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Richard D. Rosen, John M. Henderson, and David A. Salstein

that will produce the best possible forecasts most economically. Institutionally, such concerns within the United States have become the province of the North American Atmospheric Observing System (NAOS) program. As stated by NAOS (1996) , its goal “is to provide the scientific, technical, and administrative basis for governmental decision processes on how to meet the evolving needs for atmospheric observations over the region of North America and adjacent ocean areas in support of the prediction

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James D. Means and Daniel Cayan

), examine the accuracy of the estimated pressure by comparison with observations and determine the resultant error in precipitable water estimation ( section 3 ), compare our observations with other methods of determining precipitable water ( sections 4 and 5 ), and highlight some particular applications that may benefit from having a database of precipitable water ( section 6 ). 2. Methodology We have used the North American Regional Reanalysis (NARR) to estimate historical pressure and temperature

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Etor E. Lucio-Eceiza, J. Fidel González-Rouco, Jorge Navarro, and Hugo Beltrami

peninsula of Nova Scotia and in its annexed provinces (e.g., Hughes et al. 2006 ; Hughes 2007 ; Hughes and Chaudhry 2011 ). For the veracity of these analyses, however, it is paramount to handle observational databases in which the quality of different sources is brought to a common ground so that the data can later be used with confidence regardless of their provenance. The objective of this work is to analyze and improve the quality of a set of wind surface data across northeastern North America

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Daria Kluver, Tom Mote, Daniel Leathers, Gina R. Henderson, Weihan Chan, and David A. Robinson

higher-resolution dataset is preferred when examining the frequency of snowfall events, changes in the number of extreme snowfall events, or synoptic patterns associated with snowfall. This higher resolution is ideal to identify changes in the short-term weather conditions that influence and produce snowfall. Daily data also allow for the computation of snowfall intensity, event size distributions, and snowstorm statistics. Among snowfall data, heretofore a long-term gridded dataset for North America

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Hank Jenkins-Smith, Joe Ripberger, Carol Silva, Nina Carlson, Kuhika Gupta, Matt Henderson, and Amy Goodin

American Association for Public Opinion Research ( Smith 2009 ; AAPOR 2015 ). We use the following equation to calculate RR4: where S is the number of eligible cases that were successfully recruited to the pool potential respondents, U is the number of eligible cases that were not successfully recruited, e is the proportion of eligible cases among all cases in the sample for which a definitive determination of eligibility was obtained, and I is the number of indeterminate cases in the sample

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Etor E. Lucio-Eceiza, J. Fidel González-Rouco, Jorge Navarro, Hugo Beltrami, and Jorge Conte

historical data compilation of surface wind observations across northeastern North America (WNENA). Lucio-Eceiza et al. (2017 , hereafter Part I ) reports on data management issues, whereas the procedures described herein, Part II hereafter, are focused on the detection and removal/correction of measurement errors. Part I demonstrated that the problems related to data management had a very important impact on the surface wind data, with more than 90% of the data being modified during the process of

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S. J. Thomas, M. Desgagné, and R. Benoit

1. Introduction A clear trend is emerging in the procurement of new supercomputers at national weather centers in Europe and North America. The majority of these centers have installed or are migrating toward distributed or distributed-shared-memory parallel computers ( Dent and Mozdzynski 1997 ). The same can be said for climate research and ocean modeling groups around the world. The shared-memory parallel vector processors (PVP) in use at these centers for the past decade or more have now

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Martin J. Murphy, John A. Cramer, and Ryan K. Said

.1175/1520-0493(2001)129<1179:CTGLIT>2.0.CO;2 Orville , R. E. , G. R. Huffines , W. R. Burrows , and K. L. Cummins , 2011 : The North American Lightning Detection Network (NALDN)—Analysis of flash data: 2001-09 . Mon. Wea. Rev. , 139 , 1305 – 1322 , . 10.1175/2010MWR3452.1 Rakov , V.A. , and Coauthors , 2013 : Lightning parameters for engineering applications-An update on CIGRE WG C4.407 activities. 2011 Int. Symp. on Lightning Protection , Fortaleza, BR, IEEE, 294

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Chunyan Li, Eddie Weeks, Wei Huang, Brian Milan, and Renhao Wu

survey. The channel is connected in its east-northeast region to Bay Champagne ( Fig. 1 ) and a few bayous. There is a ~100-m-wide low-level salt marsh zone separating much of the channel from Bay Champagne, except where the vegetation disappears in the north, at which point the channel and Bay Champagne are narrowly connected. Bay Champagne is a semicircular and semienclosed shallow water of mostly less than 1.5-m depth not directly connected to the coastal ocean. It has a dimension of ~1.5 km × 0

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Melissa Free and Bomin Sun


This paper presents evidence of significant discontinuities in U.S. cloud cover data from the Integrated Surface Database (ISD) and its predecessor datasets. While long-term U.S. cloud records have some well-known homogeneity problems related to the introduction of the Automated Surface Observing System (ASOS) in the 1990s, the change to the international standard reporting format [aviation routine weather report (METAR)] in the United States in July 1996 introduces an additional inhomogeneity at many of the stations where humans still make or supplement cloud observations. This change is associated with an upward shift in total cloud of 0.1%–10%, statistically significant at 95 of 172 stations. The shift occurs at both National Weather Service and military weather stations, producing a mean increase in total cloud of 2%–3%. This suggests that the positive trends in U.S. cloud cover reported by other researchers for recent time periods may be exaggerated, a conclusion that is supported by comparisons with precipitation and diurnal temperature range data.

Additional discontinuities exist at other times in the frequency distributions of fractional cloud cover at the majority of stations, many of which may be explained by changes in the sources and types of data included in ISD. Some of these result in noticeable changes in monthly-mean total cloud. The current U.S. cloud cover database needs thorough homogeneity testing and adjustment before it can be used with confidence for trend assessment or satellite product validation.

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