Please use this identifier to cite or link to this item:
http://hdl.handle.net/2122/9521
DC Field | Value | Language |
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dc.contributor.authorall | Storto, A.; Ctr Euromediterraneo Cambiamenti Climat, Numer Applicat & Scenarios Div, I-40127 Bologna, Italy | en |
dc.contributor.authorall | Masina, S.; Ctr Euromediterraneo Cambiamenti Climat, Numer Applicat & Scenarios Div, I-40127 Bologna, Italy | en |
dc.contributor.authorall | Dobricic, S.; Ctr Euromediterraneo Cambiamenti Climat, Numer Applicat & Scenarios Div, I-40127 Bologna, Italy | en |
dc.date.accessioned | 2015-04-16T08:54:43Z | en |
dc.date.available | 2015-04-16T08:54:43Z | en |
dc.date.issued | 2014-10 | en |
dc.identifier.uri | http://hdl.handle.net/2122/9521 | en |
dc.description.abstract | Optimally modeling background-error horizontal correlations is crucial in ocean data assimilation. This paper investigates the impact of releasing the assumption of uniform background-error correlations in a global ocean variational analysis system. Spatially varying horizontal correlations are introduced in the recursive filter operator, which is used for modeling horizontal covariances in the Centro Euro-Mediterraneo sui Cambiamenti Climatici (CMCC) analysis system. The horizontal correlation length scales (HCLSs) were defined on the full three-dimensional model space and computed from both a dataset of monthly anomalies with respect to the monthly climatology and through the so-called National Meteorological Center (NMC) method. Different formulas for estimating the correlation length scale are also discussed and applied to the two forecast error datasets. The new formulation is tested within a 12-yr period (2000–11) in the ½° resolution system. The comparison with the data assimilation system using uniform background-error horizontal correlations indicates the superiority of the former, especially in eddy-dominated areas. Verification skill scores report a significant reduction of RMSE, and the use of nonuniform length scales improves the representation of the eddy kinetic energy at midlatitudes, suggesting that uniform, latitude, or Rossby radius-dependent formulations are insufficient to represent the geographical variations of the background-error correlations. Furthermore, a small tuning of the globally uniform value of the length scale was found to have a small impact on the analysis system. The use of either anomalies or NMC-derived correlation length scales also has a marginal effect with respect to the use of nonuniform HCLSs. On the other hand, the application of overestimated length scales has proved to be detrimental to the analysis system in all areas and for all parameters. | en |
dc.description.sponsorship | This work has received funding from the Italian Ministry of Education, University and Research and the Italian Ministry for the Environment, Land and Sea under the GEMINA project and from the European Commission's Copernicus program, previously known as the GMES program, under the MyOcean and MyOcean2 projects. | en |
dc.language.iso | English | en |
dc.publisher.name | American Meteorological Society | en |
dc.relation.ispartof | Journal of Atmospheric and Oceanic Technology | en |
dc.relation.ispartofseries | 10/31(2014) | en |
dc.subject | DATA ASSIMILATION SCHEME | en |
dc.subject | TROPICAL PACIFIC-OCEAN | en |
dc.subject | PART I | en |
dc.subject | VARIATIONAL ASSIMILATION | en |
dc.subject | COVARIANCE FUNCTIONS | en |
dc.subject | DIFFUSION EQUATION | en |
dc.subject | SYSTEM | en |
dc.subject | TEMPERATURE | en |
dc.subject | IMPLEMENTATION | en |
dc.subject | MODEL | en |
dc.title | Estimation and Impact of Nonuniform Horizontal Correlation Length Scales for Global Ocean Physical Analyses | en |
dc.type | article | en |
dc.description.status | Published | en |
dc.type.QualityControl | Peer-reviewed | en |
dc.description.pagenumber | 2330-2349 | en |
dc.identifier.URL | http://journals.ametsoc.org/doi/full/10.1175/JTECH-D-14-00042.1 | en |
dc.subject.INGV | 03. Hydrosphere::03.01. General::03.01.04. Ocean data assimilation and reanalysis | en |
dc.identifier.doi | 10.1175/JTECH-D-14-00042.1 | en |
dc.relation.references | Adani, M., S. Dobricic, and N. Pinardi, 2011: Quality assessment of a 1985–2007 Mediterranean Sea reanalysis. J. Atmos. Oceanic Technol., 28, 569–589, doi:10.1175/2010JTECHO798.1. [Abstract] Antonov, J. I., and Coauthors, 2010: Salinity. Vol. 2, World Ocean Atlas 2009, NOAA Atlas NESDIS 69, 184 pp. Barker, D. M., W. Huang, Y. R. Guo, and Q. Xiao, 2004: A three-dimensional data assimilation system for use with MM5: Implementation and initial results. Mon. Wea. Rev., 132, 897–914, doi:10.1175/1520-0493(2004)132<0897:ATVDAS>2.0.CO;2. [Abstract] Bellucci, A., S. Masina, P. Di Pietro, and A. Navarra, 2007: Using temperature salinity relations in a global ocean implementation of a multivariate data assimilation scheme. Mon. Wea. Rev., 135, 3785–3807, doi:10.1175/2007MWR1821.1. [Abstract] Belo-Pereira, M., and L. Berre, 2006: The use of an ensemble approach to study the background-error covariances in a global NWP model. Mon. Wea. Rev., 134, 2466–2489, doi:10.1175/MWR3189.1. [Abstract] Bernie, D. J., E. Guilyardi, G. Madec, J. M. Slingo, and S. J. Woolnough, 2007: Impact of resolving the diurnal cycle in an ocean–atmosphere GCM. Part 1: A diurnally forced OGCM. Climate Dyn., 29, 575–590, doi:10.1007/s00382-007-0249-6. [CrossRef] Berre, L., S. Ştefănescu, and M. Belo, 2006: The representation of analysis effect in three error simulation techniques. Tellus, 58A, 196–209, doi:10.1111/j.1600-0870.2006.00165.x. [CrossRef] Bonjean, F., and G. Lagerloef, 2002: Diagnostic model and analysis of the surface currents in the tropical Pacific Ocean. J. Phys. Oceanogr., 32, 2938–2954, doi:10.1175/1520-0485(2002)032<2938:DMAAOT>2.0.CO;2. [Abstract] Carton, J. A., B. S. Giese, X. Cao, and L. Miller, 1996: Impact of altimeter, thermistor, and expendable bathythermograph data on retrospective analyses of the tropical Pacific Ocean. J. Geophys. Res., 101, 14 147–14 159, doi:10.1029/96JC00631. [CrossRef] Carton, J. A., G. Chepurin, X. Cao, and B. S. Giese, 2000: A Simple Ocean Data Assimilation analysis of the global upper ocean 1950–95. Part I: Methodology. J. Phys. Oceanogr., 30, 294–309, doi:10.1175/1520-0485(2000)030<0294:ASODAA>2.0.CO;2. [Abstract] Cavalieri, D. J., C. L. Parkinson, P. Gloersen, J. C. Comiso, and H. J. Zwally, 1999: Deriving long-term time series of sea ice cover from satellite passive-microwave multisensor data sets. J. Geophys. Res., 104, 15 803–15 814, doi:10.1029/1999JC900081. [CrossRef] Chelton, D., R. Deszoeke, M. Schlax, K. El Naggar, and N. Siwertz, 1998: Geographical variability of the first baroclinic Rossby radius of deformation. J. Phys. Oceanogr., 28, 433–460, doi:10.1175/1520-0485(1998)028<0433:GVOTFB>2.0.CO;2. [Abstract] Courtier, P., J.-N. Thépaut, and A. Hollingsworth, 1994: A strategy for operational implementation of 4D-Var, using an incremental approach. Quart. J. Roy. Meteor. Soc., 120, 1367–1387, doi:10.1002/qj.49712051912. [CrossRef] Cummings, J. A., 2005: Operational multivariate ocean data assimilation. Quart. J. Roy. Meteor. Soc., 131, 3583–3604, doi:10.1256/qj.05.105. [CrossRef] Dai, A., and K. E. Trenberth, 2002: Estimates of freshwater discharge from continents: Latitudinal and seasonal variations. J. Hydrometeor., 3, 660–687, doi:10.1175/1525-7541(2002)003<0660:EOFDFC>2.0.CO;2. [Abstract] Daley, R., 1991: Atmospheric Data Analysis. Cambridge University Press, 457 pp. Dee, D., and Coauthors, 2011: The ERA-Interim reanalysis: Configuration and performance of the data assimilation system. Quart. J. Roy. Meteor. Soc., 137, 553–597, doi:10.1002/qj.828. [CrossRef] Derber, J., and A. Rosati, 1989: A global oceanic data assimilation system. J. Phys. Oceanogr., 19, 1333–1347, doi:10.1175/1520-0485(1989)019<1333:AGODAS>2.0.CO;2. [Abstract] Dobricic, S., and N. Pinardi, 2008: An oceanographic three-dimensional assimilation scheme. Ocean Modell., 22, 89–105, doi:10.1016/j.ocemod.2008.01.004. [CrossRef] Fichefet, T., and M. A. Morales Maqueda, 1997: Sensitivity of a global sea ice model to the treatment of ice thermodynamics and dynamics. J. Geophys. Res., 102, 12 609–12 646, doi:10.1029/97JC00480. [CrossRef] Gaspari, G., S. Cohn, J. Guo, and S. Pawson, 2006: Construction and application of correlation functions with variable length-fields. Quart. J. Roy. Meteor. Soc., 132, 1815–1838, doi:10.1256/qj.05.08. [CrossRef] Hayden, C., and R. Purser, 1995: Recursive filter objective analysis of meteorological fields: Applications to NESDIS operational processing. J. Appl. Meteor., 34, 3–15, doi:10.1175/1520-0450-34.1.3. [Abstract] Ingleby, B., and M. Huddleston, 2007: Quality control of ocean temperature and salinity profiles—Historical and real-time data. J. Mar. Syst., 65, 158–175, doi:10.1016/j.jmarsys.2005.11.019. [CrossRef] Isaksen, L., M. Fisher, and J. Berner, 2007: Use of analysis ensembles in estimating flow-dependent background error variance. Proc. ECMWF Workshop on Flow-Dependent Aspects of Data Assimilation, ECMWF, Reading, United Kingdom, 37 pp. [Available online at http://old.ecmwf.int/newsevents/meetings/workshops/2007/data_assimilation/presentations/Isaksen.pdf.] Large, W. G., and S. G. Yeager, 2004: Diurnal to decadal global forcing for ocean and sea-ice models: The data sets and flux climatologies. NCAR Tech. Note NCAR/TN-460+STR, 105 pp., doi:10.5065/D6KK98Q6. Le Traon, P. Y., F. Nadal, and N. Ducet, 1998: An improved mapping method of multisatellite altimeter data. J. Atmos. Oceanic Technol., 15, 522–534, doi:10.1175/1520-0426(1998)015<0522:AIMMOM>2.0.CO;2. [Abstract] Locarnini, R. A., A. V. Mishonov, J. I. Antonov, T. P. Boyer, H. E. Garcia, O. K. Baranova, M. M. Zweng, and D. R. Johnson, 2010: Temperature. Vol. 1, World Ocean Atlas 2009, NOAA Atlas NESDIS 68, 184 pp. Lorenc, A., 1992: Iterative analysis using covariance functions and filters. Quart. J. Roy. Meteor. Soc., 118, 569–591, doi:10.1002/qj.49711850509. [CrossRef] Madec, G., and M. Imbard, 1996: A global ocean mesh to overcome the north pole singularity. Climate Dyn., 12, 381–388, doi:10.1007/BF00211684. [CrossRef] Madec, G., P. Delecluse, M. Imbard, and C. Lévy, 1998: OPA 8.1 Ocean General Circulation Model reference manual. IPSL Note du Pôle de Modélisation 11, 91 pp. Meyers, G., H. Phillips, N. Smith, and J. Sprintall, 1991: Space and time scales for optimal interpolation—Tropical Pacific Ocean. Prog. Oceanogr., 28, 189–218, doi:10.1016/0079-6611(91)90008-A. [CrossRef] Mirouze, I., and A. Weaver, 2010: Representation of correlation functions in variational assimilation using an implicit diffusion operator. Quart. J. Roy. Meteor. Soc., 136, 1421–1443, doi:10.1002/qj.643. [CrossRef] Pannekoucke, O., L. Berre, and G. Desroziers, 2008: Background-error correlation length-scale estimates and their sampling statistics. Quart. J. Roy. Meteor. Soc., 134, 497–508, doi:10.1002/qj.212. [CrossRef] Parrish, D., and J. Derber, 1992: The National Meteorological Center’s spectral statistical interpolation analysis system. Mon. Wea. Rev., 120, 1747–1763, doi:10.1175/1520-0493(1992)120<1747:TNMCSS>2.0.CO;2. [Abstract] Pujol, M.-I., S. Dobricic, N. Pinardi, and M. Adani, 2010: Impact of multialtimeter sea level assimilation in the Mediterranean Forecasting Model. J. Atmos. Oceanic Technol., 27, 2065–2082, doi:10.1175/2010JTECHO715.1. [Abstract] Purser, R., W.-S. Wu, D. Parrish, and N. Roberts, 2003a: Numerical aspects of the application of recursive filters to variational statistical analysis. Part I: Spatially homogeneous and isotropic Gaussian covariances. Mon. Wea. Rev., 131, 1524–1535, doi:10.1175/1520-0493(2003)131<1524:NAOTAO>2.0.CO;2. [Abstract] Purser, R., W.-S. Wu, D. Parrish, and N. Roberts, 2003b: Numerical aspects of the application of recursive filters to variational statistical analysis. Part II: Spatially inhomogeneous and anisotropic general covariances. Mon. Wea. Rev., 131, 1536–1548, doi:10.1175/2543.1. [Abstract] Raynaud, L., L. Berre, and G. Desroziers, 2011: An extended specification of flow-dependent background error variances in the Météo-France global 4D-Var system. Quart. J. Roy. Meteor. Soc., 137, 607–619, doi:10.1002/qj.795. [CrossRef] Reynolds, R. W., T. M. Smith, C. Liu, D. B. Chelton, K. S. Casey, and M. G. Schlax, 2007: Daily high-resolution blended analyses for sea surface temperature. J. Climate, 20, 5473–5496, doi:10.1175/2007JCLI1824.1. [Abstract] Simmons, A., S. Uppala, D. Dee, and S. Kobayashi, 2007: ERA-Interim: New ECMWF reanalysis products from 1989 onwards. ECMWF Newsletter, No. 110, ECMWF, Reading, United Kingdom, 25–35. Storto, A., and R. Randriamampianina, 2010: Ensemble variational assimilation for the representation of background error covariances in a high-latitude regional model. J. Geophys. Res., 115, D17204, doi:10.1029/2009JD013111. [CrossRef] Storto, A., S. Dobricic, S. Masina, and P. Di Pietro, 2011: Assimilating along-track altimetric observations through local hydrostatic adjustments in a global ocean reanalysis system. Mon. Wea. Rev., 139, 738–754, doi:10.1175/2010MWR3350.1. [Abstract] Storto, A., I. Russo, and S. Masina, 2012: Interannual response of global ocean hindcasts to a satellite-based correction of precipitation fluxes. Ocean Sci. Discuss., 9, 611–648, doi:10.5194/osd-9-611-2012. [CrossRef] Storto, A., S. Masina, and S. Dobricic, 2013: Ensemble spread-based assessment of observation impact: Application to a global ocean analysis system. Quart. J. Roy. Meteor. Soc., 139, 1842–1862, doi:10.1002/qj.2071. [CrossRef] Wang, X., D. M. Barker, C. Snyder, and T. Hamill, 2008: A hybrid ETKF–3DVAR data assimilation scheme for the WRF model. Part I: Observing system simulation experiment. Mon. Wea. Rev., 136, 5116–5131, doi:10.1175/2008MWR2444.1. [Abstract] Weaver, A. T., and P. Courtier, 2001: Correlation modelling on the sphere using a generalized diffusion equation. Quart. J. Roy. Meteor. Soc., 127, 1815–1846, doi:10.1002/qj.49712757518. [CrossRef] Weaver, A. T., and I. Mirouze, 2013: On the diffusion equation and its application to isotropic and anisotropic correlation modelling in variational assimilation. Quart. J. Roy. Meteor. Soc., 139, 242–260, doi:10.1002/qj.1955. [CrossRef] Weaver, A. T., C. Deltel, E. Machu, S. Ricci, and N. Daget, 2005: A multivariate balance operator for variational ocean data assimilation. Quart. J. Roy. Meteor. Soc., 131, 3605–3625, doi:10.1256/qj.05.119. [CrossRef] Yaremchuk, M., and M. Carrier, 2012: On the renormalization of the covariance operators. Mon. Wea. Rev., 140, 637–649, doi:10.1175/MWR-D-11-00139.1. [Abstract] Zhou, G., W. Fu, J. Zhu, and H. Wang, 2004: The impact of location-dependent correlation scales in ocean data assimilation. Geophys. Res. Lett.,31, L21306, doi:10.1029/2004GL020579. | en |
dc.description.obiettivoSpecifico | 4A. Clima e Oceani | en |
dc.description.journalType | JCR Journal | en |
dc.description.fulltext | open | en |
dc.relation.issn | 0739-0572 | en |
dc.relation.eissn | 1520-0426 | en |
dc.contributor.author | Storto, A. | en |
dc.contributor.author | Masina, S. | en |
dc.contributor.author | Dobricic, S. | en |
dc.contributor.department | Ctr Euromediterraneo Cambiamenti Climat, Numer Applicat & Scenarios Div, I-40127 Bologna, Italy | en |
dc.contributor.department | Ctr Euromediterraneo Cambiamenti Climat, Numer Applicat & Scenarios Div, I-40127 Bologna, Italy | en |
dc.contributor.department | Ctr Euromediterraneo Cambiamenti Climat, Numer Applicat & Scenarios Div, I-40127 Bologna, Italy | en |
item.openairetype | article | - |
item.cerifentitytype | Publications | - |
item.languageiso639-1 | en | - |
item.grantfulltext | open | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.fulltext | With Fulltext | - |
crisitem.author.dept | CNR-Ismar | - |
crisitem.author.dept | Istituto Nazionale di Geofisica e Vulcanologia (INGV), Sezione Bologna, Bologna, Italia | - |
crisitem.author.orcid | 0000-0001-6273-7065 | - |
crisitem.author.parentorg | Istituto Nazionale di Geofisica e Vulcanologia | - |
crisitem.classification.parent | 03. Hydrosphere | - |
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