Journal
ADVANCES IN ATMOSPHERIC SCIENCES
Volume 31, Issue 6, Pages 1293-1304Publisher
SCIENCE PRESS
DOI: 10.1007/s00376-014-4030-6
Keywords
quality control; rawinsonde observation; vertical-wind-shear check
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Funding
- 973 project Assessment, Assimilation, Recompilation and Applications of Fundamental and Thematic Climate Data Records [2010CB951600]
- National Science and Technology Supporting Program of the 12th Five-Year Plan Period [2012BAC22B00]
- Monitoring and Detection of Aerial Climate Change in China project of the China Meteorological Administration [GYHY200906014]
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In 2006, the National Meteorological Information Center (NMIC) of the China Meteorological Administration (CMA) developed its real-time quality control (QC) system of rawinsonde observations coming from the Global Telecommunications System (GTS) and established the Global Upper-air Report Dataset, which, with the NMIC B01 format, is generally referred to as the B01 dataset and updated on a daily basis. However, when the B01 dataset is applied in climate analysis, some wind errors as well as some accurate values with incorrect error marks are found. To improve the quality and usefulness of Chinese rawinsonde wind observations, a new QC method (NewQC) is proposed in this paper. Different from the QC approach used for B01 datasets, the NewQC includes two vertical-wind-shear checks to analyze the vertical consistency of winds, in which the constant height level winds are used as reference data for the QC of mandatory pressure level winds. Different threshold values are adopted in the wind shear checks for different stations and different vertical levels. Several typical examples of QC of different error types by the new algorithm are shown and its performance with respect to 1980-2008 observational data is statistically evaluated. Compared with the radiosonde QC algorithms used in both the Meteorological Assimilation Data Ingest System (MADIS, http://madis.noaa.gov/madisraobqc.html) of the National Oceanic and Atmospheric Administration (NOAA) and the B01 dataset, the NewQC shows higher accuracy and better reliability, particularly when used to judge successive observation errors.
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