Evaluation of a mesoscale coupled ocean‐atmosphere configuration for tropical cyclone forecasting in the South West Indian Ocean basin

Type Article
Date 2023-03
Language English
Author(s) Corale LaëtitiaORCID1, Malardel SylvieORCID1, Bielli SolineORCID1, Bouin Marie‐noëlleORCID2, 3
Affiliation(s) 1 : Laboratoire de l’Atmosphère et des Cyclones (LACy), Université de La Réunion CNRS Météo‐France97400 Saint‐Denis, France
2 : CNRM ,Université de Toulouse CNRS Météo‐France31057 Toulouse, France
3 : Laboratoire d’Océanographie Physique et Spatiale, Université de Brest CNRS Ifremer IRD IUEM 29280 Plouzané, France
Source Earth And Space Science (2333-5084) (American Geophysical Union (AGU)), 2023-03 , Vol. 10 , N. 3 , P. e2022EA002584 (27p.)
DOI 10.1029/2022EA002584

The performance in term of tropical cyclone track and intensity prediction of the new coupled ocean-atmosphere system based on the operational atmospheric model AROME-Indian Ocean and the ocean model NEMO is assessed against that of the current operational configuration in the case of seven recent tropical cyclones. Five different configurations of the forecast system are evaluated: two with the coupled system, two with an ocean mixed layer parameterization and one with a constant sea surface temperature. For each ocean-atmosphere coupling option, one is initialized directly with the MERCATOR-Ocean PSY4 product as in the current operational configuration and the other with the ocean state that is cycled in the AROME-NEMO coupled suite since a few days before the cyclone intensification. The results show that the coupling with NEMO generally improves the intensity of cyclones in AROME-IO, reducing the mean intensity bias of the 72 h forecast of about 10 hPa. However, the impact is especially significant when the TCs encounter a slow propagation phase. For short-term forecasts (less than 36 hours), the presence of a cooling in the initial state that has been triggered by the AROME high-resolution cyclonic winds in a previous coupled forecast already improves the tropical cyclone intensity bias of 2-3 hPa for both coupled or uncoupled configurations.

Key Points

AROME/NEMO improves the forecast of tropical cyclone compared to the operational configuration with a 1D ocean mixed layer parameterization

The improvement mostly comes from quasi-stationary or very slow moving intense cyclones

The tropical cyclone forecasts are sensitive to the ocean initial conditions

Plain Language Summary

The ocean provides a large part of the energy for the intensification of tropical cyclones through warm sea surface temperature and sea-air heat and moisture exchanges. However, the ocean-atmosphere interactions also trigger processes which cools the sea surface temperature beneath the tropical cyclone and thus generates a negative feedback on the TC intensification. The numerical forecasts of the regional numerical weather prediction model AROME-IO are valuable guidance for the Regional Specialized Meteorological Centre for Tropical Cyclones, La Réunion. The objective of our study is to evaluate the possibility of replacing the current ocean mixed layer parameterization by a more realistic ocean able to represent more complex processes such as the explicit transport by the currents. Overall, we found that the new coupling improves the cyclone intensity in AROME-IO both in terms of bias and standard deviation. These improvements come almost entirely from tropical cyclones that encounter a slow propagation phase. For short-term forecasts, the presence of a cooling that is triggered by AROME high-resolution cyclonic winds in the initial state of the ocean already improves the TC intensity forecast, even when the ocean mixed layer parameterization is used.

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How to cite 

Corale Laëtitia, Malardel Sylvie, Bielli Soline, Bouin Marie‐noëlle (2023). Evaluation of a mesoscale coupled ocean‐atmosphere configuration for tropical cyclone forecasting in the South West Indian Ocean basin. Earth And Space Science, 10(3), e2022EA002584 (27p.). Publisher's official version : https://doi.org/10.1029/2022EA002584 , Open Access version : https://archimer.ifremer.fr/doc/00818/93030/