Preliminary Results of a Data Assimilation System

Stefano Federico

ISAC-CNR, UOS of Rome, via del Fosso del Cavaliere, Rome, Italy.

DOI: 10.4236/acs.2013.31009                

Abstract

A data assimilation system combines all available information on the atmospheric state in a given time-window to produce an estimate of atmospheric conditions valid at a prescribed analysis time. Nowadays, increased computing power coupled with greater access to real-time asynoptic data is paving the way toward a new generation of high-resolution (i.e. on the order of 10 km) operational mesoscale analyses and forecasting systems. Moreover, better initial conditions are increasingly considered of the utmost importance for Numerical Weather Prediction (NWP) at the short range (0 - 12 h). This paper presents a general-purpose data assimilation system, which is coupled with the Regional Atmospheric Modelling System (RAMS) to give the analyses for: zonal and meridional wind components, temperature, relative humidity, and geopotential height. In order to show its potential, the data assimilation systems applied to produce analyses over Central Europe. For this application the background field is given by a short-range forecast (12 h) of the RAMS and analyses are produced by 2D-Var with 0.25? horizontal resolution. Results show the validity of the analyses because they are closer to the observations, consistently with the settings of the data assimilation system. To quantify the impact of improved initial conditions on the forecast, the analyses are then used as initial conditions of a short-range (6 h) forecast of the RAMS model. The results show that the RMSE is effectively reduced for the one- and two hours forecast, with some improvement for the three-hours forecast.

Keywords

Meteorological Analysis; Forecasting Systems; Short-Term Forecast of Winds; Variational Methods

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	E. Kalnay, “Atmospheric Modeling, Data Assimilation and Predictability,” Cambridge University Press, Cambridge, 2003.
[2]	D. M. Barker, W. Huang, Y. R. Guo and Q. N. Xiao, “A Three-Dimensional Variational Data Assimilation System for MM5: Implementation and Initial Results,” Monthly Weather Review, Vol. 132, No. 4, 2003, pp. 897-914. doi:10.1175/1520-0493(2004)132<0897:ATVDAS>2.0.CO;2
[3]	S. M. Lazarus, C. M. Ciliberti, J. D. Horel and K. A. Brewster, “Near-Real-Time Applications of a Mesoscale Analysis System to Complex Terrain,” Weather Forecasting, Vol. 17, No. 5, 2002, pp. 149-160. doi:10.1175/1520-0434(2002)017<0971:NRTAOA>2.0.CO;2
[4]	X.-Y. Huang, Q. N. Xiao, D. M. Barker, X. Zhang, J. Michalakes, W. Huang, T. Henderson, J. Bray, Y. S. Chen, Z. Z. Ma, J. Dudhia, Y. Guo, X. Y. Zhang, D. J. Won, H. C. Lin and Y.-H. Kuo, “Four-Dimensional Variational Data Assimilation for WRF: Formulation and Preliminary Results,” MonthlyWeather Review, Vol. 137, No. 1, 2009, pp. 299-314. doi:10.1175/2008MWR2577.1
[5]	M. Zupanski, D. Zupanski, T. Vukicevic, K. Eis and T. V. Haar, “CIRA/CSU Four-Dimensional Variational Data Assimilation System,” Monthly Weather Review, Vol. 133, No. 4, 2005, pp. 829-843. doi:10.1175/MWR2891.1
[6]	F. Zhang, Z. Meng and A. Askoz, “Tests of an Ensemble Kalman Filter for Mesoscale and Regional-Scale Data Assimilation. Part I: Perfect Model Experiments,” Monthly Weather Review, Vol. 134, No. 2, 2005, pp. 722-736. doi:10.1175/MWR3101.1
[7]	A. D. Schenkman, M. Xue, A. Shapiro, K. Brewster and J. Gao, “The Analysis and Prediction of the 8-9 May 2007 Oklahoma Tornadic Mesoscale Convective System by Assimilating WSR-88D and CASA Radar Data Using 3DVAR,” Monthly Weather Review, Vol. 139, No. 1, 2011, pp. 224-246. doi:10.1175/2010MWR3336.1
[8]	W. R. Cotton, R. A. PielkeSr, R. L. Walko, G. E. Liston, C. J. Tremback, H. Jiang, R. L. McAnelly, J. Y. Harrington, M. E. Nicholls, C. G. Carrio and J. P. McFadden, “RAMS 2001: Current Status and Future Directions,” Meteorological and Atmospheric Physics, Vol. 82, No. 1-4, 2003, pp. 5-29.
[9]	R. A. Pielke, “Mesoscale Meteorological Modeling,” Academic Press, San Diego, 2002.
[10]	P. Courtier, J. N. Thépaut and A. Hollingsworth, “A Strategy for Operational Implementation of 4D-Var, Using an Incremental Approach,” Quarterly Journal of the Royal Meteorological Society, Vol. 120, No. 519, 1994, pp. 1367-1387. doi:10.1002/qj.49712051912
[11]	D. F. Parrish and J. C. Derber, “The National Meteorological Center’s Spectral Statistical Interpolation Analysis System,” Monthly Weather Review, Vol. 120, No. 8, 1992, pp. 1747-1763. doi:10.1175/1520-0493(1992)120<1747:TNMCSS>2.0.CO;2
[12]	R. Polkinghorne, T. Vukicevic and K. F. Evans, “Validation of Cloud-Resolving Model Background Data for Cloud Data Assimilation,” Monthly Weather Review, Vol. 138, No. 3, 2010, pp. 781-795. doi:10.1175/2009MWR3012.1
[13]	K. Ide, P. Courtier, M. Ghil and A. C. Lorenc, “Unified Notation for Data Assimilation: Operational, Sequential and Variational,” Journal of the Meteorological Society of Japan, Vol. 75, No. 1B, 1997, pp. 181-189.
[14]	A. C. Lorenc, “Analysis Methods for Numerical Weather Prediction,” Quarterly Journal of the Royal Meteorological Society, Vol. 112, No. 474, 1982, pp. 1177-1194.
[15]	Molinari and T. Corsetti, “Incorporation of Cloud-Scale and Mesoscale Down-Drafts into a Cumulus Parametrization: Results of One and Three-Dimensional Integrations,” Monthly Weather Review, Vol. 113, No. 4, 1985, pp. 485-501. doi:10.1175/1520-0493(1985)113<0485:IOCSAM>2.0.CO;2
[16]	R. L. Walko, W. R. Cotton, M. P. Meyers and J. Y. Harrington, “New RAMS Cloud Microphysics Parameterization Part I: The Single-Moment Scheme,” Atmospheric Research, Vol. 38, No. 1-4, 1995, pp. 29-62. doi:10.1016/0169-8095(94)00087-T
[17]	J. Smagorinsky, “General Circulation Experiments with the Primitive Equations. Part I, the Basic Experiment,” Monthly Weather Review, Vol. 91, No. 3, 1963, pp. 99-164. doi:10.1175/1520-0493(1963)091<0099:GCEWTP>2.3.CO;2
[18]	G. Mellor and T. Yamada, “Development of a Turbulence Closure Model for Geophysical Fluid Problems,” Reviews of Geophysics and Space Physics, Vol. 20, No. 4, 1982, pp. 851-875. doi:10.1029/RG020i004p00851
[19]	R. L. Walko, L. E. Band, J. Baron, T. G. Kittel, R. Lammers, T. J. Lee, D. Ojima, R. A. Sr. Pielke, C. Taylor, C. Tague, C. J. Tremback and P. L. Vidale, “Coupled Atmosphere-Biosphere-Hydrology Models for Environmental Prediction,” Journal of Applied Meteorology, Vol. 39, No. 6, 2000, pp. 931-944. doi:10.1175/1520-0450(2000)039<0931:CABHMF>2.0.CO;2
[20]	C. Chen and W. R. Cotton, “A One-Dimensional Simulation of the Stratocumulus-Capped Mixed Layer,” The Boundary Layer Meteorology, Vol. 25, No. 3, 1983, pp. 289-321. doi:10.1007/BF00119541
[21]	S. Federico, “Verification of Surface Minimum, Mean, and Maximum Temperature Forecasts in Calabria for Summer 2008,” Natural Hazards and Earth System Sciences, Vol. 11, No. 2, 2011, pp. 487-500. doi:10.5194/nhess-11-487-2011
[22]	A. Mazzarella, A. Giuliacci and I. Liritzis, “QBO of the Equatorial-Stratospheric Winds Revisited: New Methods to Verify the Dominance of 28-Month Cycle,” International Journal of Ocean and Climate Systems, Vol. 2, No. 1, 2011, pp. 19-26.
[23]	D. K. Sashegyi, D. E. Harms, R. V. Madala and S. Raman, “Application of the Bratseth Scheme for the Analysis of GALE, Data Using a Mesoscale Model,” Monthly Weather Review , Vol. 121, No. 8, 1993, pp. 207-220. doi:10.1175/1520-0493(1993)121<0207:AOVMIT>2.0.CO;2
[24]	P. Lonnberg and A. Hollingsworth, “The Statistical Structure of Short-Range Forecast Errors as Determined from Radiosonde Data. Part II: The Covariance of Height and Wind Errors,” Tellus, Vol. 38A, No. 2, 1986, pp. 137-161. doi:10.1111/j.1600-0870.1986.tb00461.x