No abstract

Microwave occultation using centimeter‐ and millimeter‐wave signals between low Earth orbit (LEO) satellites (LEO microwave occultation, LMO) is an advancement of GPS radio occultation (GRO) exploiting in addition to refraction also absorption of signals. Beyond the successful GRO refractivity profiling capability, which leaves a temperature‐humidity ambiguity in the troposphere where moisture cannot be neglected, LMO enables joint retrieval of pressure, temperature, and humidity profiles without auxiliary background information. Here we focus on the upper troposphere/lower stratosphere and advance the LMO method in two ways: (1) we introduce a new retrieval algorithm for processing LMO excess phase and amplitude data from multiple frequencies, complementing existing GRO retrieval algorithms, and (2) we employ the algorithm in an ensemble‐based end‐to‐end performance analysis and assess the accuracy of pressure, temperature, and humidity profiles retrieved from the LMO data. The end‐to‐end simulations were carried out under quasi‐realistic conditions for a day of LEO‐LEO occultation events, based on a high‐resolution atmospheric analysis of the European Centre for Medium‐Range Weather Forecasts (ECMWF) and accounting for scintillation noise from turbulence and instrumental errors. The new algorithm was found robust, fast, and versatile to adequately process LMO data under all conditions from dry and clear to moist and cloudy air as contained in the ECMWF analysis. The retrieved pressure, temperature, and humidity profiles were generally found unbiased and within target accuracy requirements, set by scientific objectives of atmosphere and climate research going to be supported by the data, of <0.2% (pressure), <0.5 K (temperature), and <10% (humidity). Extending a “minimum” LMO design with three frequencies near 22 GHz with two added frequencies near 183 GHz favorably provides humidity retrieval into the lower stratosphere but already the “minimum” design resolves the temperature‐humidity ambiguity of GRO in the upper troposphere (frequencies <15 GHz might extend this into the lower troposphere). The results are encouraging for future LMO implementation, both stand‐alone and combined with novel LEO‐LEO infrared laser occultation.

Abstract. The radio occultation (RO) technique using signals from the Global Navigation Satellite System (GNSS), in particular from the Global Positioning System (GPS) so far, is currently widely used to observe the atmosphere for applications such as numerical weather prediction and global climate monitoring. The ionosphere is a major error source in RO measurements at stratospheric altitudes, and a linear ionospheric correction of dual-frequency RO bending angles is commonly used to remove the first-order ionospheric effect. However, the residual ionospheric error (RIE) can still be significant so that it needs to be further mitigated for high-accuracy applications, especially above about 30 km altitude where the RIE is most relevant compared to the magnitude of the neutral atmospheric bending angle. Quantification and careful analyses for better understanding of the RIE is therefore important for enabling benchmark-quality stratospheric RO retrievals. Here we present such an analysis of bending angle RIEs covering the stratosphere and mesosphere, using quasi-realistic end-to-end simulations for a full-day ensemble of RO events. Based on the ensemble simulations we assessed the variation of bending angle RIEs, both biases and standard deviations, with solar activity, latitudinal region and with or without the assumption of ionospheric spherical symmetry and co-existing observing system errors. We find that the bending angle RIE biases in the upper stratosphere and mesosphere, and in all latitudinal zones from low to high latitudes, have a clear negative tendency and a magnitude increasing with solar activity, which is in line with recent empirical studies based on real RO data although we find smaller bias magnitudes, deserving further study in the future. The maximum RIE biases are found at low latitudes during daytime, where they amount to within −0.03 to −0.05 µrad, the smallest at high latitudes (0 to −0.01 µrad; quiet space weather and winter conditions). Ionospheric spherical symmetry or asymmetries about the RO event location have only a minor influence on RIE biases. The RIE standard deviations are markedly increased both by ionospheric asymmetries and increasing solar activity and amount to about 0.3 to 0.7 µrad in the upper stratosphere and mesosphere. Taking also into account the realistic observation errors of a modern RO receiving system, amounting globally to about 0.4 µrad (unbiased; standard deviation), shows that the random RIEs are typically comparable to the total observing system error. The results help to inform future RIE mitigation schemes that will improve upon the use of the linear ionospheric correction of bending angles and also provide explicit uncertainty estimates.

Global Navigation Satellite Systems (GNSS) Radio Occultation (RO) observations, globally available as a continuous record since 2001, are highly accurate and long-term stable data records. Essential climate variables for the thermodynamic state of the free atmosphere, such as temperature and tropospheric water vapor profiles (involving background information), can be derived from these records, which consequentially have the potential to serve as climate benchmark data. In order to exploit this potential, atmospheric profile retrievals need to be very accurate and the remaining uncertainties need to be quantified and traced throughout the retrieval chain. The new Reference Occultation Processing System at the Wegener Center aims to deliver such an accurate retrieval chain with integrated uncertainty propagation. Here we introduce and demonstrate the algorithms implemented for uncertainty propagation from RO bending angle profiles to dry-air variables (pressure and temperature), for estimated random and systematic uncertainties, and for coestimates of observation-to-background weighting ratio profiles. We estimated systematic uncertainty profiles with the same operators as used for the basic profiles retrieval. The random uncertainty propagation was integrated by a covariance propagation approach and validated using Monte-Carlo ensemble methods. We present the results of the validation and demonstrate how the algorithm performs for individual simulated RO events and for ensembles of real RO events. We also compare the new results from the integrated uncertainty propagation to previous ones from empirical error analyses for RO-retrieved atmospheric profiles. We find that the new uncertainty estimation chain shows robust performance and is in good agreement with previous comparable results.

The residual ionospheric error (RIE) from higher-order terms in the refractive index is not negligible when using global navigation satellite system (GNSS) radio occultation (RO) data for climate and meteorology applications in the stratosphere. In this study, a new higher-order bending angle RIE correction named “Bi-local correction approach” has been implemented and evaluated, which accounts for the ray path splitting of the dual-frequency GNSS signals, the altitude of the low Earth orbit (LEO) satellite, the ionospheric inbound (GNSS to tangent point) vs. outbound (tangent point to LEO) asymmetry, and the geomagnetic field. Statistical results based on test-day ensembles of RO events show that, over the upper stratosphere and mesosphere, the order of magnitude of the mean total RIE in the bi-local correction approach is 0.01 μrad. Related to this, the so-called electron-density-squared (Ne2) and geomagnetic (BNe) terms appear to be dominant and comparable in magnitude. The BNe term takes negative or positive values, depending on the angle between the geomagnetic field vector and the direction of RO ray paths, while the Ne2 term is generally negative. We evaluated the new approach against the existing “Kappa approach” and the standard linear dual-frequency correction of bending angles and found it to perform well and in many average conditions similar to the simpler Kappa approach. On top of this, the bi-local approach can provide added value for RO missions with low LEO altitudes and for regional-scale applications, where its capacity to account for the ionospheric inbound-outbound asymmetry as well as for the geomagnetic term plays out.