GNSS Radio Occultation Research Articles

Global GNSS-RO Electron Density in the Lower Ionosphere

Lack of instrument sensitivity to low electron density (Ne) concentration makes it difficult to measure sharp Ne vertical gradients (four orders of magnitude over 30 km) in the D/E-region. A robust algorithm is developed to retrieve global D/E-region Ne from the high-rate GNSS radio occultation (RO) data, to improve spatiotemporal coverage using recent SmallSat/CubeSat constellations. The new algorithm removes F-region contributions in the RO excess phase profile by fitting a linear function to the data below the D-region. The new GNSS-RO observations reveal many interesting features in the diurnal, seasonal, solar-cycle, and magnetic-field-dependent variations in the Ne morphology. While the D/E-region Ne is a function of solar zenith angle (χ), it exhibits strong latitudinal variations for the same χ with a distribution asymmetric about noon. In addition, large longitudinal variations are observed along the same magnetic field pitch angle. The summer midlatitude Ne and sporadic E (Es) show a distribution similar to each other. The distribution of auroral electron precipitation correlates better with the pitch angle from the magnetosphere than from one at 100 km. Finally, a new TEC retrieval technique is developed for the high-rate RO data with a top reaching at least 120 km. For better characterization of the E- to F-transition in Ne and more accurate TEC retrievals, it is recommended to have all GNSS-RO acquisition routinely up to 220 km.

Impact of assimilating Formosat-7/COSMIC-II GNSS radio occultation data on heavy rainfall prediction in Taiwan

This study investigates the impact of assimilating Formosat-7/COSMIC-II (FS7/C2) radio occultation (RO) refractivity data on predicting the heavy rainfall event that occurred in Taiwan on August 13, 2019. This event was characterized by heavy rainfall over the coastal region of central and southwestern Taiwan. Our investigation is performed using the Weather Research and Forecasting-Local Ensemble Transform Kalman Filter. Generally, assimilating the RO data increases the amount of moisture over the northern South China Sea (SCS) and the Pearl River area in southern China. It was expected that assimilating the RO data would improve low-level moisture analysis, given that more RO data are available for the lower atmosphere compared to those from Formosat-3/COSMIC-I. However, our results show that the experiment that does not include the RO data below 3 km facilitates better rainfall prediction over Taiwan in terms of the intensity and location of heavy rainfall. This heavy rainfall event can be attributed to moisture transport from the Pearl River area, where the RO data at the altitude of 3–5 km provide effective moisture enhancement to deepen the high-moisture layer. The experiment using the local spectral width (LSW) to conduct the quality control (QC) also helps improve rainfall prediction. However, such an LSW-based QC procedure tends to reject significant amounts of RO data 3 km above the land. Based on this case study, our results show that the QC procedure brings a larger impact to rainfall prediction than counterparts that adjust the observation error variance. A sophisticated QC procedure should be developed to optimize the impact of low-level RO data.

Evaluation of Forward Models for GNSS Radio Occultation Data Processing and Assimilation

In radio occultation (RO) data processing and data assimilation, the forward model (FM) is used to calculate bending angle (BA) from refractivity (N). The accuracy and precision of forward modeled BA are affected by refractivity profiles and FM methods, including Abel integral algorithms (direct, exp, exp_T, linear) and methods of interpolating refractivity during integral (log-cubic spline and log-linear). Experiment 1 compares these forward model methods by comparing the difference and relative difference (RD) of the experimental value (forward modeled ECMWF analysis) and the true value (BA of FY3D RO data). Results suggested that the exp with log-cubic spline (log-cubic) interpolation is the most accurate FM because it has better integral accuracy (less than 2%) to inputs, especially when the input is lower than an order of magnitude of 1 × 10−2 (that is, above 60 km). By contrast, the direct induced a 10% error, and the improvement of exp T to exp is limited. Experiment 2 simulated the exact errors of an FM (exp) based on inputs on different vertical resolutions. The inputs are refractivity profiles on model levels of three widely used analyses, including ECMWF 4Dvar analysis, final operational global analysis data (FNL), and ERA5. Results demonstrated that based on exp and log-cubic interpolation, BA on model level of ECMWF 4Dvar has the highest accuracy, whose RD is 0.5% between 0–35 km, 4% between 35–58 km, and 1.8% between 58–80 km. By contrast, the other two analyses have low accuracy. This paper paves the way to better understanding the FM, and simulation errors on model levels of three analyses can be a helpful FM error reference.

FY3E GNOS II GNSS Reflectometry: Mission Review and First Results

FengYun-3E (FY3E), launched on 5 July 2021, is one of China’s polar-orbiting meteorological satellite series. The GNOS II onboard FY3E is an operational GNSS remote sensor that for the first time combines GNSS radio occultation (GNSS RO) and GNSS reflectometry (GNSS-R). It has eight reflection channels that can track eight specular points at the same time, receiving reflected signals from multiple GNSS systems, including GPS, BeiDou and Galileo. The basic GNSS-R output generated by GNOS II is a 122 × 20 non-uniform delay-Doppler map whose high resolution portion captures more information near the specular point. This paper introduces the GNSS-R aspect of the FengYun-3E GNOS II, including the instrument, power calibration and wind speed retrieval algorithm. Preliminary validation results for its first four months of data are also presented. After preliminary quality control, the overall wind speed error is less than 2 m/s at wind speeds below 20 m/s for data from both GPS satellites and BeiDou satellites when compared to the ECMWF reanalysis winds.

Stability of CubeSat Clocks and Their Impacts on GNSS Radio Occultation

Global Navigation Satellite Systems’ radio occultation (GNSS-RO) provides the upper troposphere-lower stratosphere (UTLS) vertical atmospheric profiles that are complementing radiosonde and reanalysis data. Such data are employed in the numerical weather prediction (NWP) models used to forecast global weather as well as in climate change studies. Typically, GNSS-RO operates by remotely sensing the bending angles of an occulting GNSS signal measured by larger low Earth orbit (LEO) satellites. However, these satellites are faced with complexities in their design and costs. CubeSats, on the other hand, are emerging small and cheap satellites; the low prices of building them and the advancements in their components make them favorable for the GNSS-RO. In order to be compatible with GNSS-RO requirements, the clocks of the onboard receivers that are estimated through the precise orbit determination (POD) should have short-term stabilities. This is essential to correctly time tag the excess phase observations used in the derivation of the GNSS-RO UTLS atmospheric profiles. In this study, the stabilities of estimated clocks of a set of CubeSats launched for GNSS-RO in the Spire Global constellation are rigorously analysed and evaluated in comparison to the ultra-stable oscillators (USOs) onboard the Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC-2) satellites. Methods for improving their clock stabilities are proposed and tested. The results (i) show improvement of the estimated clocks at the level of several microseconds, which increases their short-term stabilities, (ii) indicate that the quality of the frequency oscillator plays a dominant role in CubeSats’ clock instabilities, and (iii) show that CubeSats’ derived UTLS (i.e., tropopause) atmospheric profiles are comparable to those of COSMIC-2 products and in situ radiosonde observations, which provided external validation products. Different comparisons confirm that CubeSats, even those with unstable onboard clocks, provide high-quality RO profiles, comparable to those of COSMIC-2. The proposed remedies in POD and the advancements of the COTS components, such as chip-scale atomic clocks and better onboard processing units, also present a brighter future for real-time applications that require precise orbits and stable clocks.

A Machine Learning-Based Method for Wind Fields Forecasting Utilizing GNSS Radio Occultation Data

With the development of computer technology and expanding environmental issues, machine learning has received more and more attention in the field of weather forecasting. Global Navigation Satellite System-Radio Occultation(GNSS-RO) technology is a kind of remote sensing technology. This investigation proposes an alternative to numerical weather forecasting model. The new method is based on machine learning utilizing GNSS-RO data to forecast the wind field in the Beijing-Tianjin-Hebei region of China. The dataset including temperature, humidity, pressure, wind speed and direction was obtained by numerical calculation in terms of historical monitoring data in Beijing-Tianjin-Hebei region. Then the models of wind fields forecasting based on machine learning were established with different neural network including Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN) and Deep Neural Networks (DNN). The prediction performance of different models was analyzed. The results demonstrate that LSTM and CNN have better performance on predicting the wind field than Deep Neural Networks. The wind speed error is about 1.4m/s, and the wind direction error is about 30°. Moreover, the time required for neural network to predict a new sample is about 1 second, which is only 0.2% of the prediction time compared with numerical model. Finally, the machine learning model can be used to predict the wind field effectively, with GNSS-RO data as the input in application. This paper pro-vides a new method in sight to use machine learning to forecast the regional wind field utilizing GNSS-RO data.

Commercial GNSS Radio Occultation on Aerial Platforms With Off-The-Shelf Receivers

<h3>Abstract</h3> In recent decades, GNSS radio occultation (RO) soundings have proven to be an invaluable input to global weather forecasting. The success of government programs such as COSMIC is now complemented by commercial low-cost cubesats. The result is access to more than 10,000 soundings per day. We examine aerial platforms for commercial GNSS-RO, specifically high-altitude balloons and commercial aviation. Meteorological balloons (radiosondes) are deployed daily in over 900 locations globally. Adding GNSS-RO capability to radiosondes would expand capability and enable local area monitoring. Commercial aviation offers scale with more than 100,000 flights daily. A barrier to entry for the inclusion of GNSS-RO sensors is cost and complexity, as GNSS-RO traditionally requires highly specialized equipment. This paper describes a low-cost and scalable approach to aerial GNSS-RO based on commercial-off-the-shelf (COTS) GNSS receivers. We present hardware prototypes and data processing techniques that demonstrate technical feasibility through the results from several flight testing campaigns.

Semi-supervised classification of lower-ionospheric perturbations using GNSS radio occultation observations from Spire Global’s Cubesat Constellation

This study presents a new methodology to automatically classify perturbations in the lower ionosphere using GNSS radio occultation (RO) observations collected using Spire’s constellation of CubeSats. This methodology combines signal processing techniques with semi-supervised machine learning by applying spectral clustering in a metric space of wavelet spectra. A “bottom-up” algorithm was applied to extract E layer information directly from Spire’s high-rate (50 Hz) GNSS-RO profiles by subtracting the effect of the F layers. This processing algorithm has been implemented in our ground segment to operationally produce high rate sTEC profiles with a vertical resolution of better than 100 m. The key idea behind the semi-supervised classification is to produce a database of labeled clusters that can be used to classify new unlabeled data by determining which cluster it belongs to. A dataset of more than 12,000 GNSS-RO profiles collected in 2019 containing sTEC perturbations is used to find the initial clusters. This dataset is used to represent the climatology of ionospheric perturbations, such as MSTIDs and sporadic Es. The wavelet power spectrum (WPS) is computed for these profiles, and a metric space is defined using the Earth mover’s distance (EMD) between the WPS. A self-tuning spectral clustering algorithm is used to cluster the profiles in this metric space. These clusters are used as a reference database of perturbations to classify new sTEC profiles by finding the cluster of the closest profile of the clustered dataset in the EMD metric space. This new methodology is used to construct an automated system to monitor ionospheric perturbations on a global scale.

Forward Simulation and Comparative Experiment Analysis of Polarimetric GNSS Radio Occultations Detecting Rainfall Events

Forward Simulation and Comparative Experiment Analysis of Polarimetric GNSS Radio Occultations Detecting Rainfall Events

Assessment of Gnss Radio Occultation Effect on Prediction of the July 2021 Henan Extreme Heavy Rainstorm

Assessment of Gnss Radio Occultation Effect on Prediction of the July 2021 Henan Extreme Heavy Rainstorm

The variations of plasma density recorded by CSES-1 satellite possibly related to Mexico Ms 7.1 earthquake on 8th September 2021

This work analyzed the Ms 7.1 earthquake occurred at 01:47 UT on September 8th, 2021, in Acapulco City, Guerrero, Mexico (17.12° N, 99.6° W, N and W represent north and west, respectively) with a 30 ​km focal depth by using multiple parameters recorded by China Seismo-Electromagnetic Satellite-1 (CSES-1). In this work, we studied the electron density (Ne) recorded by langmuir probe and O+ from plasma analyzer package at a height of 507 ​km 15 days before the earthquake. After excluding the plasma density variations caused by the geomagnetic storms on August 28th and September 7th, the results revealed that the nighttime Ne, and O+ increased on September 2nd (6 days before the earthquake) simultaneously within ±10° in latitude and ±10° in longitude around the epicenter. In addition, the enhancement was also found in electron density profile (EDP) between 200 ​km and 300 ​km derived from GNSS radio occultation data. Not only the different particle species (Ne and O+) at 507 ​km, but also electron density at 507 ​km and EDP in lower ionosphere shown anomalies 6 days before the earthquake. Basing on the results, we suggest that the enhancements of Ne, O+, and EDP on September 2nd are possible related to the Mexico Ms 7.1 earthquake on September 8th.

Diurnal Variation of the Planetary Boundary Layer Height Observed from GNSS Radio Occultation and Radiosonde Soundings over the Southern Great Plains

Abstract The planetary boundary layer (PBL) height (PBLH) is a key physical parameter of the PBL affected by numerous physical processes within the boundary layer. Specifically, the PBLH over land exhibits large spatial and temporal variation across different geographical regions. In this study, the Constellation Observing System for Meteorology, Ionosphere and Climate (COSMIC) radio occultation (RO) profiles and high-resolution radiosonde profiles from 2007 to 2013 were analyzed to estimate the diurnal cycle of the PBLH over the Southern Great Plains (SGP) in the United States. Large variations in PBLH derived from radiosonde temperature, moisture, and refractivity are observed on seasonal scales. COSMIC RO is capable of observing diurnal and seasonal variations in the terrestrial PBLH over the SGP region. Annual mean diurnal amplitude of approximately 250 m in the terrestrial PBLH was observed, with maxima occurring at around 1500 local solar time (LST) in both the collocated radiosondes and COSMIC RO profiles. Seasonal changes in the PBLH diurnal cycles ranging from approximately 100 to 400 m were also observed. Such PBL diurnal and seasonal changes can be further incorporated into PBL parameterizations to help improve weather and climate model prediction. Significance Statement The atmospheric planetary boundary layer (PBL) and its height (PBLH) control many atmospheric processes that affect our everyday lives. Observations of the PBL are usually limited to radiosondes at limited time intervals. GNSS radio occultation (RO) provide high-vertical-resolution atmospheric observations that are ideal for PBL study. This study demonstrates that the GNSS RO is capable of capturing the diurnal and seasonal variations of the PBLH over the southern Great Plains (SGP) well as compared to the collocated radiosonde observations.

Orbit Simulator for Satellite and Near-Space Platforms Supporting Observing System Simulation Experiments

Abstract Developed at the National Oceanic and Atmospheric Administration (NOAA) and the Joint Center for Satellite Data Assimilation (JCSDA), the Community Global Observing System Simulation Experiment (OSSE) Package (CGOP) provides a vehicle to quantitatively evaluate the impacts of emerging environmental observing systems or emerging in situ or remote sensing instruments on NOAA numerical weather prediction (NWP) forecast skill. The typical first step for the OSSE is to simulate observations from the so-called nature run. Therefore, the observation spatial, temporal, and view geometry are needed to extract the atmospheric and surface variables from the nature run, which are then input to the observation forward operator (e.g., radiative transfer models) to simulate the new observations. This is a challenge for newly proposed systems for which instruments are not yet built or platforms are not yet deployed. To address this need, this study introduces an orbit simulator to compute these parameters based on the specific hosting platform and onboard instrument characteristics, which has been recently developed by the NOAA Center for Satellite Applications and Research (STAR) and added to the GCOP framework. In addition to simulating existing polar-orbiting and geostationary orbits, it is also applicable to emerging near-space platforms (e.g., stratospheric balloons), cube satellite constellations, and Tundra orbits. The observation geometry simulator includes not only passive microwave and infrared sounders but also global navigation satellite system/radio occultation (GNSS/RO) instruments. For passive atmospheric sounders, it calculates the geometric parameters of proposed instruments on different platforms, such as time varying location (latitude and longitude), scan geometry (satellite zenith and azimuth angles), and ground instantaneous field of view (GIFOV) parameters for either cross-track or conical scanning mechanisms. For RO observations, it determines the geometry of the transmitters and receivers either on satellites or stratospheric balloons and computes their slant paths. The simulator has been successfully applied for recent OSSE studies (e.g., evaluating the impacts of future geostationary hyperspectral infrared sounders and RO observations from stratospheric balloons). Significance Statement An orbit simulator for satellite and near-space platforms for supporting observing system simulation experiments (OSSE) is developed in this study. It can compute spatial, temporal, and view geometry parameters of the new observations based on the specific hosting platform and onboard instrument characteristics. These parameters are important for the OSSE to extract atmospheric profiles and surface properties from nature run data and simulate new observations. In addition to simulating existing polar-orbiting and geostationary orbits, it is applicable to emerging near-space platforms (e.g., stratospheric balloons), cube satellite constellations, and Tundra orbits. The observation geometry simulator includes not only passive microwave and infrared sounders but also radio occultation instruments.

Simultaneous Radio Occultation Predictions for Inter-Satellite Comparison of Bending Angle Profiles from COSMIC-2 and GeoOptics

The Global Navigation Satellite System (GNSS) radio occultation (RO) is a remote sensing technique that uses International System of Units (SI) traceable GNSS signals for atmospheric limb soundings. The RO bending angle/sounding profiles are needed for assimilation in Numerical Weather Prediction (NWP) models, weather, climate, and space weather applications. Evaluating these RO data to ensure the high data quality for these applications is becoming more and more critical. This study presents a method for predicting radio occultation events, from which simultaneous radio occultation (SRO) for a pair of low-Earth-orbit (LEO) satellites on the limb to the same GNSS satellite can be obtained. The SRO method complements the Simultaneous Nadir Overpass (SNO) method (for nadir viewing satellite instruments), which has been widely used to inter-calibrate LEO to LEO and LEO to geosynchronous-equatorial-orbit (GEO) satellites. Unlike the SNO method, the SRO method involves three satellites: a GNSS and two LEO satellites with RO receivers. The SRO method allows for the direct comparison of bending angles when the simultaneous RO measurements for two LEO satellites receiving the same GNSS signal pass through approximately the same atmosphere within minutes in time and within less than 200 km of distance from each other. The prediction method can also be applied to radiosonde overpass prediction, and coordinate radiosonde launches for inter-comparisons between RO and radiosonde profiles. The main advantage of the SRO comparisons of bending angles is the significantly reduced uncertainties due to the much shorter time and smaller atmospheric path differences than traditional RO comparisons. To demonstrate the usefulness of this method, we present a comparison of the Constellation Observing System for Meteorology, Ionosphere, and Climate-2 (COSMIC-2) and GeoOpitcs RO profiles using SRO data for two time periods: Commercial Weather Data (CWD) data delivery order-1 (DO-1): 15 December 2020–15 January 2021 and CWD DO-2: 17 March 2021–31 August 2021. The results show good agreement in the bending angles between the COSMIC-2 RO measurements and those from GeoOptics, although systematic biases are also found in the inter-comparisons. Instrument and processing algorithm performances for the signal-to-noise ratio (SNR), penetration height, and bending angle retrieval uncertainty are also characterized. Given the efficiency of this method and the many RO measurements that are publicly and commercially available as well as the expansion of receiver capabilities to all GNSS systems, it is expected that this method can be used to validate/inter-calibrate GNSS RO measurements from different missions.

An Impact Study of GNSS RO Data on the Prediction of Typhoon Nepartak (2016) Using a Multiresolution Global Model with 3D-Hybrid Data Assimilation

AbstractConventional soundings are rather limited over the western North Pacific and can be largely compensated by GNSS radio occultation (RO) data. We utilize the GSI hybrid assimilation system to assimilate RO data and the multiresolution global model (MPAS) to investigate the RO data impact on prediction of Typhoon Nepartak that passed over southern Taiwan in 2016. In this study, the performances of assimilation with local RO refractivity and bending angle operators are compared for the assimilation analysis and typhoon forecast. Assimilations with both RO data have shown similar and comparable temperature and moisture increments after cycling assimilation and largely reduce the RMSEs of the forecast without RO data assimilation at later times. The forecast results at 60–15-km resolution show that RO data assimilation largely improves the typhoon track prediction compared to that without RO data assimilation, and assimilation with bending angle has better performances than assimilation with refractivity, in particular for wind forecast. The improvement in the forecasted track is mainly due to the improved simulation for the translation of the typhoon. Diagnostics of wavenumber-1 potential vorticity (PV) tendency budget indicates that the northwestward typhoon translation dominated by PV horizontal advection is slowed down by the southward tendency induced by the stronger differential diabatic heating south of the typhoon center for bending-angle assimilation. Simulations with the enhanced resolution of 3 km in the region of the storm track show further improvements in both typhoon track and intensity prediction with RO data assimilation. Positive RO impacts on track prediction are also illustrated for two other typhoons using the MPAS-GSI system.

An aided Abel inversion technique assisted by artificial neural network-based background ionospheric model for near real-time correction of FORMOSAT-7/COSMIC-2 data

The assumption of spherical uniformity while the retrieval of electron density profiles from the Global Navigation Satellite Systems-Radio Occultation (GNSS-RO) observations is often violated and introduces significant errors in the retrieved electron density profile data. This paper presents an improved Abel-inversion technique by incorporating the horizontal gradients in the ionosphere, which are routinely derived from the Artificial Neural Network (ANN) based background NmF2 (peak electron density of F2-layer) model (ANNC2) assimilated with near real-time Constellation Observing System for Meteorology, Ionosphere, and Climate-2 (FORMOSAT-7/COSMIC-2) NmF2 data. The ANNC2-aided Abel inversion is then implemented for more accurate retrieval of electron density profiles from COSMIC-2 in real-time. It is found that the ANNC2-aided inversion has improved the electron density values around the F2-region and below, which yields a clear separation between two anomaly crests. Further, the ANNC2-aided Abel inversion had significantly reduced the artificial plasma caves beneath the equatorial ionization anomaly crests. Furthermore, COSMIC-2 NmF2 observations obtained from both classical and the ANNC2-aided Abel inversion are compared with the ground-based Digisonde data and found that the ANNC2-aided inversion gives the better results. This study provides some new insights on the aided Abel inversion technique assisted by ANN models for the real-time correction of Abel retrieved electron density profiles.

Assimilation Impact of Early FORMOSAT-7/COSMIC-2 GNSS Radio Occultation Data with Taiwan’s CWB Global Forecast System

AbstractThe FORMOSAT-7/COSMIC-2 Global Navigation Satellite System (GNSS) Radio Occultation (RO) satellite constellation was launched on June 2019 as a successor of the FORMOSAT-3/COSMIC mission. The Central Weather Bureau (CWB) of Taiwan has received FORMOSAT-7/COSMIC-2 GNSS RO data in real time from Taiwan Analysis Center for COSMIC. With the global numerical prediction system at CWB, a parallel semi-operational experiment assimilating the FORMOSAT-7/COSMIC-2 bending angle data with all other operational observation data has been conducted to evaluate the impact of the FORMOSAT-7/COSMIC-2 data. The first seven-month results show that the quality of the early FORMOSAT-7/COSMIC-2 data has been satisfactory for assimilation. Consistent and significant positive impacts on global forecast skills have been observed since the start of the parallel experiment, with the most significant impact found in the tropical region, reflecting the low-inclination orbital design of the satellites. The impact of the FORMOSAT-7/COSMIC-2 RO data is also estimated using the Ensemble Forecast Sensitivity to Observation Impact (EFSOI) method, showing an average positive impact per observation similar to other existing GNSS RO datasets, while the total impact is impressive by virtue of its large amount. Sensitivity experiments suggest that the quality control processes built in the Gridpoint Statistical Interpolation (GSI) system for RO data work well to achieve a positive impact by the low-level FORMOSAT-7/COSMIC-2 RO data, while more effort on observation error tuning should be focused to obtain an optimal assimilation performance. This study demonstrates the usefulness of the FORMOSAT-7/COSMIC-2 RO data in global numerical weather prediction during the calibration/validation period and leads to the operational use of the data at CWB.

Analyzing the Diurnal Cycle by Bayesian Interpolation on a Sphere for Mapping GNSS Radio Occultation Data

AbstractBayesian interpolation has previously been proposed as a strategy to construct maps of radio occultation (RO) data, but that proposition did not consider the diurnal dimension of RO data. In this work, the basis functions of Bayesian interpolation are extended into the domain of the diurnal cycle, thus enabling monthly mapping of radio occultation data in synoptic time and analysis of the atmospheric tides. The basis functions are spherical harmonics multiplied by sinusoids in the diurnal cycle up to arbitrary spherical harmonic degree and diurnal cycle harmonic. Bayesian interpolation requires a regularizer to impose smoothness on the fits it produces, thereby preventing the overfitting of data. In this work, a formulation for the regularizer is proposed and the most probable values of the parameters of the regularizer determined. Special care is required when obvious gaps in the sampling of the diurnal cycle are known to occur in order to prevent the false detection of statistically significant high-degree harmonics of the diurnal cycle in the atmosphere. Finally, this work probes the ability of Bayesian interpolation to generate a valid uncertainty analysis of the fit. The postfit residuals of Bayesian interpolation are dominated not by measurement noise but by unresolved variability in the atmosphere, which is statistically nonuniform across the globe, thus violating the central assumption of Bayesian interpolation. The problem is ameliorated by constructing maps of RO data using Bayesian interpolation that partially resolve the temporal variability of the atmosphere, constructing maps for approximately every 3 days of RO data.

Statistically analyzing the effect of ionospheric irregularity on GNSS radio occultation atmospheric measurement

Abstract. The Global Navigation Satellite System (GNSS) atmospheric radio occultation (RO) has been an effective method for exploring Earth's atmosphere. RO signals propagate through the ionosphere before reaching the neutral atmosphere. The GNSS signal is affected by the ionospheric irregularity including the sporadic E (Es) and F region irregularity mainly due to the multipath effect. The effect of ionospheric irregularity on atmospheric RO data has been demonstrated by several studies in terms of analyzing singe cases. However, its statistical effect has not been investigated comprehensively. In this study, based on the Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) RO data during 2011–2013, the failed inverted RO events occurrence rate and the bending angle oscillation, which is defined as the standard deviation of the bias between the observed bending angle and the National Center for Atmospheric Research (NCAR) climatology model bending angle between 60 and 80 km, were used for statistical analysis. It is found that at middle and low latitudes during the daytime, the failed inverted RO occurrence and the bending angle oscillation show obvious latitude, longitude, and local time variations, which correspond well with the Es occurrence features. The F region irregularity (FI) contributes to the obvious increase of the failed inverted RO occurrence rate and the bending angle oscillation value during the nighttime over the geomagnetic equatorial regions. For high latitude regions, the Es can increase the failed inverted RO occurrence rate and the bending angle oscillation value during the nighttime. There also exists the seasonal dependency of the failed inverted RO event and the bending angle oscillation. Overall, the ionospheric irregularity effects on GNSS atmospheric RO measurement statistically exist in terms of failed RO event inversion and bending angle oscillation. Awareness of these effects could benefit both the data retrieval and applications of RO in the lower atmosphere.

An Evaluation of Spire Radio Occultation Data in Assimilative Ionospheric Model GPSII and Validation by Ionosonde Measurements

AbstractThis paper presents a comprehensive analysis of results obtained using NWRA’s assimilative ionospheric model GPSII when it is driven by Spire Global, Inc.’s global navigation satellite system ‐ radio occultation (GNSS‐RO) measurements. Results of the assimilative model are validated using the database of Digisonde and Dynasonde ionograms. A week’s worth of dual‐frequency phase data was collected from Spire’s constellation of low‐Earth orbiting micro‐satellites carrying GNSS receivers and assimilated into GPSII. A validation study of GNSS‐RO measurements was performed in four geographical regions. GPSII operation with the data was evaluated and the results were validated against independent ionospheric measurements. Ground‐based ionosondes were employed in diagnosing the accuracy of the derived models; we studied the differences between the F2 peak parameters (the F2 layer critical frequency, , and the peak height of the F2 layer, ) derived from ionograms and those computed with GPSII. For the 1,491 validation events used in this study, the magnitude of the deviation of the GPSII‐predicted at an ionosonde location from the corresponding ionosonde‐observed value was found on average to be 0.608 MHz (with a median of 0.461 MHz) and the magnitude of the deviation of the GPSII‐predicted at an ionosonde location from the corresponding ionosonde‐observed value was found to be on average 22.9 km (with a median value of 18.0 km). Median GPSII validation errors for are a factor of four smaller than corresponding errors for IRI, while GPSII errors are a factors of two smaller than those for IRI. The analysis showed that Spire’s TEC data was both compatible for ingestion into GPSII and that the assimilation of the data improved the comparison to ground‐based ionosondes.