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Mathieu Hamon, Eric Greiner, Pierre-Yves Le Traon, and Elisabeth Remy

1. Introduction Altimetry data are considered to be the most important input for assimilation systems. After several geophysical corrections, an accurate estimation of along-track sea surface height (SSH) is derived from altimeter measurements. Anomalies of SSH with respect to a surface reference [sea level anomalies (SLAs)] are used to constrain 3D ocean forecasts by means of a data assimilation scheme. Over the past 20 years, several studies have pointed out that multiple altimeter missions

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Wei Li, Yuanfu Xie, Zhongjie He, Guijun Han, Kexiu Liu, Jirui Ma, and Dong Li

error is removed from the observation, and it can thus provide a better or more accurate analysis. In this paper, the multigrid data assimilation scheme is applied to assimilate SST and temperature profiles of the China Seas into a numerical model in a retroactive real-time forecast experiment. A comparison of the results to those of the traditional 3DVAR using correlation scales is presented. In the following section, the theory and verification of the multigrid data assimilation scheme are

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Chengzu Bai, Ren Zhang, Senliang Bao, X. San Liang, and Wenbo Guo

1. Introduction In predicting the interannual variability of tropical cyclone (TC; see appendix A for a glossary of the acronyms) geneses over the western North Pacific (WNP), there are two outstanding problems that have caught wide attention. The first problem is unraveling the causal relation between various climate factors and the WNP TC genesis, and the other problem is how to forecast the TC genesis. Regarding the first problem, there have been many studies on cyclone

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Chih-Chiang Wei

. Because of high climatological uncertainty, the rainfall prediction provided by the CWB is a range; however, these ranges are not always accurate. At present, the CWB employs an ensemble forecasting method that is dynamic and flow dependent to quantify and communicate forecast uncertainty. However, CWB total rainfall nowcasts often have high prediction errors. These errors are primarily attributable to the island’s Central Mountain Range (CMR). The CMR is 340 km long and 80 km wide, with an average

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Mar M. Flexas, Martina I. Troesch, Steve Chien, Andrew F. Thompson, Selina Chu, Andrew Branch, John D. Farrara, and Yi Chao

) from a data-assimilating forecasting numerical ocean model, evaluates different potential glider paths (1, 2, …, n ) in real time, and selects the most promising path based on feature scoring. The algorithm will choose the path with more gradients, . After the model forecast is delivered, the whole decision-making process takes ~90 s. The true potential of the method relies on using multiple platforms. This 9 Jun 2016 ocean color image by the Visible Infrared Imaging Radiometer Suite (VIIRS) on

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Xin Zhang, Xiang-Yu Huang, and Ning Pan

1. Introduction During the past two decades, the use of the adjoint technique in meteorology and oceanography rapidly increased. The adjoint model is a powerful tool in many applications, such as data assimilation, parameter estimation, and sensitivity analysis ( Errico and Vukicevic 1992 ; Errico 1997 ; Rabier et al. 1996 ; Langland et al. 1999 ; Li et al. 1999 ; Xiao et al. 2002 , 2008 ; Kleist and Morgan 2005a , b ). The Weather Research and Forecasting (WRF) modeling system

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Richard M. Yablonsky, Isaac Ginis, Biju Thomas, Vijay Tallapragada, Dmitry Sheinin, and Ligia Bernardet

1. Introduction The U.S. National Oceanic and Atmospheric Administration (NOAA) Environmental Modeling Center provides real-time tropical cyclone (TC) track and intensity forecast guidance to NOAA’s National Hurricane Center. NOAA’s Hurricane Weather Research and Forecast Model (HWRF), which is a regional, dynamical TC model, became operational in 2007 after 5 years of development at the Environmental Modeling Center, in collaboration with NOAA’s Geophysical Fluid Dynamics Laboratory (GFDL) and

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Bertrand Bessagnet, Laurent Menut, Florian Couvidat, Frédérik Meleux, Guillaume Siour, and Sylvain Mailler

1. Introduction The deterioration of air quality is one of the major environmental threats throughout the world. The impact of pollutants on health, ecosystems, and climate are clearly highlighted by many studies ( Lim et al. 2012 ; Faustini et al. 2014 ). Modeling tools, the so-called chemical transport models such as “CHIMERE,” are useful to evaluate, analyze, and forecast air quality from urban to global scales ( Mailler et al. 2017 ). In France, the national forecast platform “PREV

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Matthias Grzeschik, Hans-Stefan Bauer, Volker Wulfmeyer, Dirk Engelbart, Ulla Wandinger, Ina Mattis, Dietrich Althausen, Ronny Engelmann, Matthias Tesche, and Andrea Riede

1. Introduction One outstanding goal of atmospheric research is the improvement of the skill of weather forecast models. Particularly critical is the prediction of precipitation and related extreme events. Only optimized models with well-specified performance can be applied to study the predictability of these meteorological situations. Corresponding research is the subject of several ongoing projects of the World Weather Research Program (WWRP) such as The Observing System Research and

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Susan Rennie, Peter Steinle, Alan Seed, Mark Curtis, and Yi Xiao

data assimilation or other applications. The classifier has been gauged as quite successful (>90%) at identifying precipitation but some types of clutter are identified correctly around only 50% of the time ( Rennie et al. 2015 ). The first major test of Doppler radar wind assimilation was during the Sydney 2014 Forecast Demonstration Project (FDP), which was a 10-week real-time test of BoM’s developmental 1.5-km horizontal-resolution (NWP) system. For the FDP, further improvements to the Bayesian

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