2018
DOI: 10.1016/j.oceano.2017.10.001
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Investigating the role of air-sea forcing on the variability of hydrography, circulation, and mixed layer depth in the Arabian Sea and Bay of Bengal

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Cited by 32 publications
(22 citation statements)
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“…The energy required for mixing (ERM) is a measure of the potential energy of the upper water column, which is computed as (Srivastava et al, 2018) italicERM=180.25em()ρhρ0gh2, where ρ 0 is the density of surface water and ρ h is the density of water at depth h . Thus, the ERM surface water to the base of the mixed layer is the difference between the potential energy of a stratified column at the base of the mixed layer and that of the same column when it is unstratified (or mixed vertically) (Shenoi et al, 2002).…”
Section: Methodsmentioning
confidence: 99%
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“…The energy required for mixing (ERM) is a measure of the potential energy of the upper water column, which is computed as (Srivastava et al, 2018) italicERM=180.25em()ρhρ0gh2, where ρ 0 is the density of surface water and ρ h is the density of water at depth h . Thus, the ERM surface water to the base of the mixed layer is the difference between the potential energy of a stratified column at the base of the mixed layer and that of the same column when it is unstratified (or mixed vertically) (Shenoi et al, 2002).…”
Section: Methodsmentioning
confidence: 99%
“…The energy required for mixing (ERM) is a measure of the potential energy of the upper water column, which is computed as (Srivastava et al, 2018)…”
Section: Estimation Of the Energy Required For Mixingmentioning
confidence: 99%
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“…ROM simulated values closely agree with observations over the homogeneous rainfall zones. We notice that the root mean square error (RMSE) between the model and observation is relatively less than the observed rainfall standard deviation for all zone except SPI, thus demonstrating our simulation's quality as a lesser RMSE of the model than observed variability (standard deviation) is good skill score to measure the quality of the model (Srivastava et al, 2016(Srivastava et al, , 2018Dwivedi et al, 2018Dwivedi et al, , 2019Mishra et al, 2020a) Noticeably, the increasing resolution results in improving the model's performance over most of the study regions except SPI where performance is found to degrade with increasing resolution. The ISM undergoes enhanced and suppressed rainfall activity over India on an intraseasonal time scale (Goswami and Ajaya Mohan, 2001;Dwivedi et al, 2006;Shahi et al, 2018).…”
Section: Spatiotemporal Variability Of Ismrmentioning
confidence: 69%
“…It is interesting to note that the LS shows greater sensitivity to horizontal resolution in comparison to CS. The strength of the Indian monsoon is significantly affected by the sea surface temperature (SST) of the IO (Srivastava et al, 2018;Mishra et al, 2020a). Therefore, the comparative skill of the ROM in representing the IO SST is worth mentioning.…”
Section: Identification Of Source Of Bias In Seasonal Mean Precipitationmentioning
confidence: 99%