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  • 1. Bojarova, Jelena
    et al.
    Gustafsson, Nils
    SMHI, Forskningsavdelningen, Meteorologi.
    Johansson, Åke
    SMHI, Forskningsavdelningen, Meteorologi.
    Vignes, Ole
    The ETKF rescaling scheme in HIRLAM2011Ingår i: Tellus. Series A, Dynamic meteorology and oceanography, ISSN 0280-6495, E-ISSN 1600-0870, Vol. 63, nr 3, s. 385-401Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    The ETKF rescaling scheme has been implemented into the HIRLAM forecasting system in order to estimate the uncertainty of the model state. The main purpose is to utilize this uncertainty information for modelling of flow-dependent background error covariances within the framework of a hybrid variational ensemble data assimilation scheme. The effects of rank-deficiency in the ETKF formulation is explained and the need for variance inflation as a way to compensate for these effects is justified. A filter spin-up algorithm is proposed as a refinement of the variance inflation. The proposed spin-up algorithm will also act to prevent ensemble collapse since the ensemble will receive 'fresh blood' in the form of additional perturbation components, generated on the basis of a static background error covariance matrix. The resulting ETKF-based ensemble perturbations are compared with ensemble perturbations based on targeted singular vectors and are shown to have more realistic spectral characteristics.

  • 2.
    Johansson, Åke
    et al.
    SMHI, Forskningsavdelningen, Meteorologi.
    Barnston, A
    Saha, S
    van den Dool, H
    On the level and origin of seasonal forecast skill in northern Europe1998Ingår i: Journal of Atmospheric Sciences, ISSN 0022-4928, E-ISSN 1520-0469, Vol. 55, nr 1, s. 103-127Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    This study examines the level and origin of seasonal forecast skill of surface air temperature in northern Europe. The forecasts are based on an empirical methodology, canonical correlation analysis (CCA), which is a method designed to find correlated patterns between predictor and predictand fields. A modified form of CCA is used where a prefiltering step precedes the CCA as proposed by T.P. Barnett and R. Preisendorfer. The predictive potential of four fields is investigated, namely, (a) surface air temperature (i.e., the predictand field itself), (b) local sea surface temperature (SST) in the northern European area on a dense grid, (c) Northern Hemisphere 700-hPa geopotential height, and (d) quasi-global SST on a coarse grid, The design is such that four contiguous predictor periods (of 3 months each) are followed by a lead time and then a single predictand period (3 months long). The shortest lead time is 1 month and the longest is 15 months. The skill of the CCA based forecasts is estimated for the 39-yr time period 1955-93, using cross-validated hindcasting. Skill estimates are expressed as the temporal correlation between the forecasts and the respective verifying observations. The forecasts are most skillful in the winter seasons with a secondary weaker skill maximum during summer. During winter the geopotential height field produces the highest skill scores of the four predictor fields. The dominant predictor pattern of the geopotential height field is confined to the predictor period that is closest to a preceding core winter season and resembles the North Atlantic Oscillation (NAG) teleconnection pattern. The time series of the expansion coefficients of this dominant predictor pattern correlates well with a low-pass filtered rime series of an NAO index. The obtained skill is similar to what is found in the United States, both with regard to seasonal distribution and level of skill. The origin of skill is however different. In the United States it is the El Nino-Southern Oscillation (ENSO) with its predominantly interannual character that is the main source of skill in winter. In northern Europe it is instead the NAO that contributes the most, and especially the lower frequency part of the NAO (periods between 4 and 10 yr). Spatially sparse station data of surface pressure extending back to the middle of the nineteenth century suggests a nonstationarity in the NAO behavior. The implications of this nonstationarity for the obtained results of this study is briefly discussed. Because finely resolved field data are not readily available for this earlier period, the level of skill realizable for that period using a pattern relationship technique such as CCA remains an open question.

  • 3. Van den Dool, H. M.
    et al.
    Peng, Peitao
    Johansson, Åke
    SMHI, Forskningsavdelningen, Meteorologi.
    Chelliah, Muthuvel
    Shabbar, Amir
    Saha, Suranjana
    Seasonal-to-decadal predictability and prediction of North American climate - The Atlantic influence2006Ingår i: Journal of Climate, ISSN 0894-8755, E-ISSN 1520-0442, Vol. 19, nr 23, s. 6005-6024Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    The question of the impact of the Atlantic on North American (NA) seasonal prediction skill and predictability is examined. Basic material is collected from the literature, a review of seasonal forecast procedures in Canada and the United States, and some fresh calculations using the NCEP-NCAR reanalysis data. The general impression is one of low predictability (due to the Atlantic) for seasonal mean surface temperature and precipitation over NA. Predictability may be slightly better in the Caribbean and the (sub) tropical Americas, even for precipitation. The NAO is widely seen as an agent making the Atlantic influence felt in NA. While the NAO is well established in most months, its prediction skill is limited. Year-round evidence for an equatorially displaced version of the NAO (named ED_NAO) carrying a good fraction of the variance is also found. In general the predictability from the Pacific is thought to dominate over that from the Atlantic sector, which explains the minimal number of reported Atmospheric Model Intercomparison Project (AMIP) runs that explore Atlantic-only impacts. Caveats are noted as to the question of the influence of a single predictor in a nonlinear environment with many predictors. Skill of a new one-tier global coupled atmosphere-ocean model system at NCEP is reviewed; limited skill is found in midlatitudes and there is modest predictability to look forward to. There are several signs of enthusiasm in the community about using "trends" (low-frequency variations): (a) seasonal forecast tools include persistence of last 10 years' averaged anomaly (relative to the official 30-yr climatology), (b) hurricane forecasts are based largely on recognizing a global multidecadal mode (which is similar to an Atlantic trend mode in SST), and (c) two recent papers, one empirical and one modeling, giving equal roles to the (North) Pacific and Atlantic in "explaining" variations in drought frequency over NA on a 20 yr or longer time scale during the twentieth century.

  • 4. van den Dool, H M
    et al.
    Saha, S
    Johansson, Åke
    SMHI, Forskningsavdelningen, Meteorologi.
    Empirical orthogonal teleconnections2000Ingår i: Journal of Climate, ISSN 0894-8755, E-ISSN 1520-0442, Vol. 13, nr 8, s. 1421-1435Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    A new variant is proposed for calculating functions empirically and orthogonally from a given space-time dataset. The method is rooted in multiple linear regression and yields solutions that are orthogonal in one direction, either space or time. In normal setup, one searches for that point in space, the base point (predictor). which, by linear regression, explains the most of the variance at all other points (predictands) combined. The first spatial pattern is the regression coefficient between the base point and all other points, and the first time series is taken to be the time series of the raw data at the base point. The original dataset is next reduced; that is, what has been accounted for by the first mode is subtracted out. The procedure is repeated exactly as before for the second, third, etc., modes. These new functions are named empirical orthogonal teleconnections (EOTs). This is to emphasize the similarity of EOT to both teleconnections and (biorthogonal) empirical orthogonal functions (EOFs). One has to choose the orthogonal direction for EOT. In the above description of the normal space-time setup, picking successive base points in space, the time series are orthogonal. One can reverse the role of time and space-in this case one picks base points in time, and the spatial maps will be orthogonal. If the dataset contains biorthogonal modes, the EOTs are the same for both setups and are equal to the EOFs. When applied to four commonly used datasets, the procedure was found to work well in terms of explained variance (EV) and in terms of extracting familiar patterns. In all examples the EV for EOTs was only slightly less than the optimum obtained by EOF. A numerical recipe was given to calculate EOF, starting from EOT as an initial guess. When subjected to cross validation the EOTs seem to fare well in terms of explained variance on independent data las good as EOF). The EOT procedure can be implemented very easily and has, for some (but not all) applications, advantages over EOFs. These novelties, advantages, and applications include the following. 1) One can pick certain modes (or base point) first-the order of the EOTs is free, and there is a near-infinite set of EOTs. 2) EOTs are linked to specific points in space or moments in time. 3) When linked to Row at specific moments in time, the EOT modes have undeniable physical reality. 4) When linked to flow at specific moments in time, EOTs appear to be building blocks for empirical forecast methods because one can naturally access the time derivative. 5) When linked to specific points in space, one has a rational basis to define strategically chosen points such that an analysis of the whole domain would benefit maximally from observations at these locations.

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