Nonlinear Forecast Error Growth of Rapidly Intensifying Hurricane Harvey (2017) Examined through Convection-Permitting Ensemble Assimilation of GOES-16 All-Sky Radiances

Abstract

The dynamics and predictability of the rapid intensification (RI) of Hurricane Harvey (2017) were examined using convection-permitting initialization, analysis, and prediction from a cycling ensemble Kalman filter (EnKF) that assimilated all-sky infrared radiances from the Advanced Baseline Imager on GOES-16. The EnKF analyses were able to evolve the various scales of the radiance fields associated with Harvey close to those observed, including those associated with scattered individual convective cells before the onset of rapid intensification (RI) and the organized vortex-scale convective system during and after RI. This was true for more than three days of a continuous assimilation cycling. Deterministic forecasts initialized from the EnKF analyses captured the rapidly deepening intensity of Harvey more than 24 hours prior to its onset. To explore the predictability of Harvey’s intensity during RI, ensemble probabilistic forecasts and sensitivity analyses were conducted. It was found that significant ensemble spread growth was induced by initial perturbations individually in either the wind or moisture fields. The nonlinear interactions between wind and moisture perturbations further limited the predictability of the intensification process of Harvey by increasing the uncertainty in the simulated wind and moisture distributions and modifying the convective activity and its feedback on vortex flow. This study highlights both the importance of better initializing the dynamic and moisture state variables simultaneously and the potential contribution of satellite all-sky radiance assimilation on constraining them and their associated convective activity that impacts RI of tropical cyclones.

Ensemble‐Based Assimilation of Satellite All‐Sky Microwave Radiances Improves Intensity and Rainfall Predictions for Hurricane Harvey (2017)

“…Zhang et al, 2009). The system configuration largely follows previous studies by F. Zhang et al (2019) and Minamide et al (2020), except that we adopted the Thompson et al (2008) microphysics scheme. Following S. B.…”

“…( 2019 ) and Minamide et al. ( 2020 ), except that we adopted the Thompson et al. ( 2008 ) microphysics scheme.…”

“…Because multiple studies have demonstrated that all‐sky IR BT assimilation improves forecasts of TC track and intensity for several different TCs (e.g., Hartman et al., 2021 ; Honda et al., 2018 ; Minamide & Zhang, 2018 ; Minamide et al., 2020 ; F. Zhang et al., 2016 , 2019 ), the baseline experiment for this study assimilates conventional surface and upper‐air observations from the GTS, TC center pressure information from TCVitals, and hourly all‐sky IR BTs from channel 8 (6.2‐µm) of the GOES‐16 Advanced Baseline Imager (ABI). This experiment is called “IR‐only” hereafter.…”

A Sampling‐Based Path Planning Algorithm for Improving Observations in Tropical Cyclones

“…The uncertainties are represented by the ensemble spread and calculated as the forecasted measurements' standard deviation. To generate the uncertainty distribution for Hurricane Harvey, conventional in situ observations (e.g., Dropsondes) and all‐sky satellite radiance from GOES‐16 were assimilated in a state‐of‐the‐art data assimilation system (ensemble Kalman filter (EnKF); Minamide et al., 2020; Zhang et al., 2019). The data assimilation system was developed at the Pennsylvania State University and built around the Advanced Weather Research and Forecasting Model (WRF‐ARW) and the Community Radiative Transfer Model (CRTM) to provide hourly temporal resolution forecast of Hurricanes.…”

Using Ensemble Data Assimilation to Explore the Environmental Controls on the Initiation and Predictability of Moist Convection

“…To alleviate those deficiencies, Minamide and Zhang (2018) introduced the Adaptive Background Error Inflation (ABEI) method that was designed to facilitate initiation of convection in observed cloudy-sky regions where clear-sky conditions are (incorrectly) modeled based on a statistical analysis. Recent investigations that have directly assimilated all-sky infrared radiances from geostationary satellites using the ABEI technique have demonstrated precise constraint of convective-scale features in numerical weather prediction models (Minamide et al, 2020;Minamide & Zhang, 2018b;Zhang et al, 2019;Zhang et al, 2018). Now, these state-of-the-art data assimilation systems are capable of providing a unique dataset of constrained realistic convective activity, which would help analyzing dominant factors to determine when and where the atmospheric moist convection occurs.…”