Argo data quality control based on climatological convex hulls

This report discusses a new method of identifying erroneous data in temperature and salinity (T/S) profiles measured by Argo profiling floats. The proposed method uses World Ocean Atlas 2009 (WOA09) climatology to classify good against bad data. An 'n' sided polygon (convex hull) with least area encompassing all the points is constructed based on the Jarvis March algorithm. The mean and standard deviation fields of temperature and salinity obtained from WOA09 corresponding to each standard depth are used for building these polygons. Subsequently Points In Polygon (PIP) principle which is implemented using ray casting algorithm is used to classify the T/S data as within or without acceptable bounds. It is observed that various types of anomalies in the Argo profile data viz., spikes, bias, sensor drifts etc can be identified using this method

Marine Meteorological Atlas of Tropical Indian Ocean

This report discusses the preparation of climatology from the data which is from the ship observations obtained from the Indian Meteorological Department (IMD) and Naval Operations Data Processing and Analysis Centre (NODPAC). Processing of these datasets, extraction of surface meteorological variables and SST (Sea surface temperature), the quality control (QC) procedures followed are discussed. The individual records of International Comprehensive Ocean-Atmosphere Dataset (ICOADS) and those obtained from IMD are compared and the unique records from IMD data are extracted. The enhancement in ICOADS climatology after adding the unique records from IMD data and NODPAC is checked. ICOADS dataset has been found to be self robust, as there is no much significant improvement in the climatology even after adding new records . Frequency distribution of the ICOADS dataset alone and after adding new records showed no much difference. Spatial correlation between ICOADS monthly climatology fields before and after adding the new records, is greater than 0.9 at all grid point

MaMeAT - A tool for visualizing marine meteorological data for Naval applications

This report discusses various functionalities of tool developed for use by Naval Operations Data Processing and Analysis Centre (NODPAC) a wing of Indian Navy. The data for this tool is obtained from the Indian Meteorological Department (IMD), Naval Operations Data Processing and Analysis Centre (NODPAC) and individual records of International Comprehensive Ocean-Atmosphere Dataset (ICOADS). These data sets are processed, quality controlled and merged to form a unique data set for enhancing the marine met climatology of Indian Ocean. The IMD and NODPAC data are compared with the unique records from COADS and duplicates are eliminated. With addition of around 12% unique records to ICOADS data, enhanced climatology is regenerated. With this base data, Graphic User Interface (GUI) based tool is build for visualizing parameters viz., SST, SLP, Wind, Relative Humidity, SSS and bathymetry. This tool has capability to generate climatology dynamically between any chosen periods apart from visualizing various plots which are useful for Navy while at sea. Also provision for adding newly observed marine met data is provided making this most robust tool for use by the Indian Navy

Assessment of Tsunami Preparedness in East Coast of India through Mega Mock Tsunami Drill conducted on 24 November 2017

Though tsunamis are infrequent, the death toll from tsunamis is huge compared with other natural disasters. The 26 December 2004 Indian Ocean tsunami resulted in disastrous loss of life and property. The major challenge with tsunamis is that they are infrequent, which requires great persistence in sustaining the process of capacity building and preparedness. Because of this infrequency, instruction through tsunami mock drills is the best way to train coastal communities to prepare for devastating actual events. The situational awareness and ability to respond quickly is best achieved through pre-event education and mock drills. The Tsunami mock drills evaluates the ability of warning centre and disaster offices to respond to a tsunami. The drills also educate the public on: where they would receive the official warnings, by which means, what those warnings indicate, how to understand them, and what they need to do in response. INCOIS in collaboration with MHA and NDMA has conducted mega mock tsunami mock drill on 24 November, 2017 to East coast of India. Disaster Management Organisations of Andhra Pradesh, Odisha, Puducherry, Tamil Nadu and West Bengal participated in the drill. They took the drill to community level and executed evacuations at different villages. The average elapsed time achieved from time of receipt of warning to activating the public notification systems was 30 minutes. This is great achievement compared with previous mock drills as it has substantially improved, though the involvement of communities was at huge level. The Tsunami mock drill was very successful which enhanced the awareness and preparedness among the coastal people of East Coast of Indi

INCOIS-Real time Automatic Weather Station(IRAWS) dataset - Quality control and significance of height correction

The INCOIS-Real time Automatic Weather Station(IRAWS) program was started in the year 2009 and was first installed onboard ORV Sagar Nidhi. Currently, there are 36 ships carrying IRAWS setup. Apart from storing one minute observations in the log onboard the ship, hourly averaged observations are reported through INSAT satellite communication. This report briefs about the hourly dataset of IRAWS and its quality control. In this report, QC results of SST and all meteorological parameters except radiation parameters is discussed. Specific quality check was applied to wind speed (WS) and sea surface temperature (SST) observations. The WS observations measured onboard few ships had a dimensional correction and SST was observed only on few ships. As SST observations are required to compute meteorological variables like DBT, RH, WS to standard height of 10 m, level-3 dataset of AVHRR SST was utilized in place of IRAWS SST wherever the data is found to be faulty. On similar terms bias correction could not be applied to IRAWS SST with the help of AVHRR SST as the error in SST observations are due to the failure of sensor. However all those IRAWS SST observations that passed the QC check were observed to be of high quality and have a correlation coefficient of 0.5 with AVHRR SST and is significant at 95% significant level. Apart from SST and radiation observations, all other parameters observations are found out to be of good quality with 70 to 90 QC pass percentage . Apart from the details of QC check, significance of representing climate variable at a homogeneous standard height is also shown in this repor

A Roadmap for Unified Ocean Modeling and Forecasting system for INCOIS

INCOIS, being the nodal organization to provide operational oceanographic services, is actively involved in the numerical modeling of ocean circulation, waves, tsunami and storm-surge as well as regional coupled ocean-atmosphere models for the prediction of track and intensity of tropical cyclones. In order to optimise the models used in INCOIS for these activities and to make a seamless prediction system from global to regional domains, it was decided to have a revisit on the ocean modeling efforts of INCOIS. Outcome of this review as well as a proposal to develop a seamless prediction system is documented in this report. It is envisaged that this document will be used as a guideline for the future ocean modeling efforts in INCOI

Antiinflammatory Therapy with Canakinumab for Atherosclerotic Disease

Background: Experimental and clinical data suggest that reducing inflammation without affecting lipid levels may reduce the risk of cardiovascular disease. Yet, the inflammatory hypothesis of atherothrombosis has remained unproved. Methods: We conducted a randomized, double-blind trial of canakinumab, a therapeutic monoclonal antibody targeting interleukin-1β, involving 10,061 patients with previous myocardial infarction and a high-sensitivity C-reactive protein level of 2 mg or more per liter. The trial compared three doses of canakinumab (50 mg, 150 mg, and 300 mg, administered subcutaneously every 3 months) with placebo. The primary efficacy end point was nonfatal myocardial infarction, nonfatal stroke, or cardiovascular death. RESULTS: At 48 months, the median reduction from baseline in the high-sensitivity C-reactive protein level was 26 percentage points greater in the group that received the 50-mg dose of canakinumab, 37 percentage points greater in the 150-mg group, and 41 percentage points greater in the 300-mg group than in the placebo group. Canakinumab did not reduce lipid levels from baseline. At a median follow-up of 3.7 years, the incidence rate for the primary end point was 4.50 events per 100 person-years in the placebo group, 4.11 events per 100 person-years in the 50-mg group, 3.86 events per 100 person-years in the 150-mg group, and 3.90 events per 100 person-years in the 300-mg group. The hazard ratios as compared with placebo were as follows: in the 50-mg group, 0.93 (95% confidence interval [CI], 0.80 to 1.07; P = 0.30); in the 150-mg group, 0.85 (95% CI, 0.74 to 0.98; P = 0.021); and in the 300-mg group, 0.86 (95% CI, 0.75 to 0.99; P = 0.031). The 150-mg dose, but not the other doses, met the prespecified multiplicity-adjusted threshold for statistical significance for the primary end point and the secondary end point that additionally included hospitalization for unstable angina that led to urgent revascularization (hazard ratio vs. placebo, 0.83; 95% CI, 0.73 to 0.95; P = 0.005). Canakinumab was associated with a higher incidence of fatal infection than was placebo. There was no significant difference in all-cause mortality (hazard ratio for all canakinumab doses vs. placebo, 0.94; 95% CI, 0.83 to 1.06; P = 0.31). Conclusions: Antiinflammatory therapy targeting the interleukin-1β innate immunity pathway with canakinumab at a dose of 150 mg every 3 months led to a significantly lower rate of recurrent cardiovascular events than placebo, independent of lipid-level lowering. (Funded by Novartis; CANTOS ClinicalTrials.gov number, NCT01327846.

Surge simulations for 1999 Orissa super cyclone using a finite element model

The Orissa super cyclone which crossed the Orissa coastal region near Paradip on October 29, 1999 proved to be disastrous. The strong winds, torrential rains with heavy rainfall and high storm surge associated with the cyclone caused havoc that resulted in the death of thousands of people, cattle and extensive damage to agricultural land, paddy crop, transmission lines, power supply, roads and buildings. In the present study, a fine resolution finite element model is used to simulate surges due to this super cyclone. The model results are compared with observed surges available from post-storm survey reports. Comparison of results show that they are in good agreement with the observed surges, and the finite element model can be used for real time surge forecasts

Calibration of a finite element surge prediction model for the east coast of India

Real time simulation of storm surges using numerical models requires calibration and validation of the model before being used for operational forecasts. The main objective of this paper is to present the calibration studies carried out on an operational level storm surge prediction model developed at NIOT using finite element method. The primary requirement for calibration of surge prediction model is the actual data on bathymetry, past cyclones and the observed surges. The bathymetric contours along the shelf of east coast of India were digitized from the hydrographic charts and the data on cyclones which crossed the east coast of India for the past fifty years were collected from India Meteorological Department. The sensitivity studies showed that the wind stress coefficient is the key sensitive parameter for the model and so the model is calibrated for this parameter. The surge simulations using the calibrated parameter compare very well with the observed surges and illustrate the predictive capability of the mode

Comparison between Argo-derived sea surface temperature and microwave sea surface temperature in tropical Indian Ocean

A comparison between in situ and microwave satellite-derived sea surface temperature (SST) for the tropical Indian Ocean (TIO) during January 2009–December 2010 is presented. All available in situ temperatures close to the surface (5 m) from Argo profiling floats are used in this study. Weekly 0.25° × 0.25° spatial resolution SST was produced from these Argo in situ data using the Data-Interpolating Variational Analysis (DIVA) method. This weekly Argo sea surface temperature (ASST) is compared with Tropical Rainfall Measuring Mission Microwave Imager (TMI)-retrieved Sea Surface Temperature (TMISST) and available blended Optimum-Interpolation Sea Surface Temperature (OISST) product, at selected regions encompassing the north, central and southern TIO. The results indicate a bias and root mean square error (RMSE) of –0.02°C and 0.45°C between ASST and OISST, whereas the bias and RMSE between ASST and TMISST is observed to be 0.11°C and 0.32°C, respectively. The correlation coefficient (r) between ASST and OISST is observed to be 0.980, whereas r between ASST and TMISST is observed to be 0.985