Performance of a Mesoscale Ensemble Kalman Filter (EnKF) during the NOAA High-Resolution Hurricane Test

Abstract

Abstract An ensemble Kalman filter (EnKF) combined with the Advanced Research Weather Research and Forecasting model (ARW-WRF; hereafter WRF) on a 36-km Atlantic basin domain is cycled over six different time periods that include the 10 tropical cyclones (TCs) selected for the NOAA High-Resolution Hurricane (HRH) test. The analysis ensemble is generated every 6 h by assimilating conventional in situ observations, synoptic dropsondes, and TC advisory position and minimum sea level pressure (SLP) data. On average, observation assimilation leads to smaller TC position errors in the analysis compared to the 6-h forecast; however, the same is true for TC minimum SLP only for tropical depressions and storms. Over the 69 HRH initialization times, TC track forecasts from a single member of the WRF EnKF ensemble has 12 h less skill compared to other operational models; the increased track error partially results from the WRF EnKF analysis having a stronger Atlantic subtropical ridge. For nonmajor TCs, the WRF EnKF forecast has lower TC minimum SLP and maximum wind speed errors compared to some operational models, particularly the GFDL model, while category-3, -4, and -5 TCs are characterized by large biases due to horizontal resolution. WRF forecasts initialized from an EnKF analysis have similar or smaller TC track, intensity, and 34-kt wind radii errors relative to those initialized from two other operational analyses, which suggests that EnKF assimilation produces the best TC forecasts for this domain. Both TC track and intensity forecasts are deficient in ensemble variance, which is at least partially due to the lack of error growth in dynamical fields and model biases.