Validation and correction of sea surface salinity retrieval from SMAP

Qin, Sisi; Wang, Hui; Zhu, Jiang; Wan, Li‐Jun; Zhang, Yu; Wang, Haoyun

doi:10.1007/s13131-020-1533-0

Cited by 30 publications

(27 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…SSS data from the GTMBA have been used in the past for comparison with satellites. Most comparisons have been done at level 3 (L3; Bao et al, 2019;Tang et al, 2017;Qin et al, 2020;Tang et al, 2014), and some at L2 (Abe & Ebuchi, 2014;Kao et al, 2018a,b;Tang et al, 2014). For example, Bao et al (2019) computed RMS differences and bias between mooring, satellite (SMOS and SMAP) and in situ gridded (EN4; Good et al, 2013) data, where the mooring data used were 8-day moving averages.…”

Section: Introductionmentioning

confidence: 99%

“…Tang et al (2017), computed similar statistical comparisons between moorings and SMAP, again using 8day average values. Qin et al (2020) reported RMS error and bias between satellite SSS and a small set of moorings. While the GTMBA moorings have been a useful point of comparison for validation, as indicated by the number of studies we have just cited, they have not to date been used to study the process by which validation is carried out.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Sea Surface Salinity Short Term Variability in the Tropics

Bingham¹,

Brodnitz²

2021

Preprint

View full text Add to dashboard Cite

Abstract. Using data from the Global Tropical Moored Buoy Array we study the validation process for satellite measurement of sea surface salinity (SSS). We compute short-term variability (STV) of SSS, variability on time scales of 5–14 days. It is meant to be a proxy for subfootprint variability as seen by a satellite measuring SSS. We also compute representation error, which is meant to mimic the SSS satellite validation process where footprint averages are compared to pointwise in situ values. We present maps of these quantities over the tropical array. We also look at seasonality in the variability of SSS and find which months have maximum and minimum amounts. STV is driven at least partly by rainfall. Moorings exhibit larger STV during rainy periods than non-rainy ones. The same computations are also done using output from a high-resolution global ocean model to see how it might be used to study the validation process. The model gives good estimates of STV, in line with the moorings, though tending to have smaller values.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Sea Surface Salinity Short Term Variability in the Tropics

Bingham¹,

Brodnitz²

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Since then two other satellites have been launched by NASA that have measured SSS from space, Aquarius [2] and Soil Moisture Active Passive (SMAP) [3]. Validation of these datasets has occurred in a number of contexts by comparison with in situ data [4][5][6][7][8][9][10][11][12][13]. Typically, individual satellite measurements are compared with nearby in situ measurements such as individual Argo floats [4], or more commonly with gridded Argo products such as that of Roemmich et al [14] or the global Hybrid Coordinate Ocean Model (HYCOM) [3].…”

Section: Introductionmentioning

confidence: 99%

“…The high-quality standards, long record duration (some over 20 years-Figure 1) and location of the buoys in this array make them ideal platforms for validating satellite SSS measurements. Several groups have been making use of the GTMBA for this purpose [4,5,7,12,13,30]. However, to date there has been little explicit comparison of mooring and satellite SSS data at a seasonal time scale.…”

Section: Introductionmentioning

confidence: 99%

Sea Surface Salinity Seasonal Variability in the Tropics from Satellites, Gridded In Situ Products and Mooring Observations

Bingham

Brodnitz

2020

Remote Sensing

View full text Add to dashboard Cite

Satellite observations of sea surface salinity (SSS) have been validated in a number of instances using different forms of in situ data, including Argo floats, moorings and gridded in situ products. Since one of the most energetic time scales of variability of SSS is seasonal, it is important to know if satellites and gridded in situ products are observing the seasonal variability correctly. In this study we validate the seasonal SSS from satellite and gridded in situ products using observations from moorings in the global tropical moored buoy array. We utilize six different satellite products, and two different gridded in situ products. For each product we have computed seasonal harmonics, including amplitude, phase and fraction of variance (R2). These quantities are mapped for each product and for the moorings. We also do comparisons of amplitude, phase and R2 between moorings and all the satellite and gridded in situ products. Taking the mooring observations as ground truth, we find general good agreement between them and the satellite and gridded in situ products, with near zero bias in phase and amplitude and small root mean square differences. Tables are presented with these quantities for each product quantifying the degree of agreement.

show abstract

“…These may take the form of comparisons with gridded Argo products such as that of [5] or [6] ( [2,3,[7][8][9][10][11][12]). There may also be comparisons with individual observations such as floats, saildrones, thermosalinographs or moorings [8][9][10][13][14][15][16][17][18][19][20]. Comparisons may be done using satellite data at level 2 (L2) (e.g.…”

Section: Introductionmentioning

confidence: 99%

Matchup Characteristics of Sea Surface Salinity using a High-resolution Ocean Model

Bingham¹,

Fournier²,

Brodnitz³

et al. 2022

Preprint

View full text Add to dashboard Cite

Sea surface salinity (SSS) satellite measurements are validated using in situ observations 8 usually made by surfacing Argo floats. Validation statistics are computed using matched values of 9 SSS from satellites and floats. This study explores how the matchup process is done using a high- 10 resolution numerical ocean model, the MITgcm. One year of model output is sampled as if the 11 Aquarius and Soil Moisture Active Passive (SMAP) satellites flew over it and Argo floats popped 12 up into it. Statistical measures of mismatch between satellite and float are computed, RMS difference 13 (RMSD) and bias. The bias is small, less than 0.002 in absolute value, but negative with float values 14 being greater than satellites. RMSD is computed using an “all salinity difference” method that av- 15 erages level 2 satellite observations within a given time and space window for comparison with 16 Argo floats. RMSD values range from 0.08 to 0.18 depending on the space-time window and the 17 satellite. This range gives an estimate of the representation error inherent in comparing single point 18 Argo floats to area-average satellite values. The study has implications for future SSS satellite mis- 19 sions and the need to specify how errors are computed to gauge the total accuracy of retrieved SSS 20 values.

show abstract

Validation and correction of sea surface salinity retrieval from SMAP

Cited by 30 publications

References 27 publications

Sea Surface Salinity Short Term Variability in the Tropics

Sea Surface Salinity Short Term Variability in the Tropics

Sea Surface Salinity Seasonal Variability in the Tropics from Satellites, Gridded In Situ Products and Mooring Observations

Matchup Characteristics of Sea Surface Salinity using a High-resolution Ocean Model

Contact Info

Product

Resources

About