Utility of dense pressure observations for improving mesoscale analyses and forecasts

Monthly Weather Review

Volumn 142, Issue 7, 2014, Pages 2398-2413

  Madaus, Luke E ^a   Hakim, Gregory J ^a   Mass, Clifford F ^a  

^a UNIVERSITY OF WASHINGTON   (United States)

Author keywords

Data assimilation; Data quality control; Kalman filters; Mesoscale forecasting; Numerical weather prediction forecasting; Surface observations

Indexed keywords

KALMAN FILTERS; QUALITY CONTROL;

DATA ASSIMILATION; DATA QUALITY CONTROL; MESOSCALE FORECASTING; NUMERICAL WEATHER PREDICTION/FORECASTING; SURFACE OBSERVATION;

FORECASTING;

ACCURACY ASSESSMENT; ATMOSPHERIC PRESSURE; DATA ASSIMILATION; KALMAN FILTER; MESOSCALE METEOROLOGY; NUMERICAL MODEL; OBSERVATIONAL METHOD; QUALITY CONTROL; WEATHER FORECASTING;

PACIFIC NORTHWEST;

EID: 84905647614     PISSN: 00270644     EISSN: 15200493     Source Type: Journal    
DOI: 10.1175/MWR-D-13-00269.1     Document Type: Article

References (28)

1. Ancell, B., 2012: Examination of analysis and forecast errors of high-resolution assimilation, bias removal, and digital filter initialization with an ensemble Kalman filter. Mon. Wea. Rev., 140, 3992-4004
2. Ancell, B., C.Mass, andG.Hakim, 2011: Evaluation of surface analyses and forecasts with a multiscale ensemble Kalman filter in regions of complex terrain. Mon. Wea. Rev., 139, 2008-2024
3. Anderson, J. L., 2007a: An adaptive covariance inflation error correction algorithm for ensemble filters. Tellus, 59A, 210- 224
4. Anderson, J. L., 2007b: Exploring the need for localization in ensemble data assimilation using a hierarchical ensemble filter. Physica D, 230, 99-111
5. Anderson, J. L., B. Wyman, S. Zhang, and T. Hoar, 2005: Assimilation of surface pressure observations using an ensemble filter in an idealized global atmospheric prediction system. J. Atmos. Sci., 62, 2925-2938
6. Anderson, J. L., T. Hoar, K. Raeder, H. Liu, N. Collins, R. Torn, and A. Avellano, 2009: The Data Assimilation Research Testbed: Acommunity facility. Bull. Amer. Meteor. Soc., 90, 1283-1296
7. Benjamin, S., B. Schwartz, and R. Cole, 1999: Accuracy of ACARS wind and temperature observations determined by collocation. Wea. Forecasting, 14, 1032-1038
8. COESA, 1976: U.S. Standard Atmosphere, 1976. NOAA, 227 pp.
9. Colle, B., C. Mass, and D. Ovens, 2001: Evolution of the timing and strength of MM5 and Eta surface trough passages over the eastern Pacific. Wea. Forecasting, 16, 553-572
10. Compo, G. P., and Coauthors, 2011: The twentieth century reanalysis project. Quart. J. Roy. Meteor. Soc., 137, 1-28
11. Dirren, S., R. Torn, and G. Hakim, 2007: A data assimilation case study using a limited-area ensemble filter. Mon. Wea. Rev., 135, 1455-1473
12. Eckel, F., and C. Mass, 2005: Aspects of effective mesoscale, shortrange ensemble forecasting. Wea. Forecasting, 20, 328-350
13. Engerer, N., D. Stensrud, and M. Coniglio, 2008: Surface characteristics of observed cold pools. Mon. Wea. Rev., 136, 4839-4849
14. Gaspari, G., and S. E. Cohn, 1999: Construction of correlation functions in two and three dimensions. Quart. J. Roy. Meteor. Soc., 125, 723-757
15. Goff, R. C., 1976: Vertical structure of thunderstorm outflows. Mon. Wea. Rev., 104, 1429-1440
16. Hanley, K., D. Kirshbaum, N. Roberts, and G. Leoncini, 2013: Sensitivities of a squall line over central Europe in a convectivescale ensemble. Mon. Wea. Rev., 141, 112-133
17. Janjic, T., and S. Cohn, 2006: Treatment of observation error due to unresolved scales in atmospheric data assimilation. Mon. Wea. Rev., 134, 2900-2915
18. Mass, C. F., 1981: Topographically forced convergence in western Washington state.Mon.Wea. Rev., 109, 1335-1347
19. Mass, C. F., and G. Ferber, 1990: Surface pressure perturbations produced by an isolated mesoscale topographic barrier. Part I: General characteristics and dynamics. Mon. Wea. Rev., 118, 2579-2596
20. Mass, C. F., J. Baars, G. Wedam, E. Grimit, and R. Steed, 2008: Removal of systematic model bias on a model grid. Wea. Forecasting, 23, 438-459
21. Melhauser, C., and F. Zhang, 2012: Practical and intrinsic predictability of severe and convective weather at the mesoscales. J. Atmos. Sci., 69, 3350-3371
22. Miller, P., M. Barth, and L. Morone, cited 2002: RSAS technical procedures bulletin. [Available online at http://msas.noaa.gov/rsas_tpb.html.
23. Roebber, P., D. Schultz, B. Colle, and D. Stensrud, 2004: Toward improved prediction: High-resolution and ensemble modeling systems in operations.Wea. Forecasting, 19, 936-949
24. Skamarock, W. C., and Coauthors, 2008: A description of the Advanced Research WRF version 3. Tech. Rep. NCAR/TN-4751STR,National Center for AtmosphericResearch, Boulder, CO, 125 pp. [Available online at http://www.mmm.ucar.edu/wrf/users/docs/arw_v3.pdf.]
25. Torn, R., and G. Hakim, 2008: Performance characteristics of a pseudo-operational ensemble Kalman filter. Mon. Wea. Rev., 136, 3947-3963
26. Torn, R., and G. Hakim., and C. Snyder, 2006: Boundary conditions for limitedarea ensemble Kalman filters. Mon. Wea. Rev., 134, 2490- 2502
27. Wheatley, D., and D. Stensrud, 2010: The impact of assimilating surface pressure observations on severe weather events in a WRF mesoscale system. Mon. Wea. Rev., 138, 1673-1694
28. Whitaker, J. S., G. P. Compo, X. Wei, and T. M. Hamill, 2004: Reanalysis without radiosondes using ensemble data assimilation. Mon. Wea. Rev., 132, 11901200