Tropical Cyclone Heat Potential & Ocean Heat Content
Precise prediction of cyclone’s track and intensity remains as a major challenge even today. Among the various efforts underway to improve the physical understanding and predictability, one approach is to identify new and efficient input parameters. While sea surface temperature (SST) has been the only oceanographic input used in most of the statistical and dynamical prediction models, new studies indicate potential use of TCHP for cyclone studies (Ali et al., 2013). Contrary to the widely accepted belief that SST has a direct relationship with cyclone intensity (CI), a study at NRSC has demonstrated that this relationship is always not true in the North Indian Ocean (Figure 1). Analysing 75 cyclones of this region during 1998-2011, Ali et al., (2012) found that SST is not an adequate indicator of CI in this region, the two fields often being negatively correlated. They suggested an improved parameterisation of subsurface thermal structure of the ocean in place of SST, for example a parameter that also depends on OHC or TCHP.
Figure 1: Pie chart of Pearson correlations (r having 95% confidence level) between cyclone intensity (CI) and sea surface temperature (SST) for (a) one-day, (b) two-day, and (c) three-day lead times (SST leading CI). The first line in the chart represents the correlation range. The second line gives the number of cyclones and the percentage of the total number of cyclones.
The need for satellite derived TCHP for cyclone intensity prediction was also shown by Goni et al., (2009). They showed how Hurricane Katrina intensified by passing over high TCHP regions. The Importance of TCHP on tropical cyclone intensity forecasting and its impact on storm surge has been demonstrated by Lin et al., (2012). Importance of TCHP for storm surge prediction is shown in Figure 3. Neerja et al., (2013) have shown how inclusion of TCHP values as one of the predictors improves the cyclone intensity prediction over the western North Pacific Ocean compared to the existing approach used at Joint Typhoon Warning Centre (JTWC). TCHP was found to be more significant than 9 meteorological parameters used by JTWC. The observed epochal variations of intense tropical cyclones in the Arabian Sea are found to be consistent with the epochal variations of TCHP (Rajeevan et al., 2013).
Figure 2: (left) Track of Hurricane Katrina in the Gulf of Mexico during August 2005 superimposed on the tropical cyclone heat potential (TCHP ) field derived from altimetry. The color of the circles indicates storm category. (right) Minimum atmospheric pressure at sea level during the passage of Hurricane Katrina in the Gulf of Mexico in August 2005, howing the actual observations (black) and the reduction of error in the Geophysical Fluid Dynamics Laboratory model output with (red) and without (green) initializing the model with the TCHP produced at the NOAA National Hurricane Centre.
Figure 3: (a) Pre-Rita SSHA map showing the presence of pre-existing warm ocean eddies (characterized by +SSHA of ~10–40 cm) along Rita’s track. Rita’s track and intensity is depicted at every 6 h interval. (b) Difference in the upper ocean thermal structure conditions under the warm ocean eddy (red profile) and the climatological (i.e., without warm eddy, black profile) conditions. (c) Pre-Rita TCHP map under the ‘with warm eddy encountering’ scenario, as derived from the observed SSHA in (a). (d) TCHP map under the ‘without warm eddy encountering’ scenario, that is, based on the climatological condition.
Figure 4.Climatic annual average (a) and standard deviation (b) of the TCHP for the period 1993-2011.
Realising the importance of OHC for cyclone studies much earlier, a workshop was conducted at NRSC in 2010 on use of OHC for cyclone studies, the proceedings of which are available at:.
Ocean Heat Content
Besides TCHP, OHC of 700 m depth (OHC700) is another promising parameter required for many climate change studies and same is available at the NRSC / Bhuvan website for real time visualisation downloading:bhuvan-noeda. Some OHC studies need this parameter with respect to the climatological value. Hence, OHC-climatology is also provided along with each OHC700 value.
- Goni, G.J. M. DeMaria, J. Knaff , C. Sampson, I. Ginis, F. Bringas, A., Mavume, C. Lauer, I-I Lin, M. M. Ali, P. Sandery, S. Ramos-Buarque, K. Kang, A. Mehra, E. Chassignet, and G. Halliwell (2009). Applications of satellite-derived ocean measurements to tropical cyclone intensity forecasting, GODAE Special Issue Feature, Oceanography, Vol. 22, pp 190-197. Lin, I-I, G. J. Goni, J. Knaff, C. Forbes and M. M. Ali (2013). Ocean Heat Content for Tropical Cyclone Intensity Forecasting and Its Impact on Storm Surge, Natural Hazards, pp 1481-1500, DOI: 10.1007/s11069-012-0214-5.
- M M Ali, P S V Jagadeesh, I-I Lin, and Je-Yuan Hsu (2012). A Neural Network Approach to Estimate Tropical Cyclone Heat Potential in the Indian Ocean, IEEE Geoscience and Remote Sensing Letters, Vol. 9, pp 1114 – 1117, DOI No: 10.1109/LGRS.2012.2190491.
- M. M. Ali, D. Swain, Tina Kashyap, J. P. McCreary and P. V. Nagamani (2013). Relationship between Cyclone Intensities and Sea Surface Temperature in the Tropical Indian Ocean, IEEE Geoscience and Rem. Sens. Letters. Vol. 10, pp 841 – 844, DOI: 10.1109/LGRS.2012.2226138.
- M. M. Ali, G. J. Goni and V. Jayaraman (2010). Satellite derived ocean heat content improves cyclone prediction, Earth Observations System, Vol. 91, p 396.
- M. M. Ali, Tina Kashyap and PV Nagamani (2013). Use of Sea Surface Temperature for Cyclone Intensity Prediction Needs a Relook, EOS, Vol. 94, pp 117.
- M. Rajeevan, J. Srinivasan, K. Niranjan Kumar, C. Gnanaseelan3 and M. M. Ali (2013). On the epochal variation of intensity of tropical cyclonesin the Arabian Sea, Atmospheric Science Letters, DOI: 10.1002/asl2.447
- N. Sharma, MM Ali, John A. Knaff and Purna Chand (2013). A soft-computing cyclone intensity prediction scheme for the Western North Pacific Ocean, Atmospheric Science Letters, Vol. 14, pp 187-192, DOI:10.1002/asl2.438.