Ground-based GNSS for climate research: review and perspectives

In climate research, the role of water vapour can hardly be overestimated. Water vapour is the most important natural greenhouse gas and is responsible for the largest known feedback mechanism for amplifying climate change. It also strongly influences atmospheric dynamics and the hydrologic cycle through surface evaporation, latent heat transport and diabatic heating, and is, in particular, a source of clouds and precipitation.Atmospheric water vapour is highly variable, both in space and in time. Therefore, measuring it remains a demanding and challenging task. The Zenith Total Delay (ZTD) estimated from GNSS observations, provided at a temporal resolution of minutes and under all weather conditions, can be converted to Integrated Water Vapour (IWV), if additional meteorological variables are available. Inconsistencies introduced into long-term time series from improved GNSS processing algorithms, instrumental, and environmental changes at GNSS stations make climate trend analyses challenging. Ongoing re-processing efforts using state-of-the-art models aim at providing consistent time series of tropospheric data, using 24+ years of GNSS observations from global and regional networks. GNSS is reaching the “maturity age” of 30 years when climate normal of ZTD/IWV (and horizontal gradients) can be derived. Being not assimilated in numerical weather prediction model reanalyses, GNSS products can also be used as independent datasets to validate climate model outputs (ZTD/IWV). However, what is the actual use of GNSS ZTDs in climate monitoring? What are the advantages of using GNSS ZTDs for climate monitoring? In addition, what would be the best ZTD time series to serve the climate community?The presentation will provide a review of the progress made in and the status of using GNSS tropospheric datasets for climate research, highlighting the challenges and pitfalls, and outlining the major remaining steps ahead. We will show examples demonstrating the benefits for climate monitoring brought by using GNSS ZTD and/or IWV datasets in complement to other observations.This contribution is related to the activities of JWG C.2: Quality control methods for climate applications of geodetic tropospheric parameters, https://iccc.iag-aig.org/joint-work-groups/216, of the IAG Inter-Commission Committee on "Geodesy for Climate Research" (ICCC)

Homogenizing GPS integrated water vapour time series: methodology and benchmarking the algorithms on synthetic datasets

We would like to thank the COST Action ES1206 GNSS4SWEC for financial support

Study on homogenization of synthetic GNSS-Retrieved IWV time series and its impact on trend estimates with autoregressive noise

Póster presentado en: EGU General Assembly celebrada del 23 al 28 de abril de 2017 en Viena, Austria.A synthetic benchmark dataset of Integrated Water Vapour (IWV) was created within the activity of “Data homogenisation” of sub-working group WG3 of COST ES1206 Action. The benchmark dataset was created basing on the analysis of IWV differences retrieved by Global Positioning System (GPS) International GNSS Service (IGS) stations using European Centre for Medium-Range Weather Forecats (ECMWF) reanalysis data (ERA-Interim). Having analysed a set of 120 series of IWV differences (ERAI-GPS) derived for IGS stations, we delivered parameters of a number of gaps and breaks for every certain station. Moreover, we estimated values of trends, significant seasonalities and character of residuals when deterministic model was removed. We tested five different noise models and found that a combination of white and autoregressive processes of first order describes the stochastic part with a good accuracy. Basing on this analysis, we performed Monte Carlo simulations of 25 years long data with two different types of noise: white as well as combination of white and autoregressive processes. We also added few strictly defined offsets, creating three variants of synthetic dataset: easy, less complicated and fully complicated. The synthetic dataset we present was used as a benchmark to test various statistical tools in terms of homogenisation task. In this research, we assess the impact of the noise model, trend and gaps on the performance of statistical methods to detect simulated change points

Performance of various homogenization tools on a synthetic benchmark dataset of GPS and ERA-interim IWV differences

Presentación realizada en: IAG-IASPEI 39th Joint Scientific Assembly celebrada en Kobe, Japón, del 30 de julio al 4 de agosto de 2017

Homogenization of tropospheric data: evaluating the algorithms under the presence of autoregressive process

Presentación realizada en: IX Hotine-Marussi Symposium celebrado en Roma del 18 al 22 de junio de 2018.This research was supported by the Polish National Science Centre, grant No. UMO-2016/21/B/ST10/02353

Interpreting the time variability of world-wide GPS and GOME/SCIAMACHY integrated water vapour retrievals, using reanalyses as auxiliary tools

This study investigates different aspects of the Integrated Water Vapour (IWV) variability at 118 globally distributed Global Positioning System (GPS) sites, using additionally UV/VIS satellite retrievals by GOME, SCIAMACHY and GOME-2 (denoted as GOMESCIA below), and ERA-Interim reanalysis output at these site locations. Apart from some spatial representativeness issues at especially coastal and island sites, those three datasets correlate rather well, the lowest correlation found between GPS and GOMESCIA (0.865 on average). In this paper, we first study the geographical distribution of the frequency distributions of the IWV time series, and subsequently analyse the seasonal IWV cycle and linear trend differences among the three different datasets. Finally, both the seasonal behaviour and the long-term variability are fitted together by means of a stepwise multiple linear regression of the station’s time series, with a selection of regionally dependent candidate explanatory variables. Overall, the variables that are most frequently used and explain the largest fractions of the IWV variability are the surface temperature and precipitation. Also the surface pressure and tropopause pressure (in particular for higher latitude sites) are important contributors to the IWV time variability. All these variables also seem to account for the sign of long-term trend in the IWV time series to a large extent, when considered as explanatory variable. Furthermore, the multiple linear regression linked the IWV variability at some particular regions to teleconnection patterns or climate/oceanic indices like the North Oscillation index for West USA, the El Niňo Southern Oscillation (ENSO) for East Asia, the East Atlantic (associated with the North Atlantic Oscillation, NAO) index for Europe

Homogenizing GPS Integrated Water Vapor Time Series: Benchmarking Break Detection Methods on Synthetic Data Sets

We assess the performance of different break detection methods on three sets of benchmark data sets, each consisting of 120 daily time series of integrated water vapor differences. These differences are generated from the Global Positioning System (GPS) measurements at 120 sites worldwide, and the numerical weather prediction reanalysis (ERA-Interim) integrated water vapor output, which serves as the reference series here. The benchmark includes homogeneous and inhomogeneous sections with added nonclimatic shifts (breaks) in the latter. Three different variants of the benchmark time series are produced, with increasing complexity, by adding autoregressive noise of the first order to the white noise model and the periodic behavior and consecutively by adding gaps and allowing nonclimatic trends. The purpose of this “complex experiment” is to examine the performance of break detection methods in a more realistic case when the reference series are not homogeneous. We evaluate the performance of break detection methods with skill scores, centered root mean square errors (CRMSE), and trend differences relative to the trends of the homogeneous series. We found that most methods underestimate the number of breaks and have a significant number of false detections. Despite this, the degree of CRMSE reduction is significant (roughly between 40% and 80%) in the easy to moderate experiments, with the ratio of trend bias reduction is even exceeding the 90% of the raw data error. For the complex experiment, the improvement ranges between 15% and 35% with respect to the raw data, both in terms of RMSE and trend estimations

Global Spatiotemporal Variability of Integrated Water Vapor Derived from GPS, GOME/SCIAMACHY and ERA-Interim: Annual Cycle, Frequency Distribution and Linear Trends

Atmospheric water vapor plays a prominent role in climate change and atmospheric, meteorological, and hydrological processes. Because of its high spatiotemporal variability, precise quantification of water vapor is challenging. This study investigates Integrated Water Vapor (IWV) variability for the period 1995–2010 at 118 globally distributed Global Positioning System (GPS) sites, using additional UV/VIS satellite retrievals by GOME, SCIAMACHY, and GOME-2 (denoted as GOMESCIA below), plus ERA-Interim reanalysis output. Apart from spatial representativeness differences, particularly at coastal and island sites, all three IWV datasets correlate well with the lowest mean correlation coefficient of 0.878 (averaged over all the sites) between GPS and GOMESCIA. We confirm the dominance of standard lognormal distribution of the IWV time series, which can be explained by the combination of a lower mode (dry season characterized by a standard lognormal distribution with a low median value) and an upper mode (wet season characterized by a reverse lognormal distribution with high median value) in European, Western American, and subtropical sites. Despite the relatively short length of the time series, we found a good consistency in the sign of the continental IWV trends, not only between the different datasets, but also compared to temperature and precipitation trends

Advanced Global Navigation Satellite Systems Tropospheric Products for Monitoring Severe Weather Events and Climate (GNSS4SWEC)

Global Navigation Satellite Systems (GNSS) have revolutionised positioning, navigation, and timing, becominga common part of our everyday life. Aside from these well-known civilian and commercial applications, GNSS is now an established atmospheric observing system which can accurately sense water vapour, the most abundant greenhouse gas, accounting for 60-70 % of atmospheric warming. Water vapour is under-sampled in the current meteorological and climate observing systems, obtaining and exploiting more high-quality humidity observations is essential to weather forecasting and climate monitoring. The European COST Action ES1206 ”Advanced Global Navigation Satellite Systems tropospheric products for monitoring severe weather eventsand climate (GNSS4SWEC)” will address new and improvedcapabilities from concurrent developments in both the GNSS and the meteorological communities. For the first time, the synergy of the three GNSS systems (GPS, GLONASS and Galileo) will be used to develop new, advanced tropospheric products, exploiting the full potential of multi-GNSS water vapour estimates on a wide range of temporal and spatial scales, from real-time monitoring and forecasting of severe weather, to climate research. In addition, the COST Action ES1206 will promote the use of meteorological data in GNSS positioning, navigation, and timing services and it will stimulate knowledge transfer and data sharing throughout Europe

A multi-site intercomparison of integrated water vapour observations for climate change analysis

Water vapour plays a dominant role in the climate change debate. However, observing water vapour over a climatological time period in a consistent and homogeneous manner is challenging. On one hand, networks of ground-based instruments able to retrieve homogeneous integrated water vapour (IWV) data sets are being set up. Typical examples are Global Navigation Satellite System (GNSS) observation networks such as the International GNSS Service (IGS), with continuous GPS (Global Positioning System) observations spanning over the last 15+ years, and the AErosol RObotic NETwork (AERONET), providing long-term observations performed with standardized and well-calibrated sun photometers. On the other hand, satellite-based measurements of IWV already have a time span of over 10 years (e. g. AIRS) or are being merged to create long-term time series (e. g. GOME, SCIAMACHY, and GOME-2). This study performs an intercomparison of IWV measurements from satellite devices (in the visible, GOME/SCIAMACHY/GOME-2, and in the thermal infrared, AIRS), in situ measurements (radiosondes) and ground-based instruments (GPS, sun photometer), to assess their use in water vapour trends analysis. To this end, we selected 28 sites world-wide for which GPS observations can directly be compared with coincident satellite IWV observations, together with sun photometer and/or radiosonde measurements. The mean biases of the different techniques compared to the GPS estimates vary only between -0.3 to 0.5mm of IWV. Nevertheless these small biases are accompanied by large standard deviations (SD), especially for the satellite instruments. In particular, we analysed the impact of clouds on the IWV agreement. The influence of specific issues for each instrument on the intercomparison is also investigated (e. g. the distance between the satellite ground pixel centre and the co-located ground-based station, the satellite scan angle, daytime/nighttime differences). Furthermore, we checked if the properties of the IWV scatter plots between these different instruments are dependent on the geography and/or altitude of the station. For all considered instruments, the only dependency clearly detected is with latitude: the SD of the IWV observations with respect to the GPS IWV retrievals decreases with increasing latitude and decreasing mean IWV