Continuous Multitrack Assimilation of Sentinel‐1 Precipitable Water Vapor Maps for Numerical Weather Prediction: How Far Can We Go With Current InSAR Data?. Issue 3 (5th February 2021)

• Record Type: Journal Article
• Authors: Mateus, P.; Miranda, P. M. A.; Nico, G.; Catalao, J.
• Is Part Of: Journal of geophysical research. Volume 126:Issue 3(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-02-05
• Subjects: assimilation -- InSAR -- precipitable water vapor -- precipitation -- Sentinel‐1 -- three‐dimensional variational; Atmospheric physics -- Periodicals; Geophysics -- Periodicals; 551.5
• DOI: 10.1029/2020JD034171
• Languages: English
• ISSNs: 2169-897X

Abstract

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. Key Points: First test of a continuous Interferometry Synthetic Aperture Radar (InSAR) water vapor data assimilation system Improved initial integrated water vapor distribution leads to better profiles of specific humidity and more skillful forecasts The assimilation of InSAR water vapor leads to a better spatial distribution of precipitation