Open AccessEvaluation of a Mesoscale Short-Range Ensemble Forecast System over the Northeast United States
Author(s) -
Matthew S. Jones,
Brian A. Colle,
Jeffrey S. Tongue
Publication year - 2007
Publication title -
weather and forecasting
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.393
H-Index - 106
eISSN - 1520-0434
pISSN - 0882-8156
DOI - 10.1175/waf973.1
Subject(s) - mm5 , mesoscale meteorology , meteorology , global forecast system , environmental science , ensemble forecasting , north american mesoscale model , climatology , precipitation , wind speed , range (aeronautics) , numerical weather prediction , physics , geology , engineering , aerospace engineering
A short-range ensemble forecast system was constructed over the northeast United States down to 12-km grid spacing using 18 members from the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5). The ensemble consisted of 12 physics members with varying planetary boundary layer schemes and convective parameterizations as well as seven different initial conditions (ICs) [five National Centers for Environmental Prediction (NCEP) Eta-bred members at 2100 UTC and the 0000 UTC NCEP Global Forecast System (GFS) and Eta runs]. The full 18-member ensemble (ALL) was verified at the surface for the warm (May–September 2003) and cool (October 2003–March 2004) seasons. A randomly chosen subset of seven physics (PHS) members at each forecast hour was used to quantitatively compare with the seven IC members. During the warm season, the PHS ensemble predictions for surface temperature and wind speed had more skill than the IC ensemble and a control (shared PHS and IC member) run initialized 12 h later (CTL12). During the cool and warm seasons, a 14-day running-mean bias calibration applied to the ALL ensemble (ALLBC) added 10%–30% more skill for temperature, wind speed, and sea level pressure, with the ALLBC far outperforming the CTL12. For the 24-h precipitation, the PHS ensemble had comparable probabilistic skill to the IC ensemble during the warm season, while the IC subensemble was more skillful during the cool season. All ensemble members had large diurnal surface biases, with ensemble variance approximating ensemble uncertainty only for wind direction. Selection of ICs was also important, because during the cool season the NCEP-bred members introduced large errors into the IC ensemble for sea level pressure, while none of the subensembles (PHS, IC, or ALL) outperformed the GFS–MM5 for sea level pressure.