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

Abstract

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 Correspondence to: B. Scherllin-Pirscher (pirscher@ucar.edu) 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).

Impact of land convection on temperature diurnal variation in the tropical lower stratosphere inferred from COSMIC GPS radio occultations

“…Extensive validation of the RO measurements against other observations and models as well as error estimations have been carried out by several authors (e.g., Kuo et al, 2004;Schreiner et al, 2007, Scherllin-Pirscher et al, 2011a. The total climatological error in dry temperature estimated by Scherllin-Pirscher et al (2011a) amounts to 0.15 K between 10 km and 20 km altitude and increases exponentially to about 0.4 K at 35 km. The uncertainties may be higher for the measurements in the strong convective systems, since the model used for temperature retrieval may not properly account for the vertical moisture transport.…”

“…The temperature and humidity profiles can then be further retrieved from the refractivity (Rocken et al, 1997). Extensive validation of the RO measurements against other observations and models as well as error estimations have been carried out by several authors (e.g., Kuo et al, 2004;Schreiner et al, 2007, Scherllin-Pirscher et al, 2011a. The total climatological error in dry temperature estimated by Scherllin-Pirscher et al (2011a) amounts to 0.15 K between 10 km and 20 km altitude and increases exponentially to about 0.4 K at 35 km.…”

Generation of a bending angle radio occultation climatology (BAROCLIM) and its use in radio occultation retrievals

“…Our experience of building atmospheric climatologies utilizing RO data (e.g., Foelsche et al, 2008;Scherllin-Pirscher et al, 2011a) showed that 10 • zonal bands were a reasonable choice for calculating mean atmospheric profiles from RO data. These bands range from 90 • S to 90 • N, resulting in 18 zonal bands.…”

Evaluation of atmospheric profiles derived from single- and zero-difference excess phase processing of BeiDou System radio occultation data of the FY-3C GNOS mission

“…At least part of the bias is likely from slight differences in vertical geolocation of GNOS and reference profiles, for which ensuring rigorous consistency is a subtle process (Scherrlin-Pirscher et al, 2017). Likewise, part of the standard deviation is from representativeness error between the GNOS and ECWMF profiles, since even though being co-located in mean location they have different 5 detailed locations and resolutions Scherllin-Pirscher et al, 2011b).…”