Recent advancements for tropical cyclone data assimilation

Abstract

In this review, data assimilation (DA) techniques used for tropical cyclones (TCs) are briefly overviewed. The strength and weakness of variational methods, ensemble methods, hybrid methods, and particle filter methods are also discussed. Several global numerical weather prediction models and their corresponding DA systems frequently used for TC forecasting and verification are described first. The DA research and development efforts in the operational regional model from the National Centers for Environmental Prediction's Hurricane Weather Research and Forecasting are then discussed in greater detail. Focused remarks on TC observations from reconnaissance, ground-based radar, enhanced satellite-derived atmospheric motion vectors and allsky satellite radiances and their impacts on TC analyses and forecasts are addressed.Recent TC DA advancements and challenges on better use of observations and more advanced DA methods for TC application are also briefly reviewed.

Exploring coastal climate adaptation through storylines: Insights from Cyclone Idai in Beira, Mozambique

“…It is based on a coupled atmosphere-wave-ocean model (Mogensen et al, 2017), has hourly temporal resolution, and offers the highest spatial resolution among global forecasts, 0.1°, which improves TC simulation (Knutson et al, 2020;Magnusson et al, 2019;Zhang et al, 2023). Previous studies have assessed the capabilities, advancements, and limitations of IFS in simulating TCs (Mogensen et al, 2017;Magnusson et al, 2019;Heming et al, 2019;Becker et al, 2021;Christophersen et al, 2022).…”

The Quasi‐Linear Absolute Angular Momentum Slope of Tropical Cyclones Under Rapid Intensification

“…A larger α represents a skinnier wind skirt outside of r m for a strong vortex, while smaller α represents a broader wind skirt outside of r m for a weaker vortex (Mallen et al., 2005). Equation achieves a continuous wind profile with very few inputs ( V m , r m , and α ), which has been widely used in vortex initialization (Braun et al., 2012; Stern & Nolan, 2012; Rappin et al., 2013; Komaromi et al., 2021; Christophersen et al., 2022; etc.) and compared with observations (DeMaria et al., 2009; Knaff et al., 2016; Mallen et al., 2005).…”

Potential Impacts of Radio Occultation Data Assimilation on Forecast Skill of Tropical Cyclone Formation in the Western North Pacific

“…Because TCs generally form over remote oceans, guidance from traditional conventional observation data is sparse. Satellite data assimilation (DA) is commonly applied to improve TC formation forecasting (Christophersen et al, 2018(Christophersen et al, , 2022Minamide & Zhang, 2018;Reale et al, 2009;Weng et al, 2007;Wu et al, 2006). The assimilation of GNSS RO data is an established method to increase the forecast hit rate for TC formation (Chen et al, 2020;Kunii et al, 2012;Liu et al, 2012;Teng et al, 2021).…”