The weather research and forecasting model's community variational/ensemble data assimilation system: WRFDA

Bulletin of the American Meteorological Society

Volumn 93, Issue 6, 2012, Pages 831-843

  Barker, Dale ^a   Huang, Xiang Yu ^b   Liu, Zhiquan ^b   Auligné, Tom ^b   Zhang, Xin ^b   Rugg, Steven ^c   Ajjaji, Raji ^d   Bourgeois, Al ^b   Bray, John ^e   Chen, Yongsh Eng ^f   Demirtas, Meral ^g   Guo, Yong Run ^b   Henderson, Tom ^h   Huang, Wei ^b   Lin, Hui Chuan ^b   Michalakes, John ⁱ   Rizvi, Syed ^b   Zhang, Xiaoyan ^b  

^a MET OFFICE   (United Kingdom)

^b NATIONAL CENTER FOR ATMOSPHERIC RESEARCH   (United States)

^c Offutt Air Force Base   (United States)

^d Air Force and Air Defense Meteorological Department   (United Arab Emirates)

^e IBM   (Sweden)

^f YORK UNIVERSITY   (Canada)

^g Turkish State Meteorological Service   (Turkey)

^h NOAA EARTH SYSTEM RESEARCH LABORATORY   (United States)

ⁱ NATIONAL RENEWABLE ENERGY LABORATORY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DATA ASSIMILATION; DATA ASSIMILATION SYSTEMS; METEOROLOGICAL DATA; NATIONAL CENTERS FOR ENVIRONMENTAL PREDICTIONS; OBSERVATION ERRORS; VARIATIONAL DATA ASSIMILATION; WEATHER RESEARCH AND FORECASTING MODELS;

ERROR STATISTICS; METEOROLOGY; RESEARCH;

WEATHER FORECASTING;

EID: 84863698671     PISSN: 00030007     EISSN: None     Source Type: Journal    
DOI: 10.1175/BAMS-D-11-00167.1     Document Type: Article

References (58)

1. Ajjaji, R., A. A. Al-Katheri, and A. Dhanhani, 2007: Tuning of WRF 3D-var data assimilation system over Middle-East and Arabian Peninsula. Extended Abstracts, Eighth WRF Users' Workshop, Boulder, CO, WRF. [Available online at www.mmm.ucar.edu/wrf/users/workshops/WS2007/abstracts/4-6-Ajjaji.pdf.]
2. Anderson, J., T. Hoar, K. Raeder, H. Liu, N. Collins, R. Torn, and A. Avellano, 2009: The Data Assimilation Research Testbed: A community facility. Bull. Amer. Meteor. Soc., 90, 1283-1296.
3. Auligné, T., A. P. McNally, and D. P. Dee, 2007: Adaptive bias correction for satellite data in a numerical weather prediction system. Quart. J. Roy. Meteor. Soc., 133, 631-642.
4. Barker, D. M., 1999: The use of synoptically-dependent background error structures in 3DVAR. Met Office VAR Scientific Documentation Paper 26, 11 pp. [Available from Met Office, Fitzroy Road, Exeter EX1 3PB, United Kingdom.]
5. 2005: Southern high-latitude ensemble data assimilation in the Antarctic Mesoscale Prediction System. Mon. Wea. Rev., 133, 3431-3449.
6. W. Huang, Y.-R. Guo, and A. Bourgeois, 2003: A three-dimensional variational (3DVAR) data assimilation system for use with MM5. NCAR Tech. Note NCAR/TN-453+STR, 68 pp. [Available online at www.mmm.ucar.edu/mm53dvar/docs/ 3DVARTechDoc.pdf.]
7. Q. N. Xiao, 2004: A threedimensional (3DVAR) data assimilation system for use with MM5: Implementation and initial results. Mon. Wea. Rev.,132, 897-914.
8. Bishop, C., B. J. Etherton, and S. J. Majumdar, 2001: Adaptive sampling with the ensemble transform Kalman filter. Part I: Theoretical aspects. Mon. Wea. Rev., 129, 420-436.
9. Bloom, S., L. L. Takacs, A. Silva, and D. Ledvian, 1996: Data assimilation using incremental analysis updates. Mon. Wea. Rev., 124, 1256-1271.
10. Chen, S.-H., 2007: The impact of assimilating SSM/I and QuikSCAT satellite winds on Hurricane Isidore simulations. Mon. Wea. Rev., 135, 549-566.
11. F. Vandenberghe, G. W. Petty, and J. Bresch, 2004: Application of SSM/I satellite data to a hurricane simulation. Quart. J. Roy. Meteor. Soc., 130, 801-825.
12. Cucurull, L., F. Vandenberghe, D. M. Barker, E. Vilaclara, and A. Rius, 2004: Three-dimensional variational data assimilation of ground-based GPS ZTD and meteorological observations during the 14 December 2001 storm event over the western Mediterranean Sea. Mon. Wea. Rev., 132, 749-763.
13. Y.-H. Kuo, D. M. Barker, and S. R. H. Rizvi, 2006: Assessing the impact of COSMIC GPS radio occultation data on weather analysis and short-term forecast over the Antarctic: A case study. Mon. Wea. Rev., 134, 3283-3296.
14. Daley, R., 1991: Atmospheric Data Analysis. Cambridge Atmospheric and Space Science Series, Cambridge University Press, 457 pp.
15. Dee, D. P., 2005: Bias and data assimilation. Quart. J. Roy. Meteor. Soc., 131, 3323-3343.
16. Demirtas, M., D. M. Barker, Y. Chen, J. Hacker, X.-Y. Huang, C. Snyder, and X. Wang, 2009: A hybrid data assimilation system (ensemble transform Kalman filter and WRF-VAR) based retrospective tests with real observations. Preprints, 19th Conf. on Numerical Weather Prediction, Omaha, NE, Amer. Meteor. Soc., 9A.5. [Available online at http://ams.confex.com/ams/pdfpapers/153340.pdf.]
17. Desroziers, G., and S. Ivanov, 2001: Diagnosis and adaptive tuning of observation-error parameters in a variational assimilation. Quart. J. Roy. Meteor. Soc., 127, 1433-1452.
18. Etherton, B., and C. Bishop, 2004: Resilience of hybrid ensemble 3DVAR analysis schemes to model error and ensemble covariance error. Mon. Wea. Rev., 132, 1065-1080.
19. Faccani, C., and R. Ferretti, 2005a: Data assimilation of high-density observations. I: Impact on initial conditions for the MAP/SOP IOP2b. Quart. J. Roy. Meteor. Soc., 131, 21-42.
20. 2005b: Data assimilation of high-density observations. II: Impact on the forecast of the precipitation for the MAP/SOP IOP2b. Quart. J. Roy. Meteor. Soc., 131, 43-61.
21. Gu, J., Q.-N. Xiao, Y.-H. Kuo, D. M. Barker, J. Xue, and X. Ma, 2005: A case study of Typhoon Rusa (2002) on its analysis and simulation using WRF 3DVAR and the WRF modeling system. Adv. Atmos. Sci., 22, 415-425.
22. Guo, Y.-R., H. Kusaka, D. M. Barker, Y.-H. Kuo, and A. Crook, 2005: Impact of ground-based GPS PW and MM5-3DVAR background error statistics on forecast of a convective case. SOLA, 1, 73-76.
23. Hamill, T. M., and C. Snyder, 2000: A hybrid ensemble Kalman filter-3D variational analysis scheme. Mon. Wea. Rev., 128, 2905-2919.
24. Han, Y., P. van Delst, Q. Liu, F. Weng, B. Yan, R. Treadon, and J. Derber, 2006: JCSDA Community Radiative Transfer Model (CRTM)-Version 1. NOAA/ NESDIS/ Tech. Rep. 122, 33 pp. [Available online at ftp://ftp.emc.ncep.noaa.gov/ jcsda/CRTM/CRTM-v1-NOAA-Tech-Report-NESDIS122.pdf.]
25. Harris, B. A., and G. Kelly, 2001: A satellite radiancebias correction for data assimilation. Quart. J. Roy. Meteor. Soc., 127, 1453-1468.
26. He, W., Z. Liu, and H. Cheng, 2011: Influence of surface temperature and emissivity on AMSU-A assimilation over land. Acta Meteor. Sin., 25, 545-557.
27. Houtekamer, P. L., and Herschel L. Mitchell, 1998: Data assimilation using an ensemble Kalman filter technique. Mon. Wea. Rev., 126, 796-811.
28. Huang, C.-Y. , Y.-H. Kuo, S.-H. Chen, and F. Vandenberghe, 2005: Improvements in typhoon forecasts with assimilated GPS occultation refractivity. Wea. Forecasting, 20, 931-953.
29. Huang, X.-Y., and Coauthors, 2009: Four-dimensional variational data assimilation for WRF: Formulation and preliminary results. Mon. Wea. Rev., 137, 299-314.
30. Hunt, B., E. Kosterlich, and I. Szunyogh, 2007: Efficient data assimilation for spatiotemporal chaos: A local ensemble Kalman filter. Physica D, 230, 112-126.
31. Kalnay, E., 2003: Atmospheric Modeling, Data Assimilation and Predictability. Cambridge University Press, 364 pp.
32. Lee, M.-S., and D. M. Barker, 2005: Preliminary tests of first guess at appropriate time (FGAT) with WRF 3DVAR and WRF model. J. Korean Meteor. Soc., 41, 495-505.
33. Y.-H. Kuo, and D. M. Barker, 2006: Incremental analysis updates initialization technique applied in 10-km MM5 3DVAR and model. Mon. Wea. Rev., 134, 1389-1404.
34. Liu, C., Q. Xiao, and B. Wang, 2008: An ensemble-based four-dimensional variational data assimilation scheme. Part I: Technical formulation and preliminary test. Mon. Wea. Rev., 136, 3363-3373.
35. 2009: An ensemble-based fourdimensional variational data assimilation scheme. Part II: Observing system simulation experiments with Advanced Research WRF (ARW). Mon. Wea. Rev., 137, 1687-1704.
36. Liu, Z., X. Zhang, T. Auligné, and H.-C. Lin, 2009: Variational analysis of hydrometeors with satellite radiance observations: A simulated study. 10th WRF Users' Workshop, Boulder, CO, WRF, 2A.1. [Available online at www.mmm.ucar.edu/wrf/users/workshops/WS2009/abstracts/2A-01.pdf.]
37. Lu, Q., W. Zhang, P. Zhang, X. Wu, F. Zhang, Z. Liu, and D. M. Barker, 2010: Monitoring the 2008 cold surge and frozen disasters snowstorm in south China based on regional ATOVS data assimilation. Sci. China; Earth Sci., 53, 1216-1228.
38. McNally, P., J. C. Derber, W. Wu, and B. B. Katz, 2000: The use of TOVS level-1b radiances in the NCEP SSI analysis system. Quart. J. Roy. Meteor. Soc., 126, 689-724.
39. Meng, Z., and F. Zhang, 2008: Tests of an ensemble Kalman filter for mesoscale and regional-scale data assimilation. Part III: Comparison with 3DVAR in a real-data case study. Mon. Wea. Rev., 136, 522-540.
40. Michel, Y., T. Auligné, and T. Montmerle, 2011: Heterogeneous convective-scale background error covariances with the inclusion of hydrometeor variables. Mon. Wea. Rev., 139, 2994-3015.
41. Parrish, D. F., and J. C. Derber, 1992: The National Meteorological Center's spectral statistical interpolation analysis system. Mon. Wea. Rev., 120, 1747-1763.
42. Powers, J. G., 2007: Numerical prediction of an Antarctic severe wind event with the Weather Research and Forecasting (WRF) model. Mon. Wea. Rev., 135, 3134-3157.
43. A. J. Monaghan, A. M. Cayette, D. H. Bromwich, Y. -H. Kuo, and K. W. Manning, 2003: Real-time mesoscale modeling over Antarctica: The Antarctic Mesoscale Prediction System (AMPS). Bull Amer. Meteor. Soc., 84, 1533-1546.
44. Rabier, F., H. Järvinen, E. Klinker, J.-F. Mahfouf, and A. Simmons, 2000: The ECMWF operational implementation of four-dimensional variational assimilation. I: Experimental results with simplified physics. Quart. J. Roy. Meteor. Soc., 126, 1143-1170.
45. Rawlins, F., S. P. Ballard, K. J. Bovis, A. M. Clayton, D. Li, G. W. Inverarity, A. C. Lorenc, and T. J. Payne, 2007: The Met Office global four-dimensional variational data assimilation scheme. Quart. J. Roy. Meteor. Soc., 133, 347-362.
46. Schwartz, C. S., Z. Liu, Y. Chen, and X.-Y. Huang, 2012: Impact of assimilating microwave radiances with a limited-area ensemble data assimilation system on forecasts of Typhoon Morakot. Wea. Forecasting, in press.
47. Simmons, A. J., and A. Hollingsworth, 2002: Some aspects of the improvement in skill of numerical weather prediction. Quart. J. Roy. Meteor. Soc., 128, 647-677.
48. Skamarock, W. C., and Coauthors, 2008: A description of the Advanced Research WRF version 3. NCAR Tech Note NCAR/TN-475+STR, 113 pp. [Available online at www.mmm.ucar.edu/wrf/users/docs/arw-v3.pdf.]
49. Torn, R. D., G. J. Hakim, and C. Snyder, 2006: Boundary conditions for limited-area ensemble Kalman filters. Mon. Wea. Rev., 134, 2490-2502.
50. Wang, X., D. M. Barker, C. Snyder, and T. M. Hamill, 2008a: A hybrid ETKF-3DVAR data assimilation scheme for the WRF model. Part I: Observing system simulation experiment. Mon. Wea. Rev., 136, 5116-5131.
51. 2008b: A hybrid ETKF-3DVAR data assimilation scheme for the WRF model. Part II: Real observation experiments. Mon. Wea. Rev., 136, 5132-5147.
52. Xiao, Q., and J. Sun, 2007: Multiple-radar data assimilation and short-range quantitative precipitation forecasting of a squall line observed during IHOP-2002. Mon. Wea. Rev., 135, 3381-3404.
53. Y. H. Kuo, J. Sun, W. C. Lee, E. Lim, Y. R. Guo, and D. M. Barker, 2005: Assimilation of Doppler radar observations with a regional 3DVAR system: Impact of Doppler velocities on forecasts of a heavy rainfall case. J. Appl. Meteor., 44, 768-788.
54. Y. Zhang, D. M. Barker, and D.-J. Won, 2006: A tropical cyclone bogus data assimilation scheme in the MM5 3D-VAR system and numerical experiments with Typhoon Rusa (2002) near landfall. J. Meteor. Soc. Japan, 84, 671-689.
55. J. Sun, W.-C. Lee, D. M. Barker, and E. Lim, 2007: An approach of Doppler reflectivity data assimilation and its assessment with the inland QPF of Typhoon Rusa (2002) at landfall. J. Appl. Meteor. Climatol., 46, 14-22.
56. Coauthors, 2008a: A successful collaboration between research institutions and operational center: Realization of Doppler radar data assimilation with WRF 3D-VAR in KMA operational forecasting. Bull. Amer. Meteor. Soc., 89, 39-43.
57. Y. Kuo, Z. Ma, W. Huang, X. Huang, X. Zhang, D. M. Barker, and J. Michalakes, 2008b: Development of the WRF adjoint modeling system and its application to the investigation of the May 2004 McMurdo Antarctica severe wind event. Mon. Wea. Rev., 136, 3696-3713.
58. Zhang, M., F. Zhang, X.-Y. Huang, and X. Zhang, 2011: Intercomparison of an ensemble Kalman filter with three-and four-dimensional variational data assimilation methods in a limited-area model over the month of June 2003. Mon. Wea. Rev., 139, 566-572.