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D. J. Lea, I. Mirouze, M. J. Martin, R. R. King, A. Hines, D. Walters, and M. Thurlow

1. Introduction Forecasting systems for short-range weather and ocean prediction have been run separately at the Met Office for many years with the weather forecasts using prescribed ocean surface temperatures and sea ice fields, and with the ocean forecasts using atmospheric forcing fields from the Met Office’s numerical weather prediction (NWP) system. It has long been known that coupling between the various earth system components (the ocean, atmosphere, sea ice, and land) produces improved

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Daryl T. Kleist and Kayo Ide

critical to ensure that the nature run is a suitable representation of the real atmosphere. To achieve realistic results, it is important to use a model within the data assimilation system that is different than that which was used to generate the nature run; if the same model is used for both, the so-called identical-twin experiment, the model error goes unaccounted for. An international, collaborative effort called the Joint OSSE has formed over the past several years. The European Centre for Medium

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Lars Nerger

the optimal parameter values. Fig . 1. Average RMS errors for the (top) EnSRF, (middle) LETKF, and (bottom) EnSRF bulk for three different observational errors: (left) 1.0, (middle) 0.5, and (right) 0.1. White fields denote filter divergence, which is defined here as where the averaged RMS error is larger than the observational error. The first two rows in Fig. 1 show the average RMS errors for the serial EnSRF and LETKF, respectively. As discussed by Nerger et al. (2012a) , the regulated

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Ting-Chi Wu, Christopher S. Velden, Sharanya J. Majumdar, Hui Liu, and Jeffrey L. Anderson

, respectively. These experiments are named noUL (no upper-layer AMVs), noML (no middle-layer AMVs), and noLL (no lower-layer AMVs), respectively. The six parallel experiments were conducted for hourly AMVs for both tropical cyclone cases. They were repeated for rapid-scan AMVs where available for Sinlaku (after its rapid intensification) and for the entire period of Ike. It is important to note that the H and RS AMV datasets are treated separately in this study, even though for much of the time in these two

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Juanzhen Sun, Hongli Wang, Wenxue Tong, Ying Zhang, Chung-Yi Lin, and Dongmei Xu

momentum variables as a function of the EOF mode, as shown by the black dotted (for the length scale ratio between ψ and u ) and dashed (for the length scale ratio between χ u and u ) lines. These two ratios increase as the wavenumber increases (except for modes 3–6), suggesting that the BE of ψχ results in larger length scales for small-scale features in the atmosphere compared to that of UV . When the eigenvalues (indicating the variance explained by each EOF mode) are plotted with respect

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Stefano Migliorini

channels currently monitored at ECMWF provide 4.479 DFS for humidity and 0.0038 for ozone over the whole atmosphere. As discussed in the next section, the (effective) degrees of freedom for signal can be used, for example, as a figure of merit for channel selection in order to select the set of channels that provide most information about the whole (selected region of the) state space. Note that and when k runs over the whole state vector. It is also important to note that the iterative channel

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Andrew C. Lorenc, Neill E. Bowler, Adam M. Clayton, Stephen R. Pring, and David Fairbairn

model and its adjoint at each iteration. In line with the recommendations of Lorenc (2013) , in this paper we reserve 4DVar, even with a prefix, to such methods. In traditional strong-constraint 4DVar, we assume that the evolution of the atmosphere is described by a forecast model and ignore model errors. Then we can “reduce the control variable” ( Le Dimet and Talagrand 1986 ) and define 4D trajectories using the forecast model and its initial conditions: . Note the unusual definition of as

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Hailing Zhang and Zhaoxia Pu

intensity forecast is not improved by the assimilation of surface wind vectors alone in SFC ( Figs. 6a,b ) outside the data assimilation period, indicating that the injection of surface wind vectors alone into the model is not enough to improve the intensity forecast. This negative impact may be attributed to the insufficient ability of single-level surface data to constrain the conditions in the middle to upper troposphere, as discussed in the following section. Fig . 6. Time series (at 6-h intervals

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Daisuke Hotta, Tse-Chun Chen, Eugenia Kalnay, Yoichiro Ota, and Takemasa Miyoshi

vital importance to explore the characteristics of 6-h EFSO, in particular to what degree it is consistent with the conventionally used 24-h FSO/EFSO. Each panel in Fig. 1 shows the EFSO impacts from each of the observation types averaged over the 31-day period evaluated with 6- (left panels), 12- (middle panels), and 24-h (right panels) lead times. The top and bottom rows of panels in Fig. 1 show the EFSO impacts measured, respectively, with moist and dry total energy norms. Despite the concern

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Chengsi Liu and Ming Xue

states of different times due to the limited ensemble size. Temporal localization should in general be applied in all 4D ensemble-based algorithms, and has been used in, for example, the 4D ensemble square root filter algorithm of S. Wang et al. (2013) . Placing the analysis time at the middle of assimilation window (when the algorithm does not involve adjoint model integration) can help somewhat, as was done in Liu et al. (2009) . Temporal localization in 4DEnVar and En4DVar algorithms can be

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