Tropospheric water vapor: a comprehensive high-resolution data collection for the transnational Upper Rhine Graben region

Tropospheric water vapor is one of the most important trace gases of the Earth\u27s climate system, and its temporal and spatial distribution is critical for the genesis of clouds and precipitation. Due to the pronounced dynamics of the atmosphere and the nonlinear relation of air temperature and saturated vapor pressure, it is highly variable, which hampers the development of high-resolution and three-dimensional maps of regional extent. With their complementary high temporal and spatial resolutions, Global Navigation Satellite Systems (GNSS) meteorology and Interferometric Synthetic Aperture Radar (InSAR) satellite remote sensing represent a significant alternative to generally sparsely distributed radio sounding observations. In addition, data fusion with collocation and tomographical methods enables the construction of detailed maps in either two or three dimensions. Finally, by assimilation of these observation-derived datasets with dynamical regional atmospheric models, tropospheric water vapor fields can be determined with high spatial and continuous temporal resolution. In the following, a collection of basic and processed datasets, obtained with the above-listed methods, is presented that describes the state and course of atmospheric water vapor for the extent of the GNSS Upper Rhine Graben Network (GURN) region. The dataset contains hourly 2D fields of integrated water vapor (IWV) and 3D fields of water vapor density (WVD) for four multi-week, variable season periods between April 2016 and October 2018 at a spatial resolution of (2.1 km)$^2$. Zenith total delay (ZTD) from GNSS and collocation and refractivities are provided as intermediate products. InSAR (Sentinel-1A/B)-derived double differential slant total delay phases (ddSTDPs) and GNSS-based ZTDs are available for March 2015 to July 2019. The validation of data assimilation with five independent GNSS stations for IWV shows improving Kling–Gupta efficiency (KGE) scores for all seasons, most notably for summer, with collocation data assimilation (KGE = 0.92) versus the open-cycle simulation (KGE = 0.69). The full dataset can be obtained from https://doi.org/10.1594/PANGAEA.936447 (Fersch et al., 2021)

Tropospheric water vapor: a comprehensive high-resolution data collection for the transnational Upper Rhine Graben region

Tropospheric water vapor is one of the most important trace gases of the Earth's climate system, and its temporal and spatial distribution is critical for the genesis of clouds and precipitation. Due to the pronounced dynamics of the atmosphere and the nonlinear relation of air temperature and saturated vapor pressure, it is highly variable, which hampers the development of high-resolution and three-dimensional maps of regional extent. With their complementary high temporal and spatial resolutions, Global Navigation Satellite Systems (GNSS) meteorology and Interferometric Synthetic Aperture Radar (InSAR) satellite remote sensing represent a significant alternative to generally sparsely distributed radio sounding observations. In addition, data fusion with collocation and tomographical methods enables the construction of detailed maps in either two or three dimensions. Finally, by assimilation of these observation-derived datasets with dynamical regional atmospheric models, tropospheric water vapor fields can be determined with high spatial and continuous temporal resolution. In the following, a collection of basic and processed datasets, obtained with the above-listed methods, is presented that describes the state and course of atmospheric water vapor for the extent of the GNSS Upper Rhine Graben Network (GURN) region. The dataset contains hourly 2D fields of integrated water vapor (IWV) and 3D fields of water vapor density (WVD) for four multi-week, variable season periods between April 2016 and October 2018 at a spatial resolution of (2.1 km)2. Zenith total delay (ZTD) from GNSS and collocation and refractivities are provided as intermediate products. InSAR (Sentinel-1A/B)-derived double differential slant total delay phases (ddSTDPs) and GNSS-based ZTDs are available for March 2015 to July 2019. The validation of data assimilation with five independent GNSS stations for IWV shows improving Kling–Gupta efficiency (KGE) scores for all seasons, most notably for summer, with collocation data assimilation (KGE = 0.92) versus the open-cycle simulation (KGE = 0.69). The full dataset can be obtained from https://doi.org/10.1594/PANGAEA.936447 (Fersch et al., 2021)

Validation of a self-completed Dystonia Non-Motor Symptoms Questionnaire

Objetive: To develop and validate a novel 14-item self-completed questionnaire (in English and German) enquiring about the presence of non-motor symptoms (NMS) during the past month in patients with craniocervical dystonia in an international multicenter study. Methods: The Dystonia Non-Motor Symptoms Questionnaire (DNMSQuest) covers seven domains including sleep, autonomic symptoms, fatigue, emotional well-being, stigma, activities of daily living, sensory symptoms. The feasibility and clinimetric attributes were analyzed. Results: Data from 194 patients with CD (65.6% female, mean age 58.96 ± 12.17 years, duration of disease 11.95 ± 9.40 years) and 102 age- and sex-matched healthy controls (66.7% female, mean age 55.67 ± 17.62 years) were collected from centres in Germany and the UK. The median total NMS score in CD patients was 5 (interquartile range 3-7), significantly higher than in healthy controls with 1 (interquartile range 0.75-2.25) (P < 0.001, Mann-Whitney U-test). Evidence for intercorrelation and convergent validity is shown by moderate to high correlations of total DNMSQuest score with motor symptom severity (TWSTRS: rs  = 0.61), clinical global impression (rs  = 0.40), and health-related quality of life measures: CDQ-24 (rs  = 0.74), EQ-5D index (rs  = -0.59), and scale (rs  = -0.49) (all P < 0.001). Data quality and acceptability was very satisfactory. Interpretation: The DNMSQuest, a patient self-completed questionnaire for NMS assessment in CD patients, appears robust, reproducible, and valid in clinical practice showing a tangible impact of NMS on quality of life in CD. As there is no specific, comprehensive, validated tool to assess the burden of NMS in dystonia, the DNMSQuest can bridge this gap and could easily be integrated into clinical practice.S

Distributed double differential slant delays and IWV from PSI analysis of Sentinel-1 data

The provided dataset consists of double differential slant delays and absolute zenith wet delays in the region of the Upper Rhine Graben. Basis is the SLC data from Sentinel 1A+B satellites provided by the Copernicus program. 169 scenes were processed which had been acquired between April 2015 and July 2019, including data of four specific study events (11 – 22 Apr 2016, 13 – 24 Jul 2018, 16 – 31 Oct 2018, 06 – 21 Jan 2017). Interferometric processing was performed using the software SNAP, continued by a Persistent Scatterer Interferometric SAR (PS-InSAR) processing, using the program StaMPS. The first product are double differential slant delays which represent the phase delay in radiant in the satellites line of sight between the master acquisition (17 Mar 2012) and each acquisition-date respectively. Further processing uses ERA5 zenith wet delay (ZWD) and mean temperature to infer absolute zenith wet delays. A mean value is subtracted for each scene, resulting in an absolute value correction. In addition, long wavelength components are corrected by fitting the trend over the scene for each date to a 2D polynomial approximation from the ERA5 data, as those parts cannot reliably be estimated solely from the SAR data. The final product for every scene is the integrated water vapor (IWV) in kg/m² for each acquisition date at the distributed PS-points – on average about 50 points per square kilometer

Water Vapor Fields by Collocation of GNSS zenith total delays and InSAR relative slant delays in the Upper Rhine Graben region

The collocation method was used to compute water vapor fields for the Upper Rhine Graben (URG) region from GNSS zenith total delays (ZTDs) and InSAR double difference slant delays (ddSTDs). Furthermore, mean temperature from ERA data was used for the conversion of GNSS ZTDs into IWV. The input data are hourly GNSS tropospheric parameters from the GURN (GNSS Upper Rhine Graben network) network for 4 different seasons in the period 2016-2018, as well as ddSTDs for 168 InSAR acquisition epochs of the Sentinel 1A+B satellites. In total, our dataset includes 2D fields of integrated water vapor (IWV) and zenith total delays (ZTDs) as well as 3D 'tomographic' products in form of refractivity fields. For 4 specific seasonal periods, also hourly water vapor density fields are provided by exploiting the relations between IWV and water vapor density in the collocation scheme. The tropospheric fields are provided for the horizontal WRF grid of data assimilation subset of this joint data collection, whereas the 3D fields are computed up to 8 km height for 16 equally distributed layers

Assimilation of GNSS, InSAR and tomography data in convection permitting RCM simulations of the Upper Rhinegraben region

Convection-permitting simulations with the Weather Research and Forecasting Modeling System (WRF) were carried out in order to provide improved water vapor fields for the Upper Rhine Valley in the border region of ​​Germany, Switzerland and France. Hourly ERA5 reanalysis data served as input for three different simulations with (1) open loop, (2) assimilation of GNSS ZTD, InSAR ZTD and synoptic station data and (3) assimilation of tomography ZTD fields. The three-dimensional variation data assimilation (3D-VAR) configuration with hourly resolution was used. The simulations were performed for four events, one in each season (April 11-22, 2016, July 13-23, 2018, October 16-31, 2018, January 6-21, 2017). Surface pressure, temperature (2m) and integrated water vapor are provided in 2D as well as pressure, temperature and water vapor density for each of the 72 vertical levels (3D)

A comprehensive high resolution data collection for tropospheric water vapor assessment for the Upper Rhine Graben, Germany

Different observation and modeling techniques were used to derive integrated water vapor (IWV) fields for the Upper Rhine Graben in the border region of Germany, Switzerland, and France. The dataset features 1) point-scale IWV and zenith total delay (ZTD) derived for 66 stations of the global navigation satellite system (GNSS) Upper Rhine Graben network (GURN), 2) area-distributed IWV and differential slant path delays from space-borne Interferometric synthetic aperture radar (InSAR) observations, 3) IWV, ZTD, refractivity (3D), and water vapor density (3D) from tomography, obtained by collocation of GNSS and InSAR products, and 4) IWV, precipitation and water vapor density (3D) simulated with the Weather Research and Forecasting Modeling system (WRF) with free run (open-loop) and three-dimensional variational data-assimilation (3D-VAR) configuration. All data products cover 4 seasonal epochs (11 – 22 Apr 2016, 13 – 24 Jul 2018, 16 – 31 Oct 2018, 06 – 21 Jan 2017). GNSS, InSAR, and tomography data are additionally available for the period Jan 2015 – Jun 2019

Tropospheric water vapor: a comprehensive high-resolution data collection for the transnational Upper Rhine Graben region

Tropospheric water vapor is one of the most important trace gases of the Earth's climate system, and its temporal and spatial distribution is critical for the genesis of clouds and precipitation. Due to the pronounced dynamics of the atmosphere and the nonlinear relation of air temperature and saturated vapor pressure, it is highly variable, which hampers the development of high-resolution and three-dimensional maps of regional extent. With their complementary high temporal and spatial resolutions, Global Navigation Satellite Systems (GNSS) meteorology and Interferometric Synthetic Aperture Radar (InSAR) satellite remote sensing represent a significant alternative to generally sparsely distributed radio sounding observations. In addition, data fusion with collocation and tomographical methods enables the construction of detailed maps in either two or three dimensions. Finally, by assimilation of these observation-derived datasets with dynamical regional atmospheric models, tropospheric water vapor fields can be determined with high spatial and continuous temporal resolution. In the following, a collection of basic and processed datasets, obtained with the above-listed methods, is presented that describes the state and course of atmospheric water vapor for the extent of the GNSS Upper Rhine Graben Network (GURN) region. The dataset contains hourly 2D fields of integrated water vapor (IWV) and 3D fields of water vapor density (WVD) for four multi-week, variable season periods between April 2016 and October 2018 at a spatial resolution of (2.1 km)2. Zenith total delay (ZTD) from GNSS and collocation and refractivities are provided as intermediate products. InSAR (Sentinel-1A/B)-derived double differential slant total delay phases (ddSTDPs) and GNSS-based ZTDs are available for March 2015 to July 2019. The validation of data assimilation with five independent GNSS stations for IWV shows improving Kling–Gupta efficiency (KGE) scores for all seasons, most notably for summer, with collocation data assimilation (KGE = 0.92) versus the open-cycle simulation (KGE = 0.69). The full dataset can be obtained from https://doi.org/10.1594/PANGAEA.936447 (Fersch et al., 2021).ISSN:1866-3516ISSN:1866-350