Remote Sensing

Research

22 pages, 852 KiB

Open AccessArticle

Variational Assimilation of Radio Occultation Observations into Numerical Weather Prediction Models: Equations, Strategies, and Algorithms

by Michael Gorbunov, Razvan Stefanescu, Vladimir Irisov and Dusanka Zupanski

Remote Sens. 2019, 11(24), 2886; https://doi.org/10.3390/rs11242886 - 4 Dec 2019

Cited by 9 | Viewed by 2877

Abstract

We review different approaches to the variational assimilation of radio occultation (RO) observations into models of global atmospheric circulation. We derive the general equation for the bending angle that reduces to the Abel integral for a spherically layered atmosphere. We review the full [...] Read more.

We review different approaches to the variational assimilation of radio occultation (RO) observations into models of global atmospheric circulation. We derive the general equation for the bending angle that reduces to the Abel integral for a spherically layered atmosphere. We review the full 3-D observation operator for bending angles, which provides the strictest solution, but is also most computationally expensive. Commonly used is the 2-D approximation that allows treating rays as plane curve. We discuss a simple 1-D approach to the assimilation of bending angles. The observation operator based on the standard form of the Abel integral has a disadvantage, because it cannot account for waveguides. Alternative approaches use 1-D ray-tracing. The most straightforward way is to use the same framework as for the 3-D observation operator, with the refractivity field reduced to a single profile independent from the horizontal coordinates. An alternative 1-D ray-tracing approach uses the form of ray equation in a spherically layered medium that uses an invariant. The assimilation of refractivity has also 1-D and 3-D options. We derive a new simple form of the refractivity-mapping operator. We present the results of numerical tests of different 3-D and 1-D observation operators, based on Spire data. Full article

(This article belongs to the Special Issue Selected Papers from IGL-1 2018 — First International Workshop on Innovating GNSS and LEO Occultations & Reflections for Weather, Climate and Space Weather)

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34 pages, 16441 KiB

Open AccessArticle

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

by Ying Li, Gottfried Kirchengast, Barbara Scherllin-Pirscher, Marc Schwaerz, Johannes K. Nielsen, Shu-peng Ho and Yun-bin Yuan

Remote Sens. 2019, 11(23), 2729; https://doi.org/10.3390/rs11232729 - 20 Nov 2019

Cited by 16 | Viewed by 4220

Abstract

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, [...] Read more.

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. Full article

(This article belongs to the Special Issue Selected Papers from IGL-1 2018 — First International Workshop on Innovating GNSS and LEO Occultations & Reflections for Weather, Climate and Space Weather)

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21 pages, 8495 KiB

Open AccessArticle

Comparison and Validation of the Ionospheric Climatological Morphology of FY3C/GNOS with COSMIC during the Recent Low Solar Activity Period

by Weihua Bai, Guangyuan Tan, Yueqiang Sun, Junming Xia, Cheng Cheng, Qifei Du, Xianyi Wang, Guanglin Yang, Mi Liao, Yan Liu, Xiangguang Meng, Danyang Zhao, Congliang Liu, Yuerong Cai, Dongwei Wang, Yingqiang Wang, Cong Yin, Peng Hu and Ziyan Liu

Remote Sens. 2019, 11(22), 2686; https://doi.org/10.3390/rs11222686 - 17 Nov 2019

Cited by 7 | Viewed by 2890

Abstract

With the accumulation of the ionospheric radio occultation (IRO) data observed by Global Navigation Satellite System (GNSS) occultation sounder (GNOS) onboard FengYun-3C (FY3C) satellite, it is possible to use GNOS IRO data for ionospheric climatology research. Therefore, this work aims to validate the [...] Read more.

With the accumulation of the ionospheric radio occultation (IRO) data observed by Global Navigation Satellite System (GNSS) occultation sounder (GNOS) onboard FengYun-3C (FY3C) satellite, it is possible to use GNOS IRO data for ionospheric climatology research. Therefore, this work aims to validate the feasibility of FY3C/GNOS IRO products in climatology research by comparison with that of Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC), laying the foundation for its application in climatology study. Since previous verification works of FY3C/GNOS were done by comparison with ionosondes, this work matched NmF2/hmF2 of FY3C/GNOS and COSMIC into data pairs to verify the profile-level accuracy of FY3C/GNOS IRO data. The statistical results show that the overall correlation coefficients of both NmF2 and hmF2 are above 0.9, the overall bias and std of NmF2 differences between FY3C/GNOS and COSMIC are −2.19% and 17.48%, respectively, and the bias and std of hmF2 differences are −3.29 and 18.01 km, respectively, indicating a high profile-level precision consistency between FY3C/GNOS and COSMIC. In ionospheric climatology comparison, we divided NmF2/hmF2 of FY3C/GNOS into four seasons, then presented the season median NmF2/hmF2 in 5° × 10° grids and compared them with that of COSMIC. The results show that the ionospheric climatological characteristics of FY3C/GNOS and COSMIC are highly matched, both showing the typical climatological features such as equatorial ionosphere anomaly (EIA), winter anomaly, semiannual anomaly, Weddell Sea anomaly (WSA) and so on, though minor discrepancies do exist like the differences in magnitude of longitude peak structures and WSA, which verifies the reliability of FY3C/GNOS IRO products in ionospheric climatology research. Full article

(This article belongs to the Special Issue Selected Papers from IGL-1 2018 — First International Workshop on Innovating GNSS and LEO Occultations & Reflections for Weather, Climate and Space Weather)

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19 pages, 5681 KiB

Open AccessArticle

Rain Monitoring with Polarimetric GNSS Signals: Ground-Based Experimental Research

by Hao An, Wei Yan, Shuangshuang Bian and Shuo Ma

Remote Sens. 2019, 11(19), 2293; https://doi.org/10.3390/rs11192293 - 1 Oct 2019

Cited by 2 | Viewed by 3335

Abstract

In recent years, there has been a preliminary research on monitoring rainfall information based on polarimetric Global Navigation Satellite System (GNSS) signals, which is a quite novel concept. After previous theoretical research on monitoring rain based on polarimetric phase shift of GNSS signals, [...] Read more.

In recent years, there has been a preliminary research on monitoring rainfall information based on polarimetric Global Navigation Satellite System (GNSS) signals, which is a quite novel concept. After previous theoretical research on monitoring rain based on polarimetric phase shift of GNSS signals, the paper aims to detect rain using polarimetric GNSS signals from a ground-based experiment. Firstly, a conical horn antenna specially designed for receiving dual-polarized (H, horizontal, and V, vertical) GNSS signals was developed, and an experimental system for polarimetric GNSS rain detection was built. Then, taking Global Positioning System (GPS) satellites as signal source, a ground-based experiment was carried out at a mountain in Nanjing, where heavy rain tends to occur frequently in rainy season. Additionally, a data processing algorithm mainly following Padullés et al. (2016) to solve the problems of quality control, unlocking, hardware effect, phase ambiguity, multipath effect was applied independently to this ground-based data from the polarimetric GNSS rain detection system. Also, the multi-source data from nearby weather radar and weather stations was used for verification. Results from 14 GPS satellites show that the obtained phase shift is zero in all no-rain days while it is not zero during rainy days, which is in accordance with the actual situation. Compared with weather radar and rain gauges’ data, the results verify that the phase shift is caused by rain. Besides, when individual cases are examined, many show that their tendencies of accumulated phase shift are quite similar to that of a weather station’s rainfall data, even some correlation coefficients are up to 0.99. These demonstrate the reliability of our experimental system and the feasibility of the data processing algorithm. This study will provide technical support for future spaceborne experiment, which has promising applications in global rain monitoring. Full article

(This article belongs to the Special Issue Selected Papers from IGL-1 2018 — First International Workshop on Innovating GNSS and LEO Occultations & Reflections for Weather, Climate and Space Weather)

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25 pages, 6350 KiB

Open AccessArticle

GNSS-R Soil Moisture Retrieval Based on a XGboost Machine Learning Aided Method: Performance and Validation

by Yan Jia, Shuanggen Jin, Patrizia Savi, Yun Gao, Jing Tang, Yixiang Chen and Wenmei Li

Remote Sens. 2019, 11(14), 1655; https://doi.org/10.3390/rs11141655 - 11 Jul 2019

Cited by 91 | Viewed by 7398

Abstract

Global navigation satellite system (GNSS)-reflectometry is a type of remote sensing technology and can be applied to soil moisture retrieval. Until now, various GNSS-R soil moisture retrieval methods have been reported. However, there still exist some problems due to the complexity of modeling [...] Read more.

Global navigation satellite system (GNSS)-reflectometry is a type of remote sensing technology and can be applied to soil moisture retrieval. Until now, various GNSS-R soil moisture retrieval methods have been reported. However, there still exist some problems due to the complexity of modeling and retrieval process, as well as the extreme uncertainty of the experimental environment and equipment. To investigate the behavior of bistatic GNSS-R soil moisture retrieval process, two ground-truth measurements with different soil conditions were carried out and the performance of the input variables was analyzed from the mathematical statistical aspect. Moreover, the feature of XGBoost method was utilized as well. As a recently developed ensemble machine learning method, the XGBoost method just emerged for the classification of remote sensing and geographic data, to investigate the characterization of the input variables in the GNSS-R soil moisture retrieval. It showed a good correlation with the statistical analysis of ground-truth measurements. The variable contributions for the input data can also be seen and evaluated. The study of the paper provides some experimental insights into the behavior of the GNSS-R soil moisture retrieval. It is worthwhile before establishing models and can also help with understanding the underlying GNSS-R phenomena and interpreting data. Full article

(This article belongs to the Special Issue Selected Papers from IGL-1 2018 — First International Workshop on Innovating GNSS and LEO Occultations & Reflections for Weather, Climate and Space Weather)

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18 pages, 5220 KiB

Open AccessArticle

Ionospheric Peak Parameters Retrieved from FY-3C Radio Occultation: A Statistical Comparison with Measurements from COSMIC RO and Digisondes Over the Globe

by Han Wang, Jia Luo and Xiaohua Xu

Remote Sens. 2019, 11(12), 1419; https://doi.org/10.3390/rs11121419 - 14 Jun 2019

Cited by 11 | Viewed by 3141

Abstract

In this study, two ionospheric peak parameters (ICPs), NmF2 and hmF2, derived from the global navigation satellite system (GNSS) radio occultation (RO) ionospheric electron density profiles (EDPs) obtained by Feng-Yun 3C (FY-3C) mission are compared with those derived from the observations of the [...] Read more.

In this study, two ionospheric peak parameters (ICPs), NmF2 and hmF2, derived from the global navigation satellite system (GNSS) radio occultation (RO) ionospheric electron density profiles (EDPs) obtained by Feng-Yun 3C (FY-3C) mission are compared with those derived from the observations of the Constellation Observing System for the Meteorology, Ionosphere, and Climate (COSMIC) mission and the measurements from 24 digisonde stations distributed around the world during the year from 2014 to 2017. The FY-3C derived ICPs and the COSMIC-derived ICPs are provided by the National Satellite Meteorological Centre (NSMC) and the COSMIC Data Analysis and Archive Center (CDAAC), respectively. The correlation and bias analyses are carried out in the comparison under the collocation criterion with the time interval of 1 h and the space interval of 3° in latitude and 5° in longitude. When comparing the ICPs derived from the two RO missions, the difference in the azimuth of occultation planes (DAOPs) between the matched pairs is limited to be within 20°. The comparison results are analyzed for different solar activity periods, and solar elevation angle (SEA) is taken for the first time as a factor that represents the comprehensive impacts of latitude zones, seasons, and local time of the observations. The results are shown as follows: (1) Both the COSMIC RO-derived and the digisonde-observed ICPs are in good agreement with the FY-3C RO-derived ones. The correlation coefficient (CC) between the NmF2 and hmF2 derived by COSMIC RO and FY-3C RO is 0.965 and 0.916, respectively, while the correlation coefficient between the NmF2 and hmF2 derived by digisonde and FY-3C RO is 0.924 and 0.832, respectively. The quality of FY-3C RO-derived ICPs are reliable enough for further applications. (2) The CC of NmF2 is, in general, higher than that of hmF2 when comparing FY-3C RO with other observations, and the overall MAB and MRB of FY-3C RO-derived ICPs during the higher solar activity period are higher than the ones during the lower solar activity period. The difference between the two RO missions is much smaller than that one between FY-3C RO and digisonde. (3) For a certain solar activity period, the standard deviations of the absolute bias (SDAB) and the standard deviations of the relative bias (SDRB) of FY-3C RO-derived ICPs compared with digisonde-derived ones generally increases with the increase of SEA, while the SDAB and SDRB of FY-3C RO-derived ICPs both get the minimum values for the AOP interval near to 90°. Full article

(This article belongs to the Special Issue Selected Papers from IGL-1 2018 — First International Workshop on Innovating GNSS and LEO Occultations & Reflections for Weather, Climate and Space Weather)

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26 pages, 3417 KiB

Open AccessArticle

The Two-Parts Step-by-Step Ionospheric Assimilation Based on Ground-Based/Spaceborne Observations and Its Verification

by Naifeng Fu, Peng Guo, Mengjie Wu, Yong Huang, Xiaogong Hu and Zhenjie Hong

Remote Sens. 2019, 11(10), 1172; https://doi.org/10.3390/rs11101172 - 16 May 2019

Cited by 8 | Viewed by 3902 | Correction

Abstract

This study introduced a Kalman filtering assimilation model that considers the DCB errors of GPS/LEO satellites and GNSS stations. The assimilation results and reliability were verified by various types of data, such as ionMap, ionosonde, ISR, and the EDP of ionPrf from COSMIC. [...] Read more.

This study introduced a Kalman filtering assimilation model that considers the DCB errors of GPS/LEO satellites and GNSS stations. The assimilation results and reliability were verified by various types of data, such as ionMap, ionosonde, ISR, and the EDP of ionPrf from COSMIC. The following analyses were carried out. Assimilating the measured ground-based/spaceborne ionospheric observation data from DOY 010, 2008 and DOY 089, 2012 revealed that the introduction of GPS/LEO satellite and GPS station DCB errors can effectively suppress the STEC observation errors caused by the single-layer hypothesis. Furthermore, the top of the ionosphere contributes 2.8 TECU (approximately 10–20% of the STEC) of electrons during the ionospheric quiet period, greatly influencing the ionospheric assimilation at altitudes of 100–800 km. The assimilation results also show that, after subtracting the influence of the top of the ionosphere, the ionospheric deviation during the quiet period improved from 1.645 TECU to 1.464 TECU; when the ionosphere was active, the standard deviation was improved from 4.408 TECU to 3.536 TECU. The IRI-Imp model introduced by Wu et al. and the IRI (2007) model were used as background fields to compare the effects of COSMIC occultation observation data on the ionospheric assimilation process. Upon comparison, the occultation data introduced by the improved model showed the greatest improvement in the vertical structure of the ionosphere; additionally, the assimilation process reused the horizontal structure information of the occultation data, and the assimilation result (IRI-Imp-Assi) was the most ideal. Due to the lack of an occultation data correction, the IRI2007 model was relatively more prone to errors. With the strategy of the IRI-Imp-Assi model, the introduction of occultation data caused a more significant reduction in the error between the assimilation model with the IRI model as the background field and the ionMap. Full article

(This article belongs to the Special Issue Selected Papers from IGL-1 2018 — First International Workshop on Innovating GNSS and LEO Occultations & Reflections for Weather, Climate and Space Weather)

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20 pages, 4219 KiB

Open AccessArticle

Validation of Preliminary Results of Thermal Tropopause Derived from FY-3C GNOS Data

by Ziyan Liu, Yueqiang Sun, Weihua Bai, Junming Xia, Guangyuan Tan, Cheng Cheng, Qifei Du, Xianyi Wang, Danyang Zhao, Yusen Tian, Xiangguang Meng, Congliang Liu, Yuerong Cai and Dongwei Wang

Remote Sens. 2019, 11(9), 1139; https://doi.org/10.3390/rs11091139 - 13 May 2019

Cited by 8 | Viewed by 3747

Abstract

The state-of-art global navigation satellite system (GNSS) occultation sounder (GNOS) onboard the FengYun 3 series C satellite (FY-3C) has been in operation for more than five years. The accumulation of FY-3C GNOS atmospheric data makes it ready to be used in atmosphere and [...] Read more.

The state-of-art global navigation satellite system (GNSS) occultation sounder (GNOS) onboard the FengYun 3 series C satellite (FY-3C) has been in operation for more than five years. The accumulation of FY-3C GNOS atmospheric data makes it ready to be used in atmosphere and climate research fields. This work first introduces FY-3C GNOS into tropopause research and gives the error evaluation results of long-term FY-3C atmosphere profiles. We compare FY-3C results with Constellation Observing System for Meteorology, Ionosphere and Climate (COSMIC) and radiosonde results and also present the FY-3C global seasonal tropopause patterns. The mean temperature deviation between FY-3C GNOS temperature profiles and COSMIC temperature profiles from January 2014 to December 2017 is globally less than 0.2 K, and the bias of tropopause height (TPH) and tropopause temperature (TPT) annual cycle derived from both collocated pairs are about 80–100 m and 1–2 K, respectively. Also, the correlation coefficients between FY-3C GNOS tropopause parameters and each radiosonde counterpart are generally larger than 0.9 and the corresponding regression coefficients are close to 1. Multiple climate phenomena shown in seasonal patterns coincide with results of other relevant studies. Our results demonstrate the long-term stability of FY-3C GNOS atmosphere profiles and utility of FY-3C GNOS data in the climate research field. Full article

(This article belongs to the Special Issue Selected Papers from IGL-1 2018 — First International Workshop on Innovating GNSS and LEO Occultations & Reflections for Weather, Climate and Space Weather)

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18 pages, 1693 KiB

Open AccessArticle

Evaluating Impact of Rain Attenuation on Space-borne GNSS Reflectometry Wind Speeds

by Milad Asgarimehr, Jens Wickert and Sebastian Reich

Remote Sens. 2019, 11(9), 1048; https://doi.org/10.3390/rs11091048 - 3 May 2019

Cited by 17 | Viewed by 3930

Abstract

The novel space-borne Global Navigation Satellite System Reflectometry (GNSS-R) technique has recently shown promise in monitoring the ocean state and surface wind speed with high spatial coverage and unprecedented sampling rate. The L-band signals of GNSS are structurally able to provide a higher [...] Read more.

The novel space-borne Global Navigation Satellite System Reflectometry (GNSS-R) technique has recently shown promise in monitoring the ocean state and surface wind speed with high spatial coverage and unprecedented sampling rate. The L-band signals of GNSS are structurally able to provide a higher quality of observations from areas covered by dense clouds and under intense precipitation, compared to those signals at higher frequencies from conventional ocean scatterometers. As a result, studying the inner core of cyclones and improvement of severe weather forecasting and cyclone tracking have turned into the main objectives of GNSS-R satellite missions such as Cyclone Global Navigation Satellite System (CYGNSS). Nevertheless, the rain attenuation impact on GNSS-R wind speed products is not yet well documented. Evaluating the rain attenuation effects on this technique is significant since a small change in the GNSS-R can potentially cause a considerable bias in the resultant wind products at intense wind speeds. Based on both empirical evidence and theory, wind speed is inversely proportional to derived bistatic radar cross section with a natural logarithmic relation, which introduces high condition numbers (similar to ill-posed conditions) at the inversions to high wind speeds. This paper presents an evaluation of the rain signal attenuation impact on the bistatic radar cross section and the derived wind speed. This study is conducted simulating GNSS-R delay-Doppler maps at different rain rates and reflection geometries, considering that an empirical data analysis at extreme wind intensities and rain rates is impossible due to the insufficient number of observations from these severe conditions. Finally, the study demonstrates that at a wind speed of 30 m/s and incidence angle of 30°, rain at rates of 10, 15, and 20 mm/h might cause overestimation as large as ≈0.65 m/s (2%), 1.00 m/s (3%), and 1.3 m/s (4%), respectively, which are still smaller than the CYGNSS required uncertainty threshold. The simulations are conducted in a pessimistic condition (severe continuous rainfall below the freezing height and over the entire glistening zone) and the bias is expected to be smaller in size in real environments. Full article

(This article belongs to the Special Issue Selected Papers from IGL-1 2018 — First International Workshop on Innovating GNSS and LEO Occultations & Reflections for Weather, Climate and Space Weather)

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17 pages, 5050 KiB

Open AccessArticle

Seeking Optimal GNSS Radio Occultation Constellations Using Evolutionary Algorithms

by Xiaohua Xu, Yi Han, Jia Luo, Jens Wickert and Milad Asgarimehr

Remote Sens. 2019, 11(5), 571; https://doi.org/10.3390/rs11050571 - 8 Mar 2019

Cited by 13 | Viewed by 4517

Abstract

Given the great achievements of the Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) mission in providing huge amount of GPS radio occultation (RO) data for weather forecasting, climate research, and ionosphere monitoring, further Global Navigation Satellite System (GNSS) RO missions are [...] Read more.

Given the great achievements of the Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) mission in providing huge amount of GPS radio occultation (RO) data for weather forecasting, climate research, and ionosphere monitoring, further Global Navigation Satellite System (GNSS) RO missions are being followingly planned. Higher spatial and also temporal sampling rates of RO observations, achievable with higher number of GNSS/receiver satellites or optimization of the Low Earth Orbit (LEO) constellation, are being studied by high number of researches. The objective of this study is to design GNSS RO missions which provide multi-GNSS RO events (ROEs) with the optimal performance over the globe. The navigation signals from GPS, GLONASS, BDS, Galileo, and QZSS are exploited and two constellation patterns, the 2D-lattice flower constellation (2D-LFC) and the 3D-lattice flower constellation (3D-LFC), are used to develop the LEO constellations. To be more specific, two evolutionary algorithms, including the genetic algorithm (GA) and the particle swarm optimization (PSO) algorithm, are used for searching the optimal constellation parameters. The fitness function of the evolutionary algorithms takes into account the spatio-temporal sampling rate. The optimal RO constellations are obtained for which consisting of 6–12 LEO satellites. The optimality of the LEO constellations is evaluated in terms of the number of global ROEs observed during 24 h and the coefficient value of variation (COV) representing the uniformity of the point-to-point distributions of ROEs. It is found that for a certain number of LEO satellites, the PSO algorithm generally performs better than the GA, and the optimal 2D-LFC generally outperforms the optimal 3D-LFC with respect to the uniformity of the spatial and temporal distributions of ROEs. Full article

(This article belongs to the Special Issue Selected Papers from IGL-1 2018 — First International Workshop on Innovating GNSS and LEO Occultations & Reflections for Weather, Climate and Space Weather)

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16 pages, 6303 KiB

Open AccessArticle

Estimating the Impact of Global Navigation Satellite System Horizontal Delay Gradients in Variational Data Assimilation

by Florian Zus, Jan Douša, Michal Kačmařík, Pavel Václavovic, Galina Dick and Jens Wickert

Remote Sens. 2019, 11(1), 41; https://doi.org/10.3390/rs11010041 - 28 Dec 2018

Cited by 26 | Viewed by 6641

Abstract

We developed operators to assimilate Global Navigation Satellite System (GNSS) Zenith Total Delays (ZTDs) and horizontal delay gradients into a numerical weather model. In this study we experiment with refractivity fields derived from the Global Forecast System (GFS) available with a horizontal resolution [...] Read more.

We developed operators to assimilate Global Navigation Satellite System (GNSS) Zenith Total Delays (ZTDs) and horizontal delay gradients into a numerical weather model. In this study we experiment with refractivity fields derived from the Global Forecast System (GFS) available with a horizontal resolution of 0.5°. We begin our investigations with simulated observations. In essence, we extract the tropospheric parameters from the GFS analysis, add noise to mimic observation errors and assimilate the simulated observations into the GFS 24h forecast valid at the same time. We consider three scenarios: (1) the assimilation of ZTDs (2) the assimilation of horizontal delay gradients and (3) the assimilation of both ZTDs and horizontal delay gradients. The impact is measured by utilizing the refractivity fields. We find that the assimilation of the horizontal delay gradients in addition to the ZTDs improves the refractivity field around 800 hPa. When we consider a single station there is a clear improvement when horizontal delay gradients are assimilated in addition to the ZTDs because the horizontal delay gradients contain information that is not contained in the ZTDs. On the other hand, when we consider a dense station network there is not a significant improvement when horizontal delay gradients are assimilated in addition to the ZTDs because the horizontal delay gradients do not contain information that is not already contained in the ZTDs. Finally, we replace simulated by real observations, that is, tropospheric parameters from a Precise Point Positioning solution provided with the G-Nut/Tefnut software, in order to show that the GFS 24h forecast is indeed improved when GNSS horizontal delay gradients are assimilated in addition to GNSS ZTDs; for the considered station (Potsdam, Germany) and period (June and July, 2017) we find an improvement in the retrieved refractivity of up to 4%. Full article

(This article belongs to the Special Issue Selected Papers from IGL-1 2018 — First International Workshop on Innovating GNSS and LEO Occultations & Reflections for Weather, Climate and Space Weather)

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18 pages, 10080 KiB

Open AccessArticle

Analysis of Precise Orbit Predictions for a HY-2A Satellite with Three Atmospheric Density Models Based on Dynamic Method

by Qiaoli Kong, Fan Gao, Jinyun Guo, Litao Han, Linggang Zhang and Yi Shen

Remote Sens. 2019, 11(1), 40; https://doi.org/10.3390/rs11010040 - 27 Dec 2018

Cited by 5 | Viewed by 4344

Abstract

HY-2A (Haiyang 2A) is the first altimetry satellite in China, and it was designed to be in a repeated ground track orbit to achieve the mission targets. Maneuvers are necessary to keep the satellite on the designed orbit according to the dynamic precise [...] Read more.

HY-2A (Haiyang 2A) is the first altimetry satellite in China, and it was designed to be in a repeated ground track orbit to achieve the mission targets. Maneuvers are necessary to keep the satellite on the designed orbit according to the dynamic precise orbital prediction. Atmospheric density models are essential for predicting the low Earth orbit (LEO) satellites, such as HY-2A. Nevertheless, it is a complex process to determine the optimal atmospheric density model for orbit prediction. In this paper, short-term and long-term orbit predictions based on the dynamic method using three different atmospheric density models are tested. Detailed comparisons and evaluation of the accuracy of the predicted results are performed. Furthermore, to assess the results for the ground tracking of the satellite, the interpolation method especially for a spherical surface is introduced. The results show that among the three models, the Jacchia 1971 model is in the closest agreement with Multi-Mission Ground Segment for Altimetry precise positioning and Orbitography (SSALTO) precise orbits. The root-mean-squares (RMSs) of radial orbit differences between the predicted and precise orbits are 0.016 m, 0.091 m, 0.176 m, 0.573 m, and 1.421 m for predicted 1-h, 12-h, 1-day, 3-day, and 7-day arcs, respectively. Full article

(This article belongs to the Special Issue Selected Papers from IGL-1 2018 — First International Workshop on Innovating GNSS and LEO Occultations & Reflections for Weather, Climate and Space Weather)

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18 pages, 7453 KiB

Open AccessArticle

Troposphere Water Vapour Tomography: A Horizontal Parameterised Approach

by Qingzhi Zhao, Yibin Yao and Wanqiang Yao

Remote Sens. 2018, 10(8), 1241; https://doi.org/10.3390/rs10081241 - 7 Aug 2018

Cited by 21 | Viewed by 3814

Abstract

Global Navigation Satellite System (GNSS) troposphere tomography has become one of the most cost-effective means to obtain three-dimensional (3-d) image of the tropospheric water vapour field. Traditional methods divide the tomography area into a number of 3-d voxels and assume that the water [...] Read more.

Global Navigation Satellite System (GNSS) troposphere tomography has become one of the most cost-effective means to obtain three-dimensional (3-d) image of the tropospheric water vapour field. Traditional methods divide the tomography area into a number of 3-d voxels and assume that the water vapour density at any voxel is a constant during the given period. However, such behaviour breaks the spatial continuity of water vapour density in a horizontal direction and the number of unknown parameters needing to be estimated is very large. This is the focus of the paper, which tries to reconstruct the water vapor field using the tomographic technique without imposing empirical horizontal and vertical constraints. The proposed approach introduces the layered functional model in each layer vertically and only an a priori constraint is imposed for the water vapor information at the location of the radiosonde station. The elevation angle mask of 30° is determined according to the distribution of intersections between the satellite rays and different layers, which avoids the impact of ray bending and the error in slant water vapor (SWV) at low elevation angles on the tomographic result. Additionally, an optimal weighting strategy is applied to the established tomographic model to obtain a reasonable result. The tomographic experiment is performed using Global Positioning System (GPS) data of 12 receivers derived from the Satellite Positioning Reference Station Network (SatRef) in Hong Kong. The quality of the established tomographic model is validated under different weather conditions and compared with the conventional tomography method using 31-day data, respectively. The numerical result shows that the proposed method is applicable and superior to the traditional one. Comparisons of integrated water vapour (IWV) of the proposed method with that derived from radiosonde and European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-Interim data show that the root mean square (RMS)/Bias of their differences are 3.2/−0.8 mm and 3.3/−1.7 mm, respectively, while the values of traditional method are 5.1/−3.9 mm and 6.3/−5.9 mm, respectively. Furthermore, the water vapour density profiles are also compared with radiosonde and ECMWF data, and the values of RMS/Bias error for the proposed method are 0.88/0.06 g/m³ and 0.92/−0.08 g/m³, respectively, while the values of the traditional method are 1.33/0.38 g/m³ and 1.59/0.40 g/m³, respectively. Full article

(This article belongs to the Special Issue Selected Papers from IGL-1 2018 — First International Workshop on Innovating GNSS and LEO Occultations & Reflections for Weather, Climate and Space Weather)

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Selected Papers from IGL-1 2018 — First International Workshop on Innovating GNSS and LEO Occultations & Reflections for Weather, Climate and Space Weather

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