Search Results

You are looking at 1 - 10 of 14 items for :

  • Data quality control x
  • North American Monsoon Experiment (NAME) x
  • All content x
Clear All
Chunmei Zhu and Dennis P. Lettenmaier

in any event the Mexican portion of the domain was largely treated as a “filler” or buffer zone when the dataset was created, recognizing that the station source data used outside the continental United States were not as carefully quality controlled as were the U.S. data. Given the importance of the NAMS domain, we describe here a dataset for all of Mexico that is compatible with the Maurer et al. (2002) data. It has been a particular challenge to generate a long-term daily gridded

Full access
Timothy J. Lang, David A. Ahijevych, Stephen W. Nesbitt, Richard E. Carbone, Steven A. Rutledge, and Robert Cifelli

organization of storms relative to major terrain features. In particular, we will identify preferred locations for convection along the SMO, as well as other locations, and will identify the preferred timing for convection in these regions. 2. Data and methodology a. Network design, data quality control, and product generation Figure 1a demonstrates the basic geometry of the NAME radar network. S-Pol was deployed in NAME during 8 July–21 August 2004 to a location 10 km west of La Cruz de Elota, Sinaloa

Full access
Wayne Higgins and David Gochis

) involves the collection of all operational/research surface network precipitation data from available sources, geographical and time subsetting, integration of data to a common time scale, resolution of duplicate data conflicts, conversion of all data to a common format, compilation of metadata with station data, provision of uniform quality control, and generation of final composite datasets at respective time resolutions. Two upper-air composites are available from operational and research rawinsonde

Full access
John E. Janowiak, Valery J. Dagostaro, Vernon E. Kousky, and Robert J. Joyce

. Yarosh , 2000 : Improved United States precipitation quality control system and analysis. NCEP/Climate Prediction Center Atlas 7, 40 pp. [Available online at http://www.cpc.ncep.noaa.gov/research_papers/ncep_cpc_atlas/7/index.html .] . Higgins , R. W. , and Coauthors , 2006 : North American Monsoon Experiment (NAME) 2004 field campaign and modeling strategy. Bull. Amer. Meteor. Soc. , 87 , 79 – 94 . Janowiak , J. E. , P. A. Arkin , and M. Morrissey , 1994 : An examination of the

Full access
Richard H. Johnson, Paul E. Ciesielski, Brian D. McNoldy, Peter J. Rogers, and Richard K. Taft

, and diurnally varying mesoscale flows. Sounding quality control procedures patterned after those used in the Tropical Ocean Global Atmosphere Coupled Ocean–Atmosphere Response Experiment (TOGA COARE) ( Loehrer et al. 1996 ) have been applied to the raw NAME sounding data. This procedure includes automated internal consistency checks (e.g., gross limit and vertical consistency checks) and the assignment of quality flags. In addition, each of the 7309 soundings from 67 sites was visually inspected

Full access
Wanqiu Wang and Pingping Xie

–longitude grid over a regional domain from 30°S to 60°N, 180° to 30°W using SST observation data from multiple platforms, including in situ observations and satellite retrievals from NOAA-16 , NOAA-17 , GOES, TMI, and AMSR. All raw individual observations within a 3-h time step and within a grid box of 0.25° × 0.25° latitude–longitude are first averaged to form superobservations. The resulting superobservations for each grid box and time step are then subject to quality control and bias correction before

Full access
Kingtse C. Mo, Eric Rogers, Wesley Ebisuzaki, R. Wayne Higgins, J. Woollen, and M. L. Carrera

. The control experiments (the operational NCEP systems) are labeled as “w” because all NAME special soundings except Yuma, Arizona, and the R/V Altair ( Fig. 1 , triangles) were included. Reports from Yuma and the Altair did not reach the GTS in real time and were excluded from all experiments in this paper. For the data-withholding studies, the model, assimilation systems, and input data for all experiments are identical to the control, with the only distinction being whether or not the NAME

Full access
David J. Gochis, Christopher J. Watts, Jaime Garatuza-Payan, and Julio Cesar-Rodriguez

USW, and C 1 (=0.00145) and C 2 [set equal to 1.0 in accordance with Duchon and Essenberg (2001) ] are regression parameters found using a least squares fitting technique and the experimental controlled flow rate data. This functional form, whose resulting bias-corrected values are shown as open circles in Fig. 2a , permits the correction for larger errors at higher flow rates while providing negligible correction at rates approaching zero. As shown in Fig. 2b , the bias in TE525USW

Full access
Andrea J. Ray, Gregg M. Garfin, Margaret Wilder, Marcela Vásquez-León, Melanie Lenart, and Andrew C. Comrie

frequently windy with relatively little precipitation, and they can raise particulate matter pollution levels to hazardous levels ( Wise and Comrie 2005 ). Particulate matter is also a factor in valley fever outbreaks. Early or late monsoon onsets alter the moisture and wind regimes controlling PM; for example, higher soil moisture levels during the monsoon keep particulate levels lower, and they rise again in the drier postmonsoon period. Local and state air quality agencies require dust mitigation (e

Full access
Myong-In Lee, Siegfried D. Schubert, Max J. Suarez, Isaac M. Held, Arun Kumar, Thomas L. Bell, Jae-Kyung E. Schemm, Ngar-Cheung Lau, Jeffrey J. Ploshay, Hyun-Kyung Kim, and Soo-Hyun Yoo

, 10 , 481 – 507 . Higgins , R. W. , W. Shi , E. Yarosh , and R. Joyce , 2000 : Improved United States precipitation quality control system and analysis. NCEP/Climate Prediction Center Atlas 7, 40 pp . Higgins , W. , and Coauthors , 2006 : North American Monsoon Experiment (NAME) 2004 field campaign and modeling strategy. Bull. Amer. Meteor. Soc. , 87 , 79 – 94 . Janowiak , J. E. , V. J. Dagostaro , V. E. Kousky , and R. J. Joyce , 2007 : An examination of

Full access