InSAR Meteorology: High‐Resolution Geodetic Data Can Increase Atmospheric Predictability

Abstract

The present study assesses the added value of high‐resolution maps of precipitable water vapor, computed from synthetic aperture radar interferograms , in short‐range atmospheric predictability. A large set of images, in different weather conditions, produced by Sentinel‐1A in a very well monitored region near the Appalachian Mountains, are assimilated by the Weather Research and Forecast (WRF) model. Results covering more than 2 years of operation indicate a consistent improvement of the water vapor predictability up to a range comparable with the transit time of the air mass in the synthetic aperture radar interferograms footprint, an overall improvement in the forecast of different precipitation events, and better representation of the spatial distribution of precipitation. This result highlights the significant potential for increasing short‐range atmospheric predictability from improved high‐resolution precipitable water vapor initial data, which can be obtained from new high‐resolution all‐weather microwave sensors.

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

“…These results are qualitatively similar to those found by Miranda et al. (2019) in the much longer (2.5 years) but sparser (two images every 12 days) Appalachian experiment. As expected, the contingency tables are slightly better in the lower resolution experiments.…”

“…In the last 2 decades, the Interferometric Synthetic Aperture Radar (InSAR) technique proved that it could provide Precipitable Water Vapor (PWV) maps with a high spatial resolution and accuracy, opening new potential applications of “InSAR Meteorology” (Mateus et al., 2018; Miranda et al., 2019). Those studies showed that PWV maps estimated from the interferometric phase could reach an accuracy of 1–2 mm, under the assumptions of (1) nonexistence of surface displacements between SAR image acquisitions, (2) removing of processing artifacts (e.g., from inaccurate orbits or digital elevation model), and (3) mitigation of phase contribution due to ionospheric and hydrostatic temporal variations (Hanssen, 2001; Kinoshita et al., 2013; Mateus et al., 2014; Mateus, Nico, & Catalão, 2013; Mateus, Nico, Tome, et al., 2013; Remy et al., 2015; Wadge et al., 2002; Zebker et al., 1997).…”

“…Miranda et al. (2019) applied the same technique to assimilate all available InSAR images (more than 50) in a sector of the Appalachian Mountains concluding that it led to better forecast in a large fraction of the cases, and neutral impact in the remaining, with a remarkable overall improvement in the climatology of precipitation.…”

Excess Path Delays From Sentinel Interferometry to Improve Weather Forecasts

“…Pioneer studies deriving PWV or ZTD from APS and assessing their impacts on NWP skill were published in the past [8]- [15]. Very recent papers are [16]- [18]. It must be considered that InSAR is an inherently differential technique that provides difference in time of PWV or ZTD referred to an unknown point on the earth (acting as a bias) and that additional factors may affect the phase difference and must be removed [3].…”

On the Definition of the Strategy to Obtain Absolute InSAR Zenith Total Delay Maps for Meteorological Applications

“…To overcome this limitation, the concept of a geosynchronous SAR is under development, and this will allow for a continuous monitoring of integrated water vapor over large areas (Ruiz Rodon et al, 2013;Monti Guarnieri and Rocca, 2017;Monti Guarnieri et al, 2018). Several experiments were done to prove the positive impact of the SAR-derived water vapor maps in the prediction of extreme rain events when assimilated into NWP models (Pichelli et al, 2015;Mateus et al, 2018;Lagasio et al, 2019;Miranda et al, 2019;Pierdicca et al, 2020).…”