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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Atmospheric Remote Sensing".

Deadline for manuscript submissions: closed (31 March 2024) | Viewed by 6811

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Special Issue Editors

Dr. Olivier Bock

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Guest Editor

IPGP, IGN, Université Paris Cité, 75013 Paris, France
Interests: GNSS meteorology and climatology; remote sensing; atmospheric water cycle; homogenization

Dr. Galina Dick

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Guest Editor

Department Geodesy, Helmholtz Centre Potsdam, GFZ German Research Centre for Geosciences, Telegrafenberg A17, D-14473 Potsdam, Germany
Interests: GNSS data processing; GNSS meteorology and climatology; atmospheric remote sensing; precise positioning

Dr. Florian Zus

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Guest Editor

Department Geodesy, Helmholtz Centre Potsdam, GFZ German Research Centre for Geosciences, Telegrafenberg A17, D-14473 Potsdam, Germany
Interests: GNSS; precise positioning; atmospheric remote sensing; reflectometry; GNSS-RO

Dr. Tong Ning

E-Mail Website
Guest Editor

Lantmäteriet—The Swedish Mapping, Cadastral and Land Registration Authority, Gävle, Sweden
Interests: GNSS data processing; GNSS meteorology; climate monitoring; modeling; homogenization

Special Issue Information

Dear Colleagues,

The Global Navigation Satellite System (GNSS) has become a key remote sensing technique of the global meteorological and climate observing systems over the past two decades. However, the timeliness and accuracy requirements from ground-based networks and low orbiting satellites for meteorology and climate monitoring are very different. On the one hand, GNSS tropospheric products derived from real-time and near real-time processing of GNSS observations are essential to improve the nowcasting and forecasting of severe weather events such as heavy precipitation and flash floods. On the other hand, the long time series of reprocessed GNSS observations have a high potential for monitoring trends and variability in atmospheric temperature and humidity and validating climate model simulations and reanalyses. This Special Issue mainly focuses on papers that address topics including but not limited to:

Advances in GNSS data processing techniques for meteorology and climate monitoring (including real-time, near real-time, and post-processing).
The study of GNSS data processing models and parametrizations (e.g., antenna models, tropospheric models) on the accuracy and homogeneity of long reprocessed time series.
Assessment of accuracy and homogeneity of existing reprocessed GNSS data sets (e.g., IGS repro3) for use in climate studies.
Advances in the homogenization of long-term GNSS data sets (e.g., statistical methods for change-point detection).
Use of GNSS integrated water vapour (IWV) data for the validation of other remote sensing techniques (e.g., from satellites).
Use of GNSS tropospheric temperature and humidity products for the validation of climate model simulations and reanalyses.
Data science/machine learning for GNSS remote sensing.

Dr. Olivier Bock
Dr. Galina Dick
Dr. Florian Zus
Dr. Tong Ning
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

weather nowcasting and forecasting
severe weather
climate monitoring
trends and variability
GNSS data processing and reprocessing
GNSS radio occultation and reflectometry
homogenization
integrated water vapour
machine learning

Published Papers (5 papers)

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Research

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22 pages, 4339 KiB

Open AccessArticle

The Novel Copernicus Global Dataset of Atmospheric Total Water Vapour Content with Related Uncertainties from GNSS Observations

by Kalev Rannat, Hannes Keernik and Fabio Madonna

Remote Sens. 2023, 15(21), 5150; https://doi.org/10.3390/rs15215150 - 27 Oct 2023

Viewed by 727

Abstract

A novel algorithm has been designed and implemented in the Climate Data Store (CDS) frame of the Copernicus Climate Change Service (C3S) with the main goal of providing high-quality GNSS-based integrated water vapour (IWV) datasets for climate research and applications. For this purpose, the related CDS GNSS datasets were primarily obtained from GNSS reprocessing campaigns, given their highest quality in adjusting systematic effects due to changes in instrumentation and data processing. The algorithm is currently applied to the International GNSS Service (IGS) tropospheric products, which are consistently extended in near real-time and date back to 2000, and to the results of a reprocessing campaign conducted by the EUREF Permanent GNSS Network (EPN repro2), covering the period from 1996 to 2014. The GNSS IWV retrieval employs ancillary meteorological data sourced from ERA5. Moreover, IWV estimates are provided with associated uncertainty, using an approach similar to that used for the Global Climate Observing System Reference Upper-Air Network (GRUAN) GNSS data product. To assess the quality of the newly introduced GNSS IWV datasets, a comparison is made against the radiosonde data from GRUAN and the Radiosounding HARMonization (RHARM) dataset as well as with the IGS repro3, which will be the next GNSS-based extension of IWV time series at CDS. The comparison indicates that the average difference in IWV among the reprocessed GNSS datasets is less than 0.1 mm. Compared to RHARM and GRUAN IWV values, a small dry bias of less than 1 mm for the GNSS IWV is detected. Additionally, the study compares GNSS IWV trends with the corresponding values derived from RHARM at selected radiosonde sites with more than ten years of data. The trends are mostly statistically significant and in good agreement. Full article

(This article belongs to the Special Issue GNSS in Meteorology and Climatology)

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24 pages, 8483 KiB

Open AccessArticle

Machine Learning-Based Estimation of Hourly GNSS Precipitable Water Vapour

by Zohreh Adavi, Babak Ghassemi, Robert Weber and Natalia Hanna

Remote Sens. 2023, 15(18), 4551; https://doi.org/10.3390/rs15184551 - 15 Sep 2023

Viewed by 928

Abstract

Water vapour plays a key role in long-term climate studies and short-term weather forecasting. Therefore, to understand atmospheric variations, it is crucial to observe water vapour and its spatial distribution. In the current era, Global Navigation Satellite Systems (GNSS) are widely used to monitor this critical atmospheric component because GNSS signals pass through the atmosphere, allowing us to estimate water vapour at various locations and times. The amount of precipitable water vapour (

P W V

) is one of the most fascinating quantities, which provides meteorologists and climate scientists with valuable information. However, calculating

P W V

accurately from processing GNSS observations usually requires the input of further observed meteorological parameters with adequate quality and latency. To bypass this problem, hourly

P W V

s without meteorological parameters are computed using the Random Forest and Artificial Neural Network algorithms in this research. The first step towards this objective is establishing a regional weighted mean temperature model for Austria. To achieve this, measurements of radiosondes launched from different locations in Austria are employed. The results indicate that Random Forest is the most accurate method compared to regression (linear and polynomial), Artificial Neural Network, and empirical methods.

P W V

models are then developed using data from 39 GNSS stations that cover Austria’s entire territory. The models are afterwards tested under different atmospheric conditions with four radiosonde stations. Based on the obtained results, the Artificial Neural Network model with a single hidden layer slightly outperforms other investigated models, with only a 5% difference in mean absolute error. As a result, the hourly

P W V

can be estimated without relying on measured meteorological parameters with an average mean absolute error of less than 2.5 mm in Austria. Full article

(This article belongs to the Special Issue GNSS in Meteorology and Climatology)

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24 pages, 8065 KiB

Open AccessEditor’s ChoiceArticle

Processing and Validation of the STAR COSMIC-2 Temperature and Water Vapor Profiles in the Neutral Atmosphere

by Shu-peng Ho, Stanislav Kireev, Xi Shao, Xinjia Zhou and Xin Jing

Remote Sens. 2022, 14(21), 5588; https://doi.org/10.3390/rs14215588 - 05 Nov 2022

Cited by 5 | Viewed by 1810

Abstract

The global navigation satellite system (GNSS) radio occultation (RO) is becoming an essential component of National Oceanic and Atmospheric Administration (NOAA) observation systems. The constellation observing system for meteorology, ionosphere, and climate (COSMIC) 2 mission and the Formosa satellite mission 7, a COSMIC follow-on mission, is now the NOAA’s backbone RO mission. The NOAA’s dedicated GNSS RO SAtellite processing and science Application Center (RO-SAAC) was established at the Center for Satellite Applications and Research (STAR). To better quantify how the observation uncertainty from clock error and geometry determination may propagate to bending angle and refractivity profiles, STAR has developed the GNSS RO data processing and validation system. This study describes the COSMIC-2 neutral atmospheric temperature and moisture profile inversion algorithms at STAR. We used RS41 and ERA5, and UCAR 1D-Var products (wetPrf2) to validate the accuracy and uncertainty of the STAR 1D-Var thermal profiles. The STAR-RS41 temperature differences are less than a few tenths of 1 K from 8 km to 30 km altitude with a standard deviation (std) of 1.5–2 K. The mean STAR-RS41 water vapor specific humidity difference and the standard deviation are −0.35 g/kg and 1.2 g/kg, respectively. We also used the 1D-Var-derived temperature and water vapor profiles to compute the simulated brightness temperature (BTs) for advanced technology microwave sounder (ATMS) and cross-track infrared sounder (CrIS) channels and compared them to the collocated ATMS and CrIS measurements. The BT differences of STAR COSMIC-2-simulated BTs relative to SNPP ATMS are less than 0.1 K over all ATMS channels. Full article

(This article belongs to the Special Issue GNSS in Meteorology and Climatology)

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22 pages, 6531 KiB

Open AccessArticle

Comparison of Weighted Mean Temperature in Greenland Calculated by Four Reanalysis Data

by Chengcheng Luo, Feng Xiao, Li Gong, Jintao Lei, Wenhao Li and Shengkai Zhang

Remote Sens. 2022, 14(21), 5431; https://doi.org/10.3390/rs14215431 - 28 Oct 2022

Cited by 1 | Viewed by 1163

Abstract

The weighted mean temperature (

T_{m}

) is a critical parameter for precipitable water vapor (PWV) retrieval in global navigation satellite system (GNSS) meteorology. Reanalysis data are an important data source for

T_{m}

calculation and

T_{m}

empirical model establishment. [...] Read more.

The weighted mean temperature (

T_{m}

) is a critical parameter for precipitable water vapor (PWV) retrieval in global navigation satellite system (GNSS) meteorology. Reanalysis data are an important data source for

T_{m}

calculation and

T_{m}

empirical model establishment. This study uses radiosonde data to evaluate the accuracy and the spatiotemporal variation of

T_{m}

that is derived from four reanalysis data, namely, the release of the fifth-generation accurate global atmospheric reanalysis (ERA5), the modern-era retrospective analysis for research and applications version 2 (MERRA-2), the NCEP/DOE, and the NCEP/NCAR, from 2005 to 2019 in Greenland, due to the paucity of research on the performance of

T_{m}

in the polar region that is derived from reanalysis data, particularly on a long temporal scale. The results were as follows: (1) The 15-year mean bias errors (MBEs) and root mean square errors (RMSEs) of

T_{m}

that were obtained from the four reanalysis data are 0.267 and 0.691 K for the ERA5, −0.247 and 0.962 K for the MERRA-2, 0.192 and 1.148 K for the NCEP/DOE, and −0.069 and 1.37 K for the NCEP/NCAR. The

T_{m}

that was derived from the ERA5 (ERA5

T_{m}

) has the highest accuracy, followed by the MERRA-2

T_{m}

, the NCEP/DOE

T_{m}

, and the NCEP/NCAR

T_{m}

. (2) In the inter-annual stability of the

T_{m}

precision compared with the radiosonde data, the results of the ERA5 are the most stable, followed by the NCEP/DOE

T_{m}

, the NCEP/NCAR

T_{m}

, and the MERRA-2

T_{m}

. The ERA5

T_{m}

have improved from 2005 to 2019. (3) The

T_{m}

accuracy that was computed by the four reanalysis data exhibits significant seasonal variation characteristics in Greenland, as follows: the summer and the autumn accuracy is higher than that in the winter and the spring, which may be related to the variation of the surface temperature (Ts) accuracy. (4) The

T_{m}

that was estimated from the four reanalysis data exhibits a consistent spatial distribution, as follows: the

T_{m}

is smaller in the middle region of Greenland and is greater at the island’s edge. The comparative study of

T_{m}

that is obtained from the four reanalysis data can serve as a reference for future research on

T_{m}

model development and water vapor retrieval in polar regions by utilizing reanalysis data. Full article

(This article belongs to the Special Issue GNSS in Meteorology and Climatology)

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15 pages, 8720 KiB

Open AccessTechnical Note

Fast Observation Operator for Global Navigation Satellite System Tropospheric Gradients

by Florian Zus, Rohith Thundathil, Galina Dick and Jens Wickert

Remote Sens. 2023, 15(21), 5114; https://doi.org/10.3390/rs15215114 - 26 Oct 2023

Viewed by 868

Abstract

From the raw measurements at a single Global Navigation Satellite System (GNSS) ground-based station, the Zenith Total Delay (ZTD) and the tropospheric gradient can be estimated. In order to assimilate such data into Numerical Weather Prediction (NWP) models, the observation operator must be developed. Our previously developed tropospheric gradient operator is based on a linear combination of tropospheric delays and, therefore, is difficult to implement into NWP Data Assimilation (DA) systems. In this technical note, we develop a fast observation operator. This observation operator is based on an integral expression which contains the north–south and east–west horizontal gradients of refractivity. We run a numerical weather model (the horizontal resolution is 10 km) and show that for stations located in central Europe and in the warm season, the root-mean-square deviation between the tropospheric gradients calculated by the fast and original approach is about 0.15 mm. This deviation is regarded acceptable for assimilation since the typical root-mean-square deviation between observed and forward modelled tropospheric gradients is about 0.5 mm. We then implement the developed operator in our experimental DA system and test the proposed approach. In particular, we analyze the impact of the assimilation on the refractivity field. The developed tropospheric gradient operator, together with its tangent linear and adjoint version, is freely available (Fortran code) and ready to be implemented into NWP DA systems. Full article

(This article belongs to the Special Issue GNSS in Meteorology and Climatology)

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