Skip to main content
SpringerLink
Log in

An analysis of spatial representativeness of air temperature monitoring stations

  • Original Paper
  • Published:
Theoretical and Applied Climatology Aims and scope Submit manuscript

Abstract

Surface air temperature is an essential variable for monitoring the atmosphere, and it is generally acquired at meteorological stations that can provide information about only a small area within an r m radius (r-neighborhood) of the station, which is called the representable radius. In studies on a local scale, ground-based observations of surface air temperatures obtained from scattered stations are usually interpolated using a variety of methods without ascertaining their effectiveness. Thus, it is necessary to evaluate the spatial representativeness of ground-based observations of surface air temperature before conducting studies on a local scale. The present study used remote sensing data to estimate the spatial distribution of surface air temperature using the advection-energy balance for air temperature (ADEBAT) model. Two target stations in the study area were selected to conduct an analysis of spatial representativeness. The results showed that one station (AWS 7) had a representable radius of about 400 m with a possible error of less than 1 K, while the other station (AWS 16) had the radius of about 250 m. The representable radius was large when the heterogeneity of land cover around the station was small.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  • Dutton EG, Nelson DW, Stone RS et al (2006) Decadal variations in surface solar irradiance as observed in a globally remote network. J Geophys Res Atmos 111:1–10. doi:10.1029/2005JD006901

    Article  Google Scholar 

  • French AN, Jacob F, Anderson MC et al (2005) Surface energy fluxes with the Advanced Spaceborne Thermal Emission and Reflection radiometer (ASTER) at the Iowa 2002 SMACEX site (USA). Remote Sens Environ 99:55–65. doi:10.1016/j.rse.2005.05.015

    Article  Google Scholar 

  • Hartkamp AD, De Beurs K, Stein A, White JW (1999) Interpolation techniques for climate variables. NRG-GIS Series 99-01, CIMMYT, Mexico, D.F

  • Huld TA, Šúri M, Dunlop ED, Micale F (2006) Estimating average daytime and daily temperature profiles within Europe. Environ Model Softw 21:1650–1661. doi:10.1016/j.envsoft.2005.07.010

    Article  Google Scholar 

  • Jacobs JD (1989) Spatial representativeness of climatic data from Baffin Island, N.W.T., with implications for muskoxen and caribou distribution. Arctic 42:50–56

    Article  Google Scholar 

  • Janssen S, Dumont G, Fierens F et al (2012) Land use to characterize spatial representativeness of air quality monitoring stations and its relevance for model validation. Atmos Environ 59:492–500. doi:10.1016/j.atmosenv.2012.05.028

    Article  Google Scholar 

  • Lakshmi V, Czajkowski K, Dubayah R, Susskind J (2001) Land surface air temperature mapping using TOVS and AVHRR. Int J Remote Sens 22:643–662. doi:10.1080/01431160050505900

    Article  Google Scholar 

  • Leclerc MY, Shen S, Lamb B (1997) Observations and large-eddy simulation modeling of footprints in the lower convective boundary layer. J Geophys Res 102:9323–9334. doi:10.1029/96JD03984

    Article  Google Scholar 

  • Li J, Heap AD (2011) A review of comparative studies of spatial interpolation methods in environmental sciences: performance and impact factors. Ecol Inform 6:228–241. doi:10.1016/j.ecoinf.2010.12.003

    Article  Google Scholar 

  • Li X, Cheng G, Liu S et al (2013) Heihe watershed allied telemetry experimental research (HiWATER): scientific objectives and experimental design. Bull Am Meteorol Soc 94:1145–1160

    Article  Google Scholar 

  • Li H, Sun D, Yu Y et al (2014) Evaluation of the VIIRS and MODIS LST products in an arid area of Northwest China. Remote Sens Environ 142:111–121. doi:10.1016/j.rse.2013.11.014

    Article  Google Scholar 

  • Liu SM, Xu ZW, Wang WZ et al (2011) A comparison of eddy-covariance and large aperture scintillometer measurements with respect to the energy balance closure problem. Hydrol Earth Syst Sci 15:1291–1306. doi:10.5194/hess-15-1291-2011

    Article  Google Scholar 

  • Liu Q, Wen J, Wu S, Qu Y (2014) Albedo dataset in 30m-resolution in the Heihe River Basin in 2012. Heihe Plan Sci Data Cent. doi:10.3972/heihe.001.2014.db

    Google Scholar 

  • Liu S, Xu Z, Song L et al (2016) Upscaling evapotranspiration measurements from multi-site to the satellite pixel scale over heterogeneous land surfaces. Agric For Meteorol. doi:10.1016/j.agrformet.2016.04.008

    Google Scholar 

  • Martín F, Fileni L, Palomino I et al (2014) Analysis of the spatial representativeness of rural background monitoring stations in Spain. Atmos Pollut Res 5:779–788

    Article  Google Scholar 

  • Masson V, Grimmond CSB, Oke TR (2002) Evaluation of the Town Energy Balance (TEB) scheme with direct measurements from dry districts in two cities. J Appl Meteorol 41:1011–1026

    Article  Google Scholar 

  • McKitrick RR, Michaels PJ (2007) Quantifying the influence of anthropogenic surface processes and inhomogeneities on gridded global climate data. J Geophys Res Atmos 112:1–14. doi:10.1029/2007JD008465

    Article  Google Scholar 

  • Prihodko L, Goward SN (1997) Estimation of air temperature from remotely sensed surface observations. Remote Sens Environ 60:335–346. doi:10.1016/s0034-4257(96)00216-7

    Article  Google Scholar 

  • Reynolds AM (1998) A two-dimensional Lagrangian stochastic dispersion model for convective boundary layers with wind shear. Boundary-Layer Meteorol 86:345–352

    Article  Google Scholar 

  • Righini G, Cappelletti A, Ciucci A et al (2014) GIS based assessment of the spatial representativeness of air quality monitoring stations using pollutant emissions data. Atmos Environ 97:121–129. doi:10.1016/j.atmosenv.2014.08.015

    Article  Google Scholar 

  • Schuepp PH, Leclerc MY, MacPherson JI, Desjardins RL (1990) Footprint prediction of scalar fluxes from analytical solutions of the diffusion equation. Boundary-Layer Meteorol 50:355–373. doi:10.1007/BF00120530

    Article  Google Scholar 

  • Shao QQ, Sun CY, Liu JY et al (2011) Impact of urban expansion on meteorological observation data and overestimation to regional air temperature in China. J Geogr Sci 21:994–1006. doi:10.1007/s11442-011-0895-9

    Article  Google Scholar 

  • Xu CD, Wang JF, Hu MG, Li QX (2013a) Interpolation of missing temperature data at meteorological stations using P-BSHADE. J Clim 26:7452–7463. doi:10.1175/JCLI-D-12-00633.1

    Article  Google Scholar 

  • Xu Z, Liu S, Li X et al (2013b) Intercomparison of surface energy flux measurement systems used during the HiWATER-MUSOEXE. J Geophys Res Atmos 118:13,140–13,157. doi:10.1002/2013JD020260

    Article  Google Scholar 

  • Xu B, Li J, Xin X et al (2015) Review of methods for evaluating representativeness of ground station observation. J Remote Sens 19:703–718

    Google Scholar 

  • Zhang R, Rong Y, Tian J et al (2015) A remote sensing method for estimating surface air temperature and surface vapor pressure on a regional scale. Remote Sens 7:6005–6025. doi:10.3390/rs70506005

    Article  Google Scholar 

Download references

Acknowledgments

This work is supported by the National Natural Science Foundation of China (grant number 41571356), the National Key Research and Development Program of China (grant number 2016YFA0602501), and the National Natural Science Foundation of China (grant numbers 41671354, 41671373, 41671368, 41371348).

Author information

Authors and Affiliations

  1. Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China

    Suhua Liu & Jing Tian

  2. University of Chinese Academy of Sciences, Beijing, 100049, China

    Suhua Liu

  3. Department of Civil, Environmental and Geomatics Engineering, Florida Atlantic University, Florida, FL, 33431, USA

    Hongbo Su

  4. Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, Lanzhou, 730000, China

    Weizhen Wang

Authors
  1. Suhua Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hongbo Su
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jing Tian
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Weizhen Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Hongbo Su or Jing Tian.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liu, S., Su, H., Tian, J. et al. An analysis of spatial representativeness of air temperature monitoring stations. Theor Appl Climatol 132, 857–865 (2018). https://doi.org/10.1007/s00704-017-2133-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00704-017-2133-6

Search

Navigation