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 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).

Highlights

  • Global climate monitoring and trend detection require accurate and long-term consistent data records

  • We introduce an empirical-analytical error model, which can be used to model all components of the total climatological error separately after which they are RMS-combined according to Eq (1)

  • Radio occultation (RO) measurements are known to be of very high accuracy, offer a high vertical resolution, are available globally, are self-calibrating and long-term stable

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Summary

Introduction

Global climate monitoring and trend detection require accurate and long-term consistent data records Such data are needed in the upper troposphere/lower stratosphere (UTLS) region since most conventional upper air measurements are based on radiometric physical devices, which often deteriorate with time. The number of high quality measurements provided by a single satellite within one month (usually larger than 3500) is sufficient to calculate monthly climatologies of atmospheric parameters with a horizontal resolution of 10◦ zonal bands. Foelsche et al (2011a) showed that monthly mean CHAMP, GRACE-A, and F3C global-average climatologies agree to within < 0.05 % in refractivity and < 0.05 K in dry temperature for almost every satellite and month, provided that the sampling error is subtracted as we suggest as a general recommendation in this paper.

RO data
ECMWF data
Error model
Statistical error estimation
Sampling error and sampling error model
Residual sampling error
Discussion and modeling of the systematic error
Total climatological error
Findings
Summary and conclusions
Full Text
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