Three-dimensional thermohaline structure estimation derived from HY-2 satellite data over the Maritime Silk Road and its applications
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Abstract: Estimated ocean subsurface fields derived from satellite observations provide potential data sources for operational marine environmental monitoring and prediction systems. This study employs a statistic regression reconstruction method, in combination with domestic autonomous sea surface height and sea surface temperature observations from the Haiyang-2 (HY-2) satellite fusion data, to establish an operational quasi-real-time three-dimensional (3D) temperature and salinity products over the Maritime Silk Road. These products feature a daily temporal resolution and a spatial resolution of 0.25° × 0.25° and exhibit stability and continuity. We have demonstrated the accuracy of the reconstructed thermohaline fields in capturing the 3D thermohaline variations through comprehensive statistical evaluations, after comparing them against Argo observations and ocean analysis data from 2022. The results illustrate that the reconstructed fields effectively represent seasonal variations in oceanic subsurface structures, along with structural changes resulting from mesoscale processes, and the upper ocean’s responses to tropical cyclones. Furthermore, the incorporation of HY-2 satellite observations notably enhances the accuracy of temperature and salinity reconstructions in the Northwest Pacific Ocean and marginally improves salinity reconstruction accuracy in the North Indian Ocean when compared to the World Ocean Atlas 2018 monthly climatology thermohaline fields. As a result, the reconstructed product holds promise for providing quasi-real-time 3D temperature and salinity field information to facilitate fast decision-making during emergencies, and also offers foundational thermohaline fields for operational ocean reanalysis and forecasting systems. These contributions enhance the safety and stability of ocean subsurface activities and navigation.
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Figure 1. Spatial coverage of the operational reconstruction system. The shaded parts are the altitude in the Maritime Silk Road. The black dots are the locations of Argo observation profiles for 2022. The red line is the trajectory of an Argo buoy (No. 5906510) between May 7, 2022, and December 31, 2022, with a black five-pointed star indicating the observation’s starting point.
Figure 2. Schematic of the method used to calculate the tropical cyclones (TC)-induced upper oceanic temperature anomalies. The black shaded part represents the study area employed for spatial composite centered on the TC location (the black dot). The spatial resolution of the grid points in this region is 10 km × 10 km. The blue-shaded part, centered around the blue dot, is the same as the black-shaded part, but for an earlier time. The colored dots are the track points at 6 h intervals for TC Malakas in 2022. The size and color of the dots are determined by the TC maximum wind speed (unit: m/s).
Figure 3. Vertical distributions of root mean square errors (RMSEs) for temperature (a) and salinity (b) of MODAS-reconstructed and WOA18 monthly climatology field in various ocean basins, and the vertical distributions of SS for the reconstructed temperature (black lines) and salinity (blue lines) fields in the NWP, NIO, and MSR, respectively (c). MODAS, Modular Ocean Data Assimilation System; WOA18, World Ocean Atlas 2018; NWP, Northwest Pacific Ocean; NIO, North Indian Ocean; MSR, Maritime Silk Road.
Figure 4. Spatial distributions of RMSET of MODAS-reconstructed (a) and WOA18 (b) monthly climatology temperature fields in every 4° × 4° bin, spatial distribution of temperature reconstruction SS in every 4° × 4° bin (c), and spatial distributions of RMSES of MODAS-reconstructed (d) and WOA18 (e) monthly climatology salinity fields in every 4° × 4° bin, spatial distribution of salinity reconstruction SS in every 4° × 4° bin (f). RMSE, root mean square error; MODAS, Modular Ocean Data Assimilation System; WOA18, World Ocean Atlas 2018.
Figure 5. The altitude (shaded part) in the Northwest Pacific Ocean (a) and North Indian Ocean (b), respectively; temperature and salinity vertical profiles for the Argo observed (red lines), the MODAS reconstructed (black solid line), and the WOA18 monthly climatology (black dashed line) in the upper 1 000 m (c). The solid red line in a is the horizontal location of the vertical sections shown in Figs 6 and 7. The red five-pointed stars in a and b represent the locations of the selected Argo observation profiles. a1–a5 and b1–b5 are the mark of these profiles. The temperature and salinity vertical profiles of a1–a5 are shown in the first and second rows in c, and those of b1–b5 showing in the third and last rows in c, respectively. MODAS, Modular Ocean Data Assimilation System; WOA18, World Ocean Atlas 2018.
Figure 6. Vertical sections of the MODAS-reconstructed temperature field (a), the GOPAF product’s temperature field (b), and the WOA18 monthly climatology temperature field (c) on the 15th day every two months from February to December 2022. The location of these vertical sections is shown in Fig. 5. MODAS, Modular Ocean Data Assimilation System; GOPAF, global ocean physics analysis and forecast; WOA18, World Ocean Atlas 2018.
Figure 7. Vertical sections of the MODAS-reconstructed salinity field (a), the GOPAF product’s salinity field (b), and the WOA18 monthly climatology salinity field (c) on the 15th day every two months from February to December 2022. The location of the these vertical sections is shown in Fig. 5. MODAS, Modular Ocean Data Assimilation System; GOPAF, global ocean physics analysis and forecast; WOA18, World Ocean Atlas 2018.
Figure 8. Vertical sections of the Argo-observed (a), the MODAS reconstructed (b), and the WOA18 monthly climatology (c) temperature as a function of depth and time; vertical sections of the Argo-observed (d), the MODAS reconstructed (e), and the WOA18 monthly climatology (f) salinity as a function of depth and time; vertical profiles of RMSET (black lines) and RMSES (red lines) for MODAS reconstructed (solid lines) and WOA18 monthly climatology fields (dashed lines) (g). MODAS, Modular Ocean Data Assimilation System; WOA18, World Ocean Atlas 2018; RMSE, root mean square error.
Figure 9. Partial snapshots of sea level anomalies (SLAs, shaded parts) and sea surface temperatures (SSTs, unit in ℃, black contours) during the observation period of Argo buoy No. 5906510 (a), vertical section of temperature anomaly (b) and salinity anomaly (c) of the Argo observation relative to the WOA18 monthly climatology field, and vertical section of temperature anomaly (d) and salinity anomaly (e) of the MODAS-reconstructed field relative to the WOA18 monthly climatology field. For a, the observation date is shown on the top of the subfigures, the green line is the observed trajectory of the buoy between May 7 and December 31, 2022, the green stars indicate the observed positions of the buoy during the specific month (corresponding to the month of the observation date), the black stars are the observed positions of the buoy on the observation date. WOA18, World Ocean Atlas 2018; MODAS, Modular Ocean Data Assimilation System.
Figure 10. Subsurface temperature anomaly within 400 km across the left-hand (distance less than 0) and right-hand (distance greater than 0) sides of the typhoon track before and after the typhoon’s passage. The green dashed line indicates the eye of the tropical cyclone.
Figure 11. A composite temperature anomaly within 400 km across the left-hand (distance less than 0) and right-hand (distance greater than 0) sides before and after the typhoon’s passage for all tropical cyclones (TCs) in the North Indian Ocean in 2022. The green dashed line indicates the eye of the TC.
Table 1. The mean RMEST and RMSES of MODAS reconstructed fields at various depth ranges in different regions
Depth/m RMEST/°C RMSES MSR NWP NIO MSR NWP NIO [0, 50)
0.77
(1.33)0.79
(1.41)0.69
(1.01)0.32
(0.34)0.31
(0.34)0.35
(0.33)[50, 150)
1.35
(1.89)1.31
(1.98)1.47
(1.54)0.20
(0.22)0.19
(0.22)0.23
(0.23)[150, 300)
1.32
(1.54)1.40
(1.66)0.97
(1.07)0.16
(0.16)0.14
(0.14)0.22
(0.20)[300, 900)
0.64
(0.81)0.70
(0.90)0.37
(0.40)0.09
(0.08)0.08
(0.08)0.10
(0.08)[900, 1 500)
0.17
(0.17)0.15
(0.16)0.23
(0.22)0.05
(0.05)0.05
(0.05)0.06
(0.05)[0, 1 500]
0.79
(1.09)0.81
(1.16)0.68
(0.79)0.17
(0.17)0.16
(0.17)0.19
(0.18)Note: The mean root mean square errors (RMSEs) of WOA18 monthly climatology fields are listed in the parenthesis. MODAS, Modular Ocean Data Assimilation System; WOA18, World Ocean Atlas 2018. 
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