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Arthur Y. Hou and Sara Q. Zhang

operational forecasts ( Treadon et al. 2002 ; Aonashi et al. 2004 ; Marecal and Mahfouf 2003 ; Bauer et al. 2006 ). Currently, precipitation information (either retrievals or rain-affected radiances) is assimilated in NWP systems much the same way as any other data to optimize the initial state for a better forecast. To this end, the system requires an “observation operator” capable of relating the observable to the initial state with reasonable accuracy. In this regard, precipitation assimilation

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Ronald M. Errico, Peter Bauer, and Jean-François Mahfouf

Centers for Environmental Prediction, the Japan Meteorological Agency, and the European Centre for Medium-Range Weather Forecasts—already incorporate precipitation observations operationally. While still unperfected, these implementations based on optimal control theory permit explicit accounting of error statistics, clear identification of the conditions for optimality, and validation in a real, state-of-the-art forecast context. Thus far much more effort has been devoted to the assimilation of

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Fuzhong Weng, Tong Zhu, and Banghua Yan

from comparisons of the simulated global emissivity distribution with satellite retrievals from AMSU and SSM/I ( Weng et al. 2001 ). b. Analysis for atmospheric temperature The direct radiance assimilation has been become a routine practice in NWP centers and results in major success in the use of clear-sky radiances in global forecast systems. However, cloud- and rain-affected satellite radiances have not been assimilated into operational forecasting models although the measurements contain

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Ronald M. Errico, George Ohring, Fuzhong Weng, Peter Bauer, Brad Ferrier, Jean-François Mahfouf, and Joe Turk

for data assimilation have varying degrees of reliability. Atmospheric dynamics at horizontal scales larger than 100 km or so are typically handled quite well both in terms of analysis and short-term forecast skill. Moreover, operational NWP models are able to predict the location in space and time of clouds associated with large-scale organized systems, but their skill degrades as the strength of synoptic forcing or the degree of larger-scale organization decreases. Large uncertainties remain in

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Fuzhong Weng

assimilation systems. In the current NOAA global operational data assimilation system, the emissivity spectra are specified as function of surface types. 3. Major impediments Direct assimilation of satellite radiances under clouds and precipitation requires detailed information on the profiles of cloud microphysical variables as background information and the error characteristics of the error covariance matrix as shown in (1) and (2) . Currently, operational forecast models and cloud prognostic schemes

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Philippe Lopez

.g., Zupanski and Mesinger 1995 ; Treadon 1997 ; Tsuyuki 1997 ; Hou et al. 2001 ; Peng and Zou 2002 ; Marécal and Mahfouf 2002 ; Marécal and Mahfouf 2003 ; Moreau et al. 2004 ; Hou et al. 2004 ; Benedetti et al. 2005 ; Lopez et al. 2006 ). All these works identified the main issues to be solved and showed that the variational assimilation of precipitation observations could bring some improvement in the analyses as well as in the subsequent forecasts. Operational satellite rain-rate assimilation

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Christopher W. O’Dell, Peter Bauer, and Ralf Bennartz

1. Introduction There is currently a need for fast yet accurate radiative transfer (RT) models for scattering atmospheres. Numerical weather prediction (NWP) models rely increasingly on assimilation of radiance data directly, rather than derived products ( English et al. 2000 ). Operational centers are beginning to assimilate microwave and infrared radiances under all weather conditions, instead of under clear skies only, as is currently done ( Greenwald et al. 2002 ). For example, recently the

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Chinnawat Surussavadee and David H. Staelin

. Observations The most extensive observations of millimeter-wave spectral images of the earth have been made by AMSU on the operational satellites NOAA-15 , NOAA-16 , NOAA-17 , and NOAA-18 , beginning in May 1998. AMSU comprises AMSU-A with ∼50-km resolution near nadir at 15 frequency bands centered between 50.3 and 89 GHz, and AMSU-B with ∼15-km resolution in five channels distributed between approximately 88 and 191 GHz ( Hewison and Saunders 1996 ; Mo 1999 ). AMSU-A and AMSU-B scan through nadir ±48

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Ruiyue Chen, Fu-Lung Chang, Zhanqing Li, Ralph Ferraro, and Fuzhong Weng

:10.1029/2003JD003906 . Chang , F-L. , Z. Li , and S. A. Ackerman , 2000 : Examining the relationship between cloud and radiation quantities derived from satellite observations and model calculations. J. Climate , 13 , 3842 – 3859 . Derber , J. C. , D. F. Parrish , and S. J. Lord , 1991 : The new global operational analysis system at the National Meteorological Center. Wea. Forecasting , 6 , 538 – 547 . Ferraro , R. R. , and Coauthors , 2005 : NOAA operational

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Qing Yue, K. N. Liou, S. C. Ou, B. H. Kahn, P. Yang, and G. G. Mace

constant-mixing-ratio gases at nadir view ( McMillin and Fleming 1976 ), for different zenith angles ( Fleming and McMillin 1977 ), and for gases with variable mixing ratios ( McMillin et al. 1979 ). More recently, Kleespies et al. (2004) reported improvements for the OPTRAN code and an implementation that has been achieved for operational use. The most distinct approach in OPTRAN is the reversal of the usual roles of the pressure and absorber amount. The advantages of this performance include that 1

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