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Article
Article Type:
Research Article

Objective Debiasing for Improved Forecasting of Tropical Cyclone Intensity with a Global Circulation Model

P. Goswami CSIR Centre for Mathematical Modelling and Computer Simulation, Council of Scientific and Industrial Research, Bangalore, India

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S. Mallick CSIR Centre for Mathematical Modelling and Computer Simulation, Council of Scientific and Industrial Research, Bangalore, India

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K. C. Gouda CSIR Centre for Mathematical Modelling and Computer Simulation, Council of Scientific and Industrial Research, Bangalore, India

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Print Publication:
01 Aug 2011
DOI:
https://doi.org/10.1175/2011MWR3600.1
Page(s):
2471–2487
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Abstract

The damage potential of a tropical cyclone is proportional to a power (generally greater than one) of intensity, which demands high accuracy in forecasting intensity for managing this natural disaster. However, the current skill in forecasting cyclone intensity is rather limited, especially over the north Indian Ocean, with very little improvement over the years. A methodology is presented here for objective nonlinear debiasing to generate intensity forecasts with enhanced reliability from raw forecasts. The intensity forecast is generated using an optimized configuration of a variable resolution global circulation model (VR-GCM) that combines the advantages of a limited area model and a global model. The hindcasts were carried out in a completely operational setting, that is, without assuming any observed information beyond the day of the initial condition. The VR-GCM and a nonlinear debiasing were found to provide skill (skill score ~0.5) in forecasting tropical cyclone intensity 2–7 days (variable depending on event) in advance for the 30 cases including storms and cyclones representing different locations, seasons, and years (1990–2005) over the Bay of Bengal. Two types of debiasing are considered: nonlinear debiasing with all observations (potential skill) and nonlinear debiasing for realizable skill (training without in-sample data). It is shown that while skill scores without debiasing are only marginally better than a climatological forecast (null hypothesis), the skill score with a nonlinear debiasing is appreciable. The climatological forecast has zero skill score, a mean absolute error of 12.9 m s−1, and 20% of the cases are in the error bin −5 to +5 m s−1; the corresponding numbers for debiased forecasts for realizable skill are 0.65, 6.4 m s−1, and 57%. It is further shown that the nonlinear debiasing is also effective in improving forecast of (3-hourly) intensity change. While a strict comparison of skill with other methods requires experiments to be carried out for the same events, a comparison of the skill of other methods and over different ocean basins based on available data shows the present method to have comparable skill. However, it may be noted that the present conclusions are based on a relatively small (30 events) sample size; evaluation with a much large sample size is desirable for actual application.

Corresponding author address: P. Goswami, Centre for Mathematical Modelling and Computer Simulation, Council of Scientific and Industrial Research, Wind Tunnel Road, Bangalore-560 037, India. E-mail: goswami@cmmacs.ernet.in

Abstract

The damage potential of a tropical cyclone is proportional to a power (generally greater than one) of intensity, which demands high accuracy in forecasting intensity for managing this natural disaster. However, the current skill in forecasting cyclone intensity is rather limited, especially over the north Indian Ocean, with very little improvement over the years. A methodology is presented here for objective nonlinear debiasing to generate intensity forecasts with enhanced reliability from raw forecasts. The intensity forecast is generated using an optimized configuration of a variable resolution global circulation model (VR-GCM) that combines the advantages of a limited area model and a global model. The hindcasts were carried out in a completely operational setting, that is, without assuming any observed information beyond the day of the initial condition. The VR-GCM and a nonlinear debiasing were found to provide skill (skill score ~0.5) in forecasting tropical cyclone intensity 2–7 days (variable depending on event) in advance for the 30 cases including storms and cyclones representing different locations, seasons, and years (1990–2005) over the Bay of Bengal. Two types of debiasing are considered: nonlinear debiasing with all observations (potential skill) and nonlinear debiasing for realizable skill (training without in-sample data). It is shown that while skill scores without debiasing are only marginally better than a climatological forecast (null hypothesis), the skill score with a nonlinear debiasing is appreciable. The climatological forecast has zero skill score, a mean absolute error of 12.9 m s−1, and 20% of the cases are in the error bin −5 to +5 m s−1; the corresponding numbers for debiased forecasts for realizable skill are 0.65, 6.4 m s−1, and 57%. It is further shown that the nonlinear debiasing is also effective in improving forecast of (3-hourly) intensity change. While a strict comparison of skill with other methods requires experiments to be carried out for the same events, a comparison of the skill of other methods and over different ocean basins based on available data shows the present method to have comparable skill. However, it may be noted that the present conclusions are based on a relatively small (30 events) sample size; evaluation with a much large sample size is desirable for actual application.

Corresponding author address: P. Goswami, Centre for Mathematical Modelling and Computer Simulation, Council of Scientific and Industrial Research, Wind Tunnel Road, Bangalore-560 037, India. E-mail: goswami@cmmacs.ernet.in
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