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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
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Volume 4, issue 9
Atmos. Meas. Tech., 4, 2019–2034, 2011
© Author(s) 2011. This work is distributed under
the Creative Commons Attribution 3.0 License.

Special issue: Observing atmosphere and climate with occultation techniques...

Atmos. Meas. Tech., 4, 2019–2034, 2011
© Author(s) 2011. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 29 Sep 2011

Research article | 29 Sep 2011

Quantifying uncertainty in climatological fields from GPS radio occultation: an empirical-analytical error model

B. Scherllin-Pirscher1,2,3, G. Kirchengast3, A. K. Steiner3, Y.-H. Kuo2, and U. Foelsche3 B. Scherllin-Pirscher et al.
  • 1Advanced Study Program, National Center for Atmospheric Research (NCAR), Boulder, Colorado, USA
  • 2COSMIC Project Office, University Corporation for Atmospheric Research (UCAR), Boulder, Colorado, USA
  • 3Wegener Center for Climate and Global Change (WEGC) and Institute for Geophysics, Astrophysics, and Meteorology/Institute of Physics (IGAM/IP), University of Graz, Graz, Austria

Abstract. Due to the measurement principle of the radio occultation (RO) technique, RO data are highly suitable for climate studies. RO profiles can be used to build climatological fields of different atmospheric parameters like bending angle, refractivity, density, pressure, geopotential height, and temperature. RO climatologies are affected by random (statistical) errors, sampling errors, and systematic errors, yielding a total climatological error. Based on empirical error estimates, we provide a simple analytical error model for these error components, which accounts for vertical, latitudinal, and seasonal variations. The vertical structure of each error component is modeled constant around the tropopause region. Above this region the error increases exponentially, below the increase follows an inverse height power-law. The statistical error strongly depends on the number of measurements. It is found to be the smallest error component for monthly mean 10° zonal mean climatologies with more than 600 measurements per bin. Due to smallest atmospheric variability, the sampling error is found to be smallest at low latitudes equatorwards of 40°. Beyond 40°, this error increases roughly linearly, with a stronger increase in hemispheric winter than in hemispheric summer. The sampling error model accounts for this hemispheric asymmetry. However, we recommend to subtract the sampling error when using RO climatologies for climate research since the residual sampling error remaining after such subtraction is estimated to be only about 30% of the original one or less. The systematic error accounts for potential residual biases in the measurements as well as in the retrieval process and generally dominates the total climatological error. Overall the total error in monthly means is estimated to be smaller than 0.07% in refractivity and 0.15 K in temperature at low to mid latitudes, increasing towards higher latitudes. This study focuses on dry atmospheric parameters as retrieved from RO measurements so for context we also quantitatively explain the difference between dry and physical atmospheric parameters, which can be significant at altitudes below about 6 km (high latitudes) to 10 km (low latitudes).

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