Track and Intensity Forecasting of Hurricanes: Impact of Convection-Permitting Resolution and Global Ensemble Kalman Filter Analysis on 2010 Atlantic Season Forecasts

Abstract

Abstract Twice-daily 48-h tropical cyclone (TC) forecasts were produced for the fall 2010 Atlantic hurricane season using the Advanced Research core of the Weather Research and Forecasting (WRF-ARW) model on a large 4-km grid covering much of the northern Atlantic. WRF forecasts initialized from operational Global Forecast System (GFS) analyses based on the gridpoint statistical interpolation (GSI) three-dimensional variational data assimilation (3DVAR) system and from experimental global ensemble Kalman filter (EnKF) analyses, and corresponding global GFS forecasts were intercompared. For the track, WRF forecasts show improvement over GFS forecasts using either set of initial conditions (ICs). The EnKF-initialized GFS and WRF are also better than the corresponding GSI-initialized forecasts, but the difference is not always statistically significant. At all lead times, the WRF track errors are comparable to or smaller than the National Hurricane Center (NHC) official track forecast error, with those of the EnKF WRF being smallest. For weaker TCs, more improvement comes from the model (resolution) than from the ICs. For hurricane intensity TCs, EnKF ICs produce better track forecasts than GSI ICs, with the best forecast coming from WRF at most lead times. For intensity, EnKF ICs consistently outperform GSI ICs in both models for weaker TCs. For hurricane-strength TCs, EnKF ICs produce forecasts statistically indistinguishable from GSI ICs in either model. For all TCs combined, WRF produces about half the error of the corresponding GFS simulation beyond 24 h, and at 36 and 48 h, the errors are smaller than those from NHC official forecasts. The improvement is even greater for hurricane-strength TCs. Overall, the WRF forecasts initialized with EnKF ICs have the smallest intensity error, and the difference is statistically significant compared to the GFS forecasts.