The availability of atmospheric water vapor content observations, with high temporal and spatial resolution, proved to have a high impact in the prediction of heavy rain events obtained from numerical weather prediction models. Several techniques can be applied to derive such observations. Some of them are well consolidated, some others are still under development. The focus of this work is to provide a statistical assessment of the consistency between four different techniques for water vapor monitoring, and specifically for precipitable water vapor (PWV) retrieval: radiometer-derived, European Centre for Medium-Range Weather Forecasts (ECMWF) meteorological model derived, GNSS-derived and atmospheric sounding derived PWV. An overview of the data processing needed to estimate such parameter in the four cases is given to highlight how the corresponding PWV is related to the actual atmospheric water vapor content. Time series of PWV obtained with the different methods are compared for a case study in Milan, over a period of one year (March 1st, 2018–February 11th, 2019). A four-channel Ka-band/W-band radiometer located in the main campus of Politecnico di Milano is employed in association with a GNSS dual-frequency receiver (MILA), part of a regional network and installed in the same campus, 280 m far from the radiometer. GNSS data are processed by the goGPS software, applying a precise point positioning strategy. A comparison with atmospheric sounding (Milano-Linate station, located at about 6 km from the GNSS receiver), as well as with PWV derived from the ECWMF model (operational products), is also given. Results show a good agreement between the outputs of the four different data sources confirming GNSS as a valid alternative to the well consolidated techniques and opening the way to its synergistic use with co-located radiometers.

Precipitable Water Vapor Content from GNSS/GPS: Validation Against Radiometric Retrievals, Atmospheric Sounding and ECMWF Model Outputs over a Test Area in Milan

Mascitelli A.;Barindelli S.;Luini L.;Venuti G.
2020-01-01

Abstract

The availability of atmospheric water vapor content observations, with high temporal and spatial resolution, proved to have a high impact in the prediction of heavy rain events obtained from numerical weather prediction models. Several techniques can be applied to derive such observations. Some of them are well consolidated, some others are still under development. The focus of this work is to provide a statistical assessment of the consistency between four different techniques for water vapor monitoring, and specifically for precipitable water vapor (PWV) retrieval: radiometer-derived, European Centre for Medium-Range Weather Forecasts (ECMWF) meteorological model derived, GNSS-derived and atmospheric sounding derived PWV. An overview of the data processing needed to estimate such parameter in the four cases is given to highlight how the corresponding PWV is related to the actual atmospheric water vapor content. Time series of PWV obtained with the different methods are compared for a case study in Milan, over a period of one year (March 1st, 2018–February 11th, 2019). A four-channel Ka-band/W-band radiometer located in the main campus of Politecnico di Milano is employed in association with a GNSS dual-frequency receiver (MILA), part of a regional network and installed in the same campus, 280 m far from the radiometer. GNSS data are processed by the goGPS software, applying a precise point positioning strategy. A comparison with atmospheric sounding (Milano-Linate station, located at about 6 km from the GNSS receiver), as well as with PWV derived from the ECWMF model (operational products), is also given. Results show a good agreement between the outputs of the four different data sources confirming GNSS as a valid alternative to the well consolidated techniques and opening the way to its synergistic use with co-located radiometers.
Communications in Computer and Information Science
978-3-030-62799-7
978-3-030-62800-0
GNSS meteorology
PWV
Radiometer
Validation
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1172628
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