• Ackerman, S. A., and Coauthors, 2018: Satellites see the world’s atmosphere. A Century of Progress in Atmospheric and Related Sciences: Celebrating the American Meteorological Society Centennial, Meteor. Monogr., No. 59, Amer. Meteor. Soc., https://doi.org/10.1175/AMSMONOGRAPHS-D-18-0009.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Alessandrini, S., S. Sperati, and L. Delle Monache, 2019: Improving the analog ensemble wind speed forecasts for rare events. Mon. Wea. Rev., 147, 26772692, https://doi.org/10.1175/MWR-D-19-0006.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Augros, C., O. Caumont, V. Ducrocq, and N. Gaussiat, 2018: Assimilation of radar dual-polarization observations in AROME model. Quart. J. Roy. Meteor. Soc., 144, 13521368, https://doi.org/10.1002/qj.3269.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Australian Bureau of Meteorology, 2012: Australian climate outlook archive. www.bom.gov.au/climate/ahead/outlooks/archive.shtml.

  • Ballard, S. P., Z. Li, D. Simonin, H. Buttery, C. Charlton-Perez, N. Gaussiat, and L. Hawkness-Smith, 2012: Use of radar data in NWP-based nowcasting in the Met Office. IAHS Publ ., 351, 336341.

    • Search Google Scholar
    • Export Citation
  • Benedetti, A., and F. Vitart, 2018: Can the direct effect of aerosols improve subseasonal predictability? Mon. Wea. Rev., 146, 34813498, https://doi.org/10.1175/MWR-D-17-0282.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Benjamin, S. G., and Coauthors, 2016: A North American hourly assimilation and model forecast cycle: The Rapid Refresh. Mon. Wea. Rev., 144, 16691694, https://doi.org/10.1175/MWR-D-15-0242.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Berner, J., and Coauthors, 2017: Stochastic parameterization: Toward a new view of weather and climate models. Bull. Amer. Meteor. Soc., 98, 565588, https://doi.org/10.1175/BAMS-D-15-00268.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bick, T., and Coauthors, 2016: Assimilation of 3D radar reflectivity with an ensemble Kalman filter on the convective scale. Quart. J. Roy. Meteor. Soc., 142, 14901504, https://doi.org/10.1002/qj.2751.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Blackwell, W. J., and Coauthors, 2018: An overview of the TROPICS NASA Earth venture mission. Quart. J. Roy. Meteor. Soc., 144, 1626, https://doi.org/10.1002/qj.3290.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Blake, E. S., and D. A. Zelinsky, 2018: Tropical cyclone report: Hurricane Harvey (AL092017). NHC Tech. Rep., 77 pp., www.nhc.noaa.gov/data/tcr/AL092017_Harvey.pdf.

    • Search Google Scholar
    • Export Citation
  • Bowler, N. E., A. Arribas, K. R. Mylne, K. B. Robertson, and S. E. Beare, 2008: The MOGREPS short-range ensemble prediction system. Quart. J. Roy. Meteor. Soc., 134, 703722, https://doi.org/10.1002/qj.234.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Buehner, M., and A. Shlyaeva, 2015: Scale-dependent background-error covariance localization. Tellus, 67A, 28027, https://doi.org/10.3402/tellusa.v67.28027.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ching, J., and Coauthors, 2018: WUADAPT: An urban weather, climate and environmental modeling infrastructure for the Anthropocene. Bull. Amer. Meteor. Soc., 99, 1907–1924, https://doi.org/10.1175/BAMS-D-16-0236.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cione, J. J., and Coauthors, 2020: Eye of the storm: Observing hurricanes with a small unmanned aircraft system. Bull. Amer. Meteor. Soc., 101, E186E205, https://doi.org/10.1175/BAMS-D-19-0169.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Clark, A. J., and Coauthors, 2018: The Community Leveraged Unified Ensemble (CLUE) in the 2016 NOAA/Hazardous Weather Testbed Spring Forecasting Experiment. Bull. Amer. Meteor. Soc., 99, 14331448, https://doi.org/10.1175/BAMS-D-16-0309.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Clayton, A. M., A. C. Lorenc, and D. M. Barker, 2013: Operational implementation of a hybrid ensemble/4D-Var global data assimilation system at the Met Office. Quart. J. Roy. Meteor. Soc., 139, 14451461, https://doi.org/10.1002/qj.2054.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • CRED, 2019: Disasters 2018: A year in review. CredCrunch, No. 54, Centre for Research on the Epidemiology of Disasters, Brussels, Belgium, 2 pp., https://cred.be/sites/default/files/CredCrunch54.pdf.

    • Search Google Scholar
  • Dixon, K., C. F. Mass, G. J. Hakim, and R. H. Holzworth, 2016: The impact of lightning data assimilation on deterministic and ensemble forecasts of convective events. J. Atmos. Oceanic Technol., 33, 18011823, https://doi.org/10.1175/JTECH-D-15-0188.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Droste, A. M., J. J. Pape, A. Overeem, H. Leijnse, G. J. Steeneveld, A. J. Van Delden, and R. Uijlenhoet, 2017: Crowdsourcing urban air temperatures through smartphone battery temperatures in São Paulo, Brazil. J. Atmos. Oceanic Technol., 34, 18531866, https://doi.org/10.1175/JTECH-D-16-0150.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Durnford, D., and Coauthors, 2018: Towards an operational water cycle prediction system for the Great Lakes and St. Lawrence River. Bull. Amer. Meteor. Soc., 99, 521546, https://doi.org/10.1175/BAMS-D-16-0155.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Édouard, S., B. Vincendon, and V. Ducrocq, 2018: Ensemble-based flash-flood modelling: Taking into account hydrodynamic parameters and initial soil moisture uncertainties. J. Hydrol., 560, 480494, https://doi.org/10.1016/j.jhydrol.2017.04.048.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Elmore, K. L., Z. L. Flamig, V. Lakshmanan, B. T. Kaney, V. Farmer, H. D. Reeves, and L. P. Rothfusz, 2014: MPING: Crowd-sourcing weather reports for research. Bull. Amer. Meteor. Soc., 95, 13351342, https://doi.org/10.1175/BAMS-D-13-00014.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fierro, A. O., E. R. Mansell, C. L. Ziegler, and D. R. MacGorman, 2012: Application of a lightning data assimilation technique in the WRF-ARW model at cloud-resolving scales for the tornado outbreak of 24 May 2011. Mon. Wea. Rev., 140, 26092627, https://doi.org/10.1175/MWR-D-11-00299.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Golding, B., E. Ebert, M. Mittermaier, A. Scolobig, S. Panchuk, C. Ross, and D. Johnston, 2019: A value chain approach to optimising early warning systems. Global Assessment Report on Disaster Risk Reduction 2019, United Nations Office for Disaster Risk Reduction, 30 pp., www.preventionweb.net/publications/view/65828.

    • Search Google Scholar
    • Export Citation
  • Goodman, S., and Coauthors, 2013: The GOES-R Geostationary Lightning Mapper (GLM). Atmos. Res., 125–126, 3449, https://doi.org/10.1016/j.atmosres.2013.01.006.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Guerova, G., and Coauthors, 2016: Review of the state of the art and future prospects of the ground-based GNSS meteorology in Europe. Atmos. Meas. Tech., 9, 53855406, https://doi.org/10.5194/amt-9-5385-2016.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gustafsson, N., and Coauthors, 2018: Survey of data assimilation methods for convective-scale numerical weather prediction at operational centres. Quart. J. Roy. Meteor. Soc., 144, 12181256, https://doi.org/10.1002/qj.3179.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hagelin, S., J. Son, R. Swinbank, A. McCabe, N. Roberts, and W. Tennant, 2017: The Met Office convective-scale ensemble, MOGREPS-UK. Quart. J. Roy. Meteor. Soc., 143, 28462861, https://doi.org/10.1002/qj.3135.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hamill, T. M., J. S. Whitaker, and S. L. Mullen, 2006: Reforecasts: An important dataset for improving weather predictions. Bull. Amer. Meteor. Soc., 87, 3346, https://doi.org/10.1175/BAMS-87-1-33.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Honda, T., S. Kotsuki, G.-Y. Lien, Y. Maejima, K. Okamoto, and T. Miyoshi, 2018: Assimilation of Himawari-8 all-sky radiance every 10 minutes: Impact on precipitation and flood risk prediction. J. Geophys. Res. Atmos., 123, 965976, https://doi.org/10.1002/2017JD027096.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hou, A. Y., and Coauthors, 2014: The Global Precipitation Measurement mission. Bull. Amer. Meteor. Soc., 95, 701722, https://doi.org/10.1175/BAMS-D-13-00164.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ingleby, B., and Coauthors, 2016: Progress towards high-resolution, real-time radiosonde reports. Bull. Amer. Meteor. Soc., 97, 21492161, https://doi.org/10.1175/BAMS-D-15-00169.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jiao, W., and Coauthors, 2016: Community Air Sensor Network (CAIRSENSE) project: Evaluation of low-cost sensor performance in a suburban environment in the southeastern United States. Atmos. Meas. Tech., 9, 52815292, https://doi.org/10.5194/amt-9-5281-2016.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kain, J. S., and Coauthors, 2017: Collaborative efforts between the United States and the United Kingdom to advance prediction of high-impact weather. Bull. Amer. Meteor. Soc., 98, 937948, https://doi.org/10.1175/BAMS-D-15-00199.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kollias, P., E. Clothiaux, M. Miller, B. A. Albrecht, G. Stephens, and T. Ackerman, 2007: Millimeter-wavelength radars: New frontier in atmospheric cloud and precipitation research. Bull. Amer. Meteor. Soc., 88, 16081624, https://doi.org/10.1175/BAMS-88-10-1608.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lean, H. W., J. F. Barlow, and C. H. Halios, 2019: The impact of spin-up and resolution on the representation of a clear convective boundary layer over London in order 100m grid-length versions of the Met Office Unified Model. Quart. J. Roy. Meteor. Soc., 145, 16741689, https://doi.org/10.1002/qj.3519.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Leroyer, S., S. Bélair, S. Z. Husain, and J. Mailhot, 2014: Subkilometer numerical weather prediction in an urban coastal area: A case study over the Vancouver Metropolitan area. J. Appl. Meteor. Climatol., 53, 14331453, https://doi.org/10.1175/JAMC-D-13-0202.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lewis, H. W., and Coauthors, 2018: The UKC2 regional coupled environmental prediction system. Geosci. Model Dev., 11, 142, https://doi.org/10.5194/gmd-11-1-2018.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Li, X., and J. Mecikalski, 2012: Impact of the dual-polarization Doppler radar data on two convective storms with a warm-rain radar forward operator. Mon. Wea. Rev., 140, 21472167, https://doi.org/10.1175/MWR-D-11-00090.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Li, Z., J. C. McWilliams, K. Ide, and J. D. Farrara, 2015: A multiscale variational data assimilation scheme: Formulation and illustration. Mon. Wea. Rev., 143, 38043822, https://doi.org/10.1175/MWR-D-14-00384.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Li, Z., S. P. Ballard, and D. Simonin, 2018: Comparison of 3D-Var and 4D-Var data assimilation in an NWP-based system for precipitation nowcasting at the Met Office. Quart. J. Roy. Meteor. Soc., 144, 404413, https://doi.org/10.1002/qj.3211.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lilly, D. K., 1990: Numerical prediction of thunderstorms—Has its time come? Quart. J. Roy. Meteor. Soc., 116, 779798, https://doi.org/10.1002/qj.49711649402.

    • Search Google Scholar
    • Export Citation
  • Liu, Q., C. Fu, M. Li, and T. Li, 2018: Storm surge forecast and numerical study of “Hato” Typhoon. Mar. Forecasts, 35, 2936.

  • Lu, X., X. Wang, Y. Li, M. Tong, and X. Ma, 2017: GSI-based ensemble-variational hybrid data assimilation for HWRF for hurricane initialization and prediction: Impact of various error covariances for airborne radar observation assimilation. Quart. J. Roy. Meteor. Soc., 143, 223239, https://doi.org/10.1002/qj.2914.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lux, O., C. Lemmerz, F. Weiler, U. Marksteiner, B. Witschas, S. Rahm, A. Schafler, and O. Reitebuch, 2018: Airborne wind lidar observations over the North Atlantic in 2016 for the pre-launch validation of the satellite mission Aeolus. Atmos. Meas. Tech., 11, 32973322, https://doi.org/10.5194/amt-11-3297-2018.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lux, O., C. Lemmerz, F. Weiler, U. Marksteiner, B. Witschas, S. Rahm, A. Geiß, and O. Reitebuch, 2020: Intercomparison of wind observations from ESA’s satellite mission Aeolus and the ALADIN airborne Doppler demonstrator. Atmos. Meas. Tech., 13, 20752097, https://doi.org/10.5194/amt-13-2075-2020.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Magnusson, L., and Coauthors, 2019: ECMWF activities for improved hurricane forecasts. Bull. Amer. Meteor. Soc., 100, 445458, https://doi.org/10.1175/BAMS-D-18-0044.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mahoney, W., III, and J. O’Sullivan, 2013: Realizing the potential of vehicle-based observations. Bull. Amer. Meteor. Soc., 94, 10071018, https://doi.org/10.1175/BAMS-D-12-00044.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mass, C. F., and L. E. Madaus, 2014: Surface pressure observations from smartphones: A potential revolution for high-resolution weather prediction? Bull. Amer. Meteor. Soc., 95, 13431349, https://doi.org/10.1175/BAMS-D-13-00188.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • McLaughlin, D., and Coauthors, 2009: Short-wavelength technology and the potential for distributed networks of small radar systems. Bull. Amer. Meteor. Soc., 90, 17971818, https://doi.org/10.1175/2009BAMS2507.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mogensen, K. S., L. Magnusson, and J.-R. Bidlot, 2017: Tropical cyclone sensitivity to ocean coupling in the ECMWF coupled model. J. Geophys. Res. Oceans, 122, 43924412, https://doi.org/10.1002/2017JC012753.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nielsen, E. R., G. R. Herman, R. C. Tournay, J. M. Peters, and R. S. Schumacher, 2015: Double impact: When both tornadoes and flash floods threaten the same place at the same time. Wea. Forecasting, 30, 16731693, https://doi.org/10.1175/WAF-D-15-0084.1.

    • Search Google Scholar
    • Export Citation
  • NOAA, 2018: Service assessment: August/September 2017 Hurricane Harvey. NOAA, 78 pp., www.weather.gov/media/publications/assessments/harvey6-18.pdf.

  • Orville, R. E., 2008: Development of the national lightning detection network. Bull. Amer. Meteor. Soc., 89, 180190, https://doi.org/10.1175/BAMS-89-2-180.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Parsons, D. B., and Coauthors, 2017: THORPEX research and the science of prediction. Bull. Amer. Meteor. Soc., 98, 807830, https://doi.org/10.1175/BAMS-D-14-00025.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Perrin, F., P. Sauzey, B. Menoret, and P.-A. Roche, 2017: Inondations de mai et juin 2016 dans les bassins moyens de la Seine et de la Loire - Retour d’expérience (in French). Rep. CGEDD 010743-01 et IGA 16080-R, Inspection générale de l’administration, Conseil général de l’environnement et du développement durable, 212 pp.

    • Search Google Scholar
    • Export Citation
  • Petersen, R. A., 2016: On the impact and benefits of AMDAR observations in operational forecasting. Bull. Amer. Meteor. Soc., 97, 585602, https://doi.org/10.1175/BAMS-D-14-00055.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Poterjoy, J., R. A. Sobash, and J. Anderson, 2017: Convective-scale data assimilation for the weather research and forecasting model using the local particle filter. Mon. Wea. Rev., 145, 18971918, https://doi.org/10.1175/MWR-D-16-0298.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rabier, F., H. Jarvinen, E. Klinker, J.-F. Mafhouf, and A. Simmons, 2000: The ECMWF operational implementation of four-dimensional variational assimilation. I: Experimental results with simplified physics. Quart. J. Roy. Meteor. Soc., 126, 11431170, https://doi.org/10.1002/qj.49712656415.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rainaud, R., C. Lebeaupin Brossier, V. Ducrocq, and H. Giordani, 2017: High-resolution air-sea coupling impact on two heavy precipitation events in the western Mediterranean. Quart. J. Roy. Meteor. Soc., 143, 24482462, https://doi.org/10.1002/qj.3098.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ramos, M.-H., C. Perrin, V. Andreassion, O. Delaigue, and J. Viatgé, 2017: Assessement report on the 2016 flood event on the Seine and Loire nasins (France). Final Rep. European Flood Awareness System (EFAS) dissemination centre, Rijkswaterstaat (NL), Vigicrues network/SCHAPI (France), Irtsea (France), 43 pp.

    • Search Google Scholar
    • Export Citation
  • Ruf, C., and Coauthors, 2016: New ocean winds satellite mission to probe hurricanes and tropical convection. Bull. Amer. Meteor. Soc., 97, 385395, https://doi.org/10.1175/BAMS-D-14-00218.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ryzhkov, A., and D. Zrnić, 2019: Radar Polarimetry for Weather Observations. Springer, 486 pp.

  • Schmit, T. J., P. Griffith, M. M. Gunshor, J. M. Daniels, S. J. Goodman, and W. J. Lebair, 2017: A closer look at the ABI on the GOES-R series. Bull. Amer. Meteor. Soc., 98, 681698, https://doi.org/10.1175/BAMS-D-15-00230.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schoetter, R., V. Masson, A. Bourgeois, M. Pellegrino, and J.-P. Lévy, 2017: Parameterisation of the variety of human behaviour related to building energy consumption in Town Energy Balance (SURFEX v. 8.2). Geosci. Model Dev., 10, 28012831, https://doi.org/10.5194/gmd-10-2801-2017.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schwartz, C. S., G. S. Romine, R. A. Sobash, K. Fossell, and M. L. Weisman, 2015: NCAR’s experimental real-time convection-allowing ensemble prediction system. Wea. Forecasting, 30, 16451654, https://doi.org/10.1175/WAF-D-15-0103.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Short, C. J., and J. Petch, 2018: How well can the Met Office Unified model forecast tropical cyclones in the western North Pacific? Wea. Forecasting, 33, 185201, https://doi.org/10.1175/WAF-D-17-0069.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Skofronick-Jackson, G., and Coauthors, 2017: The Global Precipitation Measurement (GPM) mission for science and society. Bull. Amer. Meteor. Soc., 98, 16791695, https://doi.org/10.1175/BAMS-D-15-00306.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Speight, L., and Coauthors, 2018: Developing surface water flood forecasting capabilities in Scotland: An operational pilot for the 2014 Commonwealth Games in Glasgow. J. Flood Risk Manage., 11, S884S901, https://doi.org/10.1111/jfr3.12281.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stensrud, D. J., and Coauthors, 2009: Convective-scale warn-on-forecast system. Bull. Amer. Meteor. Soc., 90, 14871500, https://doi.org/10.1175/2009BAMS2795.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stephens, G., D. Winker, J. Pelon, C. Trepte, D. Vane, C. Yuhas, T. L’Ecuyer, and M. Lebsock, 2018: CloudSat and CALIPSO within the A-train: Ten years of actively observing the Earth system. Bull. Amer. Meteor. Soc., 99, 569581, https://doi.org/10.1175/BAMS-D-16-0324.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stone, E. K., and G. Pearce, 2016: A network of Mode-S receivers for routine acquisition of aircraft-derived meteorological data. J. Atmos. Oceanic Technol., 33, 757768, https://doi.org/10.1175/JTECH-D-15-0184.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Strajnar, B., N. Žagar, and L. Berre, 2015: Impact of new aircraft observations Mode-S MRAR in a mesoscale NWP model. J. Geophys. Res. Atmos., 120, 39203938, https://doi.org/10.1002/2014JD022654.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sun, J., and H. Wang, 2013: Radar data assimilation with WRF 4D-Var. Part II: Comparison with WRF 3D-Var for a squall line over the U.S. Great Plains. Mon. Wea. Rev., 141, 22452264, https://doi.org/10.1175/MWR-D-12-00169.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sun, J., and Coauthors, 2014: Use of NWP for nowcasting convective precipitation: Recent progress and challenges. Bull. Amer. Meteor. Soc., 95, 409426, https://doi.org/10.1175/BAMS-D-11-00263.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tasmanian Government, 2013: Tasmanian bushfires inquiry. Vol. 1, 263 pp., www.dpac.tas.gov.au/__data/assets/pdf_file/0015/208131/1.Tasmanian_Bushfires_Inquiry_Report.pdf.

    • Search Google Scholar
    • Export Citation
  • Taylor, A. L., T. Kox, and D. Johnston, 2018: Communicating high impact weather: Improving warnings and decision-making processes. Int. J. Disaster Risk Reduct ., 30, 14, https://doi.org/10.1016/j.ijdrr.2018.04.002.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Theurich, G., and Coauthors, 2016: The Earth system prediction suite: Toward a coordinated US modeling capability. Bull. Amer. Meteor. Soc., 97, 12291247, https://doi.org/10.1175/BAMS-D-14-00164.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Titley, H. A., R. L. Bowyer, and H. L. Cloke, 2020: A global evaluation of multi-model ensemble tropical cyclone track probability forecasts. Quart. J. Roy. Meteor. Soc., 146, 531545, https://doi.org/10.1002/qj.3712.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tolhurst, K. G., B. Shields, and D. Chong, 2008: PHOENIX: Development and application of a bushfire risk management tool. Aust. J. Emerg. Manage., 23 (4), 4754.

    • Search Google Scholar
    • Export Citation
  • Tong, M., and M. Xue, 2005: Ensemble Kalman filter assimilation of Doppler radar data with a compressible nonhydrostatic model: OSS experiments. Mon. Wea. Rev., 133, 17891807, https://doi.org/10.1175/MWR2898.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tong, W., G. Li, J. Sun, X. Tang, and Y. Zhang, 2016: Design strategies of an hourly update 3DVAR data assimilation system for improved convective forecasting. Wea. Forecasting, 31, 16731695, https://doi.org/10.1175/WAF-D-16-0041.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • United Nations, 2018: World Urbanization Prospects. Department of Economic and Social Affairs, 126 pp.

  • van de Giesen, N., R. Hut, and J. Selker, 2014: The Trans-African Hydro-Meteorological Observatory (TAHMO). Wiley Interdiscip. Rev.: Water, 1, 341348, https://doi.org/10.1002/wat2.1034.

    • Search Google Scholar
    • Export Citation
  • van Oldenborgh, G. J., S. Philip, E. Aalbers, R. Vautard, F. Otto, K. Haustein F. Habets, R. Singh, and H. Cullen, 2016: Rapid attribution of the May/June 2016 flood-inducing precipitation in France and Germany to climate change. Hydrol. Earth Syst. Sci. Discuss ., https://doi.org/10.5194/hess-2016-308.

    • Search Google Scholar
    • Export Citation
  • Vivekanandan, J., W.-C. Lee, E. Loew, J. L. Salazar, V. Grubisic, J. Moore, and P. Isai, 2014: The next generation airborne polarimetric Doppler weather radar. Geosci. Instrum. Methods Data Syst ., 3, 111126, https://doi.org/10.5194/gi-3-111-2014.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, J., and Coauthors, 2015: A long-term, high-quality, high-vertical-resolution GPS dropsonde dataset for hurricane and other studies. Bull. Amer. Meteor. Soc., 96, 961973, https://doi.org/10.1175/BAMS-D-13-00203.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Westwater, E. R., S. Crewell, C. Mätzler, and D. Cimini, 2005: Principles of surface-based microwave and millimeter wave radiometric remote sensing of the troposphere. Quad. Soc. Ital. Elettromagnetismo, 1, 5090.

    • Search Google Scholar
    • Export Citation
  • WMO, 2017: Guidelines for nowcasting techniques. WMO-1198, 82 pp., https://library.wmo.int/doc_num.php?explnum_id=3795.

  • WMO, 2019: Concept and methodology. Vol. 1, Guidance on integrated urban hydrometeorological, climate and environmental services, WMO-1234, 52 pp., https://library.wmo.int/doc_num.php?explnum_id=9903.

    • Search Google Scholar
    • Export Citation
  • Wulfmeyer, V., and Coauthors, 2015: A review of the remote sensing of lower tropospheric thermodynamic profiles and its indispensable role for the understanding and the simulation of water and energy cycles. Rev. Geophys., 53, 819895, https://doi.org/10.1002/2014RG000476.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Xiao, X., J. Sun, X. Qie, Z. Ying, M. Chen, and L. Zhang, 2021: Lightning data assimilation scheme in a 4DVAR system and its impact on very-short-term convective forecasting. Mon. Wea. Rev., 149, 353373, https://doi.org/10.1175/MWR-D-19-0396.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, F., and Y. Weng, 2015: Predicting hurricane intensity and associated hazards: A Five-year real-time forecast experiment with assimilation of airborne Doppler radar observations. Bull. Amer. Meteor. Soc., 96, 2533, https://doi.org/10.1175/BAMS-D-13-00231.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, F., M. Minamide, R. G. Nystrom, X. Chen, S.-J. Lin, and L. M. Harris, 2019: Improving Harvey forecasts with next-generation weather satellites: Advanced hurricane analysis and prediction with assimilation of GOES-R all-sky radiances. Bull. Amer. Meteor. Soc., 100, 12171222, https://doi.org/10.1175/BAMS-D-18-0149.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, J., D. Shi, and C. Li, 2018: Analysis on the sudden change and its cause of Typhoon Hato. Mar. Forecasts, 35, 3643.

  • Zhang, Y., D. J. Stensrud, and F. Zhang, 2019: Simultaneous assimilation of radar and all-sky satellite infrared radiance observations for convection-allowing ensemble analysis and prediction of severe thunderstorms. Mon. Wea. Rev., 147, 43894409, https://doi.org/10.1175/MWR-D-19-0163.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
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Multiscale Forecasting of High-Impact Weather: Current Status and Future Challenges

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  • 1 University of Miami, Miami, Florida
  • | 2 National Center for Atmospheric Research, Boulder, Colorado
  • | 3 Met Office, Exeter, United Kingdom
  • | 4 Environment Canada, Toronto, Ontario, Canada
  • | 5 National Center for Atmospheric Research, Boulder, Colorado
  • | 6 CNRM, Université de Toulouse, Météo-France, CNRS, Toulouse, France
  • | 7 CSIR Fourth Paradigm Institute, Bangalore, India
  • | 8 Bureau of Meteorology, Melbourne, Victoria, Australia
  • | 9 Météo-France, Toulouse, France
  • | 10 China Meteorological Administration, Beijing, China
  • | 11 CIMMS, University of Oklahoma, and NOAA/NSSL, Norman, Oklahoma
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Abstract

Improving the forecasting and communication of weather hazards such as urban floods and extreme winds has been recognized by the World Meteorological Organization (WMO) as a priority for international weather research. The WMO has established a 10-yr High-Impact Weather Project (HIWeather) to address global challenges and accelerate progress on scientific and social solutions. In this review, key challenges in hazard forecasting are first illustrated and summarized via four examples of high-impact weather events. Following this, a synthesis of the requirements, current status, and future research in observations, multiscale data assimilation, multiscale ensemble forecasting, and multiscale coupled hazard modeling is provided.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Sharanya J. Majumdar, smajumdar@rsmas.miami.edu

Abstract

Improving the forecasting and communication of weather hazards such as urban floods and extreme winds has been recognized by the World Meteorological Organization (WMO) as a priority for international weather research. The WMO has established a 10-yr High-Impact Weather Project (HIWeather) to address global challenges and accelerate progress on scientific and social solutions. In this review, key challenges in hazard forecasting are first illustrated and summarized via four examples of high-impact weather events. Following this, a synthesis of the requirements, current status, and future research in observations, multiscale data assimilation, multiscale ensemble forecasting, and multiscale coupled hazard modeling is provided.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Sharanya J. Majumdar, smajumdar@rsmas.miami.edu
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