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Loïk Berre and Gérald Desroziers

these techniques, observation and background perturbations are added (either explicitly or implicitly) to the unperturbed assimilation system in order to simulate associated error contributions and their effects on the error cycling of the assimilation system. Background perturbations correspond partly to the forecast evolution of the previous analysis perturbations, and also to possible additional model perturbations representative of model errors. In all cases, background error covariances are

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P. L. Houtekamer and Fuqing Zhang

applications in the future, but a lot of work has been accomplished since. At the Canadian Meteorological Centre (CMC), a global EnKF has been used operationally since January 2005 to provide the initial conditions for a global ensemble prediction system ( Houtekamer et al. 2005 ). In this system, observation preprocessing is done by a higher-resolution variational analysis system. At the time of this writing, the global EnKF is also used to provide initial and lateral boundary conditions to a regional

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Clark Evans, Kimberly M. Wood, Sim D. Aberson, Heather M. Archambault, Shawn M. Milrad, Lance F. Bosart, Kristen L. Corbosiero, Christopher A. Davis, João R. Dias Pinto, James Doyle, Chris Fogarty, Thomas J. Galarneau Jr., Christian M. Grams, Kyle S. Griffin, John Gyakum, Robert E. Hart, Naoko Kitabatake, Hilke S. Lentink, Ron McTaggart-Cowan, William Perrie, Julian F. D. Quinting, Carolyn A. Reynolds, Michael Riemer, Elizabeth A. Ritchie, Yujuan Sun, and Fuqing Zhang

wind, waves, and precipitation. Jones et al. (2003) stressed the necessity of improving use of existing observations and exploiting new capabilities for understanding ET itself, as well as forecasting the phenomenon, and section 5 summarizes progress in and ongoing needs for both. Section 6 documents advances in the forecasting and analysis of ET. Finally, this review concludes with recommendations for future research. As noted above, this review focuses on ET and its direct impacts. A

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Volkmar Wirth, Michael Riemer, Edmund K. M. Chang, and Olivia Martius

J. Methven , 2013 : Diabatic processes modifying potential vorticity in a North Atlantic cyclone . Quart. J. Roy. Meteor. Soc. , 139 , 1270 – 1282 , . 10.1002/qj.2037 Chang , E. K. M. , 1993 : Downstream development of baroclinic waves as inferred from regression analysis . J. Atmos. Sci. , 50 , 2038 – 2053 ,<2038:DDOBWA>2.0.CO;2 . 10.1175/1520-0469(1993)050<2038:DDOBWA>2.0.CO;2 Chang , E. K. M. , 1999

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J. R. Garratt

: top linerefers to (-), bottom line to (O). The dashed curve represents the variation of Co~(10)' withV based on ~o=au.2/g with a=0.0144.scatter of Fig. 3); 3) the data of Zubkovskii andKravchenko (1967) which are suspect at the higherwinds (see comments of Stewart in Hasse, 1970); and4) the data of Kitaigorodskii et al. (1973) which arenot published in a suitable form for analysis (theirimplied Co2~ values at V ~ 3 and 11 m s-~ overlap thedata in Fig. 3). Finally we note that the C

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Julia H. Keller, Christian M. Grams, Michael Riemer, Heather M. Archambault, Lance Bosart, James D. Doyle, Jenni L. Evans, Thomas J. Galarneau Jr., Kyle Griffin, Patrick A. Harr, Naoko Kitabatake, Ron McTaggart-Cowan, Florian Pantillon, Julian F. Quinting, Carolyn A. Reynolds, Elizabeth A. Ritchie, Ryan D. Torn, and Fuqing Zhang

-0272.1 Kowaleski , A. M. , and J. L. Evans , 2016 : Regression mixture model clustering of multimodel ensemble forecasts of Hurricane Sandy: Partition characteristics . Mon. Wea. Rev. , 144 , 3825 – 3846 , . 10.1175/MWR-D-16-0099.1 Kumpf , A. , M. Rautenhaus , M. Riemer , and R. Westermann , 2019 : Visual analysis of the temporal evolution of ensemble forecast sensitivities . IEEE Trans. Visualization Comput. Graphics , 25 , 98 – 108 , https

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Robert Wood

models indicate such strong second indirect effects ( Lohmann and Feichter 2005 ; Isaksen et al. 2009 ). Fig . 25. Cloud-base precipitation rates R cb from observational case studies in subtropical marine stratocumulus, plotted against the ratio of the cube of the cloud thickness h to the cloud droplet concentration N d . The lines represent linear least-distance regressions to the case studies for each field campaign. From Brenguier and Wood (2009) . 6. Interactions between physical processes

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