On the impact of additive noise in storm-scale EnKF experiments

Monthly Weather Review

Volumn 143, Issue 8, 2015, Pages 3067-3086

  Sobash, Ryan A ^a   Wicker, Louis J ^b  

^a UNIVERSITY OF OKLAHOMA   (United States)

^b NATIONAL SEVERE STORMS LABORATORY   (United States)

Author keywords

Cloud resolving models; Convective clouds; Data assimilation; Ensembles; Kalman filters

Indexed keywords

CLOUDS; KALMAN FILTERS; RADAR; STORMS; THERMOANALYSIS; TOY MANUFACTURE;

CLOUD RESOLVING MODEL; COHERENT PERTURBATION; CONVECTIVE CLOUDS; DATA ASSIMILATION; ENSEMBLE KALMAN FILTER; ENSEMBLES; FLEXIBLE STRATEGIES; THERMO DYNAMIC ANALYSIS;

ADDITIVE NOISE;

CLIMATE MODELING; CONVECTIVE CLOUD; DATA ASSIMILATION; ENSEMBLE FORECASTING; EXPERIMENTAL STUDY; KALMAN FILTER; SIGNAL-TO-NOISE RATIO; STORM; THERMODYNAMICS;

EID: 84943421955     PISSN: 00270644     EISSN: 15200493     Source Type: Journal    
DOI: 10.1175/MWR-D-14-00323.1     Document Type: Article

References (22)

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. 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.
3. Caya, A., J. Sun, and C. Snyder, 2005: A comparison between the 4DVAR and the ensemble Kalman filter techniques for radar data assimilation. Mon. Wea. Rev., 133, 3081-3094, doi:10.1175/MWR3021.1.
4. Dawson, D. T., II, L. J. Wicker, E. R. Mansell, and R. L. Tanamachi, 2012: Impact of the environmental low-level wind profile on ensemble forecasts of the 4 May 2007 Greensburg, Kansas, tornadic storm and associated mesocyclones. Mon. Wea. Rev., 140, 696-716, doi:10.1175/MWR-D-11-00008.1.
5. Dee, D. P., 1995: On-line estimation of error covariance parameters for atmospheric data assimilation. Mon. Wea. Rev., 123, 1128-1145, doi:10.1175/1520-0493(1995)123<1128:OLEOEC>2.0.CO;2.
6. 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.
7. Dowell, D. C., F. Zhang, L. J. Wicker, C. Snyder, and N. A. Crook, 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.
8. Dowell, D. C., L. J. Wicker, and C. Snyder, 2011: Ensemble Kalman filter assimilation of radar observations of the 8 May 2003 Oklahoma City supercell: Influences of reflectivity observations on stormscale analyses. Mon. Wea. Rev., 139, 272-294, doi:10.1175/2010MWR3438.1.
9. 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.
10. Hong, S. Y., and J.-O. J. Lim, 2006: The WRF single-moment 6-class microphysics scheme (WSM6). J. Korean Meteor. Soc., 42, 129-151.
11. Houtekamer, P. L., 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.
12. Marquis, J., Y. Richardson, P. Markowski, D. Dowell, and J. Wurman, 2012: Tornado maintenance investigated with high-resolution dual-Doppler and EnKF analysis. Mon. Wea. Rev., 140, 3-27, doi:10.1175/MWR-D-11-00025.1.
13. Marquis, J., Y. Richardson, P. Markowski, D. Dowell, J. Wurman, K. Kosiba, P. Robinson, and G. Romine, 2014: An investigation of the Goshen County, Wyoming, tornadic supercell of 5 June 2009 using EnKF assimilation of mobile mesonet and radar observations collected during VORTEX2. Part I: Experiment design and verification of the EnKF analyses. Mon. Wea. Rev., 142, 530-554, doi:10.1175/MWR-D-13-00007.1.
14. Mitchell, H. L., and P. L. Houtekamer, 2000: Anadaptive ensemble Kalman filter. Mon. Wea. Rev., 128, 416-433, doi:10.1175/1520-0493(2000)128<0416:AAEKF>2.0.CO;2.
15. 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.
16. Rotunno, R., J. B. Klemp, and M. L. Weisman, 1988: A theory for strong, long-lived squall lines. J. Atmos. Sci., 45, 463-485, doi:10.1175/1520-0469(1988)045<0463:ATFSLL>2.0.CO;2.
17. 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.
18. 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.
19. Stensrud, D. J., and Coauthors, 2009: Convective-scale warn-onforecast system: A vision for 2020. Bull. Amer. Meteor. Soc., 90, 1487-1499, doi:10.1175/2009BAMS2795.1.
20. 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.
21. Weisman, M. L., 1992: The role of convectively generated rearinflow jets in the evolution of long-lived mesoconvective systems. J. Atmos. Sci., 49, 1826-1847, doi:10.1175/1520-0469(1992)049<1826:TROCGR>2.0.CO;2.
22. 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.