EGU General Assembly 2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Global assessment of linear and non-linear statistical-dynamical hindcast models of Tropical Cyclones intensity

Neetu Suresh1, Lengaigne Matthieu2, Vialard Jerome2, Mangeas Morgan3, Menkes Christophe3, Suresh Iyyappan1, Leloup Julie2, and Knaff John4
Neetu Suresh et al.
  • 1CSIR-National Institute of Oceanography (NIO), Goa, India
  • 2Sorbonne Universités (UPMC, Univ Paris 06)-CNRS-IRD-MNHN, LOCEAN Laboratory, IPSL, Paris, France
  • 3IRD / UMR ENTROPIE, BP A5, 98848 Nouméa cedex, New Caledonia
  • 4NOAA/NESDIS, Center for Satellite Research and Applications, Fort Collins, Colorado, USA

Tropical cyclones (hereafter TC) are amongst the most devastating natural phenomena for coastal regions worldwide. While there has been tremendous progress in forecasting TC tracks, intensity forecasts have been trailing behind. Most operational statistical-dynamical forecasts of TC intensity use linear regression techniques to relate the initial TC characteristics and most relevant large-scale environmental parameters along the TC track to the TC intensification rate. Historically, different operational prediction schemes have been developed independently for each TC-prone basin, making it difficult to compare skills between different TC basins. We have thus developed global TC intensity hindcasts using consistent predictors derived from a single atmospheric dataset over the same period. Linear hindcast schemes were built separately for each TC basin, based on multiple linear regression. They display comparable skill to previously-described similar hindcast schemes, and beat persistence by 20–40% in most basins, except in the North Atlantic and northern Indian Ocean, where the skill gain is only 10–25%. Most (60–80%) of the skill gain arises from the TC characteristics (intensity and its rate of change) at the beginning of the forecast, with a relative contribution from each environmental parameter that is strongly basin-dependent. Hindcast models built from climatological environmental predictors perform almost as well as using real-time values, which may allow to considerably simplify operational implementation in such models. Our results finally reveal that these models have 2 to 4 times less skill in hindcasting moderate (Category 2 and weaker) than in hindcasting strong TCs.

This last result suggests that linear models may not be sufficient for TC intensity hindcasts. Many physical processes involved in TCs intensification are indeed non-linear. We hence further investigated the benefits of non-linear statistical prediction schemes, using the same set of input parameters as for the linear models above. These schemes are based on either support vector machine (SVM) or artificial neural network algorithms. Contrary to linear schemes, which perform slightly better when trained individually over each TC basin, non-linear methods perform best when trained globally. Non-linear schemes improve TC intensity hindcasts relative to linear schemes in all TC-prone basins, especially SVM for which this improvement reaches ~10% globally, partly because they better use the non-seasonal variations of environmental predictors. The SVM scheme, in particular, partially corrects the tendency of the linear scheme to underperform for Category 2 and weaker TCs. Although the TC intensity hindcast skill improvements described above are an upper limit of what could be achieved operationally, it is comparable to that achieved by operational forecasts over the last 20 years. This improvement is sufficiently large to motivate more testing of non-linear methods for statistical TC intensity prediction at operational centres.

How to cite: Suresh, N., Matthieu, L., Jerome, V., Morgan, M., Christophe, M., Iyyappan, S., Julie, L., and John, K.: Global assessment of linear and non-linear statistical-dynamical hindcast models of Tropical Cyclones intensity, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12563,, 2020

This abstract will not be presented.