Water vapour distribution at urban scale using high-resolution numerical weather model and spaceborne SAR interferometric data

Pichelli, Emanuela; Ferretti, Rossella; Cimini, Domenico; Perissin, Daniele; Montopoli, Mario; Marzano, Frank S.; Pierdicca, Nazzareno

doi:10.5194/nhess-10-121-2010

Cited by 16 publications

(16 citation statements)

References 19 publications

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“…3118 F. Alshawaf et al: Estimating trends in atmospheric water vapor and temperature time series based estimates of zenith total delay (ZTD) or PWV have been assimilated into numerical weather prediction models to improve the quality of the output (Bock et al, 2005;Bennitt and Jupp, 2012). They have also been used to improve the performance of high-resolution atmospheric models (Pichelli et al, 2010). Besides meteorology, GNSS estimates of PWV have been employed over Scandinavia for climatological research (Elgered and Jarlemark, 1998;Gradinarsky et al, 2002;Nilsson and Elgered, 2008).…”

Section: Introductionmentioning

confidence: 99%

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

Alshawaf

Balidakis²,

Dick

et al. 2017

Atmos. Meas. Tech.

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Abstract. 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 ClausiusClapeyron equation.

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Section: Introductionmentioning

confidence: 99%

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

Alshawaf

Balidakis²,

Dick

et al. 2017

Atmos. Meas. Tech.

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“…Fiori et al (2009) investigated how the uncertainty in the numerical weather prediction of severe weather events could be affected by increasing the model grid resolution and by choosing a parameterization which is able to represent turbulent processes at such finer scales. Pichelli et al (2010) investigated the ability of high resolution meso-scale model (MM5, see Tremback, 1990) in reconstructing high resolution water vapor fields over the urban area of Rome, Italy, and its surroundings. Results showed that the high resolution Integrated Water Vapour model maps are in good agreement with the Interferometric Synthetic Aperture Radar ones.…”

Section: Modeling Studiesmentioning

confidence: 99%

Outcomes of the 10th EGU Plinius Conference on Mediterranean Storms (2008)

Michaelides¹,

Savvidou²,

Orphanou³

et al. 2011

Nat. Hazards Earth Syst. Sci.

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“…The influence of water vapor in the observations can be reduced by averaging a large number of interferograms (Zebker et al, 1997) or by time series analysis that indicates the stable persistent scatterers (Ferretti et al, 2001;Hooper et al, 2007). Besides, InSAR has recently been used to derive the phase shift caused due to the propagation in the Earth's atmosphere from the interferograms or by time series analysis (Hanssen, 2001;Meyer et al, 2008;Pichelli et al, 2010;Alshawaf et al, 2012). Global Navigation Satellite Systems (GNSS), however, have been considered since the 1990s as an efficient microwave-based tool for atmospheric sounding (Bevis et al, 1992;Rocken et al, 1995).…”

Section: Introductionmentioning

confidence: 99%

“…Numerical atmospheric prediction models are increasingly used to provide simulations of the atmospheric parameters. Various studies suggested the assimilation of atmospheric parameters, such as water vapor, estimated from the Global Positioning System (GPS) or Interferometric Synthetic Aperture Radar (InSAR), into these models to improve the quality of the simulated parameters (Pichelli et al, 2010;Bennitt and Jupp, 2008). We want to comprehend whether the model simulations of water vapor, in their current quality, can be used to even out the deficits of the measurement-based estimates, particularly in regions with no measurements.…”

Section: Introductionmentioning

confidence: 99%

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

Alshawaf

Fersch²,

Hinz

et al. 2015

Hydrol. Earth Syst. Sci.

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Abstract. 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.

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Water vapour distribution at urban scale using high-resolution numerical weather model and spaceborne SAR interferometric data

Cited by 16 publications

References 19 publications

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

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

Outcomes of the 10th EGU Plinius Conference on Mediterranean Storms (2008)

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

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