IWV observations from a network of ground-based GNSS receivers during EUREC4A

2021 ◽  
Author(s):  
Olivier Bock ◽  
Pierre Bosser ◽  
Cyrille Flamant ◽  
Erik Doerflinger ◽  
Friedhelm Jansen ◽  
...  
Keyword(s):  
Large Scale ◽  
Microwave Radiometer ◽  
Heavy Rain ◽  
Satellite System ◽  
Atmospheric Environment ◽  
Tropospheric Delay ◽  
Systematic Bias ◽  
Data Set ◽  
Radiosonde Station ◽  
Dry Bias

<p>IWV data were retrieved from a network of nearly fifty Global Navigation Satellite System (GNSS) stations distributed over the Caribbean arc for the period 1 January-29 February 2020 encompassing the EUREC4A field campaign. Two of the stations had been installed at the Barbados Cloud Observatory (BCO) during fall 2019 in the framework of the project and are still running. All other stations are permanent stations operated routinely from various geodetic and geophysical organisations in the region. High spatial and temporal Integrated Water Vapour (IWV) observations will be used to investigate the atmospheric environment during the life cycle of convection and its feedback on the large-scale circulation and energy budget.</p><p>This paper describes the ground-based GNSS data processing details and assesses the quality of the GNSS IWV retrievals as well as the IWV estimates from radiosoundings, microwave radiometer measurements and ERA5 reanalysis.</p><p>The GNSS results from five different processing streams run by IGN and ENSTA-B/IPGP are first intercompared. Four of the streams were run operationally, among one was in near-real time, and one was run after the campaign in a reprocessing mode. The uncertainties associated with each of the data sets, including the zenith tropospheric delay to IWV conversion methods and auxiliary data, are quantified and discussed. The IWV estimates from the reprocessed data set are compared to the Vaisala RS41 radiosonde measurements operated from the BCO and to the measurements from the operational radiosonde station at Grantley Adams international airport (GAIA). A significant dry bias is found in the GAIA humidity observations with respect to the BCO sondes (-2.9 kg/m2) and the GNSS results (-1.2 kg/m2). A systematic bias between the BCO sondes and GNSS is also observed (1.7 kg/m2) where the Vaisala RS41 measurements are moister than the GNSS retrievals. The HATPRO IWV estimates agree with the BCO soundings after an instrumental update on 27 January, while they exhibit a dry bias compared to GNSS and BCO sondes before that date. ERA5 IWV estimates are overall close to the GAIA observations, probably due to the assimilation of these observations in the reanalysis. However, during several events where strong peaks in IWV occurred, ERA5 is shown to significantly underestimate the IWV peaks. Two successive peaks are observed on 22 January and 23/24 January which were associated with heavy rain and deep moist layers extending from the surface up to altitudes of 3.5 and 5 km, respectively. ERA5 significantly underestimates the moisture content in the upper part of these layers. The origins of the various moisture biases are currently being investigated.</p>

scholarly journals IWV observations in the Caribbean Arc from a network of ground-based GNSS receivers during EUREC<sup>4</sup>A

2021 ◽  
Author(s):  
Olivier Bock ◽  
Pierre Bosser ◽  
Cyrille Flamant ◽  
Erik Doerflinger ◽  
Friedhelm Jansen ◽  
...  
Keyword(s):  
Large Scale ◽  
Microwave Radiometer ◽  
Heavy Rain ◽  
Tropospheric Delay ◽  
Systematic Bias ◽  
Data Set ◽  
Radiosonde Station ◽  
The Caribbean ◽  
Cloud Patterns ◽  
Dry Bias

Abstract. Ground-based Global Navigation Satellite System (GNSS) measurements from nearly fifty stations distributed over the Caribbean Arc have been analysed for the period 1 January–29 February 2020 in the framework of the EUREC4A (Elucidate the Couplings Between Clouds, Convection and Circulation) field campaign. The aim of this effort is to deliver high-quality Integrated Water Vapour (IWV) estimates to investigate the moisture environment of mesoscale cloud patterns in the Tradewinds and their feedback on the large-scale circulation and energy budget. This paper describes the GNSS data processing procedures and assesses the quality of the GNSS IWV retrievals from four operational streams and one reprocessed research stream which is the main data set used for offline scientific applications. The uncertainties associated with each of the data sets, including the zenith tropospheric delay (ZTD) to IWV conversion methods and auxiliary data, are quantified and discussed. The IWV estimates from the reprocessed data set are compared to the Vaisala RS41 radiosonde measurements operated from the Barbados Cloud Observatory (BCO) and to the measurements from the operational radiosonde station at Grantley Adams international airport (GAIA). A significant dry bias is found in the GAIA humidity observations with respect to the BCO sondes (−2.9 kg m−2) and the GNSS results (−1.2 kg m−2). A systematic bias between the BCO sondes and GNSS is also observed (1.7 kg m−2) where the Vaisala RS41 measurements are moister than the GNSS retrievals. The IWV estimates from a colocated microwave radiometer agree with the BCO soundings after an instrumental update on 27 January, while they exhibit a dry bias compared to the soundings and to GNSS before that date. IWV estimates from the ECMWF fifth generation reanalysis (ERA5) are overall close to the GAIA observations, probably due to the assimilation of these observations in the reanalysis. However, during several events where strong peaks in IWV occurred, ERA5 is shown to significantly underestimate the GNSS derived IWV peaks. Two successive peaks are observed on 22 January and 23/24 January which were associated with heavy rain and deep moist layers extending from the surface up to altitudes of 3.5 and 5 km, respectively. ERA5 significantly underestimates the moisture content in the upper part of these layers. The origins of the various moisture biases are currently being investigated. We classified the cloud organisation for five representative GNSS stations across the Caribbean Arc and found that the environment of Fish cloud patterns to be moister than that of Flowers cloud patterns which, in turn, is moister than the environment of Gravel cloud patterns. The differences in the IWV means between Fish and Gravel were assessed to be significant. Finally, the Gravel moisture environment was found to be similar to that of clear, cloud-free conditions. The moisture environment associated with the Sugar cloud pattern has not been assessed because it was hardly observed during the first two months of 2020. The reprocessed ZTD and IWV data set from 49 GNSS stations used in this study are available from the AERIS data center (https://doi.org/10.25326/79; Bock (2020b)).

scholarly journals Integrated water vapour observations in the Caribbean arc from a network of ground-based GNSS receivers during EUREC<sup>4</sup>A

2021 ◽  
Vol 13 (5) ◽  
pp. 2407-2436
Author(s):  
Olivier Bock ◽  
Pierre Bosser ◽  
Cyrille Flamant ◽  
Erik Doerflinger ◽  
Friedhelm Jansen ◽  
...  
Keyword(s):  
Water Vapour ◽  
Tropospheric Delay ◽  
Data Sets ◽  
Systematic Bias ◽  
Data Set ◽  
Radiosonde Station ◽  
Integrated Water Vapour ◽  
The Caribbean ◽  
Cloud Patterns ◽  
Dry Bias

Abstract. Ground-based Global Navigation Satellite System (GNSS) measurements from nearly 50 stations distributed over the Caribbean arc have been analysed for the period 1 January–29 February 2020 in the framework of the EUREC4A (Elucidate the Couplings Between Clouds, Convection and Circulation) field campaign. The aim of this effort is to deliver high-quality integrated water vapour (IWV) estimates to investigate the moisture environment of mesoscale cloud patterns in the trade winds and their feedback on the large-scale circulation and energy budget. This paper describes the GNSS data processing procedures and assesses the quality of the GNSS IWV retrievals from four operational streams and one reprocessed research stream which is the main data set used for offline scientific applications. The uncertainties associated with each of the data sets, including the zenith tropospheric delay (ZTD)-to-IWV conversion methods and auxiliary data, are quantified and discussed. The IWV estimates from the reprocessed data set are compared to the Vaisala RS41 radiosonde measurements operated from the Barbados Cloud Observatory (BCO) and to the measurements from the operational radiosonde station at Grantley Adams International Airport (GAIA), Bridgetown, Barbados. A significant dry bias is found in the GAIA humidity observations with respect to the BCO sondes (−2.9 kg m−2) and the GNSS results (−1.2 kg m−2). A systematic bias between the BCO sondes and GNSS is also observed (1.7 kg m−2), where the Vaisala RS41 measurements are moister than the GNSS retrievals. The IWV estimates from a collocated microwave radiometer agree with the BCO soundings after an instrumental update on 27 January, while they exhibit a dry bias compared to the soundings and to GNSS before that date. IWV estimates from the ECMWF fifth-generation reanalysis (ERA5) are overall close to the GAIA observations, probably due to the assimilation of these observations in the reanalysis. However, during several events where strong peaks in IWV occurred, ERA5 is shown to significantly underestimate the GNSS-derived IWV peaks. Two successive peaks are observed on 22 January and 23–24 January which were associated with heavy rain and deep moist layers extending from the surface up to altitudes of 3.5 and 5 km, respectively. ERA5 significantly underestimates the moisture content in the upper part of these layers. The origins of the various moisture biases are currently being investigated. We classified the cloud organization for five representative GNSS stations across the Caribbean arc using visible satellite images. A statistically significant link was found between the cloud patterns and the local IWV observations from the GNSS sites as well as the larger-scale IWV patterns from the ECMWF ERA5 reanalysis. The reprocessed ZTD and IWV data sets from 49 GNSS stations used in this study are available from the French data and service centre for atmosphere (AERIS) (https://doi.org/10.25326/79; Bock, 2020b).

scholarly journals Evaluation of the Possibility of Using the Predicted Tropospheric Delays in Real Time Gnss Positioning

2014 ◽  
Vol 49 (4) ◽  
pp. 179-189 ◽  
Author(s):  
J. Z. Kalita ◽  
Z. Rzepecka ◽  
G. Krzan
Keyword(s):  
Real Time ◽  
Satellite System ◽  
Weather Data ◽  
Observation System ◽  
Tropospheric Delay ◽  
Systematic Bias ◽  
Atmospheric Conditions ◽  
Predicted Values ◽  
Delay Component ◽  
Many Sources

ABSTRACT Among many sources of errors that influence Global Navigation Satellite System (GNSS) observations, tropospheric delay is one of the most significant. It causes nonrefractive systematic bias in the observations on the level of several meters, depending on the atmospheric conditions. Tropospheric delay modelling plays an important role in precise positioning. The current models use numerical weather data for precise estimation of the parameters that are provided as a part of the Global Geodetic Observation System (GGOS). The purpose of this paper is to analyze the tropospheric data provided by the GGOS Atmosphere Service conducted by the Vienna University of Technology. There are predicted and final delay data available at the Service. In real time tasks, only the predicted values can be used. Thus it is very useful to study accuracy of the forecast delays. Comparison of data sets based on predicted and real weather models allows for conclusions concerning possibility of using the former for real time positioning applications. The predicted values of the dry tropospheric delay component, both zenith and mapped, can be safely used in real time PPP applications, but on the other hand, while using the wet predicted values, one should be very careful.

scholarly journals IWV retrieval from ground GNSS receivers during NAWDEX

2021 ◽  
Vol 55 ◽  
pp. 13-22
Author(s):  
Pierre Bosser ◽  
Olivier Bock
Keyword(s):  
Water Vapor ◽  
North Atlantic ◽  
Regional Scale ◽  
Satellite System ◽  
Tropospheric Delay ◽  
Spatial And Temporal Variability ◽  
Data Set ◽  
The North ◽  
The North Atlantic ◽  
Global Navigation Satellite

Abstract. A ground-based network of more than 1200 Global Navigation Satellite System (GNSS) Continuously Operating Reference Stations (CORS) was analysed using GIPSY-OASIS II software package for the documentation of time and space variations of water vapor in atmosphere during the North Atlantic Waveguide and Downstream impact EXperiment (NAWDEX) during fall 2016. The network extends throughout the North Atlantic, from the Caribbeans to Morocco through Greenland. This paper presents the methodology used for GNSS data processing, screening, and conversion of Zenith Tropospheric Delay (ZTD) estimates to Integrated Water Vapor content (IWV) using surface parameters from reanalysis. The retrieved IWV are used to evaluate the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalyses ERAI and ERA5. ERA5 shows an overall improvement over ERAI in representing the spatial and temporal variability of IWV over the study area. The mean bias is decreased from 0.31±0.63 to 0.19±0.56 kg m−2 (mean ±1σ over all stations) and the standard deviation reduced from 2.17±0.67 to 1.64±0.53 kg m−2 combined with a slight improvement in correlation coefficient from 0.95 to 0.97. At regional scale, both reanalyses show a general wet bias at mid and northern latitudes but a dry bias in the Caribbeans. We hypothesize this results from the different nature of data being assimilated over the tropical oceans. This GNSS IWV data set is intended to be used for a better description of the high impact weather events that occurred during the NAWDEX experiment.

scholarly journals UAV PHOTOGRAMMETRY AND VHR SATELLITE IMAGERY FOR EMERGENCY MAPPING. THE OCTOBER 2020 FLOOD IN LIMONE PIEMONTE (ITALY)

2021 ◽  
Vol XLIII-B3-2021 ◽  
pp. 727-734
Author(s):  
L. Teppati Losè ◽  
F. Chiabrando ◽  
F. Giulio Tonolo ◽  
A. Lingua
Keyword(s):  
Data Processing ◽  
Large Scale ◽  
Heavy Rain ◽  
Satellite System ◽  
Control Points ◽  
Positional Accuracy ◽  
Multi Scale ◽  
Ground Control Points ◽  
Processing Strategies ◽  
Global Navigation Satellite

Abstract. Heavy rain between the 2nd and 3rd of October 2020 severely affected the area of Limone Piemonte, Piemonte Region (Italy). The consequence of those two days of rain was a flood that, starting from the hamlet of Limonetto severely damaged the areas close to the riverbed of the Vermegnana river and the related hydrographyc network. A synergistic multi-sensor and multi-scale approach for documenting the affected areas using VHR satellite images and UAVs (Uncrewed Aerial Vehicles) is presented. The pro and cons in terms of level of detail and processing strategies are reviewed with a focus on the workflows adopted for processing large UAV datasets. A thorough analysis of the 3D positional accuracy achievable with different georeferentation strategies for UAVs data processing is carried out, confirming that if an RTK (Reale Time Kinematic)-enabled GNSS (Global Navigation Satellite System) receiver is available on the UAV platform and proper acquisition guidelines are followed, the use of GCPs (Ground Control Points) is not impacting significantly on the overall positional accuracy. Satellite data processing is also presented, confirming the suitability for large scale mapping.

scholarly journals Validating HY-2A CMR precipitable water vapor using ground-based and shipborne GNSS observations

2020 ◽  
Vol 13 (9) ◽  
pp. 4963-4972
Author(s):  
Zhilu Wu ◽  
Yanxiong Liu ◽  
Yang Liu ◽  
Jungang Wang ◽  
Xiufeng He ◽  
...  
Keyword(s):  
Water Vapor ◽  
Microwave Radiometer ◽  
Precipitable Water ◽  
Satellite System ◽  
Precipitable Water Vapor ◽  
Tropospheric Delay ◽  
High Temporal Resolution ◽  
International Gnss Service ◽  
Global Navigation Satellite ◽  
Gnss Observations

Abstract. The calibration microwave radiometer (CMR) on board the Haiyang-2A (HY-2A) satellite provides wet tropospheric delay correction for altimetry data, which can also contribute to the understanding of climate system and weather processes. The ground-based global navigation satellite system (GNSS) provides precise precipitable water vapor (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 International GNSS Service (IGS) stations along the global coastline and 56 d shipborne GNSS observations over the Indian Ocean. The processing strategy for GNSS data and CMR data is discussed in detail. Special efforts were made in 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 as the root mean square (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.

scholarly journals Kinematic Zenith Tropospheric Delay Estimation with GNSS PPP in Mountainous Areas

Sensors ◽  
2021 ◽  
Vol 21 (17) ◽  
pp. 5709
Author(s):  
Paul Gratton ◽  
Simon Banville ◽  
Gérard Lachapelle ◽  
Kyle O’Keefe
Keyword(s):  
Ambiguity Resolution ◽  
Large Scale ◽  
Field Experiments ◽  
Integer Ambiguity Resolution ◽  
Global Navigation Satellite Systems ◽  
Integer Ambiguity ◽  
Tropospheric Delay ◽  
Mountainous Areas ◽  
Data Set ◽  
Zenith Tropospheric Delay

The use of global navigation satellite systems (GNSS) precise point positioning (PPP) to estimate zenith tropospheric delay (ZTD) profiles in kinematic vehicular mode in mountainous areas is investigated. Car-mounted multi-constellation GNSS receivers are employed. The Natural Resources Canada Canadian Spatial Reference System PPP (CSRS-PPP) online service that currently processes dual-frequency global positioning system (GPS) and Global’naya Navigatsionnaya Sputnikovaya Sistema (GLONASS) measurements and is now capable of GPS integer ambiguity resolution is used. An offline version that can process the above and Galileo measurements simultaneously, including Galileo integer ambiguity resolution is also tested to evaluate the advantage of three constellations. A multi-day static data set observed under open sky is first tested to determine performance under ideal conditions. Two long road profile tests conducted in kinematic mode are then analyzed to assess the capability of the approach. The challenges of ZTD kinematic profiling are numerous, namely shorter data sets, signal shading due to topography and forests of conifers along roads, and frequent losses of phase lock requiring numerous but not always successful integer ambiguity re-initialization. ZTD profiles are therefore often only available with float ambiguities, reducing system observability. Occasional total interruption of measurement availability results in profile discontinuities. CSRS-PPP outputs separately the zenith hydrostatic or dry delay (ZHD) and water vapour content or zenith wet delay (ZWD). The two delays are analyzed separately, with emphasis on the more unpredictable and highly variable ZWD, especially in mountainous areas. The estimated delays are compared with the Vienna Mapping Function 1 (VMF1), which proves to be highly effective to model the large-scale profile variations in the Canadian Rockies, the main contribution of GNSS PPP being the estimation of higher frequency ZWD components. Of the many conclusions drawn from the field experiments, it is estimated that kinematic profiles are generally determined with accuracy of 10 to 20 mm, depending on the signal harshness of the environment.

scholarly journals Experience of Activation of the J-SPEED(MDS) in Japan

2019 ◽  
Vol 34 (s1) ◽  
pp. s130-s131
Author(s):  
Tatsuhiko Kubo ◽  
Joji Tomioka ◽  
Hisayoshi Kondo ◽  
Yuichi Koido
Keyword(s):  
Large Scale ◽  
Heavy Rain ◽  
Electronic System ◽  
Lessons Learned ◽  
World Health ◽  
Advisory Group ◽  
Data Set ◽  
National Education ◽  
Kumamoto Earthquake ◽  
Support Team

Introduction:The Emergency Medical Team (EMT) Strategic Advisory Group of the World Health Organization has endorsed the EMT Minimum Data Set (MDS) as the standard methodology for EMT daily report. The MDS had been developed on a similar methodology called J-SPEED which developed in Japan. Thus, lessons learned from the J-SPEED can be applied to the MDS.Aim:To review previous J-SPEED activations and to extract lessons learned.Methods:Cases of the J-SPEED activation at the Kumamoto earthquake in 2016, West Japan Heavy Rain in 2018, and Hokkaido Earthquake in 2018 were reviewed.Results:The first large-scale activation of the J-SPEED at the Kumamoto earthquake revealed a significant burden in aggregations of submitted paper forms at the EMT Coordination Cell (EMTCC). To strengthen this function of the EMTCC, electronic system and human capacity development have been identified as key issues. To fulfill this gap, a smartphone app so-called J-SPEED+ has been developed. Also, the J-SPEED offsite analysis support team, which is a team to support analysis of data from outside of an affected area has been established. These two functions contributed to significant improvement of J-SPEED data flow at the West Japan Heavy Rain and Hokkaido Earthquake. These two responses reinforced the necessity of strengthening the capacity of J-SPEED onsite coordinator working at the ETMCC, and national education and training for all EMTs.Discussion:In order to strengthen the mechanism to run the J-SPEED, nationwide training for all EMTs, onsite coordinators, and the off-site analysis support team have been established. The authors regard this structural approach as a requirement for other countries to run the MDS.

ProGen:Provenance database generator for large-scale data set

2009 ◽  
Vol 28 (11) ◽  
pp. 2737-2740
Author(s):  
Xiao ZHANG ◽  
Shan WANG ◽  
Na LIAN
Keyword(s):  
Large Scale ◽  
Data Set ◽  
Large Scale Data ◽  
Scale Data

Integrative Data Analysis from a Unifying Research Synthesis Perspective

2018 ◽  
Author(s):  
Eun-Young Mun ◽  
Anne E. Ray
Keyword(s):  
Data Analysis ◽  
Large Scale ◽  
Research Synthesis ◽  
Alcohol Intervention ◽  
Data Set ◽  
Integrative Data Analysis ◽  
Level Data ◽  
Model Complex ◽  
Wide Range ◽  
Individual Participant

Integrative data analysis (IDA) is a promising new approach in psychological research and has been well received in the field of alcohol research. This chapter provides a larger unifying research synthesis framework for IDA. Major advantages of IDA of individual participant-level data include better and more flexible ways to examine subgroups, model complex relationships, deal with methodological and clinical heterogeneity, and examine infrequently occurring behaviors. However, between-study heterogeneity in measures, designs, and samples and systematic study-level missing data are significant barriers to IDA and, more broadly, to large-scale research synthesis. Based on the authors’ experience working on the Project INTEGRATE data set, which combined individual participant-level data from 24 independent college brief alcohol intervention studies, it is also recognized that IDA investigations require a wide range of expertise and considerable resources and that some minimum standards for reporting IDA studies may be needed to improve transparency and quality of evidence.

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