Revisiting the Intraseasonal Variability of Chlorophyll-a in the Adjacent Luzon Strait With a New Gap-Filled Remote Sensing Data Set

Wang, Tianhao; Yu, Peng; Wu, Zelun; Lu, Wenfang; Liu, Xin; Li, Qian P.

doi:10.1109/tgrs.2021.3067646

Cited by 15 publications

(14 citation statements)

References 52 publications

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“…Oceanic phytoplankton blooms are impacted not only by multi‐scale physical processes (for example, Lu, Oey, et al., 2018; Lu et al., 2017; Wang et al., 2021), but also by biological processes. The high‐resolution model results show that phytoplankton mortality was mainly responsible for the decrement of Chl‐a during bloom, compared with other processes such as zooplankton grazing (Figure S6 in Supporting Information ).…”

Section: Discussionmentioning

confidence: 99%

Off‐Coast Phytoplankton Bloom in the Taiwan Strait During the Northeasterly Monsoon Wind Relaxation Period

et al. 2022

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This study applied cruise, model, and satellite data to analyze the off‐coast phytoplankton blooming during the late fall to early spring monsoon period in the Taiwan Strait when northeasterly wind prevails. Based on the composite and self‐organizing map analyses, the three data sets consistently show high chlorophyll‐a concentration near the along‐shore front during the down‐front northeasterly wind relaxation period while lower concentration when relatively strong wind is persistent. Meanwhile, the off‐coast blooming always coincides with intense near‐surface stratification when the northeasterly wind relaxes. Diagnoses of balanced Richardson number, Ertel potential vorticity and instability energy budget from high‐resolution cruise observations and model results demonstrate that vigorous submesoscale symmetric and baroclinic instabilities can develop near the along‐shore front under the down‐front NE wind. Diagnoses of modeled buoyancy and chlorophyll‐a budget equations further suggest the submesoscale instabilities lead to rapid near‐surface restratification and offshore stretching of the along‐shore front within the upper 10‐m of the mixed layer when the down‐front NE wind relaxes, favoring the surface 10‐m phytoplankton growth. As comparison, contribution of the larger‐scale advection related with geostrophic adjustment and Ekman transport to the chlorophyll‐a increment reached beyond the middle layer of ∼20‐m depth.

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Section: Discussionmentioning

confidence: 99%

Off‐Coast Phytoplankton Bloom in the Taiwan Strait During the Northeasterly Monsoon Wind Relaxation Period

et al. 2022

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“…In contrast, frequency domain is more suitable because of its concentrated signal distribution. DCT is an efficient algorithm to transform signal into frequency domain (Rao and Yip, 2014), which has been widely applied in image compression (Cintra and Bayer, 2011), geophysical data filtering (El-Mahallawy and Hashim, 2013), and remote sensing data reconstruction (Wang et al, 2012(Wang et al, , 2022aFredj et al, 2016;Pham et al, 2019). In our study, a novel self-supervised fusion method based on spatiotemporal DCT, i.e., S-STDCT, is developed for the fusion task, which fully adopts the spatiotemporal knowledge of self-correlation in GOSAT and OCO-2 products.…”

Section: Spatiotemporally Self-supervised Fusion Methodsmentioning

confidence: 99%

Seamless mapping of long-term (2010–2020) daily global XCO₂ and XCH₄ from GOSAT, OCO-2, and CAMS-EGG4 with a spatiotemporally self-supervised fusion method

Wang

Yuan

et al. 2023

Preprint

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Abstract. Precise and continuous monitoring on long-term carbon dioxide (CO2) and methane (CH4) over the globe is of great importance, which can help study global warming and achieve the goal of carbon neutrality. Nevertheless, the available observations of CO2 and CH4 from satellites are generally sparse, and current fusion methods to reconstruct their long-term values on a global scale are few. To address this problem, we propose a novel spatiotemporally self-supervised fusion method to establish long-term daily seamless XCO2 and XCH4 products from 2010 to 2020 over the globe at grids of 0.25°. A total of three datasets are applied in our study, including GOSAT, OCO-2, and CAMS-EGG4. Attributed to the significant sparsity of data from GOSAT and OCO-2, the spatiotemporal Discrete Cosine Transform is considered for our fusion task. Validation results show that the proposed method achieves a satisfactory accuracy, with the σ (R2) of ~ 1.18 ppm (> 0.9) and 11.3 ppb (0.9) for XCO2 and XCH4 against TCCON measurements, respectively. Overall, the performance of fused results distinctly exceeds that of CAMS-EGG4, which is also superior or close to those of GOSAT and OCO-2. Especially, our fusion method can effectively correct the large biases in CAMS-EGG4 due to the issues from assimilation data, such as the unadjusted anthropogenic emission inventories for COVID-19 lockdowns in 2020. Moreover, the fused results present coincident spatial patterns with GOSAT and OCO-2, which accurately display the long-term and seasonal changes of globally distributed XCO2 and XCH4. The daily global seamless gridded (0.25°) XCO2 and XCH4 from 2010 to 2020 can be freely accessed at http://doi.org/10.5281/zenodo.7388893 (Wang et al., 2022b).

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“…As a comparison, we performed the inpainting of the same data using the discrete cosine transform with penalized least-squares (DCT-PLS) method, a popular smoothing technique introduced by Garcia (2010). The method is capable of handling large areas of missing values and has been extensively used in the literature (see, e.g., Wang et al 2022). The automatic choice of the amount of smoothing is performed by minimizing the generalized cross-validation score, and a Matlab code is provided in Garcia (2010).…”

Section: Analysis Of Simulated Datamentioning

confidence: 99%

Reconstruction of Photospheric Velocity Fields from Highly Corrupted Data

Rempel

Chertovskih

Davletshina

et al. 2022

ApJ

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The analysis of the photospheric velocity field is essential for understanding plasma turbulence in the solar surface, which may be responsible for driving processes such as magnetic reconnection, flares, wave propagation, particle acceleration, and coronal heating. Currently, the only available methods to estimate velocities at the solar photosphere transverse to an observer’s line of sight infer flows from differences in image structure in successive observations. Due to data noise, algorithms such as local correlation tracking may lead to a vector field with wide gaps where no velocity vectors are provided. In this paper, a novel method for image inpainting of highly corrupted data is proposed and applied to the restoration of horizontal velocity fields in the solar photosphere. The restored velocity field preserves all the vector field components present in the original field. The method shows robustness when applied to both simulated and observational data.

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Revisiting the Intraseasonal Variability of Chlorophyll-a in the Adjacent Luzon Strait With a New Gap-Filled Remote Sensing Data Set

Cited by 15 publications

References 52 publications

Off‐Coast Phytoplankton Bloom in the Taiwan Strait During the Northeasterly Monsoon Wind Relaxation Period

Off‐Coast Phytoplankton Bloom in the Taiwan Strait During the Northeasterly Monsoon Wind Relaxation Period

Seamless mapping of long-term (2010–2020) daily global XCO₂ and XCH₄ from GOSAT, OCO-2, and CAMS-EGG4 with a spatiotemporally self-supervised fusion method

Reconstruction of Photospheric Velocity Fields from Highly Corrupted Data

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Revisiting the Intraseasonal Variability of Chlorophyll-a in the Adjacent Luzon Strait With a New Gap-Filled Remote Sensing Data Set

Cited by 15 publications

References 52 publications

Off‐Coast Phytoplankton Bloom in the Taiwan Strait During the Northeasterly Monsoon Wind Relaxation Period

Off‐Coast Phytoplankton Bloom in the Taiwan Strait During the Northeasterly Monsoon Wind Relaxation Period

Seamless mapping of long-term (2010–2020) daily global XCO2 and XCH4 from GOSAT, OCO-2, and CAMS-EGG4 with a spatiotemporally self-supervised fusion method

Reconstruction of Photospheric Velocity Fields from Highly Corrupted Data

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Product

Resources

About

Seamless mapping of long-term (2010–2020) daily global XCO₂ and XCH₄ from GOSAT, OCO-2, and CAMS-EGG4 with a spatiotemporally self-supervised fusion method