Intercomparison and Validation of GNSS-IWV Derived with G-Nut and Bernese Software

2017 ◽  
Author(s):  
Pawel Golaszewski ◽  
Pawel Wielgosz ◽  
Katarzyna Stepniak
Keyword(s):  
Water Vapor ◽  
Meteorological Data ◽  
Microwave Radiometer ◽  
Severe Weather ◽  
Final Solution ◽  
Weather Forecasts ◽  
Software Packages ◽  
Gnss Measurements ◽  
Gnss Data ◽  
Permanent Station

GNSS is an important source of meteorological data. GNSS measurements can provide tropospheric Zenith Wet Delays (ZWD) over wide area covered with permanent stations. In addition, when using surface synoptical data, GNSS can provide Integrated Water Vapor (IWV) which is very valuable information utilized in weather forecasts and severe weather monitoring. Hence, there is a need to test and validate various algorithms and software used for ZWD estimation. In this research, the accuracy of the ZWD estimates was tested using two different software packages: Bernese GNSS Software v.5.2 and G-Nut/Tefnut. In addition, their computational load was evaluated. The GNSS data were obtained from POTS permanent station, which is located in Potsdam, Germany. To validate the estimation results, the derived ZWD was transformed into the IWV, and afterwards compared to the reference IWV measured by the collocated Microwave Radiometer. In addition, the ZWD estimates were also compared to the EUREF final solution.

scholarly journals Validating HY-2A CMR Precipitable Water Vapor Using Ground-based and Shipborne GNSS Observations

2020 ◽  
Author(s):  
Zhilu Wu ◽  
Yanxiong Liu ◽  
Yang Liu ◽  
Jungang Wang ◽  
Xiufeng He ◽  
...  
Keyword(s):  
Water Vapor ◽  
Microwave Radiometer ◽  
Precipitable Water ◽  
Global Navigation Satellite Systems ◽  
High Temporal Resolution ◽  
Satellite Systems ◽  
The Indian Ocean ◽  
Global Navigation Satellite ◽  
Gnss Data ◽  
Gnss Observations

Abstract. The calibration microwave radiometer (CMR) onboard Haiyang-2A satellite provides wet tropospheric delays correction for altimetry data, which can also contribute to the understanding of climate system and weather processes. Ground-based Global Navigation Satellite Systems (GNSS) provide precise PWV with high temporal resolution and could be used for calibration and monitoring of the CMR data, and shipborne GNSS provides accurate PWV over open oceans, which can be directly compared with uncontaminated CMR data. In this study, the HY-2A CMR water vapor product is validated using ground-based GNSS observations of 100 IGS stations along the coastline and 56-day shipborne GNSS observations over the Indian Ocean. The processing strategy for GNSS data and CMR data is discussed in detail. Special efforts were made to the quality control and reconstruction of contaminated CMR data. The validation result shows that HY-2A CMR PWV agrees well with ground-based GNSS PWV with 2.67 mm in RMS within 100 km. Geographically, the RMS is 1.12 mm in the polar region and 2.78 mm elsewhere. The PWV agreement between HY-2A and shipborne GNSS shows a significant correlation with the distance between the ship and the satellite footprint, with an RMS of 1.57 mm for the distance threshold of 100 km. Ground-based GNSS and shipborne GNSS agree with HY-2A CMR well with no obvious system error.

scholarly journals An improved pixel-based water vapor tomography model

2019 ◽  
Vol 37 (1) ◽  
pp. 89-100
Author(s):  
Yibin Yao ◽  
Linyang Xin ◽  
Qingzhi Zhao
Keyword(s):  
Water Vapor ◽  
Three Dimensional ◽  
Satellite System ◽  
Reference Station ◽  
Weather Forecasts ◽  
Satellite Positioning ◽  
Optimal Polynomial ◽  
Improved Model ◽  
Global Navigation Satellite ◽  
Gnss Data

Abstract. As an innovative use of Global Navigation Satellite System (GNSS), the GNSS water vapor tomography technique shows great potential in monitoring three-dimensional water vapor variation. Most of the previous studies employ the pixel-based method, i.e., dividing the troposphere space into finite voxels and considering water vapor in each voxel as constant. However, this method cannot reflect the variations in voxels and breaks the continuity of the troposphere. Moreover, in the pixel-based method, each voxel needs a parameter to represent the water vapor density, which means that huge numbers of parameters are needed to represent the water vapor field when the interested area is large and/or the expected resolution is high. In order to overcome the abovementioned problems, in this study, we propose an improved pixel-based water vapor tomography model, which uses layered optimal polynomial functions obtained from the European Centre for Medium-Range Weather Forecasts (ECMWF) by adaptive training for water vapor retrieval. Tomography experiments were carried out using the GNSS data collected from the Hong Kong Satellite Positioning Reference Station Network (SatRef) from 25 March to 25 April 2014 under different scenarios. The tomographic results are compared to the ECMWF data and validated by the radiosonde. Results show that the new model outperforms the traditional one by reducing the root-mean-square error (RMSE), and this improvement is more pronounced, at 5.88 % in voxels without the penetration of GNSS rays. The improved model also has advantages in more convenient expression.

Advanced MUlti-GNSS Array for Monitoring Severe Weather Events (AMUSE): Project overview

2020 ◽  
Author(s):  
Karina Wilgan ◽  
Jens Wickert ◽  
Galina Dick ◽  
Florian Zus ◽  
Torsten Schmidt ◽  
...  
Keyword(s):  
Water Vapor ◽  
Severe Weather ◽  
Precipitation Forecast ◽  
Spatiotemporal Resolution ◽  
Weather Forecasts ◽  
Precise Knowledge ◽  
Weather Events ◽  
Data Products ◽  
New Generation ◽  
The Impact

<p>Global Navigation Satellite Systems (GNSS) have revolutionized positioning, navigation, and timing, becoming a common part of our everyday life. Aside from these well-known civilian and commercial applications, GNSS is currently established as a powerful and versatile observation tool for geosciences. An outstanding application in this context is the operational monitoring of atmospheric water vapor with high spatiotemporal resolution. The water vapor is the most abundant greenhouse gas, which accounts for about 70% of atmospheric warming and plays a key role in the atmospheric energy exchange. The precise knowledge of its highly variable spatial and temporal distribution is a precondition for precise modeling of the atmospheric state as a base for numerical weather forecasts especially with focus to the strong precipitation and severe weather events.</p><p>The data from European GNSS networks are widely operationally used to improve regional weather forecasts in several countries. However, the impact of the currently provided data products to the forecast systems is still limited due to the exclusively focusing on GPS-only based data products; to the limited atmospheric information content, which is provided mostly in the zenith direction and to the time delay between measurement and providing the data products, which is currently about one hour.</p><p>AMUSE is a recent research project, funded by the DFG (German Research Council) and performed in close cooperation of TUB, GFZ and DWD during 2020-2022. The project foci are the major limitations of currently operationally used generation of GNSS-based water vapor data. AMUSE will pioneer the development of next generation data products. Main addressed innovations are:  1) Developments to provide multi-GNSS instead of GPS-only data, including GLONASS, Galileo and BeiDou; 2) Developments to provide high quality slant observations, containing water vapor information along the line-of-sight from the respective ground stations; 3) Developments to shorten the delay between measurements and the provision of the products to the meteorological services.</p><p>This GNSS-focused work of AMUSE will be complemented by the contribution of German Weather Service DWD to investigate in detail and to quantify the forecast improvement, which can be reached by the new generation GNSS-based meteorology data. Several dedicated forecast experiments will be conducted with focus on one of the most challenging issues, the precipitation forecast in case of severe weather events. These studies will support the future assimilation of the new generation data to the regional forecast system of DWD and potentially also to other European weather services.</p>

scholarly journals Constructing Precipitable Water Vapor Map from Regional GNSS Network Observations without Collocated Meteorological Data

2018 ◽  
Author(s):  
Biyan Chen ◽  
Wujiao Dai ◽  
Zhizhao Liu ◽  
Lixin Wu ◽  
Cuilin Kuang ◽  
...  
Keyword(s):  
Water Vapor ◽  
Meteorological Data ◽  
Precipitable Water ◽  
Satellite System ◽  
Precipitable Water Vapor ◽  
Weighted Mean Temperature ◽  
Synoptic Observations ◽  
Gnss Network ◽  
Gnss Data ◽  
Better Than

Abstract. Surface pressure (Ps) and weighted mean temperature (Tm) are two necessary variables for the accurate retrieval of precipitable water vapor (PWV) from global navigation satellite system (GNSS) data. The lack of Ps or Tm information is a concern for those GNSS sites that are not collocated with meteorological sensors. This paper investigates an alternative method of inferring accurate Ps and Tm at the GNSS station using nearby synoptic observations. Ps and Tm obtained at the nearby synoptic sites are interpolated onto the location of GNSS station by performing both vertical and horizontal adjustments, in which the parameters involved in Ps and Tm calculation are estimated from ERA-Interim reanalysis profiles. In addition, we present a method of constructing high quality PWV map through vertical reduction and horizontal interpolation of the retrieved GNSS PWVs. To evaluate the performances of the Ps and Tm retrieval and the PWV map construction, GNSS data collected from 58 stations of the Hunan GNSS network and synoptic observations from 20 nearby sites in 2015 were processed to extract the PWV so as to subsequently generate PWV map. The retrieved Ps and Tm and constructed PWV maps were assessed by the results derived from radiosonde and ERA-Interim reanalysis. The results show that (1) accuracies of Ps and Tm derived by synoptic interpolation are within the range of 1.7–3.0 hPa and 2.5–3.0 K, respectively, which are much better than the GPT2w model; (2) the constructed PWV maps have good agreements with radiosonde and ERA reanalysis data with overall accuracy better than 3 mm; and (3) PWV maps can well reveal the moisture advection, transportation and convergence during heavy rainfall.

scholarly journals Integrated water vapour content retrievals from ship-borne GNSS receivers during EUREC<sup>4</sup>A

2021 ◽  
Vol 13 (4) ◽  
pp. 1499-1517
Author(s):  
Pierre Bosser ◽  
Olivier Bock ◽  
Cyrille Flamant ◽  
Sandrine Bony ◽  
Sabrina Speich
Keyword(s):  
Water Vapour ◽  
Satellite System ◽  
Water Vapour Content ◽  
Weather Forecasts ◽  
Integrated Water Vapour ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Gnss Measurements ◽  
Global Navigation Satellite ◽  
Gnss Data

Abstract. In the framework of the EUREC4A (Elucidating the role of clouds–circulation coupling in climate) campaign that took place in January and February 2020, integrated water vapour (IWV) contents were retrieved over the open tropical Atlantic Ocean using Global Navigation Satellite System (GNSS) data acquired from three research vessels (R/Vs): R/V Atalante, R/V Maria S. Merian and R/V Meteor. This paper describes the GNSS processing method and compares the GNSS IWV retrievals with IWV estimates from the European Centre for Medium-range Weather Forecasts (ECMWF) fifth reanalysis (ERA5), from the Moderate Resolution Imaging Spectroradiometer (MODIS) infrared products and from terrestrial GNSS stations located along the tracks of the ships. The ship-borne GNSS IWV retrievals from R/V Atalante and R/V Meteor compare well with ERA5, with small biases (−1.62 kg m−2 for R/V Atalante and +0.65 kg m−2 for R/V Meteor) and a root mean square (rms) difference of about 2.3 kg m−2. The results for the R/V Maria S. Merian are found to be of poorer quality, with an rms difference of 6 kg m−2, which is very likely due to the location of the GNSS antenna on this R/V prone to multipath effects. The comparisons with ground-based GNSS data confirm these results. The comparisons of all three R/V IWV retrievals with MODIS infrared products show large rms differences of 5–7 kg m−2, reflecting the enhanced uncertainties in these satellite products in the tropics. These ship-borne IWV retrievals are intended to be used for the description and understanding of meteorological phenomena that occurred during the campaign, east of Barbados, Guyana and northern Brazil. Both the raw GNSS measurements and the IWV estimates are available through the AERIS data centre (https://en.aeris-data.fr/, last access: 20 September 2020). The digital object identifiers (DOIs) for R/V Atalante IWV and raw datasets are https://doi.org/10.25326/71 (Bosser et al., 2020a) and https://doi.org/10.25326/74 (Bosser et al., 2020d), respectively. The DOIs for the R/V Maria S. Merian IWV and raw datasets are https://doi.org/10.25326/72 (Bosser et al., 2020b) and https://doi.org/10.25326/75 (Bosser et al., 2020e), respectively. The DOIs for the R/V Meteor IWV and raw datasets are https://doi.org/10.25326/73 (Bosser et al., 2020c) and https://doi.org/10.25326/76 (Bosser et al., 2020f), respectively.

Tropospheric products as a signal of interest – overview of troposphere sensing techniques

2021 ◽  
Author(s):  
Karina Wilgan ◽  
Witold Rohm ◽  
Jaroslaw Bosy ◽  
Alain Geiger ◽  
M. Adnan Siddique ◽  
...  
Keyword(s):  
Water Vapor ◽  
New Technologies ◽  
Meteorological Data ◽  
Weather Prediction ◽  
Signal Propagation ◽  
Data Sources ◽  
Tropospheric Delay ◽  
High Temporal Resolution ◽  
Gnss Data ◽  
Tropospheric Delays

&lt;p&gt;The microwave signals passing through the troposphere are delayed by refraction. Its high variations, both in time and space, are caused mainly by water vapor. The tropospheric delay used to be considered only as a source of error that needed to be removed. Nowadays, these delays are also a source of interest. The tropospheric delays or integrated water vapor are being assimilated into nowcasting or numerical weather prediction (NWP) models. Moreover, long time series of tropospheric observations have become an important source of information for climate studies. On the other hand, the meteorological data support the space-geodetic community by providing models that can be used to reduce the troposphere impact on the signal propagation. Furthermore, the delays calculated by one microwave technique can be used to mitigate the errors in others.&lt;/p&gt;&lt;p&gt;There are several ways of observing the troposphere, especially considering water vapor. The classical meteorological are: in-situ measurements, radiosondes or radiometers, which allow to sense the amount of water vapor directly. Another, indirect way of observing the water vapor distribution is by using the Global Navigation Satellite Systems (GNSS). This method is called GNSS meteorology. Other microwave techniques such as Very Long Baseline Interferometry (VLBI) or Interferometric Synthetic Aperture Radar (InSAR) are also capable to retrieve the atmospheric information from their signals.&lt;/p&gt;&lt;p&gt;This contribution shows an overview of the troposphere sensing techniques and their applications. We present multi-comparisons of the tropospheric parameters, i.e. refractivity, tropospheric delays in zenith and slant directions and integrated water vapor. The integration of the different data sources often leads to an improved accuracy of the tropospheric products but requires a careful preparation of data. The combination of the data sources allows for using the techniques of complementary properties, for example InSAR with very high spatial resolution with GNSS observations of high temporal resolution. With the emergence of new technologies, some traditional ways of tropospheric measurements can be augmented with the new methods. For example, we have tested meteo-drones as an alternative to radiosondes. The comparisons with GNSS data shows a good agreement of the drone and microwave data, even better than with radiosonde. Moreover, we present the results of the GNSS data assimilation into NWP models and the developments towards multi-GNSS, real-time assimilation of advanced products such as slant delays and horizontal tropospheric gradients.&lt;/p&gt;

scholarly journals Arctic Total Water Vapor: Comparison of Regional Climate Simulations with Observations, and Simulated Decadal Trends

2009 ◽  
Vol 10 (1) ◽  
pp. 113-129 ◽  
Author(s):  
A. Rinke ◽  
C. Melsheimer ◽  
K. Dethloff ◽  
G. Heygster
Keyword(s):  
Water Vapor ◽  
Time Scales ◽  
Climate Model ◽  
Regional Climate ◽  
Microwave Radiometer ◽  
The Arctic ◽  
Retrieval Algorithm ◽  
Total Water ◽  
Weather Forecasts ◽  
East Siberian Sea

Abstract Satellite-retrieved data of total water vapor (TWV) over the Arctic are patchy, with large areas of missing data because of various limitations of the retrieval algorithms. To overcome these observational difficulties, a new retrieval algorithm has been developed that allows for monitoring the TWV over the Arctic during most of the year. This method retrieves TWV from satellite microwave radiometer data [the Advanced Microwave Sounding Unit B (AMSU-B)]. These new data have been made available for 4 yr (2000–03) and have been used to evaluate high-resolution simulations with the Arctic regional atmospheric climate model HIRHAM at daily, monthly, and seasonal time scales. The strong dynamic TWV variability on the daily time scale, linked with moisture transport by weather systems, is discussed for selected case studies. Both the simulated climatological seasonal mean patterns and the variability on interannual and decadal time scales are in agreement with those of the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40) data. Trends in Arctic TWV for 1958–2001, broken down by season, are presented. Although an increase in the TWV is obvious in all seasons, there are also regions where a decreasing trend appears. Significant maximum positive trends are calculated over the western Arctic in summer (up to 0.06 kg m−2 yr−1), and a significant small negative trend is calculated over the East Siberian Sea in winter.

scholarly journals GNSS Profile from the Greenland Korth Expeditions in the Context of Satellite Data

2021 ◽  
Vol 11 (3) ◽  
pp. 1115
Author(s):  
Aleš Bezděk ◽  
Jakub Kostelecký ◽  
Josef Sebera ◽  
Thomas Hitziger
Keyword(s):  
Ice Sheet ◽  
Satellite Observations ◽  
Elevation Change ◽  
Gradual Decline ◽  
Satellite Gravimetry ◽  
Surface Elevation Change ◽  
Satellite Radar ◽  
Gnss Measurements ◽  
Gnss Data ◽  
Satellite Radar Altimetry

Over the last two decades, a small group of researchers repeatedly crossed the Greenland interior skiing along a 700-km long route from east to west, acquiring precise GNSS measurements at exactly the same locations. Four such elevation profiles of the ice sheet measured in 2002, 2006, 2010 and 2015 were differenced and used to analyze the surface elevation change. Our goal is to compare such locally measured GNSS data with independent satellite observations. First, we show an agreement in the rate of elevation change between the GNSS data and satellite radar altimetry (ERS, Envisat, CryoSat-2). Both datasets agree well (2002–2015), and both correctly display local features such as an elevation increase in the central part of the ice sheet and a sharp gradual decline in the surface heights above Jakobshavn Glacier. Second, we processed satellite gravimetry data (GRACE) in order for them to be comparable with local GNSS measurements. The agreement is demonstrated by a time series at one of the measurement sites. Finally, we provide our own satellite gravimetry (GRACE, GRACE-FO, Swarm) estimate of the Greenland mass balance: first a mild decrease (2002–2007: −210 ± 29 Gt/yr), then an accelerated mass loss (2007–2012: −335 ± 29 Gt/yr), which was noticeably reduced afterwards (2012–2017: −178 ± 72 Gt/yr), and nowadays it seems to increase again (2018–2019: −278 ± 67 Gt/yr).

scholarly journals New Gridded Product for the Total Columnar Atmospheric Water Vapor over Ocean Surface Constructed from Microwave Radiometer Satellite Data

2021 ◽  
Vol 13 (12) ◽  
pp. 2402
Author(s):  
Weifu Sun ◽  
Jin Wang ◽  
Yuheng Li ◽  
Junmin Meng ◽  
Yujia Zhao ◽  
...  
Keyword(s):  
Water Vapor ◽  
Microwave Radiometer ◽  
Atmospheric Water Vapor ◽  
Global Ocean ◽  
Optimal Interpolation ◽  
Fusion Product ◽  
Mean Deviation ◽  
Atmospheric Water ◽  
Absolute Deviation ◽  
Better Than

Based on the optimal interpolation (OI) algorithm, a daily fusion product of high-resolution global ocean columnar atmospheric water vapor with a resolution of 0.25° was generated in this study from multisource remote sensing observations. The product covers the period from 2003 to 2018, and the data represent a fusion of microwave radiometer observations, including those from the Special Sensor Microwave Imager Sounder (SSMIS), WindSat, Advanced Microwave Scanning Radiometer for Earth Observing System sensor (AMSR-E), Advanced Microwave Scanning Radiometer 2 (AMSR2), and HY-2A microwave radiometer (MR). The accuracy of this water vapor fusion product was validated using radiosonde water vapor observations. The comparative results show that the overall mean deviation (Bias) is smaller than 0.6 mm; the root mean square error (RMSE) and standard deviation (SD) are better than 3 mm, and the mean absolute deviation (MAD) and correlation coefficient (R) are better than 2 mm and 0.98, respectively.

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