Domingues, R., Kuwano-Yoshida, A., Chardon-Maldonado, P., Todd, R. E., Halliwell, G., Kim, H. - S., et al. (2019). Ocean Observations in Support of Studies and Forecasts of Tropical and Extratropical Cyclones. Front. Mar. Sci. , 6 , 446.
Abstract: Over the past decade, measurements from the climate-oriented ocean observing system have been key to advancing the understanding of extreme weather events that originate and intensify over the ocean, such as tropical cyclones (TCs) and extratropical bomb cyclones (ECs). In order to foster further advancements to predict and better understand these extreme weather events, a need for a dedicated observing system component specifically to support studies and forecasts of TCs and ECs has been identified, but such a system has not yet been implemented. New technologies, pilot networks, targeted deployments of instruments, and state-of-the art coupled numerical models have enabled advances in research and forecast capabilities and illustrate a potential framework for future development. Here, applications and key results made possible by the different ocean observing efforts in support of studies and forecasts of TCs and ECs, as well as recent advances in observing technologies and strategies are reviewed. Then a vision and specific recommendations for the next decade are discussed.
Goni, G., DeMaria, M., Knaff, J., Sampson, C., Ginis, I., Bringas, F., et al. (2009). Applications of Satellite-Derived Ocean Measurements to Tropical Cyclone Intensity Forecasting. Oceanog. , 22 (3), 190–197.
Ali, M. M. (2020). Is it high time to use ocean mean temperature for monsoon prediction? Atmosphera , .
Abstract: A monsoon is a seasonal reversal in the prevailing wind direction, that is usually initiated by the land sea temperature contrast. The Indian summer monsoon, for example, is triggered when the land gets heated up more than the surrounding sea during the summer creating a pressure gradient between the land and the sea. It is well known that the ocean thermal energy required for fueling monsoon circulations comes from the upper layer of the ocean (e.g. Venugopal et al. 2018). But such amount of energy does not come from the top thin layer represented by sea surface temperature (SST) alone. Nevertheless, often SST does not represent the thermal energy available in the upper ocean, although this parameter has been the only oceanographic input to the cyclone and monsoon atmospheric numerical and statistical models.
Bhowmick, S. A., Agarwal, N., Ali, M. M., Kishtawal, C. M., & Sharma, R. (2019). Role of ocean heat content in boosting post-monsoon tropical storms over Bay of Bengal during La-Nina events. Climate Dynamics , 52 (12), 7225–7234.
Abstract: This study aims to analyze the role of ocean heat content in boosting the post-monsoon cyclonic activities over Bay of Bengal during La-Niña events. In strong La-Niña years, accumulated cyclone energy in Bay of Bengal is much more as compared to any other year. It is observed that during late June to October of moderate to strong La-Nina years, western Pacific is warmer. Sea surface temperature anomaly of western Pacific Ocean clearly indicates the presence of relatively warmer water mass in the channel connecting the Indian Ocean and Pacific Ocean, situated above Australia. Ocean currents transport the heat zonally from Pacific to South eastern Indian Ocean. Excess heat of the southern Indian Ocean is eventually transported to eastern equatorial Indian Ocean through strong geostrophic component of ocean current. By September the northward transport of this excess heat from eastern equatorial Indian Ocean to Bay of Bengal takes place during La-Nina years boosting the cyclonic activities thereafter.
Ali, M., Singh, N., Kumar, M., Zheng, Y., Bourassa, M., Kishtawal, C., et al. (2018). Dominant Modes of Upper Ocean Heat Content in the North Indian Ocean. Climate , 6 (3), 71.
Abstract: The thermal energy needed for the development of hurricanes and monsoons as well as any prolonged marine weather event comes from layers in the upper oceans, not just from the thin layer represented by sea surface temperature alone. Ocean layers have different modes of thermal energy variability because of the different time scales of ocean-atmosphere interaction. Although many previous studies have focused on the influence of upper ocean heat content (OHC) on tropical cyclones and monsoons, no study thus farparticularly in the North Indian Ocean (NIO)has specifically concluded the types of dominant modes in different layers of the ocean. In this study, we examined the dominant modes of variability of OHC of seven layers in the NIO during 1998-2014. We conclude that the thermal variability in the top 50 m of the ocean had statistically significant semiannual and annual modes of variability, while the deeper layers had the annual mode alone. Time series of OHC for the top four layers were analyzed separately for the NIO, Arabian Sea, and Bay of Bengal. For the surface to 50 m layer, the lowest and the highest values of OHC were present in January and May every year, respectively, which was mainly caused by the solar radiation cycle.
Ali, M., Singh, N., Kumar, M., Zheng, Y., Bourassa, M., Kishtawal, C., et al. (2019). Dominant Modes of Upper Ocean Heat Content in the North Indian Ocean. Climate , 6 (71), 1–8.
Abstract: The thermal energy needed for the development of hurricanes and monsoons as well as any prolonged marine weather event comes from layers in the upper oceans, not just from the thin layer represented by sea surface temperature alone. Ocean layers have different modes of thermal energy variability because of the different time scales of ocean–atmosphere interaction. Although many previous studies have focused on the influence of upper ocean heat content (OHC) on tropical cyclones and monsoons, no study thus far—particularly in the North Indian Ocean (NIO)—has specifically concluded the types of dominant modes in different layers of the ocean. In this study, we examined the dominant modes of variability of OHC of seven layers in the NIO during 1998–2014. We conclude that the thermal variability in the top 50 m of the ocean had statistically significant semiannual and annual modes of variability, while the deeper layers had the annual mode alone. Time series of OHC for the top four layers were analyzed separately for the NIO, Arabian Sea, and Bay of Bengal. For the surface to 50 m layer, the lowest and the highest values of OHC were present in January and May every year, respectively, which was mainly caused by the solar radiation cycle.
Krishnamurti, T. N., Jana, S., Krishnamurti, R., Kumar, V., Deepa, R., Papa, F., et al. (2017). Monsoonal intraseasonal oscillations in the ocean heat content over the surface layers of the Bay of Bengal. Journal of Marine Systems , 167 , 19–32.
Zheng, Y., Ali, M. M., & Bourassa, M. A. (2016). Contribution of Monthly and Regional Rainfall to the Strength of Indian Summer Monsoon. Mon. Wea. Rev. , 144 (9), 3037–3055.
Ali, M. M., Bourassa, M. A., Bhowmick, S. A., Sharma, R., & Niharika, K. (2016). Retrieval of Wind Stress at the Ocean Surface From AltiKa Measurements. IEEE Geosci. Remote Sensing Lett. , 13 (6), 821–825.
Ali, M. M., Bhowmick, S. A., Sharma, R., Chaudhury, A., Pezzullo, J. C., Bourassa, M. A., et al. (2015). An Artificial Neural Network Model Function (AMF) for SARAL-Altika Winds. IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing , 8 (11), 5317–5323.