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V. J. Cardone, R. E. Jensen, D. T. Resio, V. R. Swail, and A. T. Cox

errors in operationalsurface marine wind field analyses are the dominant source of en'ors in operational wave analyses and forecasts.However, all models were found to systematically underpredict the magnitude of tbe peak sea states in both stormsat buoys that recorded peak HS in excess of about 12 m (ESS). This bias in ESS wave heights was 3.2 m for OWIIG,1.9 m for Resio2G, 2.2 m for OWI3G, and 1.5 m for WAM4. These results provide an interesting assessment of ~heprogress made in the past decade in

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Juan Li and Xiaolei Zou

Unit (MSU) on board the early National Oceanic and Atmospheric Administration (NOAA) satellites, from the Television and Infrared Observation Satellite-N series (TIROS-N) through NOAA-14 from 1979 to 2007. It is also similar to the Advanced Microwave Sounding Unit-A (AMSU-A) channels 3, 5, 7, and 9 on board NOAA-15 , NOAA-16 , NOAA-17 , NOAA-18 , NOAA-19 , and Aqua ( You et al. 2012 ). AMSU-A data have long been incorporated in almost all operational NWP systems in the world, including the

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Xingming Liang and Alexander Ignatov

require validation against known reference data. In ACSPO, expected clear-sky BTs are simulated using the fast Community Radiative Transfer Model (CRTM; Han et al. 2006 ), similar to the Radiative Transfer Model (RTM) for the Television and Infrared Observation Satellite (TIROS) Operational Vertical Sounder (RTTOV) ( Saunders et al. 1999 ). Reynolds daily SST ( Reynolds et al. 2007 ) and National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) upper-air fields are used as

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Ananda Pascual, Christine Boone, Gilles Larnicol, and Pierre-Yves Le Traon

, either to be used alone (e.g., sea level and surface velocity maps) or as an input dataset, which is to be assimilated into numerical prediction models operated by forecasting centers [e.g., Mercator Ocean (available online at ) with the currently operational prototype systems 2 and 3 (PSY2 and PSY3, respectively) or the National Centre for Ocean Forecasting (NCOF; available online at ) using Fast Ocean Atmosphere Model (FOAM) and Proudman

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Paola Malanotte-Rizzoli and Roberta E. Young

very diverse problems and on very different timescales, from 100 years in climate problems, through interannual climate variability and extended seasonal weather forecasting, to a few weeks in regional ocean forecasting. In this latter category, an important example is the interest of navies in ocean frontal systems, such as the prediction of the Gulf Stream front and of its meandering on a timescale of two to four weeks. The operational prediction of such synoptic motions has been the objective of

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Sebastián M. Torres and David Schvartzman

warning-decision process at a given time. In other words, the radar can give forecasters the data they need when and where they need them. This is the concept of adaptive scanning, which has been explored both using simulations (e.g., Proud et al. 2009 ; Reinoso-Rondinel et al. 2010 ; Nguyen and Chandrasekar 2017 ; Weber et al. 2017 ; Schvartzman et al. 2017 ) and through implementations on research and operational radars. While some of the earlier implementations of adaptive scanning were on

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Andrew F. Bennett and Jerome R. Baugh

form to ocean tides (Bennett and Mclntosh 1982; Mclntosh andBennett 1984; Bennett 1985 ) and oceanic equatorial interannual variability (Bennett 1990). It has been appliedin parallel form to oceanic synoptic-scale circulation (Bennett and Thorburn 1992); a parallel application tooperational forecasting of tropical cyclones is in progress (Bennett et al. 1992). For the sake of simplicity, the parallel algorithm is described here for a model consisting of a linear, firstorder wave equation with

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Leonhard Scheck, Martin Weissmann, and Bernhard Mayer

, and in section 5 we compare synthetic images based on operational forecasts to 0.6- μ m observations of the SEVIRI instrument on Meteosat. Last, section 6 contains a summary and conclusions. 2. Data and methods a. COSMO forecasts The NWP model states for which we compute synthetic satellite images are 3-hourly operational forecasts of the German Weather Service [Deutscher Wetterdienst (DWD)]. These forecasts have been generated using the nonhydrostatic limited-area COSMO community model in its

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Quanhua Liu, Xingming Liang, Yong Han, Paul van Delst, Yong Chen, Alexander Ignatov, and Fuzhong Weng

Administration (NASA) Global Modeling and Assimilation Office (GMAO) for reanalysis; the Weather Research and Forecasting (WRF) model for radiance assimilation; the NOAA Microwave Integrated Retrieval System (MIRS) for operational products; and other institutions and companies. There are various requirements on radiative transfer calculation accuracy and precision, depending on application purposes and sensor characteristics. For the Advanced Very High Resolution Radiometer (AVHRR) infrared channel to the

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Likun Wang, Changyong Cao, and Mitch Goldberg

( Gunshor et al. 2009 ). In this study, we use the Infrared Atmospheric Sounding Interferometer (IASI) hyperspectral measurements on the polar-orbiting Meteorological Operation-A ( MetOp-A ) satellite to intercalibrate water vapor channels on the Geostationary Operational Environmental Satellite-11 ( GOES-11 ) and GOES-12 imagers with one year of data. In particular, the near-simultaneous nadir observations with homogeneous scenes from IASI and GOES imagers are spatially collocated. The IASI

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