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Dusanka Zupanski, Sara Q. Zhang, Milija Zupanski, Arthur Y. Hou, and Samson H. Cheung

further development. b. The observations and forward operators To use the NOAA/NCEP operational datastream, the observation operators from NCEP GSI system are incorporated in WRF-EDAS. The forward operators for conventional data and clear-sky satellite radiances are adopted along with the operational observation error specifications, quality control, and bias correction procedures for these data types. During the data assimilation cycling, the large-scale forcing for forecasts is provided only at the

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Yudong Tian, Christa D. Peters-Lidard, and John B. Eylander

and CMORPH used in this study have a 0.25° × 0.25° spatial resolution and a 3-h temporal resolution, and we used their daily accumulation to match that of the gauge data (below). The gauge data we used to correct the satellite-based estimates are the NOAA CPC unified daily gauge analysis (CPC-UNI; Xie et al. 2007 ; Chen et al. 2008 ). This dataset employs an OI technique to reproject quality-controlled gauge reports over CONUS to a 0.25° × 0.25° grid. The OI-based interpolation has been shown to

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F. M. Ralph, E. Sukovich, D. Reynolds, M. Dettinger, S. Weagle, W. Clark, and P. J. Neiman

, 48, or 72 h (day 1, day 2, and day 3, respectively) after 1200 UTC of the day the forecast was made. b. QPE verification data Six-hourly QPE data used to verify the QPF products were obtained from the CNRFC and NWRFC, as each RFC performs daily quality control of gauge data throughout their respective hydrologic service areas. These estimates are based on precipitation amounts observed at hundreds of stations throughout each domain. When gauge data are missing from one or more of these sites

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Youcun Qi, Jian Zhang, Brian Kaney, Carrie Langston, and Kenneth Howard

. The four lon defining the transitions zones are λ a (1), 121.95°W; λ a (2), 120.75°W; λ b (1), 122.35°W; and λ b (2), 121.9°W. The location of the solid green line is λ a (3) at 121.350°W and λ b (3) at 122.125°W. Hourly rainfall observations from the Hydrometeorological Automated Data System (HADS; ) gauges and the special precipitation-gauge network deployed during HMT06 were used for the evaluation of radar QPEs. Manual quality control was performed to remove

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Jonathan J. Gourley, Scott E. Giangrande, Yang Hong, Zachary L. Flamig, Terry Schuur, and Jasper A. Vrugt

(1990) . The advent of dual-polarization capability with weather radar offers opportunities in improving radar data quality and product generation through the identification and removal of nonweather targets, classifying hydrometeors, and improving rainfall rate estimation (e.g., Straka and Zrnić 1993 ; Ryzhkov and Zrnić 1995 ; Vivekanandan et al. 1999 ; Zrnić and Ryzhkov 1999 ; Bringi and Chandrasekar 2001 ). For these reasons, the U.S. National Weather Service (NWS) and other agencies will

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Timothy J. Lang, Steven A. Rutledge, and Robert Cifelli

from NAME, which can be used to estimate rainfall, differentiate between ice and liquid water, and characterize DSDs. 2. Data and methodology a. Data quality control and grid generation The primary data source for this work was obtained by the National Center for Atmospheric Research (NCAR) S-band dual-polarization Doppler radar (S-Pol). The S-Pol was stationed for six weeks near Mazatlan, Mexico during July–August 2004 as part of the NAME field campaign ( Higgins et al. 2006 ; Lang et al. 2007a

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Masamichi Ohba, Shinji Kadokura, Yoshikatsu Yoshida, Daisuke Nohara, and Yasushi Toyoda

Automated Meteorological Data Acquisition System (AMeDAS) deployed, the quality of the precipitation data was significantly improved. Available rain gauges were increased greatly in number up to more than 1100 because of installation of the observation network. Therefore it is difficult to discuss the long-term change by only using the data because its quality is not constant [readers can refer to Fig. 1 in Kamiguchi et al. (2010) ]. On the other hand, the dataset with 60 stations is better suited for

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Sandra E. Yuter, David A. Stark, Justin A. Crouch, M. Jordan Payne, and Brian A. Colle

periods were analyzed. The initial storm-day definition was refined by examining KRTX radar data to determine the start and end times of radar echo within the radar domain to the nearest hour using the MountainZebra display ( James et al. 2000 ). MountainZebra provides visualizations of radar images in horizontal and vertical cross sections with a detailed terrain field. The WSR-88D Level II data were converted to Universal Format ( Barnes 1980 ), and quality control was applied to reduce

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Jian Zhang and Youcun Qi

. 2008 ). A quality control procedure is applied to exclude outliers and to assure a stable and representative AVPR. A valid mean reflectivity is obtained at a given range (height) only when there is sufficient number of BBA pixels at the range. The minimum number of pixels is a function of range (the farther the range, the less the number because the size of the pixels increases). Second, a running 15-point average in the vertical is applied to the AVPR, and any points that deviate more than twice

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