Details

Autor(en) / Beteiligte

• Zhang, Zhan
• Tong, Mingjing
• Sippel, Jason A.
• Mehra, Avichal
• Zhang, Banglin
• Wu, Keqin
• Liu, Bin
• Dong, Jili
• Ma, Zaizhong
• Winterbottom, Henry
• Wang, Weiguo
• Zhu, Lin
• Liu, Qingfu
• Kim, Hyun-Sook
• Thomas, Biju
• Sheinin, Dmitry
• Bi, Li
• Tallapragada, Vijay

Titel

The Impact of Stochastic Physics-Based Hybrid GSI/EnKF Data Assimilation on Hurricane Forecasts Using EMC Operational Hurricane Modeling System

Ist Teil von

• Atmosphere, 2020-08, Vol.11 (8), p.801

Ort / Verlag

MDPI AG

Erscheinungsjahr

2020

Quelle

EZB Free E-Journals

Beschreibungen/Notizen

• 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.