Advanced Tropical Cyclone Prediction Using the Experimental Global ECMWF and Operational Regional COAMPS-TC Systems

Sharanya J. Majumdar aUniversity of Miami, Miami, Florida
bEuropean Centre for Medium-Range Weather Forecasts, Reading, United Kingdom

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Linus Magnusson bEuropean Centre for Medium-Range Weather Forecasts, Reading, United Kingdom

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Peter Bechtold bEuropean Centre for Medium-Range Weather Forecasts, Reading, United Kingdom

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Jean Raymond Bidlot bEuropean Centre for Medium-Range Weather Forecasts, Reading, United Kingdom

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James D. Doyle cU.S. Naval Research Laboratory, Monterey, California

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Abstract

Structure and intensity forecasts of 19 tropical cyclones (TCs) during the 2020 Atlantic hurricane season are investigated using two NWP systems. An experimental 4-km global ECMWF model (EC4) with upgraded moist physics is compared with a 9-km version (EC9) to evaluate the influence of resolution. EC4 is then benchmarked against the 4-km regional COAMPS–Tropical Cyclones (COAMPS-TC) system (CO4) to compare systems with similar resolutions. EC4 produced stronger TCs than EC9, with a >30% reduction of the maximum wind speed bias in EC4, resulting in lower forecast errors. However, both ECMWF predictions struggled to intensify initially weak TCs, and the radius of maximum winds (RMW) was often too large. In contrast, CO4 had lower biases in central pressure, maximum wind speed, and RMW. Regardless, minimal statistical differences between CO4 and EC4 intensity errors were found for ≥36-h forecasts. Rapid intensification cases yielded especially large intensity errors. CO4 produced superior forecasts of RMW, together with an excellent pressure–wind relationship. Differences in the results are due to contrasting physics and initialization schemes. ECMWF uses global data assimilation with no special treatment of TCs, whereas COAMPS-TC constructs a vortex for TCs with initial intensity ≥55 kt (∼28 m s−1) based on data provided by forecasters. Two additional ECMWF experiments were conducted. The first yielded improvements when the drag coefficient was reduced at high wind speeds, thereby weakening the coupling between the low-level winds and the surface. The second produced overly intense TCs when explicit deep convection was used, due to unrealistic mid–upper-tropospheric heating.

Significance Statement

Improved forecasts of tropical storms and hurricanes depend on advances in computer weather models. We tested an experimental high-resolution (4 km) version of the global ECMWF model against its 9-km counterpart to evaluate the influence of resolution on storm position and intensity. We also compared this with the 4-km U.S. Navy model, which is designed for tropical storms and hurricanes. Over a 3-month period during the active 2020 Atlantic hurricane season, we found that increasing the horizontal resolution improved intensity forecasts. The Navy model forecasts were superior for the radius of maximum winds and had lower intensity biases. Two additional experiments with the ECMWF model revealed the importance of simulating air–sea interaction in high winds and current challenges with explicitly simulating deep thunderstorm clouds in their system.

This article is included in the Tropical Cyclone Intensity Experiment (TCI) Special Collection.

© 2023 American Meteorological Society. This published article is licensed under the terms of the default AMS reuse license. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Sharanya J. Majumdar, s.majumdar@miami.edu

Abstract

Structure and intensity forecasts of 19 tropical cyclones (TCs) during the 2020 Atlantic hurricane season are investigated using two NWP systems. An experimental 4-km global ECMWF model (EC4) with upgraded moist physics is compared with a 9-km version (EC9) to evaluate the influence of resolution. EC4 is then benchmarked against the 4-km regional COAMPS–Tropical Cyclones (COAMPS-TC) system (CO4) to compare systems with similar resolutions. EC4 produced stronger TCs than EC9, with a >30% reduction of the maximum wind speed bias in EC4, resulting in lower forecast errors. However, both ECMWF predictions struggled to intensify initially weak TCs, and the radius of maximum winds (RMW) was often too large. In contrast, CO4 had lower biases in central pressure, maximum wind speed, and RMW. Regardless, minimal statistical differences between CO4 and EC4 intensity errors were found for ≥36-h forecasts. Rapid intensification cases yielded especially large intensity errors. CO4 produced superior forecasts of RMW, together with an excellent pressure–wind relationship. Differences in the results are due to contrasting physics and initialization schemes. ECMWF uses global data assimilation with no special treatment of TCs, whereas COAMPS-TC constructs a vortex for TCs with initial intensity ≥55 kt (∼28 m s−1) based on data provided by forecasters. Two additional ECMWF experiments were conducted. The first yielded improvements when the drag coefficient was reduced at high wind speeds, thereby weakening the coupling between the low-level winds and the surface. The second produced overly intense TCs when explicit deep convection was used, due to unrealistic mid–upper-tropospheric heating.

Significance Statement

Improved forecasts of tropical storms and hurricanes depend on advances in computer weather models. We tested an experimental high-resolution (4 km) version of the global ECMWF model against its 9-km counterpart to evaluate the influence of resolution on storm position and intensity. We also compared this with the 4-km U.S. Navy model, which is designed for tropical storms and hurricanes. Over a 3-month period during the active 2020 Atlantic hurricane season, we found that increasing the horizontal resolution improved intensity forecasts. The Navy model forecasts were superior for the radius of maximum winds and had lower intensity biases. Two additional experiments with the ECMWF model revealed the importance of simulating air–sea interaction in high winds and current challenges with explicitly simulating deep thunderstorm clouds in their system.

This article is included in the Tropical Cyclone Intensity Experiment (TCI) Special Collection.

© 2023 American Meteorological Society. This published article is licensed under the terms of the default AMS reuse license. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Sharanya J. Majumdar, s.majumdar@miami.edu
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