Cramer-Rao bounds for source localization in shallow ocean with generalized Gaussian noise

Localization of underwater acoustic sources is a problem of great interest in the area of ocean acoustics. There exist several algorithms for source localization based on array signal processing.It is of interest to know the theoretical performance limits of these estimators. In this paper we develop expressions for the Cramer-Rao-Bound (CRB) on the variance of direction-of-arrival(DOA) and range-depth estimators of underwater acoustic sources in a shallow range-independent ocean for the case of generalized Gaussian noise. We then study the performance of some of the popular source localization techniques,through simulations, for DOA/range-depth estimation of underwater acoustic sources in shallow ocean by comparing the variance of the estimators with the corresponding CRBs.

Processes of 30-90 days sea surface temperature variability in the northern Indian Ocean during boreal summer

During summer, the northern Indian Ocean exhibits significant atmospheric intraseasonal variability associated with active and break phases of the monsoon in the 30-90 days band. In this paper, we investigate mechanisms of the Sea Surface Temperature (SST) signature of this atmospheric variability, using a combination of observational datasets and Ocean General Circulation Model sensitivity experiments. In addition to the previously-reported intraseasonal SST signature in the Bay of Bengal, observations show clear SST signals in the Arabian Sea related to the active/break cycle of the monsoon. As the atmospheric intraseasonal oscillation moves northward, SST variations appear first at the southern tip of India (day 0), then in the Somali upwelling region (day 10), northern Bay of Bengal (day 19) and finally in the Oman upwelling region (day 23). The Bay of Bengal and Oman signals are most clearly associated with the monsoon active/break index, whereas the relationship with signals near Somali upwelling and the southern tip of India is weaker. In agreement with previous studies, we find that heat flux variations drive most of the intraseasonal SST variability in the Bay of Bengal, both in our model (regression coefficient, 0.9, against similar to 0.25 for wind stress) and in observations (0.8 regression coefficient); similar to 60% of the heat flux variation is due do shortwave radiation and similar to 40% due to latent heat flux. On the other hand, both observations and model results indicate a prominent role of dynamical oceanic processes in the Arabian Sea. Wind-stress variations force about 70-100% of SST intraseasonal variations in the Arabian Sea, through modulation of oceanic processes (entrainment, mixing, Ekman pumping, lateral advection). Our similar to 100 km resolution model suggests that internal oceanic variability (i.e. eddies) contributes substantially to intraseasonal variability at small-scale in the Somali upwelling region, but does not contribute to large-scale intraseasonal SST variability due to its small spatial scale and random phase relation to the active-break monsoon cycle. The effect of oceanic eddies; however, remains to be explored at a higher spatial resolution.

Estimation of heavy rainfall during cyclonic storms from microwave observations using nonlinear approach over Indian Ocean

In this study, an effort has been made to study heavy rainfall events during cyclonic storms over Indian Ocean. This estimate is based on microwave observations from tropical rainfall measuring mission (TRMM) Microwave Imager (TMI). Regional scattering index (SI) developed for Indian region based on measurements at 19-, 21- and 85-GHz brightness temperature and polarization corrected temperature (PCT) at 85 GHz have been utilized in this study. These PCT and SI are collocated against Precipitation Radar (PR) onboard TRMM to establish a relationship between rainfall rate, PCT and SI. The retrieval technique using both linear and nonlinear regressions has been developed utilizing SI, PCT and the combination of SI and PCT. The results have been compared with the observations from PR. It was found that a nonlinear algorithm using combination of SI and PCT is more accurate than linear algorithm or nonlinear algorithm using either SI or PCT. Statistical comparison with PR exhibits the correlation coefficients (CC) of 0.68, 0.66 and 0.70, and root mean square error (RMSE) of 1.78, 1.96 and 1.68 mm/h from the observations of SI, PCT and combination of SI and PCT respectively using linear regressions. When nonlinear regression is used, the CC of 0.73, 0.71, 0.79 and RMSE of 1.64, 1.95, 1.54 mm/h are observed from the observations of SI, PCT and combination of SI and PCT, respectively. The error statistics for high rain events (above 10 mm/h) shows the CC of 0.58, 0.59, 0.60 and RMSE of 5.07, 5.47, 5.03 mm/h from the observations of SI, PCT and combination of SI and PCT, respectively, using linear regression, and on the other hand, use of nonlinear regression yields the CC of 0.66, 0.64, 0.71 and RMSE of 4.68, 5.78 and 4.02 mm/h from the observations of SI, PCT and combined SI and PCT, respectively.

Signatures of Indian ocean Dipole and El Nino-Southern Oscillation events in sea level variations in the Bay of Bengal

We investigate the impact of the Indian Ocean Dipole (IOD) and El Nino and the Southern Oscillation (ENSO) on sea level variations in the North Indian Ocean during 1957-2008. Using tide-gauge and altimeter data, we show that IOD and ENSO leave characteristic signatures in the sea level anomalies (SLAs) in the Bay of Bengal. During a positive IOD event, negative SLAs are observed during April-December, with the SLAs decreasing continuously to a peak during September-November. During El Nino, negative SLAs are observed twice (April-December and November-July), with a relaxation between the two peaks. SLA signatures during negative IOD and La Nina events are much weaker. We use a linear, continuously stratified model of the Indian Ocean to simulate their sea level patterns of IOD and ENSO events. We then separate solutions into parts that correspond to specific processes: coastal alongshore winds, remote forcing from the equator via reflected Rossby waves, and direct forcing by interior winds within the bay. During pure IOD events, the SLAs are forced both from the equator and by direct wind forcing. During ENSO events, they are primarily equatorially forced, with only a minor contribution from direct wind forcing. Using a lead/lag covariance analysis between the Nino-3.4 SST index and Indian Ocean wind stress, we derive a composite wind field for a typical El Nino event: the resulting solution has two negative SLA peaks. The IOD and ENSO signatures are not evident off the west coast of India.

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.

Predicting the extremes of Indian summer monsoon rainfall with coupled ocean-atmosphere models

An analysis of the retrospective predictions by seven coupled ocean atmosphere models from major forecasting centres of Europe and USA, aimed at assessing their ability in predicting the interannual variation of the Indian summer monsoon rainfall (ISMR), particularly the extremes (i.e. droughts and excess rainfall seasons) is presented in this article. On the whole, the skill in prediction of extremes is not bad since most of the models are able to predict the sign of the ISMR anomaly for a majority of the extremes. There is a remarkable coherence between the models in successes and failures of the predictions, with all the models generating loud false alarms for the normal monsoon season of 1997 and the excess monsoon season of 1983. It is well known that the El Nino and Southern Oscillation (ENSO) and the Equatorial Indian Ocean Oscillation (EQUINOO) play an important role in the interannual variation of ISMR and particularly the extremes. The prediction of the phases of these modes and their link with the monsoon has also been assessed. It is found that models are able to simulate ENSO-monsoon link realistically, whereas the EQUINOO-ISMR link is simulated realistically by only one model the ECMWF model. Furthermore, it is found that in most models this link is opposite to the observed, with the predicted ISMR being negatively (instead of positively) correlated with the rainfall over the western equatorial Indian Ocean and positively (instead of negatively) correlated with the rainfall over the eastern equatorial Indian Ocean. Analysis of the seasons for which the predictions of almost all the models have large errors has suggested the facets of ENSO and EQUINOO and the links with the monsoon that need to be improved for improving monsoon predictions by these models.

Yanai waves in the western equatorial Indian Ocean

Observations and models have shown the presence of intraseasonal fluctuations in 20-30-day and 10-20-day bands in the equatorial Indian Ocean west of 60 degrees E (WEIO). Their spatial and temporal structures characterize them as Yanai waves, which we label low-frequency (LFYW) and high-frequency (HFYW) Yanai waves, respectively. We explore the dynamics of these intraseasonal signals, using an ocean general circulation model (Modular Ocean Model) and a linear, continuously stratified model. Yanai waves are forced by the meridional wind tau(y) everywhere in the WEIO most strongly during the monsoon seasons. They are forced both directly in the interior ocean and by reflection of the interior response from the western boundary; interference between the interior and boundary responses results in a complex surface pattern that propagates eastward and has nodes. Yanai waves are also forced by instabilities primarily during June/July in a region offshore from the western boundary (52-55 degrees E). At that time, eddies, generated by barotropic instability of the Southern Gyre, are advected southward to the equator. There, they generate a westward-propagating, cross-equatorial flow field, v(eq), with a wave number/frequency spectrum that fits the dispersion relation of a number of Yanai waves, and these waves are efficiently excited. Typically, Yanai waves associated with several baroclinic modes are excited by both wind and eddy forcing; and typically, they superpose to create beams that carry energy vertically and eastward along ray paths. The same processes generate LFYWs and HFYWs, and hence, their responses are similar; differences are traceable to the property that HFYWs have longer wavelengths than LFYWs for each baroclinic mode.

Estimating wind stress at the ocean surface from scatterometer observations

Wind stress is the most important ocean forcing for driving tropical surface currents. Stress can be estimated from scatterometer-reported wind measurements at 10 m that have been extrapolated to the surface, assuming a neutrally stable atmosphere and no surface current. Scatterometer calibration is designed to account for the assumption of neutral stability; however, the assumption of a particular sea state and negligible current often introduces an error in wind stress estimations. Since the fundamental scatterometer measurement is of the surface radar backscatter (sigma-0) which is related to surface roughness and, thus, stress, we develop a method to estimate wind stress directly from the scatterometer measurements of sigma-0 and their associated azimuth angle and incidence angle using a neural network approach. We compare the results with in situ estimations and observe that the wind stress estimations from this approach are more accurate compared with those obtained from the conventional estimations using 10-m-height wind measurements.

Dynamics of the Indian-Ocean oxygen minimum zones

In the Indian Ocean, mid-depth oxygen minimum zones (OMZs) occur in the Arabian Sea and the Bay of Bengal. The lower part of the Arabian-Sea OMZ (ASOMZ; below 400 m) intensifies northward across the basin; in contrast, its upper part (above 400 m) is located in the central/eastern basin, well east of the most productive regions along the western boundary. The Bay-of-Bengal OMZ (BBOMZ), although strong, is weaker than the ASOMZ. To investigate the processes that maintain the Indian-Ocean OMZs, we obtain a suite of solutions to a coupled biological/physical model. Its physical component is a variable-density, 6 1/2-layer model, in which each layer corresponds to a distinct dynamical regime or water-mass type. Its biological component has six compartments: nutrients, phytoplankton, zooplankton, two size classes of detritus, and oxygen. Because the model grid is non-eddy resolving (0.5 degrees), the biological model also includes a parameterization of enhanced mixing based on the eddy kinetic energy derived from satellite observations. To explore further the impact of local processes on OMZs, we also obtain analytic solutions to a one-dimensional, simplified version of the biological model. Our control run is able to simulate basic features of the oxygen, nutrient, and phytoplankton fields throughout the Indian Ocean. The model OMZs result from a balance, or lack thereof, between a sink of oxygen by remineralization and subsurface oxygen sources due primarily to northward spreading of oxygenated water from the Southern Hemisphere, with a contribution from Persian-Gulf water in the northern Arabian Sea. The northward intensification of the lower ASOMZ results mostly from horizontal mixing since advection is weak in its depth range. The eastward shift of the upper ASOMZ is due primarily to enhanced advection and vertical eddy mixing in the western Arabian Sea, which spread oxygenated waters both horizontally and vertically. Advection carries small detritus from the western boundary into the central/eastern Arabian Sea, where it provides an additional source of remineralization that drives the ASOMZ to suboxic levels. The model BBOMZ is weaker than the ASOMZ because the Bay lacks a remote source of detritus from the western boundary. Although detritus has a prominent annual cycle, the model OMZs do not because there is not enough time for significant remineralization to occur.

Data assimilation using ensemble transform Kalman filter (ETKF) in ROMS model for Indian Ocean

Study of Oceans dynamics and forecast is crucial as it influences the regional climate and other marine activities. Forecasting oceanographic states like sea surface currents, Sea surface temperature (SST) and mixed layer depth at different time scales is extremely important for these activities. These forecasts are generated by various ocean general circulation models (OGCM). One such model is the Regional Ocean Modelling System (ROMS). Though ROMS can simulate several features of ocean, it cannot reproduce the thermocline of the ocean properly. Solution to this problem is to incorporates data assimilation (DA) in the model. DA system using Ensemble Transform Kalman Filter (ETKF) has been developed for ROMS model to improve the accuracy of the model forecast. To assimilate data temperature and salinity from ARGO data has been used as observation. Assimilated temperature and salinity without localization shows oscillations compared to the model run without assimilation for India Ocean. Same was also found for u and v-velocity fields. With localization we found that the state variables are diverging within the localization scale.

A note on new indices for the equatorial Indian Ocean oscillation

It is now well known that there is a strong association of the extremes of the Indian summer monsoon rainfall (ISMR) with the El Nio and southern oscillation (ENSO) and the Equatorial Indian Ocean Oscillation (EQUINOO), later being an east-west oscillation in convection anomaly over the equatorial Indian Ocean. So far, the index used for EQUINOO is EQWIN, which is based on the surface zonal wind over the central equatorial Indian Ocean. Since the most important attribute of EQUINOO is the oscillation in convection/precipitation, we believe that the indices based on convection or precipitation would be more appropriate. Continuous and reliable data on outgoing longwave radiation (OLR), and satellite derived precipitation are now available from 1979 onwards. Hence, in this paper, we introduce new indices for EQUINOO, based on the difference in the anomaly of OLR/precipitation between eastern and western parts of the equatorial Indian Ocean. We show that the strong association of extremes of the Indian summer monsoon with ENSO and EQUINOO is also seen when the new indices are used to represent EQUINOO.

Narrowband signal detection techniques in shallow ocean by acoustic vector sensor array

This paper presents the formulation and performance analysis of four techniques for detection of a narrowband acoustic source in a shallow range-independent ocean using an acoustic vector sensor (AVS) array. The array signal vector is not known due to the unknown location of the source. Hence all detectors are based on a generalized likelihood ratio test (GLRT) which involves estimation of the array signal vector. One non-parametric and three parametric (model-based) signal estimators are presented. It is shown that there is a strong correlation between the detector performance and the mean-square signal estimation error. Theoretical expressions for probability of false alarm and probability of detection are derived for all the detectors, and the theoretical predictions are compared with simulation results. It is shown that the detection performance of an AVS array with a certain number of sensors is equal to or slightly better than that of a conventional acoustic pressure sensor array with thrice as many sensors.

Data acquisition and processing at ocean bottom for a Tsunami warning system

The design and development of a Bottom Pressure Recorder for a Tsunami Early Warning System is described here. The special requirements that it should satisfy for the specific application of deployment at ocean bed and pressure monitoring of the water column above are dealt with. A high-resolution data digitization and low circuit power consumption are typical ones. The implementation details of the data sensing and acquisition part to meet these are also brought out. The data processing part typically encompasses a Tsunami detection algorithm that should detect an event of significance in the background of a variety of periodic and aperiodic noise signals. Such an algorithm and its simulation are presented. Further, the results of sea trials carried out on the system off the Chennai coast are presented. The high quality and fidelity of the data prove that the system design is robust despite its low cost and with suitable augmentations, is ready for a full-fledged deployment at ocean bed. (C) 2013 Elsevier Ltd. All rights reserved.

Development of a regional model for the North Indian Ocean

We have developed a one-way nested Indian Ocean regional model. The model combines the National Oceanic and Atmospheric Administration (NOAA) Geophysical Fluid Dynamics Laboratory's (GFDL) Modular Ocean Model (MOM4p1) at global climate model resolution (nominally one degree), and a regional Indian Ocean MOM4p1 configuration with 25 km horizontal resolution and 1 m vertical resolution near the surface. Inter-annual global simulations with Coordinated Ocean-Ice Reference Experiments (CORE-II) surface forcing over years 1992-2005 provide surface boundary conditions. We show that relative to the global simulation, (i) biases in upper ocean temperature, salinity and mixed layer depth are reduced, (ii) sea surface height and upper ocean circulation are closer to observations, and (iii) improvements in model simulation can be attributed to refined resolution, more realistic topography and inclusion of seasonal river runoff. Notably, the surface salinity bias is reduced to less than 0.1 psu over the Bay of Bengal using relatively weak restoring to observations, and the model simulates the strong, shallow halocline often observed in the North Bay of Bengal. There is marked improvement in subsurface salinity and temperature, as well as mixed layer depth in the Bay of Bengal. Major seasonal signatures in observed sea surface height anomaly in the tropical Indian Ocean, including the coastal waveguide around the Indian peninsula, are simulated with great fidelity. The use of realistic topography and seasonal river runoff brings the three dimensional structure of the East India Coastal Current and West India Coastal Current much closer to observations. As a result, the incursion of low salinity Bay of Bengal water into the southeastern Arabian Sea is more realistic. (C) 2013 Elsevier Ltd. All rights reserved.