Seasonal variation of sea surface temperature in the Bay of Bengal during 1992 as derived from NOAA-AVHRR SST data

Murty, V. S. N.; Subrahmanyam, Bulusu; Rao, L.V.G.; Reddy, G. V.

doi:10.1080/014311698214776

Cited by 17 publications

(10 citation statements)

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“…corresponding to warmer sea surface temperature. Sea surface temperature reaches its annual maximum just before the summer monsoon onset and collapses soon after in the central areas of Bay of Bengal (Murty et al , ; Li et al , ), which agrees well with this argument. MSEs are greater in the pre‐monsoon (1.86–2.88) compared with the post‐monsoon (1.84–2.16) season because of a distinct variation of thermal forcing effect (Kattel et al , ).…”

Section: Resultsmentioning

confidence: 88%

“…corresponding to the cooler sea surface temperature, in particular at Bay of Bengal (e.g. Murty et al , ). However, steepening of TLRs values (>−9.8°C km −1 ; discussed in Section 4.2) in this season over the SETP does not show the relationship with enhancing latent heat flux across the seas, i.e.…”

Section: Resultsmentioning

confidence: 99%

“…The lower values of the constant in this season correspond to the cooling of sea surface temperature, i.e. associated with the southwest monsoon (Murty et al , ). In this season, the increasing rate of constant with elevation is higher throughout the observation period.…”

Section: Resultsmentioning

confidence: 99%

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Temperature–topographic elevation relationship for high mountain terrain: an example from the southeastern Tibetan Plateau

Kattel

2018

Intl Journal of Climatology

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This study quantifies the spatial–temporal distribution of the near‐surface temperature lapse rate (TLR) on the southeastern Tibetan Plateau (SETP) on the northern slopes of the eastern Himalayas, using the jackknife regression model. A 20‐year (1985–2004) climatic data set of 16 stations between the elevation range of 3553–4801 m asl was used for this investigation. Controls for the spatial–temporal variation on TLRs and causes of higher mean square error (MSE) from the jackknife regressions were also analysed. Dry convection due to increasing sensible heating at lower elevations associated with clear skies, the effect of cold air surges, cloud/fog, as well as pronounced long‐wave radiation loss due to snow cover at higher elevation, results in the super‐dry adiabatic TLR in the dry winter. In response to the effects of high rainfall and humidity, intense latent heating at higher elevation due to moist adiabatic cooling causes TLRs to decrease significantly in summer. The variation in net radiation due to differences in moisture, rainfall, cloud cover and air mass between higher (fewer) and lower (higher) elevations further contributes to reducing TLRs values in this season. Observed steeper values of TLRs at higher elevations and more shallow values at lower elevations are due to the thermal contrast between snow‐free ground and snow‐covered mountain terrain, as well as the effect of variations in air mass and moisture. Based on derived values, this article also estimates near‐surface temperature at higher elevations and analyses the precision of estimated values. Measurement of the lowest discrepancy (bias) between observed and estimated values from this study suggests that the estimation skills of the model are good enough. More precisely, including the smallest MSE from the jackknife regression and biases of the estimated values can be made for summer, perhaps due to the moisture controlled throughout the SETP, i.e. associated with the Indian monsoon. Higher MSE and biases are observed during winter and are due to the substantial effects of westerlies, as well as the station's geographical coordinate, local topography and microclimate. The MSE and the estimated biases are lowest at high‐elevation stations and are associated with the lower contaminating effects of the surface. This estimation model, using derived values from this investigation, could be useful for glacier‐hydro‐climatic and ecological modelling in this region.

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Section: Resultsmentioning

confidence: 88%

Section: Resultsmentioning

confidence: 99%

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Temperature–topographic elevation relationship for high mountain terrain: an example from the southeastern Tibetan Plateau

Kattel

2018

Intl Journal of Climatology

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“…왔으나 (Murty et al[1998]; Webster[1982]; Weare et al[1976]; Wyrtki [1965]), 최근의 연구추세에서는 벗어나 있는 경향을 보인다 (Kenyon [2013]…”

Section: 해양수온의 계절변동에 관한 연구는 과거부터 지속적으로 수행되어unclassified

Fluctuations and Time Series Forecasting of Sea Surface Temperature at Yeosu Coast in Korea

Seong¹,

Choi²,

Koo³

et al. 2014

Journal of the Korean Society for Marine Environment & Ener

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Seasonal variations and long term linear trends of SST (Sea Surface Temperature) at Yeosu Coast (127°37.73′E, 34°37.60′N) in Korea were studied performing the harmonic analysis and the regression analysis of the monthly mean SST data of 46 years (1965-2010) collected by the Fisheries Research and Development Institute in Korea. The mean SST and the amplitude of annual SST variation show 15.6 o C and 9.0 o C respectively. The phase of annual SST variation is 236°. The maximum SST at Yeosu Coast occurs around August 26. Climatic changes in annual mean SST have had significant increasing tendency with increase rate 0.0305 o C/Year. The warming trend in recent 30 years (1981-2010) is more pronounced than that in the last 30 years (1966-1995) and the increasing tendency of winter SST dominates that of the annual SST. The time series model that could be used to forecast the SST on a monthly basis was developed applying Box-Jenkins methodology. ARIMA(1,0,0)(2,1,0) 12 was suggested for forecasting the monthly mean SST at Yeosu Coast in Korea. Mean absolute percentage error to measure the accuracy of forecasted values was 8.3%.

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“…Ali et al (2007) have evidenced that, TC's intensify (dissipate) after traveling over anticyclonic (cyclonic) eddies. Murty et al (1998) have noted the persistence of Warm Pool (28.0°C) from April to October in the BoB. The warming of the central basin caused by trapping of Kelvin waves in the central Bay for many months creates conducive environment for cyclogenesis (Saheb et al 2009).…”

Section: Introductionmentioning

confidence: 99%

Development of tropical cyclone wind field for simulation of storm surge/sea surface height using numerical ocean model

Das

Mohanty

Indu

2015

Model. Earth Syst. Environ.

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Coupled ocean-atmospheric phenomenon such as tropical cyclones (TC's) is governed by geophysical fluid dynamics. TC associated strong wind stress transfer momentum energy to the ocean surface that acts as the prime mechanism in modulating the sea surface and in generating the storm surge. A primitive equation, Princeton ocean model (POM) with free surface, sigma (terrain-following) coordinates and realistic bottom topography is configured in Bay of Bengal (BoB) to simulate the storm surge/sea surface height (SSH) and surface currents during a super cyclone TC05B 1999. TC wind fields are developed by adopting a suitable formulation based on partial conservation of angular momentum. Modeled TC wind fields are superimposed with QuikSCAT Satellite/National Centre for Environmental Prediction (QSCAT/NCEP) blended ocean surface winds to drive the three-dimensional ocean model. Model simulated storm surge and SSH are compared with limited available surge estimates/observations and multi-satellite observed AVISO (Archive, Validation and Interpolation of Satellite Oceanography) SSH, respectively to evaluate its performance.

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Seasonal variation of sea surface temperature in the Bay of Bengal during 1992 as derived from NOAA-AVHRR SST data

Cited by 17 publications

References 0 publications

Temperature–topographic elevation relationship for high mountain terrain: an example from the southeastern Tibetan Plateau

Temperature–topographic elevation relationship for high mountain terrain: an example from the southeastern Tibetan Plateau

Fluctuations and Time Series Forecasting of Sea Surface Temperature at Yeosu Coast in Korea

Development of tropical cyclone wind field for simulation of storm surge/sea surface height using numerical ocean model

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