Debadatta Swain

Estimation of mixed-layer depth from surface parameters

Mixed layer depth (MLD) is an important oceanographic parameter. However, the lack of direct observations of MLD hampers both specification and investigation of its spatial and temporal variability. An important alternative to direct observation would be the ability to estimate MLD from surface parameters easily available from satellites. In this study, we demonstrate estimation of MLD using Artificial Neural Network methods and surface meteorology from a surface mooring in the Arabian Sea. The estimated MLD had a root mean square error of 7.36 m and a coefficient of determination (R) of 0.94. About 67% (91%) of the estimates lie within 5 m ( 10 m) of the MLD determined from temperature sensors on the mooring.

Relationship Between Cyclone Intensities and Sea Surface Temperature in the Tropical Indian Ocean

In most cyclone prediction models, sea surface temperature (SST) is the only oceanographic input, even though storms are known to be impacted by the thermal energy available through oceanic heat content, not just by SST alone. In the tropical Indian Ocean (TIO; 30° S -30° N, 30-120° E), there are no studies that examine the relationship between instantaneous cyclone intensity (CI) and SST as a function of time. Here, we explore that relationship using SST data from the Tropical Rainfall Measuring Mission Microwave Imager and CI data (maximum sustained winds) from the Joint Typhoon Warning Centre. We find that out of 75 TIO cyclones studied during 1998-2011, more than 50% of the cyclones have no significant correlation between CI and SST. The numbers having significant negative (positive) correlations are 31 (3), 13 (10), and 17 (14) with SST leading CI by one, two, and three days, respectively. These results demonstrate that SST is not a useful indicator of CI in the TIO.

Daily composite wind fields from Oceansat-2 scatterometer

Oceansat-2 scatterometer (OSCAT) is an active microwave sensor, intended to provide ocean surface wind vectors over the global oceans. In the present work, an attempt has been made to generate daily composites of OSCAT Level-3 (L3) wind vectors using Data-Interpolating Variational Analysis (DIVA) method from ascending and descending passes over the Indian Ocean region. This could be useful for operational purposes and in generating value-added products like wind stress and curl of wind stress. The daily composite wind vectors of zonal (U) and meridional (V) components have been validated by comparing with Advanced Scatterometer (ASCAT) and wind from in situ buoys for the year 2012. Wind composites thus generated using DIVA are found to match well with in situ, and ASCAT wind products. Minor deviations are observed with respect to ASCAT wind, which could be attributed to the difference in interpolation techniques used for the two scatterometer products. Given that the repeat period of ASCAT is 5 days and that of OSCAT is only 2 days, OSCAT wind products could be conveniently used for real-time met-ocean studies.

Application of Artificial Neural Networks for Sea-Surface Wind-Speed Retrieval From IRS-P4 (MSMR) Brightness Temperature

Indian Remote Sensing Satellite Multifrequency Scanning Microwave Radiometer (MSMR)-measured brightness temperatures (TB) in 6.6-, 10.65-, 18-, and 21-GHz channels with dual polarizations were utilized to retrieve sea-surface wind speed (SSWS). A concurrent and collocated database was constructed on MSMR TB- and deep-sea (DS)-buoy-recorded wind speeds for the period of June 1999-July 2001 over the north Indian Ocean. A radial-basis-function-based artificial-neural-network (ANN) algorithm was developed to estimate SSWS from MSMR TB values. Multiple ANNs were generated by the systematic variation of the architecture of input- and hidden-layer nodes. The performance of the most successful algorithm was evaluated based on statistical summary and network performance. The accuracy of the ANN-based wind-speed algorithm was compared with DS-buoy observations, and the result was then compared with the output of the regression analysis between buoy- and MSMR operational-global-retrieval-algorithm (OGRA)-derived SSWS values. On the average, 84% (92%) of ANN-estimated MSMR SSWS observations are within ±2 m/s ( ±3 m/s) when compared with DS-buoy observations. The correlation and root mean square error of 0.80 and 1.79 m/s, respectively, for ANN-predicted SSWS values are much better than that obtained from OGRA. The performance of the ANN algorithm was also evaluated during a super cyclone (October 1999) over the Bay of Bengal. The ANN algorithm could capture the high cyclonic winds, and the values match reasonably well with Special Sensor Microwave/Imager and SeaWinds Scatterometer (QuikSCAT) operational wind products.

Seasonal variability of sonic layer depth in the Central Arabian Sea

The seasonal variability of sonic layer depth (SLD) in the central Arabian Sea (CAS) (0 to 25°N and 62-66°E) was studied using the temperature and salinity (T/S) profiles from Argo floats for the years 2002–2006. The atmospheric forcing responsible for the observed changes was explored using the meteorological data from NCEP/NCAR and Quickscat winds. SLD was obtained from sound velocity profiles computed from T/S data. Net heat flux and wind forcing regulated SLD in the CAS. Up-welling and down-welling (Ekman dynamics) associated with the Findlater Jet controlled SLD during the summer monsoon. While in winter monsoon, cooling and convective mixing regulated SLD in the study region. Weak winds, high insolation and positive net heat flux lead to the formation of thin, warm and stratified sonic layer during pre and post summer monsoon periods, respectively.

First observational study during a solar eclipse event on variations in the horizontal winds simultaneously in the troposphere-stratosphere-mesosphere-lower-thermosphere region over the equatorial station Thumba (8.5°N, 77°E)

The longest annular solar eclipse of the millennium occurred on 15 January, 2010, and was visible over the equatorial station Thumba (8.5°N, 77°E) around noon time. A host of experiments were carried out to study the variations due to the solar eclipse event on various geophysical parameters, from the Earth’s surface to ionospheric heights. The present study focuses on the variation in the horizontal winds in the height regions of 0–65 km and 80–100 km, using GPS-sondes, rocket-sondes and meteor wind radar. The observations were made during, and after, the maximum obscuration on the day of the eclipse, as well as at the same time on a control day. The observations showed a strengthening/weakening of winds along with directional changes both in zonal and meridional winds in the selected height domains. A drastic change from easterly to westerly is observed at 98 km during, and after, the maximum phase, but, for the meridional wind, the reversal is observed only after the maximum phase. Variations due to the eclipse were also observed around the tropopause and stratopause in both wind components. However, the observed changes in winds around the tropopause and stratopause could not be attributed unambiguously to the eclipse as day-to-day wind variability is not available in these height regions. The significance of the present study lies in reporting the variations in the horizontal wind components from the ground to the 100-km height region (with a gap around 65–80 km), and the characteristics of the atmospheric waves generated due to the mid-day annular solar eclipse.

Mixed layer variability in Northern Arabian Sea as detected by an Argo float

Seasonal evolution of surface mixed layer in the Northern Arabian Sea (NAS) between 17° N–20.5° N and 59° E-69° E was observed by using Argo float daily data for about 9 months, from April 2002 through December 2002. Results showed that during April - May mixed layer shoaled due to light winds, clear sky and intense solar insolation. Sea surface temperature (SST) rose by 2.3 °C and ocean gained an average of 99.8 Wm−2. Mixed layer reached maximum depth of about 71 m during June - September owing to strong winds and cloudy skies. Ocean gained abnormally low ∼18 Wm−2 and SST dropped by 3.4 °C. During the inter monsoon period, October, mixed layer shoaled and maintained a depth of 20 to 30 m. November - December was accompanied by moderate winds, dropping of SST by 1.5 °C and ocean lost an average of 52.5 Wm−2. Mixed layer deepened gradually reaching a maximum of 62 m in December. Analysis of surface fluxes and winds suggested that winds and fluxes are the dominating factors causing deepening of mixed layer during summer and winter monsoon periods respectively. Relatively high correlation between MLD, net heat flux and wind speed revealed that short term variability of MLD coincided well with short term variability of surface forcing.

Relation between tropical cyclone heat potential and cyclone intensity in the North Indian Ocean

Ocean Heat Content (OHC) plays a significant role in modulating the intensity of Tropical Cyclones (TC) in terms of the oceanic energy available to TCs. TC Heat Potential (TCHP), an estimate of OHC, is thus known to be a useful indicator of TC genesis and intensification. In the present study, we analyze the role of TCHP in intensification of TCs in the North Indian Ocean (NIO) through statistical comparisons between TCHP and Cyclone Intensities (CI). A total of 27 TCs (20 in the Bay of Bengal, and 7 in the Arabian Sea) during the period 2005–2012 have been analyzed using TCHP data from Global Ocean Data Assimilation System (GODAS) model of Indian National Center for Ocean Information Services and cyclone best track data from India Meteorological Department. Out of the 27 cyclones analyzed, 58% (86%) in the Bay (Arabian Sea) have negative correlation and 42% (14%) cyclones have positive correlation between CI and TCHP. On the whole, more than 60% cyclones in the NIO show negative correlations between CI and TCHP. The negative percentage further increases for TCHP leading CI by 24 and 48 hours. Similar trend is also seen with satellite derived TCHP data obtained from National Remote Sensing Center and TC best track data from Joint Typhoon Warming Centre. Hence, it is postulated that TCHP alone need not be the only significant oceanographic parameter, apart from sea surface temperature, responsible for intensification and propagation of TCs in the NIO.

Seasonal and inter-annual variability of sea surface temperature in the North Indian ocean and associated climate change signatures

Sea surface temperature (SST) is an essential parameter for describing ocean circulation and dynamics in the study of upper ocean physical and biogeochemical processes. In conjunction with other metocean parameters, SST is used in the estimation of latent and sensible heat fluxes which are indicative of air-sea exchanges of heat. Studying the variations of SST over a long period is thus important in understanding the nature of global climate change. Classically SST is measured using conventional techniques, but satellite derived SST have advantages of synoptic view and temporal coverage.

Trends in sea ice extent as observed from microwave remote sensing

Changes in sea ice extent are strong modulators of climate change as well as indicator of the effect of global warming. The ocean-atmospheric heat budget is also affected by the sea ice dynamics owing to reflection of solar radiation back to the space by sea ice cover. In the present work, we have analysed about 30 years of sea ice data (1981–2013) over the Antarctic and Arctic regions using 25 km spatial resolution re-gridded data products obtained from National Snow and Ice Data Centre (NSIDC), USA. These sea ice data products result from combined observations by the Scanning Multi-channel Microwave Radiometer (SMMR) on Nimbus-7 platform and a series of Special Sensor Microwave Imager (SSM/I) and Special Sensor Microwave Imager/Sounder (SSMIS) instruments on board the Defense Meteorological Satellite Program (DMSP) satellites.