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Youcun Qi, Steven Martinaitis, Jian Zhang, and Stephen Cocks

evaporation in rain were attributed an additional 1%–3% increase in undercatch. The use of proper correction software was also shown to improve the accuracy of gauge measurements ( Lanza et al. 2005 ). Steiner et al. (1999) demonstrated how non-quality-controlled gauge data could substantially affect the statistical validation of radar-based rainfall estimations; thus, a comprehensive gauge quality-control (QC) procedure is necessary. Some previous studies addressed gauge errors through intercomparisons

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Jay H. Lawrimore, David Wuertz, Anna Wilson, Scott Stevens, Matthew Menne, Bryant Korzeniewski, Michael A. Palecki, Ronald D. Leeper, and Thomas Trunk

&P Rebuild (FPR), aimed to improve the quality and completeness of hourly precipitation observations while reducing maintenance costs. As a replacement for paper tape, which are subject to tearing, deterioration, and being expended between site visits, the upgrade introduced digital recording via a datalogger. Accompanying this transition to digital recording are new data acquisition, integration, and quality control processes developed at NCEI. This new approach features a change from a largely manual

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Brian R. Nelson, D-J. Seo, and Dongsoo Kim

, Table 1 ). The goal of the pilot project is to demonstrate the improvement of experimental MPR products over the operational QPE products. The main sources of improvement include additional rain gauge data, systematic quality control (QC) of rain gauge data, correction of systematic biases in radar QPE, and parameter optimization for radar–rain gauge merging. In this paper, we describe the data and the reanalysis procedure used for the pilot project and summarize the results, including comparative

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Kenneth E. Kunkel, Thomas R. Karl, and David R. Easterling

.S. precipitation data are now underway as nineteenth century data from stations operated prior to the COOP are in the process of being digitized and quality controlled. Such data are of great interest because even a 110-yr record is relatively short when evaluating multidecadal variations. Furthermore, there is evidence of very wet conditions during the nineteenth century in the central United States, including very high levels of Lakes Michigan–Huron ( Changnon 2004 ) and high streamflows on the upper

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Trent W. Ford, Steven M. Quiring, Chen Zhao, Zachary T. Leasor, and Christian Landry

methods (e.g., Dirmeyer et al. 2016 ; Ford and Quiring 2019 ), which found that short soil moisture data records exhibited high variability in temporal stability. Therefore, stations with records shorter than 365 days were not included in this study. All in situ data were acquired in units of volumetric water content θ (m 3 m −3 ), and represent the original data from the networks with no additional quality control. A general overview of each in situ network is included in Table 1 and station

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Jesse E. Bell, Michael A. Palecki, C. Bruce Baker, William G. Collins, Jay H. Lawrimore, Ronald D. Leeper, Mark E. Hall, John Kochendorfer, Tilden P. Meyers, Tim Wilson, and Howard J. Diamond

observations, which include solar radiation, surface IR temperature, 1.5-m wind speed, and a wetness detector. This article provides a brief review of the nature of soil-climate observations in the United States and the role that USCRN will play in soil-climate monitoring. The quality control of USCRN soil moisture and temperature data will also be discussed, including issues of network maintenance. Applications of the USCRN soil moisture and temperature to science and operations will be presented, with

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Vera Thiemig, Rodrigo Rojas, Mauricio Zambrano-Bigiarini, Vincenzo Levizzani, and Ad De Roo

available and hence are only used rarely in validation studies. Secondly, all data providers claim to perform quality control procedures to reduce potential errors. Therefore, and in spite of the highly varying data coverage and the uneven spatial distribution (i.e., high density at lower elevations and just few stations at higher altitude), we consider this dataset to be representative as it is the most complete and accurate, given the general data availability for each river basin. 2) SRFE This

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Edouard Goudenhoofdt and Laurent Delobbe

correlation in most cases. 1) Rain gauge measurements The hydrological service of the Walloon region (SPW) operates a dense (one gauge per 135 km 2 ) and integrated network of 90 telemetric rain gauges ( Fig. 2 ). Most of them are tipping-bucket systems providing hourly rainfall accumulation. The collected data are used for hydrological modeling and directly sent to RMIB. The rain gauges are controlled on site every 3 months and in a specialized workshop every year. Every day, a quality control of the

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R. W. Higgins and V. E. Kousky

used to produce a multiyear (1950–present) daily precipitation analysis (1200–1200 UTC) for the conterminous United States. The daily data were gridded at a horizontal resolution of (latitude, longitude) = (0.25°, 0.25°) using an optimal interpolation scheme. Several types of quality control (QC) were applied including a “duplicate station” check, a “buddy” check, a “standard deviation” check (which compares the daily data against a gridded daily climatology), and—when possible—a radar QC step (in

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Lei Ji, Gabriel B. Senay, and James P. Verdin

the data assimilation system . Quart. J. Roy. Meteor. Soc. , 137 , 553 – 597 , doi: 10.1002/qj.828 . Dorigo, W. A. , de Jeu R. , Chung D. , Parinussa R. , Liu Y. , Wagner W. , and Fernández-Prieto D. , 2012 : Evaluating global trends (1988–2010) in harmonized multi-satellite surface soil moisture . Geophys. Res. Lett. , 39 , L18405 , doi: 10.1029/2012GL052988 . Dorigo, W. A. , and Coauthors , 2013 : Global automated quality control of in situ soil moisture data from the

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