Abstract
The National Oceanic and Atmospheric Administration’s (NOAA) cloud-permitting high-resolution operational Hurricane Weather and Research Forecasting (HWRF) model includes the sophisticated hybrid grid-point statistical interpolation (GSI) and Ensemble Kalman Filter (EnKF) data assimilation (DA) system, which allows assimilating high-resolution aircraft observations in tropical cyclone (TC) inner core regions. In the operational HWRF DA system, the flow-dependent background error covariance matrix is calculated from the HWRF self-cycled 40-member ensemble. This DA system has proved to provide improved initial TC structure and therefore improved TC track and intensity forecasts. However, the uncertainties from the model physics are not taken into account in the FY2017 version of the HWRF DA system. In order to further improve the HWRF DA system, the stochastic physics perturbations are introduced in the HWRF DA, including the cumulus convection scheme, the planetary boundary layer (PBL) scheme, and model surface physics (drag coefficient), for HWRF-based ensembles. This study shows that both TC initial conditions and TC track and intensity forecast skills are improved by adding stochastic model physics in the HWRF self-cycled DA system. It was found that the improvements in the TC initial conditions and forecasts are the results of ensemble spread increases which realistically represent the model background error covariance matrix in HWRF DA. For all 2016 Atlantic storms, the TC track and intensity forecast skills are improved by about ~3% and 6%, respectively, compared to the control experiment. The case study shows that the stochastic physics in HWRF DA is especially helpful for those TCs that have inner-core high-resolution aircraft observations, such as tail Doppler radar (TDR) data.
Highlights
Ensemble forecasting has been widely used to take account of the uncertainties in both model initial conditions and model dynamics and physics
The Hurricane Weather and Research Forecasting (HWRF) is a high resolution, cloud-resolving operational tropical cyclone (TC) forecast system at the NCEP, which was developed based on the Non-Hydrostatic Meso-Scale Model (NMM) dynamical core of the WRF model, with the model physics tuned and the components designed for TC prediction, including the ocean model, the wave model, the NCEP coupler, which couples the atmospheric model with ocean/wave models, the vortex initialization, data assimilation, and the vortex tracker
The other is the introduction of stochastic model physics into the HWRF ensemble forecast to provide a background error covariance matrix for the inner-core data assimilation (DA)
Summary
Ensemble forecasting has been widely used to take account of the uncertainties in both model initial conditions and model dynamics and physics. There are two main applications of ensemble prediction system (EPS) in an NWP model system, predicting the probability density function (PDF) of the model prognostic variables due to model initial and physics uncertainties, and providing the background error covariance matrix for the data assimilation (DA) system Both applications have been widely utilized in tropical cyclone (TC) NWP modeling systems. 2000 [6]; 2013 Weber [7], Zhang et al, 2014 [8]), there have been fewer studies on the impact of EPS on the TC track and intensity, through taking account of the uncertainties in model physics, which is supposed to provide a more realistic background error covariance matrix for TC inner-core DA.
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