Severe convection-related winds in Australia and their associated environments
Andrew Brown A B and Andrew Dowdy A
+ Author Affiliations
- Author Affiliations
A Bureau of Meteorology, Melbourne, Vic., Australia.
B Corresponding author. Email: andrew.brown@bom.gov.au
Journal of Southern Hemisphere Earth Systems Science 71(1) 30-52 https://doi.org/10.1071/ES19052
Submitted: 11 May 2020 Accepted: 20 November 2020 Published: 29 January 2021
Journal Compilation © BoM 2021 Open Access CC BY-NC-ND
Abstract
Severe surface wind gusts produced by thunderstorms have the potential to damage infrastructure and are a major hazard for society. Wind gust data are examined from 35 observing stations around Australia, with lightning observations used to indicate the occurrence of deep convective processes in the vicinity of the observed wind gusts. A collation of severe thunderstorm reports is also used to complement the station wind gust data. Atmospheric reanalysis data are used to systematically examine large-scale environmental measures associated with severe convective winds. We find that methods based on environmental measures provide a better indication of the observed severe convective winds than the simulated model wind gusts from the reanalysis data, noting that the spatial scales on which these events occur are typically smaller than the reanalysis grid cells. Consistent with previous studies in other regions and idealised modelling, the majority of severe convective wind events are found to occur in environments with steep mid-level tropospheric lapse rates, moderate convective instability and strong background wind speeds. A large proportion of events from measured station data occur with relatively dry environmental air at low levels, although it is unknown to what extent this type of environment is representative of other severe wind-producing convective modes in Australia. The occurrence of severe convective winds is found to be well represented by a number of indices used previously for forecasting applications, such as the weighted product of convective available potential energy (CAPE) and vertical wind shear, the derecho composite parameter and the total totals index, as well as by logistic regression methods applied to environmental variables. Based on the systematic approach used in this study, our findings provide new insight on spatio-temporal variations in the risk of damaging winds occurring, including the environmental factors associated with their occurrence.
Keywords: climate, climatology, convection, downburst, extremes, hazards, reanalysis, thunderstorms, wind.
References
Allen, J. T., and Karoly, D. J. (2014). A climatology of Australian severe thunderstorm environments 1979–2011: inter-annual variability and ENSO influence. Int. J. Climatol. 34, 81–97.| A climatology of Australian severe thunderstorm environments 1979–2011: inter-annual variability and ENSO influence.Crossref | GoogleScholarGoogle Scholar |
Allen, J., Karoly, D., and Mills, G. (2011). A severe thunderstorm climatology for Australia and associated thunderstorm environments. Aust. Meteorol. Oceanogr. J. 61, 143–158.
| A severe thunderstorm climatology for Australia and associated thunderstorm environments.Crossref | GoogleScholarGoogle Scholar |
Allen, J. T., Tippett, M. K., and Sobel, A. H. (2015). An empirical model relating US monthly hail occurrence to large-scale meteorological environment. J. Adv. Model. Earth Syst. 7, 226–243.
| An empirical model relating US monthly hail occurrence to large-scale meteorological environment.Crossref | GoogleScholarGoogle Scholar |
Atkins, N. T., and Wakimoto, R. M. (1991). Wet microburst activity over the southeastern United States: implications for forecasting. Wea. Forecast 6, 470–482.
| Wet microburst activity over the southeastern United States: implications for forecasting.Crossref | GoogleScholarGoogle Scholar |
Australian Energy Market Operator. (2017). Black system, South Australia, 28 September 2016. Available at https://www.aemo.com.au/-/media/Files/Electricity/NEM/Market_Notices_and_Events/Power_System_Incident_Reports/2017/Integrated-Final-Report-SA-Black-System-28-September-2016.pdf [verified 25 November 2020]
Azorin‐Molina, C., Guijarro, J. A., McVicar, T. R., Trewin, B. C., Frost, A. J., and Chen, D. (2019). An approach to homogenize daily peak wind gusts: an application to the Australian series. Int. J. Climatol. 39, 2260–2277.
| An approach to homogenize daily peak wind gusts: an application to the Australian series.Crossref | GoogleScholarGoogle Scholar |
Bates, B. C., Dowdy, A. J., and Chandler, R. E. (2018). Lightning prediction for Australia using multivariate analyses of large-scale atmospheric variables. J. Appl. Meteorol. and Climatol. 57, 525–534.
| Lightning prediction for Australia using multivariate analyses of large-scale atmospheric variables.Crossref | GoogleScholarGoogle Scholar |
Bedka, K. M., Allen, J. T., Punge, H. J., Kunz, M., and Simanovic, D. (2018). A long-term overshooting convective cloud-top detection database over Australia derived from MTSAT Japanese advanced meteorological imager observations. J. Appl. Meteorol. and Climatol. 57, 937–961.
| A long-term overshooting convective cloud-top detection database over Australia derived from MTSAT Japanese advanced meteorological imager observations.Crossref | GoogleScholarGoogle Scholar |
Bednarczyk, C., and Sousounis, P. (2012). Hail climatology of Australia based on lightning and reanalysis. Available at https://ams.confex.com/ams/27SLS/webprogram/Manuscript/Paper255889/extendedAbstract_pdf.pdf [verified 25 November 2020]
Blumberg, W. G., Halbert, K. T., Supinie, T. A., Marsh, P. T., Thompson, R. L., and Hart, J. A. (2017). Sharppy: an open-source sounding analysis toolkit for the atmospheric sciences. Bull. Am. Meteorol. Soc. 98, 1625–1636.
| Sharppy: an open-source sounding analysis toolkit for the atmospheric sciences.Crossref | GoogleScholarGoogle Scholar |
Brooks, H. E., Lee, J. W., and Craven, J. P. (2003). The spatial distribution of severe thunderstorm and tornado environments from global reanalysis data. Atmos. Res. 67–68, 73–94.
| The spatial distribution of severe thunderstorm and tornado environments from global reanalysis data.Crossref | GoogleScholarGoogle Scholar |
Brown, A., and Dowdy, A. (2019). Extreme wind gusts and thunderstorms in South Australia analysed from 1979–2017. Bureau Research Report No. 034. Available at http://www.bom.gov.au/research/publications/researchreports/BRR-034.pdf [verified 25 November 2020]
Bunkers, M. J., Klimowski, B. A., Zeitler, J. W., Thompson, R. L., and Weisman, M. L. (2000). Predicting supercell motion using a new hodograph technique. Wea. Forecast 15, 61–79.
| Predicting supercell motion using a new hodograph technique.Crossref | GoogleScholarGoogle Scholar |
Bureau of Meteorology. (2016). Severe thunderstorm and tornado outbreak South Australia 28 September 2016. Available at http://www.bom.gov.au/announcements/sevwx/sa/Severe_Thunderstorm_and_Tornado_Outbreak_28_September_2016.pdf [verified 25 November 2020]
Coniglio, M. C., Stensrud, D. J., and Wicker, L. J. (2006). Effects of upper-level shear on the structure and maintenance of strong quasi-linear mesoscale convective systems. J. Atmos. Sci. 63, 1231–1252.
| Effects of upper-level shear on the structure and maintenance of strong quasi-linear mesoscale convective systems.Crossref | GoogleScholarGoogle Scholar |
Doswell, C. A., and Evans, J. S. (2003). Proximity sounding analysis for derechos and supercells: an assessment of similarities and differences. Atmos. Res. 67–68, 117–133.
| Proximity sounding analysis for derechos and supercells: an assessment of similarities and differences.Crossref | GoogleScholarGoogle Scholar |
Doswell, C. A., and Schultz, D. M. (2006). On the use of indices and parameters in forecasting severe storms. E-Journal of Severe Storms Meteorology 1, .
Doswell, C. A., Davies-Jones, R., and Keller, D. A. (1990). On summary measures of skill in rare event forecasting based on contingency tables. Wea. Forecast 5, 576–585.
| On summary measures of skill in rare event forecasting based on contingency tables.Crossref | GoogleScholarGoogle Scholar |
Dowdy, A. J. (2015) Large-scale modelling of environments favourable for dry lightning occurrence. In ‘Proceedings of 21st International Congress on Modelling and Simulation, Gold Coast, Australia, 29 November to 4 December 2015’. 1524–1530. (Modelling and Simulation Society of Australia and New Zealand.)
Dowdy, A. J. (2020). Climatology of thunderstorms, convective rainfall and dry lightning environments in Australia. Clim. Dyn. 54, 3041–3052.
| Climatology of thunderstorms, convective rainfall and dry lightning environments in Australia.Crossref | GoogleScholarGoogle Scholar |
Edwards, R., Allen, J. T., and Carbin, G. W. (2018). Reliability and climatological impacts of convective wind estimations. J. Appl. Meteorol. and Climatol. 57, 1825–1845.
| Reliability and climatological impacts of convective wind estimations.Crossref | GoogleScholarGoogle Scholar |
Ellrod, G. (1989). Environmental conditions associated with the Dallas microburst storm determined from satellite soundings. Wea. Forecast 4, 469–484.
| Environmental conditions associated with the Dallas microburst storm determined from satellite soundings.Crossref | GoogleScholarGoogle Scholar |
Evans, J. S., and Doswell, C. A. (2001). Examination of derecho environments using proximity soundings. Wea. Forecast 16, 329–342.
| Examination of derecho environments using proximity soundings.Crossref | GoogleScholarGoogle Scholar |
Gascón, E., Merino, A., Sánchez, J. L., Fernández-González, S., García-Ortega, E., López, L., and Hermida, L. (2015). Spatial distribution of thermodynamic conditions of severe storms in southwestern Europe. Atmos. Res. 164–165, 194–209.
| Spatial distribution of thermodynamic conditions of severe storms in southwestern Europe.Crossref | GoogleScholarGoogle Scholar |
Geerts, B. (2001). Estimating downburst-related maximum surface wind speeds by means of proximity soundings in New South Wales, Australia. Wea. Forecast 16, 261–269.
| Estimating downburst-related maximum surface wind speeds by means of proximity soundings in New South Wales, Australia.Crossref | GoogleScholarGoogle Scholar |
Grams, J. S., Thompson, R. L., Snively, D. V., Prentice, J. A., Hodges, G. M., and Reames, L. J. (2012). A climatology and comparison of parameters for significant tornado events in the United States. Wea. Forecast 27, 106–123.
| A climatology and comparison of parameters for significant tornado events in the United States.Crossref | GoogleScholarGoogle Scholar |
Hersbach, H., Bell, B., Berrisford, P., et al. (2020). The ERA5 global reanalysis. Quart. J. Roy. Meteorol. Soc. 146, 1999–2049.
| The ERA5 global reanalysis.Crossref | GoogleScholarGoogle Scholar |
Holmes, J., Wang, C.-H., and Oliver, S. (2018). Extreme winds for six South Australian locations. In ‘Proceedings of the 19th Australasian Wind Engineering Society Workshop. Torquay, Victoria’. (Australasian Wind Engineering Society.)
Hurlbut, M. M., and Cohen, A. E. (2014). Environments of Northeast US severe thunderstorm events from 1999 to 2009. Wea. Forecast 29, 3–22.
| Environments of Northeast US severe thunderstorm events from 1999 to 2009.Crossref | GoogleScholarGoogle Scholar |
Joint Working Group on Forecast Verification Research (2015). Forecast verification methods across time and space scales. Available at https://www.cawcr.gov.au/projects/verification/ [verified 25 November 2020]
King, A. T., and Kennedy, A. D. (2019). North American supercell environments in atmospheric reanalyses and RUC-2. J. Appl. Meteorol. Climatol. 58, 71–92.
| North American supercell environments in atmospheric reanalyses and RUC-2.Crossref | GoogleScholarGoogle Scholar |
Kounkou, R., Mills, G., and Timbal, B. (2009). A reanalysis climatology of cool-season tornado environments over southern Australia. Int. J. Climatol. 29, 2079–2090.
| A reanalysis climatology of cool-season tornado environments over southern Australia.Crossref | GoogleScholarGoogle Scholar |
Kuchera, E. L., and Parker, M. D. (2006). Severe convective wind environments. Wea. Forecast 21, 595–612.
| Severe convective wind environments.Crossref | GoogleScholarGoogle Scholar |
Kuleshov, Y., De Hoedt, G., Wright, W., and Brewster, A. (2002). Thunderstorm distribution and frequency in Australia. Aust. Met. Mag. 51, 145–154.
Ladwig, W. (2017). wrf-python (version 1.1.0) [Software]. (UCAR/NCAR: Boulder, CO.) Available at
Lang, T. J., and Rutledge, S. A. (2002). Relationships between convective storm kinematics, precipitation, and lightning. Mon. Wea. Rev. 130, 2492–2506.
| Relationships between convective storm kinematics, precipitation, and lightning.Crossref | GoogleScholarGoogle Scholar |
Mason, S. J., and Graham, N. E. (2002). Areas beneath the relative operating characteristics (ROC) and relative operating levels (ROL) curves: statistical significance and interpretation. Quart. J. Roy. Meteorol. Soc. 128, 2145–2166.
| Areas beneath the relative operating characteristics (ROC) and relative operating levels (ROL) curves: statistical significance and interpretation.Crossref | GoogleScholarGoogle Scholar |
May, R. M., Arms, S. C., Marsh, P., Bruning, E., Leeman, J. R., Goebbert, K., et al. (2019). MetPy: a Python package for meteorological data. (Unidata: Boulder, C).) Available at
McCann, D. W. (1994). WINDEX-A new index for forecasting mircoburst potenitial. Wea. Forecast 9, 532–541.
| WINDEX-A new index for forecasting mircoburst potenitial.Crossref | GoogleScholarGoogle Scholar |
Miller, R. C. (1972). Notes on analysis and severe-storm forecasting procedures of the air force global weather central. Tech. Rep. 200, Air Weather Service.
Miller, P. W., and Mote, T. L. (2018). Characterizing severe weather potential in synoptically weakly forced thunderstorm environments. Nat. Hazards and Earth Syst. Sci. 18, 1261–1277.
| Characterizing severe weather potential in synoptically weakly forced thunderstorm environments.Crossref | GoogleScholarGoogle Scholar |
Mohr, S., Kunz, M., and Keuler, K. (2015). Development and application of a logistic model to estimate the past and future hail potential in Germany. J. Geophys. Res. 120, 3939–3956.
| Development and application of a logistic model to estimate the past and future hail potential in Germany.Crossref | GoogleScholarGoogle Scholar |
Niall, S., and Walsh, K. (2005). The impact of climate change on hailstorms in southeastern Australia. Int. J. Climatol. 25, 1933–1952.
| The impact of climate change on hailstorms in southeastern Australia.Crossref | GoogleScholarGoogle Scholar |
Pang, G., He, J., Huang, Y., and Zhang, L. (2019). A binary logistic regression model for severe convective weather with numerical model data. Adv. Meteorol. 2019, 6127281.
| A binary logistic regression model for severe convective weather with numerical model data.Crossref | GoogleScholarGoogle Scholar |
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., et al. (2011). Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830.
Prein, A. F., and Holland, G. J. (2018). Global estimates of damaging hail hazard. Wea. Clim. Extrem. 22, 10–23.
| Global estimates of damaging hail hazard.Crossref | GoogleScholarGoogle Scholar |
Proctor, F. H. (1989). Numerical simulations of an isolated microburst. Part II: sensitivity experiments. J. Atmos. Sci. 46, 2143–2165.
| Numerical simulations of an isolated microburst. Part II: sensitivity experiments.Crossref | GoogleScholarGoogle Scholar |
Pryor, K. L. (2007). The GOES microburst windspeed potential index, (2004), 15. Available at https://cds.cern.ch/record/1024106/files/0703162.pdf [verified 25 November 2020]
Richter, H., Peter, J., and Collis, S. (2014). Analysis of a destructive wind storm on 16 November 2008 in Brisbane, Australia. Mon. Wea. Rev. 142, 3038–3060.
| Analysis of a destructive wind storm on 16 November 2008 in Brisbane, Australia.Crossref | GoogleScholarGoogle Scholar |
Seabold, S., and Perktold, J. (2010). Statsmodels: econometric and statistical modeling with python. In ‘Proceedings of the 9th Python in Science Conference (SciPy 2010)’, Austin Texas. (Eds S. van der Walt and J. Millman) pp. 92–96. (SciPy.org)
Sherburn, K. D., Parker, M. D., King, J. R., and Lackmann, G. M. (2016). Composite environments of severe and nonsevere high-shear, low-CAPE convective events. Wea. Forecast 31, 1899–1927.
| Composite environments of severe and nonsevere high-shear, low-CAPE convective events.Crossref | GoogleScholarGoogle Scholar |
Smith, B. T., Castellanos, T. E., Winters, A. C., Mead, C. M., Dean, A. R., and Thompson, R. L. (2012). Measured severe convective wind climatology and associated convective modes of thunderstorms in the contiguous United States, 2003–09. Wea. Forecast 28, 229–236.
| Measured severe convective wind climatology and associated convective modes of thunderstorms in the contiguous United States, 2003–09.Crossref | GoogleScholarGoogle Scholar |
Spassiani, A. C. (2020) Climatology of severe convective wind gusts in Australia. PhD thesis. The University of Queensland School of Civil Engineering
Srivastava, R. C. (1985). A simple model of evaporatively driven downdraft: application to microburst downdraft. J. Atmos. Sci. 42, 1004–1023.
| A simple model of evaporatively driven downdraft: application to microburst downdraft.Crossref | GoogleScholarGoogle Scholar |
Su, C.-H., Eizenberg, N., Steinle, P., Jakob, D., Fox-Hughes, P., White, C. J., et al. (2019). BARRA v1.0: the Bureau of Meteorology atmospheric high-resolution regional reanalysis for Australia. Geosci. Model Dev. Discuss 12, 2049–2068.
| BARRA v1.0: the Bureau of Meteorology atmospheric high-resolution regional reanalysis for Australia.Crossref | GoogleScholarGoogle Scholar |
Taszarek, M., Allen, J., Púčik, T., Groenemeijer, P., Czernecki, B., Kolendowicz, L., et al. (2019). A climatology of thunderstorms across Europe from a synthesis of multiple data sources. J. Climate 32, 1813–1837.
| A climatology of thunderstorms across Europe from a synthesis of multiple data sources.Crossref | GoogleScholarGoogle Scholar |
Thompson, R. L., Edwards, R., and Mead, C. M. (2004). ‘An update to the supercell composite and significant tornado parameters.’ (Storm Prediction Center: Norman, OK.)
Thompson, R. L., Mead, C. M., and Edwards, R. (2007). Effective storm-relative helicity and bulk shear in supercell thunderstorm environments. Wea. Forecast 22, 102–115.
| Effective storm-relative helicity and bulk shear in supercell thunderstorm environments.Crossref | GoogleScholarGoogle Scholar |
Virts, K. S., Wallace, J. M., Hutchins, M. L., and Holzworth, R. H. (2013). Highlights of a new ground-based, hourly global lightning climatology. Bull. Amer. Meteorol. Soc. 94, 1381–1391.
| Highlights of a new ground-based, hourly global lightning climatology.Crossref | GoogleScholarGoogle Scholar |
Wakimoto, R. M. (1985). Forecasting dry microburst activity over the High Plains. Mon. Wea. Rev. 113, 1131–1143.
| Forecasting dry microburst activity over the High Plains.Crossref | GoogleScholarGoogle Scholar |
