B-review list of references
Alves O., Robert C., (2005), Tropical Pacific Ocean model error covariances from Monte Carlo simulations, Q.J.R. Meteor. Soc., 131, 3643-3658
Andersson E., Haseler J., Unden P., Courtier P., Kelly G., Vasiljevic D., Brankovic C., Cardinali C., Gaffard C., Hollingsworth A, Jakob C., Janssen P., Klinker E., Lanzinger A., Miller M., Rabier F., Simmons A., Strauss B., Theoaut J-N., Viterbo P., (1998), The ECMWF implementation of three-dimensional variational assimilation (3D-Var). Part III: experimental results, QJRMS 124, 1831-1860.
- Implicity control of gravity waves via the analysis, P. 1833.
Baker W., Bloom S., Woollen J., Nestler M., Brin E., Shlatter T., Branstator G., 1987, Experiments with a three-dimensional statistical objective analysis using FGGE data, Mon. Wea. Rev., 115, 272-96.
- Horizontal scales vary in a similar way to the Rossby radius of deformation.
- Refenced from Kalnay.
Balgovind R., Dalcher A., Ghil M., Kalnay E., 1983, A stochastic-dynamic model for the spectral structure of forecast errors, Mon. Wea. Rev., 111, 701-722.
- Horizontal scales vary in a similar way to the Rossby radius of deformation.
- Refenced from Ingleby and from Kalnay.
Barker D. M., Huang W., Guo Y.-R., Bourgeois A.J., Xiao Q.N., 2004, A three-dimensional variational data assimilation system for MM5: implementation and initial results, Mon. Wea. Rev., 132, 897-914.
- General information on MM5 data assimilation scheme for discussion in review.
Barker D.M., Lorenc A.C., 2005 (and 1999), The use of synoptically-dependent background error structures in 3dVar., Var. Scientific Documentation Paper 26, Met Office, 1-11.
Barker D.M., 2005, Southern high-latitude ensemble data assimilation in the Antarctic Mesoscale Prediction System, Mon. Wea. Rev. 133, 3431-3449.
Barkmeijer J., van Gijzen M., Bouttier F., 1998, Singular vectors and estimates of the analysis-error covariance metric, Q.J.R. Meteor. Soc., 124, 1695-1713.
- Referenced by Fisher and Andersson, 2001.
Barkmeijer J., Buizza R., Palmer T.N., 1999, 3d-Var Hessian singular vectors and their potential use in the ECMWF ensemble prediction system, Q.J.R. Meteor. Soc., 125, 2333-2351.
- Referenced by Fisher and Andersson, 2001.
Bartello P., Mitchell H.L., 1992, A continuous three-dimensional model of short-range forecast error covariances, Tellus 44A, 217-235.
Benjamin S.G., Brewster K.A., Brummer R., Jewett B.F., Schlatter T.W., Smith T.L., Stamus P.A., 1991, An isentropic three-hourly data assimilation system using ACARS aircraft observations, Mon. Wea. Rev., 119, 888-906.
- Extra resolution associated with isentropic co-ordinates.
- Ref from Desroziers (1997) - ref associated with flow-dependent correlation functions.
Berre L., 2000, Estimation of synoptic and mesoscale forecast error covariances in a limited area model, Mon. Wea. Rev., 128, 644-667.
- Background error covariances in Aladin.
Berre L., Stefanescu S.E., Pereira B.M., (2006) The representation of the analysis effect in three error simulation techniques, Tellus, 58A, 196-209
- NMC, Lagged NMC, Ensemble
Boer G.T., 1983, Homogeneous and isotropic turbulance on the sphere, J. Atmos. Sci., 40 (1), 154-163.
- Homogeneous and isotropic errors.
- Referenced from Hollingsworth and Lonnberg.
Bouttier F., 1996, Application of Kalman filtering to numerical weather prediction, Proceedings of the 1996 ECMWF seminar on data assimilation, pp. 61-90.
- Weak justification for NMC method.
Bouttier F., 2003, The AROME mesoscale project, Proceedings of the 2003 ECMWF seminar on Recent developments in data assimilation for atmosphere and ocean, pp. 433-448.
Beuhner M., (2005), Ensemble derived stationary and flow dependent background error covariances: Evaluation in a quasi-operational NWP setting, Q.J.R.Meteor.Soc., 131, 1013-1043
Buehner M., Gauthier P., Liu Z., (2005) Evaluation of new estimates of background and observation error covariances for variational assimilation, Q.J.R.Meteor.Soc., 131, 3373-3384.
Buehner M., Charron M., 2007, Spectral and spatial localization of background-error correlations for data assimilation, Q.J.R.Meteor.Soc. 133, 615-630.
Charney J.G., Drazin P.G., (1961) Propagation of planetary-scale disturbances from the lower into the upper atmosphere, J. Geophys. Res., 66, 83-109.
- Longer lengthscales in stratosphere due to forcing.
- Ref from Ingleby 2001.
Corazza M., Kalnay E., Patil D.J., Yang S.-C., Morss R., Cai M., Szunyogh I., Hunt B.R., Yorke J.A., 2003, Use of the breeding technique to estimate the structure of the analysis "errors of the day", Nonlinear Processes in Geophysics, 10, 233-243.
Courtier P., Talagrand O., 1990, Variational assimilation of meteorological observations with the direct and adjoint shallow water equations, Tellus, 42A, 531-549.
- Use of balance information to improve the preconditioning.
- Ref. from Berre 2000.
Courtier P., Thepaut J.N., Hollingsworth A., 1994, A strategy for operational implementation of 4d-Var, using an incremental approach, Quart. J. Roy. Met. Soc., 120, 1367-1387.
Courtier P., Andersson E., Heckley W., Pailleux J., Vasiljevic D., Hollingsworth A., Fisher M., Rabier F, 1998?, The ECMWF implementation of three-dimensional variational assimilation (3D-Var). Part I: formulation, Quart. J. Roy. Met. Soc., ?.
- Jc term.
- Referenced from Derber & Bouttier (1999).
- See also Rabier et al (1998?). Part II: structure functions.
- See also Anderson et al (1998?). Part III: experimental results.
- Ergodicity.
- Rossby, gravity, and univariate (P. 1798).
Cullen M.J.P. (2003). Four-dimensional variational data assimilation: A new formulation of the background-error covariance matrix based on a potential-vorticity representation, Quart. J. Roy. Met. Soc. 129, 2777-2796.
Daley R., 1997, Atmospheric data assimilation, J. Metero. Soc. Japan, 75, 319-329.
Dance S.L., 2004, Issues in high resolution limited area data assimilation for quantitative precipitation forecasting, Physica D., 196, 1-27.
Deckmyn A., Berre L., 2005, A wavelet approach to representing background error covariances in a limited area model, Mon. Wea. Rev. 133, pp. 1279-1294.
Dee D., Todling R., 2000, Data assimilation in the presence of forecast bias: The GEOS moisture analysis, Mon. Wea. Rev., 128, 3268-3282.
- Stresses the importance of bias considerations.
- Need to read this!
Dee D., 2002, An adaptive scheme for cycling background error variances during data assimlation, ECMWF/GEWEX Workshop on Humidity Analysis, Reading, 1-15.
- Error propagation, error growth (model errors), error reduction.
- An analysis system that produces geostrophically balanced increments may increase errors where the flow is ageostrophic.
- Need to read this!
Dee D.P., Da Silva A.M., 2003, The choice of variable for atmospheric moisture analysis, Mon. Wea. Rev., 131, 155-171.
- Pseudo relative humidity to decouple temperature and moisture in stratosphere.
Dee D., 2004, Variational bias correction of radiance data in the ECMWF system in ECMWF workshop on assimilation of high spectral resolution sounders in NWP, June 28-July 1 2004, 97-112.
- Background errors can include first-guess of variational bias correction parameters.
- Stefano has proceedings.
Dee D.P., 2005, Bias and data assimilation, QJRMS, 131 (613), 3323-3343.
- Augmented control vector.
- Do not have this paper.
- Need to log this paper.
de Pondeca M.S.F.V., Purser R.J., Parrish D.F., Derber J.C., (year?), Comparison of strategies for the specification of anisotropies in the covariances of a three-dimensional atmospheric data assimilation, ?. 8 pp.
Derber J., Wu, 1998
Derber J., Bouttier F., 1999, A reformulation of the background error covariance in the ECMWF global data assimilation system, Tellus, 51A, 195-221.
- Reference for background errors at ECMWF.
- NMC method (modifications needed to certain quantities to avoid problems, rescaling required).
- Empirically determined relationships between variables.
- Should not use analytic balance equation in tropical regions.
- Non-separability.
- Balanced divergence included.
- Error cycling in vorticity - variances from Hessian in vorticity.
- Example single obs tests.
Desroziers G., Lafore J.-P., 1993, A coordinate transform for objective frontal analysis, Mon. Wea. Rev., 121, 1531-1553.
- Anisotropy and fronts.
- Flow dependent analysis.
Desroziers G., 1997, A coordinate change for data assimilation in spherical geometry of frontal structures, Mon. Wea. Rev., 125, 3030-3038.
Dethof A., Holm E.V., 2004, Ozone assimilation in the ERA-40 reanalysis project, QJRMS 130, 2851-2872.
- Ozone background error covariances.
Dixon M., Roulstone I., 2003, Controlling imbalance within Var by use of a weak constraint, Forecasting Research Technical Report No. 417, 1-35.
- Available from www.metoffice.com/research/nwp/publications/papers/technical_reports/2003.html
- Need for an error covariance matrix for Jc.
Dubal M.R., 2001, Use of a geostrophic coordinate transform in 3d-Var, 1-10.
Ehrendorfer M., Tribbia J.J., 1997, Optimal prediction of forecast error covariances through singular vectors, J. Atmos. Sci., 54, 286-313.
Eyring V., Butchart N., Waugh D.W., Akiyoshi H., Austin J., Bekki S., Bodeker G.E., Boville B.A., Bruhl C., Chipperfield M.P., Cordero E., Dameris M., Deushi M., Fioletov V.E., Frith SM., Garcia R.R., Gettelman A., Giorgetta M.A., Grewe V., Jourdain L., Kinnison D.E., Mancini E., Manzini E., Marchand M., Marsh D.R., Nagashima T., Newman P.A., Nielsen J.E., Pawson S., Pitari G., Plummer D.A., Rozanov E., Schraner M., Shepherd T.G., Shibata K., Stolarski R.S., Struthers H., Tian W., Yoshiki M., 2006, Assessment of temperature, trace species, and ozone in chemistry-climate model simulations of the recent past, J. GeoPhys. Res. - Atmos., 111, Art No. D22308.
- Towards chemistry-climate modelling.
- Many different climate models are compared.
Evensen,~G., 2003, The Ensemble Kalman Filter: Theoretical Formulation and Practical Implementation, Ocean Dynamics 53, 343-367.
Fletcher S.J., Zupanski M., 2006, A data assimilation method for log-normally distributed observational errors, Q.J.R. Meteor. Soc., 132, 2505-2519.
Fischer C., Montmerle T., Berre L., Auger L., Stefanescu S.E., (2005) An overview of the variational assimilation in the ALADIN/France numerical weather prediction system, .J.R.Meteor.Soc., 131, 3477-3492
Fisher M., 1998, Development of a simplified Kalman filter, ECMWF Tech Memo 260, 16 pp,
- Dynamical evolution of a subset of covariance information.
- Read with Hello and Bouttier (2001).
- Reduced rank Kalman filter.
Fisher M., Courtier P., 1995, Estimating the covariance matrix of analysis and forecast error in variational data assimilation, ECMWF Tech. Memo. 220, 1-26.
- Error variance cycling algorithm.
- Ref. from Derber and Bouttier paper.
Fisher M., 2003
- Ensemble approach to covariance modelling.
- NMC method tends to overestimate correlation lengths.
Gandin L.S., 1963, Objective analysis of meteorological fields, Gidrometerologicheskoe Izadatelstvo, Leningrad, English translation by Israleli Program for Scientific Translations, Jerusalem, 1965.
- Exponential (and isotropic) error correlation function for geopotential.
- Referenced by Kalnay, p. 162.
- Also references to Schlatter (1975), Thiebaux and Pedder (1987).
- Also referenced by H+L (importance of forecast and observation error).
Gaspari G., Cohn S.E., 1999, Construction of correlation functions in two and three dimensions, Quart. J. Roy. Met. Soc., 125, 723-757.
- Positive semi-definite.
- Correlation lengthscales.
- Compactly supported.
- Schur product.
Gauthier P., Charette C., Fillion L., Koclas P., Laroche S., 1999, Implementation of a 3d variational data assimilation system at the Canadian Meteorological Centre. Part I: The global analysis, Atmosphere Ocean 37, 103-156.
- Ref from Polavarapu (2005).
Gneiting T., 1999, Correlation functions for atmospheric data analysis, Quart. J. Roy. Met. Soc., 125, 2449-2464.
Gustafsson N. et al., (1999), Three dimensional variational data assimilation for a high resolution limited area model (HIRLAM), HIRLAM Tech. Rep. 40, 74 pp.
- NMC method.
- Is the 'balanced' control variable mass in this model?
Gustafsson N., Berre L., Hornquist S., Huang X.Y., Lindskog M., Navascues B., Mogensen K.S., Thornsteinsson S., 2001, Three-dimensional variational data assimilation for a limited area model, Tellus 53A, 425-446.
- HIRLAM.
- Parameters used as control variables.
Hakim G.J., 2005, Vertical structure of midlatitude analysis and forecast errors, Mon. Wea. Rev. 133, 567-578.
- Ensembles.
- Non-separability.
Hamill T.M., Mullen S.L., Snyder C., Toth Z., Baumhefner D.P., 2000, Ensemble forecasting in the short to medium range: report from a workshop, Bull. Am. Meteor. Soc., 81, 2653-2664.
- Need for improved forecast error statistics in data assimilation (bred modes, errors of the day).
Hamill T.M., Snyder C., 2000, A hybrid ensemble Kalman filter-3d variational analysis scheme, Mon. Wea. Rev., 128, 2905-2919.
- Discusses problems with EnKF.
- Importance of the B-matrix.
Hamill T.M., Whitaker J.S., Synder C., 2001, Distance-dependent filtering of background error covariance estimates in an ensemble Kalman filer, Mon. Wea. Rev. 129, 2776-2790.
- Dynamic propagation of forecast error covariances.
- Shur product.
Hello G., Bouttier F., 2001, Using adjoint sensitivity as a local structure function in variational data assimilation, Nonlinear Proc. in GeoPhys. 8, 347-355.
- Related to simplified Kalman filter.
- Read with Fisher (1998).
Hollingsworth A., Lonnberg P., 1986, The statistical structure of short-range forecast errors as determined from radiosonde data. Part I: The wind field, Tellus, 38A, 111-136.
- Mid-latitude errors.
- Effect of analysis - large eigenvalues are well analysed and small eigenvalues are damped.
- Importance of B.
- Baroclinic processes.
Hollingsworth A., 1987, Objective analysis for numerical weather prediction. Short- and medium-range numerical weather prediction, Proc. WMO/IUGG NWP Symp., Tokyo, Japan, Meteorological Society of Japan, 11-59.
- B-matrix used to filter noise.
- Ref. from Berre (2000).
Holm E., Andersson E., Beljaars A., Lopez P., Mahfouf J.F., Simmons A., Thepaut J.N., 2002, Assimilation and modelling of the hydrological cycle: ECMWF's status and plans, ECMWF Tech. Memo. 383, 1-55.
- Humidity analysis - humidity control variable.
- Gaussian control variable.
Holton J.R., Book
- Latent heating through condensation (section 6f for Part I).
Honda Y., Nishijima M., Koizumi K., Ohta Y., Tamiya K., Kawabata T., Tsuyuki T., (2005), A pre-operational variational data assimilation system for a non-hydrostatic model at the Japan Meteorological Agency: Formulation and preliminary results, Q.J.R.Meteor.Soc., 131, 3465-3475
- Mesoscale.
- Need to log this paper.
Hoskins, B., Bretherton F., 1972, Atmospheric frontogenesis models: mathematical formulation and solution, J. Atmos. Sci., 29, 11-27.
- Referenced by Desroziers.
- Semi-geostrophic theory.
- 'Removing' ageostrophic non-linearities.
Hoskins B., 1975, The geostrophic momentum approximation and the semi-geostrophic equations, J. Atmos. Sci., 32, 233-242.
- Geostrophic coordinate transform.
- Referenced by Desroziers.
Houtekamer P. L., Lefaivre L., Derome J., Ritchie H., Mitchell H.L., 1996, A system simulation approach to ensemble prediction, Mon. Wea. Rev., 124, 1225-1242.
- Ensemble approach to covariance modelling.
- From reviewer B's comments (wavelets paper - need to check).
Houtekamer P. L., Mitchell H. L., 2001, A sequential ensemble Kalman filter for atmospheric data assimiltion, Mon. Wea. Rev. 129, 123-137.
- Spurious long-range correlations.
Ide K., Courtier P., Ghil M., Lorenc A.C., 1997, Unified notation for data assimiltion: operational, sequential and variational, J. Meteor. Soc. Japan, 75, No. 1B, 181-189.
- Need to check that I use this notation throughout.
Ingleby N.B., 2001, The statistical structure of forecast errors and its representation in the Met Office global 3-dimensional variational data assimilation system, Quart. J. Roy. Met. Soc., 127, 209-231.
Jarvinen, 2001, Temporal evolution of innovation and residual statistics in the ECMWF variational data assimilation systems, Tellus 53A, 333-347.
- NMC method overestimates lengthscales.
- Ref. from Swinband, Jackson and Thornton (moisture control variable working paper, 30th Sept. 2004).
Jiang S., Ghil M., Dynamical properties of error statistics in a shallow water model, J. Phys. Oceanogr., 23, 2541-2566.
- Horizontal scales vary in a similar way to the Rossby radius of deformation.
- Refenced from Ingleby.
JMA Global and mesoscale data assimilation system, www.jma.go.jp/JMA_HP/jma/jma-eng/jma-center/nwp/outline-nwp/index.html
- Description of JMA data assimilation system.
Johnson C., Hoskins B., Nichols N., 2005, Filtering and interpolation in 4d-Var (incomplete title), QJRMS 131, No. 605, 1-19.
Kalnay E., Toth Z, 1994, Removing growing errors in the analysis. Preprints, 10th Conf. Numerical Weather Prediction, Portland OR, Amer. Meteor. Soc., 212-215.
- Minimize background in the direction of growing errors.
- Bred modes.
- Referenced by Barker and Lorenc (2005), and by Pu et al. (1997).
- Applied to a Lorenz 3-variable model.
Klinker E., Rabier F., Gelaro R., 1998, Estimation of ket analysis errors using the adjoint technique, QJRMS 124, 1909-1933.
Knopf B., Held H., Schellnhuber H.J., 2005, Forced versus coupled dynamics in Earth system modelling and prediction, Nonlinear Processes in Geophysics, 12, 311-320.
- Towards coupled atmosphere-ocean modelling.
Lahoz W.A., Fonteyn D., Swinbank R., -, Data assimilation of atmospheric constituents: a review, in preparation for Atmos. Chem. Phys. Discuss.
- Background error covariances in chemistry.
Lahoz W.A., Geer A.J., 2003, Some challenges in the assimilation of stratosphere / tropopause satellite data, ECMWF/SPARC Workshop on Modelling and Assimilation for the Stratosphere and Tropopause, 117-136.
- Use of RH as control variable.
Lahoz W.A., Geer A.J., Bekki S., Bormann N., Ceccherini S., Elbern H., Errera Q., Eskes H.J., Fonteyn D., Jackson D.R., Khattatov B., Massart S., Peuch V.-H., Rharmill S., Ridolfi M., Segers A., Talagrand O., Thornton H.E., Vik A.F., von Clarmann T., 2006, The assimilation of Envisat Data (ASSET) project, Atmos. Chem. Phys. Discuss., 6, 12769-12824.
- Use of RH as control variable.
- Canadian quick stat method (found better than NMC).
LeDimet F.X., Talagrand O., 1986, Variational algorithms for analysis and assimilation of meteorological observations: theoretical aspects, Tellus 38A, 97-110.
Lerner J.A., Weisz E., Kirchengast G., 2002, Temperature and humidity retrieval from simulated Infrared Atmospheric Sounding Interferometer (IASI) measurements, J. GeoPhys. Res. 107 (D14) ACH 4-1 - 4-11.
- Weighting functions, Averaging kernels.
Leutbecher M., 2003, A reduced rank estimate of forecast error variance changes due to intermittent modifications of the observing network, J. Atmos. Sci. 60, 729-742.
Lindskog M., Gustafsson N., Mogensen K.S., Representation of background error standard deviations in a limited area model data assimilation system, 2006, Tellus, 58A, 430-444.
- HIRLAM.
- Use of the B-matrix in quality control.
- Discussion of different methods of computing the B-matrix.
- The 'cimatological index' field (horizontal variations of bg error standard deviations), and the Eady-based index (flow dependency of the B-matrix).
Lonnberg P., Hollingsworth A., 1986, The statistical structure of short-range forecast errors as determined from radiosonde data. Part II: The covariance of height and wind errors, Tellus, 38A, 137-161.
- Geostrophy in the troposphere and stratosphere.
Lorenc A.C., 1981, A global three-dimensional multivariate statistical interpolation scheme, Mon. Wea. Rev., 109, 701-721.
- Synergy between observations via the B-matrix.
Lorenc A.C., 1986, Analysis methods for numerical weather prediction, Quart. J. Roy. Met. Soc., 112, 1177-1194.
- Bayesian derivation of cost function (imperfect forward model).
- Grid-box values of model (grid-box averages vs. spot values).
- Initialisation vs B to impose balance.
- Only scales resolved by model basis should be included in B.
Lorenc A.C., 1992, Iterative analysis using covariance functions and filters, Quart. J. Roy. Met. Soc., 118, 569-591.
- Recursive filters.
- Auto-regressive.
Lorenc A.C., Ballard S.P., Bell R.S., Ingleby N.B., Andrews P.L.F., Barker D.M., Bray J.R., Clayton A.M., Dalby T., Li D., Payne T.J., Saunders F.W., 2000, The Met Office global 3-dimensional variational data assimilation scheme, Quart. J. Roy. Met. Soc., 126, 2991-3012.
Lorenc A.C., 2002, Modelling of error covariances by four dimensional variational assimilation, Forecasting Research Scientific Paper No. 68, 1-19
- Kalman filter.
- Paper in QJ is actually 2003.
Lorenc A. C., 2003, The potential of the ensemble Kalman filter for NWP - a comparison with 4d-Var., Q. J. R. Meteorol. Soc., 129, 3183-3203.
Lorenc A.C., Roulstone I., White A., 2003, On the choice of control fields in Var., Forecasting Research Technical Report No. 419, 1-35.
- Available from www.metoffice.com/research/nwp/publications/papers/technical_reports/2003.html
- Definition of B in the presence of biases.
Lorenc A.C., Payne T., 2007, 4D-Var and the butterfly effect: Statistical four-dimensional data assimilation for a wide range of scales, Q.J.R.Meteor.Soc. 133, 607-614.
Lui Z.-Q., Rabier F., (2002), The interaction between model resolution, observation resolution and observation density in data assimilation: A one-dimensional study, Q.J.R.Meteor.Soc., 128, 1367-1386
Lupu C., Gautier P., 2006, Impact of a flow dependent background error covariance model based on sensitivity functions in a 3d-Var (poster from EnviSat meeting).
- Related to the work of Hello and Bouttier (2001).
Lynch P. and Huang X.-Y., 1992, Initialization of the HIRLAM model using a digital filter, Mon. Wea. Rev. 120, 1019-1034.
Menard R., Chang L.-P., 2000, Assimilation of stratospheric chemical tracer observations using a Kalman filter. Part II: chi-squared validated results and analysis of variance and correlation dynamics, Mon. Wea. Rev. 128, 2672-2686.
- Dynamic propogation of forecast errors.
Mohebalhojeh A.R., Dritschel D.G., 2001, Hierarchies of balance conditions for the f-plane shallow-water equations, Journal of the Atmospheric Sciences 58 (16), 2411-2426.
- Get this paper.
- Choice of prognostic variables in models.
- Varying Burger number.
- Got ref. from Mike Cullen.
Montmerle T., Lafore J.-P., Berre L., Fischer C., 2006, Limited-area model error statistics over Western Africa: Comparisons with midlatitude results, Q. J. R. Meteorol. Soc., 132, 213-230.
Obukhov A.M., 1954, Statistical description of continuous fields. Trudy Geofiz In-ta Akad Nauk SSSR No. 24, 151 3-42. English translation by Liason Office, Technical Information Centre, Wright -Patterson AFB F-TS-9295/v.
- Homogeneous flow, then divergent and non-divergent components of the flow are uncorrelated.
- Ref. from Hollingsworth and Lonnberg.
Okamoto K., Kazumori M., Owada H., 2005, The assimilation of ATOVS radiances in the JMA global analysis system, J. Met. Soc. Japan 83 (2), 201-217.
- Possible reference for JMA system.
- Has reference to JMA system (non-standard format).
Page C., Fillion L., Zwack P., (2007?), Diagnosing summertime mesoscale vertical motion: implications for atmospheric data assimilation, submitted to Mon.Wea. Rev.
- Omega equation derivation.
Pailleux J., 1997, Large-scale data assimilation systems: atmospheric applications to numerical weather prediction, J. Metero. Soc. Japan, 75, 347-358.
- Multivariate error correlations.
- The term 'structure functions' is used.
Pannekoucke O., Berre L., Desroziers G., 2007, Filtering properties of wavelets for local background-error correlations, Q.J.R.Meteor.Soc. 133, 363-379
Park S.K., Zupasnski D., 2003, Four-dimensional variational data assimilation for mesoscale and storm scale applications, Meteorol. Atmos. Phys. 82, 173-208.
Parrish D.F., Derber J.C., 1992, The National Meteorological Center's spectral statistical interpolation analysis system, Mon. Wea. Rev., 120, 1747-1763.
- Control variables as eigenvectors of B.
- NMC method.
- Rescaling.
- References for 4d-Var.
Parrish D.F., Derber J.C., Purser R.J., Wu W.S., Pu Z.X., 1997, The NCEP global analysis system: recent improvements and future plans, J. Meteor. Soc. Japan, 75, 359-365.
- Description of the NCEP system.
- Ref from Berre (2000).
Pereira M.B., Berre L., 2006, The use of an ensemble approach to study the background error covariances in a global NWP moel, Mon. Wea. Rev. 134, 2466-2489.
Phillips N.A., 1986, The spatial statistics of random geostrophic modes and first-guess errors, Tellus, 38A, 314-332.
- Microscale.
- Should have non-separable structure functions.
- Static B-matrix a feature of data assimilation for some time.
- Frequent observational data implies forecast error growth is restricted.
- Random modes are best expressed in terms of normal modes of forecast model (therefore expect uncorrelated).
- Forecast errors consist mostly of slow modes.
Polavarapu S., Ren S., Rochon Y., Sankey D., Ek N., Koshyk J., Tarasick D., 2005, Data assimilation with the Canadian middle atmosphere model, Atmos.-Ocean 43 (1), 77-100.
- Canadian quick stat method.
- Discussion of some method of computing background error covariances.
Pu Z.-X., Kalnay E., Parrish D., Wu W., Toth Z., 1997, The use of bred vectors in the NCEP global 3d variational analysis scheme, Weather and Forecasting, 12, 689-695.
Purser R.J., Wu W.S., Parrish D.F., Roberts N.M., Numerical aspsects of the application of recursive filters to variational analysis. Part II: spatially inhomogeneous and anisotropic general covariances. Mon. Wea. Rev.
- Ref from Holm et al, 2002.
- Get this paper, and part I.
Purser R.J., 1984, A new approach to the optimal assimilation of meteorological data by iterative Bayesian analysis, Preprints, 10th conference on weather forecasting and analysis, American Meteorological Society, 102-105.
- Ref from Lorenc, 1986.
- GET THIS PAPER.
Rabier F., McNally T., 1993, Evaluation of forecast error covariance matrix, ECMWF Tech. Memo. No. 195, 1-30.
- Referenced from Derber & Bouttier (1999).
Rabier F., Klinker E., Courtier P., Hollingsworth A., 1996, Sensitivity of forecast errors to initial conditions, QJRMS 122, 121-150.
Rabier F., et al, 1997, Recent experimentation on 4d Var anf first results from a simplified Kalman filter, ECMWF RD Technical Memo 240.
- Referenced in ECMWF training course (4d-Var).
- Propogation of b/g errors in 4d-Var. (included here?)
Rabier F., 2005, Overview of global data assimilation developments in numerical weather prediction centres, QJRMS 131, 3215-3233.
- WMO DA Symposium special edition.
Rawlins F., Ballard S.P., Bovis K.J., Clayton A.M., Li D., Inverarity G.W., Lorenc A.C., Payne T.J., 2007, The Met Office global four-dimensional variational data assimilation scheme, Q.J.R.Meteor.Soc. 133, 347-362.
Raymond W.H., Gardner A, 1991, A review of recursive and implicit filters, Mon. Wea. Rev. 119, 477-495.
Riishojgaard L., 1998, A direct way of specifying flow-dependent background error correlations for meteorological analysis systems, Tellus 50A, 42-57.
- Flow dependent background errors.
- "Structure functions" term used.
Rutherford I.D., 1972, Data assimilation by statistical interpolation of forecast error fields, J. Atmos. Sci. 29, 809-815.
- Pre-Hollingsworth and Lonnberg paper.
- Referenced by Daley's book.
- Analysis of innovations to determine forecast error covariances.
Sadiki W., Fischer C., Geleyn J.-F., 2000, Mesoscale background error covariances: recent results obtained with the limited area model ALADIN over Morocco, Mon. Wea. Rev. 128, 3927-3935.<\h3>
Sadiki W., Fischer C., 2005, A posteriori validation applied to the 3d-Var Arpege and Aladin data assimilation systems, Tellus 57A, 21-34.
- Includes references for OI, 3d-Var., 4d-Var., Kalman filter, Jmin, Argpege, Aladin.
Saha S., Nadiga S., Thiaw C., Wang J., Wang W., Zhang Q., Van den Dool H.M., Pan H.L., Moorthi S., Behringer D., Stokes D., Pena M., Lord S., White G., Ebisuzaki W., Peng P., Xie P., 2006, The NCEP Climate Forecast System, J. Clim., 19, 3483-3517.
- Operational coupled atmosphere-land-ocean modelling system.
Seaman N.L., 2003, Future directions of meteorology related to air quality research, Environment International, 29, 245-252.
- Complex models - mentions data assimilation.
Seaman R.S., 1977, Absolute and differential accuracy of analyses achievable with specified observational network characteristics, Mon. Wea. Rev., 105, 1211-1222.
- Referenced by H+L (importance of forecast and observation error).
Segers A.J., Eskes H.J., Van der A R.J., Van Oss R.F, Van Velthoven P.F.J., 2005, Assimilation of GOME ozone profiles and a global chemistry-transport model using a Kalman filter with anisotropic covariance, Quart. J. Roy. Met. Soc., 131, 477-502.
Semple A.T., 2002, An error breeding system for the Met Office 'new dynamics', Forecating Research Technical Report 413, 40 pp.
- Bred modes
- Positive and negative impact. No impact on structure functions.
Schlatter T.W., 2000, Variational assimilation of meteorological observations in the lower atmosphere: a tutorial on how it works, J. Atmos. and Solar-Terr. Phys., 62, 1057-1070.
Siroka M., Fischer C., Casse V., Brozkova R. and Geleyn J.-F., 2003, The definition of mesoscale selective forecast error covariances for a limited area variational analysis, Meteorol. Atmos. Phys. 82, 227--244.
Stajner I., Riishojgaard L.P., Rood R.B., 2001, The GEOS ozone data assimilation system: specification of error statistics, Q.J.R.M.S., 127, 1069-1094.
- Basis of work by Segers et al. (2005).
- Anisotropic errors.
Swinbank R., Jackson D.R., Thornton H., (date?), A possible new moisture control variable.
Talagrand O., Courtier P., 1987, Variational assimilation of meteorological observations with the adjoint vorticity equation. I: Theory, Quart. J. Roy. Met. Soc., 113, 1311-1328.
Thacker W.C., 1988, Three lectures on filtting numerical models to observations
Thepaut J.-N., Hoffman R., Courtier P., 1993, Interaction of dynamics and observations in a four dimensional variational assimilation, Mon. Wea. Rev., 121, 3393-3413.
- Ref. from Desroziers G., 1997.
- GET THIS PAPER.
- 4d-Var implicitly uses flow-dependent structure functions.
Thepaut J.-N., Courtier P., Belaud G., Lemaitre G., 1996, Dynamical structure functions in a four-dimensional variational assimilation: A case study, Quart. J. Roy. Met. Soc., 122, 535-561.
- Ref. from Desroziers G., 1997.
- Flow-dependent structure functions depends upon length of the assimilation period.
Todling R., Cohn S.E., 1994, Suboptimal schemes for atmospheric data assimilation based on the Kalman filter, Mon. Wea. Rev. 122, 2530-2557.
Toth Z., Kalnay E., 1993, Ensemble forecasting at NMC: the generation of perturbations, Bull. Amer. Meteor. Soc., 74, 2317-2330.
- Errors of the day, bred modes.
Toth Z., Kalnay E., 1997, Ensemble forecasting at NCEP: the breeding method, Mon. Wea. Rev., 125, 3297-3318.
- Errors of the day.
- GET THIS PAPER.
- Need original reference for the error breeding technique.
- Similarity between breeding and analysis cycles.
Tremolet Y, (), Accounting for an imperfect model in 4d-Var., ECMWF Tech. Rep. No. 477.
Weaver A.T., Deltel C., Machu E., Ricci S., Daget N., 2006, A multivariate balance operator for variational ocean data assimilation, ECMWF Tech. Memo. 491, 19 pp.
- Use of control variable transforms in ocean data assimilation.
Weaver A.T., Deltel C., Machu E., Ricci S., Daget N., 2006, A multivariate balance operator for variational ocean data assimilation, Quart. J. Roy. Met. Soc., 131, 3605-3626.
- Use of control variable transforms in ocean data assimilation.
Wlasak M., Nichols N.K., Roulstone I., 2006, Use of potential vorticity for incremental data assimilation, Q.J.R.Meteor.Soc. 132, 2867-2886.
Xie Y., Lu C., Browning G.L., (2002), Impact and formulation of cost function and constraints on three-dimensional variation data assimlation, Mon. Wea. Rev. 130, 2433-2447.
- Experiments with different forms of control variable.
- Application for mesoscale data assimilation.
- Choice of formulation can have a significant physical impact.
- NCEP system mentioned.
Xu Q., Wei L., Van Tuyl A., Barker E.H., 2001, Estimation of three-dimensional error covariances. Part I: Analysis of height innovation vectors, Mon. Wea. Rev. 129, 2126-2135.
Zagar N., Gustafsson N., Kallen E., 2004, Dynamical response of equatorial waves in four-dimensional variational data assimilation, Tellus-A, 56 (1), 29-46.
- Paper about tropical modes (Erland Kallen's DARC talk).
Zagar N., Gustafsson N., Kallen E., 2004, Variational data assimilation in the tropics: The impact of a background error covariance constraint, Quart J. Royal. Met. Soc., 130 103-125.
- Paper about tropical modes (Erland Kallen's DARC talk).
Zhang F., 2005, Dynamics and structure of mesoscale error covariance of a winter cyclone estimated through short-range ensemble forecasts, Mon. Wea. Rev. 133, 2876-2893.
- Ensemble forecasts.
- Linear error growth,
- Anisotropies and synotic dependence.
- Definition of cross covariance, spatial covariance, cross-spatial covariance.
Zupanski D., 1997, A general weak constraint applicable to operational 4d-Var data assimilation systems, Mon. Wea. Rev., 125, 2274-2291.
- Applied to NCEP's regional 4d-Var. system.
Zupanski M., Zupanski D., Vukicevic T., Eis K., Vonder Haar T., 2005, CIRA/CSU four dimensional variational data assimilation system, Mon. Wea. Rev., 133, 829-843.
- Includes references for 3d-Var. and 4d-Var.
- Mesoscale.
- Algorithm and control variables.