On the contribution of InSAR Meteorology to a Digital Twin Of The Atmosphere

In this work, we discuss the potential and perspective use of InSAR meteorology within the Destination Earth (DestineE) initiative. The joined use of high-resolution Numerical Weather Models (NWM),such as the Weather Research and Forecasting (WRF) model, and the next large availability and redundancy of C- and L-band interferometric SAR missions (besides the current Sentinel-1 A&B and SAOCOM missions and the next Sentinel-1 C&D, N.G., ROSE-L, ALOS-4, NISAR), provides an example of the digital model of Earth that could support the complex task of anticipating extreme weather events.

There are two main approaches of applied mathematics to digitalization: Physics-Based and Data Driven. Physics-based models (PBMs) can give useful information on the processes to be described without the need for huge datasets, a first idea of what variables shall be monitored and provide a means for generalization.

Data-driven approaches imply the use of methods from Machine Learning or even Deep Learning to "learn from data collected by sensors". Artificial Intelligence (AI) tools need very high amounts and can be used to find hidden patterns in the data. Such a pattern can be refined whenever new data are collected. NWMs are an example of a physics-based Digital Twin.

We focus on using WRF and InSAR meteorology to continuously update the Digital Twins of the atmosphere. The data lake consists of Sentinel-1 data (high-resolution PWV maps), the output variables of the ERA5 model. The digital twin engine consists of the 3D-Var assimilation of Sentinel-1 PWV maps, which provide a numerical tool to generate replicas of the NWM (e.g., WRF, AROME, COSMO). We want to demonstrate that it is possible to get: 1) Hints to change/modify the assumptions of NWMs; 2) Hints to reduce the extension of approximations; 3) Extend the limits of applications of WRF to better predict extreme weather events.