• Accadia, C., S. Mariani, M. Casaioli, A. Lavagnini, and A. Speranza, 2003: Sensitivity of precipitation forecast skill scores to bilinear interpolation and a simple nearest-neighbor average method on high-resolution verification grids. Wea. Forecasting, 18, 918932, https://doi.org/10.1175/1520-0434(2003)018<0918:SOPFSS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Anderson, J. L., 2001: An ensemble adjustment Kalman filter for data assimilation. Mon. Wea. Rev., 129, 28842903, https://doi.org/10.1175/1520-0493(2001)129<2884:AEAKFF>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Anderson, J. L., 2003: A local least squares framework for ensemble filtering. Mon. Wea. Rev., 131, 634642, https://doi.org/10.1175/1520-0493(2003)131<0634:ALLSFF>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Anderson, J. L., 2012: Localization and sampling error correction in ensemble Kalman filter data assimilation. Mon. Wea. Rev., 140, 23592371, https://doi.org/10.1175/MWR-D-11-00013.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Anderson, J. L., and N. Collins, 2007: Scalable implementations of ensemble filter algorithms for data assimilation. J. Atmos. Oceanic Technol., 24, 14521463, https://doi.org/10.1175/JTECH2049.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Anderson, J. L., T. Hoar, K. Raeder, H. Liu, N. Collins, R. Torn, and A. Arellano, 2009: The Data Assimilation Research Testbed: A community facility. Bull. Amer. Meteor. Soc., 90, 12831296, https://doi.org/10.1175/2009BAMS2618.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Barker, D. M., 2005: Southern high-latitude ensemble data assimilation in the Antarctic Mesoscale Prediction System. Mon. Wea. Rev., 133, 34313449, https://doi.org/10.1175/MWR3042.1.

    • 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
  • Cafaro, C., T. H. A. Frame, J. Methven, N. Roberts, and J. Bröcker, 2019: The added value of convection‐permitting ensemble forecasts of sea breeze compared to a Bayesian forecast driven by the global ensemble. Quart. J. Roy. Meteor. Soc., 145, 17801798, https://doi.org/10.1002/qj.3531.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Carley, J. R. , and Coauthors, 2021: Status of NOAA’s next generation convection-allowing ensemble: The Rapid Refresh Forecast System. Special Symp. on Global and Mesoscale Models, Amer. Meteor. Soc., 12.8, https://ams.confex.com/ams/101ANNUAL/meetingapp.cgi/Paper/378383 .

    • Search Google Scholar
    • Export Citation
  • Cavallo, S. M., J. Berner, and C. Snyder, 2016: Diagnosing model errors from time-averaged tendencies in the Weather Research and Forecasting (WRF) Model. Mon. Wea. Rev., 144, 759779, https://doi.org/10.1175/MWR-D-15-0120.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, F., and J. Dudhia, 2001: Coupling an advanced land-surface–hydrology model with the Penn State–NCAR MM5 modeling system. Part I: Model description and implementation. Mon. Wea. Rev., 129, 569585, https://doi.org/10.1175/1520-0493(2001)129<0569:CAALSH>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Clark, A. J., 2017: Generation of ensemble mean precipitation forecasts from convection-allowing ensembles. Wea. Forecasting, 32, 15691583, https://doi.org/10.1175/WAF-D-16-0199.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Clark, A. J., and Coauthors, 2011: Probabilistic precipitation forecast skill as a function of ensemble size and spatial scale in a convection-allowing ensemble. Mon. Wea. Rev., 139, 14101418, https://doi.org/10.1175/2010MWR3624.1.

    • 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
  • Computational and Information Systems Laboratory, 2017: Cheyenne: HPE/SGI ICE XA System (NCAR Community Computing). National Center for Atmospheric Research, accessed 6 May 2021, https://doi.org/10.5065/D6RX99HX.

    • Crossref
    • Export Citation
  • COSMO, 2021: MeteoSwiss Operational Applications within COSMO. Accessed 6 May 2021, http://www.cosmo-model.org/content/tasks/operational/meteoSwiss/default.htm.

  • Courtier, P., J.-N. Thépaut, and A. Hollingsworth, 1994: A strategy for operational implementation of 4D-Var, using an incremental approach. Quart. J. Roy. Meteor. Soc., 120, 13671387, https://doi.org/10.1002/qj.49712051912.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Denis, B., J. Coté, and R. Laprise, 2002: Spectral decomposition of two-dimensional atmospheric fields on limited-area domains using the discrete cosine transform (DCT). Mon. Wea. Rev., 130, 18121829, https://doi.org/10.1175/1520-0493(2002)130<1812:SDOTDA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dey, S. R., G. Leoncini, N. M. Roberts, R. S. Plant, and S. Migliorini, 2014: A spatial view of ensemble spread in convection permitting ensembles. Mon. Wea. Rev., 142, 40914107, https://doi.org/10.1175/MWR-D-14-00172.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Djalalova, I. V., and Coauthors, 2016: The POWER experiment: Impact of assimilation of a network of coastal wind profiling radars on simulating offshore winds in and above the wind turbine layer. Wea. Forecasting, 31, 10711091, https://doi.org/10.1175/WAF-D-15-0104.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dowell, D. C. , and Coauthors, 2016: Development of a High-Resolution Rapid Refresh Ensemble (HRRRE) for severe weather forecasting. 28th Conf. on Severe Local Storms, Portland, OR, Amer. Meteor. Soc., 8B.2, https://ams.confex.com/ams/28SLS/webprogram/Paper301555.html.

    • Search Google Scholar
    • Export Citation
  • Duda, J. D., X. Wang, Y. Wang, and J. Carley, 2019: Comparing the assimilation of radar reflectivity using the direct GSI based ensemble-variational (EnVar) and indirect cloud analysis methods in convection-allowing forecasts over the continental United States. Mon. Wea. Rev., 147, 16551678, https://doi.org/10.1175/MWR-D-18-0171.1.

    • Search Google Scholar
    • Export Citation
  • Durran, D. R., and M. Gingrich, 2014: Atmospheric predictability: Why butterflies are not important. J. Atmos. Sci., 71, 24762488, https://doi.org/10.1175/JAS-D-14-0007.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Durran, D. R., and J. A. Weyn, 2016: Thunderstorms do not get butterflies. Bull. Amer. Meteor. Soc., 97, 237243, https://doi.org/10.1175/BAMS-D-15-00070.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ebert, E. E., 2008: Fuzzy verification of high resolution gridded forecasts: A review and proposed framework. Meteor. Appl., 15, 5164, https://doi.org/10.1002/met.25.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ebert, E. E., 2009: Neighborhood verification: A strategy for rewarding close forecasts. Wea. Forecasting, 24, 14981510, https://doi.org/10.1175/2009WAF2222251.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Evensen, G., 1994: Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics. J. Geophys. Res., 99, 10 14310 162, https://doi.org/10.1029/94JC00572.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Feng, J., J. Sun, and Y. Zhang, 2020: A dynamic blending scheme to mitigate large‐scale bias in regional models. J. Adv. Model. Earth Syst., 12, e2019MS001754, https://doi.org/10.1029/2019MS001754.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Feng, J., M. Chen, Y. Li, and J. Zhong, 2021: An implementation of full cycle strategy using dynamic blending for rapid refresh short-range weather forecasting in China. Adv. Atmos. Sci., 38, 943956, https://doi.org/10.1007/s00376-021-0316-7.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gaspari, G., and S. E. Cohn, 1999: Construction of correlation functions in two and three dimensions. Quart. J. Roy. Meteor. Soc., 125, 723757, https://doi.org/10.1002/qj.49712555417.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gasperoni, N. A., X. Wang, and Y. Wang, 2020: A comparison of methods to sample model errors for convection-allowing ensemble forecasts in the setting of multiscale initial conditions produced by the GSI-based EnVar assimilation system. Mon. Wea. Rev., 148, 11771203, https://doi.org/10.1175/MWR-D-19-0124.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gebhardt, C., S. E. Theis, M. Paulat, and Z. Ben Bouallègue, 2011: Uncertainties in COSMO-DE precipitation forecasts introduced by model perturbations and variation of lateral boundaries. Atmos. Res., 100, 168177, https://doi.org/10.1016/j.atmosres.2010.12.008.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gehne, M., T. M. Hamill, G. T. Bates, P. Pegion, and W. Kolczynski, 2019: Land surface parameter and state perturbations in the global ensemble forecast system. Mon. Wea. Rev., 147, 13191340, https://doi.org/10.1175/MWR-D-18-0057.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gemmill, W., B. Katz, and X. Li, 2007: Daily real-time, global sea surface temperature—High-resolution analysis: RTG_SST_HR. NOAA/NWS/NCEP/EMC/MMAB, Science Application International Corporation, and Joint Center for Satellite Data Assimilation Tech. Note 260, 22 pp., http://polar.ncep.noaa.gov/mmab/papers/tn260/MMAB260.pdf.

    • Search Google Scholar
    • Export Citation
  • Gilleland, E., A. S. Hering, T. L. Fowler, and B. G. Brown, 2018: Testing the tests: What are the impacts of incorrect assumptions when applying confidence intervals or hypothesis tests to compare competing forecasts? Mon. Wea. Rev., 146, 16851703, https://doi.org/10.1175/MWR-D-17-0295.1.

    • 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., 1999: Hypothesis tests for evaluating numerical precipitation forecasts. Wea. Forecasting, 14, 155167, https://doi.org/10.1175/1520-0434(1999)014<0155:HTFENP>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Harnisch, F., and C. Keil, 2015: Initial conditions for convective-scale ensemble forecasting provided by ensemble data assimilation. Mon. Wea. Rev., 143, 15831600, https://doi.org/10.1175/MWR-D-14-00209.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Houtekamer, P. L., and F. Zhang, 2016: Review of the ensemble Kalman filter for atmospheric data assimilation. Mon. Wea. Rev., 144, 44894532, https://doi.org/10.1175/MWR-D-15-0440.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Houtekamer, P. L., H. L. Mitchell, G. Pellerin, M. Buehner, M. Charron, L. Spacek, and B. Hansen, 2005: Atmospheric data assimilation with an ensemble Kalman filter: Results with real observations. Mon. Wea. Rev., 133, 604620, https://doi.org/10.1175/MWR-2864.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hsiao, L.-F., D.-S. Chen, Y.-H. Kuo, Y.-R. Guo, T.-C. Yeh, J.-S. Hong, C.-T. Fong, and C.-S. Lee, 2012: Application of WRF 3DVAR to operational typhoon prediction in Taiwan: Impact of outer loop and partial cycling approaches. Wea. Forecasting, 27, 12491263, https://doi.org/10.1175/WAF-D-11-00131.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hsiao, L.-F., and Coauthors, 2015: Blending of global and regional analyses with a spatial filter: Application to typhoon prediction over the western North Pacific Ocean. Wea. Forecasting, 30, 754770, https://doi.org/10.1175/WAF-D-14-00047.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hu, M., S. G. Benjamin, T. T. Ladwig, D. C. Dowell, S. S. Weygandt, C. R. Alexander, and J. S. Whitaker, 2017: GSI three-dimensional ensemble-variational hybrid data assimilation using a global ensemble for the regional rapid refresh model. Mon. Wea. Rev., 145, 42054225, https://doi.org/10.1175/MWR-D-16-0418.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Iacono, M. J., J. S. Delamere, E. J. Mlawer, M. W. Shephard, S. A. Clough, and W. D. Collins, 2008: Radiative forcing by long-lived greenhouse gases: Calculations with the AER radiative transfer models. J. Geophys. Res., 113, D13103, https://doi.org/10.1029/2008JD009944.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • James, E. P., and S. G. Benjamin, 2017: Observation system experiments with the hourly updating Rapid Refresh model using GSI hybrid ensemble–variational data assimilation. Mon. Wea. Rev., 145, 28972918, https://doi.org/10.1175/MWR-D-16-0398.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • James, E. P., S. G. Benjamin, and B. D. Jamison, 2020: Commercial-aircraft-based observations for NWP: Global coverage, data impacts, and COVID-19. J. Appl. Meteor. Climatol., 59, 18091825, https://doi.org/10.1175/JAMC-D-20-0010.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Janjić, Z. I., 1994: The step-mountain eta coordinate model: Further developments of the convection, viscous sublayer, and turbulence closure schemes. Mon. Wea. Rev., 122, 927945, https://doi.org/10.1175/1520-0493(1994)122<0927:TSMECM>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Janjić, Z. I., 2002: Nonsingular implementation of the Mellor-Yamada level 2.5 scheme in the NCEP Meso model. NCEP Office Note 437, 61 pp., http://www.emc.ncep.noaa.gov/officenotes/newernotes/on437.pdf.

    • Search Google Scholar
    • Export Citation
  • Johnson, A., and X. Wang, 2017: Design and implementation of a GSI-based convection-allowing ensemble data assimilation and forecast system for the PECAN field experiment. Part I: Optimal configurations for nocturnal convection prediction using retrospective cases. Wea. Forecasting, 32, 289315, https://doi.org/10.1175/WAF-D-16-0102.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Johnson, A., X. Wang, J. Carley, L. Wicker, and C. Karstens, 2015: A comparison of multiscale GSI-based EnKF and 3DVar data assimilation using radar and conventional observations for midlatitude convective-scale precipitation forecasts. Mon. Wea. Rev., 143, 30873108, https://doi.org/10.1175/MWR-D-14-00345.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Johnson, A., X. Wang, Y. Wang, A. Reinhart, A. J. Clark, and I. L. Jirak, 2020: Neighborhood- and object-based probabilistic verification of the OU MAP ensemble forecasts during 2017 and 2018 Hazardous Weather Testbeds. Wea. Forecasting, 35, 169191, https://doi.org/10.1175/WAF-D-19-0060.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jones, T. A., and D. J. Stensrud, 2012: Assimilating AIRS temperature and mixing ratio profiles using an ensemble Kalman filter approach for convective-scale forecasts. Wea. Forecasting, 27, 541564, https://doi.org/10.1175/WAF-D-11-00090.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jones, T. A., K. Knopfmeier, D. Wheatley, G. Creager, P. Minnis, and R. Palikondo, 2016: Storm-scale data assimilation and ensemble forecasting with the NSSL Experimental Warn-on-Forecast System. Part II: Combined radar and satellite data experiments. Wea. Forecasting, 31, 297327, https://doi.org/10.1175/WAF-D-15-0107.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jones, T. A., and Coauthors, 2020: Assimilation of GOES-16 radiances and retrievals into the Warn-on-Forecast System. Mon. Wea. Rev., 148, 18291859, https://doi.org/10.1175/MWR-D-19-0379.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Keresturi, E., Y. Wang, F. Meier, F. Weidle, C. Wittmann, and A. Atencia, 2019: Improving initial condition perturbations in a convection‐permitting ensemble prediction system. Quart. J. Roy. Meteor. Soc., 145, 9931012, https://doi.org/10.1002/qj.3473.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Klasa, C., M. Arpagaus, A. Walser, and H. Wernli, 2018: An evaluation of the convection-permitting ensemble COSMO-E for three contrasting precipitation events in Switzerland. Quart. J. Roy. Meteor. Soc., 144, 744764, https://doi.org/10.1002/qj.3245.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kleist, D. T., D. F. Parrish, J. C. Derber, R. Treadon, W.-S. Wu, and S. Lord, 2009: Introduction of the GSI into the NCEP Global Data Assimilation System. Wea. Forecasting, 24, 16911705, https://doi.org/10.1175/2009WAF2222201.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kong, F. , and Coauthors, 2008: Real-time storm-scale ensemble forecasting during the 2008 Spring Experiment. 24th Conf. on Severe Local Storms, Savannah, GA, Amer. Meteor. Soc., 12.3., https://ams.confex.com/ams/pdfpapers/141827.pdf.

    • Search Google Scholar
    • Export Citation
  • Kong, F. , and Coauthors, 2009: A real-time storm-scale ensemble forecast system: 2009 Spring Experiment. 23rd Conf. on Weather Analysis and Forecasting/19th Conf. on Numerical Weather Prediction, Omaha, NE, Amer. Meteor. Soc., 16A.3., https://ams.confex.com/ams/pdfpapers/154118.pdf.

    • Search Google Scholar
    • Export Citation
  • Lin, H., S. S. Weygandt, S. G. Benjamin, and M. Hu, 2017a: Satellite radiance data assimilation within the hourly updated rapid refresh. Wea. Forecasting, 32, 12731287, https://doi.org/10.1175/WAF-D-16-0215.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lin, H., S. S. Weygandt, A. H. N. Lim, M. Hu, J. M. Brown, and S. G. Benjamin, 2017b: Radiance preprocessing for assimilation in the hourly updating rapid refresh mesoscale model: A study using AIRS data. Wea. Forecasting, 32, 17811800, https://doi.org/10.1175/WAF-D-17-0028.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lin, Y., and K. E. Mitchell, 2005: The NCEP stage II/IV hourly precipitation analyses: Development and applications. 19th Conf. on Hydrology, San Diego, CA, Amer. Meteor. Soc., 1.2., http://ams.confex.com/ams/pdfpapers/83847.pdf.

    • Search Google Scholar
    • Export Citation
  • Lorenc, A. C., and Coauthors, 2000: The Met. Office global three-dimensional variational data assimilation scheme. Quart. J. Roy. Meteor. Soc., 126, 29913012, https://doi.org/10.1002/qj.49712657002.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mason, I. B., 1982: A model for assessment of weather forecasts. Aust. Meteor. Mag., 30, 291303.

  • Mason, S. J., and N. E. Graham, 2002: Areas beneath the relative operating characteristics (ROC) and relative operating levels (ROL) curves: Statistical significance and interpretation. Quart. J. Roy. Meteor. Soc., 128, 21452166, https://doi.org/10.1256/003590002320603584.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mellor, G. L., and T. Yamada, 1982: Development of a turbulence closure model for geophysical fluid problems. Rev. Geophys. Space Phys., 20, 851875, https://doi.org/10.1029/RG020i004p00851.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mittermaier, M., and N. Roberts, 2010: Intercomparison of spatial forecast methods: Identifying skillful spatial scales using the fractions skill score. Wea. Forecasting, 25, 343354, https://doi.org/10.1175/2009WAF2222260.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mlawer, E. J., S. J. Taubman, P. D. Brown, M. J. Iacono, and S. A. Clough, 1997: Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the long-wave. J. Geophys. Res., 102, 16 66316 682, https://doi.org/10.1029/97JD00237.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nelson, B. R., O. P. Prat, D.-J. Seo, and E. Habib, 2016: Assessment and implications of NCEP stage IV quantitative precipitation estimates for product intercomparisons. Wea. Forecasting, 31, 371394, https://doi.org/10.1175/WAF-D-14-00112.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Peralta, C., Z. B. Bouallègue, S. E. Theis, C. Gebhardt, and M. Buchhold, 2012: Accounting for initial condition uncertainties in COSMO-DE-EPS. J. Geophys. Res., 117, D07108, https://doi.org/10.1029/2011JD016581.

    • Search Google Scholar
    • Export Citation
  • Politis, D. N., and J. P. Romano, 1992: A circular block-resampling procedure for stationary data. Exploring the Limits of Bootstrap, R. LePage and L. Billard, Eds., John Wiley and Sons, 263270.

    • Search Google Scholar
    • Export Citation
  • Porson, A. N., S. Hagelin, D. F. A. Boyd, N. M. Roberts, R. North, S. Webster, and J. C.-F. Lo, 2019: Extreme rainfall sensitivity in convective‐scale ensemble modelling over Singapore. Quart. J. Roy. Meteor. Soc., 145, 30043022, https://doi.org/10.1002/qj.3601.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Poterjoy, J., G. J. Alaka, Jr., and H. R. Winterbottom, 2021: The irreplaceable utility of sequential data assimilation for numerical weather prediction system development: Lessons learned from an experimental HWRF system. Wea. Forecasting, 36, 661677, https://doi.org/10.1175/WAF-D-20-0204.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Powers, J. G., and Coauthors, 2017: The Weather Research and Forecasting Model: Overview, system efforts, and future directions. Bull. Amer. Meteor. Soc., 98, 17171737, https://doi.org/10.1175/BAMS-D-15-00308.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Raymond, W. H., 1988: High‐order low‐pass implicit tangent filters for use in finite area calculations. Mon. Wea. Rev., 116, 21322141, https://doi.org/10.1175/1520-0493(1988)116<2132:HOLPIT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Raymond, W. H., and A. Garder, 1991: A review of recursive and implicit filters. Mon. Wea. Rev., 119, 477495, https://doi.org/10.1175/1520-0493(1991)119<0477:ARORAI>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Raynaud, L., and F. Bouttier, 2016: Comparison of initial perturbation methods for ensemble prediction at convective scale. Quart. J. Roy. Meteor. Soc., 142, 854866, https://doi.org/10.1002/qj.2686.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Raynaud, L., and F. Bouttier, 2017: The impact of horizontal resolution and ensemble size for convective‐scale probabilistic forecasts. Quart. J. Roy. Meteor. Soc., 143, 30373047, https://doi.org/10.1002/qj.3159.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ricard, D., C. Lac, S. Riette, R. Legrand, and A. Mary, 2013: Kinetic energy spectra characteristics of two convection-permitting limited-area models AROME and Meso-NH. Quart. J. Roy. Meteor. Soc., 139, 13271341, https://doi.org/10.1002/qj.2025.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Roberts, B., B. T. Gallo, I. L. Jirak, A. J. Clark, D. C. Dowell, X. Wang, and Y. Wang, 2020: What does a convection-allowing ensemble of opportunity buy us in forecasting thunderstorms? Wea. Forecasting, 35, 22932316, https://doi.org/10.1175/WAF-D-20-0069.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Roberts, N. M., and H. W. Lean, 2008: Scale-selective verification of rainfall accumulations from high-resolution forecasts of convective events. Mon. Wea. Rev., 136, 7897, https://doi.org/10.1175/2007MWR2123.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rogers, E. , and Coauthors, 2009: The NCEP North American Mesoscale modeling system: Recent changes and future plans. 23rd Conf. on Weather Analysis and Forecasting/19th Conf. on Numerical Weather Prediction, Omaha, NE, Amer. Meteor. Soc., 2A.4., http://ams.confex.com/ams/pdfpapers/154114.pdf.

    • Search Google Scholar
    • Export Citation
  • Romine, G. S., C. S. Schwartz, C. Snyder, J. L. Anderson, and M. L. Weisman, 2013: Model bias in a continuously cycled assimilation system and its influence on convection-permitting forecasts. Mon. Wea. Rev., 141, 12631284, https://doi.org/10.1175/MWR-D-12-00112.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schellander-Gorgas, T., Y. Wang, F. Meier, F. Weidle, C. Wittmann, and A. Kann, 2017: On the forecast skills of a convection-permitting ensemble. Geosci. Model Dev., 10, 3556, https://doi.org/10.5194/gmd-10-35-2017.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schraff, C., H. Reich, A. Rhodin, A. Schomburg, K. Stephan, A. Periáñez, and R. Potthast, 2016: Kilometre-scale ensemble data assimilation for the COSMO model (KENDA). Quart. J. Roy. Meteor. Soc., 142, 14531472, https://doi.org/10.1002/qj.2748.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schumacher, R. S., and A. J. Clark, 2014: Evaluation of ensemble configurations for the analysis and prediction of heavy-rain-producing mesoscale convective systems. Mon. Wea. Rev., 142, 41084138, https://doi.org/10.1175/MWR-D-13-00357.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schwartz, C. S., 2016: Improving large-domain convection-allowing forecasts with high-resolution analyses and ensemble data assimilation. Mon. Wea. Rev., 144, 17771803, https://doi.org/10.1175/MWR-D-15-0286.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schwartz, C. S., 2019: Medium-range convection-allowing ensemble forecasts with a variable-resolution global model. Mon. Wea. Rev., 147, 29973023, https://doi.org/10.1175/MWR-D-18-0452.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schwartz, C. S., and Z. Liu, 2014: Convection-permitting forecasts initialized with continuously cycling limited-area 3DVAR, ensemble Kalman filter, and “hybrid” variational-ensemble data assimilation systems. Mon. Wea. Rev., 142, 716738, https://doi.org/10.1175/MWR-D-13-00100.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schwartz, C. S., and R. A. Sobash, 2017: Generating probabilistic forecasts from convection-allowing ensembles using neighborhood approaches: A review and recommendations. Mon. Wea. Rev., 145, 33973418, https://doi.org/10.1175/MWR-D-16-0400.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schwartz, C. S., and Coauthors, 2010: Toward improved convection-allowing ensembles: Model physics sensitivities and optimizing probabilistic guidance with small ensemble membership. Wea. Forecasting, 25, 263280, https://doi.org/10.1175/2009WAF2222267.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schwartz, C. S., G. S. Romine, K. R. Smith, and M. L. Weisman, 2014: Characterizing and optimizing precipitation forecasts from a convection-permitting ensemble initialized by a mesoscale ensemble Kalman filter. Wea. Forecasting, 29, 12951318, https://doi.org/10.1175/WAF-D-13-00145.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schwartz, C. S., G. S. Romine, M. L. Weisman, R. A. Sobash, K. R. Fossell, K. W. Manning, and S. B. Trier, 2015: A real-time convection-allowing ensemble prediction system initialized by mesoscale ensemble Kalman filter analyses. Wea. Forecasting, 30, 11581181, https://doi.org/10.1175/WAF-D-15-0013.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schwartz, C. S., M. Wong, G. S. Romine, R. A. Sobash, and K. R. Fossell, 2020: Initial conditions for convection-allowing ensembles over the conterminous United States. Mon. Wea. Rev., 148, 26452669, https://doi.org/10.1175/MWR-D-19-0401.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schwartz, C. S., G. S. Romine, and D. C. Dowell, 2021: Toward unifying short-term and next-day convection-allowing ensemble forecast systems with a continuously cycling 3-km ensemble Kalman filter over the entire conterminous United States. Wea. Forecasting, 36, 379405, https://doi.org/10.1175/WAF-D-20-0110.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shao, H., and Coauthors, 2016: Bridging research to operations transitions: Status and plans of community GSI. Bull. Amer. Meteor. Soc., 97, 14271440, https://doi.org/10.1175/BAMS-D-13-00245.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Skamarock, W. C. , and Coauthors, 2008: A description of the Advanced Research WRF version 3. NCAR Tech. Note NCAR/TN-475+STR, 113 pp., https://doi.org/10.5065/D68S4MVH.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stensrud, D. J., and Coauthors, 2009: Convective-scale warn-on-forecast system: A vision for 2020. Bull. Amer. Meteor. Soc., 90, 14871499, https://doi.org/10.1175/2009BAMS2795.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stensrud, D. J., and Coauthors, 2013: Progress and challenges with Warn-on-Forecast. Atmos. Res., 123, 216, https://doi.org/10.1016/j.atmosres.2012.04.004.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tegen, I., P. Hollrig, M. Chin, I. Fung, D. Jacob, and J. Penner, 1997: Contribution of different aerosol species to the global aerosol extinction optical thickness: Estimates from model results. J. Geophys. Res., 102, 23 89523 915, https://doi.org/10.1029/97JD01864.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tennant, W., 2015: Improving initial condition perturbations for MOGREPS-UK. Quart. J. Roy. Meteor. Soc., 141, 23242336, https://doi.org/10.1002/qj.2524.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Theis, S. E., A. Hense, and U. Damrath, 2005: Probabilistic precipitation forecasts from a deterministic model: A pragmatic approach. Meteor. Appl., 12, 257268, https://doi.org/10.1017/S1350482705001763.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Thompson, G., P. R. Field, R. M. Rasmussen, and W. D. Hall, 2008: Explicit forecasts of winter precipitation using an improved bulk microphysics scheme. Part II: Implementation of a new snow parameterization. Mon. Wea. Rev., 136, 50955115, https://doi.org/10.1175/2008MWR2387.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tiedtke, M., 1989: A comprehensive mass flux scheme for cumulus parameterization in large-scale models. Mon. Wea. Rev., 117, 17791800, https://doi.org/10.1175/1520-0493(1989)117<1779:ACMFSF>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Torn, R. D., and C. A. Davis, 2012: The influence of shallow convection on tropical cyclone track forecasts. Mon. Wea. Rev., 140, 21882197, https://doi.org/10.1175/MWR-D-11-00246.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Torn, R. D., G. J. Hakim, and C. Snyder, 2006: Boundary conditions for limited-area ensemble Kalman filters. Mon. Wea. Rev., 134, 24902502, https://doi.org/10.1175/MWR3187.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, H., X.-Y. Huang, D. Xu, and J. Liu, 2014: A scale-dependent blending scheme for WRFDA: Impact on regional weather forecasting. Geosci. Model Dev., 7, 18191828, https://doi.org/10.5194/gmd-7-1819-2014.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, X., D. Parrish, D. Kleist, and J. Whitaker, 2013: GSI 3DVar-based ensemble–variational hybrid data assimilation for NCEP Global Forecast System: Single-resolution experiments. Mon. Wea. Rev., 141, 40984117, https://doi.org/10.1175/MWR-D-12-00141.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, Y., M. Bellus, J.-F. Geleyn, X. Ma, W. Tian, and F. Weidle, 2014: A new method for generating initial condition perturbations in a regional ensemble prediction system: Blending. Mon. Wea. Rev., 142, 20432059, https://doi.org/10.1175/MWR-D-12-00354.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Warner, T. T., R. A. Peterson, and R. E. Treadon, 1997: A tutorial on lateral boundary conditions as a basic and potentially serious limitation to regional numerical weather prediction. Bull. Amer. Meteor. Soc., 78, 25992617, https://doi.org/10.1175/1520-0477(1997)078<2599:ATOLBC>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Weyn, J. A., and D. R. Durran, 2017: The dependence of the predictability of mesoscale convective systems on the horizontal scale and amplitude of initial errors in idealized simulations. J. Atmos. Sci., 74, 21912210, https://doi.org/10.1175/JAS-D-17-0006.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wheatley, D. M., K. H. Knopfmeier, T. A. Jones, and G. J. Creager, 2015: Storm-scale data assimilation and ensemble forecasting with the NSSL experimental Warn-on-Forecast system. Part I: Radar data experiments. Wea. Forecasting, 30, 17951817, https://doi.org/10.1175/WAF-D-15-0043.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Whitaker, J. S., and T. M. Hamill, 2002: Ensemble data assimilation without perturbed observations. Mon. Wea. Rev., 130, 19131924, https://doi.org/10.1175/1520-0493(2002)130<1913:EDAWPO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Whitaker, J. S., and T. M. Hamill, 2012: Evaluating methods to account for system errors in ensemble data assimilation. Mon. Wea. Rev., 140, 30783089, https://doi.org/10.1175/MWR-D-11-00276.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Whitaker, J. S., T. M. Hamill, X. Wei, Y. Song, and Z. Toth, 2008: Ensemble data assimilation with the NCEP Global Forecast System. Mon. Wea. Rev., 136, 463482, https://doi.org/10.1175/2007MWR2018.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wilks, D. S., 1997: Resampling hypothesis tests for autocorrelated fields. J. Climate, 10, 6582, https://doi.org/10.1175/1520-0442(1997)010<0065:RHTFAF>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wilks, D. S., 2011: Statistical Methods in the Atmospheric Sciences. 3rd ed. Elsevier, 676 pp.

  • Wolff, J. K., M. Harrold, T. Fowler, J. H. Gotway, L. Nance, and B. G. Brown, 2014: Beyond the basics: Evaluating model-based precipitation forecasts using traditional, spatial, and object-based methods. Wea. Forecasting, 29, 14511472, https://doi.org/10.1175/WAF-D-13-00135.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wong, M., G. Romine, and C. Snyder, 2020: Model improvement via systematic investigation of physics tendencies. Mon. Wea. Rev., 148, 671688, https://doi.org/10.1175/MWR-D-19-0255.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Woodhams, B. J., C. E. Birch, J. H. Marsham, C. L. Bain, N. M. Roberts, and D. F. Boyd, 2018: What is the added value of a convection-permitting model for forecasting extreme rainfall over tropical East Africa? Mon. Wea. Rev., 146, 27572780, https://doi.org/10.1175/MWR-D-17-0396.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wu, W.-S., D. F. Parrish, E. Rogers, and Y. Lin, 2017: Regional ensemble–variational data assimilation using global ensemble forecasts. Wea. Forecasting, 32, 8396, https://doi.org/10.1175/WAF-D-16-0045.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Xue, M. , and Coauthors, 2007: CAPS real-time storm-scale ensemble and high-resolution forecasts as part of the NOAA Hazardous Weather Testbed 2007 Spring Experiment. 22nd Conf. on Weather Analysis and Forecasting/18th Conf. on Numerical Weather Prediction, Salt Lake City, UT, Amer. Meteor. Soc., 3B.1, http://ams.confex.com/ams/pdfpapers/124587.pdf.

    • Search Google Scholar
    • Export Citation
  • Yang, X., 2005: Analysis blending using a spatial filter in grid-point model coupling. HIRLAM Newsletter, No. 48, HIRLAM Programme, de Bilt, Netherlands, 4955, http://www.hirlam.org/index.php/hirlam-documentation/doc_view/517-hirlam-newsletter-no-48-article10-yang.

    • Search Google Scholar
    • Export Citation
  • Zhang, C., Y. Wang, and K. Hamilton, 2011: Improved representation of boundary layer clouds over the southeast Pacific in ARW-WRF using a modified Tiedtke cumulus parameterization scheme. Mon. Wea. Rev., 139, 34893513, https://doi.org/10.1175/MWR-D-10-05091.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, H., J. Chen, X. Zhi, Y. Wang, and Y. Wang, 2015: Study on multi-scale blending initial condition perturbations for a regional ensemble prediction system. Adv. Atmos. Sci., 32, 11431155, https://doi.org/10.1007/s00376-015-4232-6.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 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
  • Zhao, Q. Y., and F. H. Carr, 1997: A prognostic cloud scheme for operational NWP models. Mon. Wea. Rev., 125, 19311953, https://doi.org/10.1175/1520-0493(1997)125<1931:APCSFO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zheng, W., M. Ek, K. Mitchell, H. Wei, and J. Meng, 2017: Improving the s table surface layer in the NCEP Global Forecast System. Mon. Wea. Rev., 145, 39693987, https://doi.org/10.1175/MWR-D-16-0438.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhou, X., Y. Zhu, D. Hou, Y. Luo, J. Peng, and D. Wobus, 2017: Performance of the new NCEP Global Ensemble Forecast System in a parallel experiment. Wea. Forecasting, 32, 19892004, https://doi.org/10.1175/WAF-D-17-0023.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhu, K., M. Xue, Y. Pan, M. Hu, S. G. Benjamin, S. S. Weygandt, and H. Lin, 2019: The impact of satellite radiance data assimilation within a frequently updated regional forecast system using a GSI-based ensemble Kalman filter. Adv. Atmos. Sci., 36, 13081326, https://doi.org/10.1007/s00376-019-9011-3.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zou, X., Z. Qin, and F. Weng, 2011: Improved coastal precipitation forecasts with direct assimilation of GOES-11/12 imager radiances. Mon. Wea. Rev., 139, 37113729, https://doi.org/10.1175/MWR-D-10-05040.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
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Comparing Partial and Continuously Cycling Ensemble Kalman Filter Data Assimilation Systems for Convection-Allowing Ensemble Forecast Initialization

Craig S. SchwartzaNational Center for Atmospheric Research, Boulder, Colorado
bUniversity of Maryland, College Park, College Park, Maryland

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Jonathan PoterjoybUniversity of Maryland, College Park, College Park, Maryland

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Jacob R. CarleycNOAA/NWS/NCEP/Environmental Modeling Center, College Park, Maryland

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David C. DowelldNOAA/Earth System Research Laboratory, Boulder, Colorado

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Glen S. RomineaNational Center for Atmospheric Research, Boulder, Colorado

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Kayo IdebUniversity of Maryland, College Park, College Park, Maryland

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Abstract

Several limited-area 80-member ensemble Kalman filter (EnKF) data assimilation systems with 15-km horizontal grid spacing were run over a computational domain spanning the conterminous United States (CONUS) for a 4-week period. One EnKF employed continuous cycling, where the prior ensemble was always the 1-h forecast initialized from the previous cycle’s analysis. In contrast, the other EnKFs used a partial cycling procedure, where limited-area states were discarded after 12 or 18 h of self-contained hourly cycles and reinitialized the next day from global model fields. “Blended” states were also constructed by combining large scales from global ensemble initial conditions (ICs) with small scales from limited-area continuously cycling EnKF analyses using a low-pass filter. Both the blended states and EnKF analysis ensembles initialized 36-h, 10-member ensemble forecasts with 3-km horizontal grid spacing. Continuously cycling EnKF analyses initialized ∼1–18-h precipitation forecasts that were comparable to or somewhat better than those with partial cycling EnKF ICs. Conversely, ∼18–36-h forecasts with partial cycling EnKF ICs were comparable to or better than those with unblended continuously cycling EnKF ICs. However, blended ICs yielded ∼18–36-h forecasts that were statistically indistinguishable from those with partial cycling ICs. ICs that more closely resembled global analysis spectral characteristics at wavelengths > 200 km, like partial cycling and blended ICs, were associated with relatively good ∼18–36-h forecasts. Ultimately, findings suggest that EnKFs employing a combination of continuous cycling and blending can potentially replace the partial cycling assimilation systems that currently initialize operational limited-area models over the CONUS without sacrificing forecast quality.

SIGNIFICANCE STATEMENT

Numerical weather prediction models (i.e., weather models) are initialized through a process called data assimilation, which combines real atmospheric observations with a previous short-term weather model forecast using statistical techniques. The overarching data assimilation strategy currently used to initialize operational regional weather models over the United States has several disadvantages that ultimately limit progress toward improving weather model forecasts. Thus, we suggest an alternative data assimilation strategy be adopted to initialize a next-generation, high-resolution (∼3 km) probabilistic forecast system currently being developed. This alternative approach preserves forecast quality while fostering a framework that can accelerate weather model improvements, which in turn will lead to better weather forecasts.

© 2022 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: Craig S. Schwartz, schwartz@ucar.edu

Abstract

Several limited-area 80-member ensemble Kalman filter (EnKF) data assimilation systems with 15-km horizontal grid spacing were run over a computational domain spanning the conterminous United States (CONUS) for a 4-week period. One EnKF employed continuous cycling, where the prior ensemble was always the 1-h forecast initialized from the previous cycle’s analysis. In contrast, the other EnKFs used a partial cycling procedure, where limited-area states were discarded after 12 or 18 h of self-contained hourly cycles and reinitialized the next day from global model fields. “Blended” states were also constructed by combining large scales from global ensemble initial conditions (ICs) with small scales from limited-area continuously cycling EnKF analyses using a low-pass filter. Both the blended states and EnKF analysis ensembles initialized 36-h, 10-member ensemble forecasts with 3-km horizontal grid spacing. Continuously cycling EnKF analyses initialized ∼1–18-h precipitation forecasts that were comparable to or somewhat better than those with partial cycling EnKF ICs. Conversely, ∼18–36-h forecasts with partial cycling EnKF ICs were comparable to or better than those with unblended continuously cycling EnKF ICs. However, blended ICs yielded ∼18–36-h forecasts that were statistically indistinguishable from those with partial cycling ICs. ICs that more closely resembled global analysis spectral characteristics at wavelengths > 200 km, like partial cycling and blended ICs, were associated with relatively good ∼18–36-h forecasts. Ultimately, findings suggest that EnKFs employing a combination of continuous cycling and blending can potentially replace the partial cycling assimilation systems that currently initialize operational limited-area models over the CONUS without sacrificing forecast quality.

SIGNIFICANCE STATEMENT

Numerical weather prediction models (i.e., weather models) are initialized through a process called data assimilation, which combines real atmospheric observations with a previous short-term weather model forecast using statistical techniques. The overarching data assimilation strategy currently used to initialize operational regional weather models over the United States has several disadvantages that ultimately limit progress toward improving weather model forecasts. Thus, we suggest an alternative data assimilation strategy be adopted to initialize a next-generation, high-resolution (∼3 km) probabilistic forecast system currently being developed. This alternative approach preserves forecast quality while fostering a framework that can accelerate weather model improvements, which in turn will lead to better weather forecasts.

© 2022 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: Craig S. Schwartz, schwartz@ucar.edu
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