Probabilistic thunderstorm forecasting by blending multiple ensembles
By: François Bouttier and Hugo Marchal
References
- Beck, J., Bouttier, F., Wiegand, L., Gebhardt, C., Eagle, C. and co-authors. 2016. Development and verification of two convection-resolving multi-model ensembles over northwestern Europe. Q. J. R. Meteorol. Soc. 142, 2808–2826, doi:10.1002/qj.2870
- Ben Bouallègue, Z. 2013. Calibrated short-range ensemble precipitation forecasts using extended logistic regression with interaction terms. Weather Forecast. 28, 515–524, doi:10.1175/WAF-D-12-00062.1
- Berrocal, V. J., Raftery, A. E. and Gneiting, T. 2007. Combining spatial statistical and ensemble information in probabilistic weather forecasts. Mon. Weather Rev. 135, 1386–1402, doi:10.1175/MWR3341.1
- Bouttier, F., Vié, B., Nuissier, O. and Raynaud, L. 2012. Impact of stochastic physics in a convection-permitting ensemble. Mon. Weather Rev. 140, 3706–3721, doi:10.1175/MWR-D-12-00031.1
- Bouttier, F., Raynaud, L., Nuissier, O. and Ménétrier, B. 2016. Sensitivity of the AROME ensemble to initial and surface perturbations during HyMeX. Q. J. R. Meteorol. Soc. 142, 390–403, doi:10.1002/qj.2622
- Bröcker, J. and Smith, L. 2008. From ensemble forecasts to predictive distribution functions. Tellus A: Dyn. Meteorol. Oceanogr. 60, 663–678, doi:10.1111/j.1600-0870.2008.00333.x
- Brousseau, P., Seity, Y., Ricard, D. and Léger, J. 2016. Improvement of the forecast of convective activity from the AROME-France system. Q. J. R. Meteorol. Soc. 142, 2231–2243, doi:10.1002/qj.2822
- Casati, B. and Wilson, L. J. 2007. A new spatial-scale decomposition of the Brier score: application to the verification of lightning probability forecasts. Mon. Weather Rev. 135, 3052–3069, doi:10.1175/MWR3442.1
- Clark, A. J., Gallus, W. A. and Chen, T. C. 2008. Contributions of mixed physics versus perturbed initial/lateral boundary conditions to ensemble-based precipitation forecast skill. Mon. Weather Rev. 136, 2140–2156, doi:10.1175/2007MWR2029.1
- Clark, A. J., Gallus, W. A., Xue, M. and Kong, F. 2009. A comparison of precipitation forecast skill between small convection-allowing and large convection-parameterizing ensembles. Weather Forecast. 24, 1121–1140, doi:10.1175/2009WAF2222222.1
- Collins, W. and Tissot, P. 2015. An artificial neural network model to predict thunderstorms within 400 km2 South Texas domains. Meteorol. Appl. 22, 650–665, doi:10.1002/met.1499
- Descamps, L. and Talagrand, O. 2007. On some aspects of the definition of initial conditions for ensemble prediction. Mon. Weather Rev. 135, 3260–3272, doi:10.1175/MWR3452.1
- Descamps, L., Labadie, C., Joly, A., Bazile, E., Arbogast, P. and co-authors. 2015. PEARP, the Météo-France short-range ensemble prediction system. Q. J. R. Meteorol. Soc. 141, 1671–1685, doi:10.1002/qj.2469
- Deutsch, J. L. and Deutsch, C. V. 2012. Latin hypercube sampling with multidimensional uniformity. J. Stat. Plan. Inference 142, 763–772, (with online material at https://github.com/sahilm89/lhsmdu). doi:10.1016/j.jspi.2011.09.016
- Flowerdew, J. 2014. Calibrating ensemble reliability whilst preserving spatial structure. Tellus A: Dyn. Meteorol. Oceanogr. 66, 22662, doi:10.3402/tellusa.v66.22662
- Gijben, M., Dyson, L. L. and Mattheus, T. L. 2017. A statistical scheme to forecast the daily lightning threat over Southern Africa using the Unified Model. Atmos. Res. 194, 78–88, doi:10.1016/j.atmosres.2017.04.022
- Gneiting, T., Raftery, A. E., Westveld, A. H. and Goldman, T. 2005. Calibrated probabilistic forecasting using ensemble model output statistics and minimum CRPS estimation. Mon. Weather Rev. 133, 1098–1118, doi:10.1175/MWR2904.1
- Goodman, S. J., Blakeslee, R. J., Koshak, W. J., Mach, D., Bailey, J. and coauthors. 2013. The GOES-R geostationary lightning mapper (GLM). Atmos. Res. 125, 34–49,
- Hagedorn, R., Buizza, R., Hamill, T. M., Leutbecher, M. and Palmer, T. N. 2012. Comparing TIGGE multimodel forecasts with reforecast-calibrated ECMWF ensemble forecasts. Q. J. R. Meteorol. Soc. 138, 1814–1827, doi:10.1002/qj.1895
- Hamill, T. M., Whitaker, J. S. and Mullen, S. L. 2006. Reforecasts: an important dataset for improving weather predictions. Bull. Am. Meteorol. Soc. 87, 33–46, doi:10.1175/BAMS-87-1-33
- Hamill, T., Hagedorn, R. and Whitaker, J. S. 2008. Probabilistic forecast calibration using ECMWF and GFS ensemble reforecasts. Part II: Precipitation. Mon. Weather Rev. 136, 2620–2632, doi:10.1175/2007MWR2411.1
- Jolliffe, I. T. and Stephenson, D. B. 2011. Forecast Verification: A Practitioner's Guide in Atmospheric Science. 2nd ed. John Wiley and Sons, Hoboken, NJ, 292 pp,
- Karagiannidis, A., Lagouvardos, K., Lykoudis, S., Kotroni, V., Giannaros, T. and co-authors. 2019. Modeling lightning density using cloud top parameters. Atmos. Res. 222, 163–171, doi:10.1016/j.atmosres.2019.02.013
- Keil, C., Heinlein, F. and Craig, G. C. 2014. The convective adjustment time-scale as indicator of predictability of convective precipitation. Q. J. R. Meteorol. Soc. 140, 480–490, doi:10.1002/qj.2143
- Leutbecher, M., Lock, S.-J., Ollinaho, P., Lang, S. T. K., Balsamo, G. and co-authors. 2017. Stochastic representations of model uncertainties at ECMWF: state of the art and future vision. Q. J. R. Meteorol. Soc. 143, 2315–2339, doi:10.1002/qj.3094
- Leutbecher, M. 2018. Ensemble size: how suboptimal is less than infinity? Q. J. R. Meteorol. Soc. 145, 1–22,
- Li, N., Wei, M., Niu, B. and Mu, X. 2012. A new radar-based storm identification and warning technique. Meteorol. Appl. 19, 17–25, doi:10.1002/met.249
- Lopez, P. 2016. A lightning parameterization for the ECMWF integrated forecasting system. Mon. Weather Rev. 144, 3057–3075, doi:10.1175/MWR-D-16-0026.1
- Lu, C., Yuan, H., Schwartz, B. E. and Benjamin, S. G. 2007. Short-range numerical weather prediction using time-lagged ensembles. Weather Forecast. 22, 580–595, doi:10.1175/WAF999.1
- Mason, S. and Graham, N. 1999. Conditional probabilities, relative operating characteristics, and relative operating levels. Weather Forecast. 14, 713–725, > 2.0.CO;2. doi:10.1175/1520-0434(1999)014<;0713:CPROCA>2.0.CO;2
- Osinski, R. and Bouttier, F. 2018. Short-range probabilistic forecasting of convective risks for aviation based on a lagged-average-forecast ensemble approach. Meteorol. Appl. 25, 105–118, doi:10.1002/met.1674
- Park, Y.-Y., Buizza, R. and Leutbecher, M. 2008. TIGGE: Preliminary results on comparing and combining ensembles. Q. J. R. Meteorol. Soc. 134, 2029–2050, doi:10.1002/qj.334
- Pédeboy, S. and Schulz, W. 2014. Validation of a ground strike point identification algorithm based on ground truth data. In: Abstracts of the 23rd International Lightning Detection Conference, Tucson, AZ, 7 pp. Online at: https://www.meteorage.com/
- Raftery, A., Gneiting, T., Balabdaoui, F. and Polakowski, M. 2005. Using Bayesian model averaging to calibrate forecast ensembles. Mon. Weather Rev., 133, 1155–1174, doi:10.1175/MWR2906.1
- Raynaud, L. and Bouttier, F. 2016. Comparison of initial perturbation methods for ensemble prediction at convective scale. Q. J. R. Meteorol. Soc. 142, 854–866, doi:10.1002/qj.2686
- Richardson, D. 2000. Skill and relative economic value of the ECMWF ensemble prediction system. Q. J. R. Meteorol. Soc. 126, 649–667, doi:10.1002/qj.49712656313
- Scheuerer, M. 2014. Probabilistic quantitative precipitation forecasting using ensemble model output statistics. Q. J. R. Meteorol. Soc. 140, 1086–1096, doi:10.1002/qj.2183
- Schmeits, M. J., Kok, K. J., Vogelezang, D. H. P. and van Westrhenen, R. M. 2008. Probabilistic forecasts of (severe) thunderstorms for the purpose of issuing a weather alarm in the Netherlands. Weather Forecast. 23, 1253–1267, doi:10.1175/2008WAF2007102.1
- Schwartz, C. S. and Sobash, R. A. 2017. Generating probabilistic forecasts from convection-allowing ensembles using neighborhood approaches: a review and recommendations. Mon. Weather Rev. 145, 3397–3418, doi:10.1175/MWR-D-16-0400.1
- Seity, Y., Brousseau, P., Malardel, S., Hello, G., Bénard, P. and co-authors. 2011. The AROME-France convective scale operational model. Mon. Weather Rev. 139, 976–991, doi:10.1175/2010MWR3425.1
- Simon, T., Fabsic, P., Mayr, G. J., Umlauf, N. and Zeileis, A. 2018. Probabilistic forecasting of thunderstorms in the Eastern Alps. Mon. Weather Rev. 146, 2999–3009, doi:10.1175/MWR-D-17-0366.1
- Sobash, R. A., Kain, J. S., Bright, D. R., Dean, A. R., Coniglio, M. C. and co-authors. 2011. Probabilistic forecast guidance for severe thunderstorms based on the identification of extreme phenomena in convection-allowing model forecasts. Weather Forecast. 26, 714–728, doi:10.1175/WAF-D-10-05046.1
- Taillardat, M., Mestre, O., Zamo, M. and Naveau, P. 2016. Calibrated ensemble forecasts using quantile regression forests and ensemble model output statistics. Mon. Weather Rev. 144, 2375–2393, doi:10.1175/MWR-D-15-0260.1
- Theis, S. E., Hense, A. and Damrath, U. 2005. Probabilistic precipitation forecasts from a deterministic model: a pragmatic approach. Meteorol. Appl. 12, 257–268, doi:10.1017/S1350482705001763
- Walser, A., Lüthi, D. and Schär, C. 2004. Predictability of precipitation in a cloud-resolving model. Mon. Weather Rev. 132, 560–577, > 2.0.CO:2. doi:10.1175/1520-0493(2004)132<;0560:POPIAC>2.0.CO;2
- Weisman, M. L., Davis, C., Wang, W., Manning, K. W. and Klemp, J. B. 2008. Experiences with 0–36 h explicit convective forecasts with the WRF-ARW Model. Weather Forecast. 23, 407–437, doi:10.1175/2007WAF2007005.1
- Yair, Y., Lynn, B., Price, C., Kotroni, V., Lagouvardos, K. and co-authors. 2010. Predicting the potential for lightning activity in Mediterranean storms based on the weather research and forecasting (WRF) model dynamic and microphysical fields. J. Geophys. Res. 115, D04205,
- Zhu, Y., Toth, Z., Wobus, R., Richardson, D. and Mylne, K. 2002. The economic value of ensemble-based weather forecasts. Bull. Am. Meteorol. Soc. 83, 73–83, doi:10.1175/1520-0477(2002)083<;0073:TEVOEB>2.3.CO;2
- Ziehmann, C. 2000. Comparison of a single-model EPS with a multi-model ensemble consisting of a few operational models. Tellus A: Dyn. Meteorol. Oceanogr. 52, 280–299, doi:10.3402/tellusa.v52i3.12266
DOI: https://doi.org/10.1080/16000870.2019.1696142 | Journal eISSN: 3035-9554
Language: English
Page range: 1696142 - 1696142
Published on: Jan 1, 2020
Published by: Stockholm University Press
In partnership with: Paradigm Publishing Services
Keywords:
© 2020 François Bouttier, Hugo Marchal, published by Stockholm University Press
This work is licensed under the Creative Commons Attribution 4.0 License.