Joint Impact of Forecast Tendency and State Error Biases in Ensemble Kalman Filter Data Assimilation of Inner-Core Tropical Cyclone Observations

Abstract

n this study the properties and causes of systematic errors in high-resolution data assimilation of inner-core tropical cyclone (TC) observations were investigated using the Hurricane Weather Research and Forecasting (HWRF) Ensemble Data Assimilation System (HEDAS). Although a recent study by Aksoy et al. demonstrated overall good performance of HEDAS for 83 cases from 2008 to 2011 using airborne observations from research and operational aircraft, some systematic errors were identified in the analyses with respect to independent observation-based estimates. The axisymmetric primary circulation intensity was underestimated for hurricane cases and the secondary circulation was systematically weaker for all cases. The diagnostic analysis in this study shows that the underestimate of primary circulation was caused by the systematic spindown of the vortex core in the short-term forecasts during the cycling with observations. This tendency bias was associated with the systematic errors in the secondary circulation, temperature, and humidity. The biases were reoccurring in each cycle during the assimilation because of the inconsistency between the strength of primary and secondary circulation during the short-term forecasts, the impact of model error in planetary boundary layer dynamics, and the effect of forecast tendency bias on the background error correlations. Although limited to the current analysis the findings in this study point to a generic problem of mutual dependence of short-term forecast tendency and state estimate errors in the data assimilation of TC core observations. The results indicate that such coupling of errors in the assimilation would also lead to short-term intensity forecast bias after the assimilation for the same reasons.