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Chinnawat Surussavadee and David H. Staelin

1. Introduction To assimilate passive microwave precipitation observations or retrievals into numerical weather prediction (NWP) models, the modeled radiances must be consistent with those observed. This paper tests the sensitivity of that consistency to assumptions in a particular radiative transfer model (RTM), and in a cloud-resolving NWP model that predicts hydrometeor habits and profiles. The precipitation and water path retrieval accuracies are shown to be less sensitive to the physical

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Fuzhong Weng, Tong Zhu, and Banghua Yan

observations, many attempts were made to improve hurricane analyses for forecasts. Krishnamurti et al. (1991) developed a method to physically initialize the Florida State University global cumulus parameterization spectral model, which mainly depends upon the surface rain rates derived from the Special Sensor Microwave Imager (SSM/I). A comparison study was conducted by Tibbetts and Krishnamurti (2000) to evaluate the performance of four different rain-rate algorithms in hurricane track forecast using

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

satellites provide the bulk of observations, are now almost as accurate as those for the Northern Hemisphere. However, the progress in forecasting weather elements that are of particular public interest, such as clouds, quantitative precipitation, and precipitation type, has been less dramatic. To date, the assimilation of satellite measurements has focused on the clear atmosphere. But satellite observations in the visible, infrared, and microwave provide a great deal of information on clouds and

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Arthur Y. Hou and Sara Q. Zhang

1. Introduction Observations containing information on precipitation processes have become increasingly available from spaceborne microwave sensors in the past decade, and more is expected with the Global Precipitation Measurement (GPM) mission now in formulation ( National Aeronautics and Space Administration 2006 ). These measurements include radar reflectivity from TRMM and GPM, brightness temperatures from microwave radiometers (e.g., TMI, SSM/I, AMSR-E, SSMIS, MADRAS, GMI, CMIS) and

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

affect the accuracy of LWP estimation from microwave observations ( Lin and Rossow 1994 ; Marchand et al. 2003 ). Since microwave LWP estimation is based on the radiances emitted by cloud water droplets, it is applicable for observations during the day and night. Cloud LWP can also be estimated from solar reflectance measurements made during the daytime. In the visible/NIR method ( Nakajima and King 1990 ; Han et al. 1998 ), cloud LWP is derived based on the products of cloud optical depth and

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Graeme L. Stephens and Christian D. Kummerow

emission and scattering of electromagnetic (EM) radiation by the atmosphere and surface below. Remote sensing methods developed around such observations are referred to as passive methods, and the spectral radiances used in these methods range from the ultraviolet to the microwave regions of the EM spectrum. Unlike specific radiance measurements designed for sounding the clear atmosphere, the majority of passive methods provide very little vertical profile information about clouds and precipitation

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Fuzhong Weng

Radiative Transfer Model. Preprints, 14th Int. TOVS Study Conf., Beijing, China, Int. TOVS Working Group, 217–222 . Weng , F. , T. Zhu , and B. Yan , 2007 : Satellite data assimilation in numerical weather prediction models. Part II: Uses of rain-affected radiances from microwave observations for hurricane vortex analysis. J. Atmos. Sci. , 64 , 3910 – 3925 . Wilheit , T. T. , 1979 : A model for the microwave emissivity of the ocean’s surface as a function of wind speed. IEEE Trans

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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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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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