Observing geometry effects on a GNSS based water vapor tomography solved by Least Squares and by Compressive Sensing

Abstract. In this work, the effect of the observing geometry on the tomographic reconstruction quality of both a regularized Least Squares (LSQ) and a Compressive Sensing (CS) approach for neutrospheric water vapor tomography is compared based on synthetic Global Navigation Satellite System (GNSS) Slant Wet Delay (SWD) estimates. In this context, the term observing geometry mainly refers to the number of GNSS sites situated within a specific study area subdivided into a certain number of volumetric pixels (voxels) and to the number of signal directions available at each GNSS site. The novelties of this research are 1) the comparison of the observing geometry’s effects on the tomographic reconstruction accuracy when using LSQ resp. CS for the solution of the tomographic system and 2) the investigation of the effect of the signal directions’ variability on the tomographic reconstruction. The tomographic reconstruction is performed based on synthetic SWD data sets generated, for many samples of various observing geometry settings, based on wet refractivity information from the Weather Research and Forecasting (WRF) model. The validation of the achieved results focuses on a comparison of the refractivity estimates with the input WRF refractivities. The results show that the recommendation of Champollion et al. (2004) to discretize the analyzed study area into voxels with horizontal sizes comparable to the mean GNSS inter site distance represents a good rule of thumb for both LSQ and CS based tomography solutions. In addition, this research shows that CS needs a variety of at least 15 signal directions per site in order to estimate the refractivity field more accurately and more precisely than LSQ. Therefore, the use of CS is particularly recommended for water vapor tomography applications for which a high number of multi-GNSS SWD estimates are available.

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Observing geometry effects on a Global Navigation Satellite System (GNSS)-based water vapor tomography solved by least squares and by compressive sensing

Annales Geophysicae ◽

10.5194/angeo-38-179-2020 ◽

2020 ◽

Vol 38 (1) ◽

pp. 179-189

Author(s):

Marion Heublein ◽

Patrick Erik Bradley ◽

Stefan Hinz

Keyword(s):

Water Vapor ◽

Compressive Sensing ◽

Least Squares ◽

Global Navigation Satellite System ◽

Tomographic Reconstruction ◽

Wrf Model ◽

Satellite System ◽

Navigation Satellite ◽

Global Navigation ◽

Global Navigation Satellite

Abstract. In this work, the effect of the observing geometry on the tomographic reconstruction quality of both a regularized least squares (LSQ) approach and a compressive sensing (CS) approach for water vapor tomography is compared based on synthetic Global Navigation Satellite System (GNSS) slant wet delay (SWD) estimates. In this context, the term “observing geometry” mainly refers to the number of GNSS sites situated within a specific study area subdivided into a certain number of volumetric pixels (voxels) and to the number of signal directions available at each GNSS site. The novelties of this research are (1) the comparison of the observing geometry's effects on the tomographic reconstruction accuracy when using LSQ or CS for the solution of the tomographic system and (2) the investigation of the effect of the signal directions' variability on the tomographic reconstruction. The tomographic reconstruction is performed based on synthetic SWD data sets generated, for many samples of various observing geometry settings, based on wet refractivity information from the Weather Research and Forecasting (WRF) model. The validation of the achieved results focuses on a comparison of the refractivity estimates with the input WRF refractivities. The results show that the recommendation of Champollion et al. (2004) to discretize the analyzed study area into voxels with horizontal sizes comparable to the mean GNSS intersite distance represents a good rule of thumb for both LSQ- and CS-based tomography solutions. In addition, this research shows that CS needs a variety of at least 15 signal directions per site in order to estimate the refractivity field more accurately and more precisely than LSQ. Therefore, the use of CS is particularly recommended for water vapor tomography applications for which a high number of multi-GNSS SWD estimates are available.

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Assessment of Sampling Effects on Various Satellite-Derived Integrated Water Vapor Datasets Using GPS Measurements in Germany as Reference

Remote Sensing ◽

10.3390/rs12071170 ◽

2020 ◽

Vol 12 (7) ◽

pp. 1170 ◽

Cited By ~ 3

Author(s):

Cintia Carbajal Henken ◽

Lisa Dirks ◽

Sandra Steinke ◽

Hannes Diedrich ◽

Thomas August ◽

...

Keyword(s):

Water Vapor ◽

Pairwise Comparison ◽

Weather Conditions ◽

Satellite System ◽

High Temporal Resolution ◽

Data Sets ◽

Frequency Distributions ◽

Temporal Sampling ◽

Global Navigation Satellite ◽

Satellite Instruments

Passive imagers on polar-orbiting satellites provide long-term, accurate integrated water vapor (IWV) data sets. However, these climatologies are affected by sampling biases. In Germany, a dense Global Navigation Satellite System network provides accurate IWV measurements not limited by weather conditions and with high temporal resolution. Therefore, they serve as a reference to assess the quality and sampling issues of IWV products from multiple satellite instruments that show different orbital and instrument characteristics. A direct pairwise comparison between one year of IWV data from GPS and satellite instruments reveals overall biases (in kg/m 2 ) of 1.77, 1.36, 1.11, and −0.31 for IASI, MIRS, MODIS, and MODIS-FUB, respectively. Computed monthly means show similar behaviors. No significant impact of averaging time and the low temporal sampling on aggregated satellite IWV data is found, mostly related to the noisy weather conditions in the German domain. In combination with SEVIRI cloud coverage, a change of shape of IWV frequency distributions towards a bi-modal distribution and loss of high IWV values are observed when limiting cases to daytime and clear sky. Overall, sampling affects mean IWV values only marginally, which are rather dominated by the overall retrieval bias, but can lead to significant changes in IWV frequency distributions.

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GPS-BDS-Galileo double-differenced stochastic model refinement based on least-squares variance component estimation

Journal of Navigation ◽

10.1017/s0373463321000564 ◽

2021 ◽

pp. 1-16

Author(s):

Hong Hu ◽

Xuefeng Xie ◽

Jingxiang Gao ◽

Shuanggen Jin ◽

Peng Jiang

Keyword(s):

Stochastic Model ◽

Least Squares ◽

Stochastic Models ◽

Variance Component ◽

Satellite System ◽

Variance Component Estimation ◽

Short Baseline ◽

Component Estimation ◽

Zero Baseline ◽

Global Navigation Satellite

Abstract Stochastic models are essential for precise navigation and positioning of the global navigation satellite system (GNSS). A stochastic model can influence the resolution of ambiguity, which is a key step in GNSS positioning. Most of the existing multi-GNSS stochastic models are based on the GPS empirical model, while differences in the precision of observations among different systems are not considered. In this paper, three refined stochastic models, namely the variance components between systems (RSM1), the variances of different types of observations (RSM2) and the variances of observations for each satellite (RSM3) are proposed based on the least-squares variance component estimation (LS-VCE). Zero-baseline and short-baseline GNSS experimental data were used to verify the proposed three refined stochastic models. The results show that, compared with the traditional elevation-dependent model (EDM), though the proposed models do not significantly improve the ambiguity resolution success rate, the positioning precision of the three proposed models has been improved. RSM3, which is more realistic for the data itself, performs the best, and the precision at elevation mask angles 20°, 30°, 40°, 50° can be improved by 4⋅6%, 7⋅6%, 13⋅2%, 73⋅0% for L1-B1-E1 and 1⋅1%, 4⋅8%, 16⋅3%, 64⋅5% for L2-B2-E5a, respectively.

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Water Vapor Retrievals from Spectral Direct Irradiance Measured with an EKO MS-711 Spectroradiometer—Intercomparison with Other Techniques

Remote Sensing ◽

10.3390/rs13030350 ◽

2021 ◽

Vol 13 (3) ◽

pp. 350

Author(s):

Rosa Delia García ◽

Emilio Cuevas ◽

Victoria Eugenia Cachorro ◽

Omaira E. García ◽

África Barreto ◽

...

Keyword(s):

Water Vapor ◽

Power Plants ◽

Precipitable Water ◽

Satellite System ◽

Infrared Spectrometry ◽

Radiative Balance ◽

Direct Normal Irradiance ◽

Routine Basis ◽

Solar Power Plants ◽

Global Navigation Satellite

Precipitable water vapor retrievals are of major importance for assessing and understanding atmospheric radiative balance and solar radiation resources. On that basis, this study presents the first PWV values measured with a novel EKO MS-711 grating spectroradiometer from direct normal irradiance in the spectral range between 930 and 960 nm at the Izaña Observatory (IZO, Spain) between April and December 2019. The expanded uncertainty of PWV (UPWV) was theoretically evaluated using the Monte-Carlo method, obtaining an averaged value of 0.37 ± 0.11 mm. The estimated uncertainty presents a clear dependence on PWV. For PWV ≤ 5 mm (62% of the data), the mean UPWV is 0.31 ± 0.07 mm, while for PWV > 5 mm (38% of the data) is 0.47 ± 0.08 mm. In addition, the EKO PWV retrievals were comprehensively compared against the PWV measurements from several reference techniques available at IZO, including meteorological radiosondes, Global Navigation Satellite System (GNSS), CIMEL-AERONET sun photometer and Fourier Transform Infrared spectrometry (FTIR). The EKO PWV values closely align with the above mentioned different techniques, providing a mean bias and standard deviation of −0.30 ± 0.89 mm, 0.02 ± 0.68 mm, −0.57 ± 0.68 mm, and 0.33 ± 0.59 mm, with respect to the RS92, GNSS, FTIR and CIMEL-AERONET, respectively. According to the theoretical analysis, MB decreases when comparing values for PWV > 5 mm, leading to a PWV MB between −0.45 mm (EKO vs. FTIR), and 0.11 mm (EKO vs. CIMEL-AERONET). These results confirm that the EKO MS-711 spectroradiometer is precise enough to provide reliable PWV data on a routine basis and, as a result, can complement existing ground-based PWV observations. The implementation of PWV measurements in a spectroradiometer increases the capabilities of these types of instruments to simultaneously obtain key parameters used in certain applications such as monitoring solar power plants performance.

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Evaluation of Modeling Water-Vapor-Weighted Mean Tropospheric Temperature for GNSS-Integrated Water Vapor Estimates in Brazil

Journal of Applied Meteorology and Climatology ◽

10.1175/jamc-d-13-048.1 ◽

2014 ◽

Vol 53 (3) ◽

pp. 715-730 ◽

Cited By ~ 13

Author(s):

Luiz F. Sapucci

Keyword(s):

Water Vapor ◽

Weather Prediction ◽

Satellite System ◽

The Other ◽

Tropospheric Temperature ◽

Regional Models ◽

Weighted Mean ◽

Multivariate Statistical ◽

Global Models ◽

Global Navigation Satellite

AbstractMeteorological application of Global Navigation Satellite System (GNSS) data over Brazil has increased significantly in recent years, motivated by the significant amount of investment from research agencies. Several projects have, among their principal objectives, the monitoring of humidity over Brazilian territory. These research projects require integrated water vapor (IWV) values with maximum quality, and, accordingly, appropriate data from the installed meteorological stations, together with the GNSS antennas, have been used. The model that is applied to estimate the water-vapor-weighted mean tropospheric temperature (Tm) is a source of uncertainty in the estimate of IWV values using the ground-based GNSS receivers in Brazil. Two global models and one algorithm for Tm, developed through the use of radiosondes, numerical weather prediction products, and 40-yr ECMWF Re-Analysis (ERA-40), as well as two regional models, were evaluated using a dataset of ~78 000 radiosonde profiles collected at 22 stations in Brazil during a 12-yr period (1999–2010). The regional models (denoted the Brazilian and regional models) were developed with the use of multivariate statistical analysis using ~90 000 radiosonde profiles launched at 12 stations over a 32-yr period (1961–93). The main conclusion is that the Brazilian model and two global models exhibit similar performance if the complete dataset and the entire period are taken into consideration. However, for seasonal and local variations of the Tm values, the Brazilian model was better than the other two models for most stations. The Tm values from ERA-40 present no bias, but their scatter is larger than that in the other models.

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Validation of the Water Vapor Profiles of the Raman Lidar at the Maïdo Observatory (Reunion Island) Calibrated with Global Navigation Satellite System Integrated Water Vapor

Atmosphere ◽

10.3390/atmos10110713 ◽

2019 ◽

Vol 10 (11) ◽

pp. 713 ◽

Cited By ~ 1

Author(s):

Hélène Vérèmes ◽

Guillaume Payen ◽

Philippe Keckhut ◽

Valentin Duflot ◽

Jean-Luc Baray ◽

...

Keyword(s):

Water Vapor ◽

Global Navigation Satellite System ◽

Satellite System ◽

Navigation Satellite ◽

Reunion Island ◽

Raman Lidar ◽

Réunion Island ◽

Global Navigation ◽

Global Navigation Satellite ◽

Good Agreement

The Maïdo high-altitude observatory located in Reunion Island (21 ∘ S, 55.5 ∘ E) is equipped with the Lidar1200, an innovative Raman lidar designed to measure the water vapor mixing ratio in the troposphere and the lower stratosphere, to perform long-term survey and processes studies in the vicinity of the tropopause. The calibration methodology is based on a GNSS (Global Navigation Satellite System) IWV (Integrated Water Vapor) dataset. The lidar water vapor measurements from November 2013 to October 2015 have been calibrated according to this methodology and used to evaluate the performance of the lidar. The 2-year operation shows that the calibration uncertainty using the GNSS technique is in good agreement with the calibration derived using radiosondes. During the MORGANE (Maïdo ObservatoRy Gaz and Aerosols NDACC Experiment) campaign (Reunion Island, May 2015), CFH (Cryogenic Frost point Hygrometer) radiosonde and Raman lidar profiles are compared and show good agreement up to 22 km asl; no significant biases are detected and mean differences are smaller than 9% up to 22 km asl.

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A Method to Improve the Distribution of Observations in GNSS Water Vapor Tomography

Sensors ◽

10.3390/s18082526 ◽

2018 ◽

Vol 18 (8) ◽

pp. 2526 ◽

Cited By ~ 8

Author(s):

Fei Yang ◽

Jiming Guo ◽

Junbo Shi ◽

Lv Zhou ◽

Yi Xu ◽

...

Keyword(s):

Water Vapor ◽

Precipitable Water ◽

Absolute Error ◽

Satellite System ◽

Observation Data ◽

Radiosonde Station ◽

Satellite Signal ◽

Comparative Results ◽

Global Navigation Satellite ◽

Vapor Distribution

Water vapor is an important driving factor in the related weather processes in the troposphere, and its temporal-spatial distribution and change are crucial to the formation of cloud and rainfall. Global Navigation Satellite System (GNSS) water vapor tomography, which can reconstruct the water vapor distribution using GNSS observation data, plays an increasingly important role in GNSS meteorology. In this paper, a method to improve the distribution of observations in GNSS water vapor tomography is proposed to overcome the problem of the relatively concentrated distribution of observations, enable satellite signal rays to penetrate more tomographic voxels, and improve the issue of overabundance of zero elements in a tomographic matrix. Numerical results indicate that the accuracy of the water vapor tomography is improved by the proposed method when the slant water vapor calculated by GAMIT is used as a reference. Comparative results of precipitable water vapor (PWV) and water vapor density (WVD) profiles from radiosonde station data indicate that the proposed method is superior to the conventional method in terms of the mean absolute error (MAE), standard deviations (STD), and root-mean-square error (RMS). Further discussion shows that the ill-condition of tomographic equation and the richness of data in the tomographic model need to be discussed separately.

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MONITORING OF IONOSPHERIC SCINTILLATION PHENOMENA USING SYNTHETIC APERTURE RADAR (SAR)

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-annals-iv-5-331-2018 ◽

2018 ◽

Vol IV-5 ◽

pp. 331-337

Author(s):

S. Mohanty ◽

C. Carrano ◽

G. Singh

Keyword(s):

Satellite System ◽

Synthetic Aperture ◽

Ionospheric Scintillation ◽

Electron Content ◽

Data Sets ◽

Low Frequencies ◽

Total Electron ◽

Gnss Receiver ◽

Synthetic Aperture Radars ◽

Global Navigation Satellite

Abstract. The applications of synthetic aperture radars (SAR) have increased manifold in the past decade, which includes numerous Earth observation applications such as agriculture, forestry, disaster monitoring cryospheric- and atmospheric- studies. Among them, the potential of SAR for ionospheric studies is gaining importance. The susceptibility of SAR to space weather dynamics, and ionosphere in particular, comes at low frequencies of L- and P-bands. This paper discusses one such scintillation event that was observed by L-band Advanced Land Observation Satellite (ALOS)-2 Phased Array L-type SAR (PALSAR) over southern India on March 23, 2015. The sensors also acquired data sets on four other days on which the ionosphere was quiet. Ionospheric parameter measurements of total electron content (TEC) and amplitude scintillation (S4) index from ground-based Global Navigation Satellite System (GNSS) receiver at Tirunelveli was used to establish the ionospheric conditions on the days of SAR acquisition as well as to corroborate the S4 estimated from SAR. Multi-temporal ALOS-2 data sets were utilized to calculate S4 from two separate methods and the results have a good agreement with GNSS receiver measurements. This highlights the potential of SAR as an alternate technique of monitoring ionospheric scintillations that can be utilized as complementary to the highly accurate and dedicated measurements from the GNSS networks.

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Estimación de parámetros troposféricos en tiempo casi real para Sudamérica mediante técnicas GNSS

10.35537/10915/107223 ◽

2020 ◽

Author(s):

◽

Juan Manuel Aragón Paz

Keyword(s):

Water Vapor ◽

Satellite System ◽

Tropospheric Delay ◽

Navigation Satellite ◽

Total Delay ◽

Zenith Tropospheric Delay ◽

Zenith Total Delay ◽

El Sistema ◽

Sociedad Civil ◽

Global Navigation Satellite

En el presente trabajo de tesis se desarrolla el diseño e implementación de un sistema de cálculo, en tiempo casi real, de parámetros troposféricos mediante técnicas de navegación global por satélite (GNSS, del inglés Global Navigation Satellite System) para Sudamérica. El desarrollo de la llamada Meteorología GNSS se remonta a principios de la década del 90 donde se encuentran los trabajos fundacionales de esta disciplina. Con el correr de los años, nuevas contribuciones han ido definiendo los reales alcances de esta técnica, poniendo en práctica metodologías cada vez más contrastadas con los métodos de medición tradicionales. En los últimos años los esfuerzos se han enfocado en el desarrollo de procedimientos de cálculo que permitan la utilización de los datos GNSS, cada vez más numerosos, en la asimilación para modelos meteorológicos (en especial los de corto plazo), permitiendo así anticipar eventos con alto impacto a la sociedad civil (tormentas con granizo, inundaciones repentinas, eventos convectivos de mesoescala, etc). Numerosos trabajos se han centrado en la implementación de la meteorología GNSS en Europa, Estados Unidos y Japón. Para la región Sudamericana existen pocos y recientes antecedentes de la aplicación de estas metodologías. Se desarrolló un sistema de cálculo, que permite hacer uso de infraestructura existente en la región, tanto meteorológica como geodésica, enfocado en la obtención de las variables de interés meteorológico como son el retardo troposférico cenital (ZTD, del inglés Zenith Total Delay) y el vapor de agua integrado (IWV, del inglés Integrated Water Vapor). Por otra parte, se han realizado estudios en la aplicación del ZTD y el IWV a índices que permitan dar información rápida acerca de posibles eventos meteorológicos severos. En este trabajo se desarrollan las estrategias diseñadas para la adquisición de los datos, su disponibilidad y alcance. Las problemáticas en la disponibilidad de los mismos, de acuerdo a su procedencia, son descriptas y sorteadas. Seguidamente se brinda una detallada descripción de la metodología de estimación de las observaciones, haciendo especial foco en los parámetros de retardo troposférico cenital (ZTD, del ingles Zenith Tropospheric Delay) y vapor de agua integrado (IWV, del inglés Integrated Water Vapor) mediante el procesamiento de las observaciones GNSS y meteorológicas. Una vez que se tienen los resultados, la presentación de los mismos y los posibles formato de intercambio con las instituciones potenciales usuarias del dato son discutidos. Finalizando esta sección se hace un análisis de la performance del sistema de procesamiento contra las técnicas de radio sondeo (convencionales) y alguno de los modelos de reanálisis mas utilizados. En una segunda etapa se explora las distintas capacidades del IWV GNSS para representar las variaciones temporales y espaciales de la distribución del vapor de agua atmosférico frente a distintas situaciones meteorológicas. También, se describe el desarrollo de posibles índices de alerta que hagan utilización de la información disponible a partir del IWV GNSS. Basándose en bibliografía actualizada se comparan las distintas posibilidades de aplicación a la región de estudio en función de la frecuencia temporal y espacial de las observaciones. Los resultados son presentados analizando un evento de interés meteorológico para la región central de Argentina. Finalmente, los puntos mas salientes del presente trabajo son presentados en las conclusiones. Las mismas abarcan desde el sistema de descarga de datos hasta la implementación de los índices de alerta. Se formulan las posibles derivaciones del trabajo y sus implicancias en la mejora continua de este sistema, que en tiempo casi real, provee información sobre los parámetros de ZTD e IWV. Una sección final describe cuáles son las recomendaciones que permitirían mejoras en la utilización de los datos provistos para conseguir un máximo aprovechamiento de los mismos.

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Sparse GLONASS Signal Acquisition Based on Compressive Sensing and Multiple Measurement Vectors

Mathematical Problems in Engineering ◽

10.1155/2020/9654120 ◽

2020 ◽

Vol 2020 ◽

pp. 1-11

Author(s):

Guodong He ◽

Maozhong Song ◽

Shanshan Zhang ◽

Peng Song ◽

Xinwen Shu

Keyword(s):

Compressive Sensing ◽

Satellite System ◽

New Method ◽

Signal Acquisition ◽

Multiple Measurement ◽

Multiple Measurement Vectors ◽

Navigation Data ◽

Long Time ◽

Global Navigation Satellite ◽

Sparse Features

A sparse global navigation satellite system (GLONASS) signal acquisition method based on compressive sensing and multiple measurement vectors is proposed. The nonsparse GLONASS signal can be represented sparsely on our proposed dictionary which is designed based on the signal feature. Then, the GLONASS signal is sensed by a normalized orthogonal random matrix and acquired by the improved multiple measurement vectors acquisition algorithm. There are 10 cycles of pseudorandom codes in a navigation message, and these 10 pseudorandom codes have the same row sparse structure. So, the acquisition probability can be raised by row sparse features theoretically. A large number of simulated GLONASS signal experiments show that the acquisition probability increases with the increase in the measurement vector column dimension. Finally, the practical availability of the new method is verified by acquisition experiments with the real record GLONASS signal. The new method can reduce the storage space and energy loss of data transmission. We hope that the new method can be applied to field receivers that need to record and transmit navigation data for a long time.

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