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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

A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (31 October 2019) | Viewed by 58794

Special Issue Editors

Prof. Dr. Gottfried Kirchengast

E-Mail Website
Guest Editor
Wegener Center for Climate and Global Change (WEGC) and Institute of Physics, University of Graz, Graz, Austria
Interests: atmospheric remote sensing; GNSS radio occultation; LEO-LEO occultation methods (microwave, infrared-laser); climate and global change; GNSS remote sensing data applications
Prof. Dr. Jens Wickert

E-Mail Website
Guest Editor
German Research Centre for Geosciences GFZ Potsdam, and Technische Universität Berlin, Berlin, Germany
Interests: atmospheric and marine remote sensing; GNSS radio occultation; GNSS meteorology; GNSS reflectometry; GNSS remote sensing data applications
Prof. Dr. Kefei Zhang

E-Mail Website
Guest Editor
School of Science, RMIT University, Melbourne, Australia
Interests: GPS/GNSS; geodesy; atmospheric modelling; radio occultation; indoor positioning and tracking; space situational awareness; satellite orbit determination; space weather and severe weather
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Yueqiang Sun

E-Mail
Guest Editor
National Space Science Center, Chinese Academy of Sciences, and University of Chinese Academy of Sciences, Beijing, China
Interests: atmospheric and marine remote sensing; GNSS radio occultation; GNSS reflectometry; GNSS remote sensing data applications
Dr. Congliang Liu

E-Mail
Guest Editor
National Space Science Center, Chinese Academy of Sciences, Beijing, China
Interests: atmospheric remote sensing; GNSS radio occultation; LEO-LEO occultation methods (microwave); GNSS remote sensing data applications
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The IGL-1 2018 workshop aims to provide a platform of scientific exchange and communication for scholars, researchers and engineers related to the science, engineering, and technology of GNSS RO, GNSS-R, and LEO-LEO occultation and reflections and their applications in weather, climate and space weather. The scientific discussions among the diverse members from the space-, air- and ground-based atmospheric sounding background and the weather, climate, and space weather communities will play a crucial role in maximizing the scientific benefits provided by the past, present, and future GNSS RO, GNSS-R and LEO-LEO missions and promoting science and technology innovations. This is of great importance for a better preparedness for new space-borne atmospheric sounding related missions. A dialogue between the data providers and the data users in the weather, climate and space weather fields is equally important to ensure optimal provision and use of these data for both research and operational applications.

We are very pleased to invite you to the “First International Workshop on Innovating GNSS and LEO Occultations & Reflections for Weather, Climate and Space Weather” (IGL-1 2018) and we encourage all the participants to be part of this special issue. Apart from the publication of extended abstracts of the workshop, selected papers will be invited for formal publication in this Special Issue of the journal “Remote Sensing”. For more information please refer to the website: http://igl2018.csp.escience.cn

This Special Issue is calling for papers reporting newest advances and scientific results of GNSS remote sensing techniques using refracted and reflected signals (e.g., GNSS RO, GNSS-R) as well as of LEO-LEO occultation techniques (e.g., microwave and infrared-laser occultation), applied from space-/air-/ground-based platforms. New results, scientific experiments and innovative applications from current and planned space-based missions and air-/ground-based demonstrations are also encouraged to present at the workshop. Specifically, topics of interest for this Special Issue include (but are not limited to):

  • GNSS RO and GNSS-R methodologies (fundamentals, mathematical-physical basis, atmospheric and ionospheric signal propagation influences)
  • Precise orbit determination, raw data processing (excess phase profiles, delay Doppler maps), and retrieval techniques (algorithm advances, validation studies)
  • Applications of GNSS atmospheric sounding in atmospheric physics, meteorology, and numerical weather prediction
  • GNSS atmospheric sounding for climate monitoring and its related research and applications
  • GNSS atmospheric sounding for ionosphere and space weather and space physics related research and applications
  • Applications of GNSS-R data in oceanic, meteorological, and climate research
  • New GNSS systems (BDS, Galileo, QZSS) and their application status in GNSS RO and GNSS-R science and applications
  • LEO-LEO occultation methods (microwave, infrared-laser), science innovations, mission pre-developments, and steps towards LEO reflectometry
  • Progress in GNSS RO, GNSS-R and LEO-LEO instrument related technologies
  • Future GNSS RO, GNSS-R, and LEO-LEO occultation and reflection missions
  • Data fusion of ground-/air-/space-based atmospheric sounding techniques and its new applications in weather, climate, and other areas

Prof. Dr. Gottfried Kirchengast
Prof. Dr. Jens Wickert
Prof. Dr. Kefei Zhang
Prof. Dr. Yueqiang Sun
Assoc. Prof. Dr. Congliang Liu
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.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

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.

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (13 papers)

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Research

22 pages, 852 KiB  
Article
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 10 | Viewed by 2995
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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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Geometry of ray propagation in the presence of a waveguide. Impact parameter <math display="inline"><semantics> <msub> <mi>p</mi> <mn>1</mn> </msub> </semantics></math> corresponds to two rays: the first one is trapped inside the waveguide and cannot be observed from the space; the second one has a perigee above the waveguide. Impact parameter <math display="inline"><semantics> <msub> <mi>p</mi> <mn>2</mn> </msub> </semantics></math> corresponds to a single ray with a perigee below the waveguide. Impact parameter <math display="inline"><semantics> <msub> <mi>p</mi> <mn>0</mn> </msub> </semantics></math> corresponds to a limiting case, where there are three rays. Both the ray inside the waveguide and the ray above the waveguide asymptotically approach its upper border. The third ray slides along the upper border of the waveguide.</p> Full article ">Figure 2
<p>Average relative differences of Spire BA observations vs. different observation operators applied to GFS forecasts for the whole globe, high latitudes, middle latitudes, and tropics.</p> Full article ">Figure 3
<p>Standard deviations of relative differences of Spire BA observations vs. different observation operators applied to GFS forecasts for the whole globe, high latitudes, middle latitudes, and tropics.</p> Full article ">
34 pages, 16441 KiB  
Article
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 17 | Viewed by 4481
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)
►▼ Show Figures

Figure 1

Figure 1
<p>Schematic illustration of the algorithmic steps of the new moist air retrieval algorithm, for description see <a href="#sec2-remotesensing-11-02729" class="html-sec">Section 2</a>.</p> Full article ">Figure 2
<p>Mean profiles and uncertainties of the four input variables, i.e., observed dry temperature (first row), observed dry pressure (second row), background temperature (third row), and background specific humidity (fourth row) on 15 July 2008. For the input observed profiles (first row and second row), which the OPSv5.6 and the dynamic approach share the same uncertainties, the left, middle, and right panels show for the observed mean profile, uncertainty profile as a function of altitude, and the uncertainty profile as a function of altitude and latitude, respectively. For the background profiles (third and fourth row), the left, middle, and right panels show for the background mean profile as a function of altitude, global mean static uncertainty profile of OPSv5.6 approach as a function of altitude, and dynamic background uncertainty as a function of altitude and latitude, respectively.</p> Full article ">Figure 3
<p>Illustration of input, intermediate, and result variables relevant towards the estimation of optimal temperature (top row), specific humidity (middle row), and pressure (bottom row) of an exemplary simMetOp event (identified on top), for the dynamic approach. In the top row, the left panel shows temperature profiles for background temperature (blue), temperature calculated with specific humidity prescribed (green), and the optimally estimated temperature (red). The middle panel shows the estimated uncertainty profiles for the three profiles shown left and for the observed dry temperature (black) as well as the uncertainty of the background temperature from the OPSv5.6 approach (dashed blue). The right panel shows the differences between the temperature profiles shown left and the reference profiles, where the references profiles are the ECMWF co-located analysis profiles. In the three panels of the middle row, the same type of variables is shown as in the upper row, but for specific humidity <span class="html-italic">q</span>; thus the intermediate variable here is specific humidity with temperature prescribed (subscript “T”) and there is no dedicated input uncertainty profile in the middle panel (such as <span class="html-italic">u</span><sub>Td</sub> in the upper row). Similarly, the bottom row shows the corresponding variables for pressure, whereby here the intermediate pressures from both humidity prescribed (subscript “q”) and temperature prescribed (subscript “T”) are shown together with the optimally estimated pressure (subscript “e”), and the middle panel also illustrates the input uncertainty profile of the dry pressure <span class="html-italic">p</span><sub>d</sub>.</p> Full article ">Figure 4
<p>Illustration of input, intermediate, and result variables relevant towards the estimation of optimal temperature (top row), specific humidity (middle row), and pressure (bottom row) of an exemplary COSMIC event (identified on top), for the dynamic approach. Figure format and style are the same as for <a href="#remotesensing-11-02729-f003" class="html-fig">Figure 3</a>; see that caption for explanation.</p> Full article ">Figure 5
<p>Difference profiles between RO-retrieved temperature (left column), specific humidity (middle column), and pressure profiles (right column) and their corresponding ECMWF co-located analysis profiles, for three exemplary events (identified on top of each row) from simMetOp (top row), CHAMP (middle row), and COSMIC (bottom row), respectively. The results for the dynamic (red), OPSv5.6 (black), CDAAC (green), and ROM-SAF (blue) approaches are shown.</p> Full article ">Figure 6
<p>Number of RO profiles from simMetOp (left), CHAMP (middle), and COSMIC (right) as function of altitude for the global domain (top row) and five latitudinal bands (bottom row), including TRO (tropics; 20°S to 20°N), NHP (northern hemisphere polar; 60°N to 90°N), SHP (southern hemisphere polar; 60°S to 90°S), NHSM (northern hemisphere subtropics and mid-latitudes, 20°N to 60°N), and SHSM (southern hemisphere subtropics and mid-latitudes, 20°S to 60°S), on 15 July 2008 for simMetOp and COSMIC and on 14-16 July 2008 for CHAMP. The red, black, green, and blue colors denote the dynamic, OPSv5.6, CDAAC, and ROM-SAF approaches, respectively, with the dynamic one plotted last (hence shadowing other colors above the lower to middle troposphere) and the profiles for different latitude bands denoted by distinct symbols (see legend).</p> Full article ">Figure 7
<p>Systematic differences (SysDiff) and standard deviations (StDev) of retrieved temperature (left column), specific humidity (middle column), and pressure (right column), relative to ECMWF co-located analysis profiles as reference, of the ensemble of simMetOp events on 15 July 2008. Statistics for both the dynamic (red) and OPSv5.6 (black) approach are shown for four representative regions (top to bottom: Global, TRO, NHP, SHP). The propagated uncertainties of retrieved profiles from the dynamic approach (UncertDyn; red-dashed) are shown as well.</p> Full article ">Figure 8
<p>Systematic differences (SysDiff) and standard deviations (StDev) of retrieved temperature (left column), specific humidity (middle column), and pressure (right column), relative to ECMWF co-located analysis profiles as reference, of the ensemble of CHAMP events on 14-16 July 2008. Statistics for the dynamic (red), OPSv5.6 (black), CDAAC (green), and ROM-SAF (blue) approach are shown for four representative regions (top to bottom: Global, TRO, NHP, SHP). The propagated uncertainties of retrieved profiles from the dynamic approach (UncertDyn; red-dashed) are shown as well.</p> Full article ">Figure 9
<p>Systematic differences (SysDiff) and standard deviations (StDev) of retrieved temperature (left column), specific humidity (middle column), and pressure (right column), relative to ECMWF co-located analysis profiles as reference, of the ensemble of COSMIC events on 15 July 2008. Statistics for the dynamic (red), OPSv5.6 (black), CDAAC (green), and ROM-SAF (blue) approach are shown for four representative regions (top to bottom: Global, TRO, NHP, SHP). The propagated uncertainties of retrieved profiles from the dynamic approach (UncertDyn; red-dashed) are shown as well.</p> Full article ">Figure 10
<p>Observation-to-background weighting ratio profiles for temperature (six panels in upper two rows) and specific humidity (six panels in lower two rows), for the COSMIC data ensemble of 15 July 2008, are shown for the global ensemble (Global) and the five latitudinal bands TRO, SHSM, NHSM, SHP, and NHP (identified in the panel titles). The results for the dynamic, OPSv5.6 and ROM-SAF approaches are all shown.</p> Full article ">Figure A1
<p>Differences between RO retrieved profiles and ECMWF co-located analysis profiles obtained from using the bias-corrected background profiles (red, “Bias Corr”) and from using the original background profiles (black, “No Bias Corr”) profiles for three exemplary RO events from simMetOp (upper), CHAMP (middle), and COSMIC (bottom) from 15 July 2008.</p> Full article ">Figure A2
<p>Systematic differences and standard deviations of moist temperature and specific humidity for simulated MetOp (left), CHAMP (middle), and COSMIC (right) events in the global domain (upper two rows) and TRO regions (bottom two rows). Statistics are shown for the bias-corrected case (Bias Corr) and the no bias corrected case (No Bias Corr).</p> Full article ">Figure A3
<p>Correlation matrices (left), exemplary correlation functions at three exemplary altitude levels of 11 km, 7 km, and 3 km (middle), and estimated correlation length for correlation functions (right) for the observed dry temperature uncertainty (first row), observed dry pressure uncertainty (second row), background temperature uncertainty (third row), and background specific humidity uncertainty (fourth row). The correlation matrices are shown for 15th July 2008 only, and the correlation functions and correlation lengths are shown for 5th, 15th, and 25th of July 2008.</p> Full article ">
21 pages, 8495 KiB  
Article
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 8 | Viewed by 3050
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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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Kp variations during 2016.035–2017.035. The red line represents Kp = 4, namely the critical Kp value of a magnetic storm.</p> Full article ">Figure 2
<p>Linear fit and statistical error distribution of NmF2 between FengYun-3C (FY3C)/Global Navigation Satellite System occultation sounder (GNOS) and Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) over a whole day during 2016.035 and 2017.035: (<b>a</b>) the linear fit of NmF2 between FY3C/GNOS (horizontal axis) and COSMIC (vertical axis) over a whole day, in which the blue dots represent the matched data pairs of NmF2 and the red line depicts the linear regression fit of NmF2 data pairs; (<b>b</b>) the number (vertical axis) of NmF2 relative errors (horizontal axis) between FY3C/GNOS and COSMIC, in which the red curve represents the distribution fit of the relative errors. The correlation coefficient of NmF2 data pairs, bias, and std of relative errors of matched NmF2 data pairs are also labelled in the right side of (<b>b</b>).</p> Full article ">Figure 3
<p>Linear fits of NmF2 between FY3C/GNOS and COSMIC at daytime and nighttime during 2016.035 and 2017.035: (<b>a</b>) the linear fit of NmF2 between FY3C/GNOS (horizontal axis) and COSMIC (vertical axis) at 0600–1800LT, corresponding to daytime; (<b>b</b>) the linear fit of NmF2 at 1800–0600LT, corresponding to nighttime. The correlation coefficient of NmF2 data pairs, the bias, and std of relative errors of matched NmF2 data pairs at daytime and nighttime are presented in the bottom right corner of (<b>a</b>) and (<b>b</b>), respectively.</p> Full article ">Figure 4
<p>Linear fit and statistical error distribution of hmF2 between FY3C/GNOS and COSMIC over a whole day during 2016.035 and 2017.035: (<b>a</b>) the linear fit of hmF2 between FY3C/GNOS (horizontal axis) and COSMIC (vertical axis) over a whole day, in which the blue dots represent the matched data pairs of hmF2 and the red line depicts the linear regression fit of hmF2 data pairs; (<b>b</b>) the number (vertical axis) of hmF2 absolute errors (horizontal axis) between FY3C/GNOS and COSMIC, in which the red curve represents the distribution fit of the absolute errors. The correlation coefficient of hmF2 data pairs, bias, and std of absolute errors of matched hmF2 data pairs are also labelled in the right side of (<b>b</b>).</p> Full article ">Figure 5
<p>Linear fits of hmF2 between FY3C/GNOS and COSMIC at daytime and nighttime during 2016.035 and 2017.035: (<b>a</b>) the linear fit of hmF2 between FY3C/GNOS (horizontal axis) and COSMIC (vertical axis) at 0600–1800LT, corresponding to daytime; (<b>b</b>) the linear fit of hmF2 at 1800–0600LT, corresponding to nighttime. The correlation coefficient of hmF2 data pairs, the bias, and std of absolute errors of matched hmF2 data pairs at daytime and nighttime are presented in the bottom right corner of (<b>a</b>) and (<b>b</b>), respectively.</p> Full article ">Figure 6
<p>Ionospheric climatological characteristics of season median NmF2 observed by FY3C/GNOS and COSMIC and their differences at 0800–1100LT and 2000–2300LT during ME-month (±45 days to March equinox of 2016) from 2016.035 to 2016.125. (<b>a</b>,<b>b</b>) The climatological characteristics of season median NmF2 observed by FY3C/GNOS at 0800–1100LT and 2000–2300LT, respectively. (<b>c</b>,<b>d</b>) The climatological characteristics of season median NmF2 probed by COSMIC at 0800–1100LT and 2000–2300LT, respectively. The dip contours in (a–d) are represented by white curves, which are 60° dip, 0° dip, −60° dip from top to bottom, respectively. (<b>e</b>,<b>f</b>) The relative errors of season median NmF2 between FY3C/GNOS and COSMIC at 0800–1100LT and 2000–2300LT, respectively. The acquisition of NmF2 relative error can be seen in <a href="#sec2dot2-remotesensing-11-02686" class="html-sec">Section 2.2</a>.</p> Full article ">Figure 7
<p>Ionospheric climatological characteristics of season median NmF2 observed by FY3C/GNOS and COSMIC and their differences at 0800–1100LT and 2000–2300LT during JS-month (±45 days to June solstice of 2016) from 2016.128 to 2016.218. The red arrow points to the diurnal variation of NmF2, in which the nighttime NmF2 enhancement, namely, the general Weddell Sea anomaly (WSA) can be clearly observed. Refer to <a href="#remotesensing-11-02686-f006" class="html-fig">Figure 6</a> for more detailed annotations.</p> Full article ">Figure 8
<p>Ionospheric climatological characteristics of season median NmF2 observed by FY3C/GNOS and COSMIC and their differences at 0800–1100LT and 2000–2300LT during SE-month (±45 days to September equinox of 2016) from 2016.221 to 2016.311. Refer to <a href="#remotesensing-11-02686-f006" class="html-fig">Figure 6</a> for more detailed explanations.</p> Full article ">Figure 9
<p>Ionospheric climatological characteristics of season median NmF2 observed by FY3C/GNOS and COSMIC and their differences at 0800–1100LT and 2000–2300LT during DS-month (±45 days to December solstice of 2016) from 2016.311 to 2017.035. The regions denoted by red and black arrows indicate diurnal variation of NmF2, in which the general WSA and special WSA can be observed at nighttime, respectively. Refer to <a href="#remotesensing-11-02686-f006" class="html-fig">Figure 6</a> for more detailed descriptions.</p> Full article ">Figure 10
<p>Ionospheric climatological characteristics of season median hmF2 observed by FY3C/GNOS and COSMIC and their differences at 0800–1100LT and 2000–2300LT during ME-month (2016.035-2016.125). (<b>a</b>,<b>b</b>) The climatological characteristics of season median hmF2 observed by FY3C/GNOS at 0800–1100LT and 2000–2300LT, respectively. (<b>c</b>,<b>d</b>) The climatological characteristics of season median hmF2 probed by COSMIC at 0800–1100LT and 2000–2300LT, respectively. The dip contours in (<b>a</b>–<b>d</b>) are represented by white curves, which are 60° dip, 0° dip, −60° dip from top to bottom, respectively. (<b>e</b>,<b>f</b>) The absolute errors of season median hmF2 between FY3C/GNOS and COSMIC at 0800–1100LT and 2000–2300LT, respectively. Obtaining of the hmF2 absolute error can be seen in <a href="#sec2dot2-remotesensing-11-02686" class="html-sec">Section 2.2</a>.</p> Full article ">Figure 11
<p>Ionospheric climatological characteristics of season median hmF2 observed by FY3C/GNOS and COSMIC and their differences at 0800–1100LT and 2000–2300LT during JS-month (2016.128–2016.218). The red arrow points to the diurnal variation of hmF2, in which the nighttime hmF2 enhancement, namely, the general WSA can be clearly observed. Refer to <a href="#remotesensing-11-02686-f010" class="html-fig">Figure 10</a> for more details.</p> Full article ">Figure 12
<p>Ionospheric climatological characteristics of season median hmF2 observed by FY3C/GNOS and COSMIC and their differences at 0800–1100LT and 2000–2300LT during SE-month (2016.221–2016.311). Refer to <a href="#remotesensing-11-02686-f010" class="html-fig">Figure 10</a> for more detailed annotations.</p> Full article ">Figure 13
<p>Ionospheric climatological characteristics of season median hmF2 observed by FY3C/GNOS and COSMIC and their differences at 0800–1100LT and 2000–2300LT during DS-month (2016.311–2017.035). The regions denoted by red and black arrows indicate the diurnal variation of hmF2, where the general WSA and special WSA phenomenon can be observed at nighttime, respectively. Refer to <a href="#remotesensing-11-02686-f010" class="html-fig">Figure 10</a> for more detailed descriptions.</p> Full article ">
19 pages, 5681 KiB  
Article
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 3572
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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<p>Outdoor antenna section of the Global Navigation Satellite System (GNSS) dual-polarized rain detection system.</p> Full article ">Figure 2
<p>Sky view of Global Positioning System (GPS) satellites related to the experimental site (31.6°N, 119°E) at 21:54 on 3 June 2015 and receiving area of the dual-polarized antenna (enclosed by red dashed line).</p> Full article ">Figure 3
<p>The geographical distribution of the weather stations with respect to the observing antennas and the radar station (same with the observing antenna). The field view of the dual-polarized receiving antenna is between the two red lines in the northeast area. The radar’s detecting distances is shown by the maximum dotted circle.</p> Full article ">Figure 4
<p>Statistical characteristics of G27 data in all no-rain days.</p> Full article ">Figure 5
<p>Examples of phase shift from two no-rain days: (<b>a</b>) on 8 June 2015; (<b>b</b>) on 3 August 2015.</p> Full article ">Figure 6
<p>Examples of phase shift from two rainy days: (<b>a</b>) on 6 July 2015; (<b>b</b>) on 10 August 2015.</p> Full article ">Figure 7
<p>Examples of phase shift (red solid line with values indicated by the right <span class="html-italic">y</span> axis) from two rainy days compared with radar reflectivity (interpolated along GPS rays, whose value is shown by color scale and height is represented by left <span class="html-italic">y</span> axis) and path-averaged rain rate (“pink+” line with values indicated by the left <span class="html-italic">y</span> axis) calculated from radar reflectivity: (<b>a</b>) on 6 July 2015; (<b>b</b>) on 10 August 2015; (<b>c</b>) sky plot of G27 at this selected time.</p> Full article ">Figure 8
<p>Cases of accumulated phase shift compared with accumulated rainfall observed from four weather stations and calculated from radar, respectively: (<b>a</b>,<b>b</b>) on 6 July 2015; (<b>c</b>,<b>d</b>) on 10 August 2015.</p> Full article ">Figure 9
<p>Statistical characteristics of G22 data in all no-rain days.</p> Full article ">Figure 10
<p>Examples of phase shift from two no-rain days: (<b>a</b>) on 15 June 2015; (<b>b</b>) on 30 August 2015.</p> Full article ">Figure 11
<p>Examples of phase shift from two rainy days: (<b>a</b>) on 27 June 2015; (<b>b</b>) on 10 August 2015.</p> Full article ">Figure 12
<p>Examples of phase shift from two rainy days compared with radar reflectivity and path-averaged rain rate calculated from radar reflectivity: (<b>a</b>) on 27 June 2015; (<b>b</b>) on 10 August 2015; (<b>c</b>) sky plot of G22 at this selected time.</p> Full article ">Figure 12 Cont.
<p>Examples of phase shift from two rainy days compared with radar reflectivity and path-averaged rain rate calculated from radar reflectivity: (<b>a</b>) on 27 June 2015; (<b>b</b>) on 10 August 2015; (<b>c</b>) sky plot of G22 at this selected time.</p> Full article ">Figure 13
<p>Case of accumulated phase shift on 10 August 2015 compared with accumulated rainfall observed from four weather stations and calculated from radar, respectively: (<b>a</b>) comparison results with data from four weather stations; (<b>b</b>) comparison results with data from Jurong weather station and radar.</p> Full article ">Figure 14
<p>Statistical characteristics of four GPS satellite data in all no-rain days: (<b>a</b>) G1; (<b>b</b>) G12; (<b>c</b>) G22; (<b>d</b>) G27.</p> Full article ">Figure 15
<p>Cases of accumulated phase shift from different GPS satellites compared with accumulated rainfall observed from four nearby weather stations: (<b>a</b>) G1 in Oct. 2; (<b>b</b>) G3 in Sep. 15; (<b>c</b>) G3 in Nov. 7; (<b>d</b>) G9 in Nov. 7; (<b>e</b>) G11 in Oct. 2; (<b>f</b>) G13 in Oct. 21; (<b>g</b>) G22 in Nov. 7; (<b>h</b>) G23 in Nov. 7; (<b>i</b>) G24 in Oct. 21.</p> Full article ">Figure 15 Cont.
<p>Cases of accumulated phase shift from different GPS satellites compared with accumulated rainfall observed from four nearby weather stations: (<b>a</b>) G1 in Oct. 2; (<b>b</b>) G3 in Sep. 15; (<b>c</b>) G3 in Nov. 7; (<b>d</b>) G9 in Nov. 7; (<b>e</b>) G11 in Oct. 2; (<b>f</b>) G13 in Oct. 21; (<b>g</b>) G22 in Nov. 7; (<b>h</b>) G23 in Nov. 7; (<b>i</b>) G24 in Oct. 21.</p> Full article ">
25 pages, 6350 KiB  
Article
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 97 | Viewed by 7815
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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<p>Bistatic radar geometry.</p> Full article ">Figure 2
<p>The flowchart of global navigation satellite system-reflectometry (GNSS-R) soil moisture retrieval procedure.</p> Full article ">Figure 3
<p>The flowchart of XGBoost algorithm.</p> Full article ">Figure 4
<p>Three-dimensional data set shown for the XGBoost algorithm.</p> Full article ">Figure 5
<p>Two-dimensional data set (<b>a</b>) for the SNR and the permittivity (<math display="inline"><semantics> <mi>θ</mi> </semantics></math> = 84°), two dimensional data (<b>b</b>) set for the elevation angle and the permittivity (<math display="inline"><semantics> <mrow> <mi>S</mi> <mi>N</mi> <mi>R</mi> </mrow> </semantics></math> = 20 dB).</p> Full article ">Figure 6
<p>Variable importance sensitivity to <math display="inline"><semantics> <mrow> <mi>e</mi> <mi>s</mi> <mi>t</mi> <mi>i</mi> <mi>m</mi> <mi>a</mi> <mi>t</mi> <mi>o</mi> <mi>r</mi> <mi>s</mi> </mrow> </semantics></math> 500 (<b>a</b>) and 4000 (<b>b</b>) when <math display="inline"><semantics> <mi>n</mi> </semantics></math> = 2000 and 5000.</p> Full article ">Figure 7
<p>Variable importance sensitivity to <math display="inline"><semantics> <mrow> <mi>c</mi> <mi>o</mi> <mi>l</mi> <mo>-</mo> <mi>s</mi> <mi>a</mi> <mi>m</mi> <mi>p</mi> <mi>l</mi> <mi>e</mi> <mo>-</mo> <mi>t</mi> <mi>r</mi> <mi>e</mi> <mi>e</mi> </mrow> </semantics></math> 0.5 (<b>a</b>) and 0.6 (<b>b</b>) for <math display="inline"><semantics> <mi>n</mi> </semantics></math> = 2000 and 5000.</p> Full article ">Figure 8
<p>Variable importance sensitivity to different types of soils, Grugliasco (<b>a</b>) and Agliano (<b>b</b>), when <math display="inline"><semantics> <mrow> <mi>e</mi> <mi>s</mi> <mi>t</mi> <mi>i</mi> <mi>m</mi> <mi>a</mi> <mi>t</mi> <mi>o</mi> <mi>r</mi> <mi>s</mi> </mrow> </semantics></math> = 2000, <math display="inline"><semantics> <mi>n</mi> </semantics></math> = 5000, <math display="inline"><semantics> <mrow> <mi>c</mi> <mi>o</mi> <mi>l</mi> <mo>-</mo> <mi>s</mi> <mi>a</mi> <mi>m</mi> <mi>p</mi> <mi>l</mi> <mi>e</mi> <mo>-</mo> <mi>t</mi> <mi>r</mi> <mi>e</mi> <mi>e</mi> </mrow> </semantics></math> = 0.5 and 0.6.</p> Full article ">Figure 9
<p>Two ground-based setups in Grugliasco (<b>left</b> panel) and Agliano (<b>right</b> panel).</p> Full article ">Figure 10
<p>Precipitations in Grugliasco and Agliano during January to March 2016.</p> Full article ">Figure 11
<p>Tektronix Metallic Cable Tester 1502 for time-domain reflectometry (TDR) measurements.</p> Full article ">Figure 12
<p>The skyplot with bar and equipment position, specular points mapped on the x–y plane with Fresnel zones and antenna footprint, (<b>a</b>) 27 January 2016, (<b>b</b>) 5 February 2016, (<b>c</b>) 3 March 2016, and (<b>d</b>) 7 March 2016.</p> Full article ">Figure 12 Cont.
<p>The skyplot with bar and equipment position, specular points mapped on the x–y plane with Fresnel zones and antenna footprint, (<b>a</b>) 27 January 2016, (<b>b</b>) 5 February 2016, (<b>c</b>) 3 March 2016, and (<b>d</b>) 7 March 2016.</p> Full article ">Figure 13
<p>The results of TDR and GNSS-R soil moisture (SM) retrieval, with time series, (<b>a</b>) 27 January 2016, (<b>b</b>) 5 February 2016, (<b>c</b>) 3 March 2016, and (<b>d</b>) 7 March 2016.</p> Full article ">Figure 14
<p>The variation rate of GNSS-R permittivity for Grugliasco (<b>a</b>) and Agliano (<b>b</b>) measurement from dry to wet case.</p> Full article ">Figure 15
<p>The variation rate of GNSS-R SMC for Grugliasco (<b>a</b>) and Agliano (<b>b</b>) measurement from dry to wet case.</p> Full article ">Figure 16
<p>Variable importance sensitivity to <math display="inline"><semantics> <mrow> <mi>e</mi> <mi>s</mi> <mi>t</mi> <mi>i</mi> <mi>m</mi> <mi>a</mi> <mi>t</mi> <mi>o</mi> <mi>r</mi> <mi>s</mi> </mrow> </semantics></math> 500 (<b>a</b>) and 4000 (<b>b</b>) when <math display="inline"><semantics> <mi>n</mi> </semantics></math> = 2000 and 5000.</p> Full article ">Figure 17
<p>Variable importance sensitivity to <math display="inline"><semantics> <mrow> <mi>t</mi> <mi>e</mi> <mi>s</mi> <mi>t</mi> <mi>s</mi> <mi>i</mi> <mi>z</mi> <mi>e</mi> </mrow> </semantics></math> 0.5 (<b>a</b>) and 0.6 (<b>b</b>), <math display="inline"><semantics> <mi>n</mi> </semantics></math> = 2000 and 5000.</p> Full article ">Figure 18
<p>Variable importance sensitivity to different types of soils, Grugliasco (<b>a</b>) and Agliano (<b>b</b>), when <math display="inline"><semantics> <mrow> <mi>e</mi> <mi>s</mi> <mi>t</mi> <mi>i</mi> <mi>m</mi> <mi>a</mi> <mi>t</mi> <mi>o</mi> <mi>r</mi> <mi>s</mi> </mrow> </semantics></math> = 4000, <math display="inline"><semantics> <mi>n</mi> </semantics></math> = 5000, <math display="inline"><semantics> <mrow> <mi>t</mi> <mi>e</mi> <mi>s</mi> <mi>t</mi> <mi>s</mi> <mi>i</mi> <mi>z</mi> <mi>e</mi> </mrow> </semantics></math> = 0.5 and 0.6.</p> Full article ">
18 pages, 5220 KiB  
Article
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 3313
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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<p>Variations of daily F10.7 index (<b>a</b>) red line and Ap (the equivalent planetary daily amplitude) index (<b>b</b>) blue line during the year from 2014 to 2017.</p> Full article ">Figure 2
<p>Variations of the numbers and the percentage of RO electron density profiles before and after the Ap index check and further after quality control for (<b>a</b>) Feng-Yun 3C (FY-3C); and (<b>b</b>) the Constellation Observing System for the Meteorology, Ionosphere, and Climate (COSMIC).</p> Full article ">Figure 3
<p>Distributions of the azimuth of the occultation planes (AOPs) of the qualified RO EDPs and of the RO EDPs collocated with digisonde observations for (<b>a</b>) FY-3C and (<b>b</b>) COSMIC.</p> Full article ">Figure 4
<p>The distribution of the 24 digisonde stations.</p> Full article ">Figure 5
<p>The comparison of FY-3C RO-derived ionospheric characteristic parameters (ICPs) with ICPs derived from COSMIC RO during 2014–2017 based on the collocation space and time windows of (3°, 5°, 1 h) for the statistical parameters of (<b>a</b>,<b>d</b>) correlation coefficients, (<b>b</b>,<b>e</b>) absolute biases, and (<b>c</b>,<b>f</b>) relative biases.</p> Full article ">Figure 6
<p>The comparison of FY-3C RO-derived ICPs with ICPs derived from COSMIC RO during 2014–2017 based on the collocation space and time windows of (3°, 5°, 1 h) and the constraint of DAOP ≤20° for the statistical parameters of (<b>a</b>,<b>d</b>) correlation coefficients, (<b>b</b>,<b>e</b>) absolute biases, and (<b>c</b>,<b>f</b>) relative biases.</p> Full article ">Figure 7
<p>The comparison of FY-3C RO-derived ICPs with ICPs derived from digisonde during 2014–2017 based on the collocation space and time windows of (3°, 5°, 1 h) for the statistical parameters of (<b>a</b>,<b>d</b>) correlation coefficients, (<b>b</b>,<b>e</b>) absolute biases, and (<b>c</b>,<b>f</b>) relative biases.</p> Full article ">Figure 8
<p>The comparison between FY-3C RO-derived ICPs and ICPs from other observations (COSMIC RO and digisonde) for each year during 2014–2017 for (<b>a</b>) ABs of NmF2, (<b>b</b>) RBs of NmF2, (<b>c</b>) ABs of hmF2, (<b>d</b>) RBs of hmF2, (<b>e</b>) CCs of NmF2, (<b>f</b>) CCs of hmF2 and (<b>g</b>) the number of the couples.</p> Full article ">Figure 9
<p>The variations of the standard deviation of the absolute bias (SDAB), standard deviation of the relative bias (SDRB), mean absolute bias (MAB), and mean relative bias (MRB) with the variations of (<b>a</b>–<b>d</b>) SEAs and the variations of (<b>e</b>–<b>h</b>) AOPs for the comparison between FY-3C RO-derived ICPs and the ICPs derived from digisondes.</p> Full article ">
26 pages, 3417 KiB  
Article
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 4047 | 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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<p>Multiobserving systems: green line, signal path across bottom region of the ionosphere 800 km during occultation; yellow line, signal path across the top region during occultation; and orange line, signal path across the ionosphere of ground-based GNSS observation.</p> Full article ">Figure 2
<p>Corrected DCB value of assimilation: (<b>a</b>) DCBs’ corrected value of GPS satellites from the assimilation for the top region; (<b>b</b>) DCBs’ corrected value of GPS satellites from the assimilation for the bottom region; (<b>c</b>) DCBs’ corrected value of LEO satellites from the assimilation for the top region; and (<b>d</b>) DCBs’ corrected value of stations from the assimilation for the bottom region.</p> Full article ">Figure 3
<p>Ground-based data processing flowchart.</p> Full article ">Figure 4
<p>Calculation of the calibrated TEC.</p> Full article ">Figure 5
<p>Data processing flow in the ionospheric assimilation process.</p> Full article ">Figure 6
<p>VTEC map of the top region at 16:00–18:00 UTC on DOY 010, 2008: (<b>left</b>) the model constructed by the method of Wu et al.’s; and (<b>right</b>) the model that was re-assimilated using podTec with the constructed model.</p> Full article ">Figure 7
<p>VTEC map of the top region at 16:00–18:00 UTC on DOY 089, 2012: (<b>left</b>) the model constructed by the method of Wu et al.’s; and (<b>right</b>) the model that was re-assimilated using podTec with the constructed model.</p> Full article ">Figure 8
<p>Distribution of residual statistics of top region assimilation model and podTec observational data on DOY 10, 2008: blue bars, the model constructed by the method of Wu et al.’s; and red bars, the model assimilated using podTec with the constructed model.</p> Full article ">Figure 9
<p>Residual statistics of the bottom model compared with observation data: (<b>top</b>) blue bars are the IRI model and red bars are the model assimilated using observation data with the IRI model; and (<b>bottom</b>) blue line and blue bar are the mean and RMS of the IRI model, respectively, while red line and red bar are the mean and RMS of the model assimilated using observation data with the IRI model.</p> Full article ">Figure 10
<p><math display="inline"><semantics> <mrow> <mi>N</mi> <mi>e</mi> <mi>F</mi> <msup> <mi>o</mi> <mn>2</mn> </msup> </mrow> </semantics></math> of ionosonde and ionospheric models: (<b>left</b>) scatter plot; and (<b>right</b>) histogram plot of error.</p> Full article ">Figure 11
<p>Sample ionosonde stations’ distribution (<b>top</b>); and their NeFo2 comparing with ionospheric models on DOY 89, 2012 (<b>bottom</b>).</p> Full article ">Figure 12
<p>Densities of the ISR and ionospheric models.</p> Full article ">Figure 13
<p>Electron density profiles (EDP) of the ISR and ionospheric models of Millstone: (<b>left</b>) EDP at 09:00 UTC; (<b>right</b>) EDP at 15:00 UTC.</p> Full article ">Figure 14
<p>Biases and STDs between the initial models(the IRI and IRI-Imp models) with their assimilation models (the IRI-Assi and IRI-Imp-Assi models ) and EDP of ionPrf on DOY 010, 2008 and DOY 089, 2012: green stars, the initial models; blue star, the G+Cor strategy, only ground-based observation data with hypothesis correction; and red star, the G/S+Cor strategy, ground-based/spaceborne observation data with hypothesis correction.</p> Full article ">Figure 15
<p>Hypothesized model error: G-FULL Model refers to the deviation, standard deviation and error rms variation between the results obtained by bottom assimilation using the ground data affected by the top region and the GIM model of the IGS. G-COR Model refers to the result of a correction strategy that deducts the influence of the top region.</p> Full article ">Figure 15 Cont.
<p>Hypothesized model error: G-FULL Model refers to the deviation, standard deviation and error rms variation between the results obtained by bottom assimilation using the ground data affected by the top region and the GIM model of the IGS. G-COR Model refers to the result of a correction strategy that deducts the influence of the top region.</p> Full article ">
20 pages, 4219 KiB  
Article
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 3993
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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<p>Latitudinal (<b>a</b>) and temporal (<b>b</b>) distribution of 4 years of FY-3C (blue dot) and COSMIC (red dot) temperature profiles.</p> Full article ">Figure 2
<p>Radiosonde stations distribution, red dots representing GRUAN stations and blue dots representing IGRA stations.</p> Full article ">Figure 3
<p>Mean temperature deviation between FengYun 3 series C satellite global navigation satellite system occultation sounder (FY-3C GNOS) and Constellation Observing System for Meteorology, Ionosphere and Climate (COSMIC) in different latitude bands, and the number noted at the top-left corner of each panel is the number of collocated pairs.</p> Full article ">Figure 4
<p>Root mean square error between FY-3C GNOS and COSMIC in different latitude bands.</p> Full article ">Figure 5
<p>Panel (<b>a</b>) indicates the annual cycle mean tropopause height (TPH) retrieved from four years of collocated FY-3C and COSMIC data. Dashed lines are for COSMIC results and solid lines are for FY-3C results. Panel (<b>b</b>) and panel (<b>c</b>) show the mean and absolute deviation.</p> Full article ">Figure 6
<p>Annual cycle mean tropopause temperature (TPT) (<b>a</b>), TPT mean deviation (<b>b</b>) and TPT absolute deviation (<b>c</b>), which is similar to <a href="#remotesensing-11-01139-f005" class="html-fig">Figure 5</a>.</p> Full article ">Figure 7
<p>TPH comparison results of FY-3C and radiosonde data of nine stations. The x-axis (FY-3C TPH results) and y-axis (radiosonde TPH results) are symmetric and the correlation coefficient r and linear regression coefficient l are located at the top-left corner of each panel.</p> Full article ">Figure 8
<p>TPT comparison results, which are similar to <a href="#remotesensing-11-01139-f007" class="html-fig">Figure 7</a>.</p> Full article ">Figure 9
<p>Seasonal patterns of TPT and TPH derived from January 2014-December 2017 FY-3C data. This is for spring (March, April, May). The (<b>a</b>) panel is global TPH pattern and the (<b>b</b>) panel is for the standard deviation of global TPH. Similarly, the (<b>c</b>) panel and the (<b>d</b>) panel are for global TPT pattern and TPT standard deviation, respectively.</p> Full article ">Figure 10
<p>Seasonal patterns (<b>a</b>–<b>d</b>) of TPT and TPH for summer (June, July, August). White box points out the region where the tropopause is extremely high.</p> Full article ">Figure 11
<p>Seasonal pattern of tropical TPH for summer (June, July, August). White box is the same with that in <a href="#remotesensing-11-01139-f010" class="html-fig">Figure 10</a>.</p> Full article ">Figure 12
<p>Seasonal patterns (<b>a</b>–<b>d</b>) of TPT and TPH for autumn (September, October, November).</p> Full article ">Figure 13
<p>Seasonal patterns (<b>a</b>–<b>d</b>) of TPT and TPH for winter (December, January, February). Two white circles identify two areas where the tropopause is obviously low.</p> Full article ">
18 pages, 1693 KiB  
Article
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 4103
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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<p>Condition number of a TechDemoSat-1 (TDS-1) wind speed Geophysical Model Function (GMF) converting the Bistatic Radar Cross Section (BRCS) to wind speed.</p> Full article ">Figure 2
<p>Orientation of the model reference frame used for the numerical simulations.</p> Full article ">Figure 3
<p>Illustration of the ambiguity problem in mapping the power from the spatial domain (<b>left</b>) to the delay-Doppler domain (<b>right</b>). In the left panel, blue curves are equi-Doppler and black ellipses are equi-range. As shown, each delay-Doppler Map (DDM) pixel is not proportional to scattered power from only one pixel in the spatial domain, but from a pair of pixels located symmetrically with respect to the intersection of the incidence plane with the ocean surface, which is the y-axis in the illustrated case.</p> Full article ">Figure 4
<p>A measured DDM onboard TDS-1 (<b>a</b>) and the modeled DDM after adding an artificial Gaussian noise (<b>b</b>) at a wind speed of 14.74 m/s. Specular point position: Latitude 34.38 Longitude 16.25 degrees. Time: 3 November 2015, 13:59.47. The SGR-ReSI receiver is operated in unmonitored automatic gain control mode (AGM) so that the gain of the intermediate-frequency voltage amplifier of the global navigation satellite system (GNSS) receiver is adaptively adjusted by the automatic gain control according to the received power level [<a href="#B24-remotesensing-11-01048" class="html-bibr">24</a>]. The variable gain level is not recorded and the absolute level of incoming radiation is inaccessible.</p> Full article ">Figure 5
<p>An overview of the simulation procedure of DDMs and calculation of rain attenuation effects in this study.</p> Full article ">Figure 6
<p>Rain attenuation effects on the Bistatic Radar Cross Section <math display="inline"><semantics> <msup> <mi>σ</mi> <mn>0</mn> </msup> </semantics></math> at different rain rates and incidence angles of 0°(<b>a</b>), 10°(<b>b</b>), 20°(<b>c</b>), 30°(<b>d</b>), 40°(<b>e</b>), 50°(<b>f</b>), 60°(<b>g</b>), 70°(<b>h</b>).</p> Full article ">Figure 6 Cont.
<p>Rain attenuation effects on the Bistatic Radar Cross Section <math display="inline"><semantics> <msup> <mi>σ</mi> <mn>0</mn> </msup> </semantics></math> at different rain rates and incidence angles of 0°(<b>a</b>), 10°(<b>b</b>), 20°(<b>c</b>), 30°(<b>d</b>), 40°(<b>e</b>), 50°(<b>f</b>), 60°(<b>g</b>), 70°(<b>h</b>).</p> Full article ">Figure 6 Cont.
<p>Rain attenuation effects on the Bistatic Radar Cross Section <math display="inline"><semantics> <msup> <mi>σ</mi> <mn>0</mn> </msup> </semantics></math> at different rain rates and incidence angles of 0°(<b>a</b>), 10°(<b>b</b>), 20°(<b>c</b>), 30°(<b>d</b>), 40°(<b>e</b>), 50°(<b>f</b>), 60°(<b>g</b>), 70°(<b>h</b>).</p> Full article ">Figure 7
<p>Rain attenuation modifications in the derived BRCS (<b>a</b>) and wind speed (<b>b</b>) at incidence angle of 30° and at different rain rates. <math display="inline"><semantics> <msubsup> <mi>σ</mi> <mi>R</mi> <mn>0</mn> </msubsup> </semantics></math> and <math display="inline"><semantics> <msub> <mi>U</mi> <mrow> <mn>10</mn> <mo>,</mo> <mi>R</mi> </mrow> </msub> </semantics></math> are the rain attenuation affected BRCS and wind speed.</p> Full article ">
17 pages, 5050 KiB  
Article
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 4673
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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<p>The simulated 24 h Global Navigation Satellite System (GNSS) radio occultation events (ROEs), observed by COSMIC using the real orbits (<b>a</b>) and the simulated orbits (<b>b</b>).</p> Full article ">Figure 2
<p>The optimization algorithm flowchart of the LEO constellation with certain number of satellites and certain constellation pattern.</p> Full article ">Figure 3
<p>Fitness values of the optimal configurations for 2D-lattice flower constellation (2D-LFC) and 3D-LFC composed of 6-12 satellites based on Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) algorithms.</p> Full article ">Figure 4
<p>The numbers of ROEs observed in one day by the final optimal LEO constellations with 2D-LFC and 3D-LFC patterns composed of 6–12 satellites.</p> Full article ">Figure 5
<p>The distribution of ROEs observed within each three hours of one day by the final optimal LEO constellations with 2D-LFC pattern composed of 6 (<b>a</b>) and 12 satellites (<b>b</b>).</p> Full article ">
16 pages, 6303 KiB  
Article
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 27 | Viewed by 6920
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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<p>Tropospheric parameter differences between the NWM and PPP method. The top panel shows the station specific root-mean-square deviation for the ZTD. The middle and lower panel shows the station specific root-mean-square deviation for the north- and east-gradient coefficients, respectively. We consider four epochs per day (0, 6, 12, 18 UTC) and a period of two months (June and July, 2017).</p> Full article ">Figure 2
<p>The left panels (<b>a</b>) show the wet east (E) and north-gradient (N) coefficient and the right panels (<b>b</b>) show the approximation for the wet east (E*) and north-gradient (N*) coefficient (2 July 2017, 12 UTC). The approximations for the wet gradient coefficients are obtained by utilizing ZWD gradients. For details refer to the text.</p> Full article ">Figure 3
<p>The root mean square errors of ZTDs and the horizontal delay gradients for the Background (B), the Observation (O) and the Analysis (A). Left panel: ZTDs are assimilated. Middle panel: Horizontal delay gradients are assimilated. Right panel: ZTDs and horizontal delay gradients are assimilated. Data from the single station Potsdam are assimilated four times per day (0, 6, 12, 18 UTC) for a period of two months (June and July, 2017).</p> Full article ">Figure 4
<p>The mean (red) and one-sigma (black) refractivity deviation in percent between the background and the analysis as a function of the pressure. Left panel: ZTDs are assimilated. Middle panel: Horizontal delay gradients are assimilated. Right panel: ZTDs and horizontal delay gradients are assimilated. In each panel the four plots correspond to the grid points surrounding the station Potsdam. The labels (a), (b), (c) and (d) correspond to the grid points to the north-west, north-east, south-west and south-east respectively. Data from the single station Potsdam are assimilated four times per day (0, 6, 12, 18 UTC) for a period of two months (June and July, 2017).</p> Full article ">Figure 5
<p>The root mean square error of the background (analysis) refractivity in percent as a function of the pressure in black (red). Left panel: ZTDs are assimilated. Middle panel: horizontal delay gradients are assimilated. Right panel: ZTDs and Horizontal delay gradients are assimilated. In each panel the four plots correspond to the grid points surrounding the station Potsdam. The labels (a), (b), (c) and (d) correspond to the grid points to the north-west, north-east, south-west and south-east respectively. Data from the single station Potsdam are assimilated four times per day (0, 6, 12, 18 UTC) for a period of two months (June and July, 2017).</p> Full article ">Figure 6
<p>Same as <a href="#remotesensing-11-00041-f003" class="html-fig">Figure 3</a> but data from the station network are assimilated.</p> Full article ">Figure 7
<p>Same as <a href="#remotesensing-11-00041-f004" class="html-fig">Figure 4</a> but data from the station network are assimilated.</p> Full article ">Figure 8
<p>Same as <a href="#remotesensing-11-00041-f005" class="html-fig">Figure 5</a> but data from the station network are assimilated.</p> Full article ">Figure 9
<p>The root mean square errors of ZTDs and the horizontal delay gradients for the Background (B), the Observation (O) and the Analysis (A). Left panel: ZTDs are assimilated. Middle panel: Horizontal delay gradients are assimilated. Right panel: ZTDs and horizontal delay gradients are assimilated. GNSS ZTDs and horizontal delay gradients from the G-Nut/Tefnut software for the single station Potsdam are assimilated four times per day (0, 6, 12, 18 UTC) for a period of two months (June and July, 2017).</p> Full article ">Figure 10
<p>The mean (red) and one-sigma (black) refractivity deviation in percent between the background and the analysis as a function of the pressure. Left panel: ZTDs are assimilated. Middle panel: Horizontal delay gradients are assimilated. Right panel: ZTDs and horizontal delay gradients are assimilated. In each panel the four plots correspond to the grid points surrounding the station Potsdam. The labels (a), (b), (c) and (d) correspond to the grid points to the north-west, north-east, south-west and south-east respectively. GNSS ZTDs and horizontal delay gradients from the G-Nut/Tefnut software for the single station Potsdam are assimilated four times per day (0, 6, 12, 18 UTC) for a period of two months (June and July, 2017).</p> Full article ">Figure 11
<p>The root mean square error of the background (analysis) refractivity in percent as a function of the pressure in black (red). Left panel: ZTDs are assimilated. Middle panel: horizontal delay gradients are assimilated. Right panel: ZTDs and Horizontal delay gradients are assimilated. In each panel the four plots correspond to the grid points surrounding the station Potsdam. The labels (a), (b), (c) and (d) correspond to the grid points to the north-west, north-east, south-west and south-east respectively. GNSS ZTDs and horizontal delay gradients from the G-Nut/Tefnut software for the single station Potsdam are assimilated four times per day (0, 6, 12, 18 UTC) for a period of two months (June and July, 2017).</p> Full article ">Figure 12
<p>The root mean square error of the refractivity as a function of the pressure for the background (black line), when we assimilate ZTDs only (blue line) and when we assimilate both ZTDs and horizontal delay gradients (red line). GNSS ZTDs and horizontal delay gradients from the G-Nut/Tefnut software for the single station Potsdam are assimilated four times per day (0, 6, 12, 18 UTC) for a period of two months (June and July, 2017). For details refer to the text.</p> Full article ">
18 pages, 10080 KiB  
Article
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 4491
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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<p>The diagram for the computation of equatorial longitude of ground track: (<b>a</b>) ascending, and (<b>b</b>) descending.</p> Full article ">Figure 2
<p>Differences between the predicted orbits and precise orbits within 1 h (<b>a</b>), 2 h (<b>b</b>), 4 h (<b>c</b>), 8 h (<b>d</b>), 12 h (<b>e</b>), and 24 h (<b>f</b>) using the Jacchia 1971 atmospheric density model.</p> Full article ">Figure 3
<p>Differences between the predicted orbits and the precise orbits within 1 h (<b>a</b>), 2 h (<b>b</b>), 4 h (<b>c</b>), 8 h (<b>d</b>), 12 h (<b>e</b>), and 24 h (<b>f</b>) using the MSIS86 atmospheric density model.</p> Full article ">Figure 4
<p>Differences between the predicted orbits and the precise orbits within 1 h (<b>a</b>), 2 h (<b>b</b>), 4 h (<b>c</b>), 8 h (<b>d</b>), 12 h (<b>e</b>), and 24 h (<b>f</b>) using the DTM87 atmospheric density model.</p> Full article ">Figure 5
<p>The orbital differences between the predicted orbits and the precise ones within 3 days (<b>a</b>), and 7 days (<b>b</b>), using the Jacchia 1971 atmospheric density model.</p> Full article ">Figure 6
<p>The orbital differences between the predicted orbits and the SSALTO ones within 3 days (<b>a</b>), and 7 days (<b>b</b>), using the MSIS86 atmospheric density model.</p> Full article ">Figure 7
<p>Orbital differences between the predicted orbits and the precise ones within 3 days (<b>a</b>), and 7 days (<b>b</b>), using the DTM87 atmospheric density model.</p> Full article ">Figure 8
<p>The ground trajectory predicted using Jacchia 1971 model for the HY-2A satellite for 28 days.</p> Full article ">Figure 9
<p>The equatorial distances of equatorial crossing points between the predicted trajectory with three atmospheric density models using the 3-day arc and precise trajectory during two successive periods of 28 days.</p> Full article ">Figure 10
<p>The equatorial distances of equatorial crossing points between the predicted trajectory with three atmospheric density models using 7-day arc and precise trajectory during two successive periods of 28 days.</p> Full article ">Figure 11
<p>Equatorial distances of predicted and SSALTO ground tracks between two successive repeating cycles for the HY-2A satellite using 3-day arc.</p> Full article ">Figure 12
<p>Equatorial distances of predicted and SSALTO ground tracks between two successive repeating cycles for the HY-2A satellite using 7-day arc.</p> Full article ">Figure 13
<p>The equatorial longitudinal separation of ground track between two successive periods for HY-2A, JASON-2, and JASON-3 satellites.</p> Full article ">
18 pages, 7453 KiB  
Article
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 3993
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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Graphical abstract
Full article ">Figure 1
<p>Basic schematic diagram for the proposed horizontal parameterised approach, where the dotted blue lines in (<b>a</b>) and (<b>b</b>) are the centre of each layer while the solid black lines are the boundary of each layer.</p> Full article ">Figure 2
<p>Flowchart of steps determining the weights for the proposed tomographic model.</p> Full article ">Figure 3
<p>Geographic distribution of Global Navigation Satellite System (GNSS) receivers and radiosonde station in the tomography area.</p> Full article ">Figure 4
<p>Distribution of intersections between signal rays and every layer for different elevation angle masks, (<b>a</b>) 10°; (<b>b</b>) 20°; (<b>c</b>) 30° and (<b>d</b>) 40°, respectively.</p> Full article ">Figure 5
<p>Scatter diagram of the slant water vapor (SWV) residuals under different weather conditions, (<b>a</b>) a sunny day in Doy 138, 2013; (<b>b</b>) a rainy day in Doy 146, 2013.</p> Full article ">Figure 6
<p>Scatter diagram of the SWV residuals for HKSC station under different weather conditions, (<b>a</b>) a sunny day in Doy 138, 2013; (<b>b</b>) a rainy day in Doy 146, 2013.</p> Full article ">Figure 7
<p>Water vapor distribution of 00:00, 06:00, 12:00 and 18:00 UTC, Doy 138 at layers 1–6.</p> Full article ">Figure 8
<p>Flowchart of steps calculating the quasi-geoid height from different height systems.</p> Full article ">Figure 9
<p>Integrated water vapour (IWV) comparison derived from different tomographic schemes and radiosonde for the period of Doy 121–151, 2013.</p> Full article ">Figure 10
<p>IWV comparison derived from different tomographic schemes and ECMWF for the period of Doy 121–151, 2013.</p> Full article ">Figure 11
<p>Water vapor density profile comparison derived from schemes 1 and 2, radiosonde and ECMWF at specified epochs, (<b>a</b>) 00:00–00:05 UTC Doy 122, 2013; (<b>b</b>) 00:00–00:05 UTC Doy 135, 2013.</p> Full article ">Figure 12
<p>Comparison of RMS and relative error derived from different schemes 1 and 2, radiosonde and ECMWF during the experimental period from Doy 121 to 151, 2013.</p> Full article ">
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