A case-study: guidance on the development of long-term daily adjusted temperature datasets / by Manola Brunet ... [et al.].
Contributor(s): Brunet, Manola | World Climate Data and Monitoring Programme.
Series: WCDMP ; no. 66.Publisher: Geneva : World Climate Programme, Data and Monitoring, [2008?]Description: 43 p. : ill. ; 30 cm.Subject(s): DATA MANAGEMENT | METEOROLOGY | TEMPERATURE | TIME SERIES | SPAIN In: WCDMP / World Meteorological OrganizationSummary: "It describes the procedures followed by Brunet et al. 2006a in the development of the Spanish Daily Adjusted Temperature Series (SDATS), and as a case study it provides good practices for NMHSs and scientists to create high-quality, adjusted and reliable long instrumental climate datasets. It reviews the array of procedures adopted in order to develop the new daily adjusted dataset, the SDATS dataset, which is composed of the 22 longest and most reliable Spanish daily maximum (Tmax), minimum (Tmin) and the derived mean (Tmean) temperature records. It uses as a specific example, detailed information on the processing of the Madrid record, in order to give an extended illustration of how the whole procedure has been carried out for this station. The document is structured into ten (10) sections including the introduction. A short background on currently developed and available temperature datasets at different spatial and time scales is provided in section 2. Details of the selected temperature network, data and metadata collection and the sources used are introduced in section 3, together with a specific assessment of the sources employed for recovering Madrid's data. Quality controls applied to daily maximum and minimum temperature series are discussed in section 4, together with an assessment of the results for the entire network and, particularly, for Madrid. Section 5 describes the whole approach to produce daily adjusted temperature series, with a special emphasis on documenting Madrid's homogenization. It is divided into three subsections: In the first, we will show the procedures adopted for minimizing the bias induced by temporal changes in thermometric exposures from the raw Tmax and Tmin monthly data, which affects most of the records as it was a common and contemporary bias across the entire network. In the second subsection, the homogeneity test chosen and the results reached are shown and discussed by, first, describing the selection of the groups of reference/candidate stations; through, second, defining the detection pattern of the inhomogeneities found in the data; and by, third, showing the estimated correction scheme for adjusting time series on a monthly basis. Lastly, the third subsection is focused on describing the interpolation method used for estimating daily adjustments. Section 6 gives the approach for creating the regional temperature series for Spain, the Spanish Temperature Series (STS), and provides an assessment of long-term trends of daily Tmean, Tmax and Tmin time series included in the SDATS dataset. Section 7 reviews and compares the procedures employed by other groups in order to develop daily adjusted datasets. Section 8 gives a glossary of the principal terms for developing high-quality and homogenized datasets and, finally, section 9 lists the reference used in this guidance."--Introduction.Item type | Current library | Call number | Copy number | Status | Date due | Barcode | |
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JOURNAL | WELLINGTON OFF-SITE | STACK NO. 66 2008 | 1 | Available | J013427 |
WMO-TD 1405
Cover title.
Bibliographic references (p. 40-42).
"It describes the procedures followed by Brunet et al. 2006a in the development of the Spanish Daily Adjusted Temperature Series (SDATS), and as a case study it provides good practices for NMHSs and scientists to create high-quality, adjusted and reliable long instrumental climate datasets. It reviews the array of procedures adopted in order to develop the new daily adjusted dataset, the SDATS dataset, which is composed of the 22 longest and most reliable Spanish daily maximum (Tmax), minimum (Tmin) and the derived mean (Tmean) temperature records. It uses as a specific example, detailed information on the processing of the Madrid record, in order to give an extended illustration of how the whole procedure has been carried out for this station. The document is structured into ten (10) sections including the introduction. A short background on currently developed and available temperature datasets at different spatial and time scales is provided in section 2. Details of the selected temperature network, data and metadata collection and the sources used are introduced in section 3, together with a specific assessment of the sources employed for recovering Madrid's data. Quality controls applied to daily maximum and minimum temperature series are discussed in section 4, together with an assessment of the results for the entire network and, particularly, for Madrid. Section 5 describes the whole approach to produce daily adjusted temperature series, with a special emphasis on documenting Madrid's homogenization. It is divided into three subsections: In the first, we will show the procedures adopted for minimizing the bias induced by temporal changes in thermometric exposures from the raw Tmax and Tmin monthly data, which affects most of the records as it was a common and contemporary bias across the entire network. In the second subsection, the homogeneity test chosen and the results reached are shown and discussed by, first, describing the selection of the groups of reference/candidate stations; through, second, defining the detection pattern of the inhomogeneities found in the data; and by, third, showing the estimated correction scheme for adjusting time series on a monthly basis. Lastly, the third subsection is focused on describing the interpolation method used for estimating daily adjustments. Section 6 gives the approach for creating the regional temperature series for Spain, the Spanish Temperature Series (STS), and provides an assessment of long-term trends of daily Tmean, Tmax and Tmin time series included in the SDATS dataset. Section 7 reviews and compares the procedures employed by other groups in order to develop daily adjusted datasets. Section 8 gives a glossary of the principal terms for developing high-quality and homogenized datasets and, finally, section 9 lists the reference used in this guidance."--Introduction.
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