Radio Occultation Climatologies Research Articles

Performance of the Ionospheric Kappa-Correction of Radio Occultation Profiles Under Diverse Ionization and Solar Activity Conditions.

The kappa‐correction is an easy‐to‐use method to correct for residual ionospheric errors in radio occultation (RO) data. It is a simple empirical model term that only depends on readily available data. While its basic utility was well proven in previous studies, including a recent predecessor study on RO climatologies under solar cycle variations, its performance for individual RO profile correction under diverse and extreme ionization conditions is unclear so far. Here we tackle this gap and focus on investigating (extremely) low and high solar activity and ionization conditions of individual RO events, including inspection of ionospheric symmetry between inbound and outbound raypaths. Using a global multi‐year ensemble of MetOp‐A and GRACE‐A RO events over 2008–2015 as basis, we applied a sampling approach leading to six characteristic condition cases. These cases also relate to day and night time variations and geographic variations from the equatorial to the high latitude region. We inspected the kappa‐correction and its performance relative to the standard bending angle correction for RO‐retrieved stratospheric profiles and found mean deviations in temperature of near −0.3 K in the upper stratosphere (40–45 km) for high ionization conditions, with extreme deviations exceeding −2 K for strong inbound/outbound asymmetry. The kappa‐correction term itself reaches a mean value near 0.05 μrad under these high conditions. Low solar activity and ionization conditions lead to a mean correction smaller than 0.005 μrad and mean temperature deviations smaller than 0.02 K. An intercomparison to other quality datasets, predominantly showed a decrease in mean temperature difference when applying the kappa‐correction.

Quest over the Sampling Error of COSMIC Radio Occultation Temperature Climatologies

AbstractThe sampling error caused by the uneven distribution of radio occultation (RO) profiles in both space and time domains is an important error source of RO climatologies. In this paper, the sampling error RO temperature climatologies is investigated using the 4-yr (2007–10) data from the Constellation Observing System for Meteorology, Ionosphere, and Climate mission. The error is divided into three parts, including local time component (LTC), temporal component (TC), and spatial component (SC). The characteristics of the three components are investigated. Results show the following: 1) The LTC part of the total sampling error is characterized by a pattern of periodic positive and negative deviations, with a full cycle of about four months. The most significant LTC values are found in the area around 60°N/S and the polar regions. 2) The TC part is mainly associated with the extent of day-to-day temperature variability and the daily number of RO profiles observed in each month. The most pronounced TC part is shown in high-latitude areas in wintertime, where the day-to-day temperature variability is high. 3) The SC part shows distinct features in different altitude ranges. It is characterized by a systemic error in the lower troposphere (2–8 km) but exhibits a seasonal trend at the altitude range from 8 to 40 km. 4) The total sampling error is dominated by the TC and SC parts in the troposphere and lower stratosphere, whereas in the upper stratosphere it is dominated by the LTC part.

Sensitivity Analysis and Impact of the Kappa-Correction of Residual Ionospheric Biases on Radio Occultation Climatologies.

A new model was recently introduced to correct for higher‐order ionospheric residual biases in radio occultation (RO) data. The model depends on the α 1 and α 2 dual‐frequency bending angle difference squared, and a factor κ, which varies with time, season, solar activity, and height, needing only the F10.7 solar radio flux index as additional background information. To date, this kappa‐correction was analyzed in simulation studies. In this study, we test it on real observed Metop‐A RO data. The goal is to improve the accuracy of monthly mean RO climate records, potentially raising the accuracy of RO data toward higher stratospheric altitudes. We performed a thorough analysis of the kappa‐correction, evaluating its ionospheric sensitivity during the solar cycle for monthly RO climatologies and comparing the kappa‐corrected RO stratospheric climatologies to three other data sets from reanalysis and passive infrared sounding. We find a clear dependence of the kappa‐correction on solar activity, geographic location, and altitude; hence, it reduces systematic errors that vary with the solar cycle. From low to high solar activity conditions, the correction can increase from values of about 0.2 K to more than 2.0 K at altitudes between 40 to 45 km. The correction shifts RO climatologies toward warmer temperatures. With respect to other data sets, however, we found it difficult to draw firm conclusions, because the biases in the other data sets appear to be at similar magnitude as the size of the kappa‐correction. Further validation with more accurate data will be useful.

Evaluation of the 15-year ROM SAF monthly mean GPS radio occultation climate data record

Abstract. We here present results from an evaluation of the Radio Occultation Meteorology Satellite Application Facility (ROM SAF) gridded monthly mean climate data record (CDR v1.0), based on Global Positioning System (GPS) radio occultation (RO) data from the CHAMP (CHAllenging Minisatellite Payload), GRACE (Gravity Recovery and Climate Experiment), COSMIC (Constellation Observing System for Meteorology, Ionosphere, and Climate), and Metop satellite missions. Systematic differences between RO missions, as well as differences of RO data relative to ERA-Interim reanalysis data, are quantified. The methods used to generate gridded monthly mean data are described, and the correction of monthly mean RO climatologies for sampling errors, which is essential for combining data from RO missions with different sampling characteristics, is evaluated. We find good overall agreement between the ROM SAF gridded monthly mean CDR and the ERA-Interim reanalysis, particularly in the 8–30 km height interval. Here, the differences largely reflect time-varying biases in ERA-Interim, suggesting that the RO data record has a better long-term stability than ERA-Interim. Above 30–40 km altitude, the differences are larger, particularly for the pre-COSMIC era. In the 8–30 km altitude region, the observational data record exhibits a high degree of internal consistency between the RO satellite missions, allowing us to combine data into multi-mission records. For global mean bending angle, the consistency is better than 0.04 %, for refractivity it is better than 0.05 %, and for global mean dry temperature the consistency is better than 0.15 K in this height interval. At altitudes up to 40 km, these numbers increase to 0.08 %, 0.11 %, and 0.50 K, respectively. The numbers can be up to a factor of 2 larger for certain latitude bands compared to global means. Below about 8 km, the RO mission differences are larger, reducing the possibilities to generate multi-mission data records. We also find that the residual sampling errors are about one-third of the original and that they include a component most likely related to diurnal or semi-diurnal cycles.

Comparison study of COSMIC RO dry-air climatologies based on average profile inversion

Abstract. Global Navigation Satellite System (GNSS) radio occultation (RO) data enable the retrieval of near-vertical profiles of atmospheric parameters like bending angle, refractivity, pressure, and temperature. The retrieval step from bending angle to refractivity, however, involves an Abel integral with an upper limit of infinity. RO data are practically limited to altitudes below about 80 km and the observed bending angle profiles show decreasing signal-to-noise ratio with increasing altitude. Some kind of high-altitude background data are therefore needed in order to perform this retrieval step (this approach is known as high-altitude initialization). Any bias in the background data will affect all RO data products beyond bending angle. A reduction of the influence of the background is therefore desirable – in particular for climate applications. Recently a new approach for the production of GNSS radio occultation climatologies has been proposed. The idea is to perform the averaging of individual profiles in bending angle space and then propagate the mean bending angle profiles through the Abel transform. Climatological products of refractivity, density, pressure, and temperature are directly retrieved from the mean bending angles. The averaging of a large number of profiles suppresses noise in the data, enabling observed bending angle data to be used up to 80 km without the need for a priori information. Some background information for the Abel integral is still necessary above 80 km. This work is a follow-up study, having the focus on the comparison of the average profile inversion climatologies (API) from the two processing centers WEGC and DMI, which study monthly COSMIC (Constellation Observing System for Meteorology, Ionosphere, and Climate) data from January to March 2011. The impact of different backgrounds above 80 km is tested, and different implementations of the Abel integral are investigated. Results are compared for the climatological products with ECMWF analyses, MIPAS, and SABER data. It is shown that different implementations of the Abel integral have little impact on the API climatologies. On the other hand, different extrapolations of the bending angle profile above 80 km play a key role in the resulting monthly mean refractivities above 35 km in altitude. Below that respective altitude the API climatologies show a good agreement between the two processing centers WEGC and DMI. Due to the downward propagation within the retrieval, effects of the high-altitude initialization lead to differences in dry-temperature climatologies down to 20 km in altitude. When applying an exponential extrapolation to the bending angles above 80 km at both centers, the dry-temperature climatologies agree among WEGC, DMI, ECMWF analysis, and MIPAS up to 35 km in altitude within ±0.5 K and up to 40 km in altitude within ±1 K. We conclude that the API retrieval is a valid approach up to the lower stratosphere. It is a computationally efficient alternative method for producing dry atmospheric RO climatologies.

A simulation study with a new residual ionospheric error model for GPS radio occultation climatologies

Abstract. In this study, a new model was explored which corrects for higher order ionospheric residuals in Global Positioning System (GPS) radio occultation (RO) data. Recently, the theoretical basis of this new "residual ionospheric error model" has been outlined (Healy and Culverwell, 2015). The method was tested in simulations with a one-dimensional model ionosphere. The proposed new model for computing the residual ionospheric error is the product of two factors, one of which expresses its variation from profile to profile and from time to time in terms of measurable quantities (the L1 and L2 bending angles), while the other describes the weak variation with altitude. A simple integral expression for the residual error (Vorob’ev and Krasil’nikova, 1994) has been shown to be in excellent numerical agreement with the exact value, for a simple Chapman layer ionosphere. In this case, the "altitudinal" element of the residual error varies (decreases) by no more than about 25 % between ~10 and ~100 km for physically reasonable Chapman layer parameters. For other simple model ionospheres the integral can be evaluated exactly, and results are in reasonable agreement with those of an equivalent Chapman layer. In this follow-up study the overall objective was to explore the validity of the new residual ionospheric error model for more detailed simulations, based on modeling through a complex three-dimensional ionosphere. The simulation study was set up, simulating day and night GPS RO profiles for the period of a solar cycle with and without an ionosphere. The residual ionospheric error was studied, the new error model was tested, and temporal and spatial variations of the model were investigated. The model performed well in the simulation study, capturing the temporal variability of the ionospheric residual. Although it was not possible, due to high noise of the simulated bending-angle profiles at mid- to high latitudes, to perform a thorough latitudinal investigation of the performance of the model, first positive and encouraging results were found at low latitudes. Furthermore, first application tests of the model on the data showed a reduction in temperature level of the ionospheric residual at 40 km from about −2.2 to −0.2 K.

A modification to the standard ionospheric correction method used in GPS radio occultation

Abstract. A modification to the standard bending-angle correction used in GPS radio occultation (GPS-RO) is proposed. The modified approach should reduce systematic residual ionospheric errors in GPS radio occultation climatologies. A new second-order term is introduced in order to account for a known source of systematic error, which is generally neglected. The new term has the form κ(a) × (αL1(a)-αL2(a))2, where a is the impact parameter and (αL1, αL2) are the L1 and L2 bending angles, respectively. The variable κ is a weak function of the impact parameter, a, but it does depend on a priori ionospheric information. The theoretical basis of the new term is examined. The sensitivity of κ to the assumed ionospheric parameters is investigated in one-dimensional simulations, and it is shown that κ ≃ 10–20 rad−1. We note that the current implicit assumption is κ=0, and this is probably adequate for numerical weather prediction applications. However, the uncertainty in κ should be included in the uncertainty estimates for the geophysical climatologies produced from GPS-RO measurements. The limitations of the new ionospheric correction when applied to CHAMP (Challenging Minisatellite Payload) measurements are noted. These arise because of the assumption that the refractive index is unity at the satellite, made when deriving bending angles from the Doppler shift values.

A simplified approach for generating GNSS radio occultation refractivity climatologies

Abstract. The possibility of simplifying the retrieval scheme required to produce GNSS radio occultation refractivity climatologies is investigated. In a new, simplified retrieval approach, the main statistical analysis is performed in bending angle space and an estimate of the average bending angle profile is then propagated through an Abel transform. The average is composed of means and medians of ionospheric corrected bending angles up to 80 km. Above that, the observed profile is exponentially extrapolated to infinity using a fixed a priori scale height. The new approach circumvents the need to introduce a "statistical optimisation" processing step in which individual bending angle profiles are merged with a priori data, often taken from a climatology. This processing step can be complex, difficult to interpret, and is generally recognised as a potential source of structural uncertainty. The new scheme is compared with the more conventional approach of averaging individual refractivity profiles, produced with the implementation of statistical optimisation used in the EUMETSAT Radio Occultation Meteorology Satellite Application Facility (ROM SAF) operational processing. It is shown that the two GNSS radio occultation climatologies agree to within 0.1% from 5 km up to 35–40 km, for the three months January, February, and March 2011. During this time period, the new approach also produces slightly better agreement with ECMWF analyses between 40–50 km, which is encouraging. The possible limitations of the new approach caused by mean residual ionospheric errors and low observation numbers are discussed briefly, and areas for future work are suggested.

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

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).

Refractivity and temperature climate records from multiple radio occultation satellites consistent within 0.05%

Abstract. Data consistency is an important prerequisite to build radio occultation (RO) climatologies based on a combined record of data from different satellites. The presence of multiple RO receiving satellites in orbit over the same time period allows for testing this consistency. We used RO data from CHAMP (CHAllenging Minisatellite Payload for geoscientific research), six FORMOSAT-3/COSMIC satellites (Formosa Satellite Mission 3/Constellation Observing System for Meteorology, Ionosphere and Climate, F3C), and GRACE-A (Gravity Recovery and Climate Experiment). We show latitude-altitude-resolved results for an example month (October 2007) and the temporal evolution of differences in a climate record of global and monthly means from January 2007 to December 2009. Latitude- and altitude-resolved refractivity and dry temperature climatologies clearly show the influence of different sampling characteristics; monthly mean deviations from the multi-satellite mean over the altitude domain 10 km to 30 km typically reach 0.1% and 0.2 K, respectively. Nevertheless, the 3-yr average deviations (shorter for CHAMP) are less than 0.03% and 0.05 K, respectively. We find no indications for instrument degradation, temporal inhomogeneities in the RO records, or temporal trends in sampling patterns. Based on analysis fields from ECMWF (European Centre for Medium-Range Weather Forecasts), we can estimate – and subtract – the sampling error from each monthly climatology. After such subtraction, refractivity deviations are found reduced to <0.05% in almost any month and dry temperature deviations to <0.05 K (<0.02% relative) for almost every satellite and month. 3-yr average deviations are even reduced to <0.01% and <0.01 K (CHAMP: −0.05 K), respectively, establishing an amazing consistency of RO climatologies from different satellites. If applying the same processing scheme for all data, refractivity and dry temperature records from individual satellites with similar bending angle noise can be safely combined up to 30 km altitude (refractivity also up to 35 km) to a consistent single climate record of substantial value for climate monitoring in the upper troposphere and lower stratosphere.

Observing upper troposphere–lower stratosphere climate with radio occultation data from the CHAMP satellite

High quality observations of the atmosphere are particularly required for monitoring global climate change. Radio occultation (RO) data, using Global Navigation Satellite System (GNSS) signals, are well suited for this challenge. The special climate utility of RO data arises from their long-term stability due to their self-calibrated nature. The German research satellite CHAllenging Minisatellite Payload for geoscientific research (CHAMP) continuously records RO profiles since August 2001 providing the first opportunity to create RO based climatologies for a multi-year period of more than 5 years. A period of missing CHAMP data from July 3, 2006 to August 8, 2006 can be bridged with RO data from the GRACE satellite (Gravity Recovery and Climate Experiment). We have built seasonal and zonal mean climatologies of atmospheric (dry) temperature, microwave refractivity, geopotential height and pressure with 10° latitudinal resolution. We show representative results with focus on dry temperatures and compare them with analysis data from the European Centre for Medium-Range Weather Forecasts (ECMWF). Although we have available only about 150 CHAMP profiles per day (compared to millions of data entering the ECMWF analyses) the overall agreement between 8 and 30 km altitude is in general very good with systematic differences <0.5 K in most parts of the domain. Pronounced systematic differences (exceeding 2 K) in the tropical tropopause region and above Antarctica in southern winter can almost entirely be attributed to errors in the ECMWF analyses. Errors resulting from uneven sampling in space and time are a potential error source for single-satellite climatologies. The average CHAMP sampling error for seasonal zonal means is <0.2 K, higher values occur in restricted regions and time intervals which can be clearly identified by the sampling error estimation approach we introduced (which is based on ECMWF analysis fields). The total error of this new type of temperature climatologies is estimated to be <0.5 K below 30 km. The recently launched Taiwan/U.S. FORMOSAT-3/COSMIC constellation of 6 RO satellites started to provide thousands of RO profiles per day, but already now the single-satellite CHAMP RO climatologies improve upon modern operational climatologies in the upper troposphere–lower stratosphere and can act as absolute reference climatologies for validation of more bias-sensitive climate datasets and models.

Local time influence in single‐satellite radio occultation climatologies from Sun‐synchronous and non‐Sun‐synchronous satellites

The sampling error of Global Positioning System (GPS) radio occultation (RO) derived temperature climatologies is computed over a representative time span of 2 years and compared for Sun‐synchronous and non‐Sun‐synchronous Low Earth Orbit (LEO) satellites. The main focus lies on the sampling error's local time component, which is caused by incomplete sampling of the diurnal cycle and which depends on the geometry of the satellite orbits. The Sun‐synchronous satellite MetOp (Meteorological Operational European weather satellite) and the non‐Sun‐synchronous satellite CHAMP (Challenging Minisatellite Payload), both carrying GPS RO instruments on board, serve as representative cases. For the Sun‐synchronous satellite MetOp the local time component remains constant during the whole observation period such that the magnitude of the local time errors in monthly mean or longer‐term mean RO climatologies is generally lower than ±0.1 K. Except for potential long‐term effects of global warming on the diurnal cycle, which might require calibration, this small local time component is stable on decadal timescales and is mainly positive in the Northern Hemisphere and at low latitudes, whereas it is mainly negative in the Southern Hemisphere. These features are attributable to a slight orbit‐determined asymmetry in local time sampling. The typical (temporally stable) local time error of an annual mean MetOp climatology resolved into 18 zonal bands amounts to ∼0.04 K. For the non‐Sun‐synchronous satellite CHAMP the local time error component in monthly mean RO climatologies is also small (up to about ±0.15 K) but more variable (about zero mean) at middle and high latitudes. At low latitudes it results in sinusoidally varying positive and negative deviations with a several‐months period, resulting from the local time drift of the satellite. The magnitude of local time errors is slightly larger compared to MetOp since the monthly averaging period is too short for CHAMP to entirely sample a diurnal cycle; a longer averaging period further decreases CHAMP's local time component. An annual mean climatology resolved into 18 zonal bands shows for CHAMP a typical local time residual error component of ∼0.03 K. The overall evidence is that even with single RO satellites, monthly climatologies of high accuracy (sampling error &lt;0.3 K) with the local time component being a minor part (&lt;0.1 K to 0.15 K) can be obtained.