Evaluation of Cool-Season Extratropical Cyclones in a Multimodel Ensemble for Eastern North America and the Western Atlantic Ocean

Nathan G. Korfe School of Marine and Atmospheric Sciences, Stony Brook University, State University of New York, Stony Brook, New York

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Brian A. Colle School of Marine and Atmospheric Sciences, Stony Brook University, State University of New York, Stony Brook, New York

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Abstract

This paper evaluates the extratropical cyclones within three operational global ensembles [the 20-member Canadian Meteorological Centre (CMC), 20-member National Centers for Environmental Prediction (NCEP), and 50-member European Centre for Medium-Range Weather Forecasts (ECMWF)]. The day-0–6 forecasts were evaluated over the eastern United States and western Atlantic for the 2007–15 cool seasons (October–March) using the ECMWF’s ERA-Interim dataset as the verifying analysis. The Hodges cyclone-tracking scheme was used to track cyclones using 6-h mean sea level pressure (MSLP) data. For lead times less than 72 h, the NCEP and ECMWF ensembles have comparable mean absolute errors in cyclone intensity and track, while the CMC errors are larger. For days 4–6 ECMWF has 12–18 and 24–30 h more accuracy for cyclone intensity than NCEP and CMC, respectively. All ensembles underpredict relatively deep cyclones in the medium range, with one area near the Gulf Stream. CMC, NCEP, and ECMWF all have a slow along-track bias that is significant from 24 to 90 h, and they have a left-of-track bias from 120 to 144 h. ECMWF has greater probabilistic skill for intensity and track than CMC and NCEP, while the 90-member multimodel ensemble (NCEP + CMC + ECMWF) has more probabilistic skill than any single ensemble. During the medium range, the ECMWF + NCEP + CMC multimodel ensemble has the fewest cases (1.9%, 1.8%, and 1.0%) outside the envelope compared to ECMWF (5.6%, 5.2%, and 4.1%) and NCEP (13.7%, 10.6%, and 11.0%) for cyclone intensity and along- and cross-track positions.

© 2018 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Dr. Brian A. Colle, brian.colle@stonybrook.edu

Abstract

This paper evaluates the extratropical cyclones within three operational global ensembles [the 20-member Canadian Meteorological Centre (CMC), 20-member National Centers for Environmental Prediction (NCEP), and 50-member European Centre for Medium-Range Weather Forecasts (ECMWF)]. The day-0–6 forecasts were evaluated over the eastern United States and western Atlantic for the 2007–15 cool seasons (October–March) using the ECMWF’s ERA-Interim dataset as the verifying analysis. The Hodges cyclone-tracking scheme was used to track cyclones using 6-h mean sea level pressure (MSLP) data. For lead times less than 72 h, the NCEP and ECMWF ensembles have comparable mean absolute errors in cyclone intensity and track, while the CMC errors are larger. For days 4–6 ECMWF has 12–18 and 24–30 h more accuracy for cyclone intensity than NCEP and CMC, respectively. All ensembles underpredict relatively deep cyclones in the medium range, with one area near the Gulf Stream. CMC, NCEP, and ECMWF all have a slow along-track bias that is significant from 24 to 90 h, and they have a left-of-track bias from 120 to 144 h. ECMWF has greater probabilistic skill for intensity and track than CMC and NCEP, while the 90-member multimodel ensemble (NCEP + CMC + ECMWF) has more probabilistic skill than any single ensemble. During the medium range, the ECMWF + NCEP + CMC multimodel ensemble has the fewest cases (1.9%, 1.8%, and 1.0%) outside the envelope compared to ECMWF (5.6%, 5.2%, and 4.1%) and NCEP (13.7%, 10.6%, and 11.0%) for cyclone intensity and along- and cross-track positions.

© 2018 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Dr. Brian A. Colle, brian.colle@stonybrook.edu
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