The impact of data assimilation length scales on analysis and prediction of convective storms

Monthly Weather Review

Volumn 142, Issue 10, 2014, Pages 3781-3808

  Lange, Heiner ^a   Craig, George C ^a  

^a UNIVERSITY OF MUNICH   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

CONVECTIVE STORMS; DATA ASSIMILATION; LENGTH SCALE;

CONVECTIVE SYSTEM; COVARIANCE ANALYSIS; DATA ASSIMILATION; DOPPLER RADAR; KALMAN FILTER; PRECIPITATION ASSESSMENT; STORM; WEATHER FORECASTING;

EID: 84907444329     PISSN: 00270644     EISSN: 15200493     Source Type: Journal    
DOI: 10.1175/MWR-D-13-00304.1     Document Type: Article

References (60)

1. Aksoy, A., D. C. Dowell, and C. Snyder, 2009: A multicase comparative assessment of the ensemble Kalman filter for assimilation of radar observations. Part I: Storm-scale analyses. Mon. Wea. Rev., 137, 1805-1824, doi:10.1175/ 2008MWR2691.1.
2. Aksoy, A., D. C. Dowell, and C. Snyder, 2010: A multicase comparative assessment of the ensemble Kalman filter for assimilation of radar observations. Part II: Short-range ensemble forecasts. Mon. Wea. Rev., 138, 1273-1292, doi:10.1175/2009MWR3086.1.
3. Alpert, J. C., and V. K. Kumar, 2007: Radial wind super-obs from the WSR-88D radars in the NCEP operational assimilation system. Mon. Wea. Rev., 135, 1090-1109, doi:10.1175/ MWR3324.1.
4. Anderson, J. L., 2009: Spatially and temporally varying adaptive covariance inflation for ensemble filters. Tellus, 61A, 72-83, doi:10.1111/j.1600-0870.2008.00361.x.
5. Baldauf,M.,A. Seifert, J. Förstner,D.Majewski,M. Raschendorfer, and T. Reinhardt, 2011: Operational convective-scale numerical weather prediction with the COSMO model: Description and sensitivities. Mon. Wea. Rev., 139, 3887-3905, doi:10.1175/ MWR-D-10-05013.1.
6. Benjamin, S., S.Weygandt, and G. Manikin, cited 2012:Overview of the rapid refresh rap. [Available online at http://ruc.noaa.gov/ pdf/RAPbrief.NCEP-Dir-20Mar2012.pdf.] Bischof, M., 2011: Ensemble simulations of convective storms.
7. Bischof, M., 2011: Ensemble simulations of convective storms. M.S. thesis, Institute for Atmospheric and Climatic Sciences at Swiss Federal Institute of Technology, Zürich, Switzerland, 117 pp.
8. Bloom, S. C., L. L. Takacs, A. M. da Silva, and D. Ledvina, 1996: Data assimilation using incremental analysis updates. Mon. Wea. Rev., 124, 1256-1271, doi:10.1175/1520-0493(1996)124,1256: DAUIAU.2.0.CO;2.
9. Bocquet, M., C. A. Pires, and L. Wu, 2010: Beyond Gaussian statistical modeling in geophysical data assimilation. Mon. Wea. Rev., 138, 2997-3023, doi:10.1175/2010MWR3164.1.
10. Bryan, G. H., and H. Morisson, 2012: Sensitivity of a simulated squall line to horizontal resolution and parameterization of microphysics. Mon. Wea. Rev., 140, 202-225, doi:10.1175/ MWR-D-11-00046.1.
11. Craig, G. C., and A. Dörnbrack, 2008: Entrainment in cumulus clouds: What resolution is cloud-resolving? J. Atmos. Sci., 65, 3978-3988, doi:10.1175/2008JAS2613.1.
12. Craig, G. C., and M. Würsch, 2013: The impact of localization and observation averaging for convective-scale data assimilation in a simple stochastic model. Quart. J. Roy. Meteor. Soc., 139, 515-523, doi:10.1002/qj.1980.
13. Craig, G. C., C. Keil, and D. Leuenberger, 2012: Constraints on the impact of radar rainfall data assimilation on forecasts of cumulus convection. Quart. J. Roy. Meteor. Soc., 138, 340-352, doi:10.1002/qj.929.
14. Dance, S., 2004: Issues in high resolution limited area data assimilation for quantitative precipitation forecasting. Physica D, 196, 1-27, doi:10.1016/j.physd.2004.05.001.
15. Desroziers, G., L. Berre, B. Chapnik, and P. Poli, 2005: Diagnosis of observation, background and analysis-error statistics in observation space. Quart. J. Roy. Meteor. Soc., 131, 3385-3396, doi:10.1256/qj.05.108.
16. Done, J. M., C. A. Davis, and M. L. Weisman, 2004: The next generation of NWP: Explicit forecasts of convection using the weather research and forecasting (WRF) model. Atmos. Sci. Lett., 5, 110-117, doi:10.1002/asl.72.
17. Done, J. M., G. C. Craig, S. Gray, and P. Clark, 2012: Case-to-case variability of predictability of deep convection in a mesoscale model. Quart. J. Roy. Meteor. Soc., 138, 638-648, doi:10.1002/ qj.943.
18. Dong, J., M. Xue, and K. K. Droegemeier, 2011: The analysis and impact of simulated high-resolution surface observations in addition to radar data for convective storms with an ensemble Kalman filter. Meteor. Atmos. Sci., 112, 41-61, doi:10.1007/ s00703-011-0130-3.
19. Dowell, D. C., and L. J. Wicker, 2009: Additive noise for stormscale ensemble data assimilation. J. Atmos. Oceanic Technol., 26, 911-927, doi:10.1175/2008JTECHA1156.1.
20. Dowell, D. C., F. Zhang, L. J.Wicker, C. Snyder, and N. A. Cook, 2004: Wind and temperature retrievals in the 17 May 1981 Arcadia, Oklahoma, supercell: Ensemble Kalman filter experiments. Mon. Wea. Rev., 132, 1982-2005, doi:10.1175/ 1520-0493(2004)132,1982:WATRIT.2.0.CO;2.
21. 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.
22. Gao, J., and M. Xue, 2008: An efficient dual-resolution approach for ensemble data assimilation and tests with simulated Doppler radar data. Mon. Wea. Rev., 136, 945-963, doi:10.1175/2007MWR2120.1.
23. 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.
24. Greybush, S. J., E. Kalnay, T. Miyoshi, K. Ide, and B. R. Hunt, 2011: Balance and ensemble Kalman filter localization techniques. Mon. Wea. Rev., 139, 511-522, doi:10.1175/ 2010MWR3328.1.
25. Hohenegger, C., and C. Schär, 2007: Predictability and error growth dynamics in cloud-resolving models. J. Atmos. Sci., 64, 4467-4478, doi:10.1175/2007JAS2143.1.
26. Holland, B., and X. Wang, 2013: Effects of sequential or simultaneous assimilation of observations and localization methods on the performance of the ensemble Kalman filter. Quart. J. Roy. Meteor. Soc., 139, 758-770, doi:10.1002/qj.2006.
27. Houtekamer, P., and H. L. Mitchell, 1998: Data assimilation using an ensemble Kalman filter technique. Mon. Wea. Rev., 126, 796-811, doi:10.1175/1520-0493(1998)126,0796: DAUAEK.2.0.CO;2.
28. Hunt, B. R., E. J. Kostelich, and I. Szunyogh, 2007: Efficient data assimilation for spatiotemporal chaos: A local ensemble transform Kalman filter. Physica D, 230, 112-126, doi:10.1016/ j.physd.2006.11.008.
29. Janjić, T., L. Nerger, A. Albertella, J. Schrter, and S. Skachko, 2011: On domain localization in ensemble-based Kalman filter algorithms. Mon. Wea. Rev., 139, 2046-2060, doi:10.1175/ 2011MWR3552.1.
30. Janji±c, T., D. McLaughlin, S. E. Cohn, and M. Verlaan, 2014: Conservation of mass and preservation of positivity with ensembletype Kalman filter algorithms. Mon. Wea. Rev., 142, 755-773, doi:10.1175/MWR-D-13-00056.1.
31. Kalnay, E., and S.-C. Yang, 2010: Accelerating the spin-up of ensemble Kalman filtering. Quart. J. Roy. Meteor. Soc., 136, 1644-1651, doi:10.1002/qj.652.
32. Keil, C., and G. C. Craig, 2009: A displacement and amplitude score employing an optical flow technique. Wea. Forecasting, 24, 1297-1308, doi:10.1175/2009WAF2222247.1.
33. Kober, K., G. C. Craig, C. Keil, and A. Dörnbrack, 2012: Blending a probabilistic nowcasting method with a high-resolution numerical weather prediction ensemble for convective precipitation forecasts. Quart. J. Roy. Meteor. Soc., 138, 755-768, doi:10.1002/qj.939.
34. Kuhl, D., and Coauthors, 2007: Assessing predictability with a local ensemble Kalman filter. J. Atmos. Sci., 64, 1116-1140, doi:10.1175/JAS3885.1.
35. Lien, G.-Y., E. Kalnay, and T. Miyoshi, 2013: Effective assimilation of global precipitation: Simulation experiments. Tellus, 65A, 19915, doi:10.3402/tellusa.v65i0.19915.
36. Lilly, D. K., 1990: Numerical prediction of thunderstorms-Has its time come? Quart. J. Roy. Meteor. Soc., 116, 779-798, doi:10.1002/qj.49711649402.
37. Lorenz, E. N., 1969: The predictability of a flow which possesses many scales of motion. Tellus, 21, 289-307, doi:10.1111/ j.2153-3490.1969.tb00444.x.
38. Lu, H., and Q. Xu, 2009: Trade-offs between measurement accuracy and resolutions in configuring phased-array radar velocity scans for ensemble-based storm-scale data assimilation. J. Appl. Meteor. Climatol., 48, 1230-1244, doi:10.1175/ 2008JAMC2009.1.
39. Lynch, P., and X.-Y. Huang, 1992: Initialization of the HIRLAM model using a digital filter. Mon. Wea. Rev., 120, 1019-1034, doi:10.1175/1520-0493(1992)120,1019: IOTHMU.2.0.CO;2.
40. Reich, H., A. Rhodin, and C. Schraff, 2011: LETKF for the nonhydrostatic regional model COSMO-DE. COSMO Newsletter, No. 11, Consortium for Small-scale Modelling, 27-31. [Available online at http://www.cosmo-model.org/content/ model/documentation/newsLetters/newsLetter11/.]
41. Salonen, K., H. Järvinen, G. Haase, S. Niemelä, and R. Eresma, 2009: Doppler radar radial winds in HIRLAM. Part II: Optimizing the super-observation processing. Tellus, 61A, 288- 295, doi:10.1111/j.1600-0870.2008.00381.x.
42. Scheufele, K., K. Kober, G. C. Craig, and C. Keil, 2014: Combining probabilistic precipitation forecasts from a nowcasting technique with a time-lagged ensemble. Meteor. Appl., 21, 230- 240, doi:10.1002/met.1381.
43. Seity, Y., P. Brousseau, S.Malardel,G. Hello, P. Bnard, F. Bouttier, C. Lac, andV.Masson, 2011: TheAROME-France convectivescale operational model. Mon. Wea. Rev., 139, 976-991, doi:10.1175/2010MWR3425.1.
44. Seko, H., T. Kawabata, and T. Tsuyuki, 2004: Impacts of GPSderived water vapor and radial wind measured by Doppler radar on numerical prediction of precipitation. J. Meteor. Soc. Japan, 82, 473-489.
45. Simon, E., and L. Bertino, 2009: Application of the Gaussian anamorphosis to assimilation in a 3-d coupled physical-ecosystem model of the North Atlantic with the EnKF: A twin experiment. Ocean Sci., 5, 495-510, doi:10.5194/os-5-495-2009.
46. Skamarock, W. C., 2004: Evaluating Mesoscale NWP models using kinetic energy spectra. Mon. Wea. Rev., 132, 3019-3032, doi:10.1175/MWR2830.1.
47. Snyder, C., and F. Zhang, 2003: Assimilation of simulated Doppler radar observations with an ensemble Kalman filter. Mon. Wea. Rev., 131, 1663-1677, doi:10.1175/2555.1.
48. Sobash, R. A., and D. J. Stensrud, 2013: The impact of covariance localization for radar data on EnKF analyses of a developing MCS: Observing system simulation experiments. Mon. Wea. Rev., 141, 3691-3709, doi:10.1175/MWR-D-12-00203.1.
49. Stensrud, D. J., and J. Gao, 2010: Importance of horizontally inhomogeneous environmental initial conditions to ensemble storm-scale radar data assimilation and very short-range forecasts. Mon. Wea. Rev., 138, 1250-1272, doi:10.1175/2009MWR3027.1.
50. Stensrud, D. J., and Coauthors, 2009: Convective-Scale Warn-on-Forecast System-A vision for 2020. Bull. Amer. Meteor. Soc., 90, 1487- 1499, doi:10.1175/2009BAMS2795.1.
51. 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, 1789-1807, doi:10.1175/MWR2898.1.
52. Wang, S., M. Xue, A. D. Schenkman, and J. Min, 2013: An iterative ensemble square root filter and tests with simulated radar data for storm-scale data assimilation. Quart. J. Roy. Meteor. Soc., 139, 1888-1903, doi:10.1002/qj.2077.
53. Wernli, H., M. Paulat, M. Hagen, and C. Frei, 2008: SAL-A novel quality measure for the verification of quantitative precipitation forecasts. Mon. Wea. Rev., 136, 4470-4487, doi:10.1175/2008MWR2415.1.
54. Weygandt, S. S., S. G. Benjamin, T. G. Smirnova, and J. M. Brown, 2008: Assimilation of radar reflectivity data using a diabatic digital filter within the Rapid Update Cycle. 12th Conf. on Integrated Observing and Assimilation Systems for the Atmosphere, Oceans, and Land Surface (IOAS-AOLS), New Orleans, LA, Amer. Meteor. Soc., 8.4. [Available online at https://ams.confex.com/ams/88Annual/techprogram/ paper_134081.htm.]
55. 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, 463-482, doi:10.1175/ 2007MWR2018.1.
56. Xu, Q., 2011: Measuring information content from observations for data assimilation: Spectral formulations and their implications to observational data compression. Tellus, 63A, 793-804, doi:10.1111/j.1600-0870.2011.00524.x.
57. Yang, S.-C., E. Kalnay, B. Hunt, and N. E. Bowler, 2009: Weight interpolation for efficient data assimilation with the local ensemble transform Kalman filter. Quart. J. Roy. Meteor. Soc., 135, 251-262, doi:10.1002/qj.353.
58. Zhang, F., C. Snyder, and R. Rotunno, 2003: Effects of moist convection on mesoscale predictability. J. Atmos. Sci., 60, 1173-1185, doi:10.1175/1520-0469(2003)060,1173: EOMCOM.2.0.CO;2.
59. Zhang, F., C. Snyder, and J. Sun, 2004: Impacts of initial estimate and observation availability on convective-scale data assimilation with an ensemble Kalman filter. Mon. Wea. Rev., 132, 1238-1253, doi:10.1175/1520-0493(2004)132,1238:IOIEAO.2.0.CO;2.
60. Zhang, F., N. Bei, R. Rotunno, C. Snyder, and C. C. Epifanio, 2007: Mesoscale predictability of moist baroclinic waves: Convection-permitting experiments and multistage error growth dynamics. J. Atmos. Sci., 64, 3579-3594, doi:10.1175/ JAS4028.1.