Tropospheric Model Research Articles

Comparative analysis of four different single-frequency PPP models on positioning performance and atmosphere delay retrieval

Single-frequency precise point positioning (SF-PPP) has attracted increasing attention due to its high precision and cost effectiveness. With various strategies to handle the dominant error, i.e., ionosphere delay, the ionosphere-float (IF), ionosphere-free-half (IFH), ionosphere-corrected (IC), and ionosphere-weighted (IW) SF-PPP models are certain to possess different characteristics and performance levels. This study is dedicated to assessing and comparing the four models from model characteristics, positioning performance, and atmosphere delay retrieval. The model comparison shows that IC and IW models are full-rank while IF and IFH models have a rank deficiency of size one that will result in biased estimations, which means the better solvability of IC and IW models. The experiments are carried out based on the 7-day Global Positioning System (GPS) observations collected at 57 global Multi-GNSS Experiment (MGEX) stations and Global Ionosphere Map (GIM) products. The results indicate that the IW model can accelerate SF-PPP convergence and achieve higher positioning accuracy compared to the other three SF-PPP models, especially in kinematic mode. With convergence criteria of 0.25 m in horizontal and 0.5 m in vertical, the east/north/up convergence times of IW model are 0.5/15.0/25.0 min and 0.5/16.0/36.5 min for static and kinematic modes, respectively. The IW model is able to achieve an instantaneous positioning accuracy of 0.28/0.35/0.75 m. In addition, a real kinematic test also demonstrates the best positioning solutions of IW model. Regarding troposphere delay retrieval, the IF, IFH, and IW models obtain a comparable daily accuracy of 3.0 cm on average, while the IC model achieves the worst accuracy of 8.0 cm. For precise ionosphere delay estimation, IW model only needs an average initialization time of 34.3 min, but a longer initialization time of 51.6 min is required for IF model. The daily precision of ionosphere delay estimation for IW model can reach up to 10.8 cm. At the present accuracy of GIM products, it is suggested that the IW model should be adopted for SF-PPP first due to its superior performance in positioning and atmosphere delay retrieval.

Intercomparison of the representations of the atmospheric chemistry of pre-industrial methane and ozone in earth system and other global chemistry-transport models

An intercomparison has been set up to study the representation of the atmospheric chemistry of the pre-industrial troposphere in earth system and other global tropospheric chemistry-transport models. The intercomparison employed a constrained box model and utilised tropospheric trace gas composition data for the pre-industrial times at ninety mid-latitude surface locations. Incremental additions of four organic compounds: methane, ethane, acetone and propane, were used to perturb the constrained box model and generate responses in hydroxyl radicals and tropospheric ozone at each location and with each chemical mechanism. Although the responses agreed well across the chemical mechanisms from the selected earth system and other global tropospheric chemistry-transport models, there were differences in the detailed responses between the chemical mechanisms that could be tracked down by sensitivity analysis to differences in the representation of C1–C3 chemistry. Inter-mechanism ranges in NOx compensation points were about 0.17 ± 0.12 when expressed relative to the inter-mechanism average. Monte Carlo uncertainty analysis carried out with a single chemical mechanism put the intra-mechanism range a factor of three higher at 0.50 ± 0.12. Similar differences between inter-mechanism and intra-mechanism ranges were found for hydroxyl radical depletion but were up to a factor of six wider for ozone formation from incremental additions of organic compounds. The cause of the discrepancies between the inter- and intra-mechanism ranges was found to be the large uncertainties that are present in the laboratory determinations of the rate coefficients and product channel branching ratios of some key chemical reactions involving organic peroxy radicals and hydroperoxides. Whilst these large uncertainties are present in the laboratory determinations, there will be irreducible uncertainties in the predictions from the earth system and other chemistry-transport models of methane and tropospheric ozone trends since pre-industrial times and hence their contributions to the radiative forcing of climate change. Further definitive laboratory studies of the reaction rates and product yields of the reactions of the simple organic peroxy radicals and hydroperoxides are required to resolve and reduce current uncertainties in earth system and chemistry-transport model predictions.

Tropospheric modeling of acetic acid in the UK for Summer, Winter and Spring seasons using a mesoscale 3-dimensional chemistry and transport model, WRF-Chem-CRI

The measurement of acetic acid during the ClearfLo campaign for Winter 2012 and Summer 2012 in London and at the Weybourne Research Station (East Anglia), UK for Spring 2013 gives the average ± 1σ mixing ratios of 45.9 ± 31.5, 25.7 ± 14.3 and 55.1 ± 32.0 ppt, respectively. The WRF-Chem-CRI model was run over these three seasons and within uncertainty reproduced the data from London, with mixing ratios during Winter (32.3 ± 25.3 ppt) and Summer (55.1 ± 22.6 ppt). The model's seasonality was opposite to that observed and although within the combined uncertainty of the measurement and model data it underpredicted the levels observed at Weybourne during Spring (28.9 ± 19.3 ppt). The model-measurement correlations of the meteorological parameters (e.g. temperature, wind direction, wind speed) were good with a correlation of R > 0.7. The predicted diurnal trend of acetic acid resembled measurement data with a small negative bias during winter but performed less well during summer with a large positive bias and in spring with a large negative bias. The reasonable correlation of acetic acid mixing ratios with temperature was found to be similar for both measurement and model (Rmeasurement = 0.5, Rmodel = 0.6) during Summer suggesting the importance of the photochemical secondary source of acetic acid which was reflected both in the measurement and the model. The key processes identified from the model results were a) missing direct anthropogenic sources of acetic acid (accounting for the lower model winter values) and b) not including its loss process by Criegee intermediates (accounting for the higher model values in summer). Comparing the weekend data with weekday data revealed a likely underpredicted source of acetic acid from vehicles. The wet deposition removal process of acetic acid was found not to be as significant in the UK as anticipated.

A new simplified zenith tropospheric delay model for real-time GNSS applications

We have proposed a new simplified zenith tropospheric delay model for Global Navigation Satellite System (GNSS) real-time applications using 10 years of numerical weather prediction data from the National Center for Environmental Prediction (NCEP). Our proposed model is a blind model in which the atmospheric input parameters are tabulated based on realistic atmospheric behavior. The model with only 108 meteorological parameters is as simple as UNB3m model but has better performance; thus, it has great potential to be widely hardwired in GNSS receivers. Additionally, the accuracy of the new model is better than that of the state-of-the-art model named GPT2w in terms of the zenith tropospheric hydrostatic delay (ZHD) prediction, particularly at high altitudes. The proposed model is validated using NCEP reanalysis data and GNSS-derived International GNSS Service data. In comparison with the NCEP reanalysis data, the new model demonstrates an approximately 23% improvement in the root mean square (RMS) and a 76% improvement in the bias compared with the UNB3m model at the surface level. More significantly, this model performs better than the GPT2w model with respect to the ZHD prediction with increasing altitude, achieving up to a 96% improvement at an altitude of 10 km. The results of the zenith tropospheric delay (ZTD) model are compared with those derived from GNSS data. Notably, the RMS error and bias of the proposed model (with average values of 4.2 cm and 0.3 cm) are lower than those of the UNB3m model (with values of 5.0 cm and 1.0 cm), respectively.

Influence of the inhomogeneous troposphere on GNSS positioning and integer ambiguity resolution

The tropospheric delay is one of many error sources that affect the Global Navigation Satellite System (GNSS) positioning solutions. The widely used troposphere models assume a homogeneous atmosphere so that only the zenith delay needs to be determined and is mapped through an elevation-dependent mapping function. This procedure is to reduce the computational burden and keep the positioning model full-rank. However, this assumption fails for a realistic description of the troposphere, which is always asymmetrical at a certain elevation angle, especially during a weather event when the weather conditions are very complex. These imperfectly modelled tropospheric delays may influence the positioning accuracy and integer ambiguity resolution performance. In this case, this contribution aims to investigate the effects of the model errors due to the asymmetrical troposphere on GNSS estimations. The Numerical Weather Prediction (NWP) model is applied to generate the actual ray-tracing tropospheric delay in Western Europe, and the tropospheric model errors are calculated in a normal weather condition and a weather event condition by comparing the slant delay calculated from the NWP model and the mapping function. Case studies on the same GNSS station are conducted in two weather conditions: a normal troposphere condition and a weather event with heavy rainfall. The results based on the case studies show that the troposphere in the normal weather condition is nearly homogeneous that the azimuthal-dependent discrepancies of the tropospheric delay are less than 1cm at a very low elevation angle; meanwhile, the discrepancies between different azimuthal angles can reach to more than 25cm in the weather event. A single-frequency Single Point Positioning (SPP) model and a Precise Point Positioning (PPP) model that preserves the integer property of ambiguity are chosen for studying the estimation biases caused by the troposphere model errors. It turns out that almost all horizontal positioning biases of SPP and PPP are less than 1cm in the normal weather condition; however, the scales of the horizontal and 3D biases are concentrated in 1 to 10cm in the weather event for these two models. This contribution also contains the study of the actual integer ambiguity resolution success rate in the presence of the tropospheric model errors by applying the Monte Carlo simulation, and the success rates of PPP in the normal weather condition are consistent with the theoretical values calculated with the ideal troposphere which is totally symmetrical. However, the actual success rates in the weather event are extremely low at some epochs due to the tropospheric model errors, which means that wrong fixing may occur since the theoretical values cannot take into account these model errors. Note that the horizontal tropospheric gradients are not involved in the processing, which means that an optimistic performance might be expected if the gradients are considered.

Comparative analysis of blind tropospheric correction models in Ghana

AbstractThe impact of the earth’s atmospheric layers, particularly the troposphere on Global Navigation satellite system (GNSS) signals has become a major concern in GNSS accurate positioning, navigation, surveillance and timing applications. For precise GNSS applications, tropospheric delay has to be mitigated as accurately as possible using tropospheric delay prediction models. However, the choice of a particular prediction model can signifi-cantly impair the positioning accuracy particularly when the model does not suit the user’s environment. A performance assessment of these prediction models for a suitable one is very important. In this paper, an assessment study of the performances of five blind tropospheric delay prediction models, the UNB3m, EGNOS, GTrop, GPT2w and GPT3 models was conducted in Ghana over six selected Continuously Operating Reference Stations (CORS) using the 1˚x1˚ gridded Vienna Mapping Function 3 (VMF3) zenith tropospheric delay (ZTD) product as a reference. The gridded VMF3-ZTD which is generated for every six hours on the 1˚x1˚ grids was bilinearly interpolated both space and time and transferred from the grid heights to the respective heights of the CORS locations. The results show that the GPT3 model performed better in estimating the ZTD with an overall mean (bias: 2.05 cm; RMS: 2.53 cm), followed by GPT2w model (bias: 2.32cm; RMS: 2.76cm) and GTrop model (bias: 2.41cm; 2.82cm). UNB3m model (bias: 6.23 cm; RMS: 6.43 cm) and EGNOS model (bias: 6.70 cm; RMS: 6.89 cm) performed poorly. A multiple comparison test (MCT) was further performed on the RMSE of each model to check if there is significant difference at 5% significant level. The results show that the GPT3, GPT2w and GTrop models are significantly indifferent at 5% significance level indicating that either of these models can be employed to mitigate the ZTD in the study area, nevertheless, the choice of GPT3 model will be more preferable.

Development and performance evaluation of tropospheric scintillation model on Ku-band satellite link over Akure, Nigeria

Existing amplitude scintillation prediction models often perform less satisfactorily when deployed outside the regions where they were formulated. This necessitates the need to evaluate the performance of scintillation models developed in one region using data data from other regions while documenting their relative errors. Due to its variation with elevation angle, frequency, other link parameters and meteorological factors, we employed three years (January 2016 to December 2018) of concurrently measured satellite radio beacons and tropospheric weather parameters to develop a location-based amplitude scintillation prediction model over the Earth-space path of Akure (7.17oN, 5.18oE), South-western Nigeria. The satellite beacon measurement used Tektronix Y400 NetTek Analyzer at 1 s integration time while meteorological parameters, namely; temperature, pressure and relative humidity were measured using Davis Vantage Vue weather station at 1 min integration time. Comparative study of the model’s performance with nine (9) existing scintillation prediction models indicates that the best and worst performing models, in terms of root mean square error (RMSE), are the Statistical Temperature and Refractivity (STN) and direct physical and statistical prediction (DPSP) models with values 11.48 and 51.03 respectively. Also, worst month analysis indicates that April, with respective enhancement and fade values of 0.88 and 0.90 dB for 0.01% exceedance, is the overall worst calendar month for amplitude scintillation.

Atmospheric pressure loading in GPS positions: dependency on GPS processing methods and effect on assessment of seasonal deformation in the contiguous USA and Alaska

The Global Positioning System (GPS) has revolutionized the ability to monitor Earth-system processes, including Earth’s water cycle. Several analysis centers process GPS data to estimate ground-antenna positions at daily temporal resolution. Differences in processing strategies can lead to inconsistencies in coordinate-position estimates and therefore influence the analysis of crustal displacement associated with variations in atmospheric and hydrologic mass loading. Here, we compare five GPS data products produced by three processing centers: the Nevada Geodetic Laboratory, Jet Propulsion Laboratory, and UNAVCO Consortium. We find that 5 to 30% of the scatter in residual GPS time series (commonly considered noise) can be explained by atmospheric loading in the contiguous USA and Alaska, but that the percentages vary widely by data product. Positions derived using high-resolution troposphere models (e.g., ECMWF) exhibit significantly lower scatter after correcting for atmospheric loading than positions estimated using constant or slowly varying troposphere models (e.g., GPT2w). The data products also exhibit differences in seasonal deformation (commonly attributed, in large part, to fluctuations in hydrologic mass loading): median vector differences in estimated seasonal amplitude range from 0.4–1.0 mm in the vertical component and 0.1–0.3 mm in the horizontal components, or about 10–40% of the mean amplitudes of seasonal oscillation. Newer products exhibit lower total scatter and stronger correlations than older products. Network-coherent differences in estimates of seasonal deformation reveal reference-frame inconsistencies between data products. We also cross-check two independent models of atmospheric pressure loading: ESMGFZ and LoadDef.

Evaluation of the ZWD/ZTD Values Derived from MERRA-2 Global Reanalysis Products Using GNSS Observations and Radiosonde Data.

Tropospheric delay is one of the main errors affecting high-precision positioning and navigation and is a key parameter of water vapor detection in the Global Navigation Satellite System (GNSS). The second Modern-Era Retrospective analysis for Research and Applications (MERRA-2) is the latest generation of reanalysis data collected by the National Aeronautics and Space Administration (NASA), which can be used to calculate tropospheric delay products with high spatial and temporal resolution. However, there is no report analyzing the accuracy of the zenith tropospheric delay (ZTD) and zenith wet delay (ZWD) calculated from MERRA-2 data. This paper evaluates the performance of the ZTD and ZWD values derived from global MERRA-2 data using global radiosonde data and International GNSS Service (IGS) precise ZTD products. The results are as follows: (1) Taking the precision ZTD products of 316 IGS stations from around the world from 2015 to 2017 as the reference, the average root mean square (RMS) of the ZTD values calculated from the MERRA-2 data is better than 1.35 cm, and the accuracy difference between different years is small. The bias and RMS of the ZTD values show certain seasonal variations, with a higher accuracy in winter and a lower accuracy in summer, and the RMS decreases from the equator to the poles. However, those of the ZTD values do not show obvious variations according to elevation. (2) Relative to the radiosonde data, the RMS of the ZWD and ZTD values calculated from the MERRA-2 data are better than 1.37 cm and 1.45 cm, respectively. Furthermore, the bias and RMS of the ZWD and ZTD values also show some temporal and spatial characteristics, which are similar to the test results of the IGS stations. It is suggested that MERRA-2 data can be used for global tropospheric vertical profile model construction because of their high accuracy and good stability in the global calculation of the ZWD and ZTD.

The Impact of Different Mapping Function Models and Meteorological Parameter Calculation Methods on the Calculation Results of Single-Frequency Precise Point Positioning with Increased Tropospheric Gradient

Tropospheric delay is one of the main errors in precise point positioning (PPP). The inaccuracy of the tropospheric delay model will inevitably lead to a decrease in PPP accuracy. Therefore, the influence of the tropospheric gradient on the positioning accuracy should be considered in the processing of tropospheric delay. At the same time, the effects of different mapping function models and meteorological parameter calculation methods on the tropospheric delay accuracy of single-frequency PPP (SF PPP) are analyzed. Twelve MGEX stations, which are evenly distributed in the world, are used in this article. Taking into account the seasonal variation of the tropospheric delay, the observation times adopted are 2016, 2017, and 2018 for different seasons (winter, day of year (DOY): 22–28; spring, DOY: 92–98; summer, DOY: 199–205; and autumn, DOY: 275–281). Then, according to different mapping function models and meteorological parameter calculation methods, a total of 7056 tests and 9072 tests are performed, respectively. The following results were obtained after comparative analysis. (1) When the same method is used for calculating meteorological parameters, the percentage with improved tropospheric delay repeatability calculated by the Hopfield mapping function model (MFM3) is the highest, reaching more than 70%, and by Vienna Mapping Functions 3 (VMF3, grid resolution is 1°), the mapping function model (MFM8) is the lowest, less than 67.5%. The percentage with improved position repeatability is highest in the north (N) direction and lowest in the up (U) direction. (2) Using the same mapping function model, the correction of the tropospheric gradient model has a greater impact on calculating the repeatability percentage of the tropospheric delay and the position. Compared with standard atmospheric parameters, other calculation methods of meteorological parameters have little effect on the percentage increase of the tropospheric delay value and the positioning result after adding the tropospheric gradient model. It shows that different calculation methods of meteorological parameters have little effect on the calculation of tropospheric delay and position, different mapping function models have a large effect on the calculation of tropospheric delay and position, and the tropospheric gradient model has the greatest influence on the calculation of tropospheric delay and position.

Improved performance of ERA5 in global tropospheric delay retrieval

Reanalysis products have played an important role in space geodetic tropospheric delay retrieval and modeling in the past two decades. As the release of the fifth-generation European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA5), with improved temporal-spatial resolutions compared to its predecessor, ECMWF Re-Analysis Interim (ERAI), the performance of ERA5 in tropospheric delay retrieval was comprehensively investigated in this study. Hourly tropospheric delays in zenith and 5° elevation directions at 312 International GNSS Service (IGS) stations covering the year of 2018 from ERA5 and ERAI were ray-traced. Taking IGS Zenith Total Delay (ZTD) as reference, the reanalysis-derived ZTDs were evaluated in annual, seasonal and diurnal scales, and superior performances of ERA5 ZTD to ERAI ZTD in all scales were revealed, with bias, Root Mean Square (RMS) and standard deviation of 1.6, 11.0 and 10.1 mm for ERA5, and of 3.1, 13.8 and 12.6 mm for ERAI, respectively. Due to the absence of reliable Slant Path Delay (SPD) references, the SPDs as well as the mapping factors derived from ERA5 and ERAI were directly compared and converted to equivalent station height errors at these stations. Obvious differences were also found for SPDs and mapping factors, especially for the wet component, with slant wet delay and wet mapping factor difference RMSs of 51.9 mm and 146.1 × 10−3, respectively, corresponding to equivalent station height RMSs of about 10.4 mm. Tropospheric delays and models (e.g., mapping functions) derived from ERA5, with improved performance and temporal resolution (e.g., to support potential tropospheric parameter diurnal variation study), therefore can be expected for space geodetic applications.

Regional modeling of tropospheric delay considering vertically and horizontally separation of station for regional augmented PPP

Considering the height and horizontal separations between stations, we propose a method to regionally model the tropospheric delay. The zenith tropospheric delay (ZTD) is separated into vertically-correlated and horizontally-correlated components, which are to be regionally modeled respectively. A more accurate ZTD is thereby interpolated at the rover station, and enhanced ZTD constraints can be provided for precise point positioning (PPP). A new constraint of the ZTD interpolation error is proposed and assessed. The results show that the linear correlation between ZTD after separation and the height of the station are further enhanced, with the mean correlation coefficient reaching up to 0.99. The RMS value of the ZTD obtained from undifferenced and uncombined PPP with fixed station coordinates is less than 6.5 mm. The new method is compared with conventional modeling methods and the results show that the new zenith tropospheric delay modeling method is superior to other methods, yielding a RMS value within 1 cm. Simulated kinematic positioning experiments for five different elevation cutoff angles from 10° to 50° are performed to evaluate the performance of the new method. Experimental results show that the new method can effectively improve the convergence and positioning accuracy. The systematic deviations of the vertical component for the higher cutoff angle solution can be significantly improved by the new method.

An advanced residual error model for tropospheric delay estimation

Global navigation satellite systems (GNSS) are widely used for safety-of-life positioning applications. Such applications require high integrity, availability, and continuity of the positioning service. Integrity is assessed by the definition of a protection level, which is an estimation of the maximum positioning error at extremely low probability levels. The emergence of multi-frequency civilian signals and the availability of satellite-based augmentation systems improve the modeling of ionospheric disturbances considerably. As a result, in many applications the tropospheric delay tends to become one of the limiting factors of positioning—especially at low elevation angles. The currently adopted integrity concepts employ a global constant to model the variance of the residual tropospheric delay error. We introduce a new approach to derive residual tropospheric delay error models using the extreme value analysis technique. Seventeen years of global numerical weather model fields are analyzed, and new residual error models are derived for some recently developed tropospheric delay models. Our approach provides models that consider both the geographical location and the seasonal variation of meteorological parameters. Our models are validated with a 17-year-long time series of zenith tropospheric delay estimates as provided by the International GNSS Service. The results show that the developed models are still conservative, while the maximal residual error of the tropospheric delay is still improved by 39–55%. This improvement yields higher service availability and continuity in safety-of-life applications of GNSS.

Performance Assessment of PPP Surveys with Open Source Software Using the GNSS GPS–GLONASS–Galileo Constellations

In this work, the performance of the multi-GNSS (Global Navigation Satellite System) Precise Point Positioning (PPP) technique, in static mode, is analyzed. Specifically, GPS (Global Positioning System), GLONASS, and Galileo systems are considered, and quantifying the Galileo contribution is one of the main objectives. The open source software RTKLib is adopted to process the data, with precise satellite orbits and clocks from CNES (Centre National d’Etudes Spatiales) and CLS (Collecte Localisation Satellites) analysis centers for International GNSS Service (IGS). The Iono-free model is used to correct ionospheric errors, the GOT-4.7 model is used to correct tidal effects, and Differential Code Biases (DCB) are taken from the Deutsche Forschungsanstalt für Luftund Raumfahrt (DLR) center. Two different tropospheric models are tested: Saastamoinen and Estimate ZTD (Zenith Troposhperic Delay). For the proposed study, a dataset of 31 days from a permanent GNSS station, placed in Palermo (Italy), and a dataset of 10 days from a static geodetic receiver, placed nearby the station, have been collected and processed by the most used open source software in the geomatic community. The considered GNSS configurations are seven: GPS only, GLONASS only, Galileo only, GPS+GLONASS, GPS+Galileo, GLONASS+Galileo, and GPS+GLONASS+Galileo. The results show significant performance improvement of the GNSS combinations with respect to single GNSS cases.

Characteristic differences between IGS final and ray-traced tropospheric delays and their impact on precise point positioning and tropospheric delay estimates

The length of the convergence time in Precise Point Positioning (PPP) is a result of unmodeled errors. The tropospheric delay error is considered a major error source that affects the PPP solution accuracy and convergence time, especially in the height coordinates. Recent research showed that Numerical Weather Models (NWM)-based tropospheric correction models are superior to traditional empirical tropospheric models. We investigate the tropospheric delay difference between the ray-traced NWM and the final troposphere estimates from the International GNSS Service (IGS). Long time series of about 5 years of tropospheric delay are obtained from the European Centre for Medium-Range Weather Forecast and compared with the final IGS tropospheric delay time series for 30 globally distributed IGS stations. It is shown that the ray-traced tropospheric delay differences depend on time and experience seasonal variations with a non-zero mean. The mean is found to be 0.47 cm and the standard deviation is 1.40 cm. To model such differences, the least-squares spectral analysis approach is used to estimate the deterministic part of the tropospheric residuals (linear trend and periodic signals). The remaining tropospheric differences are estimated as a random walk process with 5 mm/√h random noise. To study the effect of the developed model on both station position and tropospheric delay estimate, we implement our model in GPS processing software, and the data from 15 IGS stations are processed. These stations are divided into two groups. The first group consists of nine IGS stations from the same network used to estimate the model, and their corresponding model values are extrapolated to the test epochs in 2018. However, the second group consists of six IGS stations, and their corresponding values are interpolated from the nearby stations. It is shown that the root means square error (RMSE) of station position in both groups can be improved by 5.68%, 0.76%, and 11.88% in Easting, Northing, and Up directions, respectively. In addition to the improvement in the RMSE of station positions, an improvement of 7.91% is obtained for total tropospheric delay estimates.

A Multi-Station Troposphere Modelling Method Based on Error Compensation Considering the Influence of Height Factor

One critical issue in network real-time kinematic (NRTK) is the interpolation of atmospheric delay for user stations. Some classic interpolation algorithms, such as linear interpolation method (LIM), ignore the strong correlation between tropospheric delay and height factors, and the interpolation accuracy is poor in areas with large height difference. To solve this problem, a troposphere modelling method based on error compensation, namely ECDIM (Error Compensation-Based DIM), is proposed, and this method can be applied to both conventional single Delaunay triangulated network (DTN) and multi-station scenarios. The results of California Real Time Network (CRTN) with large height difference show that compared with LIM, the overall modelling accuracy with ECDIM has been improved by 50.1% to 67.3%, and especially for low elevation satellites (e.g., 10–20 degree), the accuracy is increased from tens of centimetres to a few centimetres. At user end, the positioning error in up direction with LIM has an obvious systematic deviation, and the fix rate of epoch is relatively low. This situation has been improved significantly after using ECDIM. The results of Tianjin Continuously Operating Reference System (TJCORS) show that in areas with small height difference, both methods have achieved high precision interpolation accuracy, and the positioning accuracy with ECDIM in up direction is improved by 21.2% compared with LIM.

Photochemical aging of Beijing urban PM2.5: Production of oxygenated volatile organic compounds

PM2.5 has become the dominant atmospheric pollutant in many countries. Many components of PM2.5 are highly photoactive. However, the photochemical aging of PM2.5 remains poorly understood. In this study, the photoaging of real PM2.5 samples collected from 2017 to 2018 in Beijing under simulated solar radiation (λ ~ 340–850 nm) was investigated. Our study showed that large amounts of oxygenated volatile organic compounds (OVOCs), such as acetaldehyde, formic acid, acetone and acetic acid, were released during the photochemical aging of PM2.5. Furthermore, although a positive correlation between the OVOCs yield and the organic matter (OM) in PM2.5 was observed, the product distribution from the photoaging of PM2.5 was different from that in the direct photolysis of artificially synthesized SOA. Because of the release of OVOCs, the PM2.5 mass loss was evaluated to be ~1.80% per day under typical atmospheric conditions. The OVOCs released during the photoaging of PM2.5 may contribute substantially to the OVOCs sources omitted from troposphere chemistry models and may have a significant effect on the OVOCs distribution and oxidation capacity of the atmosphere.

Assessment of Empirical Troposphere Model GPT3 Based on NGL's Global Troposphere Products.

Tropospheric delay is one of the major error sources in GNSS (Global Navigation Satellite Systems) positioning. Over the years, many approaches have been devised which aim at accurately modeling tropospheric delays, so-called troposphere models. Using the troposphere data of over 16,000 global stations in the last 10 years, as calculated by the Nevada Geodetic Laboratory (NGL), this paper evaluates the performance of the empirical troposphere model GPT3, which is the latest version of the GPT (Global Pressure and Temperature) series model. Owing to the large station number, long time-span and diverse station distribution, the spatiotemporal properties of the empirical model were analyzed using the average deviation (BIAS) and root mean square (RMS) error as indicators. The experimental results demonstrate that: (1) the troposphere products of NGL have the same accuracy as the IGS (International GNSS Service) products and can be used as a reference for evaluating general troposphere models. (2) The global average BIAS of the ZTD (zenith total delay) estimated by GPT3 is −0.99 cm and the global average RMS is 4.41 cm. The accuracy of the model is strongly correlated with latitude and ellipsoidal height, showing obviously seasonal variations. (3) The global average RMS of the north gradient and east gradient estimated by GPT3 is 0.77 mm and 0.73 mm, respectively, which are strongly correlated with each other, with values increasing from the equator to lower latitudes and decreasing from lower to higher latitudes.

VMF3o: the Vienna Mapping Functions for optical frequencies

The troposphere is considered as one of the major error sources in space geodetic techniques. Thus, accurate troposphere delay models are essential to provide high-quality products, such as reference frames, satellite orbits, or Earth rotation parameters. In this paper, a new troposphere delay model for satellite laser ranging, the Vienna Mapping Functions 3 for optical frequencies (VMF3o), is introduced. The model parameters are derived from ray-traced delays generated by an in-house ray-tracing software. VMF3o comprises not only zenith delays and mapping functions, but also linear horizontal gradients, which are not part of the standard SLR analysis yet. The model parameters are dedicated to a signal wavelength of 532 nm. Since some SLR stations operate also with other wavelengths, VMF3o provides a correction formula to transform the model parameters to any requested wavelength between 350 and 1064 nm. A test demonstrates that the correction formula approximates slant delays calculated at different wavelengths very accurately. The remaining error for slant delays at a wavelength of 1064 nm adds up to only a few millimetres at 10^{circ } elevation angle. A comparison study of the modelled delays that are derived from VMF3o and ray-traced delays was carried out to examine the quality of the model approach. The remaining differences of modelled and ray-traced delays are expressed as mean absolute error. At 5^{circ } elevation angle, the mean absolute error is only a few millimetres. At 10^{circ } elevation angle, it is at the 1 mm level. The results of the comparison also reveal that introducing linear horizontal gradients reduces the mean absolute error by more than 80% for low elevation angles.

Global Warming Potential (GWP) for Methane: Monte Carlo Analysis of the Uncertainties in Global Tropospheric Model Predictions

Estimates of the global warming potential (GWP) of methane rely on the predictions from global chemistry-transport models. These models employ many uncertain input parameters representing the sources and sinks for methane and those for the tropospheric ozone, which is formed as a by-product of the methane sink process. Five thousand quasi-randomly Monte Carlo sampled model runs employing a zonally averaged global model were completed, each with a base case and a pulse case that differed from the base case only in having an additional 149 Tg (1Tg = 109 kg) emission pulse of methane. Each of the five thousand pulse case experiments had a small excess of methane that decayed away throughout the twenty-year model experiment. The radiative forcing consequences of this excess methane, and the excess tropospheric ozone formed from it, were integrated over a 100-year time horizon. The GWP was calculated in each of the five thousand model experiments from the sum of the radiative forcing consequences of methane and tropospheric ozone, by expressing them relative to the radiative forcing consequences of an identical emission pulse of carbon dioxide. The 2-sigma confidence range surrounding the methane atmospheric lifetime estimated in the Monte Carlo analysis was considerably wider than that derived from observations, suggesting that some of the input parameter combinations may have been unrealistic. The rejection of the unrealistic Monte Carlo replicates increased the mean methane GWP and narrowed its 2-sigma confidence interval to 37 ± 10 over a 100-year time horizon for emission pulses of the order of 1 Tg. Multiple linear regression was used to attribute the uncertainty in the output GWPs to each of the 183 uncertain input parameters, which represented emission source sectors, chemical kinetic rate coefficients, dry deposition velocities and biases in temperature and water vapour concentrations. Overall, the only significant contributions to the uncertainty in the methane GWP came from the chemical kinetic parameters representing the CH4 + OH, CH3O2 + HO2, CH3O2 + NO and the terpene + O3 reaction rate coefficients.