Ensemble Of Profiles Research Articles

Quantifying residual ionospheric errors in GNSS radio occultation bending angles based on ensembles of profiles from end-to-end simulations

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.

A method of online quantitative interpretation of diffuse reflection profiles of biological tissues

We have developed a method of combined interpretation of spectral and spatial characteristics of diffuse reflection of biological tissues, which makes it possible to determine biophysical parameters of the tissue with a high accuracy in real time under conditions of their general variability. Using the Monte Carlo method, we have modeled a statistical ensemble of profiles of diffuse reflection coefficients of skin, which corresponds to a wave variation of its biophysical parameters. On its basis, we have estimated the retrieval accuracy of biophysical parameters using the developed method and investigated the stability of the method to errors of optical measurements. We have showed that it is possible to determine online the concentrations of melanin, hemoglobin, bilirubin, oxygen saturation of blood, and structural parameters of skin from measurements of its diffuse reflection in the spectral range 450–800 nm at three distances between the radiation source and detector.

Error analysis for GNSS radio occultation data based on ensembles of profiles from end‐to‐end simulations

Radio occultation (RO) observations using the Global Navigation Satellite System (GNSS) globally provide high quality atmospheric data which can support the advancement of climate monitoring and modeling as well as the improvement of numerical weather prediction. In order to make optimal use of the data, e.g., via data assimilation systems, the characterization of measurement errors is of importance. Within this context we present results of an empirical error analysis based on quasi‐realistically simulated GNSS RO data. The study is based on an end‐to‐end forward‐inverse simulation involving (1) modeling of the neutral atmosphere and ionosphere, (2) simulation of RO observations, (3) forward modeling of excess phase observables including realistic observation system error modeling, and (4) retrieval of atmospheric parameters. Occultation observations were simulated for one day from which an ensemble of 300 occultation events was chosen, with 100 events in each of three latitude bands (low, middle, high). Phase path profiles were computed showing a realistic rms error of the ionosphere corrected phase paths of 2–3 mm at mesospheric and stratospheric heights at 10 Hz sampling rate. Atmospheric profiles were retrieved by applying a dry air retrieval in the stratosphere and an optimal estimation retrieval in the troposphere. The retrieved profiles were referenced to the “true” co‐located ones of the analysis field of the European Centre for Medium‐range Weather Forecasts (ECMWF). We empirically estimated bias profiles and covariance matrices (standard deviations and correlation functions) for the retrieval products such as bending angle, refractivity, pressure, geopotential height, temperature, and specific humidity. Results include the refractivity error showing a relative standard deviation of 0.1–0.75% and a relative bias of <0.1% at 5–40 km height. Temperature exhibits a standard deviation of 0.2–1 K at 3–31 km height and a bias of <0.1–0.5 K below 33 km and of <0.1 K below 20 km. Simple analytical error covariance formulations are presented for refractivity, as deduced from the empirically estimated covariance matrices. The reasonably realistic error estimates presented are a good basis for further retrieval algorithm improvements and for proper specification of observational errors in data assimilation systems.

Inferring Density from Temperature via a Density-Ratio Relation

A method is presented for determining salinity and density from temperature data in conjunction with historical or contemporaneous (but not collocated) CTD observations. The horizontal density ratio r(z) is determined from the temperature and salinity differences at each depth (δT, δS) between pairs or ensembles of profiles. These differences are expressed as a density ratio r=αδT/βδS, where α and β are the expansion coefficients for temperature and salinity, respectively. Salinity at a site where only temperature is measured, as with an expendable bathythermograph (XBT), is computed based on the temperature and salinity at a reference station (SR,TR); that is, S=SR+(T−TR)δS/δT. The method is restrictive in its application because it is most accurate when all water masses in the region of a survey are linear extrapolations from the water masses at each of the reference stations. In reality, it provides useful results when the T and S fields are not simply linear functions of horizontal distance. This approach is particularly useful in regions where, the T(z)−S(z) relation is nonunique, as in the Mediterranean Water in the North Atlantic. The corresponding expression for the lateral density difference for an observed temperature difference (δT) is δρ=−αρ0δT(1−r−1). Observations from regions offshore and along the coast of Portugal are used to evaluate the method. Errors of less than 0.05 psu are exhibited in the evaluation of salinity determined from T-5 XBT drops compared with nearly simultaneous CTD casts. A comparison of water properties and cyclostrophic velocities is made using XCP temperatures and XCP velocities in a meddy.