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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
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Volume 11, issue 5 | Copyright
Atmos. Meas. Tech., 11, 2567-2582, 2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 03 May 2018

Research article | 03 May 2018

Reducing representativeness and sampling errors in radio occultation–radiosonde comparisons

Shay Gilpin1, Therese Rieckh1,2, and Richard Anthes1 Shay Gilpin et al.
  • 1COSMIC Program Office, University Corporation for Atmospheric Research, Boulder, CO, USA
  • 2Wegener Center for Climate and Global Change, University of Graz, Austria

Abstract. Radio occultation (RO) and radiosonde (RS) comparisons provide a means of analyzing errors associated with both observational systems. Since RO and RS observations are not taken at the exact same time or location, temporal and spatial sampling errors resulting from atmospheric variability can be significant and inhibit error analysis of the observational systems. In addition, the vertical resolutions of RO and RS profiles vary and vertical representativeness errors may also affect the comparison. In RO–RS comparisons, RO observations are co-located with RS profiles within a fixed time window and distance, i.e. within 3–6h and circles of radii ranging between 100 and 500km. In this study, we first show that vertical filtering of RO and RS profiles to a common vertical resolution reduces representativeness errors. We then test two methods of reducing horizontal sampling errors during RO–RS comparisons: restricting co-location pairs to within ellipses oriented along the direction of wind flow rather than circles and applying a spatial–temporal sampling correction based on model data. Using data from 2011 to 2014, we compare RO and RS differences at four GCOS Reference Upper-Air Network (GRUAN) RS stations in different climatic locations, in which co-location pairs were constrained to a large circle ( ∼ 666km radius), small circle ( ∼ 300km radius), and ellipse parallel to the wind direction ( ∼ 666km semi-major axis,  ∼ 133km semi-minor axis). We also apply a spatial–temporal sampling correction using European Centre for Medium-Range Weather Forecasts Interim Reanalysis (ERA-Interim) gridded data. Restricting co-locations to within the ellipse reduces root mean square (RMS) refractivity, temperature, and water vapor pressure differences relative to RMS differences within the large circle and produces differences that are comparable to or less than the RMS differences within circles of similar area. Applying the sampling correction shows the most significant reduction in RMS differences, such that RMS differences are nearly identical to the sampling correction regardless of the geometric constraints. We conclude that implementing the spatial–temporal sampling correction using a reliable model will most effectively reduce sampling errors during RO–RS comparisons; however, if a reliable model is not available, restricting spatial comparisons to within an ellipse parallel to the wind flow will reduce sampling errors caused by horizontal atmospheric variability.

Publications Copernicus
Short summary
Comparing observational systems when observations are not taken at the exact same time or location can introduce sampling errors that can be come significant during error analysis. In this study, we develop two methods to reduce sampling errors: using ellipse distance constraints rather than circles and subtracting model background. We found that both the ellipses and subtracting model background from the observations reduce sampling errors caused by spatial and temporal differences.
Comparing observational systems when observations are not taken at the exact same time or...