The water vapour content or wet refractivity indices can be obtained from atmospheric models and be applied to investigate climate change, predicting storm hazards or rainfall. In situ measurements of water vapour are based on meteorological instruments (e.g., radiosonde stations). The atmospheric tomography methods are complementary techniques that use space-based observations (such as the Global Navigation Satellite System, GNSS) or existing atmospheric models to estimate vapour in the atmosphere. Wet refractivity indices are based on water vapour pressure and temperature and reveal numerous local features due to severe weather variability. Therefore, data sets with high spatial-temporal resolution are required to construct the atmospheric models to be able to represent the local fluctuations in the weather-related parameters and to improve their accuracy.
Observations of the radiosonde balloons, used for measuring temperature and humidity, are the main inputs for calculating the wet refractivity indices at different altitudes. However, due to the expensive cost and the high operational demands, the temporal and spatial resolution of these observations is limited.
Our research group works on developing tomography (e.g., Forootan et al., 2021) and data driven (e.g., Forootan et al., 2023) techniques to improve the estimates of atmospheric water vapour. Our focus is to improve weather forecasts by integrating geodetic-based water vapour data into models through Calibration and Data Assimilation (C/DA), as well as to develop tropospheric models for low cost GNSS positioning.
GNSS-based tomography for estimating water vapour, an example for the entire Germany, see Forootan et al. (2021)
Related Publications:
Forootan, E., Dehvari, M., Farzaneh, S., Karimi, S. (2023), Improving the wet refractivity estimation using the Extremely Learning Machine (ELM) technique. Atmosphere 14, doi.org/10.3390/atmos14010112.
Forootan, E., Dehvari, M., Farzaneh, S., Khaniani, AS. (2021), A functional modelling approach for reconstructing 3 and 4 dimensional wet refractivity _elds in the lower atmosphere using GNSS measurements. Advances in Space Research, 68 (10), doi:10.1016/j.asr.2021.08.012