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

applied. The vertical viscosity and diffusivity are determined by the turbulent closure scheme of Noh and Kim (1999) . The bottom friction is calculated according to the formula proposed by Weatherly (1972) . The configuration of this model is basically the same as that used in Tsujino et al. (2006) except for vertical grid spacing near the bottom and for discarding a sea ice model. The model domain spans from 117°E to 160°W zonally and from 15° to 65°N meridionally ( Fig. 2 ). Horizontal

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

) vertical cross sections along line L–M of the mixing ratio of ice water (cloud ice, snow, graupel, and hail) and temperature and (c) the mixing ratio of liquid water (cloud and rainwater) and temperature. The cloud-top limit of 6 km AGL reflects the existence of a stable layer, as shown along line O–P in Fig. 11a , which is aligned in the wind direction of the sea breeze from Tokyo Bay ( Fig. 12a ). That is, because the value of dθ / dz was high at 5–6 km AGL (the inversion layer), convection was

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Hailing Zhang and Zhaoxia Pu

tropical transition event. It was designated as a tropical depression (TD) by the National Hurricane Center (NHC) at 1800 UTC 23 August 2005 over the southeastern Bahamas and later as a tropical storm (TS) at 1200 UTC 24 August. It then moved westward and made landfall in southern Florida shortly before 0000 UTC 26 August, when it was identified as a category-1 hurricane with a minimum central sea level pressure (MSLP) of 983 hPa and a maximum surface wind speed of 70 kt (1 kt = 0.5144 m s −1 ). Then

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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

description of the tests that led to the selection of these variables is provided in section 4c , where it is shown that the adoption of this strategy succeeds in reducing spinup in the 4DEnVar-based system. h. Other changes A new global sea ice concentration analysis is used to specify the fixed ice concentration for the atmospheric forecasts. The new system uses a 3DVar approach to assimilate ice concentration retrieved from passive microwave satellite observations (SSM/I and SSMIS) and from the manual

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Stefano Migliorini

additional channels that are most effective for the joint retrieval of ice and liquid water content using IASI data without solar contamination. The impact of the additional channels on water vapor estimates was also assessed. The authors found that the additional channels provided at best only marginal improvements with respect to the case when only the standard channels are used in the retrieval. Note, however, that although in both studies the state vector is augmented with cloud-related fields, a key

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

predicted horizontal and vertical momentums as the dynamic variables, and pressure perturbations and potential temperature as the thermodynamic variables. This CRM also employed a bulk microphysical scheme in order to predict, explicitly, the mixing ratios of six hydrometers (water vapor, cloud water, cloud ice, rain, snow, and graupel) and the number concentrations of cloud ice, snow, and graupel ( Ikawa and Saito 1991 ). Similar to AE , we performed 100-member ensemble forecasts of the CRM, each of

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Lars Nerger

ensemble transformation in the ensemble square root filters . Mon. Wea. Rev. , 136 , 1042 – 1053 , doi: 10.1175/2007MWR2021.1 . Sakov , P. , G. Evensen , and L. Bertino , 2010 : Asynchronous data assimilation with the EnKF . Tellus , 62A , 24 – 29 , doi: 10.1111/j.1600-0870.2009.00417.x . Sakov , P. , F. Counillon , L. Bertino , K. A. Lisaeter , P. R. Oke , and A. Korablev , 2012 : TOPAZ4: An ocean-sea ice data assimilation system for the North Atlantic and Arctic

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