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17 pages, 14498 KiB  
Article
On-Orbit Calibration Method for Correction Microwave Radiometer of the HY-2 Satellite Constellation
by Xiaofeng Ma, Mingsen Lin, Jin Zhao, Yongjun Jia and Chengfei Jiang
Remote Sens. 2023, 15(24), 5643; https://doi.org/10.3390/rs15245643 - 6 Dec 2023
Cited by 1 | Viewed by 1231
Abstract
The HY-2D satellite was successfully launched in 2022, which marks the first phase of the HY-2 satellite constellation. In order to reduce the deviation of wet path delay (WPD) between different satellites in the HY-2 satellite constellation and increase precision in the correction [...] Read more.
The HY-2D satellite was successfully launched in 2022, which marks the first phase of the HY-2 satellite constellation. In order to reduce the deviation of wet path delay (WPD) between different satellites in the HY-2 satellite constellation and increase precision in the correction microwave radiometer (CMR) products, on-orbit calibration must be performed to the brightness temperature (BT) of the CMR in this constellation. This study describes the principle and process of on-orbit calibration for CMR in detail. For the three satellites of the HY-2 satellite constellation, after cross-matching with each other within a limited spatio-temporal range, the HY-2B satellite with sounding on the global ocean is selected to the calibration source, calibrating BT from the CMR of the HY-2C and HY-2D satellites to the BT dimension of the HY-2B satellite CMR. To check on-orbit calibration, a retrieval algorithm is built using atmospheric profile data from ECMWF and BT data, obtained from the CMR of the HY-2B satellite; this is used to calculate the atmospheric water vapor (AWV) and WPD from the HY-2 satellite constellation. After on-orbit calibration to the CMRs of the HY-2 satellite constellation, the deviation between the CMR products of different satellites is significantly reduced by over 20%, and the RMS of WPD for the same type of products from the Jason-3 satellite is less than 1 cm. It may be concluded that on-orbit calibration improves the accuracy of AWV and WPD by normalizing the BT dimension for CMRs of the HY-2 satellite constellation, so this calibration method is effective and credible for enhancing the quality of altimeter products in the HY-2 satellite constellation. Full article
(This article belongs to the Special Issue Recent Progress in Understanding Global Sea Level Rise Using Space and Earth Observations)
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<p>The HY-2 satellite constellation ground tracks. (<b>a</b>) A dual-satellite grid including HY-2C and HY-2D, with symmetrical tracks. (<b>b</b>) A constellation grid including HY-2B, HY-2C and HY-2D; it is an uneven grid due to the HY-2B orbit differing from that of HY-2C and HY-2D, but its tracks cover polar regions.</p> Full article ">Figure 2
<p>The CMR feed sources of the HY-2 satellite constellation. The HY-2B satellite flies forward along the +<span class="html-italic">x</span> axis; (<b>a</b>) shows its CMR and has three feed sources at three frequencies; these are arranged in a straight line along the flight direction, and the centers of the three-channel footprint are collinear. Both the HY-2C and HY-2D satellites fly on the yaw, which no longer stays flying forward along the +<span class="html-italic">x</span> axis. (<b>b</b>) The resulting CMRs, which have only one feed source to the three frequencies.</p> Full article ">Figure 3
<p>The distribution of crossovers with each other in the HY-2 satellite constellation; (<b>a</b>,<b>b</b>) are within the same time period and (<b>c</b>,<b>d</b>) are within another time period. (<b>a</b>,<b>c</b>) Crossovers between HY-2B and HY-2C and (<b>b</b>,<b>d</b>) crossovers between HY-2B and HY-2D. (<b>a</b>,<b>d</b>) Crossovers from 30° to 60° at south and north latitudes, respectively, and (<b>b</b>,<b>c</b>) crossovers where HY-2B crosses HY-2C and HY-2D, distributed on the globe from 60° of the north and south latitudes to the equator.</p> Full article ">Figure 3 Cont.
<p>The distribution of crossovers with each other in the HY-2 satellite constellation; (<b>a</b>,<b>b</b>) are within the same time period and (<b>c</b>,<b>d</b>) are within another time period. (<b>a</b>,<b>c</b>) Crossovers between HY-2B and HY-2C and (<b>b</b>,<b>d</b>) crossovers between HY-2B and HY-2D. (<b>a</b>,<b>d</b>) Crossovers from 30° to 60° at south and north latitudes, respectively, and (<b>b</b>,<b>c</b>) crossovers where HY-2B crosses HY-2C and HY-2D, distributed on the globe from 60° of the north and south latitudes to the equator.</p> Full article ">Figure 4
<p>Comparing brightness temperature (BT) differences of three channels in crossovers for two satellites: (<b>a</b>) BT differences between HY-2B and HY-2C, concentrated from −10 K to 20 K, and BT differences between HY-2B and HY-2D, concentrated from −20 K to 20 K, as obtained from three channels. (<b>b</b>) Correlation between any two groups of BT data at crossovers between two satellites.</p> Full article ">Figure 4 Cont.
<p>Comparing brightness temperature (BT) differences of three channels in crossovers for two satellites: (<b>a</b>) BT differences between HY-2B and HY-2C, concentrated from −10 K to 20 K, and BT differences between HY-2B and HY-2D, concentrated from −20 K to 20 K, as obtained from three channels. (<b>b</b>) Correlation between any two groups of BT data at crossovers between two satellites.</p> Full article ">Figure 5
<p>The distribution of crossovers for on-orbit calibration. Comparing the position of yellow lines in (<b>a</b>) and red lines in (<b>b</b>), more uniform crossovers are distributed between HY-2B and HY-2C on mid-low-latitudes, and crossovers between HY-2B and HY-2D have larger areas than between HY-2B and HY-2C at high latitudes.</p> Full article ">Figure 6
<p>In-situ observation at sea.</p> Full article ">Figure 7
<p>Comparing BT differences for 23.8 GHz in crossover: (<b>a</b>) BT differences, which fall from about 5 K to 0 K between HY-2B and HY-2C; (<b>b</b>) BT differences, which fall from about 10 K to 0 K between HY-2B and HY-2D.</p> Full article ">Figure 8
<p>Comparing BT differences for 18.7 GHz in crossovers.</p> Full article ">Figure 9
<p>Comparing BT differences for 37 GHz in crossovers.</p> Full article ">Figure 10
<p>The distribution of crossovers between the HY-2 satellite constellation and Jason-3 satellite. For two-month datasets for all satellites, crossovers distributed on mid–high latitudes for HY-2B are concentrated at high latitudes for HY-2D and near the equator for HY-2C.</p> Full article ">
20 pages, 8164 KiB  
Article
An Optimized Framework for Precipitable Water Vapor Mapping Using TS-InSAR and GNSS
by Qiuying Guo, Miao Yu, Dewei Li, Shoukai Huang, Xuelong Xue, Yingjun Sun and Chenghu Zhou
Atmosphere 2023, 14(11), 1674; https://doi.org/10.3390/atmos14111674 - 12 Nov 2023
Viewed by 1452
Abstract
Observations of precipitable water vapor (PWV) in the atmosphere play a crucial role in weather forecasting and global climate change research. Spaceborne Interferometric Synthetic Aperture Radar (InSAR), as a widely used modern geodetic technique, offers several advantages to the mapping of PWV, including [...] Read more.
Observations of precipitable water vapor (PWV) in the atmosphere play a crucial role in weather forecasting and global climate change research. Spaceborne Interferometric Synthetic Aperture Radar (InSAR), as a widely used modern geodetic technique, offers several advantages to the mapping of PWV, including all-weather capability, high accuracy, high resolution, and spatial continuity. In the process of PWV retrieval by using InSAR, accurately extracting the tropospheric wet delay phase and obtaining a high-precision tropospheric water vapor conversion factor are critical steps. Furthermore, the observations of InSAR are spatio-temporal differential results and the conversion of differential PWV (InSAR ΔPWV) into non-difference PWV (InSAR PWV) is a difficulty. In this study, the city of Jinan, Shandong Province, China is selected as the experimental area, and Sentinel-1A data in 2020 is used for mapping InSAR ΔPWV. The method of small baseline subset of interferometry (SBAS) is adopted in the data processing for improving the coherence of the interferograms. We extract the atmosphere phase delay from the interferograms by using SRTM-DEM and POD data. In order to evaluate the calculation of hydrostatic delay by using the ERA5 data, we compared it with the hydrostatic delay calculated by the Saastamoinen model. To obtain a more accurate water vapor conversion factor, the value of the weighted average temperature Tm was calculated by the path integral of the ERA5. In addition, GNSS PWV is used to calibrate InSAR PWV. This study demonstrates a robust consistency between InSAR PWV and GNSS PWV, with a correlation coefficient of 0.96 and a root-mean-square error (RMSE) of 1.62 mm. In conclusion, our method ensures the reliability of mapping PWV by using Sentinel-1A interferograms and GNSS observations. Full article
(This article belongs to the Section Atmospheric Techniques, Instruments, and Modeling)
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<p>Study area overview.</p> Full article ">Figure 2
<p>Retrieval flowchart of InSAR PWV. <span class="html-italic">П</span> represents the water vapor conversion factor.</p> Full article ">Figure 3
<p>(<b>a</b>) Total tropospheric delay from the InSAR interferogram. (<b>b</b>) Slant hydrostatic delay difference maps predicted from the ERA5 (taking differential interferogram-6 (master image from 22 April 2020; slave image from 4 May 2020) as an example). (<b>c</b>) The spatial distribution of the conversion factor <span class="html-italic">П</span> calculated based on ERA5. It was calculated at the time 10:00 UTC (the imaging time corresponding to Sentinel-1A). (<b>d</b>) The ΔPWV distribution map on 4 May 2020.</p> Full article ">Figure 4
<p>Results of using InSAR to retrieve PWV.</p> Full article ">Figure 5
<p>Comparison between the hydrostatic delay from the Saastamoinen model and the hydrostatic delay from ERA5. The squares indicate ZHD estimates from ERA5 data that are obtained by averaging all pixels falling within the circular area with a radius of 5.2 km centered around the station, corresponding to the observational data.</p> Full article ">Figure 6
<p>Water vapor conversion factor П calculated from the ERA5 reanalysis data (The water vapor conversion factor П from January to December are shown in the figure, respectively).</p> Full article ">Figure 7
<p>Comparison of <span class="html-italic">T</span><sub>m</sub>_RS, <span class="html-italic">T</span><sub>m</sub>_Bevis, and the method for calculating <span class="html-italic">T</span><sub>m</sub> presented in this study.</p> Full article ">Figure 8
<p>Comparison of the results of InSAR PWV and BDS PWV (The <span class="html-italic">y</span>-axis represents the PWV in millimeters, while the <span class="html-italic">x</span>-axis denotes CORS, CQRS, and YAXI, which were only included in the validation process).</p> Full article ">Figure 9
<p>(<b>a</b>) Deviation between InSAR PWV and BDS PWV (<b>b</b>) RMSE between InSAR PWV and BDS PWV.</p> Full article ">Figure 10
<p>The correlation between the deviation and liquid cloud water. The circles represent the value of liquid cloud water corresponding to deviation. The red line is the fitted linear function.</p> Full article ">Figure 11
<p>Comparison between InSAR PWV and ERA5 PWV (The first column is InSAR PWV, and the second column is ERA5 PWV).</p> Full article ">
18 pages, 3011 KiB  
Article
On-Orbit Calibration and Wet Tropospheric Correction of HY-2C Correction Microwave Radiometer
by Xiaomeng Zheng, Dehai Zhang, Jin Zhao and Maofei Jiang
Remote Sens. 2023, 15(14), 3633; https://doi.org/10.3390/rs15143633 - 21 Jul 2023
Viewed by 1332
Abstract
HY-2C is the third satellite in China’s ocean dynamic environment satellite series, and carries a correction microwave radiometer (CMR) to correct the wet tropospheric path delay for the aligned radar altimeter. To effectively use the brightness temperatures (TB) of CMR [...] Read more.
HY-2C is the third satellite in China’s ocean dynamic environment satellite series, and carries a correction microwave radiometer (CMR) to correct the wet tropospheric path delay for the aligned radar altimeter. To effectively use the brightness temperatures (TB) of CMR to retrieve path delay, an on-orbit calibration effort is required. In this study, an antenna pattern correction (APC) method and a neural network method are used to perform an on-orbit calibration for CMR’s antenna temperatures and a model based on the Whale Optimization Algorithm (WOA), Levenberg–Marquardt (LM) algorithm, and Back-Propagation neural network (WOA–LM–BP) has been proposed to retrieve the wet tropospheric correction (WTC) of CMR. The on-orbit calibration results, compared with the simulated brightness temperatures calculated by the radiative transfer model (RTM), have shown that compared with the APC method, the neural network method can almost eliminate the latitude variation, and the total bias and standard deviation of the on-orbit calibrated TB at all channels have obviously decreased. The retrieved WTC results also have shown that the retrieved WTC of CMR has a good agreement with the corresponding ones from the model-derived WTC and Jason-3. Full article
(This article belongs to the Special Issue Remote Sensing Applications in Ocean Observation (Second Edition))
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<p>Flow chart of the WOA–LM –BP model.</p> Full article ">Figure 2
<p>Diagram of OMB differences before calibration of CMR for each channel. (<b>a</b>–<b>c</b>): over time; (<b>d</b>–<b>f</b>): over latitude.</p> Full article ">Figure 3
<p>Diagram of OMB differences of CMR for each channel over time. (<b>a</b>–<b>c</b>): based on the APC method; (<b>d</b>–<b>f</b>): based on the neural network.</p> Full article ">Figure 4
<p>Diagram of OMB differences of CMR for each channel over latitude. (<b>a</b>–<b>c</b>): based on the APC method; (<b>d</b>–<b>f</b>): based on the neural network.</p> Full article ">Figure 5
<p>Histogram of OMB differences of CMR for each channel on training dataset. (<b>a</b>–<b>c</b>): based on the APC method; (<b>d</b>–<b>f</b>): based on the neural network.</p> Full article ">Figure 6
<p>Diagram of OMB differences of HY-2C for each channel with respect to the calibrated <span class="html-italic">T<sub>B</sub></span> on training dataset. (<b>a</b>–<b>c</b>): based on the APC method; (<b>d</b>–<b>f</b>): based on the neural network (x representing the calibrated <span class="html-italic">T<sub>B</sub></span> and y the simulated one). Color denotes the number of points displayed in the color bar.</p> Full article ">Figure 7
<p>(<b>a</b>): HY-2C WTC versus model-derived WTC in training dataset; (<b>b</b>) HY-2C WTC versus WTC difference between HY-2C and model-derived WTC in training dataset; (<b>c</b>): HY-2C WTC versus model-derived WTC in test dataset; (<b>d</b>) HY-2C WTC versus WTC difference between HY-2C and model-derived WTC in test dataset; Color indicates the number of points displayed in the colorbar.</p> Full article ">Figure 8
<p>Histogram of WTC differences between HY-2C and model-derived WTC. (<b>a</b>): on training dataset; (<b>b</b>): on test dataset.</p> Full article ">Figure 9
<p>Crossover points distribution between HY-2C and Jason-3 over one year.</p> Full article ">Figure 10
<p>(<b>a</b>): HY-2C WTC versus Jason-3 WTC. (<b>b</b>) HY-2C WTC versus WTC difference between HY-2C and Jason-3.</p> Full article ">Figure 11
<p>Histogram of WTC differences between CMR and Jason-3.</p> Full article ">
14 pages, 2544 KiB  
Article
Brightness Temperature and Wet Tropospheric Correction of HY-2C Calibration Microwave Radiometer Using Model-Derived Wet Troposphere Path Delay from ECMWF
by Xiaomeng Zheng, Dehai Zhang, Jin Zhao and Maofei Jiang
Remote Sens. 2023, 15(5), 1318; https://doi.org/10.3390/rs15051318 - 27 Feb 2023
Cited by 2 | Viewed by 1555
Abstract
The Calibration Microwave Radiometer (CMR) is a three-band radiometer deployed on the HY-2C satellite in a near-Earth orbit, and since it launched, there are few studies presented on the performance of CMR to date. Therefore, this paper focuses on providing an assessment of [...] Read more.
The Calibration Microwave Radiometer (CMR) is a three-band radiometer deployed on the HY-2C satellite in a near-Earth orbit, and since it launched, there are few studies presented on the performance of CMR to date. Therefore, this paper focuses on providing an assessment of HY-2C CMR brightness temperature and wet troposphere correction (WTC). CMR works at 18.7 GHz, 23.8 GHz and 37 GHz in a nadir-viewing direction, aligned with the HY-2C radar altimeter. The wet troposphere path delay of the radar altimeter signal caused by water vapour and cloud liquid water content can be monitored and corrected by CMR. In this paper, guided by the concept of antenna pattern correction algorithm and a purely statistical method, we directly establish the function between the CMR antenna temperature and the model-derived WTC calculated by the European Centre from Medium-Range Weather Forecasting (ECMWF) Reanalysis data, which can obtain the brightness temperature and the WTC of CMR simultaneously. Firstly, the algorithm principle of CMR to establish the function between the antenna temperature and the model-derived WTC is introduced, and then the brightness temperature of CMR is evaluated using reference brightness temperatures of the Advanced Microwave Radiometer 2 (AMR-2) on Jason-3 satellite at crossover points. Furthermore, the performance of the CMR WTC is validated in three ways: (1) directly comparing with the colocated WTC measured by Jason-3 AMR-2, (2) directly comparing with model-derived WTC from ECMWF, which allows a rapid check at a global scale, (3) comparing the standard deviation of the Sea Surface Height (SSH) difference at crossover points using different WTC retrieval methods. The linear fit with Jason-3 brightness temperature and WTC in all non-precipitation conditions demonstrated a good agreement with Jason-3. In addition, the WTC of CMR has an obvious decrease in the standard deviation of the SSH difference compared with model-derived WTC, indicating the CMR can significantly improve the accuracy of the HY-2C SSH measurements. All the assessments indicate that the CMR performances are satisfying the expectations and fulfilling the mission requirements. Full article
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<p>Crossover points spatial distribution of <span class="html-italic">TB</span>18.7 in K (<b>top</b> panel), <span class="html-italic">TB</span>23.8 (<b>middle</b> panel) and <span class="html-italic">TB</span>37 (<b>bottom</b> panel).</p> Full article ">Figure 2
<p>(<b>a</b>): HY-2C TB versus Jason-3 TB 18.7, in K; (<b>b</b>): HY-2C TB versus brightness temperature difference between HY-2C and Jason-3, in K; (<b>c</b>): HY-2C TB versus Jason-3 TB 23.8, in K; (<b>d</b>): HY-2C TB versus brightness temperature difference between HY-2C and Jason-3, in K.</p> Full article ">Figure 3
<p>WTC differences in the spatial distribution of crossover points between HY-2C and Jason-3 with ΔT &lt; 30 min and ΔD &lt; 30 km (~12,788 points). The colour scale indicates WTC differences in cm.</p> Full article ">Figure 4
<p>(<b>Left</b> panel) WTC of HY-2C versus WTC of Jason-3; (<b>Right</b> panel) WTC of HY-2C versus WTC differences between HY-2C and Jason-3.</p> Full article ">Figure 5
<p>(<b>a</b>): HY-2C WTC versus ERA-5 WTC in algorithm database; (<b>b</b>): HY-2C WTC versus WTC difference between HY-2C and ERA-5 in algorithm database; (<b>c</b>): HY-2C WTC versus ERA-5 WTC in retrieval database; (<b>d</b>): HY-2C WTC versus WTC difference between HY-2C and ERA-5 in retrieval database.</p> Full article ">Figure 6
<p>SSH differences histogram of crossover points for HY-2C using model-derived WTC.</p> Full article ">Figure 7
<p>SSH differences histogram of crossover points for HY-2C using CMR_WTC.</p> Full article ">Figure 8
<p>Distribution of the crossover points SSH difference for HY-2C using CMR_WTC.</p> Full article ">
26 pages, 8726 KiB  
Article
On the Impact of GPS Multipath Correction Maps and Post-Fit Residuals on Slant Wet Delays for Tracking Severe Weather Events
by Addisu Hunegnaw, Hüseyin Duman, Yohannes Getachew Ejigu, Hakki Baltaci, Jan Douša and Felix Norman Teferle
Atmosphere 2023, 14(2), 219; https://doi.org/10.3390/atmos14020219 - 20 Jan 2023
Cited by 1 | Viewed by 2405
Abstract
Climate change has increased the frequency and intensity of weather events with heavy precipitation, making communities worldwide more vulnerable to flash flooding. As a result, accurate fore- and nowcasting of impending excessive rainfall is crucial for warning and mitigating these hydro-meteorological hazards. The [...] Read more.
Climate change has increased the frequency and intensity of weather events with heavy precipitation, making communities worldwide more vulnerable to flash flooding. As a result, accurate fore- and nowcasting of impending excessive rainfall is crucial for warning and mitigating these hydro-meteorological hazards. The measurement of integrated water vapour along slant paths is made possible by ground-based global positioning system (GPS) receiver networks, delivering three-dimensional (3D) water vapour distributions at low cost and in real-time. As a result, these data are an invaluable supplementary source of knowledge for monitoring storm events and determining their paths. However, it is generally known that multipath effects at GPS stations have an influence on incoming signals, particularly at low elevations. Although estimates of zenith total delay and horizontal linear gradients make up the majority of the GPS products for meteorology to date, these products are not sufficient for understanding the full 3D distribution of water vapour above a station. Direct utilization of slant delays can address this lack of azimuthal information, although, at low elevations it is more prone to multipath (MP) errors. This study uses the convective storm event that happened on 27 July 2017 over Bulgaria, Greece, and Turkey, which caused flash floods and severe damage, to examine the effects of multipath-corrected slant wet delay (SWD) estimations on monitoring severe weather events. First, we reconstructed the one-way SWD by adding GPS post-fit phase residuals, describing the anisotropic component of the SWD. Because MP errors in the GPS phase observables can considerably impact SWD from individual satellites, we used an averaging technique to build station-specific MP correction maps by stacking the post-fit phase residuals acquired from a precise point positioning (PPP) processing strategy. The stacking was created by spatially organizing the residuals into congruent cells with an optimal resolution in terms of the elevation and azimuth at the local horizon.This enables approximately equal numbers of post-fit residuals to be distributed across each congruent cell. Finally, using these MP correction maps, the one-way SWD was improved for use in the weather event analysis. We found that the anisotropic component of the one-way SWD accounts for up to 20% of the overall SWD estimates. For a station that is strongly influenced by site-specific multipath error, the anisotropic component of SWD can reach up to 4.3 mm in equivalent precipitable water vapour. The result also showed that the spatio-temporal changes in the SWD as measured by GPS closely reflected the moisture field estimated from a numerical weather prediction model (ERA5 reanalysis) associated with this weather event. Full article
(This article belongs to the Section Atmospheric Techniques, Instruments, and Modeling)
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<p>Locations of the GPS sites used for this study. The grey shaded area in the larger scaled map depicts the city boundaries of Istanbul.</p> Full article ">Figure 2
<p>The GPS station SARY in Turkey and its surrounding multipath scattering environment.</p> Full article ">Figure 3
<p>(<b>a</b>) Sky plot of raw post-fit residuals at station SARY, Turkey, from the observed 21 days post-fit residuals. Followed by (<b>b</b>), the MPS and (<b>c</b>) for the final corrected post-fit residuals obtained by subtracting the MPS map and (<b>d</b>) shows 21 days of post-fit residuals as in (<b>b</b>) but interpolated multipath map or so-called multipath footprint.</p> Full article ">Figure 4
<p>The propagation of the convective event as seen in GPS derived PWV at KARB (<b>pink</b>), ISTN (<b>slate blue</b>), SLEE (<b>light pink</b>), and ZONG (<b>dark pink</b>). The GPS-derived PWV time series of four (3) stations oriented on an east–west axis on 27 July 2017. Separation distance between KARB and SLEE is 79 km and that of SLEE and ZONG is 186 km. The GPS names follow the local toponymy. Precipitation rate can also be plotted with different colour bars.</p> Full article ">Figure 5
<p>To measure the movements of the landfall (proxy to the maximum water vapour PWV) between GPS stations, we measured the time elapsed between PWV crests of two stations by first locating the maximum cross-correlation involving the PWV time series for a pair of stations. (<b>a</b>) The red and the blue vertical line depicts the PWV crests for stations KARB and SLEE, respectively, and (<b>b</b>) for stations SARY and SLEE. (<b>c</b>) We expounded the crest correlations between 17 pairs of GPS stations and produced a heatmap that translates the time interval to the propagation speed pf the storm. (<b>d</b>) shows all 17 stations used for the heatmap.</p> Full article ">Figure 6
<p>Sky plots of GPS post-fit residuals as a function of satellite azimuth and elevation angles for station ISTA (<b>a</b>) and SLEE (<b>b</b>), Turkey for DOY 206 during calmer atmospheric conditions. The post-fit residuals, red (positive) and yellow (negative), show an excursion normal to the satellite tracks with small signature. The lower panels (<b>c</b>,<b>d</b>) represent precipitation provided by the IMERG product on DOY 206 during the calmer atmospheric conditions.</p> Full article ">Figure 7
<p>Sky plots of GPS post-fit residuals as a function of satellite azimuth and elevation angles for the station ISTA (<b>a</b>) and SLEE (<b>b</b>), Turkey, for DOY 208 during severe atmospheric conditions. The post-fit residuals, red (positive) and yellow (negative), show an excursion normal to the satellite tracks with significantly larger signature. The lower panels (<b>c</b>,<b>d</b>) represent precipitation rate estimates provided by the IMERG product on DOY 208. The precipitation rate is much more intense during this period, and the post-fit residuals show significant variations around 17:00 UTC local time. The deepest reds depict areas getting the most rainfall. Local rainfall could be notably higher when measured from the ground level, see <a href="#sec3-atmosphere-14-00219" class="html-sec">Section 3</a>.</p> Full article ">Figure 8
<p>Constraint sensitivity of post-fit residuals by random walk process with different noise intensity when estimating ZWD and gradients. We allow the noise constraints in ZWD to vary from 100, 50, 10, 5, and 1 mm/<math display="inline"><semantics> <mrow> <mo>√</mo> <mi>h</mi> </mrow> </semantics></math> and for the gradients to vary 10, 5, 1, 0.5, and 0.1 mm/<math display="inline"><semantics> <mrow> <mo>√</mo> <mi>h</mi> </mrow> </semantics></math>. (<b>a</b>) For station SLEE and (<b>b</b>) for station ISTN.</p> Full article ">Figure 9
<p>Constraint sensitivity of (<b>a</b>) ZWD estimates by random walk processing with a noise intensity at different values for post-fit residuals, and (<b>b</b>) first differenced ZWD estimates, that is, the difference of successive values of random walk process, which is equivalent to white noise process. The applied constraints for ZWD estimates vary from 100, 50, 10, 5, and 1 mm/<math display="inline"><semantics> <mrow> <mo>√</mo> <mi>h</mi> </mrow> </semantics></math>, whereas those for horizontal gradients vary from 10, 5, 1, 0.5, and 0.1 mm/<math display="inline"><semantics> <mrow> <mo>√</mo> <mi>h</mi> </mrow> </semantics></math>.</p> Full article ">Figure 10
<p>Time series of GPS slant water vapour observations in the direction of individual satellites. (<b>a</b>,<b>c</b>) contain the isotropic component of the precipitable slant water vapour for satellite PRN10 and PRN21 for the station SLEE and ISTA, respectively. (<b>b</b>,<b>d</b>) show the anisotropic component of the precipitable slant water vapour for PRN10 and PRN21 for the station SLEE and ISTA, respectively. The satellite elevation angle is depicted in all panels as the grey line.</p> Full article ">Figure 11
<p>Time series of anisotropic slant precipitable water vapour (<math display="inline"><semantics> <mrow> <mi>δ</mi> <mi>SWV</mi> </mrow> </semantics></math>) observations in the direction of PRN08 and PRN21 satellites with and without applying site-specific multipath correction at station SARY. The panels (a,<b>c</b>) contain the anisotropic, <math display="inline"><semantics> <mrow> <mi>δ</mi> <mi>SWV</mi> </mrow> </semantics></math>, without multipath corrections in red and with multipath corrections in blue. The panels (<b>b</b>,<b>d</b>) contain the differences between the blue and the red lines. The satellite elevations angle for PRN08 and PRN21 as seen from the SARY GPS station is plotted in grey.</p> Full article ">Figure 12
<p>Slant wet delay estimates from GPS (green circles) and the corresponding ERA5 reanalysis derived SWD (magenta circles) for stations YENC (<b>a</b>), SLEE (<b>b</b>), and ZONG (<b>c</b>). (<b>d</b>) shows the SWD differences between GPS and ERA5 SWD for DOY between 206 and 210, 2017, for all 55 stations. The GPS-derived SWD includes post-fit residuals, and MPS correction maps are incorporated. (<b>e</b>) shows the SWD differences, where the GPS-derived SWD includes only post-fit residuals. (<b>f</b>) shows the SWD differences, where the GPS-derived SWD does not include both post-fit residuals and MPS correction maps. The red line represents the average biases for all the stations and the black line for the average standard error as a function of elevation angle. The ERA5 SWD has a temporal estimate every hour, while our GPS-derived SWD provides solutions every 30 s.</p> Full article ">
13 pages, 3327 KiB  
Article
Error Correction of Water Vapor Radiometers for VLBI Observations in Deep-Space Networks
by Houcai Chen, Junxiang Ge, Qingde Kong, Zhenwei Zhao and Qinglin Zhu
Atmosphere 2021, 12(12), 1601; https://doi.org/10.3390/atmos12121601 - 30 Nov 2021
Cited by 2 | Viewed by 1629
Abstract
In this paper, we present the design and implementation tests of a water vapor radiometer (WVR) suitable for very long baseline interferometry (VLBI) observation. We describe the calibration method with an analysis of the sources of measurement errors. The experimental results show that [...] Read more.
In this paper, we present the design and implementation tests of a water vapor radiometer (WVR) suitable for very long baseline interferometry (VLBI) observation. We describe the calibration method with an analysis of the sources of measurement errors. The experimental results show that the long-term measurement accuracy of the brightness temperature of the water vapor radiometer can reach 0.2 K under arbitrary ambient conditions by absolute calibration, receiver gain error calibration, and antenna feeder system temperature noise error calibration. Furthermore, we present a method for measurements of the calibration error of the oblique path measurement. This results in an oblique path wet delay measurement accuracy of the water vapor radiometer reaching 20 mm (within one month). Full article
(This article belongs to the Special Issue Radiation and Radiative Transfer in the Earth Atmosphere)
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<p>WVR next to the Shanghai Tianma telescope.</p> Full article ">Figure 2
<p>The schematic diagram of the HZD-X water vapor radiometer.</p> Full article ">Figure 3
<p>The brightness temperature of the water vapor radiometer at 23.8 GHz frequency and the brightness temperature of the black body were measured when the ambient temperature changed.</p> Full article ">Figure 4
<p>The relationship between measurement error and ambient temperature.</p> Full article ">Figure 5
<p>Block diagram of noise injection radiometer system.</p> Full article ">Figure 6
<p><span class="html-italic">V<sub>Ni</sub></span> changes on 5 March 2017.</p> Full article ">Figure 7
<p>The error of measuring blackbody after receiver gain error calibration was reduced by nearly 1 K.</p> Full article ">Figure 8
<p>Contribution of WVR components of system noise temperature <span class="html-italic">T<sub>REC</sub></span>.</p> Full article ">Figure 9
<p>The relationship between the measured feed temperature and the difference between the measured WVR and the blackbody temperature.</p> Full article ">Figure 10
<p>Calibration results of the measured data on 4 March 2017. (<b>a</b>) shows the measurement error without calibration, (<b>b</b>) shows the measurement error after system absolute calibration, (<b>c</b>) shows the measurement error after receiver gain error calibration, and (<b>d</b>) shows the measurement error after calibration of the temperature noise error of the antenna feeder system.</p> Full article ">Figure 11
<p>The optical thickness error at different elevation angles.</p> Full article ">
18 pages, 5915 KiB  
Article
Tropospheric Refractivity Profile Estimation by GNSS Measurement at China Big-Triangle Points
by Xiang Dong, Fang Sun, Qinglin Zhu, Leke Lin, Zhenwei Zhao and Chen Zhou
Atmosphere 2021, 12(11), 1468; https://doi.org/10.3390/atmos12111468 - 6 Nov 2021
Cited by 4 | Viewed by 2540
Abstract
Atmospheric radio refractivity has an obvious influence on the signal transmission path and communication group delay effect. The uncertainty of water vapor distribution is the main reason for the large error of tropospheric refractive index modeling. According to the distribution and characteristics of [...] Read more.
Atmospheric radio refractivity has an obvious influence on the signal transmission path and communication group delay effect. The uncertainty of water vapor distribution is the main reason for the large error of tropospheric refractive index modeling. According to the distribution and characteristics of water vapor pressure, temperature, and pressure, which are the basic components of the refractive index, a method for retrieving atmospheric refractivity profile based on GNSS (Global Navigation Satellite System) and meteorological sensor measurement is introduced and investigated in this study. The variation of the correlation between zenith wet delay and water vapor pressure is investigated and analyzed in detail. The partial pressure profiles of water vapor are retrieved with relevance vector machine method based on tropospheric zenith wet delay calculated by single ground-based GPS (Global Positioning System) receiver. The atmospheric temperature and pressure is calculated with the least square method, which is used to fit the coefficients of the polynomial model based on a large number of historical meteorological radiosonde data of local stations. By combining the water vapor pressure profile retrieving from single ground-based GPS and temperature and pressure profile from reference model, the refractivity profile can be obtained, which is compared to radiosonde measurements. The comparison results show that results of the proposed method are consistent with the results of radiosonde. By using over ten years’ (through 2008 to 2017) historical radiosonde meteorological data of different months at China Big-Triangle Points, i.e., Qingdao, Sanya, Kashi, and Jiamusi radiosonde stations, tropospheric radio refractivity profiles are retrieved and modeled. The comparison results present that the accuracies of refractivity profile of the proposed method at Qingdao, Sanya, Kashi, and Jiamusi are about 5.48, 5.63, 3.58, and 3.78 N-unit, respectively, and the annual average relative RMSE of refractivity at these stations are about 1.66, 1.53, 1.49, and 1.23%, respectively. Full article
(This article belongs to the Special Issue GNSS Observations in Meteorology and Climate Applications)
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<p>The distribution of radiosonde meteorological stations.</p> Full article ">Figure 2
<p>Statistical relationships between zenith wet delay and water vapor pressure at the height of (<b>a</b>) 500, (<b>b</b>) 2000, (<b>c</b>) 5000, and (<b>d</b>) 10,000 m, respectively.</p> Full article ">Figure 3
<p>Variations of the correlation coefficients with height between zenith wet delay and water vapor pressure at different months of Qingdao, Sanya, Kashi, and Jiamusi station, respectively. The horizontal axis denote the correlation coefficients, the vertical axis denote heights in meters.</p> Full article ">Figure 4
<p>Flow diagram of training procedure for retrieving profile of water vapor pressure.</p> Full article ">Figure 5
<p>Flow diagram for retrieving profile of water vapor pressure.</p> Full article ">Figure 6
<p>(<b>a</b>) The comparison between the retrieved, ITU model and radiosonde profile of water vapor pressure; (<b>b</b>) The root mean square error of retrieved and ITU model profile of water vapor pressure.</p> Full article ">Figure 7
<p>(<b>a</b>) The comparison between fitted, ITU model, and radiosonde temperature profile; (<b>b</b>) The root mean square error of fitted and ITU model temperature profile.</p> Full article ">Figure 8
<p>(<b>a</b>) The comparison between fitted, ITU model, and radiosonde atmospheric pressure profile; (<b>b</b>) The RMSE of fitted and ITU model atmospheric pressure profile.</p> Full article ">Figure 9
<p>(<b>a</b>) The comparison between fitted, ITU model, and radiosonde refractivity profile; (<b>b</b>) The root mean square error of fitted and ITU model refractivity profile.</p> Full article ">Figure 10
<p>The RMSE of refractivity profile in different month.</p> Full article ">Figure 11
<p>Boxplot of the ground refractivity in different months of 10 years’ radiosonde data, the red marks represent outlier data of refractivity.</p> Full article ">Figure 12
<p>The mean ground refractivity in different months of 10 years’ radiosonde data.</p> Full article ">Figure 13
<p>The relative RMSE of refractivity profile in different months.</p> Full article ">
24 pages, 8731 KiB  
Article
Sentinel-3 Microwave Radiometers: Instrument Description, Calibration and Geophysical Products Performances
by Marie-Laure Frery, Mathilde Siméon, Christophe Goldstein, Pierre Féménias, Franck Borde, Alexandre Houpert and Ana Olea Garcia
Remote Sens. 2020, 12(16), 2590; https://doi.org/10.3390/rs12162590 - 12 Aug 2020
Cited by 13 | Viewed by 4410
Abstract
Copernicus Sentinel-3 Surface Topography Mission embarks a two-channel microwave radiometer combined with the altimeter in order to correct the altimeter range for the excess path delay resulting from the presence of water vapour in the troposphere. The in-flight calibration of a single instrument [...] Read more.
Copernicus Sentinel-3 Surface Topography Mission embarks a two-channel microwave radiometer combined with the altimeter in order to correct the altimeter range for the excess path delay resulting from the presence of water vapour in the troposphere. The in-flight calibration of a single instrument is the critical point to achieve the expected performances. In the context of a constellation, the inter-calibration is even more important. After a presentation of the instrument design, we present the diagnoses used for the calibration of Sentinel-3A, using vicarious calibration over specific areas and double difference methods. The inter-calibration of Sentinel-3B with Sentinel-3A is performed during the tandem phase, using the residual differences of co-located measurements. Finally performances are assessed at crossover points with two parameters, first the wet troposphere correction by comparison with Jason-3; secondly on the Sea Surface Height by difference of variance. Analysis results have shown that Sentinel-3A is well calibrated, consistent with other instruments, and that Sentinel-3B is calibrated within 0.4 K with Sentinel-3A as a reference. Performances and stability fulfill the requirements for both missions. Full article
(This article belongs to the Special Issue Calibration and Validation of Satellite Altimetry)
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Full article ">Figure 1
<p>Schematic drawing of the operating modes.</p> Full article ">Figure 2
<p>Side-lobe correction map centered on Mediterranean sea. (<b>a</b>) On-Earth side-lobe correction for 23.8 GHz channel computed with Envisat antenna pattern; (<b>b</b>) On-Earth side-lobe correction for 23.8 GHz channel computed with S3A antenna pattern.</p> Full article ">Figure 3
<p>Raw counts for both channels (23.8 GHz, 36.5 GHz) and both instruments (Sentinel-3A, Sentinel-3B) in coastal areas during the tandem phase: (<b>a</b>) over a specific land/sea transition and (<b>b</b>) one-cycle statistics with respect to shoreline distance.</p> Full article ">Figure 4
<p>Brightness temperature spectra for both channels of S3A and S3B.</p> Full article ">Figure 5
<p>Time series of coldest brightness temperatures over ocean for measurements (full line) and simulations (dotted lines) at 23.8 GHz (<b>a</b>) and Cloud Liquid Water Contentchannels (<b>b</b>) for Altika, J3, MetOp-A and S3A. The right panels are showing the average single difference computed over the period defined by the two vertical dotted lines.</p> Full article ">Figure 6
<p>Mean Single (MES-SIM) and double difference for Altika, J3, Metop-A and S3A. (<b>a</b>) Single difference 23.8 Ghz channel; (<b>b</b>) Single difference Cloud Liquid Water Content (CLWC) channel; (<b>c</b>) Double difference 23.8 Ghz channel; (<b>d</b>) Double difference CLWC channel.</p> Full article ">Figure 7
<p>Single difference (MES-SIM) for ascending and descending passes for Altika, J3, Metop-A and S3A.</p> Full article ">Figure 8
<p>(<b>a</b>) Glob cover mask and (<b>b</b>) variation of Jason-2 brightness temperatures measurements averaged in one hour bins of local time.</p> Full article ">Figure 9
<p>Time series of hottest brightness temperatures over Amazon forest at 23.8 GHz (<b>a</b>) and CLWC channel (<b>b</b>) for AltiKa, Jason-3, MetOp-A and Sentinel-3A. The right panels are showing the average temperature computed over the period defined by the two vertical dotted lines.</p> Full article ">Figure 10
<p>Mean temperatures of single difference for coldest ocean points (<b>a</b>,<b>b</b>), hottest temperatures over Amazon forest (<b>c</b>,<b>d</b>) and single difference over ocean (<b>e</b>,<b>f</b>) after in-flight calibration.</p> Full article ">Figure 11
<p>Residual difference between S3A and S3B brightness temperatures before S3B inter-calibration for cycle 10 of S3B.</p> Full article ">Figure 12
<p>Histogram of Residual differences between S3A and S3B brightness temperatures (<b>a</b>) before and (<b>b</b>) after S3B inter-calibration for cycle 10 of S3B.</p> Full article ">Figure 13
<p>Residual differences between S3A and S3B brightness temperatures after S3B inter-calibration for cycle 10 of S3B.</p> Full article ">Figure 14
<p>Monthly average of WTC differences at crossover points with Jason-3 for (<b>a</b>), (<b>b</b>) S3A, S3B (<b>c</b>) and AltiKa (<b>d</b>) missions.</p> Full article ">Figure 15
<p>Maps of Wet Troposphere Correction (WTC) differences at S3A/J3 (<b>a</b>) and AL/J3 (<b>b</b>) crossover points.</p> Full article ">Figure 16
<p>Difference of variance of SSH at crossover points for AL, J3, S3A, S3B for global and low oceanic variability selections.</p> Full article ">Figure 17
<p>Performances of MWR solutions with respect to ECMWF. S3A, Jason-3 and AltiKa data cover a similar period of time of 4 years, S3B data cover only 1.5 year.</p> Full article ">Figure A1
<p>Architecture.</p> Full article ">
21 pages, 4145 KiB  
Article
Modelling the Altitude Dependence of the Wet Path Delay for Coastal Altimetry Using 3-D Fields from ERA5
by Telmo Vieira, M. Joana Fernandes and Clara Lázaro
Remote Sens. 2019, 11(24), 2973; https://doi.org/10.3390/rs11242973 - 11 Dec 2019
Cited by 14 | Viewed by 3888
Abstract
Wet path delay (WPD) for satellite altimetry has been provided from external sources, raising the need of converting this value between different altitudes. The only expression available for this purpose considers the same altitude reduction, irrespective of geographic location and time. The focus [...] Read more.
Wet path delay (WPD) for satellite altimetry has been provided from external sources, raising the need of converting this value between different altitudes. The only expression available for this purpose considers the same altitude reduction, irrespective of geographic location and time. The focus of this study is the modelling of the WPD altitude dependence, aiming at developing improved expressions. Using ERA5 pressure level fields (2010–2013), WPD vertical profiles were computed globally. At each location and for each vertical profile, an exponential function was fitted using least squares, determining the corresponding decay coefficient. The time evolution of these coefficients reveals regions where they are highly variable, making this modelling more difficult, and regions where an annual signal exists. The output of this modelling consists of a set of so-called University of Porto (UP) coefficients, dependent on geographic location and time. An assessment with ERA5 data (2014) shows that for the location where the Kouba coefficient results in a maximum Root Mean Square (RMS) error of 3.2 cm, using UP coefficients this value is 1.2 cm. Independent comparisons with WPD derived from Global Navigation Satellite Systems and radiosondes show that the use of UP coefficients instead of Kouba’s leads to a decrease in the RMS error larger than 1 cm. Full article
(This article belongs to the Special Issue Remote Sensing of Coastal and Inland Waters)
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<p>Atmospheric variables provided by ERA5 on 1 January 2010, at 00:00 UTC on model levels (blue) and on pressure levels (orange): (<b>a</b>) temperature (T) in Kelvin and (<b>b</b>) specific humidity (q) in kg/kg at location 00°, 120°E.</p> Full article ">Figure 2
<p>Wet path delay (WPD) vertical profiles at (<b>a</b>) 10°N, 90°W; (<b>b</b>) 00°, 100°E; (<b>c</b>) 25°S, 65°E. Grey profiles represent those every 3h over the year 2010, solid line represents the annual mean profile, squares with dashed line and circles with dotted line represent the mean profiles for January and July, respectively.</p> Full article ">Figure 3
<p>Spatial representation of the radiosondes (RS) network from Integrated Global Radiosonde Data (IGRA). Blue points represent all RS since 1905, green squares represent the RS with valid measurements of temperature and humidity over the year 2014, and red triangles represent those selected for the validation.</p> Full article ">Figure 4
<p>Time evolution of the α coefficients at locations: (<b>a</b>) 10°N, 90°W; (<b>b</b>) 00°, 100°E; (<b>c</b>) 25°S, 65°E. Grey points represent the α coefficients every 3h, orange line represents the overall mean (UP-01) and purple squares and green points represent the seasonally averaged (UP-04) and monthly averaged coefficients (UP-12), respectively.</p> Full article ">Figure 4 Cont.
<p>Time evolution of the α coefficients at locations: (<b>a</b>) 10°N, 90°W; (<b>b</b>) 00°, 100°E; (<b>c</b>) 25°S, 65°E. Grey points represent the α coefficients every 3h, orange line represents the overall mean (UP-01) and purple squares and green points represent the seasonally averaged (UP-04) and monthly averaged coefficients (UP-12), respectively.</p> Full article ">Figure 5
<p>Spatial representation of the α coefficient, computed as the mean for each point (UP-01) in a 5° × 5° grid.</p> Full article ">Figure 6
<p>Root Mean Square (RMS) (cm) of the WPD differences between 3-D (WPD retrieved from the original ERA5 PL fields) and 2-D with Kouba reduction, using profiles every 3h in a 5° × 5° grid over the year 2014.</p> Full article ">Figure 7
<p>RMS (cm) of the WPD differences between 3-D and 2-D with UP-01 reduction, using profiles every 3h in a 5° × 5°grid over the year 2014.</p> Full article ">Figure 8
<p>RMS (cm) of the differences between WPD computed with 3-D approach and that computed at surface level and then reduced with UP-04 (<b>left</b>) and UP-12 (<b>right</b>) coefficients.</p> Full article ">Figure 9
<p>RMS (cm) of the differences between WPD computed with 3-D approach using atmospheric variables from IGRA and those computed at lowest level and then reduced with Kouba (blue bars), UP-01 (orange bars), UP-04 (purple bars), and UP-12 (green bars) coefficients.</p> Full article ">Figure 10
<p>RMS (cm) of the differences at Global Navigation Satellite Systems (GNSS) station height between WPD derived from GNSS and those computed at ERA5 orography level using single level atmospheric variables and then reduced with Kouba (blue bars), UP-01 (orange bars), UP-04 (purple bars), and UP-12 (green bars) coefficients to the height of each GNSS station (identified by its four characters).</p> Full article ">
19 pages, 4216 KiB  
Article
Adaptive Air-Fuel Ratio Regulation for Port-Injected Spark-Ignited Engines Based on a Generalized Predictive Control Method
by Lei Meng, Xiaofeng Wang, Chunnian Zeng and Jie Luo
Energies 2019, 12(1), 173; https://doi.org/10.3390/en12010173 - 6 Jan 2019
Cited by 16 | Viewed by 7611
Abstract
The accurate air-fuel ratio (AFR) control is crucial for the exhaust emission reduction based on the three-way catalytic converter in the spark ignition (SI) engine. The difficulties in transient cylinder air mass flow measurement, the existing fuel mass wall-wetting phenomenon, and the unfixed [...] Read more.
The accurate air-fuel ratio (AFR) control is crucial for the exhaust emission reduction based on the three-way catalytic converter in the spark ignition (SI) engine. The difficulties in transient cylinder air mass flow measurement, the existing fuel mass wall-wetting phenomenon, and the unfixed AFR path dynamic variations make the design of the AFR controller a challenging task. In this paper, an adaptive AFR regulation controller is designed using the feedforward and feedback control scheme based on the dynamical modelling of the AFR path. The generalized predictive control method is proposed to solve the problems of inherent nonlinearities, time delays, parameter variations, and uncertainties in the AFR closed loop. The simulation analysis is investigated for the effectiveness of noise suppression, online prediction, and self-correction on the SI engine system. Moreover, the experimental verification shows an acceptable performance of the designed controller and the potential usage of the generalized predictive control in AFR regulation application. Full article
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<p>The structure of the AFR control system.</p> Full article ">Figure 2
<p>The schematic diagram of the dynamic AFR path.</p> Full article ">Figure 3
<p>The scheme of the adaptive controller.</p> Full article ">Figure 4
<p>The parameter identification result of <math display="inline"><semantics> <mover accent="true"> <mi>X</mi> <mo stretchy="false">^</mo> </mover> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mover accent="true"> <mi>τ</mi> <mo stretchy="false">^</mo> </mover> <mi>f</mi> </msub> </mrow> </semantics></math>.</p> Full article ">Figure 5
<p>The feedback control scheme.</p> Full article ">Figure 6
<p>The simulation result of Case 1 (u as the fuel injection calculated by the controller, y as the engine equivalence ratio output).</p> Full article ">Figure 7
<p>Simulation parameters of Case 2. (<b>Up</b>) the measured engine parameters; (<b>Down</b>) the engine model parameters.</p> Full article ">Figure 8
<p>The simulation result of system output in Case 2 (yr as the referenced equivalence ratio, y as the engine equivalence ratio output).</p> Full article ">Figure 9
<p>The online identification result of the controller parameters (a<sub>1</sub>, a<sub>2</sub>, b<sub>1</sub>, and b<sub>2</sub> as the parameters in Equation (32)).</p> Full article ">Figure 10
<p>The control system scheme of the engine test bench.</p> Full article ">Figure 11
<p>Experimental results of the control performance under load disturbance.</p> Full article ">Figure 12
<p>Comparison experimental results of the ECU controllers.</p> Full article ">Figure 13
<p>Experimental results of different control methods.</p> Full article ">
44 pages, 11672 KiB  
Article
Radar Path Delay Effects in Volcanic Gas Plumes: The Case of Láscar Volcano, Northern Chile
by Stefan Bredemeyer, Franz-Georg Ulmer, Thor H. Hansteen and Thomas R. Walter
Remote Sens. 2018, 10(10), 1514; https://doi.org/10.3390/rs10101514 - 21 Sep 2018
Cited by 14 | Viewed by 4654
Abstract
Modern volcano monitoring commonly involves Interferometric Synthetic Aperture Radar (InSAR) measurements to identify ground motions caused by volcanic activity. However, InSAR is largely affected by changes in atmospheric refractivity, in particular by changes which can be attributed to the distribution of water (H [...] Read more.
Modern volcano monitoring commonly involves Interferometric Synthetic Aperture Radar (InSAR) measurements to identify ground motions caused by volcanic activity. However, InSAR is largely affected by changes in atmospheric refractivity, in particular by changes which can be attributed to the distribution of water (H2O) vapor in the atmospheric column. Gas emissions from continuously degassing volcanoes contain abundant water vapor and thus produce variations in the atmospheric water vapor content above and downwind of the volcano, which are notably well captured by short-wavelength X-band SAR systems. These variations may in turn cause differential phase errors in volcano deformation estimates due to excess radar path delay effects within the volcanic gas plume. Inversely, if these radar path delay effects are better understood, they may be even used for monitoring degassing activity, by means of the precipitable water vapor (PWV) content in the plume at the time of SAR acquisitions, which may provide essential information on gas plume dispersion and the state of volcanic and hydrothermal activity. In this work we investigate the radar path delays that were generated by water vapor contained in the volcanic gas plume of the persistently degassing Láscar volcano, which is located in the dry Atacama Desert of Northern Chile. We estimate water vapor contents based on sulfur dioxide (SO2) emission measurements from a scanning UV spectrometer (Mini-DOAS) station installed at Láscar volcano, which were scaled by H2O/SO2 molar mixing ratios obtained during a multi-component Gas Analyzer System (Multi-GAS) survey on the crater rim of the volcano. To calculate the water vapor content in the downwind portion of the plume, where an increase of water vapor is expected, we further applied a correction involving estimation of potential evaporation rates of water droplets governed by turbulent mixing of the condensed volcanic plume with the dry atmosphere. Based on these estimates we obtain daily average PWV contents inside the volcanic gas plume of 0.2–2.5 mm equivalent water column, which translates to a slant wet delay (SWD) in DInSAR data of 1.6–20 mm. We used these estimates in combination with our high resolution TerraSAR-X DInSAR observations at Láscar volcano, in order to demonstrate the occurrence of repeated atmospheric delay patterns that were generated by volcanic gas emissions. We show that gas plume related refractivity changes are significant and detectable in DInSAR measurements. Implications are two-fold: X-band satellite radar observations also contain information on the degassing state of a volcano, while deformation signals need to be interpreted with care, which has relevance for volcano observations at Láscar and for other sites worldwide. Full article
(This article belongs to the Special Issue Remote Sensing of Volcanic Processes and Risk)
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<p>Láscar and adjacent Aguas Calientes volcanoes. The location of the scanning Mini-DOAS station is indicated by the <span class="html-italic">white dot</span> on the southern flank of Aguas Calientes volcano. (<b>a</b>) Aster visible range composite of 29 April 2013 draped onto SRTM-1 grid; (<b>b</b>) Close-up of high temperature fumaroles in the active crater; (<b>c</b>) View from the southern crater rim towards NNW into the active crater of Láscar showing fumarolic activity during the Multi-GAS survey on the Southern crater rim on 2 December 2012. The <span class="html-italic">white bounding box</span> framing the area shown in (<b>b</b>) roughly spans 200 m in width and 100 m in height; (<b>d</b>) Panoramic view from South towards NNW showing a dispersed gas plume emanating from Láscars’ active crater on 5 December 2012, and drifting towards SE, which corresponds to the main transport direction during daylight time. Distance between Mini-DOAS in the foreground and active crater in the background of the image is about 6 km.</p> Full article ">Figure 2
<p>Spatio-temporal relationships between the methods used for the gas plume estimate. (<b>a</b>) Sketch showing the measurement geometry and location of the Multi-GAS and scanning Mini-DOAS instruments at Láscar volcano during an overpass of TerraSAR-X. Refractive delay of the radar occurs inside the volcanic plume, which is heading towards East, due to predominantly westerly winds. See text for discussion; (<b>b</b>) Date versus time plot depicting acquisition times of scanning DOAS measurements and SAR images. Measurement times are indicated using Coordinated Universal Time (UTC), which is offset by +3 h with respect to Chile Summer Time (CLST). <span class="html-italic">Red arrows</span> indicate the temporal offset between SAR images and scanning DOAS data chosen for analysis; (<b>c</b>) Spatial and temporal baselines of SAR images used in DInSAR time series subsets 01 (<span class="html-italic">blue</span>) and 02 (<span class="html-italic">red</span>). Master scenes of <span class="html-italic">subset 01</span> and <span class="html-italic">subset 02</span> are from 18 October 2013 and 12 December 2013, respectively, and each computed interferogram is represented by a line between the corresponding two images.</p> Full article ">Figure 3
<p>Technical diagram depicting the processing steps of the WBDD algorithm. In a DInSAR time series each DInSAR pixel (respectively range azimuth position) is characterized by a certain temporal evolution of its differential signal strength. Computation of the <math display="inline"><semantics> <mrow> <mi>D</mi> <mi>T</mi> <mo>-</mo> <mi>ℂ</mi> <mi>W</mi> <mi>T</mi> </mrow> </semantics></math> representations of each DInSAR enables to capture the temporal evolution of the signal strength of all SAR pixels of the DInSAR time series by means of a small number of complex wavelet coefficients. Similarly each process which causes changes in SAR signal strength can be described by a time series, which reflects the temporal variations of the process (e.g., variations of water vapor contents in the volcanic gas plume, variations in spatial baseline, relative humidity, ground temperature, and pressure). The algorithm uses the time series of these processes as an a priori knowledge of a related possible change in SAR signal strength and assigns the SAR signals to their likely causes by comparison of the <span class="html-italic">prior time series</span> with the temporal evolution of SAR signal strength at each range azimuth position, which decomposes the interferograms into different phase screens.</p> Full article ">Figure 4
<p>(<b>a</b>) Time series of PWV contents determined for plume cross-sections recorded by scanning DOAS on days with TerraSAR-X acquisitions (18 December 2013 to 16 February 2014). The time scale is not linear. Result without consideration of evaporation; (<b>b</b>) Daily average PWV contents in the centerline of the plume (<span class="html-italic">stippled line</span>) and the bulk plume (<span class="html-italic">solid line</span>), which are representative for the moisture distribution above the crater rim on days with available SAR acquisitions; (<b>c</b>) Time series of potential evaporation rates at plume height above the summit of Láscar volcano. Evaporation rates at the times of SAR observations are indicated by <span class="html-italic">red</span> (master scenes) and <span class="html-italic">blue</span> (slave scenes) <span class="html-italic">dots</span>; (<b>d</b>) Time series of wind speeds at summit altitude used for calculation of potential evaporation rates; (<b>e</b>) Time series of PWV contents in plume cross-sections considering downwind evaporation; (<b>f</b>) Daily average PWV content in the centerline of the plume (<span class="html-italic">stippled line</span>) and the bulk plume (<span class="html-italic">solid line</span>) downwind of the volcano on days with available SAR acquisitions.</p> Full article ">Figure 5
<p>(<b>a</b>) PWV contents in plume cross-sections obtained for the downwind portion of the volcanic gas plume on acquisition dates of SAR master and slave scenes, and (<b>b</b>–<b>e</b>) corresponding DInSAR maps (10 × 10 km in azimuth and range direction). Scales of the DInSAR maps indicate range change in units of mm, and are unique to each image using the same scale bounds for the sake of comparability with other phase contributions. Incoherent areas are masked out; (<b>b</b>) Coarsely corrected DInSAR maps used for decomposition analysis and retrieval of the gas plume estimate. DEM and major APS contributions derived from WRF were removed; (<b>c</b>) Phase difference maps of the gas plume estimates obtained for the downwind portion of the volcanic gas plume. Corresponding theoretical dSWDs are indicated in the upper right corner of each map; (<b>d</b>) DInSAR maps from 5b, where the phase contributions of the gas plume were removed; (<b>e</b>) Linear deformation estimates obtained from the refined correction using the delay estimates obtained from WBDD analysis.</p> Full article ">Figure 6
<p>(<b>a</b>–<b>h</b>) Delay correlation maps depicting the estimated repeating patterns of SAR delays obtained from <span class="html-italic">priors</span> included in the WBDD analysis. Location of active crater is indicated by the <span class="html-italic">red circle</span>; (<b>a</b>) Delay estimate for the gas plume aloft the crater (<b>b</b>–<b>d</b>) Delay estimates for the downwind portion of the gas plume (<b>b</b>) with and (<b>c</b>) without additional removal of pressure and temperature dependent phase screens by including/excluding respective <span class="html-italic">priors</span> in the WBDD analysis; (<b>d</b>) Gas plume estimate as in c), however omitting the SAR observation of 3 January 2014. Scales correspond to estimated delay (mm) per theoretical delay (mm). Scale bar limits of the gas plume estimate obtained for the proximal part of the plume above the crater indicate that theoretical delays are by a factor 100 smaller than the delay estimate. Upper and lower limits of the scale bars of the estimates obtained for the downwind portion of the gas plume are equal to unity, indicating concurrence of theoretical and estimated delay. The red to yellow colored signature in the lower right corner of the image indicates a lengthening of the delay, where the effect of H<sub>2</sub>O emissions is large. The affected area corresponds to the most common plume transport directions (see <a href="#app4-remotesensing-10-01514" class="html-app">Appendix D</a>, <a href="#remotesensing-10-01514-f0A3" class="html-fig">Figure A3</a>a,b) and locations where the volcanic plume regularly touches the ground (fumigation); (<b>e</b>) Estimates of surface temperature and (<b>f</b>) surface pressure related delays. Scales indicate mm estimated delay per Kelvin, respectively hPa of the input <span class="html-italic">prior</span>; (<b>g</b>) Delay estimate obtained for the relative humidity <span class="html-italic">prior</span> (<b>h</b>) Delay estimate for the spatial baseline <span class="html-italic">prior</span>; (<b>i</b>) Scatter plot of theoretical delay (mm) versus determined signal strength of the gas plume contribution in the DInSARs used for the gas plume estimate in (<b>b</b>). Each asterisk symbol represents one of the DInSARs and corresponding slave dates (mm-dd) are indicated.</p> Full article ">Figure 7
<p>(<b>a</b>) H<sub>2</sub>O emission rates from Láscar volcano along with a sequence of Aster, Landsat-8 and EO-1 ALI scenes depicting snow and cloud coverage (upper row: false-color composites of SWIR, NIR and visible red spectral bands; lower row: true-color images using a combination of visible red, green and blue spectral bands). Days with incandescence observations are indicated by <span class="html-italic">stippled grey vertical lines</span>. Precipitation events that were recorded in Toconao (on 10 November 2013, 17 January 2014, and 26 January 2014) are indicated by <span class="html-italic">vertical blue lines</span>, including information on amount and duration of the events; (<b>b</b>) Background atmospheric PWV contents estimated from GDAS1 soundings above Láscar volcano (<span class="html-italic">red curve</span>), compared to radiometer measurements conducted at APEX (<span class="html-italic">blue curve</span>). Good agreement between the two curves reflects similar weather conditions caused by a similar morphologic exposition of both sites, and may additionally be attributed to the coarse spatial resolution of the GDAS1 data. PWV estimates for the times of SAR observations are indicated by <span class="html-italic">red</span> (master scene) and <span class="html-italic">blue</span> (slave scene) <span class="html-italic">dots</span>, respectively.</p> Full article ">Figure A1
<p>(<b>a</b>,<b>b</b>) Time series of vertical atmospheric profiles depicting variations in (<b>a</b>) wind speed and (<b>b</b>) wind direction. Note the weak winds accompanied by strong variations of wind directions during austral summer (ranging from December 2013 to February 2014). (<b>c</b>,<b>d</b>) Time series of wind directions some 100 m (<b>c</b>) above and (<b>d</b>) below the summit of Láscar (500 and 550 mbar pressure levels of the GDAS1 soundings, respectively). Wind directions at the time of SAR observations are indicated by <span class="html-italic">red</span> (master scene) and <span class="html-italic">blue dots</span> (slave scene). Wind directions during austral summer (ranging from December 2013 to February 2014) were predominantly easterly at the time of SAR observations.</p> Full article ">Figure A2
<p>Time series of surface wind fields obtained from the lowest eta level of WRF, determined for acquisition times of each SAR image. Katabatic (downslope) mountain winds prevail at the time of SAR acquisitions, which were recorded during the early morning hours at about 10:04 a.m. (UTC), respectively 07:04 a.m. (CLST).</p> Full article ">Figure A3
<p>Averaged wind fields combining wind fields of all SAR observation times. (<b>a</b>) Average surface winds from eta level 1 (<b>b</b>) Average winds up to 387 m above surface (<b>c</b>) Average winds up to 1402 m above surface (<b>d</b>) Average winds up to 3193 m above surface.</p> Full article ">Figure A4
<p><span class="html-italic">Single event APS estimates</span> superimposed by surface wind fields obtained from the lowest eta level of WRF (<span class="html-italic">thin white arrows</span>). Scales indicate range change in millimeters, and are unique to each image, in order to enhance contrast by depicting the full range of each image. Katabatic (downslope) mountain winds prevail at the time of SAR acquisitions recorded during the early morning hours (10:04 a.m. GMT, local time is offset −3 h). <span class="html-italic">Wind barbs</span> indicate wind directions and wind speeds above the summit of Láscar volcano, which were obtained from GFS hindcasts at the times of SAR acquisitions. The <span class="html-italic">barbs</span> are displaced upstream in order not to cover the delay signatures of the summit area. The individual lines of the <span class="html-italic">barbs</span> represent the wind speeds in units of knots (half strokes correspond to 5 knots and full strokes correspond to 10 knots). Wind directions (clockwise degrees from North), wind speed (m·sec<sup>−1</sup>) and estimated average PWV contents (mm) are additionally indicated in the upper right corner of each image.</p> Full article ">Figure A5
<p>Comparison of the absolute values of the predicted dSWDs with (<b>a</b>) estimates of the mean measured APS amplitudes, and with (<b>b</b>) RMSDs of phase delay amplitudes in DInSARs. Best fitting linear regression lines (<span class="html-italic">thick black lines</span>) are depicted along with their corresponding equations and R-squared values. Additionally, to guide the eye, linear regression lines that are forced through zero (<span class="html-italic">thin dashed lines</span>) are given as reference.</p> Full article ">Figure A6
<p>Delay correlation maps depicting estimated interferometric gas plume patterns from (<b>a</b>) <span class="html-italic">subset 01</span> and (<b>b</b>) <span class="html-italic">subset 02</span>. (<b>c</b>) Estimated interferometric pattern from <span class="html-italic">subset 02</span>, where the SAR observation of 03 Januray 2014 was omitted. All three estimates contain phase contributions of the temperature and pressure related phase screens (TPS &amp; PPS not removed). Scales correspond to estimated delay (mm) per theoretical delay (mm). Upper and lower bounds of the scale bar are equal to unity in <span class="html-italic">subset 01</span>, but not in <span class="html-italic">subset 02</span>, indicating that the estimated delay in <span class="html-italic">subset 02</span> was by a factor 2 larger than the theoretical delay.</p> Full article ">
29 pages, 25739 KiB  
Article
Independent Assessment of Sentinel-3A Wet Tropospheric Correction over the Open and Coastal Ocean
by Maria Joana Fernandes and Clara Lázaro
Remote Sens. 2018, 10(3), 484; https://doi.org/10.3390/rs10030484 - 20 Mar 2018
Cited by 28 | Viewed by 5783
Abstract
Launched on 16 February 2016, Sentinel-3A (S3A) carries a two-band microwave radiometer (MWR) similar to that of Envisat, and is aimed at the precise retrieval of the wet tropospheric correction (WTC) through collocated measurements using the Synthetic Aperture Radar Altimeter (SRAL) instrument. This [...] Read more.
Launched on 16 February 2016, Sentinel-3A (S3A) carries a two-band microwave radiometer (MWR) similar to that of Envisat, and is aimed at the precise retrieval of the wet tropospheric correction (WTC) through collocated measurements using the Synthetic Aperture Radar Altimeter (SRAL) instrument. This study aims at presenting an independent assessment of the WTC derived from the S3A MWR over the open and coastal ocean. Comparisons with other four MWRs show Root Mean Square (RMS) differences (cm) of S3A with respect to these sensors of 1.0 (Global Precipitation Measurement (GPM) Microwave Imager, GMI), 1.2 (Jason-2), 1.3 (Jason-3), and 1.5 (Satellite with ARgos and ALtika (SARAL)). The linear fit with respect to these MWR shows scale factors close to 1 and small offsets, indicating a good agreement between all these sensors. In spite of the short analysis period of 10 months, a stable temporal evolution of the S3A WTC has been observed. In line with the similar two-band instruments aboard previous European Space Agency (ESA) altimetric missions, strong ice and land contamination can be observed, the latter mainly found up to 20–25 km from the coast. Comparisons with the European Centre for Medium-Range Weather Forecasts (ECMWF) and an independent WTC derived only from third party data are also shown, indicating good overall performance. However, improvements in both the retrieval algorithm and screening of invalid MWR observations are desirable to achieve the quality of the equivalent WTC from Jason-3. The outcome of this study is a deeper knowledge of the measurement capabilities and limitations of the type of MWR aboard S3A and of the present WTC retrieval algorithms. Full article
(This article belongs to the Special Issue Satellite Altimetry for Earth Sciences)
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<p>Location of a set of 60 Global Navigation Satellite System (GNSS) stations used in this study (adapted from [<a href="#B43-remotesensing-10-00484" class="html-bibr">43</a>]). The background map represents the Root Mean Square (RMS) of wet path delay (WPD) in cm.</p> Full article ">Figure 2
<p>Sentinel-3A (S3A) points for cycle 06 with invalid microwave radiometer (MWR) observations: green—land contamination; blue—ice contamination; pink—rain, outliers, or additional condition such as all points above latitude 70°N or below 70°S.</p> Full article ">Figure 3
<p>Spatial coverage of match points between S3A and the Global Precipitation Measurement (GPM) Microwave Imager (GMI) with time difference ΔT &lt; 45 min and distance ΔD &lt; 50 km, for S3A cycles 05–16, used in this study (~219,000 points). Colour scale indicates WPD differences between the GMI and S3A in cm.</p> Full article ">Figure 4
<p>(<b>a</b>) Wet path delay from S3A versus WPD from the GMI; (<b>b</b>) WPD from S3A versus WPD differences between the GMI and S3A (~219,000 points).</p> Full article ">Figure 5
<p>(<b>Top panel</b>): time evolution of the WPD from the GMI (blue) and S3A (pink); (<b>Bottom Panel</b>): time evolution of WPD differences between the GMI and S3A for ascending passes (blue) and descending passes (red). Colour bars refer to periods when the GMI/S3A match points are all located at high latitudes (green points in <a href="#remotesensing-10-00484-f007" class="html-fig">Figure 7</a>) or low latitudes (orange points in <a href="#remotesensing-10-00484-f007" class="html-fig">Figure 7</a>) to which correspond smaller or larger WPD variability.</p> Full article ">Figure 6
<p>Time evolution of the daily RMS of the WPD differences between the GMI and S3A. The number of points is represented by “np”.</p> Full article ">Figure 7
<p>Match points between the GMI and S3A. Colours correspond to different time periods, indicated in <a href="#remotesensing-10-00484-f005" class="html-fig">Figure 5</a> by the corresponding colour bars.</p> Full article ">Figure 8
<p>(<b>Top panel</b>): Crossover points between J2 and S3A with ΔT &lt; 180 min (~11,600 points); (<b>Middle panel</b>): crossovers between J3 and the S3A with ΔT &lt; 180 min (~12,600 points); (<b>Bottom panel</b>): crossovers between SARAL and S3A with ΔT &lt; 240 min (~13,700 points).</p> Full article ">Figure 9
<p>(<b>Left panels</b>): WPD from S3A versus WPD from J2 (<b>top</b>), J3 (<b>middle</b>), and SARAL (<b>bottom</b>), in cm; (<b>Right panels</b>): WPD from S3A versus WPD difference between J2 and S3A (<b>top</b>), between J3 and S3A (<b>middle</b>) and between SARAL and S3A (<b>bottom</b>).</p> Full article ">Figure 10
<p>Time evolution of daily and 27-day RMS WPD differences between J2 and S3A (<b>Top</b>), between J3 and S3A (<b>Middle</b>), and between SARAL and S3A (<b>Bottom</b>), in cm.</p> Full article ">Figure 11
<p>RMS differences between the WTC from the GNSS at coastal stations and the WTC from the S3A MWR, in cm. The grey and red coloured bars represent the number of points in each class of distance for GPD1 and the MWR, respectively.</p> Full article ">Figure 12
<p>WTC (in metres) for S3A pass 340, cycle 06: ECMWF Operational (blue), MWR (red) and GPD (black, GPD1 in the top plot, GPD2 in the bottom plot) functions of latitude. The plot order is as mentioned in this caption. Thus, whenever the blue points cannot be seen, they are overlaid by the red and/or black points.</p> Full article ">Figure 13
<p>Same as in <a href="#remotesensing-10-00484-f012" class="html-fig">Figure 12</a> for S3A pass 462, cycle 06.</p> Full article ">Figure 13 Cont.
<p>Same as in <a href="#remotesensing-10-00484-f012" class="html-fig">Figure 12</a> for S3A pass 462, cycle 06.</p> Full article ">Figure 14
<p>Spatial distribution of the RMS values of the WPD differences (cm) between GPD1 and the S3A MWR, for cycles 05 to 16.</p> Full article ">Figure 15
<p>Time evolution of the RMS differences between the S3A MWR-based WTC and those from GPD1 and ECMWF.</p> Full article ">Figure 16
<p>(<b>Left panels</b>): WPD from S3A versus WPD from ECMWF (<b>top</b>) and GPD1 (<b>bottom</b>), in cm; (<b>Right panels</b>): WPD from S3A versus WPD difference between ECMWF and S3A (<b>top</b>) and between GPD1 and S3A (<b>bottom</b>).</p> Full article ">Figure 17
<p>Spatial distribution of the weighted sea level anomaly (SLA) variance differences at crossovers, for SLA datasets computed using the MWR- and ECMWF-derived WTC (<b>Top</b>) and those using the WTC from the MWR and GPD1 (<b>Bottom</b>) for the period corresponding to S3A cycles 05 to 16. Only points with valid observations have been used.</p> Full article ">Figure 18
<p>(<b>Top panel</b>): temporal evolution of weighted SLA variance differences at crossovers for SLA datasets computed using the WTC from the S3A MWR and ECMWF (orange) and the WTC from GPD1 and ECMWF (blue) using only points with a valid MWR; (<b>Bottom panel</b>): temporal evolution of weighted SLA variance differences at crossovers between SLA datasets computed using the WTC from GPD1 and that from ECMWF (blue) and between GPD2 and ECMWF (green), using all points with valid SLA.</p> Full article ">Figure 18 Cont.
<p>(<b>Top panel</b>): temporal evolution of weighted SLA variance differences at crossovers for SLA datasets computed using the WTC from the S3A MWR and ECMWF (orange) and the WTC from GPD1 and ECMWF (blue) using only points with a valid MWR; (<b>Bottom panel</b>): temporal evolution of weighted SLA variance differences at crossovers between SLA datasets computed using the WTC from GPD1 and that from ECMWF (blue) and between GPD2 and ECMWF (green), using all points with valid SLA.</p> Full article ">Figure 19
<p>Temporal evolution of weighted along-track SLA variance differences for SLA datasets computed using the WTC from the on-board MWR and from ECMWF (orange) and those from GPD1 and ECMWF (blue) for S3A (<b>Top</b>) and J3 (<b>Bottom</b>). Only points with a valid MWR have been used.</p> Full article ">Figure 20
<p>(<b>Top panel</b>): variance differences of SLA versus latitude, for SLA datasets computed using the WTC from the MWR and ECMWF (orange) and those from GPD1 and ECMWF (blue), over the period of S3A cycles 05 to 16 and using only points with a valid MWR; (<b>Bottom panel</b>): variance differences of SLA versus latitude between GPD1 and ECMWF (blue) and between GPD2 and ECMWF (green), over the period of S3A cycles 05 to 16 using all points with valid SLA.</p> Full article ">Figure 21
<p>Variance differences of SLA versus distance from coast for SLA datasets computed using the WTC from MWR and ECMWF (orange), those from GPD1 and ECMWF (blue) and those from GPD2 and ECMWF (green) over the period of S3A cycles 05 to 16. In the first case only valid MWR points were selected while in the last two cases all points with valid SLA were considered</p> Full article ">Figure 22
<p>From top to bottom and left to right: WTC (in metres) for S3A cycle 06 passes 041, 646, 660, and 670, as a function of latitude (degrees). Shown are: ECMWF Operational (blue), MWR (red), GPD2 (black), and composite (green) WTC.</p> Full article ">
19624 KiB  
Article
GPD+ Wet Tropospheric Corrections for CryoSat-2 and GFO Altimetry Missions
by M. Joana Fernandes and Clara Lázaro
Remote Sens. 2016, 8(10), 851; https://doi.org/10.3390/rs8100851 - 16 Oct 2016
Cited by 64 | Viewed by 7702
Abstract
Due to its large space-time variability, the wet tropospheric correction (WTC) is still considered a significant error source in satellite altimetry. This paper presents the GNSS (Global Navigation Satellite Systems) derived Path Delay Plus (GPD+), the most recent algorithm developed at the University [...] Read more.
Due to its large space-time variability, the wet tropospheric correction (WTC) is still considered a significant error source in satellite altimetry. This paper presents the GNSS (Global Navigation Satellite Systems) derived Path Delay Plus (GPD+), the most recent algorithm developed at the University of Porto to retrieve improved WTC for radar altimeter missions. The GPD+ are WTC estimated by space-time objective analysis, by combining all available observations in the vicinity of the point: valid measurements from the on-board microwave radiometer (MWR), from GNSS coastal and island stations and from scanning imaging MWR on board various remote sensing missions. The GPD+ corrections are available both for missions which do not possess an on-board microwave radiometer such as CryoSat-2 (CS-2) and for all missions which carry this sensor, by addressing the various error sources inherent to the MWR-derived WTC. To ensure long-term stability of the corrections, the large set of radiometers used in this study have been calibrated with respect to the Special Sensor Microwave Imager (SSM/I) and the SSM/I Sounder (SSM/IS). The application of the algorithm to CS-2 and Geosat Follow-on (GFO), as representative altimetric missions without and with a MWR aboard the respective spacecraft, is described. Results show that, for both missions, the new WTC significantly reduces the sea level anomaly (SLA) variance with respect to the model-based corrections. For GFO, the new WTC also leads to a large reduction in SLA variance with respect to the MWR-derived WTC, recovering a large number of observations in the coastal and polar regions and full sets of tracks and several cycles when MWR measurements are missing or invalid. Overall, the algorithm allows the recovery of a significant number of measurements, ensuring the continuity and consistency of the correction in the open-ocean/coastal transition zone and at high latitudes. Full article
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<p>Spatial correlation scales (in km) for the wet tropospheric correction (WTC) as determined from a set of European Centre for Medium-Range Weather Forecasts (ECMWF) Operational model grids at 0.125° × 0.125°, well distributed over the year 2013.</p> Full article ">Figure 2
<p>Location of GNSS stations used in the GPD+ estimations. The background picture is the map of the standard error of the wet tropospheric correction, in metres, computed from two years of ECMWF model fields.</p> Full article ">Figure 3
<p>Set of SI-MWR sensors used in the GPD+ estimations (DMSP-F15 (see <a href="#remotesensing-08-00851-t001" class="html-table">Table 1</a>) was not used due to its instable behaviour [<a href="#B24-remotesensing-08-00851" class="html-bibr">24</a>,<a href="#B25-remotesensing-08-00851" class="html-bibr">25</a>]).</p> Full article ">Figure 4
<p>(<b>a</b>) Time evolution of the differences in WTC (cm) from AMSU-A and from ECMWF ReAnalysis (ERA) Interim; (<b>b</b>) Corresponding differences from SI-MWR sensors (SSM/I-SSM/IS, TMI (TRM), AMSR-E (AQU), AMSR-2 (GCW), WindSat (COR), GMI (GPM)) and from ERA Interim; (<b>c</b>) Corresponding differences from the SSM/I-SSM/IS sensors and from ERA Interim for the altimeter era (1992-present).</p> Full article ">Figure 5
<p>Wet path delay (symmetric of WTC) from SSM/I and SSM/IS versus the corresponding values from the MWR on board the reference missions: TOPEX/Poseidon (TP) (<b>a</b>); J1 (<b>b</b>); and J2 (<b>c</b>). The red straight lines represent the linear fit to each dataset.</p> Full article ">Figure 6
<p>(<b>a</b>) Time evolution of the differences in WTC (cm) from SSM/I, SSM/IS and from MWR on board satellite altimetry reference missions, before and after calibration; (<b>b</b>) Corresponding differences in WTC (cm) from ERA Interim and from MWR on board satellite altimetry reference missions.</p> Full article ">Figure 7
<p>Wet path delay (symmetric of WTC) from the reference altimetric missions vs. the corresponding values from GFO WVR. The solid red line represents the linear fit of these datasets.</p> Full article ">Figure 8
<p>Time evolution of the differences between the WTC (cm) derived from MWR on board altimetric reference missions and from GFO WVR, before and after calibration.</p> Full article ">Figure 9
<p>Wet path delay (symmetric of WTC) from the reference altimetric missions vs. the corresponding values from NOAA-15/AMSU-A (<b>a</b>); and from GCW/AMSR-2 (<b>b</b>). The red lines represent the linear fit of these datasets.</p> Full article ">Figure 10
<p>Time evolution of the differences between the WTC (cm) derived from MWR on board altimetric reference missions and from AMSR-E (AQU), AMSR-2 (GCW), TMI (TRM), WindSat (COR), and from GMI (GPM) before (<b>a</b>); and after (<b>b</b>) calibration.</p> Full article ">Figure 11
<p>Time evolution of the differences between the WTC (cm) derived from MWR on board altimetric reference missions and from AMSU-A before (<b>a</b>); and after (<b>b</b>) calibration.</p> Full article ">Figure 12
<p>Spatial coverage of the various datasets for CS-2 sub-cycles 31 (<b>a</b>); and 35 (<b>b</b>). The red triangles represent the location of the Global Navigation Satellite Systems (GNSS) stations. The black dots indicate points where there are no SI-MWR measurements available for the estimations, either because they do not exist or because they are too far away in space or time.</p> Full article ">Figure 13
<p>Formal error (in cm) of the GPD wet tropospheric correction for CryoSat-2 sub-cycles 31 (<b>a</b>); and 35 (<b>b</b>).</p> Full article ">Figure 14
<p>WTC for Jason-2, cycle 127 pass 223: GPD+ version using AMR (red), GPD+ version without AMR (black) and ECMWF Operational model (blue).</p> Full article ">Figure 15
<p>(<b>a</b>) Temporal evolution of weighted along-track SLA variance differences between each GPD+ version (with AMR in blue and without AMR in orange) and the corresponding values from ECMWF Operational model over the period of J2 cycles 74 to 273; (<b>b</b>) Corresponding variance differences at crossovers. “N. Xovers” represents the number of crossovers per cycle. “Obs (%)” represents, for each cycle, the percentage of points with available observations for the GPD+ WTC estimation.</p> Full article ">Figure 16
<p>Temporal evolution of weighted sea level anomaly (SLA) variance differences along-track (blue) and at crossovers (green) between GPD+ and ECMWF Operational model over the period of CS-2 sub-cycles 04 to 78 (RADS convention). “N. Xovers” represents the number of crossovers per sub-cycle. “Obs (%)” represents, for each sub-cycle, the percentage of points with available observations for the GPD+ WTC estimation.</p> Full article ">Figure 17
<p>Variance differences of SLA versus latitude (<b>a</b>) and distance from coast (<b>b</b>) between GPD and ECMWF Operational model over the period of CS-2 sub-cycles 04 to 78 (RADS convention). In the right panel, the orange and blue plots represent the results for the whole range of latitudes and for the latitude band |ϕ| &lt; 50°, respectively.</p> Full article ">Figure 18
<p>Weighted along-track SLA variance differences (<b>a</b>); and weighted SLA variance differences at crossovers (<b>b</b>) between GPD and ECMWF Operational model over the period of CS-2 sub-cycles 04 to 78 (RADS convention).</p> Full article ">Figure 18 Cont.
<p>Weighted along-track SLA variance differences (<b>a</b>); and weighted SLA variance differences at crossovers (<b>b</b>) between GPD and ECMWF Operational model over the period of CS-2 sub-cycles 04 to 78 (RADS convention).</p> Full article ">Figure 19
<p>Location of along-track points selected for the GPD+ computation (points with flag_MWR_rej ≠ 0) for GFO cycles 98 (<b>a</b>); and 135 (<b>b</b>). Brown: land points near the coast; dark green: points with radiometer land flag set to 1; light green: points with distance from coast less than 30 km; blue: points contaminated by ice or outside limits if located at latitudes |ϕ| &gt; 45°; pink: points rejected by outlier detection criteria or with the MWR WTC outside limits (see text for details). It should be noted that for the tracks for which all points are rejected, the algorithm attributes the ice rejection criterion to all points in the latitude bands |ϕ| &gt; 45°, while indeed all points in the track are outside limits.</p> Full article ">Figure 20
<p>Formal error (in cm) of the GPD+ wet tropospheric correction for GFO cycle 135.</p> Full article ">Figure 21
<p>Comparison of the along-track WTC for GFO cycle 49, passes 13 (<b>a</b>); and pass 296 (<b>b</b>) the GPD+ WTC is shown in black, while MWR- and ERA-derived WTC are shown in red and blue, respectively. Colour bars indicate MWR WTC values flagged as invalid due to ice contamination (cyan) or due to land proximity (distance from coast less than 30 km, light green) and rejected by outlier detection criteria or with the MWR WTC outside limits (grey).</p> Full article ">Figure 22
<p>Along-track GFO GPD+ WTC for pass 2 of cycle 136 (<b>a</b>); and pass 61 of cycle 166 (<b>b</b>). For both these cycles, the MWR-derived WTC is unavailable and has been set to a positive value, and therefore is not shown within the chosen <span class="html-italic">y</span>-axis limits.</p> Full article ">Figure 23
<p>Temporal evolution of weighted along-track SLA variance differences between GPD+ and ERA (blue) and between GPD+ and on-board MWR (orange) for GFO cycles 37 to 223. “Estimated GPD points (%)” represents, for each cycle, the percentage of points with a new GPD+ estimate. In the differences with respect to WVR, the cycles with the largest differences, most of them with 100% of points with invalid MWR-derived WTC, are not shown.</p> Full article ">Figure 24
<p>Temporal evolution of weighted SLA variance differences at crossovers between GPD+ and ERA (blue) and between GPD+ and on-board MWR (green) for GFO cycles 37 to 223. “N. Xovers” represents the number of crossovers per cycle. In the differences with respect to the WVR-derived WTC, the cycles with the largest differences, occurring after cycle 180, are not shown.</p> Full article ">Figure 25
<p>Temporal evolution of weighted SLA variance differences along-track (green) and at crossovers (blue) between GPD+ and ERA Interim over the period of GFO sub-cycles 37 to 223. “N. Xovers” represents the number of crossovers per cycle. Only points with valid WVR measurements, according to the GPD+ validity criteria, have been used in this comparison.</p> Full article ">Figure 26
<p>Spatial distribution of the weighted SLA variance differences at crossovers between GPD+ and ERA (<b>a</b>); and between GPD+ and WVR-derived WTC (<b>b</b>) over the period corresponding to GFO cycles 37 to 223. In the differences with respect to MWR, observations outside limits and those from the cycles with the largest differences have not been considered.</p> Full article ">Figure 27
<p>Variance differences of SLA versus latitude (<b>a</b>); and distance from coast (<b>b</b>) between GPD+ and ERA (blue) and between GPD+ and WVR-derived WTC (orange) over the period of GFO cycles 37 to 223. Note the two different scales in the right panel. In the calculation of the differences with respect to WVR-derived WTC, the cycles with the largest differences have not been considered.</p> Full article ">
3087 KiB  
Article
Evaluation of Empirical Tropospheric Models Using Satellite-Tracking Tropospheric Wet Delays with Water Vapor Radiometer at Tongji, China
by Miaomiao Wang and Bofeng Li
Sensors 2016, 16(2), 186; https://doi.org/10.3390/s16020186 - 2 Feb 2016
Cited by 14 | Viewed by 4577
Abstract
An empirical tropospheric delay model, together with a mapping function, is commonly used to correct the tropospheric errors in global navigation satellite system (GNSS) processing. As is well-known, the accuracy of tropospheric delay models relies mainly on the correction efficiency for tropospheric wet [...] Read more.
An empirical tropospheric delay model, together with a mapping function, is commonly used to correct the tropospheric errors in global navigation satellite system (GNSS) processing. As is well-known, the accuracy of tropospheric delay models relies mainly on the correction efficiency for tropospheric wet delays. In this paper, we evaluate the accuracy of three tropospheric delay models, together with five mapping functions in wet delays calculation. The evaluations are conducted by comparing their slant wet delays with those measured by water vapor radiometer based on its satellite-tracking function (collected data with large liquid water path is removed). For all 15 combinations of three tropospheric models and five mapping functions, their accuracies as a function of elevation are statistically analyzed by using nine-day data in two scenarios, with and without meteorological data. The results show that (1) no matter with or without meteorological data, there is no practical difference between mapping functions, i.e., Chao, Ifadis, Vienna Mapping Function 1 (VMF1), Niell Mapping Function (NMF), and MTT Mapping Function (MTT); (2) without meteorological data, the UNB3 is much better than Saastamoinen and Hopfield models, while the Saastamoinen model performed slightly better than the Hopfield model; (3) with meteorological data, the accuracies of all three tropospheric delay models are improved to be comparable, especially for lower elevations. In addition, the kinematic precise point positioning where no parameter is set up for tropospheric delay modification is conducted to further evaluate the performance of tropospheric delay models in positioning accuracy. It is shown that the UNB3 model is best and can achieve about 10 cm accuracy for the N and E coordinate component while 20 cm accuracy for the U coordinate component no matter the meteorological data is available or not. This accuracy can be obtained by the Saastamoinen model only when meteorological data is available, and degraded to 46 cm for the U component if the meteorological data is not available. Full article
(This article belongs to the Section Remote Sensors)
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Figure 1

Figure 1
<p>The water vapor radiometer used for data collection on the roof of Cehui building on the Tongji campus (Shanghai, China).</p> Full article ">Figure 2
<p>The configuration interface of satellite-tracking function of the used water vapor radiometer. In this configuration, it allows changes to the observation period, the ranges of satellite elevation, and azimuth, the tracking satellite and so on.</p> Full article ">Figure 3
<p>Histograms of absolute wet delay errors derived from all combinations of tropospheric delay models and mapping functions, where the standard atmosphere was applied.</p> Full article ">Figure 4
<p>Mean of absolute wet delay errors as function of elevation. The standard atmosphere was applied for wet delay calculation.</p> Full article ">Figure 5
<p>Mean of the absolute wet delay errors as a function of azimuth. The standard atmosphere was applied for wet delay calculation.</p> Full article ">Figure 6
<p>Mean elevation in each azimuth angle interval.</p> Full article ">Figure 7
<p>Histograms of absolute wet delay errors derived from all combinations of tropospheric delay models and mapping functions, where the real meteorological data were applied.</p> Full article ">Figure 8
<p>Mean of absolute wet delay errors as function of elevation. The meteorological measurement was applied for the wet delay calculation.</p> Full article ">Figure 9
<p>Mean of absolute wet delay errors as a function of azimuth. The real meteorological data was applied for wet delay calculation.</p> Full article ">Figure 10
<p>The absolute wet delay errors calculated with SAAS and NMF for satellites PRN 8 and 13 without and with meteorological observations. The subplots (<b>a</b>,<b>b</b>) denote the results without meteorological observations for PRN 8 and 13; while (<b>c</b>,<b>d</b>) denote the results with meteorological observations for these satellites.</p> Full article ">Figure 11
<p>The positioning errors of kinematic PPP, where N, E, and U denote the North, East, and Up components. The subplots (<b>a</b>,<b>c</b>) denote the results of the SAAS and UNB3 where no meteorological data were used; and the subplots (<b>b</b>,<b>d</b>) denote the results of the SAAS and UNB3 where meteorological data were used.</p> Full article ">Figure 12
<p>The temperature, atmospheric pressure, and partial water vapor pressure used to calculate the tropospheric delay on 22 November. The ″SAAS + STAND″ and ″UNB3 + STAND″ are denoted as the modeled meteorological data used in the SAAS and UNB3 model. ″Measurement″ is denoted as the observed meteorological data.</p> Full article ">Figure 13
<p>Statistics of kinematic PPP positioning errors in the Up coordinate component for nine-day data. The subplots, (<b>a</b>–<b>c</b>) denote the results of mean difference (its absolute value), STD, and RMS of positioning errors.</p> Full article ">
4573 KiB  
Article
Analysis and Inter-Calibration of Wet Path Delay Datasets to Compute the Wet Tropospheric Correction for CryoSat-2 over Ocean
by M. Joana Fernandes, Alexandra L. Nunes and Clara Lázaro
Remote Sens. 2013, 5(10), 4977-5005; https://doi.org/10.3390/rs5104977 - 14 Oct 2013
Cited by 27 | Viewed by 8653
Abstract
Unlike most altimetric missions, CryoSat-2 is not equipped with an onboard microwave radiometer (MWR) to provide wet tropospheric correction (WTC) to radar altimeter measurements, thus, relying on a model-based one provided by the European Center for Medium-range Weather Forecasts (ECMWF). In the ambit [...] Read more.
Unlike most altimetric missions, CryoSat-2 is not equipped with an onboard microwave radiometer (MWR) to provide wet tropospheric correction (WTC) to radar altimeter measurements, thus, relying on a model-based one provided by the European Center for Medium-range Weather Forecasts (ECMWF). In the ambit of ESA funded project CP4O, an improved WTC for CryoSat-2 data over ocean is under development, based on a data combination algorithm (DComb) through objective analysis of WTC values derived from all existing global-scale data types. The scope of this study is the analysis and inter-calibration of the large dataset of total column water vapor (TCWV) products from scanning MWR aboard Remote Sensing (RS) missions for use in the WTC computation for CryoSat-2. The main issues regarding the computation of the WTC from all TCWV products are discussed. The analysis of the orbital parameters of CryoSat-2 and all other considered RS missions, their sensor characteristics and inter-calibration is presented, providing an insight into the expected impact of these datasets on the WTC estimation. The most suitable approach for calculating the WTC from TCWV is investigated. For this type of application, after calibration with respect to an appropriate reference, two approaches were found to give very similar results, with root mean square differences of 2 mm. Full article
(This article belongs to the Special Issue Calibration and Verification of Remote Sensing Instruments and Observations)
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<p>Mean value of the WTC (in centimeters) computed from ECMWF operational model grids over the period of two years.</p> Full article ">
<p>Standard deviation of the WTC (in centimeters) computed from ECMWF operational model grids over the period of two years.</p> Full article ">
<p>(<b>a</b>) Mean and (<b>b</b>) standard deviation (sd) of the differences between the WTC computed from ERA Interim and the ECMWF operational model (in cm) for each cycle of the three reference altimetric missions: T/P, J1 and J2.</p> Full article ">
<p>NOAA-17 (AMSU-A) and TRMM (TMI) images closest in time to CS-2 ascending pass 3 (in black), sub-cycle 26 (16 March 2012). Color scale is TCWV in mm.</p> Full article ">
<p>Coriolis (WindSat) ascending images for the same day of CS-2 ascending pass 3 (in black), sub-cycle 26 (16 March 2012). Color scale is TCWV in mm.</p> Full article ">
<p>Longitude of equator crossings (Asc. and Desc.) <span class="html-italic">vs.</span> time, at the middle of CS-2 sub-cycle 17 (July 2011), for all 11 sun-synchronous RS satellites.</p> Full article ">
<p>Time evolution of the local time of ascending node (LTAN, solid lines) and local time of descending node (LTDN, dashed lines) of all 11 sun-synchronous RS satellites and of CS-2 passes from the middle of sub-cycle 11 (February 2011) to the middle of sub-cycle 35 (December 2012) The vertical grey bars highlight cycles 23 and 26, which are representative of extreme conditions for CS-2 coverage.</p> Full article ">
<p>Number of images available for each CS-2 point, for sub-cycle 23 (January 2012), using ΔT = 180 min and ΔD = 75 km. The points with N = 0 (10.2%) are shown in black. DMSP-F15 images were not considered.</p> Full article ">
<p>Number of images available for each CS-2 point, for sub-cycle 26 (April 2012), using ΔT = 180 min and ΔD = 75 km. The points with N = 0 (0.3%) are shown in black. DMSP-F15 images were not considered.</p> Full article ">
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