Inter-comparison of model, satellite and in situ tropical cyclone heat potential in the North Indian Ocean

Abstract

The North Indian Ocean (NIO) experiences frequent tropical cyclones (TCs). TC heat potential (TCHP) is a major ocean parameter responsible for TC genesis and intensification changes. In this study, Indian National Centre for Ocean Information Services-Global Ocean Data Assimilation System (INCOIS-GODAS) model and satellite-derived TCHP data from National Remote Sensing Centre (NRSC) and National Oceanic and Atmospheric Administration (NOAA) are validated against TCHP from in situ profiles in the NIO during the period 2011-2013 for buoys and during 2005-2015 for Argo data. Data from eight moored buoys (6 in Bay of Bengal and 2 in Arabian Sea) under the Ocean Moored Buoy Network are used. Comparison of model and in situ TCHP yields correlation coefficients (root-mean-square errors in kJ/cm 2 ) of 0.74 (17.75), 0.59 (15.34), 0.70 (17.68), 0.60 (22.24), 0.57 (19.52), 0.73 (17.88) and 0.77 (39.17) at buoy locations BD08, BD09, BD10, BD11, BD13, AD06 and AD10. The scatter indices between collocated TCHP values at these locations were 0.32, 0.22, 0.30, 0.30, 0.31, 0.58 and 0.41. Further, it was found that satellite-based TCHP from NRSC match better with in situ as compared to near-real-time TCHP data obtained from NOAA. TCHP from INCOIS-GODAS model, NOAA delayed time data and NRSC TCHP data set are also in good agreement with those from Argo profiles. As a case study, model and in situ TCHP were compared during a TC, "Thane" at two buoy locations (BD11 and BD13), closest to its track. The analysis revealed underestimation of model TCHP at BD11, but good correlation at BD13. This could be attributed to the existence of a strong temperature inversion at BD11. It is observed that although the model is able to capture features like barrier and inversion layers, the temperature and depth of such layers are underestimated. Further, the recovery time from the influence of TC on the ocean subsurface is also much longer in case of the model which thus needs to be fine-tuned. Seasonal comparison of TCHP from various sources with in situ estimated TCHP also shows better correlation between all the products for the pre-summer monsoon compared to the post-summer monsoon season.

Interactions Between a Marine Heatwave and Tropical Cyclone Amphan in the Bay of Bengal in 2020

“…The build-up of TCHP since March 2020 can be seen for both boxes and this increased TCHP (also considered as ocean heat content) acts as preconditioning to sustain the MHW and tropical cyclone. Previous studies showed that subsurface ocean warming can play a key role in the generation of MHWs (Elzahaby and Schaeffer, 2019;Scannell et al, 2020) as well as tropical cyclones (Jangir et al, 2020(Jangir et al, , 2021.…”

“…This study focuses on the Bay of Bengal (BoB; Figure 1) whose dynamics are very complex due to the strong stratification from freshwater input, high SST values, and its vulnerability toward tropical cyclones that form during the pre-monsoon (March-May) and post-monsoon (October-November) seasons. The North Indian Ocean accounts for the highest number of fatalities globally from tropical cyclones (Mohanty et al, 2015), and the BoB experiences ∼5 to 7% of the total number of tropical cyclones that occur globally each year (Neetu et al, 2012;Jangir et al, 2020Jangir et al, , 2021. Out of 23 recorded deadly storms with fatalities > 10,000 in the last 300 years, 20 tropical cyclones formed over the BoB (Mohanty et al, 2015).…”

Tropical Cyclones and Coastal Vulnerability: Assessment and Mitigation

“…TC landfalls are known to be dangerous and devastating natural disasters with huge effects on life, property and ecosystem throughout the world and thus are multi-hazard weather phenomena (Anthes 2016;Jangir et al 2020). A TC is a large atmospheric system comprising a rapid inward circulation of air masses with a low-pressure centre and accompanied by stormy destructive weather.…”