD) and neural networks (NN), denoted EMD-NN. In the algorithm, EMD is employed as a low pass filter for eliminating high frequency signals from observed pole coordinates data. Then the annual and Chandler wobbles are removed a priori from pole coordinates data with high frequency signals eliminated. Finally, the radial basis function (RBF) networks are used to model and predict the residuals. The prediction performance of the EMD-NN approach is compared with that of the NN-only solution and the prediction methods and techniques involved in the Earth orientation parameters prediction comparison campaign (EOP PCC). The results show that the prediction accuracy of the EMD-NN algorithm is better than that of the NN-only solution and is also comparable with that of the other existing prediction method and techniques.

Development of a Technique for Short-Term Prediction of Hydrometeors Using Advection and Physical Forcing.

1983 ◽  
Author(s):  
Gregory S. Forbes ◽  
John J. Cahir ◽  
Paul B. Dorian ◽  
Walter D. Lottes ◽  
Kathy Chapman
Keyword(s):  
Short Term ◽  
Physical Forcing ◽  
Term Prediction ◽  
Short Term Prediction
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