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Seoyeon Lee and Kwang-Yul Kim

1. Introduction General circulation models (GCMs) are a widespread means of understanding future climate and various aspects of climate changes ( Hansen et al. 1988 ; Cox et al. 1999 ; Murphy et al. 2004 ; IPCC 2013 ). They also serve as a useful tool for seasonal forecasts and long-term predictions. Considerable effort to improve the performance of GCMs has been made for the past decades, and GCMs are capable of simulating large-scale climatological features and their changes in the

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Xiangming Zeng, Yizhen Li, and Ruoying He

of life and property damage in many Gulf coastal communities. Much effort has been expended to predict LC variation and its eddy shedding process using remote sensing observations and primitive equation numerical models. Oey et al. (2005a) performed a study to predict the LC and its eddy frontal position using the Princeton Ocean Model (POM). Yin and Oey (2007) applied the bred-ensemble forecast technique to estimate the locations and strengths of the LC and LC eddies from July to September

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Michael L. Van Woert, Cheng-Zhi Zou, Walter N. Meier, Philip D. Hovey, Ruth H. Preller, and Pamela G. Posey

1. Introduction The meteorological community has a long-standing history of environmental forecasting. In concert with developing new forecast systems, that community has put considerable energy into developing methods for assessing changes in forecast skill. Operational ocean forecasting systems are in their infancy, but there is considerable interest in developing robust, skillful forecast systems for that environment (e.g., Koblinsky and Smith 2001 ; Pinardi and Woods 2002 ). Sea ice

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Qinghua Yang, Martin Losch, Svetlana N. Losa, Thomas Jung, and Lars Nerger

and forecasts, the obvious way is to combine available sea ice observations and coupled ice–ocean models with advanced data assimilation techniques ( Lisæter et al. 2003 ). In contrast to the successfully observed sea ice concentration with satellite-based passive microwave instruments ( Cavalieri and Parkinson 2012 ; Stroeve et al. 2012 ), observing sea ice thickness from space is still a great challenge ( Kwok and Sulsky 2010 ; Kaleschke et al. 2012 ; Tian-Kunze et al. 2014 ). Because of the

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Renée Elio, Johannes De Haan, and G. S. Strong

December 1986)ABSTRACT An experienced forecaster can use several different types of knowledge in forecasting Fh-st, there is his theoreticalunderstanding of meteorology, which is (veil entrenched in current numerical models. A second type is his"local knowledge," gained over years of experience, of how weather is likely to form in his forecast area. Thiskind of local familiarity is not easily captured with traditional numeric techniques, but might provide additionalinsights for prediction that

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Harry T. Ochs III and Stanley Q. Kidder

218 JOURNAL OF ATMOSPHERIC AND OCEANIC TECHNOLOGY VOLUME6A Forecasting/Nowcasting System for Remote Field Locations HARRY T. OCHS III AND STANLEY Q. KIDDERClimate and Meteorology Section, Illinois State Water Survey, Champaign, Illinois12 April 1988 and I August 1988ABSTRACT Vast quantities of frequently updated weather data for both forecasting and nowcasting are generally requiredin meteorological

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Kalpesh Patil, M. C. Deo, and M. Ravichandran

. More recently Mahongo and Deo (2013) predicted the SST over a subsequent month and season at sites near an East African shore using different NNs and also based on the autoregressive integrated moving average (ARIMA) method. It was observed that among all of the NNs, the nonlinear autoregressive network had better skills not only in forecasting monthly and seasonal SST anomalies, but also in capturing ENSO and Indian Ocean dipole (IOD) events. This observation with respect to single

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Ricardo Martins Campos, Vladimir Krasnopolsky, Jose-Henrique G. M. Alves, and Stephen G. Penny

1. Introduction The U.S. National Centers for Environmental Prediction (NCEP) have produced atmospheric forecasts using ensembles since 1992 and wave ensembles since 2005. Kalnay (2003) describes the two main advantages of using ensemble forecasts: the ensemble members tend to smooth out uncertain components, which lead to better skill than single deterministic forecasts; and the spread of the ensemble members provides an estimation of the uncertainty. The mean of the ensemble members is

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Birgitte Rugaard Furevik, Harald Schyberg, Gunnar Noer, Frank Tveter, and Johannes Röhrs

1. Introduction and state of art Since synoptic observations are sparse in the polar regions, forecasting and monitoring of polar lows (PL) are to a large degree based on satellite observations from passive radiometers, in particular the National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR), and from scatterometer winds in addition to numerical models. Polar lows are relatively small (200–1000 km) short-lived (1–2 days) cyclones that form over

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Valliappa Lakshmanan, Robert Rabin, Jason Otkin, John S. Kain, and Scott Dembek

1. Introduction Precipitation forecasts from numerical weather prediction (NWP) models have traditionally been presented to forecasters as accumulated depth over specified time intervals (e.g., 6 h). Guidance regarding clouds has generally been lacking, aside from postprocessed statistics about general cloud properties such as cloud cover (e.g., Jakob 1999 ; Teixeira and Hogan 2002 ). Part of the reason for this rather ambiguous presentation of clouds and precipitation is that many aspects of

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