Continuous Multitrack Assimilation of Sentinel‐1 Precipitable Water Vapor Maps for Numerical Weather Prediction: How Far Can We Go With Current InSAR Data?

Abstract

The present study assesses the viability of including water vapor data from Interferometry Synthetic Aperture Radar (InSAR) in the initialization of numerical weather prediction (NWP) models, using already available Sentinel‐1 A and B products. Despite the limitations resulting from the 6‐day return period of images produced by the 2‐satellite system, it is found that for a sufficiently large domain designed to contain a set of images every 12 h (at varying locations), the impact on model performance is beneficial or at least neutral. The proposed methodology is tested in 24 consecutive 12 h forecasts, covering two cycles of the Sentinel‐1 system and 214 images, for a domain containing Iberia. A statistical analysis of the forecast precipitable water vapor (PWV) against independent GNSS observations concluded for relevant improvements in the different scores, especially during a consecutive 3‐day period where the standard initial data were less accurate. An analysis of the rain forecasts against gridded remote sensing observations further indicates an overall improvement in the grid‐point distribution of different precipitation classes throughout the simulation, even when the mean impact of PWV assimilation was not significant. It is suggested that current InSAR data are already a useful source of NWP data and will only become more relevant as new systems are put into operation.

Error Evaluation of L-Band InSAR Precipitable Water Vapor Measurements by Comparison with GNSS Observations in Japan

“…The height dependence was not suggested in our analysis. This study clarified that the InSAR water vapor observation using L-band InSAR has significant accuracy compared to C-band InSAR, which showed its usefulness for the data assimilation into meso-scale weather prediction [7,9,18,44], indicating that L-band InSAR also has the potential for improving the precipitation forecast. At present, there are no experiments on the precipitation forecast using the L-band InSAR data assimilation.…”

“…In a previous study that assimilated InSAR-derived water vapor information into a meso-scale weather model, precipitation prediction improved by up to 9 h [7]. In addition, a continuous use of InSAR water vapor information for short-term weather forecasting at the Iberian Peninsula using Sentinel-1 indicated a positive impact for improving precipitation prediction [18]; however, InSAR water vapor information has not yet been put into practical use for operational weather forecasting, and thus it is necessary to further investigate the potential of InSAR water vapor observation for the improvement of precipitation prediction.…”

A Novel Procedure for Generation of Sar-Derived ZTD Maps for Weather Prediction: Application to South Africa Use Case

“…The temporal high-resolution of water vapor variability information is crucial in obtaining benefits from the assimilation process. Several investigations have been performed taking advantage of Sentinel-1 data (Mateus et al, 2018;Miranda et al, 2019;Lagasio et al, 2019;Mateus et al, 2020), confirming the InSAR water vapor products potential in enhancing NWP models accuracy.…”

A New Unconstrained Approach to GNSS Atmospheric Water Vapor Tomography

“…If GNSS tomographic results can be made operationally for large domains one may expect a significant impact on weather forecasting, maybe complementing InSAR observations that will be available at much higher horizontal resolutions but only offer 2D PWV fields. Recent results indicate that InSAR data assimilation can consistently improve forecasts (Mateus et al, 2021;Miranda et al, 2019). The joint use of both data sources is a promising prospect, although there are many challenges concerning the real-time availability of such data.…”

“…The simulations were initialized every 24 h (at 18 UTC), with the first 6 h taken out to account for model spin-up. Parameterization options are the same as in Mateus et al (2021).…”