scholarly journals Data assimilation of SMOS observations into the Mercator Ocean operational system: focus on the Nino 2015 event

2018 ◽  
Author(s):  
Benoît Tranchant ◽  
Elisabeth Remy ◽  
Eric Greiner ◽  
Olivier Legalloudec
Keyword(s):  
Data Assimilation ◽  
Ocean Circulation ◽  
Positive Impact ◽  
Atmospheric Precipitation ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Global Ocean ◽  
Data Sets ◽  
The Impact ◽  
Over The Top

Abstract. Monitoring Sea Surface Salinity (SSS) is important for understanding and forecasting the ocean circulation. It is even crucial in the context of the acceleration of the water cycle. Until recently, SSS was one of the less observed essential ocean variables. Only sparse in situ observations, most often closer to 5 meters deep than the surface, were available to estimate the SSS. The recent satellite missions of ESA's SMOS, NASA's Aquarius, and now SMAP have made possible for the first time to measure SSS from space. The SSS drivers can be quite different than the temperature ones. The model SSS can suffer from significant errors coming not only from the ocean dynamical model but also the atmospheric precipitation and evaporation as well as ice melting and river runoff. Satellite SSS can bring a valuable additional constraint to control the model salinity. In the framework of the SMOS Nino 2015 ESA project (https://www.godae-oceanview.org/projects/smos-nino15/), the impact of satellite SSS data assimilation is assessed with the Met Office and Mercator Ocean global ocean analysis and forecasting systems with a focus on the Tropical Pacific region. This article presents the analysis of an Observing System Experiment (OSE) conducted with the 1/4° resolution Mercator Ocean analysis and forecasting system. SSS data assimilation constrains the model SSS to be closer to the observations in a coherent way with the other data sets already routinely assimilated in an operational context. Globally, the SMOS SSS assimilation has a positive impact in salinity over the top 30 meters. Comparisons to independent data sets show a small but positive impact. The sea surface height (SSH) has also been impacted by implying a reinforcement of TIWs during the El-Niño 2015/16 event. Finally, this study helped us to progress in the understanding of the biases and errors that can degrade the SMOS SSS performance.

scholarly journals Impact of Initialization Methods on the Predictive skill in NorCPM - An Arctic-Atlantic Case Study

2021 ◽  
Author(s):  
Leilane Passos ◽  
Helene Langehaug ◽  
Marius Årthun ◽  
Tor Eldevik ◽  
Ingo Bethke ◽  
...  
Keyword(s):  
Data Assimilation ◽  
North Atlantic ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
The Arctic ◽  
Complex Data ◽  
Surface Salinity ◽  
Norwegian Sea ◽  
Predictive Skill ◽  
The Impact

Abstract The skilful prediction of climatic conditions on a forecast horizon of months to decades into the future remains a main scientific challenge of large societal benefit. Here we assess the hindcast skill of the Norwegian Climate Prediction Model (NorCPM) – for sea surface temperature (SST) and sea surface salinity (SSS) in the Arctic-Atlantic region – focusing on the impact of different initialization methods. We find the skill to be distinctly larger for the Subpolar North Atlantic than for the Norwegian Sea, and generally for all lead years analyzed. For the Subpolar North Atlantic, there is furthermore consistent benefit in increasing the amount of data assimilated, and also in updating the sea ice based on SST with strongly coupled data assimilation. The predictive skill is furthermore significant for at least two model versions up to 8-10 lead years with the exception for SSS at the longer lead years. For the Norwegian Sea, significant predictive skill is more rare; there is relatively higher skill with respect to SSS than for SST. A systematic benefit from more complex data assimilation approach can not be identified for this region. Somewhat surprisingly, skill deteriorates quite consistently for both the Subpolar North Atlantic and the Norwegian Sea when going from CMIP5 to corresponding CMIP6 versions. We find this to relate to change in the regional performance of the underlying physical model that dominates the benefit from initialization.

scholarly journals How essential are Argo observations to constrain a global ocean data assimilation system?

Ocean Science ◽  
2016 ◽  
Vol 12 (1) ◽  
pp. 257-274 ◽  
Author(s):  
V. Turpin ◽  
E. Remy ◽  
P. Y. Le Traon
Keyword(s):  
Data Assimilation ◽  
Surface Temperature ◽  
Sea Surface ◽  
Global Ocean ◽  
Argo Data ◽  
In Situ Observations ◽  
The Impact ◽  
Data Assimilation System ◽  
Assimilation System

Abstract. Observing system experiments (OSEs) are carried out over a 1-year period to quantify the impact of Argo observations on the Mercator Ocean 0.25° global ocean analysis and forecasting system. The reference simulation assimilates sea surface temperature (SST), SSALTO/DUACS (Segment Sol multi-missions dALTimetrie, d'orbitographie et de localisation précise/Data unification and Altimeter combination system) altimeter data and Argo and other in situ observations from the Coriolis data center. Two other simulations are carried out where all Argo and half of the Argo data are withheld. Assimilating Argo observations has a significant impact on analyzed and forecast temperature and salinity fields at different depths. Without Argo data assimilation, large errors occur in analyzed fields as estimated from the differences when compared with in situ observations. For example, in the 0–300 m layer RMS (root mean square) differences between analyzed fields and observations reach 0.25 psu and 1.25 °C in the western boundary currents and 0.1 psu and 0.75 °C in the open ocean. The impact of the Argo data in reducing observation–model forecast differences is also significant from the surface down to a depth of 2000 m. Differences between in situ observations and forecast fields are thus reduced by 20 % in the upper layers and by up to 40 % at a depth of 2000 m when Argo data are assimilated. At depth, the most impacted regions in the global ocean are the Mediterranean outflow, the Gulf Stream region and the Labrador Sea. A significant degradation can be observed when only half of the data are assimilated. Therefore, Argo observations matter to constrain the model solution, even for an eddy-permitting model configuration. The impact of the Argo floats' data assimilation on other model variables is briefly assessed: the improvement of the fit to Argo profiles do not lead globally to unphysical corrections on the sea surface temperature and sea surface height. The main conclusion is that the performance of the Mercator Ocean 0.25° global data assimilation system is heavily dependent on the availability of Argo data.

Improving Ocean Circulations Using Lagrangian Data Assimilation of Surface Drifters During Grand Lagrangian Deployments

2021 ◽  
Author(s):  
Luyu Sun ◽  
Stephen Penny ◽  
Matthew Harrison
Keyword(s):  
Data Assimilation ◽  
Ocean Circulation ◽  
World Ocean ◽  
Global Ocean ◽  
Observation System ◽  
Geophysical Fluid Dynamics Laboratory ◽  
Near Surface ◽  
Lagrangian Data ◽  
Surface Drifters ◽  
The Impact

<p>Accurate forecast of ocean circulation is important in many aspects. A lack of direct ocean velocity observations has been one of the overarching issues in nowadays operational ocean data assimilation (DA) system. Satellite-tracked surface drifters, providing measurement of near-surface ocean currents, have been of increasing importance in global ocean observation system. In this work, the impact of an augmented-state Lagrangian data assimilation (LaDA) method using Local Ensemble Transform Filter (LETKF) is investigated within a realistic ocean DA system. We use direct location data from 300 surface drifters released in the Gulf of Mexico (GoM) by the Consortium for Advanced Research on Transport of Hydrocarbon in the Environment (CARTHE) during the summer 2012 Grand Lagrangian Deployment (GLAD) experiment. These drifter observations are directly assimilated into a realistic eddy-resolving GoM configuration of the Modular Ocean Model version 6 (MOM6) of the Geophysical Fluid Dynamics Laboratory (GFDL). Ocean states (T/S/U/V) are updated at both the surface and at depth by utilizing dynamic forecast error covariance statistics. Four experiments are conducted: (1) a free run generated by MOM6; 2) a DA experiment assimilating temperature and salinity profile observations from World Ocean Database 2018 (WOD18); and 3) a DA experiment assimilating both drifter and the profile observations. The LaDA results are then compared with the traditional assimilation using the drifter-derived velocity field from the same GLAD database. In addition, we evaluate the impact of the LaDA algorithm on different eddy-permitting and eddy-resolving model resolutions to determine the most effective horizontal resolutions for assimilating drifter position data using LaDA.</p>

scholarly journals Data assimilation of Soil Moisture and Ocean Salinity (SMOS) observations into the Mercator Ocean operational system: focus on the El Niño 2015 event

Ocean Science ◽  
2019 ◽  
Vol 15 (3) ◽  
pp. 543-563 ◽  
Author(s):  
Benoît Tranchant ◽  
Elisabeth Remy ◽  
Eric Greiner ◽  
Olivier Legalloudec
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Bias Correction ◽  
Positive Impact ◽  
El Nino ◽  
Sea Surface ◽  
Convergence Zone ◽  
Data Sets ◽  
Surface Salinity ◽  
Ocean Salinity

Abstract. Monitoring sea surface salinity (SSS) is important for understanding and forecasting the ocean circulation. It is even crucial in the context of the intensification of the water cycle. Until recently, SSS was one of the less observed essential ocean variables. Only sparse in situ observations, mostly closer to 5 m depth than the surface, were available to estimate the SSS. The recent satellite ESA Soil Moisture and Ocean Salinity (SMOS), NASA Aquarius SAC-D and Soil Moisture Active Passive (SMAP) missions have made it possible for the first time to measure SSS from space and can bring a valuable additional constraint to control the model salinity. Nevertheless, satellite SSS still contains some residual biases that must be removed prior to bias correction and data assimilation. One of the major challenges of this study is to estimate the SSS bias and a suitable observation error for the data assimilation system. It was made possible by modifying a 3D-Var bias correction scheme and by using the analysis of the residuals and errors with an adapted statistical technique. This article presents the design and the analysis of an observing system experiment (OSE) conducted with the 0.25∘ resolution Mercator Ocean global analysis and forecasting system during the El Niño 2015/16 event. The SSS data assimilation constrains the model to be closer to the near-surface salinity observations in a coherent way with the other data sets already routinely assimilated in an operational context. This also shows that the overestimation of E–P is corrected by data assimilation through salting in regions where precipitations are higher. Globally, the SMOS SSS assimilation has a positive impact in salinity over the top 30 m. Comparisons to independent salinity data sets show a small but positive impact and corroborate the fact that the impact of SMOS SSS assimilation is larger in the Intertropical Convergence Zone (ITCZ) and South Pacific Convergence Zone (SPCZ) regions. There is little impact on the sea surface temperature (SST) and sea surface height (SSH) error statistics. Nevertheless, the SSH seems to be impacted by the tropical instability wave (TIW) propagation, itself linked to changes in barrier layer thickness (BLT). Finally, this study helped us to progress in the understanding of the biases and errors that can degrade the SMOS SSS data assimilation performance.

On the impact of sea ice in a global ocean circulation model

1997 ◽  
Vol 25 ◽  
pp. 111-115 ◽  
Author(s):  
Achim Stössel
Keyword(s):  
Southern Ocean ◽  
Sea Ice ◽  
Ocean Circulation ◽  
General Circulation ◽  
Thermohaline Circulation ◽  
Circulation Model ◽  
Deep Ocean ◽  
Global Ocean ◽  
Convective Activity ◽  
The Impact

This paper investigates the long-term impact of sea ice on global climate using a global sea-ice–ocean general circulation model (OGCM). The sea-ice component involves state-of-the-art dynamics; the ocean component consists of a 3.5° × 3.5° × 11 layer primitive-equation model. Depending on the physical description of sea ice, significant changes are detected in the convective activity, in the hydrographic properties and in the thermohaline circulation of the ocean model. Most of these changes originate in the Southern Ocean, emphasizing the crucial role of sea ice in this marginally stably stratified region of the world's oceans. Specifically, if the effect of brine release is neglected, the deep layers of the Southern Ocean warm up considerably; this is associated with a weakening of the Southern Hemisphere overturning cell. The removal of the commonly used “salinity enhancement” leads to a similar effect. The deep-ocean salinity is almost unaffected in both experiments. Introducing explicit new-ice thickness growth in partially ice-covered gridcells leads to a substantial increase in convective activity, especially in the Southern Ocean, with a concomitant significant cooling and salinification of the deep ocean. Possible mechanisms for the resulting interactions between sea-ice processes and deep-ocean characteristics are suggested.

scholarly journals The Role of Averaging for Improving Sea Surface Salinity Retrieval from the Soil Moisture and Ocean Salinity (SMOS) Satellite and Impact of Auxiliary Data

2007 ◽  
Vol 24 (2) ◽  
pp. 255-269 ◽  
Author(s):  
Sabine Philipps ◽  
Christine Boone ◽  
Estelle Obligis
Keyword(s):  
Soil Moisture ◽  
Sensitivity Study ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Global Ocean ◽  
Surface Salinity ◽  
Auxiliary Data ◽  
Additional Information ◽  
Ocean Salinity

Abstract Soil Moisture and Ocean Salinity (SMOS) was chosen as the European Space Agency’s second Earth Explorer Opportunity mission. One of the objectives is to retrieve sea surface salinity (SSS) from measured brightness temperatures (TBs) at L band with a precision of 0.2 practical salinity units (psu) with averages taken over 200 km by 200 km areas and 10 days [as suggested in the requirements of the Global Ocean Data Assimilation Experiment (GODAE)]. The retrieval is performed here by an inverse model and additional information of auxiliary SSS, sea surface temperature (SST), and wind speed (W). A sensitivity study is done to observe the influence of the TBs and auxiliary data on the SSS retrieval. The key role of TB and W accuracy on SSS retrieval is verified. Retrieval is then done over the Atlantic for two cases. In case A, auxiliary data are simulated from two model outputs by adding white noise. The more realistic case B uses independent databases for reference and auxiliary ocean parameters. For these cases, the RMS error of retrieved SSS on pixel scale is around 1 psu (1.2 for case B). Averaging over GODAE scales reduces the SSS error by a factor of 12 (4 for case B). The weaker error reduction in case B is most likely due to the correlation of errors in auxiliary data. This study shows that SSS retrieval will be very sensitive to errors on auxiliary data. Specific efforts should be devoted to improving the quality of auxiliary data.

scholarly journals Statistically, Does peer assisted learning make a difference on a UK engineering degree programme? HETL Scotland 2017

2019 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Anne Louise Nortcliffe ◽  
Sajhda Parveen ◽  
Cathy Pink-Keech
Keyword(s):  
Academic Performance ◽  
Statistical Analysis ◽  
Positive Impact ◽  
Data Sets ◽  
Social Categories ◽  
Peer Assisted Learning ◽  
Student Records ◽  
Content Type ◽  
Attainment Gap ◽  
The Impact

Purpose Black British minority ethnics (BME) students are nationally underachieving in comparison to their Ethnic Chinese and White peers, showing typically a 16 per cent graduate attainment gap in the UK. Previous research has suggested that the attainment gap could be explained by BME student disengagement, as the students typically commute from family home to University, and they work part time. However, peer-assisted learning (PAL) has been shown to have a positive impact on addressing and resolving student alienation and disengagement. However, a question still remains regarding whether student perceptions hold up to statistical analysis when scrutinised in comparison to similar cohorts without PAL interventions. The paper aims to discuss these issues. Design/methodology/approach This paper presents the results of a statistical study for two cohorts of students on engineering courses with a disproportionately high representation of BME students. The research method involved a statistical analysis of student records for the two cohorts to ascertain any effect of correlation between: PAL; student ethnicity; and student parental employment on student academic performance and placement attainment. Findings The results indicate that PAL has no significant impact on the academic performance; however, PAL has a positive impact on the placement/internship attainment for BME students and students from parental households with parents in non-managerial/professional employment. Research limitations/implications The research limitations are that the cohorts are small, but more equal diverse mix of different social categories than any other courses. However, as the cohorts are less than 30 students, comparing social categories the data sets are small to have absolute confidence in the statistical results of academic performance. Even the t-test has its limitations as the subjects are human, and there are multiple personal factors that can impact an individual academic performance; therefore, the data sets are heterostatic. Practical implications The results highlight that there is need for pedagogy interventions to support: ideally all BME students from all social categery to secure placements; BME students who are unable to go on placement to gain supplementary learning that has the same impact on their personal development and learning as placement/internship experience; and White students from managerial/professional family households to engage more in their studies. Social implications Not addressing and providing appropriate pedagogy interventions, in the wider context not addressing/resolving the BME academic and placement attainment gap, a set of students are being disadvantaged to their peers through no fault of their own, and compounding their academic attainment. As academics we have a duty to provide every opportunity to develop our student attainment, and as student entry is generally homogeneous, all students should attain it. Originality/value Previous research evaluation of PAL programmes has focused on quantitative students surveys and qualitative semi-structured research interviews with students on their student engagement and learning experience. On the other hand, this paper evaluates the intervention through conducting a quantitative statistical analysis of the student records to evaluate the impact of PAL on a cohort’s performance on different social categories (classifications) and compares the results to a cohort of another group with a similar student profile, but without PAL intervention implementation.

scholarly journals SMOS Neural Network Soil Moisture Data Assimilation in a Land Surface Model and Atmospheric Impact

2019 ◽  
Author(s):  
Nemesio Rodriguez-Fernandez ◽  
Patricia de Rosnay ◽  
Clement Albergel ◽  
Philippe Richaume ◽  
Filipe Aires ◽  
...  
Keyword(s):  
Neural Network ◽  
Soil Moisture ◽  
Data Assimilation ◽  
Land Surface ◽  
Positive Impact ◽  
Land Surface Model ◽  
Surface Model ◽  
Northern Asia ◽  
The Impact

The assimilation of Soil Moisture and Ocean Salinity (SMOS) data into the ECMWF (European Centre for Medium Range Weather Forecasts) H-TESSEL (Hydrology revised - Tiled ECMWF Scheme for Surface Exchanges over Land) model is presented. SMOS soil moisture (SM) estimates have been produced specifically by training a neural network with SMOS brightness temperatures as input and H-TESSEL model SM simulations as reference. This can help the assimilation of SMOS information in several ways: (1) the neural network soil moisture (NNSM) data have a similar climatology to the model, (2) no global bias is present with respect to the model even if regional differences can exist. Experiments performing joint data assimilation (DA) of NNSM, 2 metre air temperature and relative humidity or NNSM-only DA are discussed. The resulting SM was evaluated against a large number of in situ measurements of SM obtaining similar results to those of the model with no assimilation, even if significant differences were found from site to site. In addition, atmospheric forecasts initialized with H-TESSEL runs (without DA) or with the analysed SM were compared to measure of the impact of the satellite information. Although, NNSM DA has an overall neutral impact in the forecast in the Tropics, a significant positive impact was found in other areas and periods, especially in regions with limited in situ information. The joint NNSM, T2m and RH2m DA improves the forecast for all the seasons in the Southern Hemisphere. The impact is mostly due to T2m and RH2m, but SMOS NN DA alone also improves the forecast in July- September. In the Northern Hemisphere, the joint NNSM, T2m and RH2m DA improves the forecast in April-September, while NNSM alone has a significant positive effect in July-September. Furthermore, forecasting skill maps show that SMOS NNSM improves the forecast in North America and in Northern Asia for up to 72 hours lead time.

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