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Ning Wang, Jian-Wen Bao, Jin-Luen Lee, Fanthune Moeng, and Cliff Matsumoto

and distribution of numerical forecasts from these high-resolution global models is their large data sizes. Datasets produced by these models typically have sizes in tens to hundreds of gigabytes. Efficient transmission and storage of these datasets poses a practical and important problem for both operational and research communities. Efforts have been made to compress high-resolution model data on Cartesian grids in two of the most widely used data formats for geoscience data—Network Common Data

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Christopher C. Hennon, Charles N. Helms, Kenneth R. Knapp, and Amanda R. Bowen

1. Introduction For many reasons, accurate forecasts of tropical cyclone development (tropical cyclogenesis) remain elusive. Tropical cyclones form in areas where little (if any) in situ data are available; thus, numerical models are initialized almost exclusively with data retrieved from satellites. These data, with little exception, do not provide the necessary space and time resolution at the surface and other atmospheric levels to properly initialize the large areas of convection [tropical

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Wenfeng Lai, Jianping Gan, Ye Liu, Zhiqiang Liu, Jiping Xie, and Jiang Zhu

1. Introduction In ocean model simulation, data assimilation (DA) provides an efficient way to address the uncertainties and improve the forecasts of the ocean model by using observations. Due to the uncertainties over model parameterization, driving forces, and initial and boundary conditions, ocean models are imperfect representations of the actual environments. Assimilation techniques for ocean forecasts are commonly classified into sequential methods (e.g., Kalman filter) and variational

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Yicun Zhen, Pierre Tandeo, Stéphanie Leroux, Sammy Metref, Thierry Penduff, and Julien Le Sommer

estimating upper-ocean circulation at scales larger than the first Rossby radius of deformation where the geostrophic balance holds. Satellite altimetry is therefore a key source of information for ocean monitoring systems, and an essential constraint in ocean forecasting systems. In practice, many oceanographic applications of satellite altimetry rely on gridded SSH products rather than on raw along-track SSH data. Satellite altimeters indeed provide SSH measurements along ground tracks, following a

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James A. Cummings and Ole Martin Smedstad

1. Introduction Assessment of the impact of observations on reducing ocean model forecast error from data assimilation is a fundamental aspect of any ocean analysis and forecasting system. The purpose of assimilation is to reduce the model initial condition error. Improved initial conditions should lead to an improved forecast. However, it is likely that not all observations assimilated have equal value in reducing forecasting error. Estimation of which observations are best and the

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Will McCarty, David Carvalho, Isaac Moradi, and Nikki C. Privé

radiation measured from space to information of the emitting molecules (e.g., Derber and Wu 1998 ; Susskind et al. 1998 ; McNally et al. 2000 ). The improved use of these radiance observations and advances in the observing system have resulted in equally skillful forecasts in the Northern Hemisphere, which is well-observed by conventional observations, and the Southern Hemisphere, which is sparsely observed by conventional observations ( Bauer et al. 2015 ; Diniz and Todling 2020 ). There remains a

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Hua Xie, Nicholas R. Nalli, Shanna Sampson, Walter W. Wolf, Jun Li, Timothy J. Schmit, Christopher D. Barnet, Everette Joseph, Vernon R. Morris, and Fanglin Yang

calculation. Li et al. (2010) found that a regression is usually better than a short-term forecast field since the regression uses combined forecast and ABI IR radiances as predictors, so the LAP algorithm starts with a statistical nonlinear regression technique. The regression coefficients can be obtained using the general least squares method with a global training database. The “SeeBor database” ( Borbas et al. 2005 ) is composed of global temperature, humidity, and ozone profiles from the

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S. J. Thomas, M. Desgagné, and R. Benoit

given way to scalable computer architectures. “Scalable” implies that the sustainable performance of a parallel scientific application program increases as the problem size increases with the number of processors. A more precise definition of scalability can be found in the text by Kumar et al. (1994) . In the context of real-time weather forecasting, a 24–48-h weather forecast must be produced within a wall-clock time of approximately 1 h in order for the forecast to be useful. Thus, the problem

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Huizhen Yu, Hongli Wang, Zhiyong Meng, Mu Mu, Xiang-Yu Huang, and Xin Zhang

1. Introduction The question of predictability in the atmospheric sciences has received considerable attention since the work of Lorenz (1963 , 1975) . Sensitivity analysis, which examines, but is not limited to, the forecast response to a change in the initial conditions, is one way to study predictability, and a potential application of sensitivity analysis is targeted observation. Targeted observation is a method in which a special area is determined to gather extra observations that

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Sean Waugh and Terry J. Schuur

1. Introduction Radiosondes are used the world over to provide fundamental observations throughout the depth of the lower atmosphere ( Luers and Eskridge 1998 ). In the United States, National Weather Service (NWS) forecast offices across the country coordinate radiosonde launches of weather balloons twice a day (valid at 0000 and 1200 UTC) to obtain vertical profiles of the atmosphere that are used to produce local forecast products and initialize a variety of forecast models. During major

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