Abstract
Quality assurance of meteorological data is crucial for ensuring the reliability of applications and models that use such data as input variables, especially in the field of environmental sciences. Spatial validation of meteorological data is based on the application of quality control procedures using data from neighbouring stations to assess the validity of data from a candidate station (the station of interest). These kinds of tests, which are referred to in the literature as spatial consistency tests, take data from neighbouring stations in order to estimate the corresponding measurement at the candidate station. These estimations can be made by weighting values according to the distance between the stations or to the coefficient of correlation, among other methods. The test applied in this study relies on statistical decision-making and uses a weighting based on the standard error of the estimate. This paper summarizes the results of the application of this test to maximum, minimum and mean temperature data from the Agroclimatic Information Network of Andalusia (southern Spain). This quality control procedure includes a decision based on a factor f, the fraction of potential outliers for each station across the region. Using GIS techniques, the geographic distribution of the errors detected has been also analysed. Finally, the performance of the test was assessed by evaluating its effectiveness in detecting known errors.
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References
Abraha MG, Savage MJ (2008) Comparison of estimates of daily solar radiation from air temperature range for application in crop simulations. Agric For Meteorol 148:401–416
AEMET (2012) Resumen de extremos climatológicos en España. <http://www.aemet.es/documentos/es/divulgacion/resumen_efemerides/Resumen_extremos.pdf>. Accessed 05.07.15, in Spanish
Brunet M, Saladie O, Jones P, Sigro J, Aguilar E, Moberg A, Lister D, Walther A, Lopez D, Almarza C (2006) The development of a new dataset of Spanish Daily Adjusted Temperature Series (SDATS) (1850–2003). Int J Climatol 26:1777–1802
Camillo PJ, Gurney RJ (1984) A sensitivity analysis of a numerical model for estimating evapotranspiration. Water Res Research 12(5):873–879
Eischeid JK, Baker CB, Karl T, Diaz HF (1995) The quality control of long-term climatological data using objective data analysis. J Appl Meteor 34:2787–2795
Estévez J, Gavilán P, Berengena J (2009) Sensitivity analysis of a Penman-Monteith type equation to estimate reference evapotranspiration in southern Spain. Hydrol Proc 23:3342–3353
Estévez J, Gavilan P, Garcia-Marin AP (2011a) Data validation procedures in agricultural meteorology. A prerequisite for their use. Adv Science Res 6:141–146
Estévez J, Gavilan P, Giraldez JV (2011b) Guidelines on validation procedures for meteorological data from automatic weather stations. J Hydrol 402:144–154
Estévez J, Padilla F, Gavilán P (2012) Evaluation and regional calibration of solar radiation prediction models in southern Spain. J Irrig and Drainage Eng 138(10):868–879
Estévez J, Gavilán P, García-Marín AP, Zardi D (2015) Detection of spurious precipitation signals from automatic weather stations in irrigated areas. Int J Climatol 35:1556–1568
Feng S, Hu Q, Qian Q (2004) Quality control of daily meteorological data in China, 1951-2000: a new dataset. Int J Climatol 24:853–870
Fiebrich CA, Morgan CR, McCombs AG, Hall PK, McPherson RA (2010) Quality assurance procedures for mesoscale meteorological data. J Atmos Ocean Technol 27:1565–1582
Gandin LS (1988) Complex quality control of meteorological observations. Mon Wea Rev 116:1137–1156
García-Marín AP, Estévez J, Medina-Cobo MT, Ayuso-Muñoz JL (2015) Delimiting homogeneous regions using the multifractal properties of validated rainfall data series. J of Hydrol 529:106–119
Graybeal DY (2006) Relationships among daily mean and maximum wind speeds with application to data quality assurance. Int J Climatol 26:29–43
Guttman NB, Quayler RG (1990) A review of cooperative temperature data validation. J Atmos Ocean Technol 7:334–339
Hubbard KG (2001) Multiple station quality control procedures. Automated weather stations for applications in agriculture and water resources management. In: Hubbard KG, Sivakumar MVK (eds) Tech. Doc. AGM-3 WMO/TD 1074. High Plains Regional Climate Center, Lincoln, NE, p. 248
Hubbard KG, You J (2005) Sensitivity analysis of quality assurance using the spatial regression approach. A case study of the maximum/minimum air temperature. J Atmos Ocean Technol 22:1520–1530
Hubbard KG, Goddard S, Sorensen WD, Wells N, Osugi TT (2005) Performance of quality assurance procedures for an applied climate information system. J Atmos Ocean Technol 22:105–112
IPCC (2014) Field CB, VR Barros DJ, Dokken KJ, Mach MD, Mastrandrea TE, Bilir M, Chatterjee KL, Ebi YO, Estrada RC, Genova B, Girma ES, Kissel AN, Levy S, MacCracken PR, Mastrandrea, White LL (eds) Climate Change 2014: Impacts, adaptation, and vulnerability. Part A: global and sectoral aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, p 1132
Ley TW, Hill RW, Jensen DT (1994) Errors in Penman-Wright alfalfa reference evapotranspiration estimates: I. Model sensitivity analyses. Transactions of the ASAE 37(6):1863–1870
Liu X (2009) Evaluation of temperature-based global solar radiation models in China. Agric and Forest Meteor 149(9):1433–1446
Mahmood R, Hubbard KG (2002) Effect the time of temperature observation and estimation of daily solar radiation for the Northern Great Plains, USA. Agronomic Journal 94(4):723–733
Meek DW, Hatfield JL (1994) Data quality checking for single station meteorological databases. Agric and Forest Meteor 36:85–109
Olson, JE (2003) Data quality: the accuracy dimension. Morgan Kaufmann Publishers, p 293
PostGIS (2014) http://postgis.refractions.net. Accessed december 2014
PostgreSQL (2014) http://www.postgresql.org. Accessed december 2014
Reek T, Doty SR, Owen TW (1992) A deterministic approach to the validation of historical daily temperature and precipitation data from the Cooperative Network. Bull Amer Meteor Soc 73:753–765
Rivington M, Bellocchi G, Matthews KB, Buchan K (2005) Evaluation of three model estimations of solar radiation at 24 UK stations. Agric For Meteorol 132(3–4):228–443
Shafer MA, Fiebrich CA, Arndt DS, Fredrickson SE, Hughes TW (2000) Quality assurance procedures in the Oklahoma Mesonet. J Atmos Ocean Technol 17:474–494
Steinacker R, Mayer D, Steiner A (2011) Data quality control based on self-consistency. Mon Wea Rev 139:3974–3991
Wade CG (1987) A quality control program for surface meso-meteorological data. J Atmos Ocean Technol 4:435–453
WMO (2008) Guide to meteorological instruments and methods of observation, 7th edn. WMO-8, World Meteorological Organization, Geneva, p. 681
Zahumensky I (2004) Guidelines on quality control procedures for data from automatic weather stations. WMO-No. 955, Geneva
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Estévez, J., Gavilán, P. & García-Marín, A.P. Spatial regression test for ensuring temperature data quality in southern Spain. Theor Appl Climatol 131, 309–318 (2018). https://doi.org/10.1007/s00704-016-1982-8
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DOI: https://doi.org/10.1007/s00704-016-1982-8