Validation of HWRF-based Probabilistic TC Wind and Precipitation forecasts

Abstract

Tropical cyclones are associated with a variety of significant social hazards, including wind, rain, and storm surge. Despite this, most of the model validation effort has been directed toward track and intensity forecasts. In contrast, few studies have investigated the skill of state-of-the-art, high-resolution ensemble prediction systems in predicting associated TC hazards, which is crucial since TC position and intensity do not always correlate with the TC-related hazards, and can result in impacts far from the actual TC center. Furthermore, dynamic models can provide flow-dependent uncertainty estimates, which in turn can provide more specific guidance to forecasters than statistical uncertainty estimates based on past errors. This study validates probabilistic forecasts of wind speed and precipitation hazards derived from the HWRF ensemble prediction system and compares its skill to forecasts by the stochastically-based operational Monte Carlo Model (NHC), the IFS (ECMWF), and the GEFS (NOAA) in use 2017-2019. Wind and Precipitation forecasts are validated against NHC best track wind radii information and the National Stage IV QPE Product. The HWRF 34 kn wind forecasts have comparable skill to the global models up to 60 h lead time before HWRF skill decreases, possibly due to detrimental impacts of large track errors. In contrast, HWRF has comparable quality to its competitors for higher thresholds of 50 kn and 64 kn throughout 120 h lead time. In terms of precipitation hazards, HWRF performs similar or better than global models, but depicts higher, although not perfect, reliability, especially for events over 5 in120h−1. Post-processing, like Quantile Mapping, improves forecast skill for all models significantly and can alleviate reliability issues of the global models.

Performance of High-Resolution WRF Modeling System in Simulation of Severe Tropical Cyclones over the Bay of Bengal Using IMDAA Regional Reanalysis Dataset

“…Therefore, there is a need to improve the forecast skill of intense TCs over the NIO. In the last two decades, the forecast accuracy of TCs has increased by using high-resolution regional and global numerical weather prediction models [12][13][14][15][16] and improved and proper representations of physical parameterization schemes [17][18][19][20][21][22]. Additionally, TC forecast accuracy has improved by using advanced data assimilation techniques such as 3D/4D variational techniques, hybrid, and ensemble methods [5,[23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38][39].…”

Heavy Rainfall Forecast of Landfalling Tropical Cyclone Over China With an Upgraded DSAEF_LTP Model

“…However, our ability to predict TC rainfall, primarily through NWP models, is still limited, especially in predicting the distribution of heavy rainfall (Ma, 2014; Marchok et al., 2007; Wang et al., 2012; Yu et al., 2019). This problem can be largely attributed to the lack of sufficient high‐performance computing resources (Bachmann & Torn, 2021; Judt, 2018; Selz, 2019) and the limited ability to parameterize sophisticated TC rainfall physical processes in NWP models; significant progress in both cannot be achieved in a short period. Therefore, some scientists have aimed to fill in this gap by exploring TC rainfall forecasts with statistic‐ or analog‐based techniques (Bagtasa, 2021; Li et al., 2015; Kim et al., 2019, 2020; Ren et al., 2018; Wei, 2012a, 2012b; Zhong et al., 2009).…”

Illustration of an object‐based approach to identify structural differences in tropical cyclone wind fields

“…Further, Cangialosi and Landsea (2016) argue that an improved understanding of the limitations of dynamical models in skillfully predicting storm size "would be of substantial assistance in operations and potentially could help in improving models' predictions of wind radii." More recently, Bachmann and Torn (2021) evaluated probabilistic forecasts of wind speed and precipitation from the Hurricane Weather Research and Forecast ensemble prediction system compared with an operational Monte Carlo model and global forecast models. All models were verified against observations.…”

“…A 2016 NHC study found that their official forecasts for 34‐, 50‐, and 64‐kt wind radii were skillful compared with persistence and climatology, whereas dynamical models were not skillful in predicting the radius of hurricane‐force winds and other important size metrics (Cangialosi and Landsea, 2016). Further, Cangialosi and Landsea (2016) argue that an improved understanding of the limitations of dynamical models in skillfully predicting storm size “would be of substantial assistance in operations and potentially could help in improving models' predictions of wind radii.” More recently, Bachmann and Torn (2021) evaluated probabilistic forecasts of wind speed and precipitation from the Hurricane Weather Research and Forecast ensemble prediction system compared with an operational Monte Carlo model and global forecast models. All models were verified against observations.…”