GNSS, SAR and NWP Assimilation to Enhance the Prediction of Extreme Weather Events

Dear Colleagues,

Atmospheric interaction with GNSS and satellite-borne SAR microwave signals can be properly modeled and estimated in terms of extra-path or propagation delay.

From the data collected by a single dual frequency GNSS receiver, it is possible to estimate time series of the propagation delay specifically due to the tropospheric water vapor component, allowing for a description of the temporal evolution of the columnar water vapor content over the receiver itself.

GNSS can provide also a direct description of the tropospheric refractivity field via the radio occultation technique, applied to the signals received by sensors on board of a low orbiting satellite, or by a tomographic inversion of the propagation delays affecting the signals of a GNSS network.

From SAR interferometry (InSAR), it is possible to estimate atmospheric delay maps relative to a reference unknown one, which has to be derived using external sources.

Tropospheric water vapor distribution, which plays a key role in the prediction of atmospheric dynamics over a broad range of scales, has a turbulent behavior, which is difficult to describe via atmospheric dynamic models.

Therefore, the assimilation of GNSS and SAR water vapor products into local high-resolution numerical weather prediction (NWP) models can improve the description of time and spatial evolution of the cloud process variables, with special reference to deep moist convection phenomena.

The application of GNSS and SAR to meteorology, through assimilation into NWP models, although not new, still requires scientific investigations guided by experimental activities.

Some examples, far from being exhaustive, can be as follows:

• The use of local and dense GNSS networks (especially made by low-cost receivers) in the prediction of deep moist convection processes;
• The limits towards a near real time exploitation of GNSS time series;
• The effects of multi constellation data availability on the water vapor estimates;
• A deeper analysis of the anisotropic description of the water vapor distribution above a GNSS station and the exploitation of the derived information into NWP models;
• Assimilation of radio occultation profiles;
• Tomography reconstruction from dense GNSS networks and refractivity field assimilation;
• The impact of ionosphere interactions in InSAR derived maps;
• Improvement on assimilation by a correct modeling of the observation spatial correlation;
• Inconsistencies between GNSS SAR data in mountain areas.

Prof. Dr. Giovanna Venuti
Prof. Dr. Andrea Monti Guarnieri
Dr. Antonio Parodi
Guest Editors

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• GNSS meteorology 
• InSAR meteorology 
• NWP assimilation