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Edward A. O’Lenic, David A. Unger, Michael S. Halpert, and Kenneth S. Pelman

. Also, the term “outlook” is used in reference to the final 1- or 3-month outlook, while “forecast” is used when describing tools used to produce an outlook, or forecasting in general. While the instantaneous details of the weather are unpredictable beyond a limit of about 2 weeks, statistics such as weekly and longer means, and standard deviations, are predictable to some degree ( Lorenz 1982 ). Evaluations of the skill of operational LRFs by Namias (1953) and subsequently Barnston (1994a , b

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Jenni L. Evans, Justin M. Arnott, and Francesca Chiaromonte

improved operational numerical modeling of these storms ( McAdie and Lawrence 2000 ). Model forecasts for TCs are traditionally assessed predominantly on track, with some attention paid to intensity validation on central pressure ( McBride and Holland 1987 ; Fiorino et al. 1993 ; Surgi et al. 1998 ; Nagata et al. 2001 ; Goerss et al. 2004 ). Little if any attention is paid to the storm structure and validation of quantitative precipitation forecasts for TCs in their infancy. More recently, the

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Benjamin J. E. Schroeter, Phil Reid, Nathaniel L. Bindoff, and Kelvin Michael

implemented in late 2000 to provide experimental real-time meteorological forecasts for the Antarctic region ( Powers et al. 2003 ) and has been run operationally as a real-time implementation of the Weather Research and Forecasting (WRF) Model ( Skamarock et al. 2008 ) since 2008. Initial conditions in AMPS are generated from the National Centers for Environmental Prediction (NCEP) global forecasting system as well as space-borne and surface observations. As a limited-area model, AMPS covers six domains

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Julie Demargne, Limin Wu, Satish K. Regonda, James D. Brown, Haksu Lee, Minxue He, Dong-Jun Seo, Robert Hartman, Henry D. Herr, Mark Fresch, John Schaake, and Yuejian Zhu

-based decision making, such integrated uncertainty information needs to be communicated to forecasters and users effectively. In an operational environment, ensembles are an effective means of producing uncertainty-quantified forecasts. Ensemble forecasts can be ingested in a user's downstream application (e.g., reservoir management decision support system) and used to derive probability statements about the likelihood of specific future events (e.g., probability of exceeding a flood threshold). Atmospheric

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Shih-Yu Wang and Adam J. Clark

handling planetary boundary layer evolution (e.g., Bukovsky et al. 2006 ; Coniglio et al. 2009 ). Further difficulties in forecasting warm-season precipitation in current operational NWP models such as the National Centers for Environmental Prediction (NCEP) North American Mesoscale Model (NAM; Janjić 2003 ) arise from the use of cumulus parameterization (CP), which is needed to depict the effects of subgrid-scale convective processes (e.g., Molinari and Dudek 1992 ). Specifically, previous works

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Prakash Pithani, Sachin D. Ghude, R. K. Jenamani, Mrinal Biswas, C. V. Naidu, Sreyashi Debnath, Rachana Kulkarni, Narendra G. Dhangar, Chinmay Jena, Anupam Hazra, R. Phani, P. Mukhopadhyay, Thara Prabhakaran, Ravi S. Nanjundiah, and M. Rajeevan

estimated economic loss to IGIA during 2011–16 was estimated at $3.9 million USD ( Kulkarni et al. 2019 ). Therefore, fog prediction plays an essential role in road/rail transport, and for airline operations. There has been increasing concern in India to examine the genesis of fog formations and potential methods for operational fog forecasting on local to regional scales to mitigate the issue. However, the accurate prediction of fog is very challenging for the present numerical weather prediction (NWP

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Chermelle Engel and Elizabeth E. Ebert

, with an inset of Tasmania to highlight differences in topography over small regions. [Reprinted from Engel and Ebert (2007) .] In addition to NWP models run locally at the Bureau of Meteorology, Australian forecasters have access to a variety of model output from international forecasting centers. Current Australian public weather forecasts make heavy use of an automated first-guess forecast that combines local and international forecasts into an operational consensus forecast (OCF; Woodcock and

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Xiping Zhang and Hui Yu

computational power and numerical weather prediction (NWP) models, track forecasts of TCs have been improved significantly during the past few decades. Additionally, the use of a multimodel consensus in operational centers became widespread about 15 years ago in an effort to improve track forecasts, which usually outperforms the individual models ( Krishnamurti et al. 2010 ; Sampson et al. 2005 ; Goerss et al. 2004 ). There are two main methods for creating a consensus. The first is to take a simple

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A. Sankarasubramanian, Upmanu Lall, and Susan Espinueva

diagnostic analyses for each forecasting time step. Given that many research institutions and agencies are issuing climate forecasts on a monthly basis using general circulation models (GCMs), an alternate approach would be to utilize GCM-predicted fields to develop operational streamflow forecasts. However, GCM-predicted fields are typically available at larger spatial scales (2.5° × 2.5°), which need to be downscaled to obtain streamflow forecasts. Dynamical downscaling, a physical approach of nesting

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Paul J. Roebber

586 WEATHER AND FORECASTING VOLUME5Variability in Successive Operational Model Forecasts of Maritime CyclogenesisPAUL J. ROEBBERDepartment of Meteorology. McGill University. Montreal(Manuscript received 2 February 1990, in final form 25 June 1990)ABSTRACT The level of variability present in operational model simulations of marine cyclogenesis was examined. Successive forecasts valid for the same

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