Ncar's experimental real-time convection-allowing ensemble prediction system

Weather and Forecasting

Volumn 30, Issue 6, 2015, Pages 1645-1654

  Schwartz, Craig S ^a   Romine, Glen S ^a   Sobash, Ryan A ^a   Fossell, Kathryn R ^a   Weisman, Morris L ^a  

^a NATIONAL CENTER FOR ATMOSPHERIC RESEARCH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

FORECASTING;

ENSEMBLE PREDICTION SYSTEMS; EXPERIMENTAL SYSTEM; INITIAL CONDITIONS; NATIONAL CENTER FOR ATMOSPHERIC RESEARCHES; OPERATIONAL COMMUNITY; OPERATIONAL FORECASTERS; PAPER DOCUMENTS; SEVERE WEATHER;

WEATHER FORECASTING;

ATMOSPHERIC CONVECTION; ENSEMBLE FORECASTING; KALMAN FILTER; MESOSCALE METEOROLOGY; REAL TIME; SEVERE WEATHER;

EID: 84953376862     PISSN: 08828156     EISSN: 15200434     Source Type: Journal    
DOI: 10.1175/WAF-D-15-0103.1     Document Type: Article

References (54)

1. Accadia, C., S.Mariani,M. Casaioli,A. Lavagnini, andA. Speranza, 2003: Sensitivity of precipitation forecast skill scores to bilinear interpolation and a simple nearest-neighbor aver- age method on high-resolution verification grids. Wea.Forecasting, 18, 918-932, doi:10.1175/1520-0434(2003)018,0918: SOPFSS.2.0.CO;2.
2. Anderson, J. L., 2001: An ensemble adjustment Kalman filter for data assimilation. Mon. Wea. Rev., 129, 2884-2903, doi:10.1175/1520-0493(2001)129,2884:AEAKFF.2.0.CO;2.
3. Anderson, J. L., 2003: A local least squares framework for ensemble filtering. Mon. Wea. Rev., 131, 634-642, doi:10.1175/ 1520-0493(2003)131,0634:ALLSFF.2.0.CO;2.
4. Anderson, J. L., 2009: Spatially and temporally varying adaptive covariance infiation for ensemble filters. Tellus, 61A, 72-83, doi:10.1111/ j.1600-0870.2008.00361.x.
5. Anderson, J. L., 2012: Localization and sampling error correction in ensemble Kalman filter data assimilation. Mon. Wea. Rev., 140, 2359- 2371, doi:10.1175/MWR-D-11-00013.1.
6. Anderson, J. L., T. Hoar, K. Raeder, H. Liu, N. Collins, R. Torn, and A. Arellano, 2009: The Data Assimilation Research Testbed: Acommunity facility. Bull.Amer.Meteor. Soc., 90, 1283-1296, doi:10.1175/2009BAMS2618.1.
7. Barker, D. M., 2005: Southern high-latitude ensemble data assimi-lation in theAntarcticMesoscale Prediction System.Mon.Wea.Rev., 133, 3431-3449, doi:10.1175/MWR3042.1.
8. Barker, D. M., and Coauthors, 2012: The Weather Research and Forecasting Model's Community Variational/Ensemble Data Assimila- tion System:WRFDA. Bull. Amer. Meteor. Soc., 93, 831-843, doi:10.1175/BAMS-D-11-00167.1.
9. Bouttier, F., B. Vié, O. Nuissier, and L. Raynaud, 2012: Impact of stochastic physics in a convection-permitting ensemble. Mon.Wea. Rev., 140, 3706-3721, doi:10.1175/MWR-D-12-00031.1.
10. Chen, F., and J. Dudhia, 2001: Coupling an advanced land surface- hydrology model with the Penn State-NCARMM5 modeling system. Part I: Model description and implementation. Mon.\Wea. Rev., 129, 569-585, doi:10.1175/1520-0493(2001)129,0569: CAALSH.2.0.CO;2.
11. Cintineo, J. L., T. M. Smith, V. Lakshmanan, H. E. Brooks, and K. L. Ortega, 2012: An objective high-resolution hail clima- tology of the contiguous United States. Wea. Forecasting, 27, 1235-1248, doi:10.1175/WAF-D-11-00151.1.
12. 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, 1410-1418, doi:10.1175/2010MWR3624.1.
13. Clark, A. J.,W.A.Gallus Jr.,M.Xue, and F.Kong, 2009:Acomparison of precipitation forecast skill between small convection-allowing and large convection-parameterizing ensembles. Wea. Fore- casting, 24, 1121-1140, doi:10.1175/2009WAF2222222.1.
14. Du, J., and Coauthors, 2009: NCEP Short-Range Ensemble Fore- cast (SREF) system upgrade in 2009. Preprints, 19th Conf. on Numerical Weather Prediction/23rd Conf. on Weather Analysis and Forecasting, Omaha, NE, Amer. Meteor. Soc., 4A.4.
15. Ebert, E. E., 2001: Ability of a poor man's ensemble to predict the probability and distribution of precipitation. Mon. Wea.Rev., 129, 2461-2480, doi:10.1175/1520-0493(2001)129,2461: AOAPMS.2.0.CO;2.
16. Evensen, G., 1994: Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics. J. Geophys. Res., 99, 10 143-10 162, doi:10.1029/94JC00572.
17. Gaspari, G., and S. E. Cohn, 1999: Construction of correlation functions in two and three dimensions. Quart. J. Roy. Meteor.Soc., 125, 723-757, doi:10.1002/qj.49712555417.
18. 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. At- mos. Res., 100, 168-177, doi:10.1016/j.atmosres.2010.12.008.
19. Hacker, J., and Coauthors, 2011: The U.S. Air Force Weather Agency's mesoscale ensemble: Scientific description and performance results. Tellus, 63A, 625-641, doi:10.1111/ j.1600-0870.2010.00497.x.
20. Houtekamer, P. L.,H. L.Mitchell, and X. Deng, 2009:Model error representation in an operational ensemble Kalman filter. Mon.\ Wea. Rev., 137, 2126-2143, doi:10.1175/2008MWR2737.1.
21. Houtekamer, P. L., X. Deng, H. L. Mitchell, S.-J. Baek, and N. Gagnon, 2014: Higher resolution in an operational ensemble Kalman filter. Mon. Wea. Rev., 142, 1143-1162, doi:10.1175/ MWR-D-13-00138.1.
22. 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, 1249-1263, doi:10.1175/WAF-D-11-00131.1.
23. 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, doi:10.1029/2008JD009944.
24. JanjićZ. I., 1994: The step-mountain eta coordinate model: Fur-ther developments of the convection, viscous sublayer, and turbulence closure schemes. Mon. Wea. Rev., 122, 927-945, doi:10.1175/1520-0493(1994)122,0927:TSMECM.2.0.CO;2.
25. Janjić Z. I., 2002: Nonsingular implementation of the Mellor-Yamada level 2.5 scheme in the NCEP Mesomodel. NCEP Office Note 437, 61 pp. [Available online at http://www.emc.ncep.noaa.gov/officenotes/newernotes/on437.pdf.]
26. Jones, T. A., and D. J. Stensrud, 2012: Assimilating AIRS tem- perature and mixing ratio profiles using an ensemble Kalman filter approach for convective-scale forecasts. Wea. Fore- casting, 27, 541-564, doi:10.1175/WAF-D-11-00090.1.
27. Jones, T. A.,and D. J. Stensrud, L. Wicker, P. Minnis, and R. Palikonda, 2015: Simul- taneous radar and satellite data storm-scale assimilation using an ensemble Kalman ?lter approach for 24 May 2011. Mon. Wea. Rev., 143, 165-194, doi:10.1175/MWR-D-14-00180.1.
28. Kain, J. S., S. R. Dembek, S. J. Weiss, J. L. Case, J. J. Levit, and R. A. Sobash, 2010: Extracting unique information from high- resolution forecast models: Monitoring selected fields and phenomena every time step. Wea. Forecasting, 25, 1536-1542, doi:10.1175/2010WAF2222430.1.
29. Kuchera, E., S. Rentschler, G. Creighton, and J. Hamilton, 2014: The Air Force weather ensemble prediction suite. Preprints, 15th WRF Users' Workshop, Boulder, CO, National Center for Atmospheric Research, 2.3. [Available online at http://www2.mmm.ucar.edu/wrf/users/workshops/WS2014/ppts/2.3.pdf.]
30. Kühnlein, C., C. Keil, G. C. Craig, and C. Gebhardt, 2014: The impact of downscaled initial condition perturbations onconvective-scale ensemble forecasts of precipitation. Quart.J. Roy. Meteor. Soc.
31. Lin, Y., and K. E. Mitchell, 2005: The NCEP stage II/IV hourly precipitation analyses: Development and applications. Pre- prints, 19th Conf. on Hydrology, San Diego, CA, Amer. Me- teor. Soc., 1.2. [Available online at http://ams.confex.com/ams/pdfpapers/83847.pdf.]
32. 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, 2145-2166, doi:10.1256/ 003590002320603584.
33. Mellor, G. L., and T. Yamada, 1982: Development of a turbulence closure model for geophysical fluid problems. Rev. Geophys.Space Phys., 20, 851-875, doi:10.1029/RG020i004p00851.
34. Mlawer, E. J., S. J. Taubman, P. D. Brown,M. J. Iacono, and S. A. Clough, 1997: Radiative transfer for inhomogeneous atmo- spheres: RRTM, a validated correlated-k model for the long- wave. J. Geophys. Res., 102, 16 663-16 682, doi:10.1029/ 97JD00237.
35. Peralta, C., Z. B. Bouallegue, S. E. Theis, C. Gebhardt, and M. Buchhold, 2012: Accounting for initial condition uncer- tainties in COSMO-DE-EPS. J. Geophys. Res., 117, D07108, doi:10.1029/2011JD016581.
36. Rogers, E., and Coauthors, 2009: The NCEP North American Mesoscale modeling system: Recent changes and future plans.Preprints, 23rd Conf. on Weather Analysis and Forecasting/ 19th Conf. on Numerical Weather Prediction, Omaha, NE, Amer. Meteor. Soc., 2A.4. [Available online at http://ams.confex.com/ams/pdfpapers/154114.pdf.]
37. Romine, G. S., C. S. Schwartz, C. Snyder, J. Anderson, and M. Weisman, 2013: Model bias in a continuously cycled as-similation system and its influence on convection-permitting forecasts. Mon. Wea. Rev., 141, 1263-1284, doi:10.1175/ MWR-D-12-00112.1.
38. Romine, G. S., C. S. Schwartz, J. Berner, K. R. Fossell, C. Snyder, J. L. Anderson, and M. L. Weisman, 2014: Representing forecast error in a convection-permitting ensemble system.Mon.Wea. Rev., 142, 4519-4541, doi:10.1175/MWR-D-14-00100.1.
39. Schumacher, R. S., and A. J. Clark, 2014: Evaluation of ensemble configurations for the analysis and prediction of heavy-rain- producingmesoscale convective systems.Mon.Wea.Rev., 142, 4108-4138, doi:10.1175/MWR-D-13-00357.1.
40. 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 me- soscale ensemble Kalman filter. Wea. Forecasting, 29, 1295- 1318, doi:10.1175/WAF-D-13-00145.1.
41. 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,doi:10.1175/ WAF-D-15-0013.1,inpress.
42. Skamarock, W. C., and Coauthors, 2008: A description of the Advanced Research WRF version 3. NCAR Tech. NoteNCAR/TN-4751STR, 113 pp. [Available from http://nldr.library.ucar.edu/repository/assets/technotes/TECH-NOTE-000-000000-855.pdf.]
43. Sobash, R.A., J. S. Kain,D. R. Bright,A. R.Dean,M. C. Coniglio, and S. J. Weiss, 2011: Probabilistic forecast guidance for se- vere thunderstorms based on the identification of extreme phenomena in convection-allowing model forecasts. Wea. Forecasting, 26, 714-728, doi:10.1175/WAF-D-10-05046.1.
44. Stensrud, D. J., J. Bao, and T. T. Warner, 2000: Using initial con- dition and model physics perturbations in short-range ensem- ble simulations of mesoscale convective systems. Mon. Wea.Rev., 128, 2077-2107, doi:10.1175/1520-0493(2000)128,2077: UICAMP.2.0.CO;2.
45. 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, 23 895-23 915, doi:10.1029/ 97JD01864.
46. Tennant, W., 2015: Improving initial condition perturbations for MOGREPS-UK. Quart. J. Roy.Meteor. Soc., 141, 2324-2336, doi:10.1002/qj.2524.
47. Thompson,G., P.R. Field,R.M.Rasmussen, andW.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, 5095-5115, doi:10.1175/2008MWR2387.1.
48. Tiedtke, M., 1989: A comprehensive mass flux scheme for cu- mulus parameterization in large-scale models. Mon. Wea.Rev., 117, 1779-1800, doi:10.1175/1520-0493(1989)117,1779: ACMFSF.2.0.CO;2.
49. Torn, R. D., 2010: Performance of a mesoscale ensemble Kalman filter (EnKF) during the NOAA High-Resolution Hurricane test.Mon.Wea. Rev., 138, 4375-4392, doi:10.1175/ 2010MWR3361.1.
50. Torn, R. D., G. J. Hakim, and C. Snyder, 2006: Boundary conditions for limited-area ensemble Kalman filters. Mon. Wea. Rev., 134, 2490-2502, doi:10.1175/MWR3187.1.
51. Weisman, M. L., and Coauthors, 2015: The Mesoscale Pre- dictability Experiment (MPEX). Bull. Amer. Meteor. Soc., doi:10.1175/BAMS-D-13-00281, in press.
52. Wilks, D. S., 2006: Statistical Methods in the Atmospheric Sciences: An Introduction. 2nd ed. Academic Press, 627 pp.
53. Xue, M., and Coauthors, 2007: CAPS real-time storm-scale en- semble and high-resolution forecasts as part of the NOAA Hazardous Weather Testbed 2007 Spring Experiment. Pre- prints, 22nd Conf. on Weather Analysis and Forecasting/18th Conf. on Numerical Weather Prediction, Salt Lake City, UT, Amer. Meteor. Soc., 3B. [Available online at http://ams.confex.com/ams/pdfpapers/124587.pdf.]
54. Zhang, C., Y. Wang, and K. Hamilton, 2011: Improved represen- tation of boundary layer clouds over the southeast Pacific in ARW-WRF using a modified Tiedtke cumulus parameteri- zation scheme. Mon. Wea. Rev., 139, 3489-3513, doi:10.1175/ MWR-D-10-05091.1.