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B. S. Sandeepan, V. G. Panchang, S. Nayak, K. Krishna Kumar, and J. M. Kaihatu

the wind pattern changes on short spatial and temporal scales. This regional model is implemented through the dynamical downscaling of the global NCEP Final Analysis using the WRF Model ( Skamarock et al. 2005 ), essentially following the approach of Olsson et al. (2013) and Carvalho et al. (2014) . No fewer than 19 automated weather stations in Qatar and two offshore buoys ( Fig. 1 ) are available for evaluation of model performance. These data sources comprise a relatively high density of

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Zhaoyan Liu, Mark Vaughan, David Winker, Chieko Kittaka, Brian Getzewich, Ralph Kuehn, Ali Omar, Kathleen Powell, Charles Trepte, and Chris Hostetler

to complement current measurements and improve our understanding of weather and climate. The availability of a global, multiyear set of vertically resolved measurements of the earth’s atmosphere should ultimately lead to great improvements in both weather and climate models. CALIOP is the first satellite-borne lidar optimized specifically for aerosol and cloud measurements, and is also the first polarization lidar in space. CALIOP is a dual-wavelength, polarization-sensitive elastic backscatter

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Emily M. Riley Dellaripa, Aaron Funk, Courtney Schumacher, Hedanqiu Bai, and Thomas Spangehl

1. Introduction Comparisons between general circulation models (GCMs) and observations are critical to assess the fidelity of a GCM and pinpoint GCM deficiencies, such as precipitation biases ( Dai 2006 ; Sun et al. 2006 ; Stephens et al. 2010 ; Tan et al. 2018 ). Many satellite–GCM comparisons are done using surface rainfall, which is a highly derived quantity from both satellite measurements and GCM equations. In addition, focusing only on surface rain severely limits model evaluation

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R. Harikumar, T. M. Balakrishnan Nair, G. S. Bhat, Shailesh Nayak, Venkat Shesu Reddem, and S. S. C. Shenoi

1. Introduction Indian National Centre for Ocean Information Services (INCOIS) has established an ocean forecast system, named the Indian Ocean Forecasting System (INDOFOS). The purpose of this system is to predict ocean surface waves, general circulation features, and oil spill trajectories at various spatiotemporal scales. Forecast models of this system are forced by the analyzed and forecasted atmospheric products from the National Centre for Medium Range Weather Forecasting (NCMRWF), the

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

northwest shelf operational model. Based on EnOI, D. Liu et al. (2018) carried on observing system simulation experiments (OSSE) to study the impact of assimilating moored velocity on the improvement of the simulation in the intraseasonal variability. Crosby et al. (2017) reported that assimilating buoy observations can improve model predictions and wave hindcasts and suggested that dense observational networks lead to a significant improvement in model performance. Despite successful applications

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V. Sanil Kumar and T. Muhammed Naseef

and ERA-I. Wave data in ERA-I were produced on a grid with a resolution of the order of 110 km. Even though shallow-water physics are active in the model, due to the coarseness of the grid, the coupled wave component ( Janssen 2004 ) is coarser at approximately 110 km and the mean water depth at the point of interest is different from the buoy location. Also, the data used for evaluating the ERA-I data in the present study were collected using buoys located 1.5–5 km from the coast ( Table 1

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Je-Yuan Hsu, Ren-Chieh Lien, Eric A. D’Asaro, and Thomas B. Sanford

from float measurements and surface wave properties at the float positions assuming the empirical JONSWAP spectrum ( Hasselmann et al. 1973 ; appendix D , section a ). In section 5 we estimate surface waves under Fanapi using two methods—one assuming the JONSWAP spectrum and one assuming a single dominant surface wave ( Sanford et al. 2011 ). The oceanic surface wave model WAVEWATCH III (ww3) is used to simulate the surface wave field under Typhoon Fanapi. In section 5 the ww3 model outputs

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Sean C. Crosby, William C. O’Reilly, and Robert T. Guza

) directional bias in the southern end of the SBC. A ranking of buoy site skill, averaged across select boundary conditions ( Table 4 ), indicates the poorest model performance at buoy site 107 (in SBC) and at the southernmost buoy site, 093. Fig . 4. Model performance at each SC nearshore buoy location for (a) wave height and (b) wave direction . Each circle quadrant shows a different boundary condition (key, upper right). Circle quadrant size shows RMSE (key, lower left), and color shows bias (color

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Shawn R. Smith, Kristen Briggs, Nicolas Lopez, and Vassiliki Kourafalou

that was not captured by the research vessel data. Comparisons over specific cruise tracks are also performed for a more detailed evaluation. An example is given in Fig. 11 , where three individual cruises provided by SAMOS allow for examination of the regional variations in model performance in the northern Gulf of Mexico ( Fig. 11 ). This examination compares all three HYCOM-based models (each having different horizontal grid resolutions) discussed herein: GLB (¾°), GoM (⅜°), and NGoM (⅝°) HYCOM

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Katherine A. Pingree-Shippee, Norman J. Shippee, and David E. Atkinson

useful in this regard because the source code is available and it has a well-established validation track record. Previous validation efforts that have included the Alaska region (in particular, the Bering Sea and the Gulf of Alaska) indicate that modeled significant wave heights are generally underestimated ( Chawla et al. 2009 , 2011 ; Spindler and Tolman 2010 ; Stopa and Cheung 2014 ). These studies consist of climatological evaluations of the wave states, they do not perform analyses on a

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