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

mentioned above, there may be reductions in the mean surface air temperature increments in the same regions in the atmosphere ( Fig. 7 ). However, in the South Pacific and other Southern Hemisphere oceans the standard deviation of the increments is increased in the coupled case. This is strongly linked to the increased SST innovations in the coupled case, discussed in section 3b , as a result of the diurnal cycle representation. When a new diurnal model is implemented in the coupled model, the mean and

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David Halpern, Dimitris Menemenlis, and Xiaochun Wang

the western Pacific Ocean . J. Climate , 3 , 1102 – 1110 , doi: 10.1175/1520-0442(1990)003<1102:WPOASL>2.0.CO;2 . Philander, S. G. , 1990 : El Niño, La Niña, and the Southern Oscillation . International Geophysics Series, Vol. 46, Academic Press, 293 pp . Qu, T. , Mitsudera H. , and Yamagata T. , 1999 : A climatology of the circulation and water mass distribution near the Philippine coast . J. Phys. Oceanogr. , 29 , 1488 – 1505 , doi: 10.1175/1520-0485(1999)029<1488:ACOTCA>2.0.CO

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Norihisa Usui, Yosuke Fujii, Kei Sakamoto, and Masafumi Kamachi

1. Introduction Ocean data assimilation, which synthesizes observations and numerical models to obtain the statistically best estimate of the ocean state, has been widely used for various purposes such as ocean monitoring, forecast, and reanalysis (e.g., Bennett 1992 , 2002 ; Ghil and Malanotte-Rizzoli 1991 ; Wunsch 1996 ; Talagrand 1997 ; Lewis et al. 2006 ; Evensen 2007 ). The variational method is one of the major approaches in data assimilation. Based on the maximum likelihood

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Daryl T. Kleist and Kayo Ide

( Fig. 6 , bottom) reveals that 3DHYB is almost uniformly better than the 3DVar control for the simulated August period. Interestingly, the errors in the 3DVar control are quite large over the Southern Ocean, as expected, but quite small over the central and North Pacific. This is likely an artifact of the errors that were added to the simulated observations and trying to match the impact of each observing platform relative to realistic observing system experiments (OSEs; N. C. Privé 2010, personal

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Hailing Zhang and Zhaoxia Pu

’s landfall in southern Florida, Quick Scatterometer (QuikSCAT) observations are available near 0000 and 1200 UTC 25 August 2005. Meanwhile, there are surface mesonet observations ( Horel et al. 2002 ) over land, complementing the QuikSCAT ocean surface winds. Figure 3 displays the distributions of each type of observation at 0000 UTC 25 August. It shows that QuikSCAT ocean surface wind vectors and mesonet wind vectors are not included in the NCEP ADP data decoded in this study, creating an opportunity

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

the nature to be and, as in Peña and Kalnay (2004) , and . This configuration implies that the tropical atmosphere and the ocean are strongly coupled, while the two atmospheres are only weakly coupled. The system is integrated using a second-order Runge–Kutta scheme with a time step of . According to Peña and Kalnay (2004) , the model (12) represents an El Niño–Southern Oscillation (ENSO)-like configuration with an almost slave, small amplitude atmosphere whose regime changes are

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María E. Dillon, Yanina García Skabar, Juan Ruiz, Eugenia Kalnay, Estela A. Collini, Pablo Echevarría, Marcos Saucedo, Takemasa Miyoshi, and Masaru Kunii

precipitation forecasted by the GFS is displaced to the southeast with respect to the observations, and it overestimates the precipitation over the southern part of the domain, particularly over the ocean. As can be seen in Fig. 10 , the LETKF-single and LETKF-multi experiments have better performance compared to WRF-GFS, mainly because in the latter there is a displacement of the precipitation area to the north with respect to TMPA. The spatial distribution of the accumulated precipitation is better

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Hyo-Jong Song and In-Hyuk Kwon

severe error of Ps changes to a positive error reduction even on the Southern Ocean ( Figs. 13a,b ), because the surface observations are distributed well on both hemispheres ( Fig. 4 ). Fig . 13. (a) The background error and (b) the error reduction that is the subtraction of the absolute value of the analysis error from that of the background error for the surface pressure at 0000 UTC 10 Mar. Figure 14 presents background and analysis RMSEs of U , V , T , Q , and Ps for eight experimental cases

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Daryl T. Kleist and Kayo Ide

the Canadian operational global NWP model, and found that the 4DEnVar improved upon their operational, nonhybrid 4DVar in the tropics and Southern Hemisphere, but not in the Northern Hemisphere. It was also found that 4DEnVar performed slightly worse than a hybrid 4DVar. Buehner et al. (2013) , again using the operational Canadian system, found that while the use of 4D instead of 3D ensemble covariances did result in small, consistent improvements in their EnVar for deterministic NWP, the gains

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

number of radar data (10 2 and 10 5 , respectively) and thus they had much smaller weight. When only DWL observations were assimilated, a clear influence on the northern part of MCS A was apparent ( Fig. 9d ), perhaps because southerly winds increased but water vapor did not such that MCS A fully strengthened. When we assimilated only GPS-PWV data, only the southern part of MCS A appeared ( Fig. 9e ). Combining DWL and GPS-PWV data appeared to smoothly merge their separate assimilation effects ( Fig

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