World Library  

Add to Book Shelf
Flag as Inappropriate
Email this Book

Implementation of a 3D-var System for Atmospheric Profiling Data Assimilation Into the Rams Model: Initial Results : Volume 6, Issue 12 (17/12/2013)

By Federico, S.

Click here to view

Book Id: WPLBN0003999263
Format Type: PDF Article :
File Size: Pages 14
Reproduction Date: 2015

Title: Implementation of a 3D-var System for Atmospheric Profiling Data Assimilation Into the Rams Model: Initial Results : Volume 6, Issue 12 (17/12/2013)  
Author: Federico, S.
Volume: Vol. 6, Issue 12
Language: English
Subject: Science, Atmospheric, Measurement
Collections: Periodicals: Journal and Magazine Collection (Contemporary), Copernicus GmbH
Publication Date:
Publisher: Copernicus Gmbh, Göttingen, Germany
Member Page: Copernicus Publications


APA MLA Chicago

Federico, S. (2013). Implementation of a 3D-var System for Atmospheric Profiling Data Assimilation Into the Rams Model: Initial Results : Volume 6, Issue 12 (17/12/2013). Retrieved from

Description: ISAC-CNR, UOS of Rome, Rome (RM), Italy. This paper presents the current status of development of a three-dimensional variational data assimilation system (3D-Var). The system can be used with different numerical weather prediction models, but it is mainly designed to be coupled with the Regional Atmospheric Modelling System (RAMS). Analyses are given for the following parameters: zonal and meridional wind components, temperature, relative humidity, and geopotential height.

Important features of the data assimilation system are the use of incremental formulation of the cost function, and the representation of the background error by recursive filters and the eigenmodes of the vertical component of the background error covariance matrix. This matrix is estimated by the National Meteorological Center (NMC) method.

The data assimilation and forecasting system is applied to the real context of atmospheric profiling data assimilation, and in particular to the short-term wind prediction. The analyses are produced at 20 km horizontal resolution over central Europe and extend over the whole troposphere. Assimilated data are vertical soundings of wind, temperature, and relative humidity from radiosondes, and wind measurements of the European wind profiler network.

Results show the validity of the analyses because they are closer to the observations (lower root mean square error (RMSE)) compared to the background (higher RMSE), and the differences of the RMSEs are in agreement with the data assimilation settings.

To quantify the impact of improved initial conditions on the short-term forecast, the analyses are used as initial conditions of three-hours forecasts of the RAMS model. In particular two sets of forecasts are produced: (a) the first uses the ECMWF analysis/forecast cycle as initial and boundary conditions; (b) the second uses the analyses produced by the 3D-Var as initial conditions, then it is driven by the ECMWF forecast.

The improvement is quantified by considering the horizontal components of the wind, which are measured at asynoptic times by the European wind profiler network. The results show that the RMSE is effectively reduced at the short range. The results are in agreement with the set-up of the numerical experiment.

Implementation of a 3D-Var system for atmospheric profiling data assimilation into the RAMS model: initial results

Mellor, G. and Yamada, T.: Development of a Turbulence Closure Model for Geophysical Fluid Problems, Rev. Geophys. Space Phys., 20, 851–875, 1982.; Anderson, J.: An ensemble adjustment Kalman filter for data assimilation, Mon. Weather Rev., 129, 2884–2903, 2001.; Andersson, E., Haseler, J., Undén, P., Courtier, P., Kelly, G., Vasiljevic, D., Brankovic, C., Gaffard, C., Hollingsworth, A., Jakob, C., Janssen, P., Klinker, E., Lanzinger, A., Miller, M., Rabier, F., Simmons, A., Strauss, B., Viterbo, P., Cardinali, C., and Thépaut, J. N.: The ECMWF implementation of three-dimensional variational assimilation (3D-Var). III: Experimental results, Q. J. Roy. Meteorol. Soc., 124, 1831–1860, 1998.; Barker, D. M., Huang, W., Guo, Y.-R., and Bourgeois, A.: Athree-dimensional variational (3DVAR) data assimilation system for use with MM5. NCAR Tech. Note. NCAR/TN-453 1 STR, available from UCAR Communications, P.O. Box 3000,Boulder, CO 80307, 68 pp., 2003.; Lorenc, A. C.: Analysis methods for numerical weather prediction, Q. J. Roy. Meteorol. Soc., 112, 1177–1194, 1986.; Barker, D. M., Huang, W., Guo, Y.-R., and Xiao, Q. N.: A Three-Dimensional Variational Data Assimilation System For MM5: Implementation And Initial Results, Mon. Weather Rev., 132, 897–914, 2004.; Chen, C. and Cotton, W. R.: A One-Dimensional Simulation of the Stratocumulus-Capped Mixed Layer, Bound.-Lay. Meteorol., 25, 289–321, 1983.; Clifford, S., Kaimal, J. C., Lataitis, R. J., and Strauch, R. G.: Ground-based remote profiling in atmospheric studies: an overview, P. IEEE, 82, 313–355, 1994.; Cotton, W. R., Pielke Sr., R. A., Walko, R. L., Liston, G. E., Tremback, C. J., Jiang, H., McAnelly, R. L., Harrington, J. Y., Nicholls, M. E., Carrio, G. G., and McFadden, J. P.: RAMS 2001: Current satus and future directions, Meteorol. Atmos., Phys., 82, 5–29, 2003.; Courtier, P., Thépaut, J. N., and Hollingsworth, A.: A strategy for operational implementation of 4D-Var, using an incremental approach, Q. J. Roy. Meteorol. Soc., 120, 1367–1387, 1994.; DiMego, G. J., Phoebus, P. A., and McDonell, J. E: Data processing and quality control for optimum interpolation analyses at the National Meteorological Center, Washington, DC, US Dept of Commerce, National Oceanic and Atmospheric Administration, National Weather Service, Office Note 306, 1985.; Federico, S.: Preliminary Results of a Data Assimilation System, Atmospheric and Climate Sciences, 3, 61–72, 2013.; Huang, X. Y., Xiao, Q., Barker, D. M., Zhang, X., Michalakes, J., Huang, W., Henderson, T., Bray, J., Chen, Y., Ma, Z., Dudhia, J., Guo, Y., Zhang, X., Won, D. J., Lin, H. C., and Kuo, Y. H.: Four-Dimensional Variational Data Assimilation for WRF: Formulation and Preliminary Results, Mon. Weather Rev., 137, 299–314, doi:10.1175/2008MWR2577.1, 2009.; Kalnay, E.: Atmospheric Modeling, Data Assimilation and Predictability, Cambridge University Press, 2003.; Ide, K., Courtier, P., Ghil, M., and Lorenc, A. C.: Unified notation for data assimilation: Operational, sequential and variational, J. Meteorol. Soc. Jpn., 75, 181–189, 1997.; Lazarus, S. M., Ciliberti, C. M., Horel, J. D., and Brewster, K. A.: Near-Real-Time Applications of a Mesoscale Analysis System to Complex Terrain, Weather Forecast., 17, 149–160, 2002.; Lönnberg, P. and Hollingsworth, A.: The statistical structure of short-range forecast errors as determined from radiosonde data. Part II: The covariance of height and wind errors, Tellus, 38A, 137–161, 1986.; Molinari, J. and Corsetti, T.: Incorporation of cloud-scale and mesoscale down-drafts into a cumulus parametrization: results of one and three-dimensional integrations, Mon. Weather Rev., 113, 485–501, 1985.; Moninger, W. R., Benjamin, S. G., Jamison, B. D., Schlatter, T. W., Smith, T. L., and Szoke, E. D.: Evaluation of Regional Aircraft Observations Using TAMDAR, Weather Forecast., 25, 627–645, 2010.; Otkin, J. A., Hartung, D. C., Turner, D. D., Peterse


Click To View

Additional Books

  • Application of Spectral Analysis Techniq... (by )
  • A Field-deployable, Chemical Ionization ... (by )
  • Processing of Gras/Metop Radio Occultati... (by )
  • Block Based Cloud Classification with St... (by )
  • Atmospheric Influence on a Laser Beam Ob... (by )
  • Intercomparison of Two Comparative React... (by )
  • Reference Quality Upper-air Measurements... (by )
  • Observations of Precipitable Water Vapou... (by )
  • Characterization of Disdrometer Uncertai... (by )
  • Vertical Level Selection for Temperature... (by )
  • Measurements of Atmospheric Aerosol Vert... (by )
  • Retrieval of Aerosol Absorption Properti... (by )
Scroll Left
Scroll Right


Copyright © World Library Foundation. All rights reserved. eBooks from World eBook Library are sponsored by the World Library Foundation,
a 501c(4) Member's Support Non-Profit Organization, and is NOT affiliated with any governmental agency or department.