Global Navigation Satellite System Radio Research Articles

Volcanic clouds detection applying machine learning techniques to GNSS radio occultations

Volcanic clouds detection is a challenge especially when meteorological clouds are present in the same area. Several algorithms have been developed to detect and monitor volcanic clouds by using satellite instruments based on different remote sensing techniques. This work aims at classifying volcanic clouds based on atmospheric profiles retrieved by the GNSS (Global Navigation Satellite Systems) radio occultation technique. We collocated the radio occultations with the volcanic cloud detection from AIRS (Atmospheric InfraRed Sounder) and IASI (Infrared Atmospheric Sounding Interferometer) for 11 big eruptions happening in the period 2008–2015 resulting in about 15000 profiles. We created an archive with the collocations and a corresponding number of profiles in “non-volcanic” environment in the same area and on the same period of the year. A support vector machine algorithm was applied to the archive in order to classify the clouds and to distinguish the volcanic clouds from the other types. The model performances are promising: the GNSS radio occultations are able to distinguish the volcanic clouds with an accuracy higher than 80% when the eruption occurs at high latitudes. The performances of the model are affected by the number of collocations used for the training. Nowadays, the number of radio occultations is higher than in the period considered in this research, making this work a pioneering study for a future operational product.

Assessment of satellite images terrestrial surface temperature and WVP using GNSS radio occultation data

Abstract Land Surface Temperature (LST) and Water Vapor Pressure (WVP) contour maps can be produced using cameras aboard satellites, for instance, under the name “Remote Sensing (RS)”. Satellite image observations should be verified before using based on a reliable data. Global Navigation Satellite System Radio Occultation (GNSS-RO) method is observing accurate Earth atmosphere parameters continuously. In the present research, LST and WVP differences between Landsat 8 (LC08), Sentinel-3 (S3), and MODIS (Terra and Aqua) images and GNSS-RO are assessed in Egypt depending on the satellites operating periods and data availability during the years from 2015 to 2020. Statistically, S3 and Terra have insignificant differences with RO temperature with an average bias of 3.48 °C and 1.47 °C, respectively, but LC08 and Aqua have significant differences with it. For WVP, Aqua and LC08 have insignificant differences with an average bias of 0.02 kg/m2 and 2.31 kg/m2, respectively, but S3 and Terra have significant differences with RO observations. When comparing LC08 LST data to other satellites, it was found that there were insignificant differences between LC08 and S3 as well as Terra. However, significant differences were observed when comparing LC08 LST data to Aqua. Additionally, significant differences were noted when comparing LC08 WVP data to other satellites. In response to these differences, improvement models have been developed to enhance the estimation of terrestrial data through remote sensing, particularly for satellites that exhibited significant disparities when compared to reference observations (RO).

Structure and variations of global planetary boundary layer top from 2008-2022 multiple GNSS RO observations

The structural changes at the planetary boundary layer (PBL) top are very complex and closely related to climate and environmental changes. With the development of Global Navigation Satellite System Radio Occultation (GNSS RO), it provides a good opportunity to estimate and study PBL variations. In this paper, long-term variations and structures of planetary boundary layer height (PBLH) from 2008 to 2022 are investigated by the Wavelet Covariance Transform (WCT) method based on refractivity profiles from Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) and Korea Multi-purpose Satellite-5 (KOMPSAT-5). Furthermore, the temperature and pressure of PBL top are detected using the temperature and pressure profiles from COSMIC and KOMPSAT-5 occultation data. The results demonstrate the latitudinal distribution of the PBL top height, temperature, and pressure, with the more apparent temperature's latitudinal characteristics. The pressure is more strongly correlated with PBL height than temperature. The PBL height in most land and oceans is high in summer and low in winter, and the PBL pressure is low in summer and high in winter, according to seasonal variation characteristics. The global PBL temperature is high in summer and low in winter, which is more obvious in high latitude region. A positive correlation between latitude and the magnitude of seasonal variation in PBL temperature is observed worldwide. Seasonal variation is less pronounced in the Southern Hemisphere (SH) than in the Northern Hemisphere (NH) due to sea-land differences. The annual average value of the global PBL height, temperature, and pressure do not change significantly over the long term, and also there is no a discernible upward or downward trend.

Influences of Sudden Stratospheric Warming Events on Tropopause Based on GNSS Radio Occultation Data

Sudden Stratospheric Warming (SSW) events have a strong impact on the tropospheric weather and climate. Past researchers have carried out extensive studies investigating the theories of interactions between the stratosphere and the troposphere. However, detailed studies on the influences of the global tropopause are rarely shown. This study uses Global Navigation Satellite System (GNSS) Radio Occultation (RO) data from the years 2007 to 2013 to investigate the influences of different types of SSW events on the tropopause over latitude bands from 30° S to 90° N. It was found that SSW events have strong influences on the tropopause over 60° N–90° N and over 20° N–30° N regions. In 60° N–90° N, SSW events cause a tropopause temperature increase and, therefore, a tropopause height decrease. The increment in the tropopause temperature are more than 10 K and the decrement in the tropopause height is about to 2 km during strong events. Such influences last for about 1.5 months for strong split events and about 10 days for weaker and/or displacement type events. The influences of SSW events on 20° N–30° N are weaker. Only the January 2009 SSW event shows a visible influence on the tropopause layer with a tropopause temperature decrease of about 4 K and a tropopause height increase of about 1 km. Other SSW events share no common characteristics on the tropical tropopause. This is mainly because SSW events are not strong enough to dominate the tropopause variations and other factors, especially the planetary waves in the troposphere, have stronger impacts on the tropopause layer.

Using the Commercial GNSS RO Spire Data in the Neutral Atmosphere for Climate and Weather Prediction Studies

Recently, the NOAA has included GNSS (Global Navigation Satellite System) Radio Occultation (RO) data as one of the crucial long-term observables for weather and climate applications. To include more GNSS RO data in its numerical weather prediction systems, the NOAA Commercial Weather Data Pilot program (CWDP) started to explore the commercial RO data available on the market. After two rounds of pilot studies, the CWDP decided to award the first Indefinite Delivery Indefinite Quantity (IDIQ) contract to GeoOptics and Spire Incs. in 2020. This study examines the quality of Spire RO data products for weather and climate applications. Spire RO data collected from commercial CubeSats are carefully compared with data from Formosa Satellite Mission 7–Constellation Observing System for Meteorology, Ionosphere, and Climate-2 (COSMIC-2), the fifth-generation European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalysis (ERA5), and high-quality radiosonde data. The results demonstrate that, despite their generally lower Signal-Noise-Ratio (SNR), Spire RO data show a pattern of lowest penetration height similar to that of COSMIC-2. The Spire and COSMIC-2 penetration heights are between 0.6 and 0.8 km altitude over tropical oceans. Although using different GNSS RO receivers, the precision of Spire STRATOS receivers is of the same quality as those of the COSMIC-2 TriG (Global Positioning System—GPS, GALILEO, and GLObal NAvigation Satellite System—GLONASS) RO Receiver System (TGRS) receivers. Furthermore, the Spire and COSMIC-2 retrieval accuracies are quite comparable. We validate the Spire temperature and water vapor profiles by comparing them with collocated radiosonde observation (RAOB) data. Generally, over the height region between 8 km and 16.5 km, the Spire temperature profiles match those from RS41 RAOB very well, with temperature biases of <0.02 K. Over the height range from 17.8 to 26.4 km, the temperature differences are ~−0.034 K, with RS41 RAOB being warmer. We also estimate the error covariance matrix for Spire, COSMIC-2, and KOMPSAT-5. The results show that the COSMIC-2 estimated error covariance values are slightly more significant than those from Spire over the oceans at the mid-latitudes (45°N–30°N and 30°S–45°S), which may be owing to COSMIC-2 SNR being relatively lower at those latitudinal zones.

On the Kalman Smoother Interpolation Error Distribution in Collocation Comparison of Atmospheric Profiles

The intercomparison between different atmospheric monitoring systems is key for instrument calibration and validation. Common cases involve satellites, radiosonde and atmospheric model outputs. Since instruments and/or measures are not perfectly collocated, miss-collocation uncertainty must be considered in related intercomparison uncertainty budgets. This paper is motivated by the comparison of GNSS-RO, the Global Navigation Satellite System Radio Occultation, with ERA5, the version 5 Reanalysis of the European Centre for Medium-range Weather Forecasts. We consider temperature interpolation observed at GNSS-RO pressure levels to the ERA5 levels. We assess the interpolation uncertainty using as ‘truth’ high-resolution reference data obtained by GRUAN, the Reference Upper-Air Network of the Global Climate Observing System. In this paper, we propose a mathematical representation of the interpolation problem based on the well-known State-space model and the related Kalman filter and smoother. We show that it performs the same (sometimes better) than linear interpolation and, in addition, provides an estimate of the interpolation uncertainty. Moreover, with both techniques, the interpolation error is not Gaussian distributed, and a scaled Student’s t distribution with about 4.3 degrees of freedom is an appropriate approximation for various altitudes, latitudes, seasons and times of day. With our data, interpolation uncertainty results larger at the equator, the Mean Absolute Error being MAE≅0.32 K, and smaller at a high latitude, MAE≅0.21 K at −80° latitude. At lower altitudes, it is close to the measurement uncertainty, with MAE<0.2 K below the tropopause. Around 300 hPa, it starts increasing and reaches about 0.8 K above 100 hPa, except at the equator, where we observed MAE about 1 K.

Recent Applications of Smart Technologies for Monitoring the Sustainability of Forest Operations

Precision forestry is a useful technique to help forest stakeholders with proper sustainable forest management. Modern sensors and technologies, with special reference to the sustainability of forest operations, can be applied on a variety of levels, including the monitoring of forest activities regarding the three pillars (economy, environment, and society). In this review, we summarised the current level of knowledge regarding the use of precision forestry techniques for monitoring forest operations. We concentrated on recent data from the last five years (2019–2023). We demonstrated how an Industry 4.0 strategy for remote and proximal monitoring of working performance can be effective when using CAN-bus and StanForD data collected by modern forest machines. The same information can be effectively used to create maps of soil trafficability and to evaluate the patterns of skid tracks or strip roads built as a result of forest intervention. Similar information can be gathered in the case of small-scale forestry by using GNSS-RF (Global Navigation Satellite Systems—Radio Frequency) or even monitoring systems based on smartwatches or smartphones. LiDAR and Structure for Motion (SfM) photogrammetry are both useful tools for tracking soil rutting and disturbances caused by the passage of forest machinery. SfM offers denser point clouds and a more approachable method, whereas laser scanning can be considerably faster but needs a more experienced operator and better data-processing skills. Finally, in terms of the social component of sustainability, the use of location sharing technologies is strongly advised, based for instance on GNSS—RF to monitor the security of forest workers as they operate.

Assimilation of GNSS Zenith Total Delay in NAVGEM

AbstractA new data source from ground‐based stations that track global navigation satellite system (GNSS) transmitters has been implemented in the Navy Global Environmental Model (NAVGEM) with the NRL Atmospheric Variational Data Assimilation System‐Accelerated Representer. The observable is the ground‐based zenith total delay (ZTD) at each ground station. This ground‐based GNSS ZTD represents the tropospheric delay of the propagation of GNSS radio L‐band signal between the transmitters and the ground stations after correcting for ionospheric effects. Here, we present the implementation procedure, quality control, single observation test, bias correction scheme, and data impact assessment. One of the critical elements in the quality control scheme is to correct for the difference between the observing ground station elevation and the numerical model terrain. A single observation test shows comparable impact from this new data source to a single radiosonde observation at a single level. The ZTD biases estimated from a 6‐month experiment run are generally small, but have a dependency on the processing center and are computed separately for each station. The forecast sensitivity to observation impact diagnosis from a 3‐month experiment demonstrates that a single ZTD observation shows similar impact as the average impact from a single tangent point of a GNSS Radio Occultation profile. The assimilation of ZTD observations also significantly improves the forecast skill by 0.25%–0.75% for wind, temperature, and precipitable water and by 1%–2% for geopotential height in the Southern Hemisphere beyond 3 days.

Systematic Ionospheric Residual Errors in GNSS Radio Occultation: Theory for Spherically Stratified Media

AbstractThe standard ionospheric correction of bending angles in Global Navigation Satellite System (GNSS) radio occultation (RO) measurements removes most of the influence from the ionosphere, but leaves small systematic residual errors in the corrected data. The main reasons for residual errors at stratospheric and mesospheric altitudes are the neglect of higher‐order terms in the expansion of the ionospheric refractive index, and the fact that the two GNSS signals follow slightly different paths through the ionosphere. Another reason is the neglect of the local electron density at the receiver in orbit. These residual errors depend on the geomagnetic field and the spatial distribution of the ionospheric electron density and its gradients. In this work we derive this dependency to high accuracy for a spherically stratified ionosphere, including the “bi‐local” situation where the ionosphere is different (though still spherically stratified) on the inbound and outbound side of the RO event tangent points. As part of the derivations, we find a small residual error term not previously noted, which can become appreciable for elliptical satellite orbits. The results are verified by ray tracing through simple ionospheric and geomagnetic field models. The accuracy of a higher‐order bi‐local ionospheric residual error correction based on these results would be limited by the uncertainty in knowledge of the electron density and by horizontal electron density gradients along the ray paths.

On the Moments of Probability Distribution Function of Amplitude Scintillation in the Polar Region

AbstractThe distortions seen in the Global Navigation Satellite Systems radio signal caused by ionospheric irregularities in the polar region are examined through the lens of probability distributions of fluctuations in the recorded scintillation signals. The first four moments of the probability distribution function (PDF) of the amplitude scintillation are computed and analyzed for 106 events collected at Pond Inlet station [Magnetic Coordinates = (80.0°N, 2.6°E)]. As a starting point, the background variation level is investigated using similar computations carried out with about 400 30‐s window segments. The resulting distribution for the skewness (third moment) and the kurtosis (fourth moment) strongly indicate that the background fluctuations are nearly Gaussian; given the computed variances, the Rayleigh distribution hypothesis is ruled out. In the case where scintillation is present, we found that the skewness distribution nearly overlaps with the background while the excess kurtosis strongly indicates a departure from normality. More precisely, the PDF appear mostly symmetric and leptokurtic (positive kurtosis K) and exhibit, in some cases, significant flatness (K ∼ 1). These results reveal that the analyzed scintillations are statistically consistent with stationarity and therefore vanishing skewness. Moreover, the empirical moments are compared with those derived from probability densities referred in the literature regarding scintillations. We have found that none of the log‐normal, Nakagami, Rician and α–μ distributions account for the analyzed data. The generalized Gaussian distribution with a shape parameter 1 < β < 2 seems the most probable and the most adequate to account for the results.

Using GNSS Radio Occultation Data to Monitor Tropical Atmospheric Anomalies during the January–February 2009 Sudden Stratospheric Warming Event

We used Global Navigation Satellite System (GNSS) radio occultation (RO) temperature, density, and bending angle profiles to monitor tropical atmospheric anomalies during the January–February 2009 sudden stratospheric warming (SSW) event on a daily basis. We constructed RO anomaly profiles (tropical mean (30°S–30°N)) and gridded mean anomalies, as well as tropopause height and temperature anomalies. Based on the anomalies, we investigated the response time and region of the tropical atmosphere to SSW. It was found that the GNSS RO data were robust in monitoring tropical atmospheric anomalies during SSW. The tropical stratosphere revealed cooling simultaneously with polar stratospheric warming, although the magnitudes of the maximum tropical mean anomalies were 6–7 times smaller than the polar mean. Altitude variations showed that tropical stratospheric anomalies were largest within 35–40 km, which were 5 km higher than those in the polar region. On the onset day of 23 January, temperature anomalies over 0–30°N were mostly more than −5 K, which were larger than those of −2 K detected over the 0–30°S band, and the largest anomalies were detected over northern Africa with values more than −10 K. RO density and bending angle anomalies responded to SSW in a similar way as temperature but were 20 km higher. Following cooling, the tropical upper stratosphere and lower mesosphere revealed visible warming, with anomalies more than 10 K in the sector of 15°S–15°N. Tropopause anomalies revealed the largest variations over 20°N–30°N, further confirming that the extratropical region of the northern hemisphere is a key region for the dynamical coupling between the polar and tropical regions. Tropopause height anomalies had clear increase trends from 16 January to 8 February, with anomalies of the 20°N–30°N band that were −2 km on Jan 16 and increased to −0.5 km on Feb 6 with a variation of 1.5 km, while variations in other bands were within 0.5 km. Tropopause temperature anomalies had clear decrease trends over the same period, with anomalies at 20°N–30°N of 4 K on 16 January and decreasing to about −1 K on 8 February, while anomalies in other bands showed variations within 3 K.

Investigation of the Potential Saturation of Information from Global Navigation Satellite System Radio Occultation Observations with an Observing System Simulation Experiment

Abstract The potential impact of large numbers of Global Navigation Satellite System radio occultation (GNSS-RO) observations on numerical weather prediction is investigated using a global observing system simulation experiment (OSSE). The hybrid four-dimensional ensemble variational Gridpoint Statistical Interpolation (GSI) data assimilation system and Global Earth Observing System (GEOS) model are used to ingest up to 100 000 GNSS-RO soundings per day in addition to the current suite of conventional and radiance data. Analysis quality, forecast skill, and forecast sensitivity to observation impact are examined with differing quantities of additional GNSS-RO profiles. It is found that saturation of information from additional RO soundings has not been reached with 100 000 soundings per day. There are some indications of suboptimal performance of the GSI in handling GNSS-RO observations particularly in the middle- and lower-tropospheric extratropics.

Stereo Plume Height and Motion Retrievals for the Record‐Setting Hunga Tonga‐Hunga Ha'apai Eruption of 15 January 2022

AbstractStereo methods using GOES‐17 and Himawari‐8 applied to the Hunga Tonga‐Hunga Ha'apai volcanic plume on 15 January 2022 show overshooting tops reaching 50–55 km altitude, a record in the satellite era. Plume height is important to understand dispersal and transport in the stratosphere and climate impacts. Stereo methods, using geostationary satellite pairs, offer the ability to accurately capture the evolution of plume top morphology quasi‐continuously over long periods. Manual photogrammetry estimates plume height during the most dynamic early phase of the eruption and a fully automated algorithm retrieves both plume height and advection every 10 min during a more frequently sampled and stable phase beginning 3 hr after the eruption. Stereo heights are confirmed with Global Navigation Satellite System Radio Occultation bending angles, showing that much of the plume was lofted 30–40 km into the atmosphere. Cold bubbles are observed in the stratosphere with brightness temperature of ∼173 K.

Comparison of different machine learning approaches for tropospheric profiling based on COSMIC-2 data

Precise and reliable information on the tropospheric temperature and water vapor profiles play a key role in weather and climate studies. Among the sensors supporting the observations of the troposphere, one can distinguish the Global Navigation Satellite System Radio Occultation (RO) technique, which provides accurate and high-quality meteorological profiles. However, external knowledge about temperature is essential to estimate other physical atmospheric parameters. To overcome this constraint, I trained and evaluated four different machine learning models comprising Artificial Neural Network (ANN) and Random Forest regression algorithms, where no auxiliary meteorological data is needed. To develop the models, I employed 150,000 globally distributed (45°S–45°N) RO profiles between October 2019 and December 2020. Input vectors consisted of bending angle or refractivity profiles from the Formosa Satellite-7/Constellation Observing System for Meteorology, Ionosphere, and Climate-2 mission together with the month, hour, and latitude of the RO event. While temperature, pressure, and water vapor profiles derived from the modern ERA5 reanalysis and interpolated to the RO location served as the models’ targets. Evaluation on the testing data set revealed a good agreement between all model outputs and ERA5 targets, where slightly better statistics were noted for ANN and refractivity inputs. Vertically averaged root mean square error (RMSE) did not exceed 1.7 K for the temperature and reached around 1.4 hPa and 0.45 hPa for the total and water vapor pressures. Additional validation with 477 co-located radiosonde observations and the operational one-dimensional variational product showed slightly larger discrepancies with the mean RMSE of around 1.9 K, 1.9 hPa, and 0.5 hPa for the temperature, pressure, and water vapor, respectively.Graphical

Short vertical-wavelength gravity wave activities in the upper troposphere lower stratosphere observed with global navigation satellite system radio occultation under different QBO phases

Gravity Waves (GWs) are believed to play important role in the generation of the driving force of the stratospheric Quasi-Biennial Oscillation (QBO). Deep convection in the equatorial region can generate large amount of GW with short vertical wavelength (λz <1 km) but studies of these wave activities in the upper troposphere lower stratosphere (UTLS) region are still limited. Recent advances in Global Navigation Satellite System (GNSS) Radio Occultation (RO) retrieval techniques have made it possible to derive global temperature profile with vertical resolution of less than 1 km. In this research, activities of GW with λz from 0.5 to 3.5 km in the UTLS region of 20-27 km heights are identified by calculating the GW potential energy (E p). Correlation between GW activities and QBO phases is examined using 50 hPa zonal wind as the QBO index. The results show that during both easterly and westerly QBO phases, the GW E p value increases gradually with time and reaches its peak in the transition periods. This pattern is seen in E p with all vertical wavelengths between 0.5-3.5 km but the percentage value of E p for λz<1 km is higher during the transition from westerly to easterly QBO. The GW E p values exhibit downward propagation with the QBO phase but there are also discernible upward propagations of GW activities below 24 km height and intersect those two bring large changes in QBO phases. Additionally, higher percentage of E p with λz<1 km is also found to be associated with El Niño events.

Using A Sliding Window Phase Matching Method for Imaging of GNSS Radio Occultation Signals

Global Navigation Satellite System Radio Occultation (GNSS-RO) is a technique used to sound the atmosphere and derive vertical profiles of refractivity. Signals from GNSS satellites are received in a low-Earth orbit, and they are then processed to produce bending angle profiles, from which meteorological parameters can be retrieved. Generating two-dimensional images in the form of spectrograms from GNSS-RO signals is commonly done to, for instance, investigate reflections or estimate signal quality in the lower troposphere. This is typically implemented using, e.g., the Short-Time Fourier Transform (STFT) to produce a time-frequency representation that is subsequently transformed to bending angle (BA) and impact height (IH) coordinates by non-linear mapping. In this paper, we propose an alternative method based on a straightforward extension of the Phase Matching (PM) operator to produce two-dimensional spectral images in the BA-IH domain by applying a sliding window. This Sliding Window Phase Matching (SWPM) method generates the spectral amplitude on an arbitrary grid in BA and IH, e.g., along the coordinate axes. To illustrate, we show both SWPM and STFT methods applied to operational MetOp-A data. For SWPM we use a constant window in the BA-dimension, whereas for STFT we use a conventional constant time window. We show that the SWPM method produces the same result as STFT when the same window length is used for both methods. The sample points in impact parameter and bending angle are those generated by and the main advantage is that SWPM offers the user a convenient way to freely sample the BA-IH space. The cost for this is processing time that is somewhat longer than implementations based on the Fast Fourier Transform, such as the STFT method.

Assessments of the Retrieval of Atmospheric Profiles from GNSS Radio Occultation Data in Moist Tropospheric Conditions Using Radiosonde Data

The Global Navigation Satellite System (GNSS) Radio Occultation (RO) retrieved temperature and specific humidity profiles can be widely used for weather and climate studies in troposphere. However, some aspects, such as the influences of background data on these retrieved moist profiles have not been discussed yet. This research evaluates RO retrieved temperature and specific humidity profiles from Wegener Center for Climate and Global Change (WEGC), Radio Occultation Meteorology Satellite Application Facility (ROM SAF) and University Corporation for Atmospheric Research (UCAR) Boulder RO processing centers by comparing with measurements from 10 selected Integrated Global Radiosonde Archive (IGRA) radiosonde stations in different latitudinal bands over 2007 to 2010. The background profiles used for producing their moist profiles are also compared with radiosonde. We found that RO retrieved temperature profiles from all centers agree well with radiosonde. Mean differences at polar, mid-latitudinal and tropical stations are varying within ±0.2 K, ±0.5 K and from −1 to 0.2 K, respectively, with standard deviations varying from 1 to 2 K for most pressure levels. The differences between RO retrieved and their background temperature profiles for WEGC are varying within ±0.5 K at altitudes above 300 hPa, and the differences for ROM SAF are within ±0.2 K, and that for UCAR are within 0.5 K at altitudes below 300 hPa. Both RO retrieved and background specific humidity above 600 hPa are found to have large positive differences (up to 40%) against most radiosonde measurements. Discrepancies of moist profiles among the three centers are overall minor at altitudes above 300 hPa for temperature and at altitudes above 700 hPa for specific humidity. Specific humidity standard deviations are largest at tropical stations in June July August months. It is expected that the outcome of this research can help readers to understand the characteristics of moist products among centers.

Revised GNSS‐RO observation uncertainties in the Met Office NWP system

AbstractThe diagnostics of Desroziers et al. have been used to estimate the uncertainties of Global Navigation Satellite System Radio Occultation (GNSS‐RO) observations. Compared with the uncertainties that are currently operational, the new uncertainties are smaller and vary with height, latitude, satellite identifier, and other variables. The initial tests allowed the observation uncertainties to vary with latitude, satellite identifier, and the tangent‐point height of the observation. Using these uncertainties for a winter period demonstrated clear improvements in the weather forecast skill. Further tests showed that this improvement derived from the variation of the uncertainties with satellite identifier and from using a smooth variation with latitude—the current method gives a sharp change in the uncertainty at 30° intervals in latitude. The diagnosis method indicated a maximum in the observation uncertainty just above the tropical tropopause (around 20 km). Experiments demonstrated that the high values for the observation uncertainty in this region degrade the forecast performance. It is suggested that this is a sign that this method is identifying some model errors incorrectly as observation errors, which may be a consequence of the known assumptions of the method being violated. The uncertainties diagnosed in this study were calculated over a one‐month period in winter 2017/2018, and the initial tests were conducted using a three‐month period for the same winter. Using uncertainties that vary with latitude, height, and satellite identifier in an experiment for summer 2018 resulted in a much smaller increase in forecast skill than for the winter period. Therefore uncertainties were diagnosed that vary with quantities other than latitude. The most effective of these is to allow the observation uncertainties to vary with the average temperature between the Earth's surface and 20 km. Uncertainties that vary with this and with satellite identifier and height give good forecast skill improvements in both seasons.

Precise Orbit Determination for Climate Applications of GNSS Radio Occultation including Uncertainty Estimation

Global Navigation Satellite System (GNSS) Radio Occultation (RO) is a highly valuable remote sensing technique for probing the Earth’s atmosphere, due to its global coverage, high accuracy, long-term stability, and essentially all-weather capability. In order to ensure the highest quality of essential climate variables (ECVs), derived from GNSS signal tracking by RO satellites in low Earth orbit (LEO), the orbit positions and velocities of the GNSS transmitter and LEO receiver satellites need to be determined with high and proven accuracy and reliability. Wegener Center’s new Reference Occultation Processing System (rOPS) hence aims to integrate uncertainty estimation at all stages of the processing. Here we present a novel setup for precise orbit determination (POD) within the rOPS, which routinely and in parallel performs the LEO POD with the two independent software packages Bernese GNSS software (v5.2) and NAPEOS (v3.3.1), employing two different GNSS orbit data products. This POD setup enables mutual consistency checks of the calculated orbit solutions and is used for position and velocity uncertainty estimation, including estimated systematic and random uncertainties. For LEOs enabling laser tracking we involve position uncertainty estimates from satellite laser ranging. Furthermore, we intercompare the LEO orbit solutions with solutions from other leading orbit processing centers for cross-validation. We carefully analyze multi-month, multi-satellite POD result statistics and find a strong overall consistency of estimates within LEO orbit uncertainty target specifications of 5 cm in position and 0.05 mm/s in velocity for the CHAMP, GRACE-A, and Metop-A/B missions. In 92% of the days investigated over two representative 3-month periods (July to September in 2008 and 2013) these POD uncertainty targets, which enable highly accurate climate-quality RO processing, are satisfied. The moderately higher uncertainty estimates found for the remaining 8% of days (∼5–15 cm) result in increased uncertainties of RO-retrieved ECVs. This allows identification of RO profiles of somewhat reduced quality, a potential benefit for adequate further use in climate monitoring and research.

A New Algorithm for the Retrieval of Atmospheric Profiles from GNSS Radio Occultation Data in Moist Air and Comparison to 1DVar Retrievals

The Global Navigation Satellite System (GNSS) Radio Occultation (RO) is a key technique for obtaining thermodynamic profiles of temperature, humidity, pressure, and density in the Earth’s troposphere. However, due to refraction effects of both the dry air and water vapor at low altitudes, retrieval of accurate profiles is challenging. Here we introduce a new moist air retrieval algorithm aiming to improve the quality of RO-retrieved profiles in moist air and including uncertainty estimation in a clear sequence of steps. The algorithm first uses RO dry temperature and pressure and background temperature/humidity and their uncertainties to retrieve humidity/temperature and their uncertainties. These temperature and humidity profiles are then combined with their corresponding background profiles by optimal estimation employing inverse-variance weighting. Finally, based on the optimally estimated temperature and humidity profiles, pressure and density profiles are computed using hydrostatic and equation-of-state formulas. The input observation and background uncertainties are dynamically estimated, accounting for spatial and temporal variations. We show results from applying the algorithm on test datasets, deriving insights from both individual profiles and statistical ensembles, and from comparison to independent 1D-Variational (1DVar) algorithm-derived moist air retrieval results from Radio Occultation Meteorology Satellite Application Facility Copenhagen (ROM-SAF) and University Corporation for Atmospheric Research (UCAR) Boulder RO processing centers. We find that the new scheme is comparable in its retrieval performance and features advantages in the integrated uncertainty estimation that includes both estimated random and systematic uncertainties and background bias correction. The new algorithm can therefore be used to obtain high-quality tropospheric climate data records including uncertainty estimation.