An eddy resolving tidal-driven model of the South China Sea assimilating along-track SLA data using the EnOI

The upper ocean circulation in the South China Sea (SCS) is driven by the Asian monsoon, the Kuroshio intrusion through the Luzon Strait, strong tidal currents, and a complex topography. Here, we demonstrate the benefit of assimilating along-track altimeter data into a nested configuration of the HYbrid Coordinate Ocean Model that includes tides. Including tides in models is important because they interact with the main circulation. However, assimilation of altimetry data into a model including tides is challenging because tides and mesoscale features contribute to the elevation of ocean surface at different time scales and require different corrections. To address this issue, tides are filtered out of the model output and only the mesoscale variability is corrected with a computationally cheap data assimilation method: the Ensemble Optimal Interpolation (EnOI). This method uses a running selection of members to handle the seasonal variability and assimilates the track data asynchronously. The data assimilative system is tested for the period 1994–1995, during which time a large number of validation data are available. Data assimilation reduces the Root Mean Square Error of Sea Level Anomalies from 9.3 to 6.9 cm and improves the representation of the mesoscale features. With respect to the vertical temperature profiles, the data assimilation scheme reduces the errors quantitatively with an improvement at intermediate depth and deterioration at deeper depth. The comparison to surface drifters shows an improvement of surface current by approximately −9% in the Northern SCS and east of Vietnam. Results are improved compared to an assimilative system that does not include tides and a system that does not consider asynchronous assimilation

Data assimilation as a learning tool to infer ordinary differential equation representations of dynamical models

Recent progress in machine learning has shown how to forecast and, to some extent, learn the dynamics of a model from its output, resorting in particular to neural networks and deep learning techniques. We will show how the same goal can be directly achieved using data assimilation techniques without leveraging on machine learning software libraries, with a view to high-dimensional models. The dynamics of a model are learned from its observation and an ordinary differential equation (ODE) representation of this model is inferred using a recursive nonlinear regression. Because the method is embedded in a Bayesian data assimilation framework, it can learn from partial and noisy observations of a state trajectory of the physical model. Moreover, a space-wise local representation of the ODE system is introduced and is key to coping with high-dimensional models. It has recently been suggested that neural network architectures could be interpreted as dynamical systems. Reciprocally, we show that our ODE representations are reminiscent of deep learning architectures. Furthermore, numerical analysis considerations of stability shed light on the assets and limitations of the method. The method is illustrated on several chaotic discrete and continuous models of various dimensions, with or without noisy observations, with the goal of identifying or improving the model dynamics, building a surrogate or reduced model, or producing forecasts solely from observations of the physical model

Impact of assimilating a merged sea-ice thickness from CryoSat-2 and SMOS in the Arctic reanalysis

Accurately forecasting the sea-ice thickness (SIT) in the Arctic is a major challenge. The new SIT product (referred to as CS2SMOS) merges measurements from the CryoSat-2 and SMOS satellites on a weekly basis during the winter. The impact of assimilating CS2SMOS data is tested for the TOPAZ4 system – the Arctic component of the Copernicus Marine Environment Monitoring Services (CMEMS). TOPAZ4 currently assimilates a large set of ocean and sea-ice observations with the Deterministic Ensemble Kalman Filter (DEnKF). Two parallel reanalyses are conducted without (Official run) and with (Test run) assimilation of CS2SMOS data from 19 March 2014 to 31 March 2015. Since only mapping errors were provided in the CS2SMOS observation, an arbitrary term was added to compensate for the missing errors, but was found a posteriori too large. The SIT bias (too thin) is reduced from 16 to 5&thinsp;cm and the standard errors decrease from 53 to 38&thinsp;cm (by 28&thinsp;%) when compared to the assimilated SIT. When compared to independent SIT observations, the error reduction is 24&thinsp;% against the ice mass balance (IMB) buoy 2013F and by 12.5&thinsp;% against SIT data from the IceBridge campaigns. The improvement of sea-ice volume persists through the summer months in the absence of CS2SMOS data. Comparisons to sea-ice drift from the satellites show that dynamical adjustments reduce the drift errors around the North Pole by about 8&thinsp;%–9&thinsp;% in December 2014 and February 2015. Finally, using the degrees of freedom for signal (DFS), we find that CS2SMOS makes the prime source of information in the central Arctic and in the Kara Sea. We therefore recommend the assimilation of C2SMOS for Arctic reanalyses in order to improve the ice thickness and the ice drift.</p

Do pain-related beliefs influence adherence to multidisciplinary rehabilitation? A systematic review

OBJECTIVES: To understand how pain-related cognitions predict and influence treatment retention and adherence during and after a multidisciplinary rehabilitation program. METHODS: Electronic databases including Medline, CINAHL, PsycINFO, Academic Search Complete, and Scopus were used to search three combinations of keywords: chronic pain, beliefs, and treatment adherence. RESULTS: The search strategy yielded 591 results, with an additional 12 studies identified through reference screening. 81 full-text papers were assessed for eligibility and 10 papers met the inclusion and exclusion criteria for this review. The pain-related beliefs that have been measured in relation to treatment adherence include: pain-specific self-efficacy, perceived disability, catastrophizing, control beliefs, fear-avoidance beliefs, perceived benefits and barriers, as well as other less commonly measured beliefs. The most common pain-related belief investigated in relation to treatment adherence was pain-related self-efficacy. Findings for the pain-related beliefs investigated among the studies were mixed. Collectively, all of the aforementioned pain-related beliefs, excluding control beliefs, were found to influence treatment adherence behaviours. DISCUSSION: The findings suggest that treatment adherence is determined by a combination of pain-related beliefs either supporting or inhibiting chronic pain patients\u27 ability to adhere to treatment recommendations over time. In the studies reviewed, self-efficacy appears to be the most commonly researched predictor of treatment adherence, its effects also influencing other pain-related beliefs. More refined and standardised methodologies, consistent descriptions of pain-related beliefs and methods of measurement will improve our understanding of adherence behaviours

Adsorption of Rhodamine B from Wastewater on the Arsenic- Hyperaccumulator Pteris Vittata Waste Roots

The Pteris vittata fern, which is a perennial plant known for hyper-accumulating Arsenic, can be grown in hydroponic cultures and is often used for phytoremediation of contaminated water. To reduce the cost of disposing As-contaminated biomass, this study examined the potential of using waste roots from Pteris vittata as a new and inexpensive bio-adsorbent for removing Rhodamine B (RB) dye, which is commonly used in industrial applications. Batch tests were performed at 25°C in order to observe both the rate and the equilibrium conditions of the system. The isotherm showed a typical Langmuir behavior exhibiting a maximum adsorption capacity of 42.7 mg/g. Kinetics tests were conducted at different solid-liquid ratios and fitted by a mathematical model. The maximum likelihood method was employed to estimate the effective diffusivity of RB in the solid which resulted 4.48 10-9 cm2/min. This study lays the groundwork for future investigations into the use of this material in continuous systems to determine its feasibility for application in industrial apparatus

TOPAZ4: an ocean-sea ice data assimilation system for the North Atlantic and Arctic

We present a detailed description of TOPAZ4, the latest version of TOPAZ – a coupled ocean-sea ice data assimilation system for the North Atlantic Ocean and Arctic. It is the only operational, large-scale ocean data assimilation system that uses the ensemble Kalman filter. This means that TOPAZ features a time-evolving, state-dependent estimate of the state error covariance. Based on results from the pilot MyOcean reanalysis for 2003–2008, we demonstrate that TOPAZ4 produces a realistic estimate of the ocean circulation in the North Atlantic and the sea-ice variability in the Arctic. We find that the ensemble spread for temperature and sea-level remains fairly constant throughout the reanalysis demonstrating that the data assimilation system is robust to ensemble collapse. Moreover, the ensemble spread for ice concentration is well correlated with the actual errors. This indicates that the ensemble statistics provide reliable state-dependent error estimates – a feature that is unique to ensemble-based data assimilation systems. We demonstrate that the quality of the reanalysis changes when different sea surface temperature products are assimilated, or when in-situ profiles below the ice in the Arctic Ocean are assimilated. We find that data assimilation improves the match to independent observations compared to a free model. Improvements are particularly noticeable for ice thickness, salinity in the Arctic, and temperature in the Fram Strait, but not for transport estimates or underwater temperature. At the same time, the pilot reanalysis has revealed several flaws in the system that have degraded its performance. Finally, we show that a simple bias estimation scheme can effectively detect the seasonal or constant bias in temperature and sea-level

Flap reconstruction of the hypopharynx: a defect orientated approach

The present retrospective analysis evaluated the outcomes of different flap reconstructions for several hypopharyngeal defects in 136 patients who underwent hypopharyngeal reconstruction with a free or pedicled flap after excision of pharyngeal or laryngeal carcinoma.Functional and oncological outcome were the main measures. Nine patients had a type I-a hypopharyngeal defect (partial with larynx preserved), 33 type I-b (partial without larynx preserved), 85 type II (circumferential), 5 type III (extensive superior) and 4 vertical hemipharyngolaryngectomy. The flaps used to reconstruct these defects were pectoralis major (n = 34), free radial forearm (n = 25), jejunum (n = 72), pedicled latissimus dorsi (n = 2), sternocleidomastoid (n = 1), lateral thigh (n = 1) and deltopectoral (n = 1). Twelve defects (9%) needed a secondary flap reconstruction. Surgical and medical complications were seen in 29% and 8% of patients, respectively; 18% of patients developed a fistula. No difference in complication rate or admission days was found for pre-operative versus no previous radiotherapy, type of defect or free versus pedicled flap. After 12 months follow-up, 38% of patients had a tracheo-oesophageal voice prosthesis, in 82% a fully oral diet was obtained and the average body weight gain was 0.9 kg. Five-year overall and disease-specific survival rates were 35% and 49%, respectively, while local and regional control rates were 65% and 91%, respectively. Considering these results, a defect orientated approach may be helpful for deciding which flap should be used for reconstruction of the hypopharynx. An algorithm is proposed with similar functional and oncological outcomes for the different groups. The choice of flap should be based on expected morbidity and functional outcome