Preliminary Meteorological Results of a Four-Dimensional Data Assimilation Technique in Southern Italy
Elenio Avolio, S. Federico, A.M Sempreviva, C.R Calidonna, L. De Leo, C Bellecci
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DOI: 10.4236/acs.2011.13015   PDF    HTML     4,707 Downloads   8,877 Views  

Abstract

A four-dimensional data assimilation (FDDA) scheme based on a Newtonian relaxation (or “nudging”) was tested using observational asynoptic data collected at a coastal site in the Central Mediterranean peninsula of Calabria, southern Italy. The study is referred to an experimental campaign carried out in summer 2008. For this period a wind profiler, a sodar and two surface meteorological stations were considered. The collected measurements were used for the FDDA scheme, and the technique was incorporated into a tailored version of the Regional Atmospheric Modeling System (RAMS). All instruments are installed and operated routinely at the experimental field of the CRATI-ISAC/CNR located at 600 m from the Tyrrhenian coastline. Several simulations were performed, and the results show that the assimilation of wind and/or temperature data, both throughout the simulation time (continuous FDDA) and for a 12 h time window (forecasting configuration), produces improvements of the model performance. Considering a whole single day, improvements are sub-stantial in the case of continuous FDDA while they are smaller in the case of forecasting configuration. En-hancements, during the first six hours of each run, are generally higher. The resulting meteorological fields are finalised as input into air quality and agro-meteorological models, for short-term predictions of renew-able energy production forecast, and for atmospheric model initialization.

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E. Avolio, S. Federico, A. Sempreviva, C. Calidonna, L. Leo and C. Bellecci, "Preliminary Meteorological Results of a Four-Dimensional Data Assimilation Technique in Southern Italy," Atmospheric and Climate Sciences, Vol. 1 No. 3, 2011, pp. 134-141. doi: 10.4236/acs.2011.13015.

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1] E. Kalnay, “Atmospheric Modeling, Data Assimilation, and Predictability”, Cambridge Univ. Press, New York, 2002.
[2] R. A. Anthes, “Data assimilation and initialization of hur- ricane prediction models”, J. Atmos. Sci., 31, 1974, pp. 702-719.
[3] R. E. Kistler, “A study of data assimilation techniques in an autobarotropic primitive equation channel model”, Ph.D dissertation, The Penn. State University, 1974.
[4] D. R. Stauffer and N. L. Seaman, “Use of four-dimen- sional data assimilation in a limited-area mesoscale model. part I: Experiments with synoptic-scale data”, Mon. Weather Rev., 118, 1990, pp. 1250-1277.
[5] D. R. Stauffer and N. L. Seaman, “Multiscale four-di- mensional data assimilation”, J. Appl. Meteorol., 33, 1994, pp. 416-434.
[6] N. L. Seaman, D. R. Stauffer, G. K. Hunter and A. M. Lario-Gibbs, “The use of the San Jaoquin Valley Meteorological Model in preparation of a Field program in the South Coast Air Basin and surrounding regions of Southern California. Vol. II. Numerical modelling studies in support of design for the 1997 Southern California Ozone Study (SCOS-97) Field program. Final Report. California Air Resources Board, Sacramento, CA, 1997.
[7] J. D. Fast, “Mesoscale modeling and four-dimensional data assimilation in areas of highly complex terrain”, Journal of Applied Meteorology, 34, 1995, pp. 2762- 2782.
[8] N. L. Seaman, “Meteorological modeling for air quality assessments”, Atmos. Environ., 34, 2000, pp. 2231-2259.
[9] S. Tanrikulu, D. R. Stauffer, N. L. Seaman and A. J. Ran- zieri, “A field-coherence technique for meteorological field-program design for air quality studies. part II: Eva- luation in the San Joaquin Valley”, J. Appl. Meteorol., 39, 2000, pp. 317-334.
[10] S. A. Michelson and N. L. Seaman, “Assimilation of NEXRAD-VAD winds in summertime meteorological simulations over the north-eastern United States”, J. Appl. Meteorol., 39, 2000, pp. 367-383.
[11] J. W. Nielsen-Gammon, R. T. McNider, W. M. Angevine, A. B. White and K. Knupp, “Mesoscale model performance with assimilation of wind profiler data: Sensitivity to assimilation parameters and network configuration”, J. Geophys. Res., 112, 2007, doi:10.1029/2006JD007633.
[12] T. Umeda and P. T. Martien, “Evaluation of a data assimilation technique for a mesoscale meteorological model used for air quality modelling”, J. Appl. Meteorol., 41, 2002, pp. 12-29.
[13] M. Barna and B. Lamb, “Improving ozone modeling in regions of complex terrain using observational nudging in a prognostic meteorological model”, Atmos. Environ., 34, 2000, pp. 4889-4906.
[14] S. Federico, L. Pasqualoni, L. De Leo, C. Bellecci, “A study of the breeze circulation during summer and fall 2008 in Calabria, Italy”, Atmospheric Research, 97, 2010a, pp. 1-13.
[15] S. Federico, L. Pasqualoni, A.M. Sempreviva, L. De Leo, E. Avolio, C.R. Calidonna and C. Bellecci, “The seasonal characteristics of the breeze circulation at a coastal Mediterranean site in South Italy”, Adv. Sci. Res., 4, 2010b, pp. 47-56. http://www.adv-sci-res.net/4/47/2010/ doi:10.5194/asr-4-47-2010.
[16] E. Avolio, L. Pasqualoni, S. Federico, M. Fornaciari, T. Bonofiglio, F. Orlandi, C. Bellecci and B. Romano, “Cor- relation between large scale atmospheric fields and olive pollen season in Central Italy”, Int J Biometeorol., 52, 2008, pp. 787-796.
[17] S. Federico, E. Avolio, C. Bellecci and M. Colacino, “The meteorological model RAMS at Crati Scrl”, Adv. Geosci., 2, 2005, pp. 177-180, doi 10.5194/adgeo-2-177-2005.
[18] W. R. Cotton, R. A. Pielke Sr., R. L. Walko, G. E. Liston, C. J. Tremback, H. Jiang, R. L. McAnelly, J. Y. Harrington, M. E. Nicholls, G. G. Carrio, J. P. McFadden, “RAMS 2001: Current status and future directions”, Meteorol. Atmos. Phys., 82, 2003, pp. 5-29.
[19] R. L. Walko, W. R. Cotton, M. P. Meyers, J. Y. Harrington, “New RAMS cloud microphysics parameterization part I: the single-moment scheme”, Atmos. Research, 38, 2995, pp. 29-62.
[20] J. Molinari and T. Corsetti, “Incorporation of cloud-scale and mesoscale down-drafts into a cumulus parameterization: results of one and three-dimensional integrations”, Mon. Wea. Rev., 113, 1985, pp. 485-501.
[21] 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”, J. Appl. Met., 39, 2000, pp. 931-944.
[22] G. Mellor and T. Yamada, “Development of a Turbulence Closure Model for Geophysical Fluid Problems”, Reviews of Geophysics and Space Physics, 20, 1982, pp. 851-875.
[23] J. E. Hoke and R. A. Anthes, “Initialization of numerical models by a dynamic-initialization technique”, Mon. Wea- ther Rev., 104, 1976, pp. 1551-1556.

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