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20 pages, 8372 KiB

Open AccessArticle

A Study on Assimilation of CYGNSS Wind Speed Data for Tropical Convection during 2018 January MJO

by Xuanli Li, John R. Mecikalski and Timothy J. Lang

Remote Sens. 2020, 12(8), 1243; https://doi.org/10.3390/rs12081243 - 14 Apr 2020

Cited by 16 | Viewed by 3351

Abstract

The National Aeronautics and Space Administration (NASA) Cyclone Global Navigation Satellite System (CYGNSS) mission was launched in December 2016. CYGNSS provides ocean surface wind speed retrieval along specular reflection tracks at an interval resolution of approximately 25 km. With a median revisit time [...] Read more.

The National Aeronautics and Space Administration (NASA) Cyclone Global Navigation Satellite System (CYGNSS) mission was launched in December 2016. CYGNSS provides ocean surface wind speed retrieval along specular reflection tracks at an interval resolution of approximately 25 km. With a median revisit time of 2.8 h covering a ±35° latitude, CYGNSS can provide more frequent and accurate measurements of surface wind over the tropical oceans under heavy precipitation, especially within tropical cyclone cores and deep convection regions, than traditional scatterometers. In this study, CYGNSS v2.1 Level 2 wind speed data were assimilated into the Weather Research and Forecasting (WRF) model using the WRF Data Assimilation (WRFDA) system with hybrid 3- and 4-dimensional variational ensemble technology. Case studies were conducted to examine the impact of the CYGNSS data on forecasts of tropical cyclone (TC) Irving and a westerly wind burst (WWB) during the Madden–Julian oscillation (MJO) event over the Indian Ocean in early January 2018. The results indicate a positive impact of the CYGNSS data on the wind field. However, the impact from the CYGNSS data decreases rapidly within 4 h after data assimilation. Also, the influence of CYGNSS data only on precipitation forecast is found to be limited. The assimilation of CYGNSS data was further explored with an additional experiment in which CYGNSS data was combined with Global Precipitation Mission (GPM) Integrated Multi-satellitE Retrievals for GPM (IMERG) hourly precipitation and Advanced Scatterometer (ASCAT) wind vector and were assimilated into the WRF model. A significant positive impact was found on the tropical cyclone intensity and track forecasts. The short-term forecast of wind and precipitation fields were also improved for both TC Irving and the WWB event when the combined satellite data was assimilated. Full article

(This article belongs to the Special Issue GPS/GNSS for Earth Science and Applications)

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24 pages, 6379 KiB

Open AccessArticle

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

by Josef Innerkofler, Gottfried Kirchengast, Marc Schwärz, Christian Pock, Adrian Jäggi, Yago Andres and Christian Marquardt

Remote Sens. 2020, 12(7), 1180; https://doi.org/10.3390/rs12071180 - 7 Apr 2020

Cited by 10 | Viewed by 5045

Abstract

Global Navigation Satellite System (GNSS) Radio Occultation (RO) is a highly valuable remote sensing technique for probing the Earth’s atmosphere, due to its global coverage, high accuracy, long-term stability, and essentially all-weather capability. In order to ensure the highest quality of essential climate [...] Read more.

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

(This article belongs to the Special Issue GPS/GNSS for Earth Science and Applications)

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18 pages, 10188 KiB

Open AccessArticle

Characterizing Extratropical Tropopause Bimodality and its Relationship to the Occurrence of Double Tropopauses Using COSMIC GPS Radio Occultation Observations

by Benjamin Johnston and Feiqin Xie

Remote Sens. 2020, 12(7), 1109; https://doi.org/10.3390/rs12071109 - 31 Mar 2020

Cited by 5 | Viewed by 2829

Abstract

Lapse rate tropopause (LRT) heights in the extratropics have been shown to display a bimodal distribution, with one modal maxima above 15 km (typical of the tropical tropopause) and the other below 13 km (typical of the extratropical tropopause). The climatology of the [...] Read more.

Lapse rate tropopause (LRT) heights in the extratropics have been shown to display a bimodal distribution, with one modal maxima above 15 km (typical of the tropical tropopause) and the other below 13 km (typical of the extratropical tropopause). The climatology of the tropopause is studied by characterizing tropopause bimodality and how it relates to the occurrence of double tropopauses (DTs). LRT heights are derived from Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) GPS Radio Occultation temperature profiles from 2006 to 2017. Tropopause bimodality occurs most frequently within a subtropical band (20°–40°) in both hemispheres. A distinct seasonality is observed as bimodality occurs most frequently in winter except for another local maximum along the northern edge of the Asian summer monsoon. The regions with a bimodal height distribution nearly overlap the regions that experience a high frequency of DTs. DTs occur most frequently in winter (50%–70% of the time) along the poleward edge of the bimodal band, and most LRT heights are within the extratropical mode (>80%), whereas DT occurrence decreases quickly toward the equatorward edge (<20%) along with fewer LRT heights in the extratropical mode (<50%). These results indicate that LRT height bimodality occurs along the equatorward edge due to the occurrences of double tropopauses, while the poleward edge is due to single tropopause profiles that are more tropical in nature. Full article

(This article belongs to the Special Issue GPS/GNSS for Earth Science and Applications)

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18 pages, 6501 KiB

Open AccessArticle

Retrieval of Ocean Wind Speed Using Super-Resolution Delay-Doppler Maps

by Hao-Yu Wang and Jyh-Ching Juang

Remote Sens. 2020, 12(6), 916; https://doi.org/10.3390/rs12060916 - 12 Mar 2020

Cited by 5 | Viewed by 4234

Abstract

The use of reflected Global Navigation Satellite System (GNSS) signals has shown to be effective for some remote sensing applications. In a GNSS Reflectometry (GNSS-R) system, a set of delay-Doppler maps (DDMs) related to scattered GNSS signals is formed and serves as a [...] Read more.

The use of reflected Global Navigation Satellite System (GNSS) signals has shown to be effective for some remote sensing applications. In a GNSS Reflectometry (GNSS-R) system, a set of delay-Doppler maps (DDMs) related to scattered GNSS signals is formed and serves as a measurement of ocean wind speed and roughness. The design of the DDM receiver involves a trade-off between computation/communication complexity and the effectiveness of data retrieval. A fine-resolution DDM reveals more information in data retrieval while consuming more resources in terms of onboard processing and downlinking. As a result, existing missions typically use a compressed or low-resolution DDM as a data product, and a high-resolution DDM is processed for special purposes such as calibration. In this paper, a deep learning, super resolution algorithm is developed to construct a high-resolution DDM based on a low-resolution DDM. This may potentially enhance the data retrieval results with no impact on the instrument design. The proposed method is applied to process the DDM products disseminated by the Cyclone GNSS (CYGNSS) and the effectiveness of wind speed retrieval is demonstrated. Full article

(This article belongs to the Special Issue GPS/GNSS for Earth Science and Applications)

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16 pages, 4780 KiB

Open AccessFeature PaperArticle

Desert Roughness Retrieval Using CYGNSS GNSS-R Data

by Donato Stilla, Mehrez Zribi, Nazzareno Pierdicca, Nicolas Baghdadi and Mireille Huc

Remote Sens. 2020, 12(4), 743; https://doi.org/10.3390/rs12040743 - 24 Feb 2020

Cited by 36 | Viewed by 4792

Abstract

The aim of this paper is to assess the potential use of data recorded by the Global Navigation Satellite System Reflectometry (GNSS-R) Cyclone Global Navigation Satellite System (CYGNSS) constellation to characterize desert surface roughness. The study is applied over the Sahara, the largest [...] Read more.

The aim of this paper is to assess the potential use of data recorded by the Global Navigation Satellite System Reflectometry (GNSS-R) Cyclone Global Navigation Satellite System (CYGNSS) constellation to characterize desert surface roughness. The study is applied over the Sahara, the largest non-polar desert in the world. This is based on a spatio-temporal analysis of variations in Cyclone Global Navigation Satellite System (CYGNSS) data, expressed as changes in reflectivity (Γ). In general, the reflectivity of each type of land surface (reliefs, dunes, etc.) encountered at the studied site is found to have a high temporal stability. A grid of CYGNSS Γ measurements has been developed, at the relatively fine resolution of 0.03° × 0.03°, and the resulting map of average reflectivity, computed over a 2.5-year period, illustrates the potential of CYGNSS data for the characterization of the main types of desert land surface (dunes, reliefs, etc.). A discussion of the relationship between aerodynamic or geometric roughness and CYGNSS reflectivity is proposed. A high correlation is observed between these roughness parameters and reflectivity. The behaviors of the GNSS-R reflectivity and the Advanced Land Observing Satellite-2 (ALOS-2) Synthetic Aperture Radar (SAR) backscattering coefficient are compared and found to be strongly correlated. An aerodynamic roughness (Z₀) map of the Sahara is proposed, using four distinct classes of terrain roughness. Full article

(This article belongs to the Special Issue GPS/GNSS for Earth Science and Applications)

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20 pages, 9937 KiB

Open AccessArticle

Evaluation of Zenith Tropospheric Delay Derived from ERA5 Data over China Using GNSS Observations

by Chunhua Jiang, Tianhe Xu, Shuaimin Wang, Wenfeng Nie and Zhangzhen Sun

Remote Sens. 2020, 12(4), 663; https://doi.org/10.3390/rs12040663 - 17 Feb 2020

Cited by 55 | Viewed by 7680

Abstract

The latest reanalysis of the European Center for Medium-Range Weather Forecasts (ECMWF), ERA5, can provide atmospheric data for calculating Zenith Tropospheric Delay (ZTD) with hourly temporal resolution, which is a key factor in Global Navigation Satellite System (GNSS) high-precision application. This paper is [...] Read more.

The latest reanalysis of the European Center for Medium-Range Weather Forecasts (ECMWF), ERA5, can provide atmospheric data for calculating Zenith Tropospheric Delay (ZTD) with hourly temporal resolution, which is a key factor in Global Navigation Satellite System (GNSS) high-precision application. This paper is aimed at evaluating the performance of ZTD derived from ERA5 reanalysis data over China using 219 GNSS stations of the Crustal Movement Observation Network of China (CMONOC) covering the period from 2015 to 2016. The site-specific hourly ZTD at these stations is obtained by integration method and Saastamoinen model method on ERA5 pressure-level and surface-level reanalysis data with the temporal resolution of 1 h and the spatial resolution of 0.25° × 0.25°. Firstly, the atmospheric temperature and pressure that derived from ERA5 are compared with temperature and pressure obtained from meteorological sensors available at 193 GNSS stations. The biases are 2.31 °C and 1.26 mbar implying the accuracy and feasibility of ERA5 pressure and temperature for calculating ZTD over China. Secondly, the performance of ERA5 ZTD is systematically evaluated using ZTD from 219 GNSS sites. The average bias and Root Mean Square (RMS) of ERA5 pressure-level ZTD at all test stations in integration method are approximately 2.97 mm and 11.49 mm respectively, and those of ERA5 surface-level ZTD in model method are 7.97 mm, 39.25 mm, which indicates that ERA5 pressure-level ZTD has a higher accuracy over China. Further analysis indicates that the accuracies of ZTD derived from ERA5 pressure-level and surface-level data are approximately 13.8% and 10.9% higher than those from of ERA-Interim pressure-level and surface-level data. Moreover, ERA5 is able to accurately capture the short-term (hourly) variation of ZTD, which further indicates the better performance of ERA5. Thirdly, the temporal and spatial variation characteristics of ERA5 ZTD accuracy are further analyzed over China. The results show that the ZTD in the southern region has the lower accuracy compared with that in the northern region over China due to the influence of latitude and altitude. Furthermore, it is found that the ERA5 ZTD over China has obvious seasonality, with higher accuracy in winter and lower accuracy in summer. Full article

(This article belongs to the Special Issue GPS/GNSS for Earth Science and Applications)

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16 pages, 5469 KiB

Open AccessArticle

A New Approach of the Global Navigation Satellite System Tomography for Any Size of GNSS Network

by Yuchen Wang, Nan Ding, Yu Zhang, Long Li, Xiaoyan Yang and Qingzhi Zhao

Remote Sens. 2020, 12(4), 617; https://doi.org/10.3390/rs12040617 - 13 Feb 2020

Cited by 7 | Viewed by 2909

Abstract

Global Navigation Satellite System (GNSS) tomography is a popular method for measuring and modelling water vapor in the troposphere. Presently, most studies use a cuboid-shaped tomographic region in their modelling, which represents the modelling region for all measurement epochs. This region is defined [...] Read more.

Global Navigation Satellite System (GNSS) tomography is a popular method for measuring and modelling water vapor in the troposphere. Presently, most studies use a cuboid-shaped tomographic region in their modelling, which represents the modelling region for all measurement epochs. This region is defined by the distribution of the GNSS signals skywards from a network of ground based GNSS stations for all epochs of measurements. However, in reality at each epoch the shape of the GNSS tomographic region is more likely to be an inverted cone. Unfortunately, this fixed conic tomographic region does not properly reflect the fact that the GNSS signal changes quickly over time. Therefore a dynamic or adaptive tomographic region is better suited. In this study, a new approach that adjusts the GNSS tomographic model to adapt the size of the GNSS network is proposed, which referred to as The High Flexibility GNSS Tomography (HFGT). Test data from different numbers of the GNSS stations are used and the results from HFGT are compared against that of radiosonde data (RS) to assess the accuracy of the HFGT approach. The results showed that the new approach is feasible for different numbers of the GNSS stations when a sufficient and uniformed distribution of GNSS signals is used. This is a novel approach for GNSS tomography. Full article

(This article belongs to the Special Issue GPS/GNSS for Earth Science and Applications)

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26 pages, 2342 KiB

Open AccessArticle

GNSS-RO Refractivity Bias Correction Under Ducting Layer Using Surface-Reflection Signal

by Kuo-Nung Wang, Chi O. Ao and Manuel de la Torre Juárez

Remote Sens. 2020, 12(3), 359; https://doi.org/10.3390/rs12030359 - 22 Jan 2020

Cited by 10 | Viewed by 3790

Abstract

Due to its high vertical resolution and cloud-penetrating capability, GNSS-Radio Occultation (RO) remote sensing technique has been utilized to observe the vertical structure of the Planetary Boundary Layer (PBL) in recent years. However, the critical refraction, or ducting, caused by large refractivity gradients [...] Read more.

Due to its high vertical resolution and cloud-penetrating capability, GNSS-Radio Occultation (RO) remote sensing technique has been utilized to observe the vertical structure of the Planetary Boundary Layer (PBL) in recent years. However, the critical refraction, or ducting, caused by large refractivity gradients usually associated with the top of the stratocumulus clouds, can negatively bias the retrieved refractivity and humidity within the PBL. Previous research has shown that combining RO retrievals and the external information, such as collocated precipitable water (PW) estimates, can effectively reduce the negative bias and enhance the retrieval quality. Nevertheless, the requirement of collocated observations from other techniques limits the applicability of this reconstruction method in practice. Here, we describe an alternative approach that uses the coherent grazing signals from the same RO event that are reflected by the Earth’s surface to remove the bias due to ducting. Additional observations are no longer necessary in this approach because the reflected signals provide the extra constraint. A least squares framework is used to select the candidate from a family of solutions wherein reflected bending angle best matches the corresponding observation. This new method was validated by both multiple phase screen (MPS) simulation and the simulated RO bending angle via forward Abel transform, and it was tested with the actual GPS-RO measurements. While, in general, the reflected bending retrieved from the current mission was noisy, the results show that this approach can potentially reduce the negative bias and improve RO observation within the PBL without assistance by the external information, such as PW. Full article

(This article belongs to the Special Issue GPS/GNSS for Earth Science and Applications)

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18 pages, 4936 KiB

Open AccessArticle

A Novel Approach for the Determination of the Height of the Tropopause from Ground-Based GNSS Observations

by Jorge Mendez Astudillo, Lawrence Lau, Yu-Ting Tang and Terry Moore

Remote Sens. 2020, 12(2), 293; https://doi.org/10.3390/rs12020293 - 16 Jan 2020

Cited by 6 | Viewed by 3487

Abstract

In this paper, we present a new method to calculate the height of the second lapse-rate tropopause (LRT2) using GNSS high-precision data. The use of GNSS data for monitoring the atmosphere is possible because as the radio signals propagate through the troposphere, they [...] Read more.

In this paper, we present a new method to calculate the height of the second lapse-rate tropopause (LRT2) using GNSS high-precision data. The use of GNSS data for monitoring the atmosphere is possible because as the radio signals propagate through the troposphere, they are delayed according to the refractive index of the path of the signal. We show that by integrating the vertical profile of the refractive index in the troposphere, we are able to determine the altitude of LTR2. Furthermore, as GNSS data is available from many stations around all latitudes of the globe and make up a network with high spatial and temporal resolution, we can monitor the diurnal cycle of the variables related to the refractive index of the path of the signal. A comparison between the heights of the LRT2 obtained with radiosonde data and with this novel method is presented in the paper, and it shows good agreement. The average difference found is ≤1 km for stations between the latitudes of 30°S and 30°N. Full article

(This article belongs to the Special Issue GPS/GNSS for Earth Science and Applications)

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35 pages, 10601 KiB

Open AccessArticle

Cross-Comparison and Methodological Improvement in GPS Tomography

by Hugues Brenot, Witold Rohm, Michal Kačmařík, Gregor Möller, André Sá, Damian Tondaś, Lukas Rapant, Riccardo Biondi, Toby Manning and Cédric Champollion

Remote Sens. 2020, 12(1), 30; https://doi.org/10.3390/rs12010030 - 19 Dec 2019

Cited by 24 | Viewed by 5007

Abstract

GPS tomography has been investigated since 2000 as an attractive tool for retrieving the 3D field of water vapour and wet refractivity. However, this observational technique still remains a challenging task that requires improvement of its methodology. This was the purpose of this [...] Read more.

GPS tomography has been investigated since 2000 as an attractive tool for retrieving the 3D field of water vapour and wet refractivity. However, this observational technique still remains a challenging task that requires improvement of its methodology. This was the purpose of this study, and for this, GPS data from the Australian Continuously Operating Research Station (CORS) network during a severe weather event were used. Sensitivity tests and statistical cross-comparisons of tomography retrievals with independent observations from radiosonde and radio-occultation profiles showed improved results using the presented methodology. The initial conditions, which were associated with different time-convergence of tomography inversion, play a critical role in GPS tomography. The best strategy can reduce the normalised root mean square (RMS) of the tomography solution by more than 3 with respect to radiosonde estimates. Data stacking and pseudo-slant observations can also significantly improve tomography retrievals with respect to non-stacked solutions. A normalised RMS improvement up to 17% in the 0–8 km layer was found by using 30 min data stacking, and RMS values were divided by 5 for all the layers by using pseudo-observations. This result was due to a better geometrical distribution of mid- and low-tropospheric parts (a 30% coverage improvement). Our study of the impact of the uncertainty of GPS observations shows that there is an interest in evaluating tomography retrievals in comparison to independent external measurements and in estimating simultaneously the quality of weather forecasts. Finally, a comparison of multi-model tomography with numerical weather prediction shows the relevant use of tomography retrievals to improving the understanding of such severe weather conditions. Full article

(This article belongs to the Special Issue GPS/GNSS for Earth Science and Applications)

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19 pages, 6117 KiB

Open AccessArticle

Benefits of a Closely-Spaced Satellite Constellation of Atmospheric Polarimetric Radio Occultation Measurements

by F. Joseph Turk, Ramon Padullés, Chi O. Ao, Manuel de la Torre Juárez, Kuo-Nung Wang, Garth W. Franklin, Stephen T. Lowe, Svetla M. Hristova-Veleva, Eric J. Fetzer, Estel Cardellach, Yi-Hung Kuo and J. David Neelin

Remote Sens. 2019, 11(20), 2399; https://doi.org/10.3390/rs11202399 - 16 Oct 2019

Cited by 11 | Viewed by 3913

Abstract

The climate and weather forecast predictive capability for precipitation intensity is limited by gaps in the understanding of basic cloud-convective processes. Currently, a better understanding of the cloud-convective process lacks observational constraints, due to the difficulty in obtaining accurate, vertically resolved pressure, temperature, [...] Read more.

The climate and weather forecast predictive capability for precipitation intensity is limited by gaps in the understanding of basic cloud-convective processes. Currently, a better understanding of the cloud-convective process lacks observational constraints, due to the difficulty in obtaining accurate, vertically resolved pressure, temperature, and water vapor structure inside and near convective clouds. This manuscript describes the potential advantages of collecting sequential radio occultation (RO) observations from a constellation of closely spaced low Earth-orbiting satellites. In this configuration, the RO tangent points tend to cluster together, such that successive RO ray paths are sampling independent air mass quantities as the ray paths lie “parallel” to one another. When the RO train orbits near a region of precipitation, there is a probability that one or more of the RO ray paths will intersect the region of heavy precipitation, and one or more would lie outside. The presence of heavy precipitation can be discerned by the use of the polarimetric RO (PRO) technique recently demonstrated by the Radio Occultations through Heavy Precipitation (ROHP) receiver onboard the Spanish PAZ spacecraft. This sampling strategy provides unique, near-simultaneous observations of the water vapor profile inside and in the environment surrounding heavy precipitation, which are not possible from current RO data. Full article

(This article belongs to the Special Issue GPS/GNSS for Earth Science and Applications)

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32 pages, 6949 KiB

Open AccessArticle

High Spatio-Temporal Resolution CYGNSS Soil Moisture Estimates Using Artificial Neural Networks

by Orhan Eroglu, Mehmet Kurum, Dylan Boyd and Ali Cafer Gurbuz

Remote Sens. 2019, 11(19), 2272; https://doi.org/10.3390/rs11192272 - 28 Sep 2019

Cited by 164 | Viewed by 7644

Abstract

This paper presents a learning-based, physics-aware soil moisture (SM) retrieval algorithm for NASA’s Cyclone Global Navigation Satellite System (CYGNSS) mission. The goal of the proposed novel method is to advance CYGNSS-based SM estimations, exploiting the spatio-temporal resolution of the GNSS reflectometry (GNSS-R) signals [...] Read more.

This paper presents a learning-based, physics-aware soil moisture (SM) retrieval algorithm for NASA’s Cyclone Global Navigation Satellite System (CYGNSS) mission. The goal of the proposed novel method is to advance CYGNSS-based SM estimations, exploiting the spatio-temporal resolution of the GNSS reflectometry (GNSS-R) signals to its highest potential within a machine learning framework. The methodology employs a fully connected Artificial Neural Network (ANN) regression model to perform SM predictions through learning the nonlinear relations of SM and other land geophysical parameters to the CYGNSS observables. In situ SM measurements from several International SM Network (ISMN) sites are used as reference labels; CYGNSS incidence angles, derived reflectivity and trailing edge slope (TES) values, as well as ancillary data, are exploited as input features for training and validation of the ANN model. In particular, the utilized ancillary data consist of normalized difference vegetation index (NDVI), vegetation water content (VWC), terrain elevation, terrain slope, and h-parameter (surface roughness). Land cover classification and inland water body masks are also used for the intermediate derivations and quality control purposes. The proposed algorithm assumes uniform SM over a 0.0833

^{\circ} \times

0.0833

^{\circ}

(approximately 9 km × 9 km around the equator) lat/lon grid for any CYGNSS observation that falls within this window. The proposed technique is capable of generating sub-daily and high-resolution SM predictions as it does not rely on time-series or spatial averaging of the CYGNSS observations. Once trained on the data from ISMN sites, the model is independent from other SM sources for retrieval. The estimation results obtained over unseen test data are promising: SM predictions with an unbiased root mean squared error of 0.0544 cm

^{3}

/cm

^{3}

and Pearson correlation coefficient of 0.9009 are reported for 2017 and 2018. Full article

(This article belongs to the Special Issue GPS/GNSS for Earth Science and Applications)

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13 pages, 5657 KiB

Open AccessArticle

Establishment and Assessment of a New GNSS Precipitable Water Vapor Interpolation Scheme Based on the GPT2w Model

by Fei Yang, Jiming Guo, Xiaolin Meng, Junbo Shi and Lv Zhou

Remote Sens. 2019, 11(9), 1127; https://doi.org/10.3390/rs11091127 - 10 May 2019

Cited by 10 | Viewed by 4349

Abstract

With the development of Global Navigation Satellite System (GNSS) reference station networks that provide rich data sources containing atmospheric information, the precipitable water vapor (PWV) retrieved from GNSS remote sensing has become one of the most important bodies of data in many meteorological [...] Read more.

With the development of Global Navigation Satellite System (GNSS) reference station networks that provide rich data sources containing atmospheric information, the precipitable water vapor (PWV) retrieved from GNSS remote sensing has become one of the most important bodies of data in many meteorological departments. GNSS stations are distributed in the form of scatters, generally, these separations range from a few kilometers to tens of kilometers. Therefore, the spatial resolution of GNSS-PWV can restrict some applications such as interferometric synthetic aperture radar (InSAR) atmospheric calibration and regional atmospheric water vapor analysis, which inevitably require the spatial interpolation of GNSS-PWV. This paper explored a PWV interpolation scheme based on the GPT2w model, which requires no meteorological data at an interpolation station and no regression analysis of the observation data. The PWV interpolation experiment was conducted in Hong Kong by different interpolation schemes, which differed in whether the impact of elevation was considered and whether the GPT2w model was added. In this paper, we adopted three skill scores, i.e., compound relative error (CRE), mean absolute error (MAE), and root mean square error (RMSE), and two approaches, i.e., station cross-validation and grid data validation, for our comparison. Numerical results showed that the interpolation schemes adding the GPT2w model could greatly improve the PWV interpolation accuracy when compared to the traditional schemes, especially at interpolation points away from the elevation range of reference stations. Moreover, this paper analyzed the PWV interpolation results under different weather conditions, at different locations, and on different days. Full article

(This article belongs to the Special Issue GPS/GNSS for Earth Science and Applications)

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8 pages, 3048 KiB

Open AccessTechnical Note

Response to Variations in River Flowrate by a Spaceborne GNSS-R River Width Estimator

by April Warnock and Christopher Ruf

Remote Sens. 2019, 11(20), 2450; https://doi.org/10.3390/rs11202450 - 22 Oct 2019

Cited by 26 | Viewed by 4641

Abstract

In recent years, the use of Global Navigation Satellite System-Reflectometry (GNSS-R) for remote sensing of the Earth’s surface has gained momentum as a means to exploit existing spaceborne microwave navigation systems for science-related applications. Here, we explore the potential for using measurements made [...] Read more.

In recent years, the use of Global Navigation Satellite System-Reflectometry (GNSS-R) for remote sensing of the Earth’s surface has gained momentum as a means to exploit existing spaceborne microwave navigation systems for science-related applications. Here, we explore the potential for using measurements made by a spaceborne GNSS-R bistatic radar system (CYGNSS) during river overpasses to estimate its width, and to use that width as a proxy for river flowrate. We present a case study utilizing CYGNSS data collected in the spring of 2019 during multiple overpasses of the Pascagoula River in southern Mississippi over a range of flowrates. Our results demonstrate that a measure of river width derived from CYGNSS is highly correlated with the observed flowrates. We show that an approximately monotonic relationship exists between river flowrate and a measure of river width which we define as the associated GNSS-R width (AGW). These results suggest the potential for GNSS-R systems to be utilized as a means to estimate river flowrates and widths from space. Full article

(This article belongs to the Special Issue GPS/GNSS for Earth Science and Applications)

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18 pages, 3596 KiB

Open AccessTechnical Note

A Real-Time On-Orbit Signal Tracking Algorithm for GNSS Surface Observations

by Scott Gleason

Remote Sens. 2019, 11(16), 1858; https://doi.org/10.3390/rs11161858 - 9 Aug 2019

Cited by 9 | Viewed by 4396

Abstract

This manuscript describes real-time on-orbit instrument compatible open loop signal tracking techniques for Global Navigation Satellite Systems (GNSS) reflection observations. All GNSS-reflection (GNSS-R) satellite instruments require algorithms which run in real-time on-board the satellite, that are capable of predicting the code phase time [...] Read more.

This manuscript describes real-time on-orbit instrument compatible open loop signal tracking techniques for Global Navigation Satellite Systems (GNSS) reflection observations. All GNSS-reflection (GNSS-R) satellite instruments require algorithms which run in real-time on-board the satellite, that are capable of predicting the code phase time delay and Doppler frequency of surface reflected signals. The algorithms presented here are for open loop tracking techniques in reflected GNSS signals for the purposed of making surface remote sensing observations. Initially, the algorithms are demonstrated using high resolution sampled data from the NASA Cyclone GNSS (CYGNSS) mission over ocean and land surfaces. Subsequently. the algorithm performance over ocean regions is analyzed in detail using a larger data set. As part of the analysis, the algorithm is assessed for its speed of convergence, to demonstrate general compatibility with spacecraft instrument processing limitations. Results indicate that over ocean regions is it possible to robustly predict in real time the Doppler frequency and code phase time delay of multiple reflected signal to sufficient precision to make science observations of the scattering surface. These algorithms are intended to provide a baseline technique and variations from which the scientific community can design more specialized algorithms for individual applications. Full article

(This article belongs to the Special Issue GPS/GNSS for Earth Science and Applications)

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