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Xin Zhang, Ying-Hwa Kuo, Shu-Ya Chen, Xiang-Yu Huang, and Ling-Feng Hsiao

RO soundings are currently being used at several global operational NWP centers, including the National Centers for Environmental Prediction (NCEP; Cucurull and Derber 2008 ), the European Centre for Medium-Range Weather Forecasts (ECMWF; Healy 2008 ), the Met Office (UKMO; Rennie 2010 ), and Météo-France ( Poli et al. 2009 ). Because of the success of COSMIC, U.S. agencies and Taiwan have decided to move forward with a follow-up RO mission [called Formosa Satellite Mission 7 (FORMOSAT-7

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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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Jared W. Marquis, Mayra I. Oyola, James R. Campbell, Benjamin C. Ruston, Carmen Córdoba-Jabonero, Emilio Cuevas, Jasper R. Lewis, Travis D. Toth, and Jianglong Zhang

1. Introduction The atmospheric science community has become largely dependent on the growing database of operational satellite atmospheric remote sensing observations for weather monitoring, analysis, and forecasting (e.g., Rabier 2005 ; Mecikalski and Bedka 2006 ). In particular, Hyperspectral Infrared Sounder (HIS) observations, which are ingested into numerical weather prediction (NWP) models through data assimilation (DA), are a critical component of NWP, directly causing significant

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

, Hennon et al. (2005) analyzed 4 yr (1998–2001) of Atlantic TCC activity by tracking TCCs manually in Geostationary Operational Environmental Satellite (GOES) infrared (IR) imagery, a process that took over 1 yr. More recently, Gierach et al. (2007) tracked TCCs using GOES imagery and noted that the process was time consuming, such that the number of analyzed tracks was limited. Second, the statistical models, especially those developed for operational forecasting, are usually reliant on the global

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Allen B. White, Daniel J. Gottas, Eric T. Strem, F. Martin Ralph, and Paul J. Neiman

technique used during PACJET to make the output from this algorithm available on the internet in near–real time. The remainder of this section explains our motivation for using profiles of DVV in addition to radar reflectivity to detect the bright band. Section 2 shows how accurate information about the melting level is critical to the success of operational river forecasts, providing an example of a user community who could benefit from real-time measurements of BBH. Section 3 describes the

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Steven D. Miller, Daniel T. Lindsey, Curtis J. Seaman, and Jeremy E. Solbrig

advances are improvements to the overall quality and information content of the imagery. However, with the copious volumes of new data come unique challenges as well. Namely, with so many independent pieces of information now available, it is impractical for a human analyst to consider them independently, particularly in a time-critical operational forecast setting. In many cases, isolating a unique signal (or physically based “spectral fingerprint”) characteristic of a given environmental parameter

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Peter A. E. M. Janssen, Saleh Abdalla, Hans Hersbach, and Jean-Raymond Bidlot

fourth independent dataset by running the wave forecasting system with 6-hourly ECMWF-analyzed winds over the 4-yr period of January 2000 until December 2003 (after a 2-month warm-up period to eliminate any altimeter impacts). No ERS-2 altimeter data were assimilated so that the hindcast results are independent of altimeter and buoy wave height data. From the operational results and the hindcast the following collocated dataset was generated: X = hindcast, Y = altimeter, Z = buoy, V = first

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Susan Rennie, Peter Steinle, Alan Seed, Mark Curtis, and Yi Xiao

. Wakimoto , and R. W. Russell , 1994 : Boundary layer clear-air radar echoes: Origin of echoes and accuracy of derived winds . J. Atmos. Oceanic Technol. , 11 , 1184 – 1206 , https://doi.org/10.1175/1520-0426(1994)011<1184:BLCARE>2.0.CO;2 . 10.1175/1520-0426(1994)011<1184:BLCARE>2.0.CO;2 Xiao , Q. , and Coauthors , 2008 : Doppler radar data assimilation in KMA’s operational forecasting . Bull. Amer. Meteor. Soc. , 89 , 39 – 43 , https://doi.org/10.1175/BAMS-89-1-39 . 10.1175/BAMS-89

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Rui M. Ponte and Joel Dorandeu

actually 6-h forecasts. (For simplicity we will refer to both ECMWF and NCEP fields as analyses, but this difference should be kept in mind.) The original NCEP times were thus shifted by 6 h, to correct for the time tag problem. To compare with the analyses, we obtained a number of island station pressure data that were available online at a site maintained by the National Oceanic and Atmospheric Administration/National Ocean Service's (NOAA/NOS) Center for Operational Oceanographic Products and

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Bomin Sun, Tony Reale, Steven Schroeder, Michael Pettey, and Ryan Smith

1. Introduction Balloonborne radiosonde observations (raobs) play a critical role in upper-air climate change detection, numerical weather prediction (NWP) data assimilation and forecasting, and satellite data calibration/validation (cal/val). Vaisala RS92 is a major sonde type in the current global operational upper-air network and a reference sonde in the Global Climate Observing System (GCOS) Reference Upper Air Network (GRUAN; Bodeker et al. 2016 ). However, RS92 has gradually been

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