The Implementation of Cloud and Vertical Velocity Relocation/Cycling System in the Vortex Initialization of the HAFS
Abstract
:1. Introduction and Motivation
2. Cloud and Vertical Velocity Improvements in the New VI
2.1. Details Regarding the New VI
2.2. Example Cases
3. Effects of the Updated VI on the HAFS Forecast
3.1. Experimental Configuration
3.2. Overall Statistics from Three VI Experiments
4. Detailed Structure Analysis
4.1. Model Initial Cloud Structure Improvements
4.2. Model Initial Condition Validation with Respect to Observations
5. Important Effects of Vertical Velocity on the Intensity Forecast
6. Summary
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A. Relocating–Cycling Cloud and Vertical Velocity at Constant Pressure Level
References
- Aksoy, A.; Lorsolo, S.; Vukicevic, T.; Sellwood, K.J.; Aberson, S.D.; Zhang, F. The HWRF Hurricane Ensemble Data Assimilation System (HEDAS) for high-resolution data: The impact of airborne Doppler radar observations in an OSSE. Mon. Weather Rev. 2012, 140, 1843–1862. [Google Scholar] [CrossRef]
- Weng, Y.; Zhang, F. Assimilating airborne Doppler radar observations with an Ensemble Kalman Filter for convection-permitting hurricane initialization and prediction: Katrina (2005). Mon. Weather Rev. 2012, 140, 841–859. [Google Scholar] [CrossRef]
- Sippel, J.A.; Zhang, F.; Weng, Y.; Tian, L.; Heymsfield, G.M.; Braun, S.A. Ensemble Kalman Filter assimilation of HIWRAP observations of Hurricane Karl (2010) from the unmanned Global Hawk aircraft. Mon. Weather Rev. 2014, 142, 4559–4580. [Google Scholar] [CrossRef]
- Lu, X.; Wang, X.; Tong, M.; Tallapragada, V. GSI-based, continuously cycled, dual-resolution hybrid ensemble–variational data assimilation system for HWRF: System description and experiments with Edouard (2014). Mon. Weather Rev. 2017, 145, 4877–4898. [Google Scholar] [CrossRef]
- Li, J.; Li, J.; Velden, C.; Wang, P.; Schmit, T.J.; Sippel, J. Impact of rapid-scan-based dynamical information from GOES-16 on HWRF hurricane forecasts. J. Geophys. Res. Atmos. 2020, 125, e2019JD031647. [Google Scholar] [CrossRef]
- Kwon, I.-H.; Cheong, H.-B. Tropical cyclone initialization with a spherical high-order filter and an idealized three-dimensional bogus vortex. Mon. Weather Rev. 2010, 138, 1344–1367. [Google Scholar] [CrossRef]
- Kwon, I.-H.; Cheong, H.-B.; Kang, H.-G.; Han, H.-J.; Park, J.-R. Structure change of Typhoon Nari (2007) in the weakening stage. Asia-Pac. J. Atmos. Sci. 2010, 46, 327–340. [Google Scholar] [CrossRef]
- Doyle, J.D.; Hodur, R.M.; Chen, S.; Jin, Y.; Moskaitis, J.R.; Wang, S.; Hendricks, E.A.; Jin, H.; Smith, T.A. Tropical Cyclone prediction using COAMPS-TC. Oceanography 2014, 27, 104–115. [Google Scholar] [CrossRef]
- Kurihara, Y.; Bender, M.A.; Ross, R.J. An initialization scheme of hurricane models by vortex specification. Mon. Weather Rev. 1993, 121, 2030–2045. [Google Scholar] [CrossRef]
- Kurihara, Y.; Bender, M.A.; Tuleya, R.E.; Ross, R.J. Improvements in the GFDL hurricane prediction system. Mon. Weather Rev. 1995, 123, 2791–2801. [Google Scholar] [CrossRef]
- Lee, J.-D.; Wu, C.-C. The role of polygonal eyewalls in rapid intensification of Typhoon Megi (2010). J. Atmos. Sci. 2018, 75, 4175–4199. [Google Scholar] [CrossRef]
- Lee, J.-D.; Park, D.-S.R.; Ito, K.; Wu, C.-C. Effects of the assimilation of relative humidity reproduced from T-PARC II and Himawari-8 satellite imagery using dynamical initialization and ocean-coupled model: A case study of Typhoon Lan (2017). J. Geophys. Res. Atmos. 2021, 126, e2020JD034516. [Google Scholar] [CrossRef]
- Mehra, A.; Tallapragada, V.; Zhang, Z.; Liu, B.; Zhu, L.; Wang, W.; Kim, H.-S. Advancing the state of the art in operational tropical cyclone forecasting at NCEP. Trop. Cyclone Res. Rev. 2018, 7, 51–56. [Google Scholar] [CrossRef]
- Liu, Q.; Zhang, X.; Tong, M.; Zhang, Z.; Liu, B.; Wang, W.; Zhu, L.; Zhang, B.; Xu, X.; Trahan, S.; et al. Vortex initialization in the NCEP operational hurricane models. Atmosphere 2020, 11, 968. [Google Scholar] [CrossRef]
- Han, J.; Pan, H.-L. Revision of convection and vertical diffusion schemes in the NCEP Global Forecast System. Weather Forecast. 2011, 26, 520–533. [Google Scholar] [CrossRef]
- Han, J.; Wang, W.; Kwon, Y.C.; Hong, S.-Y.; Tallapragada, V.; Yang, F. Updates in the NCEP GFS cumulus convection schemes with scale and aerosol awareness. Weather Forecast. 2017, 32, 2005–2017. [Google Scholar] [CrossRef]
- Han, J.; Witek, M.L.; Teixeira, J.; Sun, R.; Pan, H.-L.; Fletcher, J.K.; Bretherton, C.S. Implementation in the NCEP GFS of a Hybrid Eddy-Diffusivity Mass-Flux (EDMF) boundary layer parameterization with dissipative heating and modified stable boundary layer mixing. Weather Forecast. 2016, 31, 341–352. [Google Scholar] [CrossRef]
- Iacono, M.J.; Delamere, J.S.; Mlawer, E.J.; Shephard, M.W.; Clough, S.A.; Collins, W.D. Radiative forcing by long-lived greenhouse gases: Calculations with the AER radiative transfer models. J. Geophys. Res. 2008, 113, D13103. [Google Scholar] [CrossRef]
- Lin, S.-J.; Chao, W.C.; Sud, Y.C.; Walker, G.K. A class of the van Leer-type transport schemes and its application to the moisture transport in a general circulation model. Mon. Weather Rev. 1994, 122, 1575–1593. [Google Scholar] [CrossRef]
- Lin, S.-J. A “Vertically Lagrangian” finite-volume dynamical core for global models. Mon. Weather Rev. 2004, 132, 2293–2307. [Google Scholar] [CrossRef]
- Chen, X.; Andronova, N.; Leer, B.V.; Penner, J.P.; Boyd, J.P.; Jablonowski, C.; Lin, S.-J. A Control-Volume model of the compressible euler Equations with a vertical Lagrangian coordinate. Mon. Weather Rev. 2013, 141, 2526–2544. [Google Scholar] [CrossRef]
- Harris, L.; Chen, X.; Putman, W.; Zhou, L.; Chen, J.-H. A Scientific Description of the GFDL Finite-Volume Cubed-Sphere Dynamical Core; NOAA Technical Memorandum OAR GFDL; 2021-001; National Oceanic and Atmospheric Administration/Geophysical Fluid Dynamics Laboratory: Princeton, NJ, USA, 2021. [Google Scholar] [CrossRef]
- Wu, T.-C.; Zupanski, M.; Grasso, L.D.; Kummerow, C.D.; Bouckabara, S.-A. All-Sky Radiance Assimilation of ATMS in HWRF: A Demonstration Study. Mon. Weather Rev. 2019, 147, 85–106. [Google Scholar] [CrossRef]
- Lin, Y.-L.; RFarley, D.; Orville, H.D. Bulk parameterization of the snow field in a cloud model. J. Appl. Meteorol. Climatol. 1983, 22, 1065–1092. [Google Scholar] [CrossRef]
- Zhou, L.; Lin, S.-J.; Chen, J.-H.; Harris, L.M.; Chen, X.; Rees, S.L. Toward convective-scale prediction within the next generation global prediction system. Bull. Am. Meteorol. Soc. 2019, 100, 1225–1243. [Google Scholar] [CrossRef]
- Harris, L.; Zhou, L.; Lin, S.-J.; Chen, J.-H.; Chen, X.; Gao, K.; Morin, M.; Rees, S.; Sun, Y.; Tong, M.; et al. GFDL SHiELD: A Unified System for Weather-to-Seasonal Prediction. J. Adv. Model. Earth Syst. 2020, 12, e2020MS002223. [Google Scholar] [CrossRef]
- Thompson, G.; Rasmussen, R.M.; Manning, K. Explicit forecasts of winter precipitation using an improved bulk microphysics scheme. Part I: Description and sensitivity analysis. Mon. Weather Rev. 2004, 132, 519–542. [Google Scholar] [CrossRef]
- Thompson, G.; Eidhammer, T. A study of aerosol impacts on clouds and precipitation development in a large winter cyclone. J. Atmos. Sci. 2014, 71, 3636–3658. [Google Scholar] [CrossRef]
- Fischer, M.S.; Rogers, R.F.; Reasor, P.D.; Gamache, J.F. An analysis of tropical cyclone vortex and convective characteristics in relation to storm intensity using a novel airborne Doppler radar database. Mon. Weather Rev. 2022, 150, 2255–2278. [Google Scholar] [CrossRef]
- Gamache, J.F. Evaluation of a fully three-dimensional variational Doppler analysis technique. In Proceedings of the 28th Conference on Radar Meteorology, Austin, TX, USA, 9–12 September 1997; pp. 422–423. [Google Scholar]
- Reasor, P.D.; Eastin, M.D.; Gamache, J.F. Rapidly intensifying Hurricane Guillermo (1997). Part I: Low-wavenumber structure and evolution. Mon. Weather Rev. 2009, 137, 603–630. [Google Scholar] [CrossRef]
- Rogers, R.F. Convective-scale structure and evolution during a high-resolution simulation of tropical cyclone rapid intensification. J. Atmos. Sci. 2010, 67, 44–70. [Google Scholar] [CrossRef]
- Munsell, E.B.; Zhang, F.; Braun, S.A.; Sippel, J.A.; Didlake, A.C. The inner-core temperature structure of Hurricane Edouard (2014): Observations and ensemble variability. Mon. Weather Rev. 2018, 146, 135–155. [Google Scholar] [CrossRef]
- Shin, J.H. Vortex spin-up process in the extratropical transition of Hurricane Sandy (2012). J. Atmos. Sci. 2019, 76, 3589–3609. [Google Scholar] [CrossRef]
- Bucci, L.; Alaka, L.; Hagen, A.; Delgado, S.; Beven, J. National Hurricane Center Hurricane Ian Report. 2022; 72p. Available online: https://www.nhc.noaa.gov/data/tcr/AL092022_Ian.pdf (accessed on 7 July 2024).
- Beven, J.L., II. National Hurricane Center Hurricane Franklin Report. 2023; 29p. Available online: https://www.nhc.noaa.gov/data/tcr/AL082023_Franklin.pdf (accessed on 7 July 2024).
- Kleist, D.T.; Parrish, D.F.; Derber, J.C.; Treadon, R.; Wu, W.-S.; Lord, S. Introduction of the GSI into the NCEP Global Data Assimilation System. Weather Forecast. 2009, 24, 1691–1705. [Google Scholar] [CrossRef]
HAFSv1A (Operation HAFS for 2023) | HAFSv1.1A (Real-time experiment for 2023) | |
Horizontal and vertical resolution | ~6 km for parent domain ~2 km for moving nest 81 vertical levels | ~5.4 km for parent domain ~1.8 km for moving nest 81 vertical levels |
Convective parameterization | Scale-aware Simplified Arakawa–Schubert convection scheme [15,16] | |
Microphysics | GFDL | Thompson |
Planetary boundary layer | Scale-aware TKE-based eddy-diffusivity–mass-flux [17] | |
Data assimilation | 4D-EnVar | |
Vortex initialization | Warm cycling threshold: 50 kt No cloud/vertical velocity relocation/cycling Vortex modification option: Auto | Warm cycling threshold: 40 kt Cloud/vertical velocity relocation/cycling Composite vortex is updated Vortex modification option: Yes |
Ocean model | HYbrid Coordinate Ocean Model | MOM6 |
Radiation scheme | RRTMG [18] |
Cloud Relocation/Cycling | Vertical Velocity Relocation/Cycling | Description | |
---|---|---|---|
HFXA | Yes | Yes | HAFSv1.1A configuration with new VI |
HFNN | No | No | HAFSv1.1A configuration with old VI |
HFNW | Yes | No | For testing the impact of vertical velocity |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Shin, J.; Zhang, Z.; Liu, B.; Weng, Y.; Liu, Q.; Mehra, A.; Tallapragada, V. The Implementation of Cloud and Vertical Velocity Relocation/Cycling System in the Vortex Initialization of the HAFS. Atmosphere 2024, 15, 1006. https://doi.org/10.3390/atmos15081006
Shin J, Zhang Z, Liu B, Weng Y, Liu Q, Mehra A, Tallapragada V. The Implementation of Cloud and Vertical Velocity Relocation/Cycling System in the Vortex Initialization of the HAFS. Atmosphere. 2024; 15(8):1006. https://doi.org/10.3390/atmos15081006
Chicago/Turabian StyleShin, JungHoon, Zhan Zhang, Bin Liu, Yonghui Weng, Qingfu Liu, Avichal Mehra, and Vijay Tallapragada. 2024. "The Implementation of Cloud and Vertical Velocity Relocation/Cycling System in the Vortex Initialization of the HAFS" Atmosphere 15, no. 8: 1006. https://doi.org/10.3390/atmos15081006