Anomalous circulation around the southern Indian peninsula observed during winter, 2009; and the generation mechanisms

343-351In the present study we have analysed the currents data collected on board INS Sagardhwani using Vessel-Mounted Acoustic Doppler Current Profiler (VM-ADCP) along the southern Indian peninsula and Sri Lanka during December 2009. The observed currents deviated from climatological circulation pattern except around Sri Lanka. The southeastern region of peninsular India has evidenced the most anomalous currents from in-situ observations, with a northward flowing east India coastal current which is often southward (climatologically). The ROMS model is unable to simulate the observed anomalous currents, whereas the high-resolution (1/12 degree) HYCOM ocean model had reasonably well simulated these currents. From HYCOM simulations, we have found that small scale eddies (which are not captured or resolved by altimeter) are responsible for the observed anomalous behaviour of currents (east India coastal current) over southeastern India. This work further emphasizes the need of high-resolution ocean modelling for proper representation of key physical process and associated circulation systems

An observing system simulation experiment for Indian Ocean surface pCO2 measurements

An observing system simulation experiment (OSSE) is conducted to identify potential locations for making surface ocean pCO2 measurements in the Indian Ocean using the Bayesian Inversion method. As of the SOCATv3 release, the pCO2 data is limited in the Indian Ocean. To improve our modeling of this region, we need to identify where and what observation systems would produce the most good or benefit for their cost. The potential benefits of installing pCO2 sensors in the existing RAMA and OMNI moorings of the Indian Ocean, the potential of Bio-Argo floats (with pH measurements), and the implementation of the ship of opportunity program (SOOP) for underway sampling of pCO2 are evaluated. A cost function of dissolved inorganic carbon as a model state vector and CO2 flux mismatch as the source of error is minimized, and the basin-wide CO2 flux uncertainty reduction is estimated for different seasons. The maximum flux uncertainty reduction achievable by installing pCO2 sensors in the existing RAMA and OMNI moorings is limited to 30% during different seasons. One may consider that around 20 Bio-Argos are still the right choice over installing mooring based pCO2 sensors and achieve uncertainty reduction up to 50% with additional benefit of profiling the sub-surface upto 1000 & ndash;2000 m. However, a single track SOOP has the potential to reduce the uncertainty by approximately 62%. This study identifies vital RAMA and OMNI moorings and SOOP tracks for observing Indian Ocean pCO2. Plain Language Summary. Surface ocean partial pressure of CO2 (pCO2) information is vital for estimating sea-to-air CO2 exchanges. This parameter is least available from the Indian Ocean as compared to other global tropical and southern oceans. There has been no effort made so far to measure surface ocean pCO2 in the Indian Ocean with routine monitoring such as by mounting instruments to moorings or by underway sampling via any ship of opportunity program. Therefore there is a considerable demand to start pCO2 observations in the Indian Ocean. However, one key question that emerges is where to deploy pCO2 instruments in the Indian Ocean to learn the most with limited resources. This study addresses this question with inverse modeling techniques. The study finds that the existing moorings of the Indian Ocean are capable of hosting pCO2 sensors, and data from those are useful to reduce the uncertainty in the surface sea-to-air CO2 flux estimation by a quarter magnitude. In contrast, the Bio-Argo floats with pH sensors, and the ship of opportunity underway sampling of pCO2 may benefit from reducing the same up to 50% and 62%, respectively

Screening COVID-19 by Swaasa AI platform using cough sounds: a cross-sectional study

Abstract The Advent of Artificial Intelligence (AI) has led to the use of auditory data for detecting various diseases, including COVID-19. SARS-CoV-2 infection has claimed more than six million lives to date and therefore, needs a robust screening technique to control the disease spread. In the present study we created and validated the Swaasa AI platform, which uses the signature cough sound and symptoms presented by patients to screen and prioritize COVID-19 patients. We collected cough data from 234 COVID-19 suspects to validate our Convolutional Neural Network (CNN) architecture and Feedforward Artificial Neural Network (FFANN) (tabular features) based algorithm. The final output from both models was combined to predict the likelihood of having the disease. During the clinical validation phase, our model showed a 75.54% accuracy rate in detecting the likely presence of COVID-19, with 95.45% sensitivity and 73.46% specificity. We conducted pilot testing on 183 presumptive COVID subjects, of which 58 were truly COVID-19 positive, resulting in a Positive Predictive Value of 70.73%. Due to the high cost and technical expertise required for currently available rapid screening methods, there is a need for a cost-effective and remote monitoring tool that can serve as a preliminary screening method for potential COVID-19 subjects. Therefore, Swaasa would be highly beneficial in detecting the disease and could have a significant impact in reducing its spread