Search

Upcoming satellite missions will observe the sea surface height (SSH) fields at a very high spatial resolution, which has generated an urgent need to understand better how well geostrophy can represent the ocean current field at finer scales, particularly in coastal regions characterized by complex flow geometry. We conduct statistical and spectral analyses of high-resolution surface currents and SSHs off the Oregon coast to examine the relative contribution of geostrophy and ageostrophy in coastal ocean currents. We analyze forward numerical simulations based primarily on a regional ocean model (ROMS) and use regional observations of high-frequency radar (HFR)-derived surface currents and altimeter-derived geostrophic currents and a subset of global domain numerical simulations (MITgcm) as secondary resources. Regional submesoscale ageostrophic currents account for up to 50% of the total variance and are primarily associated with near-inertial currents and internal tides. Geostrophy becomes dominant at time scales longer than 3 to 10 days and at spatial scales longer than 50 km, and is dependent on the depth and distance from the coast in the cross-shore direction. Ageostrophy dominates in the near-inertial and super-inertial frequency bands, which correspond to near-inertial motions (Coriolis force dominates) and high-frequency internal waves/tides (pressure gradient dominates), respectively. Because of ageostrophy, it may not be possible to estimate submesoscale currents from SSHs obtained from upcoming satellite missions using the geostrophic relationship. Thus, other concurrent high-resolution in-situ observations such as HFR-derived surface currents, together with data assimilation techniques, should be used for constructive data integration to resolve submesoscale currents.

This study proposes an approach to estimate the ocean sea surface height signature of coherent internal tides from a 25-year along-track altimetry record, with a single inversion over time, resolving both internal tide contributions and mesoscale eddy variability. The inversion is performed on a reduced-order basis of topography and practically achieved with a conjugate gradient. The particularity of this approach is to mitigate the potential aliasing effects between mesoscales and internal tide estimation from the uneven altimetry sampling (observing the sum of these components) by accounting for their statistics simultaneously, while other methods generally use a prior mesoscale. The four major tidal components are considered (M2, K1, S2, O1) over the period 1992–2017 on a global configuration. From the solution, we use altimetry data after 2017 for independent validation in order to evaluate the performance of the simultaneous inversion and compare it with an existing model.

Severe ice conditions in the Bohai Sea could cause serious harm to maritime traffic, offshore oil exploitation, aquaculture, and other economic activities in the surrounding regions. In addition to providing sea ice forecasts for disaster prevention and risk mitigation, sea ice numerical models could help explain the sea ice variability within the context of climate change in marine ecosystems, such as spotted seals, which are the only ice-dependent animal that breeds in Chinese waters. Here, we developed NEMO-Bohai, an ocean–ice coupled model based on the Nucleus for European Modelling of the Ocean (NEMO) model version 4.0 and Sea Ice Modelling Integrated Initiative (SI3) (NEMO4.0-SI3) for the Bohai Sea. This study will present the scientific design and technical choices of the parameterizations for the NEMO-Bohai model. The model was calibrated and evaluated with in situ and satellite observations of the ocean and sea ice. The model simulations agree with the observations with respect to sea surface height (SSH), temperature (SST), salinity (SSS), currents, and temperature and salinity stratification. The seasonal variation of the sea ice area is well simulated by the model compared to the satellite remote sensing data for the period of 1996–2017. Overall agreement is found for the occurrence dates of the annual maximum sea ice area. The simulated sea ice thickness and volume are in general agreement with the observations with slight overestimations. NEMO-Bohai can simulate seasonal sea ice evolution and long-term interannual variations. Hence, NEMO-Bohai is a valuable tool for long-term ocean and ice simulations and climate change studies.

The Regional Ocean Modeling System (ROMS), including an ice shelf component, has been applied on a circum-Antarctic domain to derive estimates of ice shelf basal melting. Significant improvements made compared to previous models of this scale are the inclusion of tides and a horizontal spatial resolution of 2 km, which is sufficient to resolve on-shelf heat transport by bathymetric troughs and eddy-scale circulation. We run the model with ocean–atmosphere–sea ice conditions from the year 2007 to represent nominal present-day climate. We force the ocean surface with buoyancy fluxes derived from sea ice concentration observations and wind stress from ERA-Interim atmospheric reanalysis. Boundary conditions are derived from the ECCO2 ocean state estimate; tides are incorporated as sea surface height and barotropic currents at the open boundary. We evaluate model results using satellite-derived estimates of ice shelf melting and established compilations of ocean hydrography. The Whole Antarctic Ocean Model (WAOM v1.0) qualitatively captures the broad scale difference between warm and cold regimes as well as many of the known characteristics of regional ice–ocean interaction. We identify a cold bias for some warm-water ice shelves and a lack of high-salinity shelf water (HSSW) formation. We conclude that further calibration and development of our approach are justified. At its current state, the model is ideal for addressing specific, process-oriented questions, e.g. related to tide-driven ice shelf melting at large scales.

Accurate digitization of synoptic ocean features is crucial for climate studies and the operational forecasting of ocean and coupled ocean-atmosphere systems. Today, for some North Atlantic operational regional models, skilled human experts visualize and extract the gulf stream and rings (warm and cold eddies) through an extensive and knowledge-based manual process. To automate this task, we develop a dynamics-inspired deep learning system that extracts the Gulf Stream and rings from concurrent satellite images of sea surface temperature (SST) and sea surface height (SSH). We pose the above problem as a multilabel semantic image segmentation problem. A novel deep convolutional neural network architecture named W-Net, with two parallel encoder-decoder branches, is developed to perform the segmentation. The W-Net's one branch is the SST branch (accepts SST image as input) and another is the SSH branch (accepts SSH as input), and the final output is a segmentation of gulf stream, warm rings, and cold rings. A dataset consisting of SST, SSH, and manual feature annotation (ground truth) from 2014 to 2018 is used for training. For gulf stream, we obtain 82.7% raw test accuracy and a low error of 4.39% in the detected path length. For the Rings, we obtain more than 71% raw eddy detection accuracy. A detailed ablation study and an examination of both SST and SSH parts of the network are presented to understand how the deep neural network learns to segment the gulf stream's meandering path and Rings accurately.

Marine forecasting techniques based on data-driven method generally treat each variable as independent and analyze the time series of a single and specific variable, while the real marine environment is the result of the interaction of multiple variables. In this letter, a data-driven method combining the empirical orthogonal function of multivariate (MEOF), complete ensemble empirical mode decomposition (CEEMD), and multilayer perceptron (MEOF-CEEMD-MLP in brief) is proposed to perform mid-term prediction of daily sea surface height anomaly (SSHA) and sea surface temperature (SST) simultaneously, considering that there is a correlation between them in the real marine environment. In this model, application of MEOF not only considers the correlation between SSHA and SST but also establishes the temporal and spatial relationship between discrete points, making predictions more accurate. A case study in the South China Sea (SCS) that predicts the daily SSHA and SST 30 days ahead shows that MEOF-CEEMD-MLP model is highly promising for mid-term daily prediction of SSHA and SST simultaneously. Also, the correlation between these two kinds of ocean variables can be simulated very well by this prediction model.

Sea surface height anomaly (SSHA) is vitally important for climate and marine ecosystems. This paper develops a multi-layer fusion recurrent neural network (MLFrnn) to achieve an accurate and holistic prediction of the SSHA field, given only as a series of past SSHA observations. The proposed approach learns long-term dependencies within the SSHA time series and spatial correlations among neighboring and remote regions. A new multi-layer fusion cell as the building block of MLFrnn model was designed, which fully fused spatial and temporal features. The daily average satellite altimeter SSHA data in the South China Sea from January 1, 2001, to May 13, 2019, were used to train and test the model. We performed a 21-day ahead SSHA prediction and our MLFrnn model has very high accuracy, with the RMSE of 0.027 m. Compared with existing deep learning networks, the proposed model was superior both in prediction performance and stability, especially on the wide-scale and long-term predictions.

Multiple CubeSat altimeters can work independently or corporately to form altimeter constellations. Different configurations of the constellations can acquire distinguished advantages: improved spatial/temporal sampling and high cross-track resolution, which will be helpful for observations of oceanic small-scale structures and weather forecasting. Compared to single conventional altimeters, CubeSat altimeter constellations may achieve better performances with lower costs. To fully understand these systems, this article focuses on the performance analysis and methodology for CubeSat altimeter constellations. Besides the typical analyses of the resolution, revisit, and absolute sea surface height (SSH) accuracy, the performance analysis was conducted by considering the characteristics of multiple measurements provided by CubeSat altimeter constellations. Local and global spatial sampling performances are investigated for various constellations and compared by sampling density and swath size. Moreover, relative SSH accuracy is introduced and evaluated based on the spatial structure functions of errors to effectively evaluate the measurement performance. Related system requirements on power, delta-v, etc., to achieve the performance are also discussed, which ensures that the analysis fits the boundary conditions of implementation. Finally, different concepts of the CubeSat altimeter constellations are compared, where their limitations and possible solutions are also discussed.

Sea surface height (SSH) prediction is theoretically and practically significant for global and regional ocean-related research. Numerous studies have been conducted to acquire accurate prediction results. However, most investigations on SSH ignore the importance of data at each time step on the prediction, which limits the accuracy of the final prediction. Therefore, a deep learning model combined Long Short-Term Memory (LSTM) network and Attention mechanism is proposed in this letter. This model integrates attention mechanism in both of time and space dimensions into LSTM. For time dimension, it assigns reasonable weight for data at each time step. For space dimension, it groups the data points close to each other, let model concentrate on points in the same group and eliminates the impact from other points. Daily absolute dynamic topography (ADT) in the South China Sea from January 2010 to December 2017 is adopted to conduct experiments. The proposed model demonstrates reliable results, the root mean square error is 0.38 cm, the mean absolute error is 0.0031, and the correlation coefficient reaches up to 0.999. The results show that the deep learning method based on attention mechanism is reliable for SSH prediction with high performance.

Radar altimeters have so far had limited use in the coastal zone, the area with most societal impact. This is due to both lack of, or insufficient accuracy in the necessary corrections, and more complicated altimeter signals. This letter examines waveform data from the Envisat RA-2 as it passes regularly over Pianosa (a 10-km2 island in the northwestern Mediterranean). Forty-six repeat passes were analyzed, with most showing a reduction in signal upon passing over the island, with weak early returns corresponding to the reflections from land. Intriguingly, one third of cases showed an anomalously bright hyperbolic feature. This feature may be due to extremely calm waters in the Golfo della Botte (northern side of the island), but the cause of its intermittency is not clear. The modeling of waveforms in such a complex land/sea environment demonstrates the potential for sea surface height retrievals much closer to the coast than is achieved by routine processing. The long-term development of altimetric records in the coastal zone will not only improve the calibration of altimetric data with coastal tide gauges but also greatly enhance the study of storm surges and other coastal phenomena.

The impact of assimilating simulated wide-swath altimetry observations from the upcoming Surface Water and Ocean Topography (SWOT) mission is assessed using observing system simulation experiments (OSSEs). These experiments use the Met Office 1.5 km resolution North West European Shelf analysis and forecasting system. In an effort to understand the importance of future work to account for correlated errors in the data assimilation scheme, we simulate SWOT observations with and without realistic correlated errors. These are assimilated in OSSEs along with simulated observations of the standard observing network, also with realistic errors added. It was found that while the assimilation of SWOT observations without correlated errors reduced the RMSE (root mean squared error) in sea surface height (SSH) and surface current speeds by up to 20 %, the inclusion of correlated errors in the observations degraded both the SSH and surface currents, introduced an erroneous increase in the mean surface currents and degraded the subsurface temperature and salinity. While restricting the SWOT data to the inner half of the swath and applying observation averaging with a 5 km radius negated most of the negative impacts, it also severely limited the positive impacts. To realise the full benefits in the prediction of the ocean mesoscale offered by wide-swath altimetry missions, it is crucial that methods to ameliorate the effects of correlated errors in the processing of the SWOT observations and account for the correlated errors in the assimilation are implemented.

We re-examined the slip distribution on faults of the 2004 Sumatra–Andaman (M 9.1 according to USGS) earthquake by the inversion of tsunami data with phase-corrected Green’s functions applied to linear long waves. The correction accounts for the effects of compressibility of seawater, elasticity of solid earth, and gravitational potential variation associated with the motion of mass to reproduce the delayed arrivals and the reversed phase of the first tsunami waves. We used sea surface height (SSH) data from satellite altimetry (SA) measurements along five tracks, and the tsunami waveforms recorded at tide gauges (TGs) and ocean bottom pressure gauges (OBPGs) in and around the Indian Ocean. The inversion results for both data sets for different rupture velocities (Vr) show that the reproducibility of the spatiotemporal SSHs and tsunami waveforms is improved by the phase corrections, although the effects are not so significant within the Indian Ocean. The best slip distribution model from joint inversion of SA, TG and OBPG data with Vr of 1.3 km/s shows the largest slips of 16–25 m off Sumatra Island, large slips of 2–11 m off the Nicobar Islands, and moderate slips of 2–6 m in the Andaman Islands. The inversion results reproduce the far-field tsunami waveforms well at distant stations even more than 13,000–25,000 km from the epicenter. The total source length is about 1400 km and the seismic moment is Mw 9.2, longer and larger than that of our previous estimates based on TG records.

In this study, daily outgoing longwave radiation (OLR) product is used to detect the atmospheric intraseasonal oscillation (ISO) in the eastern tropical Indian Ocean (TIO). A 50–80-day ISO is identified south of the equator, peaking in boreal winter and propagating eastward. The mechanisms underneath are investigated using observational data and reanalysis products. The results suggest that the 50–80-day atmospheric ISO is enhanced by ocean dynamic processes during December–January. Monsoon transition in October–November causes large wind variability along the equator. Equatorial sea surface height/thermocline anomalies appear off Sumatra due to the accumulative effects of the wind variability, leading the atmospheric 50–80-day ISO by ~ 5–6 weeks. The wind-driven ocean equatorial dynamics are reflected from the Sumatra coast as downwelling oceanic Rossby waves, which deepen the thermocline and contribute to the SST warming in the southeastern TIO, affecting local atmospheric conditions. It offers insights into the role of ocean dynamics in the intensification of 50–80-day atmospheric ISOs over the eastern TIO and explains the seasonal peak of the eastward-propagating ISO during boreal winter. These results have implications for intraseasonal predictability.

The interaction between waves, surges, and astronomical tides can lead to high coastal total water level (TWL), which can in turn trigger coastal flooding. Here, a high-resolution (1.5 km) simulation from a UK-focused regional coupled environmental prediction system is used to investigate the extreme events of winter 2013/4 around the UK and Irish coasts. The aim is to analyse the spatial distribution of coastal TWL and its components during this period by assessing (1) the relative contribution of different TWL components around the coast; (2) how extreme waves, surges, and tide interacted and if they occurred simultaneously; and (3) if this has implications in defining the severity of coastal hazard conditions. The TWL components' coastal distribution in winter 2013/4 was not constant in space, impacting differently over different regions. High (>90th percentile) waves and high surges occurred simultaneously at any tidal stage, including high tide (7.7 % of cases), but more often over the flood tide. During periods of high flood risk, a hazard proxy, defined as the sum of the sea surface height and half the significant wave height, at least doubled from average over three-quarters of the coast. These results have important implications for the risk management sector.

Variations of chlorophyll-a (chl-a), sea surface temperature (SST) and net primary productivity (NPP) along with fish catch were studied along the coastal waters off Karnataka, west coast of India from January 2007 to December 2016. These variations are linked with the Indian Ocean Dipole and El Nino/Southern Oscillation—Nino3.4 SST index. NPP was estimated by using vertically generalized production model. Change in NPP values over the decade (2007–2016) was studied in context of global reports of decline in productivity over the period. It is very important to understand the pattern of NPP variability as well the interrelationship with SST over a long period to study its impact on distribution of the fishery resources. Trend showed that there is reduction in productivity after 2012 and this trend continued in subsequent years. During the study period, an increasing trend in SST is observed along the Karnataka coast. The present study shows that a strong El Nino event was seen in 2015, along with declined productivity in the coastal waters off Karnataka by ~ 24% and increase in SST by ~ 1% from the average of 10 years (2007–2016). Increased SST and low ocean productivity resulted in reduced fish catch with the decline of 6.7% in total fish catch along the Karnataka coast from 2014 to 2015. In 2016, 5.29 lakh tones of fish catch, was estimated resulting in 19.6%, increase compared to 2015. During southwest monsoon, sea surface height anomalies (SSHa) showed negative anomaly trend and increased Ekman pumping resulted in upwelling over the study area.

In this study, the Nucleus European Modelling of Ocean (NEMO) based global ocean analysis and forecast system configured at National Centre for Medium Range Weather Forecasting (NCMWRF) is used to compute the upper ocean heat content up to 26° isotherm depth called as Tropical Cyclone Heat Potential (TCHP). NCMRWF has the real-time TCHP monitoring capability on daily basis. It is produced in real time using the global ocean forecast system up to 10 days using the ocean only model and up to 15 days using the coupled atmosphere-ocean model for monitoring the upper ocean and also for research purpose mainly for Tropical Cyclone (TC) study. Four pre-monsoon TCs with category above cyclonic storms in which two from Arabian Sea and two from Bay of Bengal are also examined. The Sea Surface Temperature drop is much more over the regions where the surface wind stress anomaly is strong. In both regions, various small scale warm and cold eddies are observed during TCs. TCHP anomaly is positive/negative over the region where Sea Surface Height (SSH) anomaly is positive/negative. From TCHP, it is manifest that the magnitude of TCHP anomaly is higher in BoB than AS region during TCs.

In this study, the Adriatic Sea and Coast (AdriSC) kilometre-scale atmosphere–ocean climate model covering the Adriatic Sea and northern Ionian Sea is presented. The AdriSC ocean results of a 31-year-long (i.e. 1987–2017) climate simulation, derived with the Regional Ocean Modeling System (ROMS) 3 km and 1 km models, are evaluated with respect to a comprehensive collection of remote sensing and in situ observational data. In general, it is found that the AdriSC model is capable of reproducing the observed sea surface properties, daily temperatures and salinities, and the hourly ocean currents with good accuracy. In particular, the AdriSC ROMS 3 km model demonstrates skill in reproducing the main variabilities of the sea surface height and the sea surface temperature, despite a persistent negative bias within the Adriatic Sea. Furthermore, the AdriSC ROMS 1 km model is found to be more capable of reproducing the observed thermohaline and dynamical properties than the AdriSC ROMS 3 km model. For the temperature and salinity, better results are obtained in the deeper parts than in the shallow shelf and coastal parts, particularly for the surface layer of the Adriatic Sea. The AdriSC ROMS 1 km model is also found to perform well in reproducing the seasonal thermohaline properties of the water masses over the entire Adriatic–Ionian domain. The evaluation of the modelled ocean currents revealed better results at locations along the eastern coast and especially the northeastern shelf than in the middle eastern coastal area and the deepest part of the Adriatic Sea. Finally, the AdriSC climate component is found to be a more suitable modelling framework to study the dense water formation and long-term thermohaline circulation of the Adriatic–Ionian basin than the available Mediterranean regional climate models.

The sea surface height data volume of the future wide-swath two-dimensional (2D) altimetric satellite is thousands of times greater than that of nadir altimetric satellites. The time complexity of the 2D altimetry mapping reaches O(n3). It is challenging to map the global grid products of future 2D altimetric satellites. In this study, to improve the efficiency of global data mapping, a new algorithm called parallel-dynamic interpolation (PA-DI) was designed. Through the use of 2D data segmentation and fine-grained data mosaic methods, the parallel along-track DI processes were accelerated, and a fast and efficient spatial-temporal high-resolution and low-error enhanced mapping method was obtained. As determined from a comparison of the single-threaded DI with the PA-DI, the new algorithm optimized the time complexity from O(n3) to O(n3/KL), which improved the mapping efficiency and achieved the expected results. According to the test results of the observing system simulation experiments, the PA-DI algorithm may provide an efficient and reliable method for future wide-swath 2D altimetric satellite mapping.

The throughflow in the Canadian Arctic Archipelago (CAA) had a significant impact on the North Atlantic Ocean with the Arctic climate change. The findings of physical mechanisms driving the throughflow in the CAA differed and few studies about the impact of sea level pressure (SLP) on the CAA throughflow were made. A high-resolution ice-ocean coupled Arctic Ocean Finite-Volume Community Ocean Model (AO-FVCOM) was used over the period 1978–2016 to examine the interannual and seasonal variability of the outflows in the CAA and the mechanism of SLP in driving the variation of the CAA throughflow quantitively. The simulated volume transport through Davis Strait, Nares Strait, Lancaster Sound and Jones Sound showed consistent increasing trends over 1978–2016 and the larger flux in winter and spring than in summer and fall. The variation of volume transport through Nares Strait contributed more than Lancaster and Jones Sound to the variation through Davis Strait. Five process-oriented experiments were made to further explore the role of SLP in setting up and controlling the sea surface height (SSH) difference and thus the throughflow transport in the CAA. The SLP was a primary forcing to control the SSH difference and the outflow transport compared with the wind forcing. The memory of the SSH to the SLP was short and an equilibrium state could be reached if the SLP varied with time. The upstream and downstream SLP difference, however, made a slight direct contribution to driving the volume transport of the CAA throughflow. In addition to the external forcing of SLP and wind, the variability of the CAA outflow was also influenced by the variability of the inflow/outflow and SSH on boundaries connected to the Pacific and Atlantic Oceans. The choice of SLP datasets from CORE-v2, ECMWF and NCEP was sensitive to the simulated uncertainty of volume transport.

A divide-and-conquer (DAC) machine learning approach was first proposed by Wang et al. to forecast the sea surface height (SSH) of the Loop Current System (LCS) in the Gulf of Mexico. In this DAC approach, the forecast domain was divided into non-overlapping partitions, each of which had their own prediction model. The full domain SSH prediction was recovered by interpolating the SSH across each partition boundaries. Although the original DAC model was able to predict the LCS evolution and eddy shedding more than two months and three months in advance, respectively, growing errors at the partition boundaries negatively affected the model forecasting skills. In the study herein, a new partitioning method, which consists of overlapping partitions is presented. The region of interest is divided into 50%-overlapping partitions. At each prediction step, the SSH value at each point is computed from overlapping partitions, which significantly reduces the occurrence of unrealistic SSH features at partition boundaries. This new approach led to a significant improvement of the overall model performance both in terms of features prediction such as the location of the LC eddy SSH contours but also in terms of event prediction, such as the LC ring separation. We observed an approximate 12% decrease in error over a 10-week prediction, and also show that this method can approximate the location and shedding of eddy Cameron better than the original DAC method.

A deep-learning-based method, called ConvLSTMP3, is developed to predict the sea surface heights (SSHs). ConvLSTMP3 is data-driven by treating the SSH prediction problem as the one of extracting the spatial-temporal features of SSHs, in which the spatial features are “learned” by convolutional operations while the temporal features are tracked by long short term memory (LSTM). Trained by a reanalysis dataset of the South China Sea (SCS), ConvLSTMP3 is applied to the SSH prediction in a region of the SCS east off Vietnam coast featured with eddied and offshore currents in summer. Experimental results show that ConvLSTMP3 achieves a good prediction skill with a mean RMSE of 0.057 m and accuracy of 93.4% averaged over a 15-d prediction period. In particular, ConvLSTMP3 shows a better performance in predicting the temporal evolution of mesoscale eddies in the region than a full-dynamics ocean model. Given the much less computation in the prediction required by ConvLSTMP3, our study suggests that the deep learning technique is very useful and effective in the SSH prediction, and could be an alternative way in the operational prediction for ocean environments in the future.

Oceanic geostrophic turbulence theory predicts significant inverse kinetic energy (KE) cascades at scales larger than the energy injection wavelength. However, the characteristics of the mesoscale variabilities associated with the inverse KE cascade in the real oceans have not been clear enough up to now. To further examine this problem, we analyzed the spectral characteristics of the oceanic mesoscale motions over the scales of inverse KE cascades based on high-resolution gridded altimeter data. The applicability of the quasigeostrophic (QG) turbulence theory and the surface quasigeostrophic (SQG) turbulence theory in real oceans is further explored. The results show that the sea surface height (SSH) spectral slope is linearly related to the eddy-kinetic-energy (EKE) level with a high correlation coefficient value of 0.67. The findings also suggest that the QG turbulence theory is an appropriate dynamic framework at the edge of high-EKE regions and that the SQG theory is more suitable in tropical regions and low-EKE regions at mid-high latitudes. New anisotropic characteristics of the inverse KE cascade are also provided. These results indicate that the along-track spectrum used by previous studies cannot reveal the dynamics of the mesoscale variabilities well.

This paper explores the impact of the individual components of a coastal ocean observing system on estimates of the circulation derived from a state-of-the-art analysis and forecast system for the Mid-Atlantic Bight and Gulf of Maine. The foundation of these activities is the Regional Ocean Modeling System 4-dimensional variational (4D-Var) data assimilation platform, which is run in support of the Mid-Atlantic Regional Association Coastal Ocean Observing System as part of the U.S. Integrated Ocean Observing System. The specific focus of this study is on the impact of remote sensing observations from both space- and land-based platforms on estimates of cross-shelf transport in the vicinity of the National Science Foundation Ocean Observatories Initiative Pioneer array. Sea surface temperature (SST) and sea surface height (SSH) were found to have, on average, a similar impact on the transport estimates. However, during a typical 3-day 4D-Var assimilation cycle, approximately two orders of magnitude more observations of SST than SSH are used in the model, and closer analysis shows that each altimeter measurement has approximately 50 times more impact on the transport estimates than an individual SST observation. This highlights the value of altimetry data for ocean state estimation, and the significance of expanding the altimeter constellation. The observations that are most impactful of all are in situ measurements of temperature and salinity, which have typically 3-4 times more impact than an individual SSH datum. A robust geographical distribution of the observation impacts emerges across a range of transport metrics which results from the combined influence of space-time dynamical interpolation and error covariance information within the 4D-Var system. The observation impact calculations suggest that High Frequency (HF) radar estimates of surface currents have relatively little direct influence on cross-shelf transport estimates. However, quantification of the sensitivity of these same estimates to changes in the observing system indicate that HF radar observations indirectly provide important information. This is understood in the current system by appealing to the idea of borrowing strength from the field of statistics in which some observations (satellite remote sensing in the case considered here) can borrow strength from other, seemingly less important observations.

Most of the part of India is already under water-stressed condition. In this regard, the continuous monitoring of the water levels (WL) and storage capacity of reservoirs, lakes, and rivers is very important for the estimation and utilization of water resources effectively. The long term ground observed WL of many of the water bodies is not easily available, which may be very critical for proper water resources management. Satellite radar altimetry is the remote sensing technique, which is being used to study sea surface height for the last 25 years. The advancement in radar technology with time has provided the opportunity to exploit the technique to retrieve the WL of inland water bodies. In the current study, an attempt has been made to generate long term time series on WL of around 29 geometrically complicated inland water bodies in India. These water bodies are mainly large reservoirs namely Ban Sagar, Balimela, Bargi, Bhakra, Gandhi Sagar, Hasdeo, Indravati, Jalaput, Kadana, Kolab, Mahi Bajaj, Maithon, Massanjore, Pong, Ramganga, Ranapratap Sagar, Rihand, Sardar Sarovar, Shivaji Sagar, Tilaiya, Ujjani, and Ukai. The WL of these water bodies was retrieved for around two decades using the European Remote-Sensing Satellite – 2 (ERS-2), ENVISAT Radar Altimeter – 2 (ENVISAT RA-2), and Saral-AltiKa altimeters data through Ice-1 retracking algorithm. Further, an attempt has also been made to estimate the WL of gauged/ungauged lakes namely Mansarovar, Pangong, Chilika, Bhopal, and Rann of Kutch over which Saral-AltiKa pass was there. As after July 2016, the SARAL-AltiKa is operating in the drifting orbit, systematic repeated observation of WL data of all reservoirs was not possible. The data of drifted tracks of Saral-AltiKa were tested for WL estimation of Ban Sagar reservoir. As the ERS-2, ENVISAT RA-2 and Saral-AltiKa all were having almost the same passing tracks, a long term WL series of these lakes could be generated from 1997 - 2016. However, at present only Sentinel – 3 is in orbit, the continuous altimeter based WL monitoring of some of these reservoirs (Gandhi Sagar, Nathsagar, Ranapratap, Ujjani, and Ukai) was attempted through Sentinel-3A satellite data from 2016 to 2018. The accuracy of the retrieved WL was than validated against the observed WL. In most of the reservoirs, a systematic bias was found due to the different characteristics and geoid height of each reservoir. The coefficient of determination, R2, value for a majority of reservoirs was as good as 0.9. In the case of ERS-2, the values of R2 varied for 0.44 to 0.97 with root mean square error (RMSE) in the range of 0.63 to 2.72m. These statistics improved with the ENVISAT RA-2 data analysis, the R2 value reached more than 0.90 for around 11 reservoirs. The highest, 0.99, for Hasdeo and Shivaji Sagar Reservoirs with RMSE of 0.44 and 0.56, respectively. Further, the accuracy improved with the analysis of Saral-AltiKa data. The R2 was always more than 0.9 for each reservoir and the lowest RMSE reduced to 0.03. Therefore, it can be said that the accuracy and consistency of WL retrieval through satellite altimetry has improved with time. Furthermore, the altimeter based retrieved WL may be used in hydrological studies and can contribute to better water resources management.

The investigation of ocean surface topography variation by low-resolution mode (LRM) altimeters at length scales shorter than 100 km is limited by cm-scale measurement noise. Spectral analysis of along-track altimeter data has been used to highlight the issue and to illustrate that one significant noise source stems from correlation between significant wave height (SWH) and range errors that is inherent to any waveform retracking algorithm. This paper focuses on improved characterization and reduction of these high-rate correlated errors that arise from this altimeter measurement process. Specifically, it addresses the effects of sea-state related errors in high rate (20-Hz) Jason-3 satellite sea level data that may possibly impact both short-scale and longer-scale data via interplay with the sea state bias (SSB) correction. Among the suite of standard corrections used to extract sea surface height (SSH) from the raw altimeter range, the empirical SSB term is designed to remove correlation between range and SWH measurements, but not explicitly short-scale error between the two. In this paper, we report that the efficacy of the SSB correction varies with wavelength and it does not remove all correlated signal at high wavenumbers. Following several recent studies, an independent high-frequency adjustment (HFA) is developed to remove these residual correlated errors. Both SSB and HFA are applied on SSH estimations at the 20-Hz rate, improving both 20-Hz and 1-Hz data. Jason-3 data are used as a test bed. Both corrections and net results are specific to MLE4 Jason-3 waveform retracking. Because the reported updated SSB models provide nearly unbiased continuity from Jason-1 to Jason-3 SSB time-series and the MLE4 retracking algorithm is the standard for these missions, the Jason-3 HFA solution can be applied to earlier Jason data with implications for improving the combined 16-year Jason record. By design, the HFA does not impact wavelengths greater than 200 km which is the acting domain of the SSB correction. Applying both the SSB and HFA corrections leads to a global SSH variance reduction nearing 35% in average. The SSH denoising approach through the computation of an additional HFA term is applicable for any LRM ocean altimeter.

In this paper we analyze the effect of increasing the posting rate of delay/Doppler altimeters on the retrieval of open ocean geophysical parameters. The conventional posting rate for synthetic aperture radar altimeters data is 20 Hz, which on-ground corresponds to the typical delay/Doppler along-track resolution of about 320 m. However, the speckle autocorrelation properties of delay/Doppler waveforms over the open ocean show a decorrelation distance much shorter than the typical along-track resolution, suggesting that further information can be extracted if more frequent observations of the surface were obtained. By processing one cycle of Sentinel-3A data at different posting rates we have verified that a relative improvement of more than 20% in the measurement noise of both sea surface height and significant wave height can be achieved, just by increasing the posting rate from 20 to 40 Hz, and an even further improvement is obtained at 80 Hz. We believe this result has significant implications for the current CryoSat-2 and Sentinel-3 missions and the upcoming Sentinel-6/Jason-CS mission.

The Surface Water Ocean Topography mission (SWOT), scheduled for launch in 2021, is the first space-borne radar interferometer capable of providing wide-swath height maps of water surfaces with centimetric precision. In addition to its primary objectives in oceanography and hydrography, the SWOT instrument offers opportunities for other applications. Here, we explore the feasibility of sea ice freeboard and sea surface height retrievals in the ice-covered oceans from SWOT data. The quality of SWOT height estimates depends on the backscatter strength and number of samples used for multi-looking. We use near-nadir radar backscatter estimates from sea ice and water over the range of SWOT incidence angles to simulate SWOT height maps and assess the retrieval precision under different backscatter, surface type and roughness conditions. Unlike wind-roughened open water, the available observations suggest that backscatter over sea ice has a moderate dependence on look angle (specularity), and the backscatter of younger, flatter sea ice has a greater degree of specularity than older, more deformed and colder sea ice. To achieve a similar freeboard precision to conventional altimeters (∼3 cm) requires averaging over 15–40 km2 in the near- to mid-swath and 90–175 km2 in the far-swath for lower northern latitudes (

An ensemble optimal interpolation (EnOI) data assimilation method is applied in the BCC_CSM1.1 to investigate the impact of ocean data assimilations on seasonal forecasts in an idealized twin experiment framework. Pseudo-observations of sea surface temperature (SST), sea surface height (SSH), sea surface salinity (SSS), temperature and salinity (T/S) profiles were first generated in a free model run. Then, a series of sensitivity tests initialized with predefined bias were conducted for a one-year period; this involved a free run (CTR) and seven assimilation runs. These tests allowed us to check the analysis field accuracy against the “truth”. As expected, data assimilation improved all investigated quantities; the joint assimilation of all variables gave more improved results than assimilating them separately. One-year predictions initialized from the seven runs and CTR were then conducted and compared. The forecasts initialized from joint assimilation of surface data produced comparable SST root mean square errors to that from assimilation of T/S profiles, but the assimilation of T/S profiles is crucial to reduce subsurface deficiencies. The ocean surface currents in the tropics were better predicted when initial conditions produced by assimilating T/S profiles, while surface data assimilation became more important at higher latitudes, particularly near the western boundary currents. The predictions of ocean heat content and mixed layer depth are significantly improved initialized from the joint assimilation of all the variables. Finally, a central Pacific El Nino was well predicted from the joint assimilation of surface data, indicating the importance of joint assimilation of SST, SSH, and SSS for ENSO predictions.

Satellite altimeter needs to be calibrated to evaluate the accuracy of sea surface height data. The dedicated altimeter calibration field needs to establish a special calibration strategy and needs to evaluate its calibration ability. This paper describes absolute calibration of HY-2B altimeter SSH using the GPS calibration method at the newly Wanshan calibration site, located in the Wanshan Islands, China. There are two HY-2B altimeter passes across the Wanshan calibration site. Pass No. 362 is descending and the ground track passes the east of Dan’gan Island. Pass No. 375 is ascending and crosses the Zhiwan Island. The GPS data processing strategy of Wanshan calibration site was established and the accuracy of GPS calibration method of Wanshan calibration site was evaluated. Meanwhile, the processing strategies of the HY-2B altimeter for the Wanshan calibration site were established, and a dedicated geoid model data were used to benefit the calibration accuracy. The time-averaged HY-2B altimeter bias was approximately 2.12 cm with a standard deviation of 2.08 cm. The performance of the HY-2B correction microwave radiometer was also evaluated in terms of the wet troposphere path delay and showed a mean difference −0.2 cm with a 1.4 cm standard deviation with respect to the in situ GPS radiosonde.

This study examines interannual variability in sea surface height (SSH) at southern midlatitudes of the Indian Ocean (10°–35°S). Our focus is on the relative role of local wind forcing and remote forcing from the equatorial Pacific Ocean. We use satellite altimetry measurements, an atmospheric reanalysis, and a one-dimensional wave model tuned to simulate observed SSH anomalies. The model solution is decomposed into the part driven by local winds and that driven by SSH variability radiated from the western coast of Australia. Results show that variability radiated from the Australian coast is larger in amplitude than variability driven by local winds in the central and eastern parts of the south Indian Ocean at midlatitudes (between 19° and 33°S), whereas the influence from eastern boundary forcing is confined to the eastern basin at lower latitudes (10° and 17°S). The relative importance of eastern boundary forcing at midlatitudes is due to the weakness of wind stress curl anomalies in the interior of the south Indian Ocean. Our analysis further suggests that SSH variability along the west coast of Australia originates from remote wind forcing in the tropical Pacific, as is pointed out by previous studies. The zonal gradient of SSH between the western and eastern parts of the south Indian Ocean is also mostly controlled by variability radiated from the Australian coast, indicating that interannual variability in meridional geostrophic transport is driven principally by Pacific winds.

To achieve better observation for sea surface, a new generation of wide-swath interferometric altimeter satellites is proposed. Before satellite launch, it is particularly important to study the data processing methods and carry out the detailed error analysis of ocean satellites, because it is directly related to the ultimate ability of satellites to capture ocean information. For this purpose, ocean eddies are considered a specific case of ocean signals, and it can cause significant changes in sea surface elevation. It is suitable for theoretical simulation of the sea surface and systematic simulation of the altimeter. We analyzed the impacts of random error and baseline error on the sea surface and ocean signals and proposed a combined strategy of low-pass filtering, empirical orthogonal function (EOF) decomposition, and linear fitting to remove the errors. Through this strategy, sea surface anomalies caused by errors were considerably improved, and the capability of satellite for capturing ocean information was enhanced. Notably, we found that the baseline error in sea surface height data was likely to cause inaccuracy in eddy boundary detection, as well as false eddy detection. These abnormalities could be prevented for “clean” sea surface height after the errors removal.

SARAL/AltiKA (SRL) is the first altimetry satellite with a Ka-band altimeter. To validate the advantages of the Ka-band altimeter over traditional Ku-band altimeters in marine geodetic applications, a comprehensive analysis is carried out over the South China Sea (SCS) (0–30° N, 105–125° E) from three aspects, namely the influence of load on waveforms, the precision of sea surface heights (SSHs), and the precision of altimeter-derived marine gravity field. Coastal waveforms of SRL, Jason-2, and CryoSat-2 are separately compared with corresponding ocean-type waveforms. The radius of coastal influence on SSHs of SRL/exact repeat mission (SRL/ERM) is the smallest, being about 3 km. Crossover discrepancies, global mean sea surface models, and tide gauge data are used to assess the precision of altimetric SSHs. Compared with the SSH precision of Ku-band Jason-2/ERM, the SSH precision of Ka-band SRL/ERM is 4.6% higher over the SCS and 10% higher in offshore areas. Gridded gravity anomalies are derived from measurements of SRL/drifting phase (SRL/DP) and CryoSat-2 through the inverse Vening-Meinesz formula, respectively. According to the assessment by shipborne gravity data and global marine gravity models, the precision of SRL/DP-derived gravity is higher than that of CryoSat-2-derived gravity over the SCS, especially in offshore areas. In some cycles, ground tracks of SRL/ERM have large drifting of more than 10 km from nominal tracks. The results show that the Ka-band altimeter of SRL has better precision in SSHs and marine gravity recovery than the Ku-band altimeter over the SCS, particularly in offshore areas.

High-precision observation of ocean surface topography is of great significance in studying the ocean mesoscale and sub-mesoscale processes. Spaceborne near-nadir interferometric synthetic aperture...

In Release 001 and 002 of the ICESat-2 sea ice products, candidate height segments used to estimate the reference sea surface height for freeboard calculations included two surface types: specular and smooth dark leads. We found that the uncorrected photon rates, used as proxies of surface reflectance, are attenuated due to clouds resulting in the potential misclassification of sea ice as dark leads, biasing the reference sea surface height relative to those derived from the more reliable specular returns. This results in higher reference sea surface heights and lower estimated ice freeboards. The resolution of available cloud flags from the ICESat-2 atmosphere data product is too coarse to provide useful filtering at the lead segment scale. In Release 003, we have modified the surface-reference-finding algorithm so that only specular leads are used. The consequence of this change can be seen in the composites of mean freeboard of the Arctic and Southern oceans. Broadly, coverages have decreased by ∼10–20 % because there are fewer leads (by excluding the dark leads), and the composite means have increased by 0–4 cm because of the use of more consistent specular leads.

The livelihoods of most residents of Tanga (Northern Tanzania) and Malindi (Northern Kenya), rely strongly on fishing activities in the East African shelf region. Thus, understanding variations in sea surface temperature (SST) and its related parameters such as thermocline depths and upper ocean circulation are crucial. This study applies a regional model to understand interannual spatial relationships between ocean circulation and SST off Northern Tanzania and on the Northern Kenya Bank. The results indicate slight differences in variations off the Northern Tanzanian shelf region and the Northern Kenya Bank. Such small variations might have local impacts on the human population through influencing primary productivity and fisheries. The coastal waters off Malindi indicate stronger variations, particularly in 1997 (cold SST) and 1998 (warm SST), than those off Tanga region. The SST anomalies seem to be associated with thermocline and sea surface height (SSH) off Malindi, while off Tanga they relate only to SSH. This information provides further understanding of parameters that may affect fishing activities in these regions and can be used for planning and management processes.

Future climate change in the western North Pacific at the end of the 21st century (2081–2100) was examined using a high-resolution regional ocean model (10-km mesh) under the RCP2.6 and RCP8.5 scenarios. The range of uncertainty in future projections was estimated from ensemble simulations. Projected results indicated no significant change in the Kuroshio net transport and the latitude of the Kuroshio Extension under both RCP scenarios; the changes were within the range of variability associated with the present climate. Projected sea surface temperature (SST) increased by as much as several degrees Celsius, especially in SST fronts, including the subarctic frontal zone. The significant increase of SST east of Japan was attributed to the northward expansion of the northern part of the subtropical gyre in response to basin-scale atmospheric changes. The projected area of sea ice in the Sea of Okhotsk decreased in both RCP scenarios. The projected offshore sea-level rise was larger in the subtropical gyre and smaller in the subpolar gyre. The sea-level rise along the coast of Japan, in contrast, showed no significant spatial variation. The mean sea-level rise along the coast of Japan was mostly comparable to the global mean sea-level rise.

The climate variability of the Kuroshio-Oyashio Extension (KOE) and North Pacific Transition Zone (NPTZ) exerts a strong control on marine populations that are sensitive to the strong productivity gradients between the subtropical and subpolar recirculation gyres. In observations, the relationship between KOE, NPTZ and productivity is evident in the first two dominant covariability modes between sea surface height and Chlorophyll-a anomalies, which are associated with a meridional shift in the location of the KOE (e.g. shift mode 1) and an intensification of the mean circulation of the KOE (e.g. intensification mode 2). To understand the projected impacts of anthropogenic forcing on the NPTZ, we examine these two dominant modes of variability in the Community Earth System Model Large Ensemble (CESM-LE) and an ensemble of climate models from the Coupled Model Intercomparing Project (CMIP5-E) under the Representative Concentration Pathways (RCP8.5). A significant poleward shift has been found in the KOE mean location associated with an equivalent shift of the Aleutian Low atmospheric pressure system. Superimposed to the changes of the mean, we find a significant increase (15–20%) in the variability of the shift mode in both the CESM-LE and CMPI5-E suggesting that variation in the meridional position of the NPTZ are also becoming stronger. These changes in variance of the shift mode are linked to an increase variability of the atmospheric forcing.

Variability of surface water masses of the Laptev and the East Siberian seas in August–September 2018 is studied using in situ and satellite data. In situ data were collected during the ARKTIKA-2018 expedition and then complemented with satellite-derived sea surface temperature (SST), salinity (SSS), sea surface height, wind speed, and sea ice concentration. The estimation of SSS fields is challenging in high-latitude regions, and the precision of soil moisture and ocean salinity (SMOS) SSS retrieval is improved by applying a threshold on SSS weekly error. For the first time in this region, the validity of DMI (Danish Meteorological Institute) SST and SMOS SSS products is thoroughly studied using ARKTIKA-2018 expedition continuous thermosalinograph measurements and conductivity–temperature–depth (CTD) casts. They are found to be adequate to describe large surface gradients in this region. Surface gradients and mixing of the river and the sea water in the ice-free and ice-covered areas are described with a special attention to the marginal ice zone at a synoptic scale. We suggest that the freshwater is pushed northward, close to the marginal ice zone (MIZ) and under the sea ice, which is confirmed by the oxygen isotope analysis. The SST-SSS diagram based on satellite estimates shows the possibility of investigating the surface water mass transformation at a synoptic scale and reveals the presence of river water on the shelf of the East Siberian Sea. The Ekman transport is calculated to better understand the pathway of surface water displacement on the shelf and beyond.

This article describes the development and ocean forecast evaluation of an atmosphere–ocean coupled prediction system for the Maritime Continent (MC) domain, which includes the eastern Indian and western Pacific oceans. The coupled system comprises regional configurations of the atmospheric model MetUM and ocean model NEMO at a uniform horizontal resolution of 4.5 km × 4.5 km, coupled using the OASIS3-MCT libraries. The coupled model is run as a pre-operational forecast system from 1 to 31 October 2019. Hindcast simulations performed for the period 1 January 2014 to 30 September 2019, using the stand-alone ocean configuration, provided the initial condition to the coupled ocean model. This paper details the evaluations of ocean-only model hindcast and 6 d coupled ocean forecast simulations. Direct comparison of sea surface temperature (SST) and sea surface height (SSH) with analysis, as well as in situ observations, is performed for the ocean-only hindcast evaluation. For the evaluation of coupled ocean model, comparisons of ocean forecast for different forecast lead times with SST analysis and in situ observations of SSH, temperature, and salinity have been performed. Overall, the model forecast deviation of SST, SSH, and subsurface temperature and salinity fields relative to observation is within acceptable error limits of operational forecast models. Typical runtimes of the daily forecast simulations are found to be suitable for the operational forecast applications.

With the ongoing climate change affecting the ocean, there is a need to understand and predict the future distributions of marine species in order to assess the sustainability of marine ecosystem. In this study, remotely-sensed satellite oceanographic data together with Indian mackerel (Rastrelliger kanagurta) fishery dataset were used to predict potential fishing grounds in the Exclusive Economic Zone (EEZ) of Peninsular Malaysia using boosted regression trees (BRT) model. The model was developed using three years (2008–2010) fish catch data and environmental variables of chlorophyll-a (chl-a), sea surface temperature (SST) and sea surface height (SSH). Result indicated that potential fishing grounds were closely associated with SSH, followed by SST and chl-a. The performance of the BRT model indicated acceptable fishing grounds prediction accuracy (AUC value of 0.749). Seasonal variability in fishing grounds was related to favorable environmental conditions of SSH (1.1–1.3 m), SST (29–32 °C) and chl-a (0.3–0.6 mg/m3). The projection of increases in SST due to climate change according to IPCC-AR5-RCPs was observed to influence the spatial and temporal distributions R. kanagurta. Increased temperature at 1.80 °C resulted in high potential catch areas for R. kanagurta in the EEZ. Meanwhile, elevated temperature at 2.60 °C and 3.30 °C showed decreased in potential catch areas for R. kanagurta in the EEZ. Most of the future fishing grounds area were projected to decline, and it was observed to shift outside the EEZ off South China Sea. Hence, by understanding these relationships, this analysis identifies where strategies can be adapted to face the ecological impacts under changing environmental conditions.

In this paper, a two-stage method is proposed for predicting the catch of skipjack tuna (Katsuwonus pelamis) from a 2D sea environmental pattern. Following the assumption that sea water temperature and sea surface height (SSH) which fishermen often use for finding fishing spots has a correlation with the skipjack tuna catch, a new approach of using Faster R-CNN in object detection is proposed. The proposed method consists of two part. In the first part, taking a sea temperature map as input, Faster R-CNN extracts the candidates of where skipjack tuna would be on the map in order to imitate the behaviors of fishers. In the second part, Support Vector Regression (SVR) estimates the catch amount in each candidate. Fater R-CNN is applied to several sea environmental patterns with three different loss functions and compares each performance. The proposed model is evaluated by comparing the result with average fishers’ ability on the skipjack tuna catches and several criteria for evaluating the proposed model. The results show that the proposed method is able to outperform the average fishers’ ability by an average of 3%.

In this paper we consider the different locations of Zhejiang (ZJ) provincial area including Hangzhou Bay (HZB), Ningbo (NB), Zhoushan (ZS), Taizhou (TZ) and Wenzhou (WZ) subareas to find out the sea level (SL) variations. Sea Surface Height Relative to Geoid over these areas has been obtained from the satellite altimetry data produced routinely by the European Centre for Medium-Range Weather Forecasting (ECMWF) and Archiving, Validation and Interpretation of Satellite Oceanographic (AVISO) data. Monthly, seasonal and interannual variations of SL over ZJ provincial areas have been presented and discussed. SL is indicating an increasing trend, however there is a decreasing trend between the years 1975 and 1987, in later years SL increasing. Over Zhejiang provincial areas revealing increasing trend in SL. Negative relation between SL and Sea surface temperature (SST) found over ZJ province areas.

Implementation of effective conservation planning relies on a robust understanding of the spatiotemporal distribution of the target species. In the marine realm, this is even more challenging for species rarely seen at the sea surface due to their extreme diving behavior like the sperm whales. Our study aims at (a) investigating the seasonal movements, (b) predicting the potential distribution, and (c) assessing the diel vertical behavior of this species in the Mascarene Archipelago in the south‐west Indian Ocean. Using 21 satellite tracks of sperm whales and eight environmental predictors, 14 supervised machine learning algorithms were tested and compared to predict the whales' potential distribution during the wet and dry season, separately. Fourteen of the whales remained in close proximity to Mauritius, while a migratory pattern was evidenced with a synchronized departure for eight females that headed towards Rodrigues Island. The best performing algorithm was the random forest, showing a strong affinity of the whales for sea surface height during the wet season and for bottom temperature during the dry season. A more dispersed distribution was predicted during the wet season, whereas a more restricted distribution to Mauritius and Reunion waters was found during the dry season, probably related to the breeding period. A diel pattern was observed in the diving behavior, likely following the vertical migration of squids. The results of our study fill a knowledge gap regarding seasonal movements and habitat affinities of this vulnerable species, for which a regional IUCN assessment is still missing in the Indian Ocean. Our findings also confirm the great potential of machine learning algorithms in conservation planning and provide highly reproductible tools to support dynamic ocean management., 21 satellite tracked sperm whales in the south‐west Indian Ocean. The use of 14 machine learning algorithms predicted probabilities of the sperm whale's distribution during the wet and dry seasons.

Short-wavelength (15–150 km) ocean phenomena are difficult to be observed by conventional altimeter due to the sampling method limited the resolution of the data. It is, therefore, the future wide-swath interferometric altimetry (WIA) missions like “SWOT” and “Guanlan” are designed to observe these phenomena. However, observing the short-wavelength dynamic process of the high eddy kinetic energy (EKE) region is still a challenge. The short-wavelength validation of WIA is also an urgent problem to be solved. In-situ observation platforms array can provide data set with high spatial and temporal resolution, which can capture short-wavelength dynamic process and validate the altimeter data. Simulation is a key to verify the feasibility of the observation strategy. In this article, we carried out simulated observation experiments based on high spatial-temporal resolution numerical models and introduced motion model of mooring and keep-station glider to improve the accuracy of in-situ observation simulation under the influence of the dynamic ocean currents which optimized by deep learning methods. In this experiment, the steric height, reconstructed by different observation array strategies, and the sea surface height of the measured positions are compared based on wavenumber spectrum. We tested the performance of the mooring array, station-keeping glider array, mooring and wave-energy profiling buoy (M&WPB) combination array, and multidevice array. The results show that the glider and M&WPB arrays have better performance of strong current regions. It could be helpful for the observation of dynamic processes in high EKE regions and has potential application for the validation of WIA.

Distributed space missions can offer significant advantages in cost and complexity over traditional, monolithic spacecraft. The ability of a distributed space mission to perform synthetic aperture radar interferometry for the purpose of sea surface height monitoring is assessed. A multiobjective genetic algorithm is used to vary payload distribution, payload parameters and orbital parameters and to explore the impact of these parameters on measurement error, mission cost, revisit time, and resilience to failure. It is shown that a distributed space mission offers an estimated savings of 26% compared to the upcoming monolithic Surface Water and Ocean Topography mission, suggesting that distributed space missions can meet requirements at similar or reduced costs while offering benefits such as reduced payload complexity and schedule flexibility. Additionally, an extension of the Helix formation to an arbitrary number of satellites is presented.

Due to the diffuse scattering on sea surface and dynamic geometry of coastal global navigation satellite system-reflectometry, the altimetry performance is strongly dependent on elevation angle. As the elevation angle decreases, the bias in retrieved height using the peak of the derivative waveform and the fractional power point of the waveform leading edge increases. In this article, a multiparameter estimator is proposed to combine the peak of the derivative waveform and several fractional power points to obtain unbiased height. The simulated results show that the bias of the uncalibrated height using the proposed algorithm has no significant dependence on elevation angle, but the standard derivation for the low elevation angle case still is larger than that of the high elevation angle. In addition, the calibrated performance depends on the used fractional values so that proper fractional values should be chosen. The data from the Dongying experiment acquiring GPS L1 CA and L5 signal and the Qingdao experiment for BeiDou B1I signal are used to demonstrate the proposed algorithm. After calibrating using the proposed algorithm, regardless of GPS L1 CA, L5, or BeiDou B1I signal, the calibrated heights all are in agreement with in situ heights. Finally, the calibrated heights from the BeiDou B1I signal are used to retrieve sea surface height. The results show that retrieved sea surface heights from geostationary Earth orbit (GEO) and inclined geosynchronous orbit/middle Earth orbit (IGSO/MEO) satellites all appear in the same trend as that of the in situ ones; furthermore, GEO satellites can provide a better retrieved performance than IGSO/MEO satellites. Through averaging the heights from several satellites and smoothing with the half-hour span, compared to in situ sea surface height, the standard derivation of 0.67 m is obtained.

Swath altimetry is likely to revolutionize our ability to monitor and forecast ocean dynamics. To meet the requirements of the EU Copernicus Marine Service, a constellation of two wide-swath altimeters is envisioned for the long-term (post-2030) evolution of the Copernicus Sentinel 3 topography mission. A series of observing system simulation experiments (OSSEs) is carried out to quantify the expected performances. The OSSEs use a state-of-the-art high-resolution (1/12∘) global ocean data assimilation system similar to the one used operationally by the Copernicus Marine Service. Flying a constellation of two wide-swath altimeters will provide a major improvement of our capabilities to monitor and forecast the oceans. Compared to the present situation with three nadir altimeters flying simultaneously, the sea surface height (SSH) analysis and 7 d forecast error are globally reduced by about 50 % in the OSSEs. With two wide-swath altimeters, the quality of SSH 7 d forecasts is equivalent to the quality of SSH analysis errors from three nadir altimeters. Our understanding of ocean currents is also greatly improved (30 % improvements at the surface and 50 % at 300 m depth). The resolution capabilities will be drastically improved and will be closer to 100 km wavelength compared to about 250 km today. Flying a constellation of two wide-swath altimeters thus looks to be a very promising solution for the long-term evolution of the Sentinel 3 constellation and the Copernicus Marine Service.