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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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Graeme L. Stephens and Christian D. Kummerow

such observations also varies according to where in the EM spectrum the observations are made. Figure 2 summarizes three different classes of interactions used as the basis of forward models of satellite retrievals of cloud and precipitation properties. One class of retrieval approach relies on measurements of transmission where the attenuation of a defined source of radiation is used to determine some property of clouds. An example of this includes lidar transmission methods for thin cirrus cloud

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

that affect them. One difficult but obvious way to achieve this is to appropriately assimilate observations that are specifically affected by, and therefore indicative of, clouds and precipitation. These include data from surface rain gauges, ground-based and satellite radar or lidar reflectivity, and satellite radiances from passive infrared and microwave radiometers. Besides potentially improving forecasts, such improved analysis will provide better datasets required for validating, and thus

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Philippe Lopez

onboard radars. Observations from the ground are available from the national operational networks of precipitation radars and rain gauges as well as from several experimental sites equipped with microwave radiometers, cloud lidars, and radars, such as those of the Atmospheric Radiation Measurement Program (ARM; Stokes and Schwartz 1994 ). So far four main methods have been developed to assimilate this kind of data: nudging ( Macpherson 2001 ), diabatic or physical initialization (e.g., Puri and

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

1. Introduction Satellite data assimilation in numerical weather prediction models requires an efficient and accurate radiative transfer model for the computation of radiances and Jacobians. Present thermal infrared radiative transfer models for satellite data assimilation have been developed primarily for clear conditions (i.e., pure absorbing atmospheres). However, many studies have found that a great majority of satellite observations is “contaminated” by clouds. For example, Saunders (2000

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

several more computationally efficient approaches to serve as fast alternatives to the reference scheme. Section 5 characterizes the accuracy of the fast models as compared to the reference overlap model, whereas section 6 examines the full forward-model errors as compared with actual microwave observations. A brief discussion of the results is given in section 7 . 2. Base profiles and microphysics The profile datasets were drawn from the ECMWF efforts to assimilate cloud- and precipitation

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