The capability of NOTHAS in the prediction of extreme weather events across different climatic areas

• Autorzy: Spiridonov Vlado , Grcić Marija , Sladić Nedim , Ćurić Mladjen , Jakimovski Boro

Identyfikatory

DOI

10.1007/s11600-023-01122-4

• Języki publikacji: EN

Abstrakty

EN

Climate changes are accelerating and leading to climate and weather extremes with the most destructive impacts and negative consequences on the planet. For these reasons, precise forecasting, and announcement of weather disasters of a convective nature, from local to synoptic scales, is very important. The Novel Thunderstorm Alert System (NOTHAS) has shown outstanding results in forecasting and early warning of different modes of convection, including local hazards in mid-latitudes. In this study, an attempt has been made to apply this tool in the prediction of different atmospheric systems that occur in different climatic regions. The upgraded prognostic and diagnostic algorithm with adjusted complex parameters and criteria representative of tropical storms and tropical cyclones showed good coincidence with the available observations. NOTHAS showed skill and success in assessing the dynamics and intensity of Hurricane Ian, which hit the west coast of Florida on 30 September 2022 and caused great material damage and human losses. This advanced tool also detected the most intense-extreme Level-5 on 1 September 2021, over New York, when catastrophic flooding occurred within the remnants of Hurricane Ida. Likewise, the upgraded model configuration very correctly predicted the trajectory, modifications, and strength of super typhoon Nanmadol over Japan (19 September 2022), 24-48 h in advance, and super typhoon Noru over the Philippines (25 September 2022). The system showed the temporal and spatial accuracy of the location of the heavy rainfall and flash flood. In general, the obtained results for all evaluated cases are encouraging and provide a good basis for further testing, verification, and severe weather warnings and guidance for weather services worldwide.

Słowa kluczowe

EN

weather and climate extremes; WRF-model forecast; diagnostic tool; convective weather alert; tropical cyclone

• Wydawca: Instytut Geofizyki PAN ; Springer
• Czasopismo: Acta Geophysica
• Rocznik: 2023
• Tom: Vol. 71, no. 6
• Strony: 3007--3024
• Opis fizyczny: Bibliogr. 41 poz., rys., tab.

Twórcy

autor

Spiridonov Vlado

• Faculty of Natural Sciences and Mathematics, Institute of Physics - Meteorology, Ss. Cyril, and Methodius University in Skopje, Skopje, North Macedonia

• https://orcid.org/0000-0001-9731-3940

autor

Grcić Marija

• Vienna, Austria

autor

Sladić Nedim

• Department of Meteorology, University of Reading, Reading, UK

autor

Ćurić Mladjen

• Institute of Meteorology, University of Belgrade, Belgrade, Serbia

autor

Jakimovski Boro

• Faculty of Computer Science and Engineering, Ss. Cyril and Methodius University in Skopje, Skopje, North Macedonia

Bibliografia

• 1. Alfieri L, Burek P, Dutra E, Krzeminski B, Muraro D, Thielen J, Pappenberger F (2013) GloFAS—global ensemble streamflow forecasting and flood forecasting. Hydrol Earth Syst Sci 17(3):1161–1175. https://doi.org/10.5194/hess-17-1161-2013
• 2. Ali R, Li Q, Chen J, Huang D, Lu X (2021) The spatial characteristics of hourly rainfall induced by tropical cyclones along the south China coast. Earth Space Sci 8:e2020EA001623. https://doi.org/10.1029/2020EA001623
• 3. Bauer P, Thorpe A, Brunet G (2015) The quiet revolution of numerical weather prediction. Nature 525(7567):47–55
• 4. Calhoun KM, Smith TM, Kingfield DM, Gao J, Stensrud DJ (2014) Forecaster use and evaluation of real-time 3DVAR analyses during severe thunderstorm and tornado warning operations in the Hazardous Weather Testbed. Weather Forecast 29:601–613. https://doi.org/10.1175/WAF-D-13-00107.1
• 5. Creighton GA, Creighton G, Kuchera E, Adams-Selin R, McCormick J, Rentschler S, Wickard B (2021) AFWA diagnostics in WRF. Available online: https://www2.mmm.ucar.edu/wrf/users/docs/AFWA_Diagnostics_in_WRF.pdf. Accessed 22 Mar 2021
• 6. 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
• 7. Ferrier B (1994) A double-moment multiple-phase four-class bulk ice scheme. Part 1: description. J Atmos Sci 51(2):249–280
• 8. Flora ML, Potvin CK, Skinner PS, Handler S, McGovern A (2021) Using machine learning to generate storm-scale probabilistic guidance of severe weather hazards in the warn-on-forecast system. Mon Weather Rev 149:1535–1557
• 9. Frame TH, Methven J, Roberts NM, Titley HA (2015) Predictability of frontal waves and cyclones. Weather Forecast 30(5):1291–1302
• 10. Gálvez JM, Davison M (2020) The Gálvez–Davison index for tropical convection. Available online: http://www.wpc.ncep.noaa.gov/international/gdi/GDI_Manuscript_V20161021.pdf
• 11. Golding B, Roberts NM, Leoncini G, Mylne K, Swinbank R (2016) MOGREPS-UK convection-permitting ensemble products for surface water flood forecasting: rationale and first results. J Hydrometeorol 17:1383–1406. https://doi.org/10.1175/JHM-D-15-0083.1
• 12. Han JY, Hong SY (2018) Precipitation forecast experiments using the weather research and forecasting (WRF) model at gray-zone resolutions. Weather Forecast 33:1605–1616
• 13. Han JY, Wang W, Kwon YC, Hong SY, Tallapragada V, Yang F (2017) Updates in the NCEP GFS cumulus convection schemes with scale and aerosol awareness. Weather Forecast 32:2005–2017. https://doi.org/10.1175/WAF-D-17-0046.1
• 14. Heming JT (2017) Tropical cyclone tracking and verification techniques for Met Office numerical weather prediction models. Meteorol Appl 24:1–8. https://doi.org/10.1002/met.1599
• 15. Hong S-Y (2010) A new stable boundary-layer mixing scheme and its impact on the simulated East Asia summer monsoon. Q J R Meteorol Soc 136(651):1481–1496. https://doi.org/10.1002/qj.665
• 16. Hong SY, Lim JOJ (2006) The WRF single-moment 6-class microphysics 515 scheme (WSM6). J Korean Meteorol Soc 42:129–151
• 17. Janjic ZI (2003) A nonhydrostatic model based on a new approach. Meteorol Atmos Phys 82:271–285
• 18. Jankov I, Gallus WA Jr, Segal M, Koch SE (2007) Influence of initial conditions on the WRF–894 ARW model QPF response to physical parameterization changes. Weather Forecast 22(3):501–519
• 19. Kuchera EL, Rentschler SA, Creighton GA, Rugg SA (2021) A review of operational ensemble forecasting efforts in the United States Air Force. Atmosphere 12(6):677. https://doi.org/10.3390/atmos12060677
• 20. Liu Y, Chen Y, Chen O, Wang J, Zhuo L, Rico-Ramirez MA, Han D (2021) To develop a progressive multimetric configuration optimisation method for WRF simulations of extreme rainfall events over Egypt. J Hydrol 598:126237
• 21. Magnusson L, Bidlot JR, Lang ST, Thorpe A, Wedi N, Yamaguchi M (2014) Evaluation of medium-range forecasts for hurricane Sandy. Mon Weather Rev 142(5):1962–1981
• 22. Majumdar SJ, Torn RD (2014) Probabilistic verification of global and mesoscale ensemble forecasts of tropical cyclogenesis. Weather Forecast 29(5):1181–1198
• 23. Marsigli C, Ebert E, Ashrit R, Casati B, Chen J, Coelho CAS, Dorninger M, Gilleland E, Haiden T, Landman S, Mittermaier M (2021) Review article: observations for high-impact weather and their use in verification. Nat Hazards Earth Syst Sci 21:1297–1312. https://doi.org/10.5194/nhess-21-1297-2021
• 24. Mlawer E, Taubman S, Brown P, Iacono M, Clough S (1997) Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave. J Geophys Res 102:16663–16682
• 25. Neal RA, Boyle P, Grahame N, Mylne K, Sharpe M (2014) Ensemble based first guess support towards a risk-based severe weather warning service. Meteorol Appl 21:563–577. https://doi.org/10.1002/met.1377
• 26. Park H, Kim G, Cha DH, Chang EC, Kim J, Park, SH, Lee DK (2022) Effect of a scale-aware convective parameterization scheme on the simulation of convective cells related heavy rainfall in South Korea. J Adv Model Earth Syst 14:e2021MS002696. https://doi.org/10.1029/2021MS002696
• 27. Roberts RD, Anderson AS, Nelson E, Brown BG, Wilson JW, Pocernich M, Saxen T (2012) Impacts of forecaster involvement on convective storm initiation and evolution nowcasting. Weather Forecast 27:1061–1089. https://doi.org/10.1175/WAF-D-11-00087.1
• 28. Shin H, Hong S-Y (2015) Representation of the subgrid-scale turbulent transport in convective boundary layers at grey-zone resolutions. Mon Weather Rev 143:250–271
• 29. Skamarock WC, Klemp JB (2008) A time-split non-hydrostatic atmospheric model for weather 938 research and forecasting applications. J Comput Phys 227(7):3465–3485
• 30. Skamarock WC, Klemp JB, Dudhia J et al (2008) A description of the advanced research WRF version 3 (No. NCAR/TN-475+STR). Univ Corp Atmos Res. https://doi.org/10.5065/D68S4MVH
• 31. Spiridonov V, Curic M, Sladic N et al (2021) Novel thunderstorm alert system (NOTHAS). Asia Pac J Atmos Sci 57:479–498. https://doi.org/10.1007/s13143-020-00210-5
• 32. Stensrud DJ et al (2009) Convective-scale warn-on-forecast system. Bull Am Meteorol Soc 90:1487–1500. https://doi.org/10.1175/2009BAMS2795.1
• 33. Stensrud DJ et al (2013) Progress and challenges with the warn-on-forecast. Atmos Res 123:2–16. https://doi.org/10.1016/j.atmosres.2012.04.004
• 34. Stumpf GJ, Gerard AE (2021) National Weather Service severe weather warnings as threats-in-motion (TIM). Weather Forecast 36:627–643. https://doi.org/10.1175/WAF-D20-0159.1
• 35. Thompson G, Eidhammer T (2014) A study of aerosol impacts on clouds and precipitation development in a large winter cyclone. J Atmos Sci. https://doi.org/10.1175/JAS-D-13-0305.1
• 36. Thompson G, Field PR, Rasmussen RM, Hall WD (2008) Explicit forecasts of winter precipitation using an improved bulk microphysics scheme. Part II: implementation of a new snow parameterization. Mon Weather Rev 136:5095–5115
• 37. Tsonevsky I, Doswell CA, Brooks HE (2018) Early warnings of severe convection using the ECMWF extreme forecast index. Weather Forecast 33:857–871. https://doi.org/10.1175/WAFD-18-0030.1
• 38. WMO (2017) WMO-No.1198: guidelines for nowcasting techniques, 2017 edn. World Meteorological Organization, Geneva. ISBN 978–92–63–11198–2
• 39. WMO (2020) State of climate services. WMO, Geneva. https://library.wmo.int/doc_num.php?explnum_id=10385. Accessed 2 Sept 2021
• 40. Yu X, Zhou X, Wang X (2012) The advances in the nowcasting techniques on thunderstorms and severe convection. Acta Meteorol Sin 70(3):311–337
• 41. Zhang Q, Li L, Ebert B, Golding B, Johnston D, Mills B, Panchuk S, Potter S, Riemer M, Sun J, Taylor A, Jones S, Ruti P, Keller J (2019) Increasing the value of weather-related warnings. Sci Bull 64:647–649. https://doi.org/10.1016/j.scib.2019.04.003
• 1. Alfieri L, Burek P, Dutra E, Krzeminski B, Muraro D, Thielen J, Pappenberger F (2013) GloFAS—global ensemble streamflow forecasting and flood forecasting. Hydrol Earth Syst Sci 17(3):1161–1175. https://doi.org/10.5194/hess-17-1161-2013
• 2. Ali R, Li Q, Chen J, Huang D, Lu X (2021) The spatial characteristics of hourly rainfall induced by tropical cyclones along the south China coast. Earth Space Sci 8:e2020EA001623. https://doi.org/10.1029/2020EA001623
• 3. Bauer P, Thorpe A, Brunet G (2015) The quiet revolution of numerical weather prediction. Nature 525(7567):47–55
• 4. Calhoun KM, Smith TM, Kingfield DM, Gao J, Stensrud DJ (2014) Forecaster use and evaluation of real-time 3DVAR analyses during severe thunderstorm and tornado warning operations in the Hazardous Weather Testbed. Weather Forecast 29:601–613. https://doi.org/10.1175/WAF-D-13-00107.1
• 5. Creighton GA, Creighton G, Kuchera E, Adams-Selin R, McCormick J, Rentschler S, Wickard B (2021) AFWA diagnostics in WRF. Available online: https://www2.mmm.ucar.edu/wrf/users/docs/AFWA_Diagnostics_in_WRF.pdf. Accessed 22 Mar 2021
• 6. 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
• 7. Ferrier B (1994) A double-moment multiple-phase four-class bulk ice scheme. Part 1: description. J Atmos Sci 51(2):249–280
• 8. Flora ML, Potvin CK, Skinner PS, Handler S, McGovern A (2021) Using machine learning to generate storm-scale probabilistic guidance of severe weather hazards in the warn-on-forecast system. Mon Weather Rev 149:1535–1557
• 9. Frame TH, Methven J, Roberts NM, Titley HA (2015) Predictability of frontal waves and cyclones. Weather Forecast 30(5):1291–1302
• 10. Gálvez JM, Davison M (2020) The Gálvez–Davison index for tropical convection. Available online: http://www.wpc.ncep.noaa.gov/international/gdi/GDI_Manuscript_V20161021.pdf
• 11. Golding B, Roberts NM, Leoncini G, Mylne K, Swinbank R (2016) MOGREPS-UK convection-permitting ensemble products for surface water flood forecasting: rationale and first results. J Hydrometeorol 17:1383–1406. https://doi.org/10.1175/JHM-D-15-0083.1
• 12. Han JY, Hong SY (2018) Precipitation forecast experiments using the weather research and forecasting (WRF) model at gray-zone resolutions. Weather Forecast 33:1605–1616
• 13. Han JY, Wang W, Kwon YC, Hong SY, Tallapragada V, Yang F (2017) Updates in the NCEP GFS cumulus convection schemes with scale and aerosol awareness. Weather Forecast 32:2005–2017. https://doi.org/10.1175/WAF-D-17-0046.1
• 14. Heming JT (2017) Tropical cyclone tracking and verification techniques for Met Office numerical weather prediction models. Meteorol Appl 24:1–8. https://doi.org/10.1002/met.1599
• 15. Hong S-Y (2010) A new stable boundary-layer mixing scheme and its impact on the simulated East Asia summer monsoon. Q J R Meteorol Soc 136(651):1481–1496. https://doi.org/10.1002/qj.665
• 16. Hong SY, Lim JOJ (2006) The WRF single-moment 6-class microphysics 515 scheme (WSM6). J Korean Meteorol Soc 42:129–151
• 17. Janjic ZI (2003) A nonhydrostatic model based on a new approach. Meteorol Atmos Phys 82:271–285
• 18. Jankov I, Gallus WA Jr, Segal M, Koch SE (2007) Influence of initial conditions on the WRF–894 ARW model QPF response to physical parameterization changes. Weather Forecast 22(3):501–519
• 19. Kuchera EL, Rentschler SA, Creighton GA, Rugg SA (2021) A review of operational ensemble forecasting efforts in the United States Air Force. Atmosphere 12(6):677. https://doi.org/10.3390/atmos12060677
• 20. Liu Y, Chen Y, Chen O, Wang J, Zhuo L, Rico-Ramirez MA, Han D (2021) To develop a progressive multimetric configuration optimisation method for WRF simulations of extreme rainfall events over Egypt. J Hydrol 598:126237
• 21. Magnusson L, Bidlot JR, Lang ST, Thorpe A, Wedi N, Yamaguchi M (2014) Evaluation of medium-range forecasts for hurricane Sandy. Mon Weather Rev 142(5):1962–1981
• 22. Majumdar SJ, Torn RD (2014) Probabilistic verification of global and mesoscale ensemble forecasts of tropical cyclogenesis. Weather Forecast 29(5):1181–1198
• 23. Marsigli C, Ebert E, Ashrit R, Casati B, Chen J, Coelho CAS, Dorninger M, Gilleland E, Haiden T, Landman S, Mittermaier M (2021) Review article: observations for high-impact weather and their use in verification. Nat Hazards Earth Syst Sci 21:1297–1312. https://doi.org/10.5194/nhess-21-1297-2021
• 24. Mlawer E, Taubman S, Brown P, Iacono M, Clough S (1997) Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave. J Geophys Res 102:16663–16682
• 25. Neal RA, Boyle P, Grahame N, Mylne K, Sharpe M (2014) Ensemble based first guess support towards a risk-based severe weather warning service. Meteorol Appl 21:563–577. https://doi.org/10.1002/met.1377
• 26. Park H, Kim G, Cha DH, Chang EC, Kim J, Park, SH, Lee DK (2022) Effect of a scale-aware convective parameterization scheme on the simulation of convective cells related heavy rainfall in South Korea. J Adv Model Earth Syst 14:e2021MS002696. https://doi.org/10.1029/2021MS002696
• 27. Roberts RD, Anderson AS, Nelson E, Brown BG, Wilson JW, Pocernich M, Saxen T (2012) Impacts of forecaster involvement on convective storm initiation and evolution nowcasting. Weather Forecast 27:1061–1089. https://doi.org/10.1175/WAF-D-11-00087.1
• 28. Shin H, Hong S-Y (2015) Representation of the subgrid-scale turbulent transport in convective boundary layers at grey-zone resolutions. Mon Weather Rev 143:250–271
• 29. Skamarock WC, Klemp JB (2008) A time-split non-hydrostatic atmospheric model for weather 938 research and forecasting applications. J Comput Phys 227(7):3465–3485
• 30. Skamarock WC, Klemp JB, Dudhia J et al (2008) A description of the advanced research WRF version 3 (No. NCAR/TN-475+STR). Univ Corp Atmos Res. https://doi.org/10.5065/D68S4MVH
• 31. Spiridonov V, Curic M, Sladic N et al (2021) Novel thunderstorm alert system (NOTHAS). Asia Pac J Atmos Sci 57:479–498. https://doi.org/10.1007/s13143-020-00210-5
• 32. Stensrud DJ et al (2009) Convective-scale warn-on-forecast system. Bull Am Meteorol Soc 90:1487–1500. https://doi.org/10.1175/2009BAMS2795.1
• 33. Stensrud DJ et al (2013) Progress and challenges with the warn-on-forecast. Atmos Res 123:2–16. https://doi.org/10.1016/j.atmosres.2012.04.004
• 34. Stumpf GJ, Gerard AE (2021) National Weather Service severe weather warnings as threats-in-motion (TIM). Weather Forecast 36:627–643. https://doi.org/10.1175/WAF-D20-0159.1
• 35. Thompson G, Eidhammer T (2014) A study of aerosol impacts on clouds and precipitation development in a large winter cyclone. J Atmos Sci. https://doi.org/10.1175/JAS-D-13-0305.1
• 36. Thompson G, Field PR, Rasmussen RM, Hall WD (2008) Explicit forecasts of winter precipitation using an improved bulk microphysics scheme. Part II: implementation of a new snow parameterization. Mon Weather Rev 136:5095–5115
• 37. Tsonevsky I, Doswell CA, Brooks HE (2018) Early warnings of severe convection using the ECMWF extreme forecast index. Weather Forecast 33:857–871. https://doi.org/10.1175/WAFD-18-0030.1
• 38. WMO (2017) WMO-No.1198: guidelines for nowcasting techniques, 2017 edn. World Meteorological Organization, Geneva. ISBN 978–92–63–11198–2
• 39. WMO (2020) State of climate services. WMO, Geneva. https://library.wmo.int/doc_num.php?explnum_id=10385. Accessed 2 Sept 2021
• 40. Yu X, Zhou X, Wang X (2012) The advances in the nowcasting techniques on thunderstorms and severe convection. Acta Meteorol Sin 70(3):311–337
• 41. Zhang Q, Li L, Ebert B, Golding B, Johnston D, Mills B, Panchuk S, Potter S, Riemer M, Sun J, Taylor A, Jones S, Ruti P, Keller J (2019) Increasing the value of weather-related warnings. Sci Bull 64:647–649. https://doi.org/10.1016/j.scib.2019.04.003

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).