Objective Debiasing for Improved Forecasting of Tropical Cyclone Intensity with a Global Circulation Model
IR@C-MMACS: CSIR-Centre for Mathematical Modelling and Computer Simulation, Bangalore
View Archive Info| Field | Value | |
| Title |
Objective Debiasing for Improved Forecasting of Tropical Cyclone Intensity
with a Global Circulation Model
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| Creator |
Goswami, P
Gouda, K C |
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| Subject |
Climate Modelling
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| Description |
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 s21, and 20% of the cases are in the error bin25 to15 m s21; the corresponding numbers for debiased
forecasts for realizable skill are 0.65, 6.4 m s21, 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.
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| Publisher |
American Meteorological Society
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| Date |
2011-08
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| Type |
Article
PeerReviewed |
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| Format |
application/pdf
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| Identifier |
http://cir.cmmacs.ernet.in/232/1/2011_mwr_CYCLONE.pdf
Goswami, P and Gouda, K C (2011) Objective Debiasing for Improved Forecasting of Tropical Cyclone Intensity with a Global Circulation Model. Monthly Weather Review, 139 (8). pp. 2471-2487. ISSN Online ISSN: 1520-0493, Print ISSN: 0027-0644 |
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| Relation |
http://journals.ametsoc.org
http://cir.cmmacs.ernet.in/232/ |
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