LOGIN TO YOUR ACCOUNT

Username
Password
Remember Me
Or use your Academic/Social account:

CREATE AN ACCOUNT

Or use your Academic/Social account:

Congratulations!

You have just completed your registration at OpenAire.

Before you can login to the site, you will need to activate your account. An e-mail will be sent to you with the proper instructions.

Important!

Please note that this site is currently undergoing Beta testing.
Any new content you create is not guaranteed to be present to the final version of the site upon release.

Thank you for your patience,
OpenAire Dev Team.

Close This Message

CREATE AN ACCOUNT

Name:
Username:
Password:
Verify Password:
E-mail:
Verify E-mail:
*All Fields Are Required.
Please Verify You Are Human:
fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Soci, Cornel; Bazile, Eric; Besson, François; Landelius, Tomas (2016)
Publisher: Co-Action Publishing
Journal: Tellus A
Languages: English
Types: Article
Subjects: Meteorology. Climatology, QC851-999, rain gauge data, Optimum interpolation, limited area, various background resolutions, rain gauge data, GC1-1581, optimum interpolation, Oceanography, limited area, Data assimilation, various background resolutions
In this article, we describe the design and the validation of the Mescan precipitation analysis system developed for climatological purposes under the EURO4M project. The system is based on an optimal interpolation algorithm using the 24-h aggregated gauge measurements from the surface network. The background fields are the total accumulated precipitation forecasts at different resolutions from the ALADIN or HIRLAM mesoscale models, downscaled to 5.5 km grid spacing, chosen to match the time period of the climatological gauge reports. The validation of the Mescan system is carried out over the French territory employing various metrics and by providing forcing to a hydrological model to produce river discharges. The investigations have shown that the precipitation analyses have almost the same quality as the well-validated SAFRAN analysis system. In addition, the analysis of the precipitation variance spectra computed on the same horizontal domain has indicated that at short wavelengths the downscaled fields have significantly lower variability than a field produced by time integrating a forecast model. The Mescan precipitation analysis system has successfully been used to produce 24-h total accumulated precipitation re-analyses on a 5.5 km grid over Europe for the period 2007–2010.Keywords: optimum interpolation, limited area, various background resolutions, rain gauge data(Published: 8 April 2016)Citation: Tellus A 2016, 68, 29879, http://dx.doi.org/10.3402/tellusa.v68.29879
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • Amodei, M. and Stein, J. 2009. Deterministic and fuzzy verification methods for a hierarchy of numerical models. Meteorol. Appl. 16, 191 203.
    • Bhargava, M. and Danard, M. 1994. Application of optimum interpolation to the analysis of precipitation in complex terrain. J. Appl. Meteorol. 33, 508 518.
    • Courtier, P., Freydier, C., Geleyn, J.-F., Rabier, F. and Rochas, M. 1991. The ARPEGE project at M e´te´ o-France. In: Proceedings of the ECMWF Workshop on Numerical Methods in Atmospheric Models. Reading, UK, ECMWF, pp. 193 231.
    • Daley, R. 1991. Atmospheric Data Analysis. Cambridge University Press, Cambridge, UK.
    • Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P. and co-authors. 2011. The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Q. J. Roy. Meteorol. Soc. 137, 553 597.
    • Denis, B., Coˆ t e´, J. and Laprise, R. 2002. Spectral decomposition of two-dimensional atmospheric fields on limited-area domains using the discrete cosine transform (DCT). Mon. Weather. Rev. 130, 1812 1829.
    • Desroziers, G., Berre, L., Chapnik, B. and Poli, P. 2005. Diagnosis of observation, background, and analysis-error statistics in observation space. Q. J. Roy. Meteorol. Soc. 131, 3385 3396.
    • Durand, Y., Brun, E., Me´ rindol, L., Guyomarc'h, G., Lesaffre, B. and co-authors. 1993. A meteorological estimation of relevant parameters for snow models. Ann. Glaciol. 18, 65 71.
    • Durand, Y., Giraud, G., Brun, E., Me´ rindol, L. and Martin, E. 1999. A computer-based system simulating snowpack structures as a tool for regional avalanche forecasting. J. Glaciol. 45, 469 484.
    • Ebert, E. 2008. Fuzzy verification of high-resolution gridded forecasts: a review and proposed framework. Meteorol. Appl. 15, 51 64.
    • Fortin, V. 2007. Analyse de pre´cipitation CaPA: Proposition d'installation d'une passe paralle`le. Se´minaire Recherche en Pre´vision Nume´rique [Precipitation Analysis CaPA: Recommendation to install a parallel suite. Workshop Research in Numerical Weather Prediction]. Dorval, QC, Environment Canada, 65 pp. Online at: http://collaboration.cmc.ec.gc.ca/science/rpn/SEM/dossiers/2007/ seminaires/2007-10-26/Fortin2007RPN_CaPA_final.pdf Fortin, V., Roy, G., Dobaldson, N. and Mahidjiba, A. 2015. Assimilation of radar quantitative precipitation estimation in the Canadian Precipitation Analysis (CaPA). J. Hydrol. 531(Part 2), 296 307.
    • Fowler, H. J., Blenkinsop, S. and Tebaldi, C. 2007. Review linking climate change modelling to impacts studies: recent advances in downscaling techniques for hydrological modelling. Int. J. Climatol. 27, 1547 1578.
    • Gelman, A., Carlin, J. B., Stern, H. S. and Rubin, D. B. 1995. Bayesian Data Analysis. Texts in Statistical Science. Chapman and Hall, London.
    • Ghelli, A. and Lalaurette, F. 2000. Verifying precipitation forecasts using upscaled observations. ECMWF Newslett. 87, 9 17.
    • Golaz-Cavazzi, C., Etchevers, P., Habets, F., Ledoux, E. and Noilhan, J. 2001. Comparison of two hydrological simulations of the Rhoˆ ne basin. Phys. Chem. Earth B. 26(5 6), 461 466.
    • Habets, F., Boone, A., Champeaux, J.-L., Etchevers, P., Franchisteguy, L. and co-authors. 2008. The Safran-Isba-Modcou (SIM) hydrometorological model applied over France. J. Geophys. Res. 113, D06113. DOI: http://dx.doi.org/10.1029/2007JD008548 H a¨ggmark, L., Ivarsson, K.-I., Gollvik, S. and Olofsson, P.-O. 2000. Mesan, an operational mesoscale analysis system. Tellus A. 52, 2 20.
    • Hora´ nyi, A., Iha´ sz, I. and Radno´ ti, G. 1996. ARPEGE-ALADIN: a numerical weather prediction model for Central-Europe with the participation of the Hungarian Meteorological Service. Ido¨ ja´ra´s. 100, 277 301.
    • Ingleby, B. 2015. Global assimilation of air temperature, humidity, wind and pressure from surface stations. Q. J. Roy. Meteorol. Soc. 141, 504 507.
    • Jolliffe, I. T. and Stephenson, D. B. 2003. Forecast Verification: A Practitioner's Guide in Atmospheric Science. John Wiley and Sons, Chichester, UK, pp. 240.
    • Kalnay, E. 2002. Atmospheric Modeling, Data Assimilation and Predictability. Cambridge University Press, Cambridge, UK.
    • Kalnay, E., Kanamitsu, M., Kistler, R., Collins, W., Deaven, D., Gandin, L. and co-authors. 1996. The NCEP/NCAR 40-Year reanalysis project. Bull. Am. Meteorol. Soc. 77, 437 471.
    • Kistler, R., Kalnay, E., Collins, W., Saha, S., White, G., Wollen, J. and co-authors. 2001. The NCEP-NCAR 50-Year reanalysis: monthly means CD-ROM and documentation. Bull. Am. Meteorol. Soc. 82, 247 267.
    • Laurantin, O. 2008. ANTILOPE: Hourly rainfall analysis merging radar and rain gauge data. In: Proceedings of the International Symposium on Weather Radar and Hydrology, Grenoble,. France, 10 12 March 2008, pp. 2 8.
    • Ledoux, E., Girard, G., de Marsily, G. and Deschenes, J. 1989. Spatially distributed modelling: conceptual approach, coupling surface water and ground water. In: Unsaturated Flow Hydrologic Modelling Theory and Practice (ed. Kluwer Academic Publishers), Vol. 275. Kluwer Academic, Norwell, MA, NATO ASI Series C, pp. 435 454.
    • Lespinas, F., Fortin, V., Roy, G., Rasmussen, P. and Stadnyk, T. 2015. Performance evaluation of the Canadian Precipitation Analysis (CaPA). J. Hyrometeorol. 16, 2045 2064.
    • Lopez, P. 2011. Direct 4D-Var assimilation of NCEP Stage IV radar and gauge precipitation data at ECMWF. Mon. Weather Rev. 139, 2098 2116.
    • Lorenc, A. C. 1981. A global three-dimensional multivariate statistical interpolation scheme. Mon. Weather Rev. 109, 701 721.
    • Mahfouf, J.-F., Brasnett, B. and Gagnon, S. 2007. A Canadian precipitation analysis (CaPA) project: description and preliminary results. Atmos. Ocean. 45, 1 16.
    • Noilhan, J. and Mahfouf, J.-F. 1996. The ISBA land surface parameterization scheme. Global Planet. Change. 13, 145 159.
    • Onogi, K., Koide, H., Sakamoto, M., Kobayashi, S., Tsutsui, J., Hatsushika, H. and co-authors. 2005. JRA-25: Japanese 25-year re-analysis project progress and status. Q. J. Roy. Meteorol. Soc. 131, 3259 3268.
    • Parrish, D. and Derber, J. 1992. The National Meteorological Center's spectral statistical interpolation analysis system. Mon. Weather Rev. 120, 1747 1763.
    • Quintana-Segu ı´, P., Le Moigne, P., Durand, Y., Martin, E., Habets, F. and co-authors. 2008. Analysis of near-surface atmospheric variables: validation of the SAFRAN analysis over France. J. Appl. Meteorol. Climatol. 47, 92 107.
    • Ricard, D., Lac, C., Riette, S., Legrand, R. and Mary, A. 2013. Kinetic energy spectra characteristics of two convection-permitting limited-area models AROME and Meso-NH. Q. J. Roy. Meteorol. Soc. 139, 1327 1341.
    • Rousset, F., Habets, F., Gomez, E., Le Moigne, P., Morel, S. and co-authors. 2004. Hydrometeorological modeling of the Seine basin using the SAFRAN-ISBA-MODCOU system. J. Geophys. Res. 109, 1 20. DOI: http://dx.doi.org/10.1029/2003JD004403 Seity, Y., Brousseau, P., Malardel, S., Hello, G., B e´nard, P. and co-authors. 2011. The AROME-France convective-scale operational model. Mon. Weather. Rev. 139, 976 991.
    • Soci, C., Bazile, E., Besson, F., Landelius, T., Mahfouf, J.-F. and co-authors. 2013. D2.6 Report describing the new system in D2.5 EURO4M report, 26 pp. Online at: http://www.euro4m.eu/ downloads/D2.6_Report_describing_the_new_system_in_D2.5.pdf Talagrand, O. 1997. Assimilation of observations, an introduction. J. Meteorol. Soc. Jpn. 75, 191 209.
    • Uppala, S. M., Ka˚ llberg, P. W., Simmons, A. J., Andrae, U., Da Costa Bechtold, V., Fiorino, M. and co-authors. 2005. The ERA-40 re-analysis. Q. J. Roy. Meteorol. Soc. 131, 2961 3012.
    • Wilks, D. S. 2006. Statistical Methods in the Atmospheric Sciences. 2nd ed. Academic Press, San Diego, CA.
    • World Meteorological Organization. 2012. WMO-8: Guide to Meteorological Instruments and Methods of Observation. updated 2010 ed. WMO, Geneva, Switzerland.
  • No related research data.
  • No similar publications.

Share - Bookmark

Funded by projects

  • EC | EURO4M

Cite this article