Correction to: High-resolution surface salinity maps in coastal oceans based on geostationary ocean color images: quantitative analysis of river plume dynamics

The Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership (SNPP) satellite has been a reliable source of ocean color data products, including five moderate (M) bands and one imagery (I) band normalized water-leaving radiance spectra nLw(λ). The spatial resolutions of the M-band and I-band nLw(λ) are 750 m and 375 m, respectively. With the technique of convolutional neural network (CNN), the M-band nLw(λ) imagery can be super-resolved from 750 m to 375 m spatial resolution by leveraging the high spatial resolution features of I1-band nLw(λ) data. However, it is also important to enhance the spatial resolution of VIIRS-derived chlorophyll-a (Chl-a) concentration and the water diffuse attenuation coefficient at the wavelength of 490 nm (Kd(490)), as well as other biological and biogeochemical products. In this study, we describe our effort to derive high-resolution Kd(490) and Chl-a data based on super-resolved nLw(λ) images at the VIIRS five M-bands. To improve the network performance over extremely turbid coastal oceans and inland waters, the networks are retrained with a training dataset including ocean color data from the Bohai Sea, Baltic Sea, and La Plata River Estuary, covering water types from clear open oceans to moderately turbid and highly turbid waters. The evaluation results show that the super-resolved Kd(490) image is much sharper than the original one, and has more detailed fine spatial structures. A similar enhancement of finer structures is also found in the super-resolved Chl-a images. Chl-a filaments are much sharper and thinner in the super-resolved image, and some of the very fine spatial features that are not shown in the original images appear in the super-resolved Chl-a imageries. The networks are also applied to four other coastal and inland water regions. The results show that super-resolution occurs mainly on pixels of Chl-a and Kd(490) features, especially on the feature edges and locations with a large spatial gradient. The biases between the original M-band images and super-resolved high-resolution images are small for both Chl-a and Kd(490) in moderately to extremely turbid coastal oceans and inland waters, indicating that the super-resolution process does not change the mean values of the original images.

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Spatial Variations of Phytoplankton Biomass Controlled by River Plume Dynamics Over the Lower Changjiang Estuary and Adjacent Shelf Based on High-Resolution Observations

Frontiers in Marine Science ◽

10.3389/fmars.2020.587539 ◽

2020 ◽

Vol 7 ◽

Author(s):

Zhaoru Zhang ◽

Meng Zhou ◽

Yisen Zhong ◽

Guosen Zhang ◽

Shan Jiang ◽

...

Keyword(s):

High Resolution ◽

Phytoplankton Biomass ◽

River Plume ◽

Changjiang Estuary ◽

Spatial Variations ◽

Plume Dynamics

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Improved representation of river runoff in Estimating the Circulation and Climate of the Ocean Version 4 (ECCOv4) simulations: implementation, evaluation, and impacts to coastal plume regions

Geoscientific Model Development ◽

10.5194/gmd-14-1801-2021 ◽

2021 ◽

Vol 14 (3) ◽

pp. 1801-1819

Author(s):

Yang Feng ◽

Dimitris Menemenlis ◽

Huijie Xue ◽

Hong Zhang ◽

Dustin Carroll ◽

...

Keyword(s):

High Resolution ◽

Mississippi River ◽

River Runoff ◽

Mixed Layer Depth ◽

Skill Score ◽

River Plume ◽

Grid Resolution ◽

Sea Surface Salinity ◽

Model Resolution ◽

Surface Salinity

Abstract. In this study, we improve the representation of global river runoff in the Estimating the Circulation and Climate of the Ocean Version 4 (ECCOv4) framework, allowing for a more realistic treatment of coastal plume dynamics. We use a suite of experiments to explore the sensitivity of coastal plume regions to runoff forcing, model grid resolution, and grid type. The results show that simulated sea surface salinity (SSS) is reduced as the model grid resolution increases. Compared to Soil Moisture Active Passive (SMAP) observations, simulated SSS is closest to SMAP when using daily, point-source runoff (DPR) and the intermediate-resolution LLC270 grid. The Willmott skill score, which quantifies agreement between models and SMAP, yields up to 0.92 for large rivers such as the Amazon. There was no major difference in SSS for tropical and temperate coastal rivers when the model grid type was changed from the ECCO v4 latitude–longitude–polar-cap grid to the ECCO2 cube–sphere grid. We also found that using DPR forcing and increasing model resolution from the coarse-resolution LLC90 grid to the intermediate-resolution LLC270 grid elevated the river plume area, volume, stabilized the stratification and shoal the mixed layer depth (MLD). Additionally, we find that the impacts of increasing model resolution from the intermediate-resolution LLC270 grid to the high-resolution LLC540 grid are regionally dependent. The Mississippi River Plume is more sensitive than other regions, possibly because the wider and shallower Texas–Louisiana shelf drives a stronger baroclinic effect, as well as relatively weak sub-grid vertical mixing and adjustment in this region. Since rivers deliver large amounts of freshwater and anthropogenic materials to coastal regions, improving the representation of river runoff in global, high-resolution models will advance studies of coastal hypoxia, carbon cycling, and regional weather and climate and will ultimately help to predict land–ocean–atmospheric feedbacks seamlessly in the next generation of Earth system models.

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Quantitative Analysis of Pharmaceutical Drugs Using a Combination of Acoustic Levitation and High Resolution Mass Spectrometry

Analytical Chemistry ◽

10.1021/acs.analchem.1c00762 ◽

2021 ◽

Vol 93 (15) ◽

pp. 6019-6024

Author(s):

Sebastian van Wasen ◽

Yi You ◽

Sebastian Beck ◽

Jens Riedel ◽

Dietrich A. Volmer

Keyword(s):

Mass Spectrometry ◽

Quantitative Analysis ◽

High Resolution ◽

High Resolution Mass Spectrometry ◽

Acoustic Levitation ◽

Pharmaceutical Drugs ◽

High Resolution Mass ◽

Resolution Mass

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Ocean Color Hyperspectral Remote Sensing With High Resolution and Low Latency--The HYPSO-1 CubeSat Mission

IEEE Transactions on Geoscience and Remote Sensing ◽

10.1109/tgrs.2021.3080175 ◽

2021 ◽

pp. 1-19

Author(s):

Mariusz E. Grotte ◽

Roger Birkeland ◽

Evelyn Honore-Livermore ◽

Sivert Bakken ◽

Joseph L. Garrett ◽

...

Keyword(s):

Remote Sensing ◽

High Resolution ◽

Ocean Color ◽

Hyperspectral Remote Sensing ◽

Low Latency

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Matchup Characteristics of Sea Surface Salinity Using a High-Resolution Ocean Model

Remote Sensing ◽

10.3390/rs13152995 ◽

2021 ◽

Vol 13 (15) ◽

pp. 2995

Author(s):

Frederick M. Bingham ◽

Severine Fournier ◽

Susannah Brodnitz ◽

Karly Ulfsax ◽

Hong Zhang

Keyword(s):

High Resolution ◽

Time Window ◽

Single Point ◽

Ocean Model ◽

Sea Surface ◽

Sea Surface Salinity ◽

Surface Salinity ◽

Argo Floats ◽

Statistical Measures ◽

Salinity Difference

Sea surface salinity (SSS) satellite measurements are validated using in situ observations usually made by surfacing Argo floats. Validation statistics are computed using matched values of SSS from satellites and floats. This study explores how the matchup process is done using a high-resolution numerical ocean model, the MITgcm. One year of model output is sampled as if the Aquarius and Soil Moisture Active Passive (SMAP) satellites flew over it and Argo floats popped up into it. Statistical measures of mismatch between satellite and float are computed, RMS difference (RMSD) and bias. The bias is small, less than 0.002 in absolute value, but negative with float values being greater than satellites. RMSD is computed using an “all salinity difference” method that averages level 2 satellite observations within a given time and space window for comparison with Argo floats. RMSD values range from 0.08 to 0.18 depending on the space–time window and the satellite. This range gives an estimate of the representation error inherent in comparing single point Argo floats to area-average satellite values. The study has implications for future SSS satellite missions and the need to specify how errors are computed to gauge the total accuracy of retrieved SSS values.

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