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Patrik Benáček and Máté Mile

spatially and temporally collocated reference. The NWP centers use as reference the model forecast or an analysis as best estimate of the atmosphere to diagnose the bias in the observations. Because this reference is not completely unbiased, the bias thus diagnosed contains a mixture of observation and NWP model errors that cannot be distinguished ( Dee 2005 ). Therefore, good knowledge of the source and nature of the biases is essential for its subsequent removal. The background biases come mostly from

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Bruce Albrecht, Virendra Ghate, Johannes Mohrmann, Robert Wood, Paquita Zuidema, Christopher Bretherton, Christian Schwartz, Edwin Eloranta, Susanne Glienke, Shaunna Donaher, Mampi Sarkar, Jeremy McGibbon, Alison D. Nugent, Raymond A. Shaw, Jacob Fugal, Patrick Minnis, Robindra Paliknoda, Louis Lussier, Jorgen Jensen, J. Vivekanandan, Scott Ellis, Peisang Tsai, Robert Rilling, Julie Haggerty, Teresa Campos, Meghan Stell, Michael Reeves, Stuart Beaton, John Allison, Gregory Stossmeister, Samuel Hall, and Sebastian Schmidt

; van der Dussen et al. 2013 ). The second was a satellite-derived composite ( Sandu et al. 2010 ; de Roode et al. 2016 ) of several thousand Lagrangian trajectories based on Moderate Resolution Imaging Spectroradiometer (MODIS) cloud observations with trajectories based on European Centre for Medium-Range Weather Forecasts (ECMWF) reanalyses. Neither case includes a good accompanying set of aerosol observations in or above the boundary layer or the robust statistics on horizontal cloud and

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Jenny V. Turton, Thomas Mölg, and Dirk Van As

investigate the meteorology and atmospheric processes present in this region. Because of the relatively short observational period in northeast Greenland (and especially over 79N glacier, where only four incomplete years of data are available), reanalysis data are used to extend the climatology back to 1979, within the region. A regional case study using the Weather Research and Forecasting (WRF) Model complements the observations and provides additional information on the links between synoptic

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Bradley W. Klotz and David S. Nolan

1. Introduction Invaluable strides toward improving tropical cyclone (TC) track and intensity forecasts have been made over the past few decades. While track forecasts have steadily improved ( Gall et al. 2013 ), intensity forecasts are improving more slowly due to deficiencies in the understanding of physical processes in TCs and the ability to model those processes in numerical simulations ( DeMaria et al. 2014 ). As part of this desire to understand the deficiencies in TC forecasting, data

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Johna E. Rudzin, Lynn K. Shay, and Benjamin Jaimes de la Cruz

. Gray arrows represent ocean mixing. Black arrow represents wind forcing. From a TC forecasting perspective, Halliwell et al. (2011) stated that to correctly forecast intensity evolution within a TC, the ocean component of a coupled forecast model must accurately predict the pattern and rate of SST cooling. Furthermore, they found that the ocean model component is most sensitive to ocean initialization with regards to upper-ocean temperature and salinity profiles. However, the Caribbean Sea is one

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Vasubandhu Misra and Amit Bhardwaj

year leads to unrealistic onset and demise dates of the NEM season as a result of the CA curve overlapping with the SISM season in boreal summer and fall seasons. It also becomes clear, from the analysis presented in the following section, why we avoid the use of rainfall for defining the onset and demise of the NEM. We make use of the Climate Forecast System Reanalysis (CFSR; Saha et al. 2010 ) to make composites of upper-air and upper-ocean variables, and these are presented in the following

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Christopher S. Bretherton, Isabel L. McCoy, Johannes Mohrmann, Robert Wood, Virendra Ghate, Andrew Gettelman, Charles G. Bardeen, Bruce A. Albrecht, and Paquita Zuidema

with large-eddy simulations of many of the cruises ( McGibbon and Bretherton 2017 ) as well as satellite retrievals ( Painemal et al. 2015 ), global weather forecast model output ( Ahlgrimm et al. 2018 ), and reanalysis ( Kalmus et al. 2015 ). The focus of this paper is the Cloud System Evolution in the Trades (CSET) campaign in July–August 2015 ( Albrecht et al. 2019 ; hereafter A19 ). CSET aimed to document and understand cloud processes in the summertime Sc–Cu transition using airborne

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Johannes Mohrmann, Christopher S. Bretherton, Isabel L. McCoy, Jeremy McGibbon, Robert Wood, Virendra Ghate, Bruce Albrecht, Mampi Sarkar, Paquita Zuidema, and Rabindra Palikonda

was empirically chosen to balance the competing interests in reducing noise in box-averaged quantities while avoiding including observations from regions subject to significantly different large-scale forcings; a comparison of the GOES cloud fraction estimate to that derived from a radar-lidar cloud mask can be found in Bretherton et al. (2019) . Supplemental data are drawn from the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis, version 5 (ERA5) [ Copernicus Climate Change

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Mampi Sarkar, Paquita Zuidema, Bruce Albrecht, Virendra Ghate, Jorgen Jensen, Johannes Mohrmann, and Robert Wood

Lagrangian resampling 2 days later, constrains the “outbound” CA-to-HI flight path to a more northerly route. This route initially follows along 40°N, with the first boundary layer module beginning at 40°N, 130°W, or 10°N and at the western edge of the climatological maximum. Forward trajectories, calculated using HYSPLIT ( Stein et al. 2015 ) and the National Centers for Environmental Predication (NCEP) Global Forecast System meteorology, were initialized at 40°N, 130°W and further west at approximately

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