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José A. Aravéquia, Istvan Szunyogh, Elana J. Fertig, Eugenia Kalnay, David Kuhl, and Eric J. Kostelich

observations we assimilate are the Advanced Microwave Sounding Unit-A (AMSU-A) level 1B brightness temperature data from an instrument flown on the Earth Observing System (EOS) Aqua spacecraft ( Olsen 2007 ). Hereafter, we refer to brightness temperature and radiance observations collectively as radiance observations, as the assimilation of both of these types of data requires the use of a radiative transfer model. The performance of the LETKF in assimilating radiance observations is assessed by

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Mark Buehner, P. L. Houtekamer, Cecilien Charette, Herschel L. Mitchell, and Bin He

geostationary and polar-orbiting satellites; surface winds over water from the Quick Scatterometer (QuikSCAT); and radiances from the Advanced Microwave Sounding Unit A and B (AMSU-A/B), Atmospheric Infrared Sounder (AIRS), Special Sensor Microwave Imager (SSM/I), and geostationary satellites. The 2008 operational EnKF assimilates the same observations except for the radiances from AIRS, SSM/I, and geostationary satellites. Several modifications were made to procedures related to the assimilated

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Takemasa Miyoshi, Yoshiaki Sato, and Takashi Kadowaki

1. Introduction The ensemble Kalman filter (EnKF), first proposed by Evensen (1994) , is now a feasible choice for use with operational numerical weather prediction (NWP). The Canadian Meteorological Centre (CMC) started to use an EnKF method with perturbed observations as an operational ensemble prediction system (EPS) in January 2005 ( Houtekamer and Mitchell 1998 , 2001 , 2005 ; Houtekamer et al. 2005 ). In the summer of 2005, the Met Office started to use the ensemble transform Kalman

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Zhiyong Meng and Fuqing Zhang

Advanced Microwave Sounding Unit (AMSU) radiance for a tropical cyclone event in comparison to 3DVar (see online at http://hfip.psu.edu/EDA2010/LiuZQ.pdf ). However, many issues remain to be explored in satellite radiance assimilation. For example, observation bias correction requires long-term stationary statistics of satellite observations over large areas, which is usually not available for mesoscale models. Additionally, mesoscale models usually do not have a high-enough model top, which may

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Mark Buehner, P. L. Houtekamer, Cecilien Charette, Herschel L. Mitchell, and Bin He

observations; wind from profilers over the United States; atmospheric motion wind from geostationary and polar-orbiting satellites; surface wind over water from the Quick Scatterometer (QuikSCAT); and radiances from the Advanced Microwave Sounding Unit A and B (AMSU-A/B), the Atmospheric Infrared Sounder (AIRS), the Special Sensor Microwave Imager (SSM/I), and geostationary satellites. In the operational EnKF, the same observations were assimilated as those used in the 4D-Var system except for the

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Thomas M. Hamill and Jeffrey S. Whitaker

characteristics of ensemble predictions initialized from EnKFs with real observations. Of particular concern is ensuring that the spread (the standard deviation of ensemble perturbations about the mean) of ensemble forecast perturbations are consistent with the ensemble-mean forecast error; commonly, spread growth is smaller than error growth. The spread growth in forecasts from operational EnKFs is likely to be affected in part by the choice of methods for dealing with the model uncertainty during the

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Mark Buehner and Ahmed Mahidjiba

, temperature, and humidity from radiosondes; wind and temperature from aircraft; wind, temperature, pressure, and humidity from in situ surface observations; atmospheric motion wind from geostationary and polar-orbiting satellites; and radiances from Advanced Microwave Sounding Unit-A/B (AMSU-A/B). In addition to these observation types, the 4D-Var also assimilated wind from profilers over the United States and radiances from geostationary satellites. Because of the difference in horizontal and vertical

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