Skip to main content
  • Print publication year: 2016
  • Online publication date: May 2016

3 - Methods of Observation and Instrumentation

from Part I - Controls of Microclimate


The task of observing the microclimate of a region or object in a quantitative manner is important to the discipline for many reasons. As a consequence, many of the innovations and developments in meteorological instrumentation stem from the microclimate community. Initially (and currently), the agricultural community had a great interest in measuring the key variables that were (are) important for crop growth and yields: solar radiation (photosynthetic active radiation, or PAR), air and soil temperature, precipitation, and soil properties (texture for proper drainage and water retention, water content). As such, instruments and techniques for measuring the spatial and temporal variability of these and other crop variables were developed within agriculture and soil physics departments at many universities. Given the need to understand the spatial aspects of microclimate, such as the spatial interpolation between measurement locations, geography departments also often fostered and refined microclimate observations and instrumentation.

This chapter provides a review of some of the main techniques, instrumentation, and theory behind micrometeorological observations. The fundamental principles governing these observations are first discussed. This is followed by discussions of the measurement of temperature, soil properties (temperature, moisture, and heat flux), radiation, wind, precipitation, total and partial atmospheric pressures, and turbulent fluxes.


Any measurement strives to quantify reality. The measurement's accuracy refers to the difference between reality and what the instrument actually detects. For example, if the actual air temperature is 25 °C, a thermometer reading 24 °C is more accurate than one reading 28 °C. In addition to accuracy, a well-designed instrument should have a high precision, meaning that the instrument should read the same value if the conditions are not changing. For example, an object is at a steady temperature of 10 °C. A thermometer that reads 10 °C for five consecutive measurements would be said to have a high precision and high accuracy. If the thermometer “drifted” so that the five consecutive readings were, 10, 9, 8, 7, 6 °C, respectively, the thermometer would have a low precision and low accuracy. Note that an instrument could be precise, but not accurate (see Figure 3.1). In the previous example, consecutive readings of 4 °C while the actual temperature is 10 °C would indicate a high-precision, but low accuracy, thermometer. Ideally an instrument should be accurate and precise.

Recommend this book

Email your librarian or administrator to recommend adding this book to your organisation's collection.

Microclimate and Local Climate
  • Online ISBN: 9781316535981
  • Book DOI:
Please enter your name
Please enter a valid email address
Who would you like to send this to *
Aubinet, M., Vesala, T., and Papale, D. (eds.) 2012. Eddy covariance: A practical guide to measurement and data analysis. Dordrecht: Springer.
Blanken, P. D., Rouse, W. R., and Schertzer, W. M. 2003. Enhancement of evaporation from a large northern lake by the entrainment of warm, dry air. J. Hydromet. 4(4), 680–93.
Burba, G. 2013. Eddy covariance method. Lincoln, NE: LI-COR.
Farouki, O. T. 1981. Thermal properties of soils. Hanover, NH: CRREL Monograph 81-1.
Goodison, B. E., Louie, P. Y. T., and Yang, D. 1998. WMO solid precipitation measurement intercomparison. Final report no. 67. WMO/TD – No. 872.
Leclerc, M. Y. and Foken, T. 2014. Footprints in micrometeorology and ecology.Berlin: Springer-Verlag.
Merz, S. et al. 2014. Moisture profiles of the upper soil layer during evaporation monitored by NMR. Water Resour. Res. doi: 10.1002/2013WR014809.
Mudelsee, M. 2014. Climate time series analysis. Netherlands: Springer.
Navarra, A., and Simoncini, V. 2010. A guide to empirical orthogonal functions for climate data analysis.Netherlands: Springer.
Obukhov, A. M. 1971. Turbulence in an atmosphere with a non-uniform temperature, Boundary-Layer Met. 2, 7–29.
Perez, P. J., Castellvi, F., Ibañez, M., and Rosell, J. I. 1999. Assessment of reliability of Bowen ratio method for partitioning fluxes, Agric. Forest Met. 97, 141–50.
Plummer, N., Allsopp, T., and Lopez, J. A. 2003. Guidelines on climate observation networks and systems. World Meteorological Organization, WMO/TD no. 1185.
Rasmussen, R et al. 2012. How well are we measuring snow?Bull. Amer. Met. Soc. 93(6), 811–29. doi: 10.1175/BAMS-D-11-00052.1.
Robock, A. et al., 2000. The global soil moisture data bank. Bull. Amer. Met. Soc. 81(6), 1281–99.
Rötzer, K., Montzka, C. and Vereecken, H. 2015. Spatio-temporal variability of global soil moisture products. J. Hydrol. 522, 187–202.
Savage, M. J., Everson, C. S., and Metelerkamp, B. R. 2009. Bowen ratio evaporation measurement in a remote montane grassland: Data integrity and fluxes. J. Hydrol. 376, 249–60.
Sayde, C. et al. 2014. Mapping variability of soil water content and flux across 1–1000 m scales using the Actively Heated Fiber Optic method, Water Resour. Res. 50. doi:10.1002/2013WR014983.
Smith, J. A., Seo, D. J., Baeck, M. L., and Hudlow, M. D. 2010. An intercomparison study of NEXRAD precipitation estimates. Water Resour. Res. 32(7), 2035–45.
Storch, H., von, and Navarra, A (eds.) 1999. Analysis of Climate Variability, 2nd Ed. Berlin: Springer-Verlag.
Storch, H., von, and Zwiers, F. W. 2002. Statistical analysis in climate research. Cambridge: Cambridge University Press.
Vereecken, H. et al. 2008. On the value of soil moisture measurements in vadose zone hydrology: a review. Water Resour. Res. 44, W00D06.
Zreda, M. et al. 2012. COSMOS: the cosmic-ray Soil Moisture Observing System. Hydrol. Earth System Sci. 16(11).