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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union

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https://doi.org/10.5194/amt-2018-20
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Research article
31 Jan 2018
Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Atmospheric Measurement Techniques (AMT).
Reducing representativeness and sampling errors in radio occultation–radiosonde comparisons
Shay Gilpin1, Therese Rieckh1,2, and Richard Anthes1 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. Typically in RO–RS comparisons, RS observations are co-located with RO profiles within a fixed time window and distance, i.e. within 3–6 h and circles of radii ranging between 100–500 km. 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 correction based on model data. 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 (∼ 666 km radius), small circle (∼ 300 km radius) and ellipse parallel to the wind direction (∼ 666 km semi-major axis, ∼ 133 km semi-minor axis). We also apply a spatial 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 either reduce or are comparable to RMS differences within circles of similar area. Applying the spatial correction shows the most significant reduction in RMS differences, such that RMS differences are nearly identical with the spatial correction regardless of the geometric constraints. We conclude that implementing the spatial 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.

Citation: Gilpin, S., Rieckh, T., and Anthes, R.: Reducing representativeness and sampling errors in radio occultation–radiosonde comparisons, Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2018-20, in review, 2018.
Shay Gilpin et al.
Shay Gilpin et al.
Shay Gilpin et al.

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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 reduces sampling errors caused by spatial and temporal differences.
Comparing observational systems when observations are not taken at the exact same time or...
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