Water vapor mapping by fusing InSAR and GNSS remote sensing data and atmospheric simulations

Data fusion aims at integrating multiple data sources that can be redundant or complementary to produce complete, accurate information of the parameter of interest. In this work, data fusion of precipitable water vapor (PWV) estimated from remote sensing observations and data from the Weather Research and Forecasting (WRF) modeling system are applied to provide complete grids of PWV with high quality. Our goal is to correctly infer PWV at spatially continuous, highly resolved grids from heterogeneous data sets. This is done by a geostatistical data fusion approach based on the method of fixed-rank kriging. The first data set contains absolute maps of atmospheric PWV produced by combining observations from the Global Navigation Satellite Systems (GNSS) and Interferometric Synthetic Aperture Radar (InSAR). These PWV maps have a high spatial density and a millimeter accuracy; however, the data are missing in regions of low coherence (e.g., forests and vegetated areas). The PWV maps simulated by the WRF model represent the second data set. The model maps are available for wide areas, but they have a coarse spatial resolution and a still limited accuracy. The PWV maps inferred by the data fusion at any spatial resolution show better qualities than those inferred from single data sets. In addition, by using the fixed-rank kriging method, the computational burden is significantly lower than that for ordinary kriging. © 2015 Author(s)

Water vapor mapping by fusing InSAR and GNSS remote sensing data and atmospheric simulations

Data fusion aims at integrating multiple data sources that can be redundant or complementary to produce complete, accurate information of the parameter of interest. In this work, data fusion of precipitable water vapor (PWV) estimated from remote sensing observations and data from the Weather Research and Forecasting (WRF) modeling system are applied to provide complete grids of PWV with high quality. Our goal is to correctly infer PWV at spatially continuous, highly resolved grids from heterogeneous data sets. This is done by a geostatistical data fusion approach based on the method of fixed-rank kriging. The first data set contains absolute maps of atmospheric PWV produced by combining observations from the Global Navigation Satellite Systems (GNSS) and Interferometric Synthetic Aperture Radar (InSAR). These PWV maps have a high spatial density and a millimeter accuracy; however, the data are missing in regions of low coherence (e.g., forests and vegetated areas). The PWV maps simulated by the WRF model represent the second data set. The model maps are available for wide areas, but they have a coarse spatial resolution and a still limited accuracy. The PWV maps inferred by the data fusion at any spatial resolution show better qualities than those inferred from single data sets. In addition, by using the fixed-rank kriging method, the computational burden is significantly lower than that for ordinary kriging

Water vapor mapping by fusing InSAR and GNSS remote sensing data and atmospheric simulations

Data fusion aims at integrating multiple data sources that can be redundant or complementary to produce complete, accurate information of the parameter of interest. In this work, data fusion of precipitable water vapor (PWV) estimated from remote sensing observations and data from the Weather Research and Forecasting (WRF) modeling system is applied to provide complete, accurate grids of PWV. Our goal is to infer spatially continuous, precise grids of PWV from heterogeneous data sets. This is done by a geostatistical data fusion approach based on the method of fixed-rank kriging. The first data set contains absolute maps of atmospheric water vapor produced by combining observations from Global Navigation Satellite Systems (GNSS) and Interferometric Synthetic Aperture Radar (InSAR). These PWV maps have a high spatial density and an accuracy of submillimeter; however, data are missing in regions of low coherence (e.g., forests and vegetated areas). The PWV maps simulated by the WRF model represent the second data set. The model maps are available for wide areas, but they have a coarse spatial resolution and a yet limited accuracy. The PWV maps inferred by the data fusion at any spatial resolution are more accurate than those inferred from single data sets. In addition, using the fixed-rank kriging method, the computational burden is significantly lower than that for ordinary kriging

Delayed subsidence of the Dead Sea shore due to hydro-meteorological changes

Many studies show the sensitivity of our environment to manmade changes, especially the anthropogenic impact on atmospheric and hydrological processes. The effect on Solid Earth processes such as subsidence is less straightforward. Subsidence is usually slow and relates to the interplay of complex hydro-mechanical processes, thus making relations to atmospheric changes difficult to observe. In the Dead Sea (DS) region, however, climatic forcing is strong and over-use of fresh water is massive. An observation period of 3 years was thus sufficient to link the high evaporation (97 cm/year) and the subsequent drop of the Dead Sea lake level (− 110 cm/year), with high subsidence rates of the Earth’s surface (− 15 cm/year). Applying innovative Global Navigation Satellite System (GNSS) techniques, we are able to resolve this subsidence of the “Solid Earth” even on a monthly basis and show that it behaves synchronous to atmospheric and hydrological changes with a time lag of two months. We show that the amplitude and fluctuation period of ground deformation is related to poro-elastic hydro-mechanical soil response to lake level changes. This provides, to our knowledge, a first direct link between shore subsidence, lake-level drop and evaporation

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

Water vapor mapping by fusing InSAR and GNSS remote sensing data and atmospheric simulations

Data fusion aims at integrating multiple data sources that can be redundant or complementary to produce complete, accurate information of the parameter of interest. In this work, data fusion of precipitable water vapor (PWV) estimated from remote sensing observations and data from the Weather Research and Forecasting (WRF) modeling system are applied to provide complete grids of PWV with high quality. Our goal is to correctly infer PWV at spatially continuous, highly resolved grids from heterogeneous data sets. This is done by a geostatistical data fusion approach based on the method of fixed-rank kriging. The first data set contains absolute maps of atmospheric PWV produced by combining observations from the Global Navigation Satellite Systems (GNSS) and Interferometric Synthetic Aperture Radar (InSAR). These PWV maps have a high spatial density and a millimeter accuracy; however, the data are missing in regions of low coherence (e.g., forests and vegetated areas). The PWV maps simulated by the WRF model represent the second data set. The model maps are available for wide areas, but they have a coarse spatial resolution and a still limited accuracy. The PWV maps inferred by the data fusion at any spatial resolution show better qualities than those inferred from single data sets. In addition, by using the fixed-rank kriging method, the computational burden is significantly lower than that for ordinary kriging

Estimating trends in atmospheric water vapor and temperature time series over Germany

Ground-based GNSS (Global Navigation Satellite System) has efficiently been used since the 1990s as a meteorological observing system. Recently scientists have used GNSS time series of precipitable water vapor (PWV) for climate research. In this work, we compare the temporal trends estimated from GNSS time series with those estimated from European Center for Medium-Range Weather Forecasts (ECMWF) reanalysis (ERA-Interim) data and meteorological measurements. We aim to evaluate climate evolution in Germany by monitoring different atmospheric variables such as temperature and PWV. PWV time series were obtained by three methods: (1) estimated from ground-based GNSS observations using the method of precise point positioning, (2) inferred from ERA-Interim reanalysis data, and (3) determined based on daily in situ measurements of temperature and relative humidity. The other relevant atmospheric parameters are available from surface measurements of meteorological stations or derived from ERA-Interim. The trends are estimated using two methods: the first applies least squares to deseasonalized time series and the second uses the Theil–Sen estimator. The trends estimated at 113 GNSS sites, with 10 to 19 years temporal coverage, vary between −1.5 and 2.3 mm decade−1 with standard deviations below 0.25 mm decade−1. These results were validated by estimating the trends from ERA-Interim data over the same time windows, which show similar values. These values of the trend depend on the length and the variations of the time series. Therefore, to give a mean value of the PWV trend over Germany, we estimated the trends using ERA-Interim spanning from 1991 to 2016 (26 years) at 227 synoptic stations over Germany. The ERA-Interim data show positive PWV trends of 0.33 ± 0.06 mm decade−1 with standard errors below 0.03 mm decade−1. The increment in PWV varies between 4.5 and 6.5 % per degree Celsius rise in temperature, which is comparable to the theoretical rate of the Clausius–Clapeyron equation

Delayed subsidence of the Dead Sea shore due to hydro-meteorological changes

Many studies show the sensitivity of our environment to manmade changes, especially the anthropogenic impact on atmospheric and hydrological processes. The effect on Solid Earth processes such as subsidence is less straightforward. Subsidence is usually slow and relates to the interplay of complex hydro-mechanical processes, thus making relations to atmospheric changes difficult to observe. In the Dead Sea (DS) region, however, climatic forcing is strong and over-use of fresh water is massive. An observation period of 3 years was thus sufficient to link the high evaporation (97 cm/year) and the subsequent drop of the Dead Sea lake level (− 110 cm/year), with high subsidence rates of the Earth’s surface (− 15 cm/year). Applying innovative Global Navigation Satellite System (GNSS) techniques, we are able to resolve this subsidence of the “Solid Earth” even on a monthly basis and show that it behaves synchronous to atmospheric and hydrological changes with a time lag of two months. We show that the amplitude and fluctuation period of ground deformation is related to poro-elastic hydro-mechanical soil response to lake level changes. This provides, to our knowledge, a first direct link between shore subsidence, lake-level drop and evaporation

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