A method to generate fully multi-scale optimal interpolation by combining efficient single process analyses, illustrated by a DINEOF analysis spiced with a local optimal interpolation

Beckers, Jean-Marie; Barth, Alexander; Tomažić, Igor; Alvera‐Azcárate, Aida

doi:10.5194/os-10-845-2014

Cited by 9 publications

(5 citation statements)

References 39 publications

(38 reference statements)

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“…In contrast, this distance reaches values of 5 km after 24 h in the case of OMA. The values of D obtained in our computations after 24 h of simulation are smaller than those reported in Molcard et al (2009), Bellomo et al (2015) and Kalampokis et al (2016) for separation distances between real and virtual drifter trajectories. It is important to keep in mind that in these experiments the trajectories are calculated with data from different sources (HFR and drifters) and different samples (spatial resolution), which are collected in a different region (Mediterranean Sea).…”

Section: Comparison Of Trajectoriescontrasting

confidence: 84%

“…Over the last 20-25 years there has been a rapid growth in the use of coastal radars, demonstrating the possibility of observing and monitoring complex surface current dynamics and leading to a fast spread of installations of HFR observatories in many coastal regions. Currently, HFR is a unique technology able to provide autonomously continuous hourly surface velocity measurements over wide coastal areas (typical range of 30-100 km from the coast) at high spatial (a few kilometers) resolution depending on the working frequency (Paduan and Washburn, 2013;Bellomo et al, 2015;Lana et al, 2016;Rubio et al, 2017). Contrary to satellite altimetry, the use of HFR data can potentially provide gridded velocities with the necessary resolution in both space and time to unravel the role of small scales on dynamical properties (Hernández-Carrasco et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

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Impact of HF radar current gap-filling methodologies on the Lagrangian assessment of coastal dynamics

et al. 2018

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Abstract. High-frequency radar, HFR, is a cost-effective monitoring technique that allows us to obtain high-resolution continuous surface currents, providing new insights for understanding small-scale transport processes in the coastal ocean. In the last years, the use of Lagrangian metrics to study mixing and transport properties has been growing in importance. A common condition among all the Lagrangian techniques is that complete spatial and temporal velocity data are required to compute trajectories of virtual particles in the flow. However, hardware or software failures in the HFR system can compromise the availability of data, resulting in incomplete spatial coverage fields or periods without data. In this regard, several methods have been widely used to fill spatiotemporal gaps in HFR measurements. Despite the growing relevance of these systems there are still many open questions concerning the reliability of gap-filling methods for the Lagrangian assessment of coastal ocean dynamics. In this paper, we first develop a new methodology to reconstruct HFR velocity fields based on self-organizing maps (SOMs). Then, a comparative analysis of this method with other available gap-filling techniques is performed, i.e., open-boundary modal analysis (OMA) and data interpolating empirical orthogonal functions (DINEOFs). The performance of each approach is quantified in the Lagrangian frame through the computation of finite-size Lyapunov exponents, Lagrangian coherent structures and residence times. We determine the limit of applicability of each method regarding four experiments based on the typical temporal and spatial gap distributions observed in HFR systems unveiled by a K-means clustering analysis. Our results show that even when a large number of data are missing, the Lagrangian diagnoses still give an accurate description of oceanic transport properties.

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Section: Comparison Of Trajectoriescontrasting

confidence: 84%

Section: Introductionmentioning

confidence: 99%

Impact of HF radar current gap-filling methodologies on the Lagrangian assessment of coastal dynamics

et al. 2018

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“…25 The training process of DINCAE is based on OI, which aims to minimize the analysis error (i.e., between the estimated and observed values). The basic OI approach can be found in the previous studies [25,54,55]. DINCAE estimates the anomaly of SST ( ŷij ) from the inverse of the error variance and provides the error standard deviation ( σij ) of the reconstructed SST field.…”

Section: Variable Type Variablementioning

confidence: 99%

High-Resolution Seamless Daily Sea Surface Temperature Based on Satellite Data Fusion and Machine Learning over Kuroshio Extension

Jung

Yoo

2022

Remote Sensing

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Sea SurfaceTemperature (SST) is a critical parameter for monitoring the marine environment and understanding various ocean phenomena. While SST can be regularly retrieved from satellite data, it often suffers from missing data due to various reasons including cloud contamination. In this study, we proposed a novel two-step data fusion framework for generating high-resolution seamless daily SST from multi-satellite data sources. The proposed approach consists of (1) SST reconstruction based on Data Interpolate Convolutional AutoEncoder (DINCAE) using the SSTs derived from two satellite sensors (i.e., Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Microwave Scanning Radiometer 2(AMSR2)), and (2) SST improvement through data fusion using random forest for consistency with in situ measurements with two schemes (i.e., scheme 1 using the reconstructed MODIS SST variables and scheme 2 using both MODIS and AMSR2 SST variables). The proposed approach was evaluated over the Kuroshio Extension in the Northwest Pacific, where a highly dynamic SST pattern can be found, from 2015 to 2019. The results showed that the reconstructed MODIS and AMSR2 SSTs through DINCAE yielded very good performance with Root Mean Square Errors (RMSEs) of 0.85 and 0.60 °C and Mean Absolute Errors (MAEs) of 0.59 and 0.45 °C, respectively. The results from the second step showed that scheme 2 and scheme 1 produced RMSEs of 0.75 and 0.98 °C and MAEs of 0.53 and 0.68 °C, respectively, compared to the in situ measurements, which proved the superiority of scheme 2 using multi-satellite data sources. Scheme 2 also showed comparable or even better performance than two operational SST products with similar spatial resolution. In particular, scheme 2 was good at simulating features with fine resolution (~50 km). The proposed approach yielded promising results over the study area, producing seamless daily SST products with high quality and high feature resolution.

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“…In the data assimilation analysis, the errors components on different temporal and spatial scales are related to scale aliasing and can negatively impact different scales analyses to a certain extent (Li et al, 2015a, 2015b). The advantages of scale‐selective data assimilation were widely researched by previous studies to eliminate scale aliasing (Beckers et al, 2014; Levin et al, 2018; Peng et al, 2010). Oceanic processes in NWTPO on intraseasonal and low‐frequency time scales respond to different controlling factors and thus different horizontal spatial scales (Kashino et al, 2011; Kessler, 1990; Meyers, 1979; Qiu & Lukas, 1996; Wang, Li, et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Impact of Assimilation of Moored Velocity Data on Low‐Frequency Current Estimation in Northwestern Tropical Pacific

et al. 2020

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This study investigates the impact of assimilation of Northwestern Tropical Pacific Ocean (NWTPO) moored currents observation system on low‐frequency (over a period of more than 90 days) current estimation. A low‐frequency scale‐selective ensemble optimal interpolation (EnOI) scheme is designed to assimilate the horizontally sparse moored currents into an eddy‐permitting ocean model. In this assimilation scheme, low‐frequency current signals are selected by removing intraseasonal signals in both observation and ensemble data, and a horizontal spatial filter is implemented to match the model forecast to the low‐frequency ensemble and observation. The assimilation of currents efficiently resolves simulated low‐frequency current displacements, especially for the Equatorial Undercurrent (EUC), the North Equatorial Countercurrent (NECC), and the North Equatorial Current (NEC). More realistic intensities of the NWTPO low‐frequency currents are also reproduced. With respect to the moored velocity profiles, the root‐mean‐square error (RMSE) of the simulated low‐frequency zonal velocity above a 500‐m depth is reduced from 10.9 to 5.8 cm/s using this low‐frequency EnOI scheme. The low‐frequency variation of NWTPO currents is reproduced by correcting the structures of different baroclinic modes. The increments of zonal velocities mainly correspond to the first baroclinic mode for the NECC and NEC, while for the EUC, they correspond to the leading three and higher‐order baroclinic modes. The improvements of the simulated stratification and pressure gradient remark the role of the assimilation of moored currents in adjusting available potential energy.

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A method to generate fully multi-scale optimal interpolation by combining efficient single process analyses, illustrated by a DINEOF analysis spiced with a local optimal interpolation

Cited by 9 publications

References 39 publications

Impact of HF radar current gap-filling methodologies on the Lagrangian assessment of coastal dynamics

Impact of HF radar current gap-filling methodologies on the Lagrangian assessment of coastal dynamics

High-Resolution Seamless Daily Sea Surface Temperature Based on Satellite Data Fusion and Machine Learning over Kuroshio Extension

Impact of Assimilation of Moored Velocity Data on Low‐Frequency Current Estimation in Northwestern Tropical Pacific

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