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Robert P. d’Entremont, Richard Lynch, Gennadi Uymin, Jean-Luc Moncet, Ryan B. Aschbrenner, Mark Conner, and Gary B. Gustafson

comprehensive understanding of global climate and climate change processes. Global numerical weather prediction (NWP) models rely on the remote sensing of important atmospheric attributes such as temperature, water vapor, and winds in data-sparse regions. Proper retrieval of the first two properties relies on accurate prescriptions of cloud cover in satellite imager data, and the third requires the accurate detection and vertical placement of clouds. Satellite-based atmospheric aerosol prescriptions require

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Christopher S. Velden, Christopher M. Hayden, W. Paul Menzel, James L. Franklin, and James S. Lynch

-resolution infrared-window imagery; 2) automatically derived vectors from animated imagery of two VAS (VISSR Atmospheric Sounder) water vapor channels (6.7 and7.3 ~tm); and 3) gradient winds derived fkom multispectral VAS-retrieved height fields. The complementary distribution of these wind vectors over oceanicregions is illustrated in Fig. 1. Cloud-motion winds areabundant in the cirrus outflow regions, in areas of exposed midlevel cloudiness, and in low-level trade-windcumulus air masses surrounding the stoma

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Christian Keil, Arnold Tafferner, Hermann Mannstein, and Ulrich Schättler

(ECMWF) to assimilate satellite radiance observations [Radiative Transfer for Television and Infrared Observation Satellite Operational Vertical Sounder (RTTOV); Saunders et al. 1999 ]. In this study synthetic brightness temperatures are calculated in the infrared and water vapor channels of a Meteosat satellite by using the “Cloudy RTTOV” radiative transfer code ( Chevallier et al. 2001 ), which also takes model clouds into account. For radar, synthetic reflectivities can be constructed from model

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Min Wen, Song Yang, Augustin Vintzileos, Wayne Higgins, and Renhe Zhang

reduced significantly ( Figs. 6d–f ) and the improvement mainly occurs from resolution T126 to resolution T254 ( Fig. 6e ). However, the western Pacific warm pool becomes even warmer, especially from T62 to T126 ( Fig. 6d ), due to the increased low-level northeasterly wind and the decreased precipitation in situ. Fig . 5. As in Fig. 1 , but for SST (°C). Fig . 6. As in Fig. 2 , but for SST. Water vapor transportation is a key process in the monsoon rainfall cycle, and abundant vapor import often

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Thomas F. Lee and James S. Boyle

represent a pixel-bypixel application of the nonlinear regression equationderived by Alishouse et al. (1990). Valid for all theunfrozen oceans of the world, the equation uses a combination of the 19-, 22-, and 37-GHz channels. Whileprecipitable water represents a vertical integration, itsmagnitude is strongly correlated with absolute humidity in the marine boundary layer (Liu 1986; Hsu andBlanchard 1989). Thus, the synoptic evolution of theprecipitable water field strongly depends on vapor advection

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Raymond M. Zehr, James F. W. Purdom, John F. Weaver, and Robert N. Green

. The MSI mode allows frequentinterval water vapor imagery. The three ]nodes of operation are not independent; when the satellite is operated in one mode, data from the other modes are notavailable. When VAS is operated in the dwell sounding ~--/ I \ \ ~ '~.X .,Y1 ' I ' I ' ~2Sl-/ ~ \ x, VI2 ~5oI- ~ \ \ L.."~ / co~ C,AN,ELS 850k/ ~' ~ I 6 ~ 5 1000

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Randhir Singh, P. K. Pal, C. M. Kishtawal, and P. C. Joshi

and second European Remote Sensing satellites ( ERS-1/2 ; Isakessen and Stoffelen 2000 ; Leidner et al. 2003 ), and water vapor or cloud-derived satellite winds. The scatterometer provides both surface wind speed and direction, which significantly increases the tropical cyclone (TC) forecaster’s knowledge of TC formation and TC surface wind structure. Global coverage of scatterometer data has been routinely available ( Tomassini et al. 1998 ) to forecasters and researchers since 1991 from the

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Rich F. Coleman, James F. Drake, Michael D. McAtee, and Leslie O. Belsma

forecast day, but this difference is not statistically significant. c. Water vapor mixing ratio verification over the entire domain Verification was performed on the forecast water vapor mixing ratio, using reports from the 31 available AFWA surface stations as truth. AQMD stations do not report variables from which the water vapor mixing ratio may be determined, so no verification was possible with the AQMD station set. Here again, the biases are comparable for the two cases over the entire domain

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Richard P. James and John H. E. Clark

tropical meteorology. The Tropical Ocean Global Atmosphere (TOGA) Coupled Ocean–Atmosphere Response Experiment (COARE) found that lateral advection of dry air into the tropical western Pacific occurs frequently and is of primary importance in the atmospheric water vapor budget ( Parsons et al. 2000 ). Several studies have demonstrated the suppression of deep convection by the dry intrusions (e.g., Lucas and Zipser 2000 ; Brown and Zhang 1997 ). This may be attributable either to increased convective

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Stanley G. Benjamin and William R. Moninger

model forecast cycle: The Rapid Refresh . Mon. Wea. Rev. , 144 , 1669 – 1694 , doi: 10.1175/MWR-D-15-0242.1 . Cardinali, C. , Isaksen L. , and Andersson E. , 2003 : Use and impact of automated aircraft data in a global 4DVAR data assimilation system . Mon. Wea. Rev. , 131 , 1865 – 1877 , doi: 10.1175//2569.1 . Petersen, R., Cronce, L. Mamrosh, R. Baker, and R. Pauley P. , 2016 : On the impact and future benefits of AMDAR observations in operational forecasting. Part II: Water

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