Joint Session J3.13 Improvements to and Status of ARM's Data Quality Health and Status System

Thursday, 23 June 2005: 11:15 AM
South Ballroom (Hilton DeSoto)
Randy A. Peppler, CIMMS/Univ. of Oklahoma, Norman, OK; and K. E. Kehoe, K. L. Sonntag, S. T. Moore, and K. J. Doty

Presentation PDF (821.6 kB)

The Atmospheric Radiation Measurement (ARM) Program Data Quality Office was formed in July 2000 to coordinate the data quality activities of the ARM Program, in response to a program review in 1999 identifying such a need. The ARM Program fields instrumentation and collects data from Climate Research Facilities located in the U.S. Southern Great Plains, North Slope of Alaska, and Tropical Western Pacific. These data are used by ARM scientists to learn more about the climate system and to apply this knowledge to improve the treatment of clouds and atmospheric radiation in climate models. Thus, to support the research properly, the data the ARM Program collects must be of high qualilty.

The Data Quality Office is responsible for making sure that ARM data are usable, so that data users are able to readily determine whether the data have been reviewed, how they were reviewed, and whether there are known problems. To facilitate this process, the Data Quality Office has developed a web-based tool called the Data Quality Health and Status (DQ HandS) system (http://dq.arm.gov/). DQ HandS reads ARM data files, displays flag information in the form of color tables, provides pop-up information indicating the nature of the flags violated, produces diagnostic plots of key parameters and allows for the interactive plotting of any file variables, and hosts various assessment and problem reporting mechanisms.

Since the last Conference on Applied Climatology in Seattle in 2004, the Data Quality Office has made a number of additions to DQ HandS, which will be shown. Among these are: incorporation of more ARM datastreams; inclusion of ARM Mobile Facility data; a new thumbnail plot browser to facilitate the viewing of a string of diagnostic plots for trends; an improved method for writing and databasing weekly data quality assessment reports; and an improved method for searching ARM report databases for determining problem context. The presentation will also include new ideas on how to include more automated quality control information within ARM data files.

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