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Patrick Nima Raanes, Alberto Carrassi, and Laurent Bertino

1. Introduction The ensemble Kalman filter (EnKF) is a popular method for doing data assimilation (DA) in the geosciences. This study is concerned with the treatment of model noise in the EnKF forecast step. a. Relevance and scope While uncertainty quantification is an important end product of any estimation procedure, it is paramount in DA because of the sequentiality and the need to correctly weight the observations at the next time step. The two main sources of uncertainty in a forecast are

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Robin J. T. Weber, Alberto Carrassi, and Francisco J. Doblas-Reyes

caused by the displacement of the model state onto the observed values lying outside the model attractor. At the expense of larger initial errors, the objective of AI is to keep the initial state close to the model attractor and reduce the drift. The mean forecast error is less dependent on lead time and, as argued by Magnusson et al. (2013) , the use of standard a posteriori bias correction techniques is more robust. Anomaly initialization can reduce initialization shocks, but is unable to avoid

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Nicholas A. Gasperoni and Xuguang Wang

sampling error; however, a time-forecast component is added to the localization problem, such that a straightforward application of fixed localization techniques would not guarantee accurate impact estimates. To partially address the issue, Kalnay et al. (2012) proposed two methods of moving localization: 1) using a model-forecast nonlinear incremental evolution of the localization function, and 2) advecting the localization center using the climatological group velocity of dominant wavenumbers. Ota

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

observation improved or degraded the 24-h forecast. Their formulation exploits an adjoint sensitivity technique and is applicable to variational DA systems. Major operational NWP centers soon adopted this technique (e.g., Cardinali 2009 ; Gelaro and Zhu 2009 ; Ishibashi 2010 ; Lorenc and Marriott 2014 ) and showed that it is a powerful diagnostic. Its ensemble-based formulation, ensemble FSO (EFSO), was devised by Liu and Kalnay (2008) and Li et al. (2010) for the local ensemble transform Kalman

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Daryl T. Kleist and Kayo Ide

Appropriate Time (3DVar-FGAT) method ( Rabier et al. 1998 ; Lawless 2010 ), which employs 4D model states at the appropriate time to compute innovations but only solves for a solution at a single time, typically at the center of a window. The major drawbacks to the 4DVar technique are the computational cost, complications related to developing and maintaining linearized forecast models and their corresponding adjoints, and the basic assumption of linearity for the incremental formulation, which may be

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Kazumasa Aonashi, Kozo Okamoto, Tomoko Tashima, Takuji Kubota, and Kosuke Ito

. , 129 , 2776 – 2790 , doi: 10.1175/1520-0493(2001)129<2776:DDFOBE>2.0.CO;2 . Houtekamer , P. L. , and H. L. Mitchell , 1998 : Data assimilation using an ensemble Kalman filter technique . Mon. Wea. Rev. , 126 , 796 – 811 , doi: 10.1175/1520-0493(1998)126<0796:DAUAEK>2.0.CO;2 . Ikawa , M. , and K. Saito , 1991 : Description of a nonhydrostatic model developed at the Forecast Research Department of the MRI. MRI Tech. Rep. 28, 238 pp. [Available online at http

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Jean-François Caron, Thomas Milewski, Mark Buehner, Luc Fillion, Mateusz Reszka, Stephen Macpherson, and Judy St-James

. 7c,d ). Similar improvements can also be seen at shorter lead times (not shown). Fig . 7. As in Fig. 5 , but for experiments ENF (red) and 4DF (blue). 2) Ground-based GPS Forecasts of precipitable water were compared to the values derived from GB-GPS ZTD observations. 4 The confidence intervals for the scores reported here and in the following subsection were estimated from the bootstrap resampling technique described in Candille et al. (2007 , see their section 2c). Figure 8 shows the

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

1. Introduction The variational data assimilation (DA) technique has been widely used in operational centers as well as in research communities to provide analysis and initialization for numerical models. The technique can be implemented with a three-dimensional (3DVar) or a four-dimensional (4DVar) variational data assimilation approach; the latter requires the use of a prediction model as the constraint. The variational method seeks to find the optimal analysis by minimizing a cost function

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Mark Buehner, Ron McTaggart-Cowan, Alain Beaulne, Cécilien Charette, Louis Garand, Sylvain Heilliette, Ervig Lapalme, Stéphane Laroche, Stephen R. Macpherson, Josée Morneau, and Ayrton Zadra

for the 4DEnVar-based system is shown in Fig. 2 . Throughout the integration using 4DIAU, gravity wave activity remains near baseline levels, a degree of balance achieved only after 4 h of integration in a comparable simulation initialized with a full-field digital filter. Fig . 2. The global-mean second time derivative of surface pressure is shown for each forecast lead time, for the initialization techniques shown in the legend. The ability of the different initialization techniques to limit

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Takuya Kawabata, Hironori Iwai, Hiromu Seko, Yoshinori Shoji, Kazuo Saito, Shoken Ishii, and Kohei Mizutani

. We conclude that simultaneous assimilation of radar, DWL, and GPS-PWV data is a robust technique for improved forecasting of intense MCSs. We used our simulation to explore the mechanisms of the intense MCS. Only liquid water particles and no ice water particles were present ( Yamada 2012 ). The MCS was, therefore, formed by warm rain clouds, as the cloud tops were kept below 6 km AGL by an inversion layer. A stable layer of this type was present over the Kanto Plain in our simulation. It also

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