ABSTRACT This paper presents a general-purpose analysis package able to solve two- and three- dimensional analysis problems. The system can use the following methods of solution: Successive Approximation (SA), Optimal Interpolation (OI), and 3D-Var. Analyses are given for the following parameters: zonal and meridional wind components, temperature, relative humidity, and geopotential height. The analysis package was applied to produce analyses at 6 h time interval for the period 1-11 August 2008. The period was selected for data availability and forty-one analyses were collected. The results show the validity of the different solutions, which can be chosen depending on the physical problem to solve and on the computational resources available. In particular, assuming the observations as the reference, all solutions show a decrease of the RMSE compared to the background. The decrease is consistent with the particular setting of the analysis system used in this paper. The comparison between different solutions shows that the SA converges to OI in few iterations, and that the SA solution with ten iteration is, in practice, equal to OI. Moreover, the 3D-Var method shows its potential to improve the analysis, once the horizontal and vertical length-scales and the background and observational errors are set optimally, because its solution may be sizeably different from two-dimensional methods.
Cite this paper
S. Federico, "A General Purpose Analysis Package," Atmospheric and Climate Sciences, Vol. 2 No. 2, 2012, pp. 210-221. doi: 10.4236/acs.2012.22022.
 E. Kalnay, “Atmospheric Modeling, Data Assimilation and Predictability,”Cambridge University Press, Cambridge, 2003.
D. M. Barker, Huang, W., 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, 897-914.
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 and Forecasting, Vol. 17, No.5, 2002, pp. 971-1000.
F. Zhang, Z. Meng and A. Askoy, “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, 2006, pp. 722-736.
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
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.
P. L?nnberg 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, 137-161.
W. R. Cotton, R. A. Pielke Sr.,, R. L. Walko, G. E. Liston, C. Tremback, H. Jiang, R. L. McAnelly, J. Y. Harrington, M. E. Nicholls, G. G. Carrio and J. P. McFadden, “RAMS 2001: Current Satus and Future Directions,” Meteorological and Atmospheric Physics, Vol. 82, No. 1-4, 2003, pp. 5-29.
J. 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
R. L. Walko, W. R. Cotton, W. R., 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.
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.
R. A. Pielke Sr., “Mesoscale Meteorological Modeling,” Academic Press, San Diego, 2002.
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
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 Pre- diction,” Journal of Applied Meteorology, Vol. 39, No. 6, 2000, pp. 931-944.
C. Chen and W. R. Cotton, “A One-Dimensional Simulation of the Stratocumulus-Capped Mixed Layer,” The Boundary Layer Meteorology, Vol. 25, 1983, pp. 289-321.
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.
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.
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, 2011, pp. 181-189.
A. C. Lorenc, “Analysis Methods for Numerical Weather Prediction”, Quarterly Journal of the Royal Meteorological Society, Vol. 112, No. 474, 1982, pp. 1177-1194.
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
D. M. Barker, W. Huang, Y-R. Guo, A. J. Bourgeois and Q. N. Xiao, “A Three-Dimensional Variational Data Assimi- lation System for MM5: Implementation and Initial Results,” Monthly Weather Review, Vol. 132, No. 4, pp. 897-914.