Observational perspective of SST changes during life cycle of tropical cyclones over Bay of Bengal

Pothapakula, Praveen Kumar; Osuri, Krishna K.; Pattanayak, Sujata; Mohanty, U. C.; Sil, Sourav; Nadimpalli, Raghu

doi:10.1007/s11069-017-2945-9

Cited by 24 publications

(25 citation statements)

References 40 publications

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“…The primary (secondary) maximum cooling of ∼0.33 °C (∼0.28 °C) is more (less) frequent at ∼25% (∼12%). The observed smaller range of maximum cooling spread in the post‐monsoon season could be attributed to the presence of thermal inversions and salinity‐stratified thick BL (Neetu et al ., ; Pothapakula et al ., ; Prakash and Pant, 2017). A similar kind of PDF analysis constructed for the TCs moving with different translation speeds is presented in Figure b.…”

Section: Resultsmentioning

confidence: 99%

“…The quality of INCOIS‐GODAS analyses has been assessed in various assessments (e.g. Ravichandran et al ., ; Sivareddy, ; Sivareddy et al ., ) including for tropical cyclone studies (Pothapakula et al ., ). For instance, the INCOIS‐GODAS compared to the in situ observations show a better SST representation with a root‐mean‐square error (RMSE) of 0.26 and 0.48 °C, respectively, for the pre‐ and post‐monsoon seasons over the BoB (Pothapakula et al ., ).…”

Section: Methodsmentioning

confidence: 97%

“…Studies such as Pothapakula et al . () concluded that slow and pre‐monsoon storms produce higher SST cooling of ∼2 °C than that of the post‐monsoon season in the BoB. As a feedback reverse, the amplitude of the storm‐induced SSTA affects translation speed and thereby the TC evolution (Zedler, ; Mei et al ., ; Pothapakula et al ., ).…”

Section: Introductionmentioning

confidence: 99%

“…Considering the SST feedbacks, slower (faster) moving TCs leads to relatively stronger (weaker) negative SSTA (Zedler, 2009). Studies such as Pothapakula et al (2017) concluded that slow and pre-monsoon storms produce higher SST cooling of ∼2 • C than that of the post-monsoon season in the BoB. As a feedback reverse, the amplitude of the storm-induced SSTA affects translation speed and thereby the TC evolution (Zedler, 2009;Mei et al, 2012;Pothapakula et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

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The response of ocean parameters to tropical cyclones in the Bay of Bengal

Busireddy

Ankur

Osuri

et al. 2019

Quart J Royal Meteoro Soc

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The Bay of Bengal (BoB) exhibits notable seasonal variations in tropical cyclone heat potential (TCHP), barrier layer thickness (BLT) and sea‐surface temperature (SST). These parameters also undergo profound changes in the presence of tropical cyclones (TCs). The composite structures of these ocean parameters as a function of the season of TC formation, intensity, and translation speed are unknown and are developed in the present study. Composite structures are examined based on 1,222 instantaneous samples from 83 TCs during 2003–2016 using INCOIS‐GODAS analyses. A BLT of 10–30 m and TCHP of 40–80 kJ/cm2 favours TC intensification in the central BoB. The multivariate regression of BLT and TCHP appears to be better for TC intensity up to 64 knots and is highly underestimated for the stronger TCs (>64 knots). The TC right‐rear sector experiences significant changes in TCHP anomaly (TCHPA) as the intensity increases. The TCHPA ranges ∼10–15, ∼20–25 and ∼25–30 kJ/cm2 when a TC is at Cyclone Storm (CS), Severe Cyclonic Storm (SCS) and Very SCS (VSCS) stages respectively. The maximum TCHPA is generally aligned along the TC track during the post‐monsoon season. Slow‐moving TCs produce maximum TCHPA cooling of ∼20 kJ/cm2 within 250 km storm radius in the rear sector, while it is less and away from the storm centre for normal and fast‐movers. The seasonal changes showed opposite relations between BLT and TCHP from pre‐ to post‐monsoon seasons during the TC intensification. TC‐induced SST cooling is maximum (∼0.5–1.2 °C) in the inner core for the strong (VSCS and above) and slow‐moving TCs. The cooling decreases with an increase in the translation speed and is more pronounced in the pre‐monsoon season. This study provides a baseline to verify and understand the limitations of the models, and also develop a climatological perspective of BoB TCs.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

The response of ocean parameters to tropical cyclones in the Bay of Bengal

Busireddy

Ankur

Osuri

et al. 2019

Quart J Royal Meteoro Soc

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“…These coefficients are set as in the operational INCOIS‐GODAS, the quality of which was assessed on various occasions (e.g. Ravichandran et al ., ; Sivareddy, ; Pothapakula et al ., ; Sivareddy et al ., ). We are also motivated by the weak sensitivity of the dynamic RE experiments results to different values of this coefficient, as discussed in the Supplementary Information Appendix S1 (sections 1 and 2). Assim_DynMod – same as Assim_DynObs but uses model‐based dynamic REs as OEs after adding the uniform SE s of 0.75 °C and 0.35 psu for temperature and salinity, respectively.…”

Section: Impact Of the Estimated Dynamic Re's On The Ocean Analysesmentioning

confidence: 99%

Impact of dynamical representational errors on an Indian Ocean ensemble data assimilation system

Sanikommu

Banerjee

Baduru

et al. 2019

Quart J Royal Meteoro Soc

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This study investigates the impact of dynamical representational error (RE) on the analysis of an ocean ensemble Kalman filter‐based data assimilation system, LETKF‐ROMS (Local Ensemble Transform Kalman Filter – Regional Ocean Modelling System) configured for the Indian Ocean and assimilating in situ temperature and salinity observations from Argo. Three different approaches to account for the RE are studied and inter‐compared: (a) static RE (varies in horizontal and vertical direction), (b) dynamic RE (varies in space and time) estimated from concurrent observations, and (c) dynamic RE estimated using concurrent high‐resolution model outputs. RE estimated from the model outputs exhibits rich spatial and temporal variability with an estimated temporal mean RE for temperature below 0.5 and 0.2 °C in the surface and deep layers, respectively, and reaching up to 1 °C in the thermocline layers. The region encompassing the Great Whirl displays a large seasonal variability reaching up to 0.8 °C, and the South Equatorial Current (SEC) a large interannual variability reaching up to 0.4 °C. Neglecting such spatio‐temporal variations of RE and assimilating with a static RE limited the benefits of assimilation by entertaining over‐fitting issues that caused degradations in the Bay of Bengal, the western parts of the Arabian Sea, and the equatorial Indian Ocean. Assimilating with the observations‐based dynamic RE improved the results in these regions, but the best performances were obtained with the configuration using the model‐based dynamic RE, which yielded further improvements, e.g. reduction of sea surface height root‐mean‐square errors reaches 30% with respect to the observations‐based dynamic RE. The latter also better handled the rich spatial variability regions and areas not well sampled by the observations. Improved estimates of the spatial and temporal variations of RE helped to better exploit the assimilated observations and provided enhanced analyses less prone to assimilation shocks.

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