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Arthur Y. Hou and Sara Q. Zhang

poses a special challenge. Unlike conventional data, precipitation observations do not directly provide information on the atmospheric state in terms of temperature, wind, and moisture but are related indirectly to these variables through parameterized moist physics with simplifying assumptions. As a result, precipitation observation operators are inherently less accurate than those for conventional data or observables in clear-sky regions. In variational data assimilation, moist physics schemes are

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Peter M. Norris and Arlindo M. da Silva

assimilating clear-sky fluxes to determine temperature and moisture profiles. This approach is still very new, but progress is being made ( Janisková et al. 2002 ; Greenwald et al. 2004 ; Chevallier et al. 2004 ). The challenge is not only to have a forward model that accurately accounts for cloud optical properties, but also that passive radiative observations still only partially constrain the cloud properties (especially in multilayer cloud schemes, which are common). Janisková et al. conduct some

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Philippe Lopez

redistribution of moisture, through the heating of upper-tropospheric levels due to condensation in the updrafts and to large-scale subsidence outside convective cells, through the large-scale compensating subsidence around convective areas, and through the low-level cooling caused by precipitation–evaporation induced downdrafts. Weather and climate modelers soon realized that some way of including the impact of convection on their model’s large-scale variables was needed to obtain realistic simulations of

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

critical for characterizing climate. Since they directly or indirectly affect many human activities, their accurate prediction on several time scales is also strongly desired. Remote sensing now provides critical observations for analyzing the atmosphere. The propagation of infrared or microwave radiation is strongly affected by details of clouds or precipitation, including the shape and size distributions of hydrometeors. Thus retrieving temperature and moisture fields from radiance observations in

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Fuzhong Weng, Tong Zhu, and Banghua Yan

through clouds and precipitation and provide rich information on hurricane structures (e.g., temperature, moisture, rain rate, and surface winds) from the signals received by the sensor. In this study, we are applying the measurements from two microwave sensors for hurricane model initialization. Currently, NCEP’s Global Data Assimilation System (GDAS) uses a 3DVAR approach to assimilate microwave radiances and products, such as AMSU radiances, SSM/I surface wind speed, and precipitation products, in

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Graeme L. Stephens and Christian D. Kummerow

cloud signal is small relative to the background surface emissions. Effects such as soil moisture changed by recent precipitation events, for example, can introduce significant sources of error (e.g., Greenwald et al. 1997 ). Although microwave radiance has been applied to estimate LWP for over 20 yr, detailed assessment of the uncertainties attached to the estimates of LWP are generally lacking. Greenwald et al. (1993) describe a sensitivity analysis for ocean-based LWP retrievals and show how

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