Advanced Method for Forecasting and Warning of Severe Convective Weather and Local-scale Hazards
Source: By:Author(s)
DOI: https://doi.org/10.30564/jasr.v5i1.4375
Abstract:Hurricane Ida ferociously affected many south-eastern and eastern parts of the United States, making it one of the strongest hurricanes in recent years. Advanced forecast and warning tool has been used to track the path of the ex-Hurricane, Ida, as it left New Orleans on its way towards the northeast, accurately predicting significant supercell development above New York City on September 01, 2021. This advanced method accurately detected the area with the highest possible level of convective instability with 24-h lead time and even Level 5, devised in the categorical outlooks legend of the system. Therefore, an extreme level implied a very high probability of the local-scale hazard occurring above the NYC. Cloud model output fields (updrafts and downdrafts, wind shear, near-surface convergence, the vertical component of relative vorticity) show the rapid development of a strong supercell storm with rotating updrafts and a mesocyclone. The characteristic hook-shaped echo signature visible in the reflectivity patterns indicates a signal for a highly precipitable (HP) supercell with the possibility of tornado initiation. Open boundary conditions represent a good basis for simulating a tornado that evolved from a supercell storm, initialized with initial data obtained from a real-time simulation in the period when the bow echo and tornado-like signature occurred. Тhe modeled results agree well with the observations.
References:[1] Simpson, R.H., 1974. The hurricane disaster-potential scale. Weatherwise. 27, 169-186. [2] Klemp, J.B., Rotunno, R., 1983. A Study of the Tornadic Region within a Supercell Thunderstorm. J. Atmos. Sci. 40, 359-377. DOI: https://doi.org/10.1175/1520-0469(1983)040%3C0359:ASOTTR%3E2.0.CO;2 [3] Doswell, C.A., III, Brooks, H.E., Maddox, R.A., 1996. Flash Flood Forecasting: An Ingredients-Based Methodology. Wea. Forecasting. 11, 560-581. DOI: https://doi.org/10.1175/1520-0434(1996)0112.0.CO;2. [4] Markowski, P.M., Straka, J.M., Rasmussen, E.N., 2003. Tornadogenesis resulting from the transport of circulation by a downdraft: Idealized numerical simulations. J. Atmos. Sci. 60, 795-823. [5] Markowski, P.M., Richardson, Y.P., 2009. Tornadogenesis: our current understanding, forecasting considerations, and questions to guide future research. Atmos. Res. 93, 3-10. [6] Markowski, P.M., Richardson, Y.P., 2014a. The influence of environmental low-level shear and cold pools on tornadogenesis: Insights from idealized simulations. J. Atmos. Sci. 71, 243-275. DOI: https://doi.org/10.1175/JAS-D-13-0159.1 [7] Markowski, P.M., Richardson, Y.P., 2014b. What we know and don’t know about tornado formation. Physics Today. 67, 26-31. DOI: https://doi.org/10.1063/PT.3.2514 [8] Klemp, J.B., 2006. Advances in the WRF model for convection-resolving forecasting. Adv. Geosci. 7, 25- 29. DOI: https://doi.org/10.5194/adgeo-7-25-2006 [9] Skamarock, W.C., Klemp, J.B., Dudhia, J., et al., 2008. A Description of the Advanced Research WRF Version 3 (No. NCAR/TN-475+STR). University Corporation for Atmospheric Research. DOI: https://doi.org/10.5065/D68S4MVH [10] Skamarock, W.C., Klemp, J.B., Dudhia, J., et al., 2021. A Description of the Advanced Research WRF Model Version 4.3. DOI: https://doi.org/10.5065/1dfh-6p97 [11] Kain, J.S., Weiss, S.J., Levit, J.J., et al., 2006. Examination of Convection-Allowing Configurations of the WRF Model for the Prediction of Severe Convective Weather: The SPC/NSSL Spring Program 2004. Wea. Forecasting. 21, 167-181. https://10.1175/WAF906 [12] Litta, A.J., Mohanty, U.C., 2008. Simulation of a Severe Thunderstorm Event during the Field Experiment of STORM Programme 2006, Using WRFNMM Model. Current Sci. 95 (2), 204-215. [13] Spiridonov, V., Ćurić, M., 2015. A storm modeling system as an advanced tool in the prediction of well-organized slowly moving convective cloud system and early warning of severe weather risk. Asia-Pacific J. Atmos. Sci. 51, 61-75. DOI: https://doi.org/10.1007/s13143-014-0060-3 [14] Ćurić, M., Janc, D., 2012. Differential heating influence on hailstorm vortex pair evolution. Q. J. R. Meteorol. Soc. 138, 72-80. DOI: https://doi.org/10.1002/qj.918 [15] Karacostas, T., Spiridonov, V., Pytharoulis, B.I., et al., 2016. Analysis and numerical simulation of a real cell merger using a three-dimensional cloud-resolving model. Atmos. Res. 169(2), 547-555. DOI: https://doi.org/10.1016/j.atmosres.2015.09.011 [16] Spiridonov, V., Baez, J., Telenta, B., et al., 2020. Prediction of extreme convective rainfall intensities using a free-running 3-D sub-km-scale cloud model initialized from WRF km-scale NWP forecasts, J. Atmos. Solar-Terres. Phys. 209, 1364-6826. DOI: https://doi.org/10.1016/https:j.jastp.2020.105401 [17] Schenkman, A.D., Xue, M., Hu, M., 2014. Tornadogenesis in a High-Resolution Simulation of the 8 May 2003 Oklahoma City Supercell. J. Atmos. Sci. 71, 130-154. DOI: https://doi.org/10.1175/JAS-D-13-073.1 [18] Xue, M., Hu, M., Schenkman, A.D., 2014. Numerical Prediction of the 8 May 2003 Oklahoma City Tornadic Supercell and Embedded Tornado Using ARPS with the Assimilation of WSR-88D Data. Wea. and Forecasting. 29(1), 39-62. DOI: https://doi.org/10.1175/WAF-D-13-00029.1 [19] Orf, L., Wilhelmson, R., Lee, B., et al., 2017. Evolution of along-track violent tornado within a simulated supercell. Bull. Amer. Meteor. Soc. 98, 45-68. DOI: https://doi.org/10.1175/bams-d-15-00073 [20] Spiridonov, V., Ćurić, M., Velinov,G., et al., 2021b. Numerical simulation of a violent supercell tornado over Vienna airport initialized and initiated with a cloud model, Atmos. Res. Vol. 261. DOI: https://doi.org/10.1016/j.atmosres.2021.105758 [21] Xue, M., Coauthors, 2007. CAPS real-time stormscale ensemble and high-resolution forecasts as part of the NOAA Hazardous Weather Testbed 2007 spring experiment. 22nd Conf. Weather Analysis and Forecasting and 18th Conf. Numerical Weather Prediction, Park City, UT, Amer. Meteor. Soc., CDROM 3B. 1. [22] Stensrud, D.J., Coauthors, 2009. Convective-scale Warn-on-Forecast system. Bull. Amer. Meteor. Soc. 90, 1487-1500. DOI: https://doi.org/10.1175/2009BAMS2795.1 [23] Stensrud, D.J., Coauthors, 2013. Progress and challenges with the warn-on-forecast. Atmos. Res. 123, 2-16. DOI: https://doi.org/10.1016/j.atmosres.2012.04.004 [24] Stumpf, G.J., Gerard, A.E., 2021. National Weather Service Severe Weather Warnings as Threats-in-Motion. Wea. Forecasting. 36, 627-643. [25] Spiridonov, V., Ćurić, M., Sladic, N., 2021a. Novel Thunderstorm Alert System (NOTHAS) Asia-Pacific J Atmos Sci. 57, 479-498. DOI: https://doi.org/10.1007/s13143-020-00210-5 [26] Calhoun, K.M., Berry, K.L., Kingfield, D.M., et al., 2021. The Experimental Warning Program of NOAA’s Hazardous Weather Testbed. Bull. Amer. Meteor. Soc. 1-51. DOI: https://doi.org/10.1175/BAMS-D-21-0017.1 [27] Flora, M.L., Potvin, C.K., Skinner, P.S., et al., 2021. Using Machine Learning to Generate Storm-Scale Probabilistic Guidance of Severe Weather Hazards in the Warn-on-Forecast System. Mon. Wea. Rev. 149, 1535-1557. [28] Telenta, B., Aleksić, N., 1988. A three-dimensional simulation of the 17 June 1978 HIPLEX case with observed ice multiplication. WMO/TD No. 268, 277- 285. [29] Klemp, J.B., Wilhelmson, R.B., 1978. The Simulation of Three-Dimensional Convective Storm Dynamics. J. Atmos. Sci. 35, 1070-1096. [30] Orville, H.D., Kopp, F.J., 1977. Numerical Simulation of the Life History of a Hailstorm. J. Atmos. Sci. 34, 1596-1618. DOI: https://doi.org/10.1175/1520-0469(1977)034%3C1596:NSOTLH%3E2.0.CO;2 [31] Lin, Y.L., Farley, R.D., Orville, H.D., 1983. Bulk Parameterization of the Snow Field in a Cloud Model. J. Climate Appl. Meteor. 22, 1065-1092. DOI: https://doi.org/10.1175/1520-0450(1983)022%3C1065:BPOTSF%3E2.0.CO;2 [32] Ćurić, M., Janc, D., 1995. On the sensitivity of the continuous accretion rate equation used in bulk-water parameterization schemes. Atmos. Res. 39, 313-332. DOI: https://doi.org/10.1016/0169-8095(95)00022-4 [33] Ćurić, M., Janc. D.,1997. On the sensitivity of hail accretion rates in numerical modeling, Tellus Ser. A and Ser. B. 49(A), 100-107. [34] Barth, M.C., Kim, S.W., Wang, C., et al., 2007. Cloud-scale model intercomparison of chemical constituent transport in deep convection. Atmos. Chem. Phys. 7, 4709-4731. DOI: https://doi.org/10.5194/acp-7-4709-2007 [35] Spiridonov, V., Ćurić, M., 2005. The Relative Importance of Scavenging, Oxidation, and Ice-Phase Processes in the Production and Wet Deposition of Sulfate. J. Atmos. Sci. 62, 2118-2135. [36] Thompson, G., Field, P.R., Rasmussen, R.M., et al., 2008. Explicit forecasts of winter precipitation using an improved bulk microphysics scheme. Part II: Implementation of a new snow parameterization. Mon. Wea. Rev. 136, 5095-5115. [37] Thompson, G., Eidhammer, T., 2014. A Study of Aerosol Impacts on Clouds and Precipitation Development in a Large Winter Cyclone, J. Atmos. Sci. DOI: https://doi.org/10.1175/JAS-D-13-0305.1 [38] Hong, S.Y., Lim, J.O.J., 2006. The WRF Single-Moment 6-Class Microphysics 515 Scheme (WSM6). Journal of the Korean Meteorological Society. 42, 129-151. [39] Shin, H.H., Hong, S.Y., 2011. Intercomparison of Planetary Boundary-Layer Parametrizations in the WRF Model for a Single Day from CASES-99. Boundary-Layer Meteorol. 139, 261-281. DOI: https://doi.org/10.1007/s10546-010-9583-z [40] Chen, F., Dudhia, J., 2001. Coupling an Advanced Land Surface-Hydrology Model with the Penn StateNCAR MM5 Modeling System. Part I: Model Implementation and Sensitivity. Mon. Wea. Rev. 129, 569- 585. DOI: https://doi.org/10.1175/1520-0493(2001)129%3C0569:CAALSH%3E2.0.CO;2 [41] Janjic, Z.I., 1994. The Step-Mountain Eta Coordinate Model: Further Developments of the Convection, Viscous Sublayer, and Turbulence Closure Schemes. Mon. Wea. Rev. 122, 927-945. DOI: http://dx.doi.org/10.1175/1520-0493(1994)1222.0.CO;2 [42] Shin, H.H., Hong, S.Y., 2015. Representation of the Subgrid-Scale Turbulent Transport in Convective Boundary Layers at Gray-Zone Resolutions. Monthly Weather Review. 143, 99250-271. DOI: https://doi.org/10.1175/MWR-D-14-00116.1 [43] Han, J.W., Wang, W., Kwon, Y.C., et al., 2017. Updates in the NCEP GFS cumulus convection schemes with scale and aerosol awareness. Wea. Forecasting. 32, 2005-2017. DOI: https://doi.org/10.1175/WAF-D-17-0046.1 [44] Dudhia, J., 1989. Numerical Study of Convection Observed during the Winter Monsoon Experiment Using a Mesoscale Two-Dimensional Model. J.Atmos.Sci. 46, 3077-3107. DOI: https://doi.org/10.1175/1520-0469(1989)046%3C3077:NSOCOD%3E2.0.CO;2 [45] Mlawer, E.J., Taubman, S.J., Brown, P.D., et al., 1997. Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave. J. Geophys. Res. 02, 16,663-16,682. DOI: https://doi.org/10.1029/97JD00237 [46] Lean, H.W, Clark, P.A., Dixon, M., et al., 2008. Characteristics of high-resolution versions of the met office unified model for forecasting convection over the United Kingdom. Mon Weather Rev. 136(9), 3408-3424. [47] Roberts, N.M., Lean, H.W., 2008. Scale-selective verification of rainfall accumulations from high-resolution forecasts of convective events. Mon. Wea. Rev. 136, 78-97. DOI: https://doi.org/10.1175/2007MWR2123.1