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Peter M. Norris and Arlindo M. da Silva

paid to predicted cloud properties, due to the slower time scales associated with cloud-induced radiative heating rates compared with the forecast duration. Nevertheless, clouds do have an important societal impact from day to day, in terms of their effects on diurnal temperature range and sunlight exposure. Furthermore, since NWP and GCM models have become more merged, typically sharing the same physics, advances in cloud parameterization in either climate or weather studies ought to benefit the

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Arthur Y. Hou and Sara Q. Zhang

sounders (e.g., AMSU, ATMS, MHS) (see list of acronyms in the appendix ), as well as precipitation rates and latent heating profiles derived from these measurements ( Simpson et al. 2000 ). In recent years, significant progress has been made in using these observations in data assimilation to improve atmospheric analyses and forecasts. Numerical weather prediction centers such as the NCEP, JMA, and ECMWF have begun using precipitation data or rain-affected microwave brightness temperatures in

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

1. Introduction As a result of better numerical weather prediction (NWP) models, more powerful computers, new satellite observations, and more efficient and effective data assimilation systems, the forecast skill of midtropospheric synoptic flow patterns has steadily improved over the past few decades. Today’s 4-day forecasts of those patterns are as accurate as 3-day predictions were just a decade ago and as 2-day forecasts were 2 decades ago. Forecasts for the Southern Hemisphere, where

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

using variational (1DVAR) techniques were developed using a priori information provided by short-term weather forecasts. The retrievals were then assimilated as pseudo-observations of temperature and humidity in the full three-dimensional atmospheric analysis systems ( Eyre et al. 1993 ; Gérard and Saunders 1999 ). More recently, improvements in model accuracy and data assimilation techniques have allowed the direct assimilation of raw radiances in weather prediction models ( McNally et al. 1999

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Fuzhong Weng

1. Introduction Satellite observations of the atmosphere, land, and oceans are now a major component of the environmental observing system, since they provide critically important information to better understand and forecast short-term as well as climatic changes in weather. Through data assimilation techniques, the satellite observations as well as other sources of atmospheric and oceanic data, sampled at different times, intervals, and locations can be combined into a unified and consistent

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Philippe Lopez

therefore be seen as the combination of the background state and the observations, weighted by the inverse of their respective statistical errors. In practice the minimization of the cost function involves the computation of its gradient through In this equation, 𝗛 T is the adjoint of operator 𝗛, which includes the adjoint of a set of moist physical parameterizations that are usually simplified versions of the ones used in the forecast model (see section 3b ). More details on the adjoint technique

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Fuzhong Weng, Tong Zhu, and Banghua Yan

( McNally 2002 ). In the European Centre for Medium-Range Weather Forecasts (ECMWF) 4DVAR system, two approaches were tested for assimilating water vapor information from satellite microwave imager data. In the first approach, 1DVAR technique was used to generate increments of total column water vapor, which is then assimilated through 4DVAR, which is referred to as “1DVAR + 4DVAR” ( Bauer et al. 2006a , b ). The second approach is direct assimilation of surface rain-rate observations from satellite

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

estimation can be of significance in cloud water and radiation budget studies. d. Implication for warm rain clouds IR rain detection algorithms ( Adler and Negri 1988 ; Arkin 1979 ) generally miss the presence of precipitation in warm clouds because these algorithms depend on the cloud-top temperature. Microwave techniques cannot detect warm rain over land since the techniques rely on ice scattering over land ( McCollum and Ferraro 2003 ). Over oceans, warm rain can be estimated from satellite microwave

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K. Franklin Evans

function is computed from the radiance field in spherical harmonics. The same series acceleration technique as in SHDOM is used to improve the convergence of these iterations, which is important for optically thick media with little absorption. The iterations continue until the normalized rms change in the source function in one iteration (the solution criterion) is below a specified value (the solution accuracy). The discrete ordinates are defined by double Gaussian quadrature in zenith angle and

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Graeme L. Stephens and Christian D. Kummerow

this case, the forward model of this system is complex with uncertainties now defined not only by the radiative transfer component of the model but also by (generally unknown) errors of the CRM model and the representativeness (or lack thereof) of these CRM profiles. Bauer et al. (2005) describe a different profile retrieval scheme using combinations of microwave window and sounding channel radiances and a priori profiles of clouds and precipitation drawn from the (background) state of a forecast

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