Abstract

Six-hourly three-dimensional ensemble variational (3DEnVar) (6H-3DEnVar) data assimilation (DA) assumes constant background error covariance (BEC) during a six-hour DA window and is, therefore, unable to account for temporal evolution of the BEC. This study evaluates the one-hourly 3DEnVar (1H-3DEnVar) and six-hourly 4DEnVar (6H-4DEnVar) DA methods for the analyses and forecasts of hurricanes with rapidly evolving BEC. Both methods account for evolving BEC in a hybrid EnVar DA system. In order to compare these methods, experiments are conducted by assimilating inner core Tail Doppler Radar (TDR) wind for Hurricane Edouard (2014) and by running the Hurricane Weather Research and Forecasting (HWRF) model. In most metrics, 1H-3DEnVar and 6H-4DEnVar analyses and forecasts verify better than 6H-3DEnVar. 6H-4DEnVar produces better thermodynamic analyses than 1H-3DEnVar. Radar reflectivity shows that 1H-3DEnVar produces better structure forecasts. For the first 24–48 h of the intensity forecast, 6H-4DEnVar forecast performs better than 1H-3DEnVar verified against the best track. Degraded 1H-3DEnVar forecasts are found to be associated with background storm center location error as a result of underdispersive ensemble storm center spread. Removing location error in the background improves intensity forecasts of 1H-3DEnVar.

Highlights

  • Tropical Cyclones (TCs) can cause losses of life and billions of dollars in damage

  • While forecasts can be improved through several avenues, this study focuses on improving the forecasts of hurricanes by applying advanced data assimilation (DA) techniques

  • Stepped Frequency Microwave Radiometer (SFMR) wind speed verification of the analyses shows that 6H-3DEnVar has a mean root mean square error (RMSE) 40% larger than those of both 6H-4DEnVar and 1H-3DEnVar (Figure 6a), and 6H-3DEnVar has a larger RMSE than both 1H-3DEnVar and 6H-4DEnVar for 4 of the 5 analyses

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Summary

Introduction

Tropical Cyclones (TCs) can cause losses of life and billions of dollars in damage. Recent category 5 hurricanes Irma (2017), Maria (2017), Michael (2018), and Dorian (2019) each caused more than $5 billion in damage and several dozen direct deaths along with hundreds of injuries and indirect deaths. Summaries can be found at (https://www.nhc.noaa.gov/data/tcr, accessed on 12 April 2021). One way to reduce the significant risk to life and property is through improving numerical predictions of hurricanes. If rapid intensification (RI) can be more confidently forecasted in advance, the decision to evacuate could be made sooner. While forecasts can be improved through several avenues, this study focuses on improving the forecasts of hurricanes by applying advanced data assimilation (DA) techniques

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