Comparison of Satellite‐Derived Sea Surface Salinity Products from SMOS, Aquarius, and SMAP

Bao, Senliang; Wang, Huizan; Ren, Zhaohui; Yan, Hengqian; Chen, Jian

doi:10.1029/2019jc014937

Cited by 71 publications

(74 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Within the Arabian Sea, there is a consistent improvement in SMAP statistical skills from the west, where the Gulfs of Aden and Oman are, to the east (Figure 8). Offshore the western boundary, all products show that SMAP captures SSS in the Arabian Sea with similar performance to the global evaluations [3,8], in which correlation in open water away from the coast is above 0.7 and rmsd was less than 0.2…”

Section: Discussionmentioning

confidence: 58%

“…and histograms were calculated for each SMAP product for the collocated datasets to assess SMAP performance in the North Indian Ocean in both the spatial and temporal domains. These statistics are generally used in studies evaluating satellite salinity observations [1,3,8,9,12,13,[16][17][18]21,61].…”

Section: Collocation Between Smap and In Situ Observations And Statismentioning

confidence: 99%

See 1 more Smart Citation

Statistical Assessment of Sea-Surface Salinity from SMAP: Arabian Sea, Bay of Bengal and a Promising Red Sea Application

Menezes

2020

Remote Sensing

View full text Add to dashboard Cite

Sea-surface salinity (SSS) is an essential climate variable connected to Earth's hydrological cycle and a dynamical component of ocean circulation, but its variability is not well-understood. Thanks to Argo floats, and the first decade of salinity remote sensing, this is changing. While satellites can retrieve salinity with some confidence, accuracy is regionally dependent and challenging within 500-1000 km offshore. The present work assesses the first four years of the National Aeronautics and Space Administration's Soil Moisture Active Passive (SMAP) satellite in the North Indian Ocean. SMAP's improved spatial resolution, better mitigation for radio-frequency interference, and land contamination make it particularly attractive to study coastal areas. Here, regions of interest are the Bay of Bengal, the Arabian Sea, and the extremely salty Red Sea (the last of which has not yet received attention). Six SMAP products, which include Levels 2 and 3 data, were statistically evaluated against in situ measurements collected by a variety of instruments. SMAP reproduced SSS well in both the Arabian Sea and the Bay of Bengal, and surprisingly well in the Red Sea. Correlations there were 0.81-0.93, and the root-mean-square difference was 0.38-0.67 for Level 3 data.

show abstract

Section: Discussionmentioning

confidence: 58%

Section: Collocation Between Smap and In Situ Observations And Statismentioning

confidence: 99%

Statistical Assessment of Sea-Surface Salinity from SMAP: Arabian Sea, Bay of Bengal and a Promising Red Sea Application

Menezes

2020

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…[4]) which are commonly used for this purpose are derived from measurements at an average scale of 1 measurement per 3 • ×3 • square per 10 days. For the most part, validation of satellite SSS has been done comparing values to individual in situ measurements such as Argo floats [5][6][7]. Floats come to the surface in relatively random locations, and, if close enough in space and time, can be matched up with satellite samples [8].…”

Section: Introductionmentioning

confidence: 99%

“…The satellite estimate of an SSS snapshot at point E is equivalent to averaging the SSS over a region, the central part of which we will call the 'footprint'. in situ measurements such as Argo floats [5][6][7]. Floats come to the surface in relatively random locations, and, if close enough in space and time, can be matched up with satellite samples [8].…”

Section: Introductionmentioning

confidence: 99%

Subfootprint Variability of Sea Surface Salinity Observed during the SPURS-1 and SPURS-2 Field Campaigns

Bingham

2019

Remote Sensing

View full text Add to dashboard Cite

Subfootprint variability (SFV), variability within the footprint of a satellite measurement, is a source of error associated with the validation process, especially for a satellite measurement with a large footprint such as those measuring sea surface salinity (SSS). This type of error has not been adequately quantified in the past. In this study, I have examined SFV using in situ ocean data from the SPURS-1 (Salinity Processes in the Upper ocean Regional Studies-1) and SPURS-2 field campaigns in the subtropical North Atlantic and eastern tropical North Pacific respectively. I computed SFV from these data over two one-year periods of intense sampling. The results show that SFV is highly seasonal. I have computed SFV errors in several different forms, a median value of the weekly snapshot error, a total snapshot error, an absolute error of the Aquarius and SMAP (Soil Moisture Active Passive) measurement, a part of that error associated with SFV and a bias due to the skewness of the distribution of SSS. These results are characteristic only of the particular regions studied. However, comparison of the results with high resolution models, and in situ data from moorings gives the possibility of getting global estimates of SFV from these other more common sources of SSS data.

show abstract

“…A number of other studies showed positive impacts due to the assimilation of SSS in controlled experiments, including improved upper ocean salinity (Vernieres et al, 2014), improved surface currents, mixed-layer depth, and barrier layer thickness (Chakraborty et al (2014(Chakraborty et al ( , 2015, and improved temporal variability of the vertical distribution of salinity in areas with large freshwater input (Seelanki et al, 2018). Still, the low temporal frequency of the data, large uncertainty estimates attributed to instantaneous observations, and large platform-specific biases (Bao et al, 2019), make the assimilation of SSS a continuing challenge. A nextgeneration technology that could produce SSS observations with the frequency, accuracy, and coverage of SST observations would be a high-impact capability.…”

Section: Prediction At Subseasonal To Seasonal Timescalesmentioning

confidence: 99%

Observational Needs for Improving Ocean and Coupled Reanalysis, S2S Prediction, and Decadal Prediction

et al. 2019

View full text Add to dashboard Cite

Developments in observing system technologies and ocean data assimilation (DA) are symbiotic. New observation types lead to new DA methods and new DA methods, such as coupled DA, can change the value of existing observations or indicate where new observations can have greater utility for monitoring and prediction. Practitioners of DA are encouraged to make better use of observations that are already available, for example, taking advantage of strongly coupled DA so that ocean observations can be used to improve atmospheric analyses and vice versa. Ocean reanalyses are useful for the analysis of climate as well as the initialization of operational long-range prediction models. There are many remaining challenges for ocean reanalyses due to biases and abrupt changes in the ocean-observing system throughout its history, the presence of biases and drifts in models, and the simplifying assumptions made in DA solution methods. From a governance point of view, more support is needed to bring the ocean-observing and DA communities together. For prediction applications, there is wide agreement that protocols are needed for rapid communication of oceanobserving data on numerical weather prediction (NWP) timescales. There is potential for new observation types to enhance the observing system by supporting prediction on multiple timescales, ranging from the typical timescale of NWP, covering hours to weeks, out to multiple decades. Better communication between DA and observation communities is encouraged in order to allow operational prediction centers the ability to provide guidance for the design of a sustained and adaptive observing network.

show abstract

Comparison of Satellite‐Derived Sea Surface Salinity Products from SMOS, Aquarius, and SMAP

Cited by 71 publications

References 46 publications

Statistical Assessment of Sea-Surface Salinity from SMAP: Arabian Sea, Bay of Bengal and a Promising Red Sea Application

Statistical Assessment of Sea-Surface Salinity from SMAP: Arabian Sea, Bay of Bengal and a Promising Red Sea Application

Subfootprint Variability of Sea Surface Salinity Observed during the SPURS-1 and SPURS-2 Field Campaigns

Observational Needs for Improving Ocean and Coupled Reanalysis, S2S Prediction, and Decadal Prediction

Contact Info

Product

Resources

About