TY - JOUR

T1 - Continuous Multitrack Assimilation of Sentinel-1 Precipitable Water Vapor Maps for Numerical Weather Prediction: How Far Can We Go With Current InSAR Data?

AU - Mateus, P.

AU - Miranda, P. M.A.

AU - Nico, G.

AU - Catalao, J.

PY - 2021/2/16

Y1 - 2021/2/16

N2 - 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.

AB - 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.

KW - assimilation

KW - InSAR

KW - precipitable water vapor

KW - precipitation

KW - Sentinel-1

KW - three-dimensional variational

UR - http://www.scopus.com/inward/record.url?scp=85101000627&partnerID=8YFLogxK

U2 - 10.1029/2020JD034171

DO - 10.1029/2020JD034171

M3 - Article

AN - SCOPUS:85101000627

VL - 126

JO - Journal of Geophysical Research D: Atmospheres

JF - Journal of Geophysical Research D: Atmospheres

SN - 2169-897X

IS - 3

ER -