Improving the Use of Dropsondes for NOAA Operations: HWRF Vortex-Scale Data Assimilation Applications

The assimilation of tropical cyclone (TC) inner-core (e.g., vortex scale) observations (i.e., Global Hawk, inner-core dropsondes, etc.,) within numerical weather prediction (NWP) models presents a unique opportunity to improve the forecast skill for TC intensity. However, in order to get the most benefit from these types of observations, the NWP model TC must first be accurately po- sitioned such that the assimilated observations can be applied to correct both the TC structure and initial intensity. The current operational NWP models, namely HWRF, employ variants of the methodologies prescribed by Kurihara et al., [1993] and Kurihara et al., [1995].

The aforementioned methodologies are highly tuned as a function of NWP model type and there- fore must be modified in accordance with the respective NWP model changes. This can often result in countless forecast cycles to determine if the tuning coefficients required to filter the TC from the synoptic environment and then relocate the respective TC, have been chosen correctly. In this study we seek an algorithm that is agnostic to both the atmospheric NWP model’s grid-length resolution and grid architecture and is thus applicable to any NWP model (regional-scale or global) capable of producing TCs. Further, the TC filtering methodology presented in this study is bound by meteorological analysis techniques and current observational understandings of TC structure.

We will first present an overview and application of the TC filtering algorithm to several TC cases. We will then apply the algorithm to relocate the TC for HWRF vortex-scale data assimilation and assess the impacts compared to the current operational HWRF forecast results.