The Indian Ocean forecast system

In order to meet the ever growing demand for the prediction of oceanographic parametres in the Indian Ocean for a variety of applications, the Indian National Centre for Ocean Information Services (INCOIS) has recently set-up an operational ocean forecast system, viz. the Indian Ocean Forecast System (INDOFOS). This fully automated system, based on a state-of-the-art ocean general circulation model issues six-hourly forecasts of the sea-surface temperature, surface currents and depths of the mixed layer and the thermocline up to five-days of lead time. A brief account of INDOFOS and a statistical validation of the forecasts of these parametres using in situ and remote sensing data are presented in this article. The accuracy of the sea-surface temperature forecasts by the system is high in the Bay of Bengal and the Arabian Sea, whereas it is moderate in the equatorial Indian Ocean. On the other hand, the accuracy of the depth of the thermocline and the isothermal layers and surface current forecasts are higher near the equatorial region, while it is relatively lower in the Bay of Bengal

pyParaOcean: A System for Visual Analysis of Ocean Data

Visual analysis is well adopted within the field of oceanography for the analysis of model simulations, detection of different phenomena and events, and tracking of dynamic processes. With increasing data sizes and the availability of multivariate dynamic data, there is a growing need for scalable and extensible tools for visualization and interactive exploration. We describe pyParaOcean, a visualization system that supports several tasks routinely used in the visual analysis of ocean data. The system is available as a plugin to Paraview and is hence able to leverage its distributed computing capabilities and its rich set of generic analysis and visualization functionalities. pyParaOcean provides modules to support different visual analysis tasks specific to ocean data, such as eddy identification and salinity movement tracking. These modules are available as Paraview filters and this seamless integration results in a system that is easy to install and use. A case study on the Bay of Bengal illustrates the utility of the system for the study of ocean phenomena and processes.Comment: 8 pages, EnvirVis202

Intraseasonal variability of air-sea fluxes over the Bay of Bengal during the southwest monsoon

In the Bay of Bengal (BoB), surface heat fluxes play a key role in monsoon dynamics and prediction. The accurate representation of large-scale surface fluxes is dependent on the quality of gridded reanalysis products. Meteorological and surface flux variables from five reanalysis products are compared and evaluated against in situ data from the RAMA moored array in the BoB. The reanalysis products: ERA-Interim (ERA-I), TropFlux, MERRA-2, JRA-55 and CFSR are assessed for their characterisation of air-sea fluxes during the southwest monsoon season (JJAS). ERA-I captured radiative fluxes best while TropFlux captured turbulent and net heat fluxes (Qnet) best, and both products outperformed JRA-55, MERRA-2 and CFSR, showing highest correlations and smallest biases when compared to the in situ data. In all five products, the largest errors were in shortwave radiation (QSW) and latent heat flux (QLH), with non-negligible biases up to ∼75 W m−2. The QSW and QLH are the largest drivers of the observed Qnet variability, thus highlighting the importance of the results from the buoy comparison. There are also spatially coherent differences in the mean basin-wide fields of surface flux variables from the reanalysis products, indicating that the biases at the buoy position are not localized. Biases of this magnitude have severe implications on reanalysis products ability to capture the variability of monsoon processes. Hence, the representation of intraseasonal variability was investigated through the boreal summer intraseasonal oscillation and we found that TropFlux and ERA-I perform best at capturing intraseasonal climate variability during the southwest monsoon season

The Dynamics of the Southwest Monsoon Current in 2016 from High-Resolution In Situ Observations and Models

The strong stratification of the Bay of Bengal (BoB) causes rapid variations in sea surface temperature (SST) that influence the development of monsoon rainfall systems. This stratification is driven by the salinity difference between the fresh surface waters of the northern bay and the supply of warm, salty water by the Southwest Monsoon Current (SMC). Despite the influence of the SMC on monsoon dynamics, observations of this current during the monsoon are sparse. Using data from high-resolution in situ measurements along an east–west section at 8°N in the southern BoB, we calculate that the northward transport during July 2016 was between 16.7 and 24.5 Sv (1 Sv ≡ 106 m3 s−1), although up to ⅔ of this transport is associated with persistent recirculating eddies, including the Sri Lanka Dome. Comparison with climatology suggests the SMC in early July was close to the average annual maximum strength. The NEMO 1/12° ocean model with data assimilation is found to faithfully represent the variability of the SMC and associated water masses. We show how the variability in SMC strength and position is driven by the complex interplay between local forcing (wind stress curl over the Sri Lanka Dome) and remote forcing (Kelvin and Rossby wave propagation). Thus, various modes of climatic variability will influence SMC strength and location on time scales from weeks to years. Idealized one-dimensional ocean model experiments show that subsurface water masses advected by the SMC significantly alter the evolution of SST and salinity, potentially impacting Indian monsoon rainfall

Appositeness of artificial intelligence in modern medicine

Artificial intelligence (AI) can be demonstrated as intelligence demonstrated by machines.AI research has gone through different phases like simulating the brain, modeling human problem solving, formal logic, large databases of knowledge and imitating animal behavior. In the beginning of 21st century, highly mathematical statistical machine learning has dominated the field, was found useful and considered in helping to solve many challenging problems throughout industry and academia. The domain was discovered and work was done on the assumption that human intelligence can be simulated by machines. These initiate some discussions in raising queries about the mind and the ethics of creating artificial beings with human-like intelligence. Myth, fiction, and philosophy are involved in the creation of this field. The debates and discussion also point to concerns of misuse regarding this technology.

Understanding Iodine Chemistry Over the Northern and Equatorial Indian Ocean

Observations of halogen oxides, ozone, meteorological parameters, and physical and biogeochemical water column measurements were made in the Indian Ocean and its marine boundary layer as a part of the Second International Indian Ocean Expedition (IIOE-2). The expedition took place on board the oceanographic research vessel Sagar Nidhi during 4–22 December 2015 from Goa, India, to Port Louis, Mauritius. Observations of mixed layer depth, averaged temperature, salinity, and nitrate concentrations were used to calculate predicted iodide concentrations in the seawater. The inorganic iodine ocean-atmosphere flux (hypoiodous acid [HOI] and molecular iodine [I2]) was computed using the predicted iodide concentrations, measured atmospheric ozone, and wind speed. Iodine oxide (IO) mixing ratios peaked at 0.47 ± 0.29 pptv (parts per trillion by volume) in the remote open ocean environment. The estimated iodide concentrations and HOI and I2 fluxes peaked at 200/500 nM, 410/680 nmol·m−2·day−1, and 20/80 nmol·m−2·day−1, respectively, depending on the parameterization used. The calculated fluxes for HOI and I2 were higher closer to the Indian subcontinent; however, atmospheric IO was only observed above the detection limit in the remote open ocean environment. We use NO2 observations to show that titration of IO by NO2 is the main reason for this result. These observations show that inorganic iodine fluxes and atmospheric IO show similar trends in the Indian Ocean marine boundary layer, but the impact of inorganic iodine emissions on iodine chemistry is buffered in elevated NOx environments, even though the estimated oceanic iodine fluxes are higher

The railroad switch effect of seasonally reversing currents on the Bay of Bengal high salinity core

The Southwest Monsoon Current (SMC) flows eastward from the Arabian Sea into the Bay of Bengal (BoB) during summer, advecting a core of high salinity water. This high salinity core has been linked with Arabian Sea High Salinity Water that is presumed to enter the BoB directly from the Arabian Sea via the SMC. Here we show that the high salinity core originates primarily from the western equatorial Indian Ocean, reaching the BoB via the Somali Current, the Equatorial Undercurrent and the SMC. Years with anomalously saline high salinity cores are linked with the East Africa Coastal Current and the Somali Current winter convergence, and an anomalously strong Equatorial Undercurrent. Seasonal reversals that occur at the Somali Current and SMC junctions act as 'railroad switches' diverting water masses to different basins in the northern Indian Ocean. Interannual fluctuations of the Equatorial Undercurrent are linked to wind stress and El Nino

BoBBLE: ocean-atmosphere interaction and its impact on the South Asian monsoon

The Bay of Bengal (BoB) plays a fundamental role in controlling the weather systems that make up the South Asian summer monsoon system. In particular,the southern BoB has cooler sea surface temperature (SST) that influence ocean-atmosphere interaction and impact on the monsoon. Compared to the southeast, the southwestern BoB is cooler, more saline, receives much less rain, and is influenced by the Summer Monsoon Current(SMC). To examine the impact of these features on the monsoon, the BoB Boundary Layer Experiment (BoBBLE) was jointly undertaken by India and the UK during June–July 2016. Physical and bio-geochemical observations were made using a CTD, five ocean gliders, a uCTD, a VMP, two ADCPs, Argo floats, drifting buoys, meteorological sensors and upper air radiosonde balloons. The observations were made along a zonal section at 8◦N between 85.3◦E and 89◦E with a 10-day time series at 89◦E, 8◦N. This paper presents the new observed features of the southern BoB from the BoBBLE field program, supported by satellite data. Key results from the BoBBLE field campaign show the Sri Lanka Dome and the SMC in different stages of their seasonal evolution and two freshening events during which salinity decreased in the upper layer leading to the formation of thick barrier layers. BoBBLE observations were taken during a suppressed phase of the intraseasonal oscillation; they captured in detail the warming of the ocean mixed layer and preconditioning of the atmosphere to convection

Injection of oxygenated Persian Gulf Water into the southern Bay of Bengal

Persian Gulf Water (PGW) is an oxygenated, high-salinity water mass that has recently been detected in the Bay of Bengal (BoB). However, little is known about the transport pathways of PGW into the BoB. Ocean glider observations presented here demonstrate the presence of PGW in the southwestern BoB. Output from an ocean reanalysis product shows that this PGW signal is associated with a northward-flowing filament of high-salinity water. Particle tracking experiments reveal two pathways: one in the eastern Arabian Sea that takes a minimum of 2 years and another in the western Arabian Sea that takes a minimum of 3 years. The western pathway connects to the BoB via equatorial currents. The greatest influx of PGW occurs between 82° and 87°E during the southwest monsoon. We propose that injection of PGW to the BoB oxygen minimum zone (OMZ) contributes to keeping oxygen concentrations in the BoB above the level at which denitrification occurs