Excess Path Delays From Sentinel Interferometry to Improve Weather Forecasts

A synthetic aperture radar can offer not only an accurate monitoring of the earth surface deformation, but also information on the troposphere, such as the total path delay or the columnar water vapor at high horizontal resolution. This can be achieved by proper interferometric processing and postprocessing of the radar interferograms. The fine and unprecedented horizontal resolution of the tropospheric products can offer otherwise unattainable information to be assimilated into numerical weather prediction models, which are progressively increasing their resolving capabilities. A number of tricks on the most effective processing approaches, as well as a novel method to pass from multipass differential interferometry products to absolute tropospheric columnar quantities are discussed. The proposed products and methods are assessed using real Sentinel-1 data. The experiment aims at evaluating the accuracy of the derived information and its impact on the weather prediction skill for two meteorological events in Italy. The main perspective of the study is linked to the possibility of exploiting interferometric products from a geosynchronous platform, thus complementing the inherent high resolution of SAR sensors with the required frequent revisit needed for meteorological applications.

[1]  Frank S. Marzano,et al.  Water vapour distribution at urban scale using high-resolution numerical weather model and spaceborne SAR interferometric data , 2010 .

[2]  G. Nico,et al.  InSAR Meteorology: High‐Resolution Geodetic Data Can Increase Atmospheric Predictability , 2019, Geophysical Research Letters.

[3]  Franz J. Meyer,et al.  The Potential of Low-Frequency SAR Systems for Mapping Ionospheric TEC Distributions , 2006, IEEE Geoscience and Remote Sensing Letters.

[4]  Frank S. Marzano,et al.  A Synergistic Use of a High-Resolution Numerical Weather Prediction Model and High-Resolution Earth Observation Products to Improve Precipitation Forecast , 2019, Remote. Sens..

[5]  Gianfranco Fornaro,et al.  A two-dimensional region growing least squares phase unwrapping algorithm for interferometric SAR processing , 1999, IEEE Trans. Geosci. Remote. Sens..

[6]  Riccardo Lanari,et al.  Space‐borne radar interferometry techniques for the generation of deformation time series: An advanced tool for Earth's surface displacement analysis , 2010 .

[7]  Marina Ruggieri,et al.  Satellite communication and propagation experiments through the alphasat Q/V band Aldo Paraboni technology demonstration payload , 2016, IEEE Aerospace and Electronic Systems Magazine.

[8]  Pedro M. A. Miranda,et al.  Experimental Study on the Atmospheric Delay Based on GPS, SAR Interferometry, and Numerical Weather Model Data , 2013, IEEE Transactions on Geoscience and Remote Sensing.

[9]  Gianfranco Fornaro,et al.  A new algorithm for surface deformation monitoring based on small baseline differential SAR interferograms , 2002, IEEE Trans. Geosci. Remote. Sens..

[10]  Fabio Rocca,et al.  Permanent scatterers in SAR interferometry , 2001, IEEE Trans. Geosci. Remote. Sens..

[11]  Henrik Vedel,et al.  Impact of Ground Based GPS Data on Numerical Weather Prediction , 2004 .

[12]  T. Herring,et al.  GPS Meteorology: Remote Sensing of Atmospheric Water Vapor Using the Global Positioning System , 1992 .

[13]  Soichiro Sugimoto,et al.  An examination of WRF 3DVAR radar data assimilation on its capability in retrieving unobserved variables and forecasting precipitation through observing system simulation experiments , 2009 .

[15]  Michele Manunta,et al.  An On-Demand Web Tool for the Unsupervised Retrieval of Earth's Surface Deformation from SAR Data: The P-SBAS Service within the ESA G-POD Environment , 2015, Remote. Sens..

[16]  Mirko Reguzzoni,et al.  goGPS: open source software for enhancing the accuracy of low-cost receivers by single-frequency relative kinematic positioning , 2013 .

[17]  Yong-Run Guo,et al.  A Three-demiensional Variational (3DVAR) Data Assimilation System for Use With MM5 , 2003 .

[18]  Kristian Mogensen,et al.  Calculation of zenith delays from meteorological data comparison of NWP model, radiosonde and GPS delays , 2001 .

[19]  G. Powers,et al.  A Description of the Advanced Research WRF Version 3 , 2008 .

[20]  Giulia Panegrossi,et al.  InSAR Water Vapor Data Assimilation into Mesoscale Model MM5: Technique and Pilot Study , 2015, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[21]  F. Casu,et al.  Large areas surface deformation analysis through a cloud computing P-SBAS approach for massive processing of DInSAR time series , 2017 .

[22]  Wei Tang,et al.  High-spatial-resolution mapping of precipitable water vapour using SARinterferograms, GPS observations and ERA-Interim reanalysis , 2016 .

[23]  Giovanni Nico,et al.  Three-Dimensional Variational Assimilation of InSAR PWV Using the WRFDA Model , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[24]  John Derber,et al.  The National Meteorological Center's spectral-statistical interpolation analysis system , 1992 .

[25]  Y. Kuo,et al.  Application of WRF 3DVAR to operational typhoon prediction in Taiwan: Impact of outer loop and partial cycling approaches , 2012 .

[26]  Xiufeng He,et al.  Remote sensing of atmospheric water vapor from synthetic aperture radar interferometry: case studies in Shanghai, China , 2016 .

[27]  Pierre Héroux,et al.  Precise Point Positioning Using IGS Orbit and Clock Products , 2001, GPS Solutions.

[28]  R. Hanssen.  Radar Interferometry: Data Interpretation and Error Analysis , 2001 .

[30]  H. Zebker,et al.  High-Resolution Water Vapor Mapping from Interferometric Radar Measurements. , 1999, Science.

[31]  Joao P. S. Catalao,et al.  Mapping Precipitable Water Vapor Time Series From Sentinel-1 Interferometric SAR , 2020, IEEE Transactions on Geoscience and Remote Sensing.

[32]  Giovanni Nico,et al.  Sentinel-1 Interferometric SAR Mapping of Precipitable Water Vapor Over a Country-Spanning Area , 2017, IEEE Transactions on Geoscience and Remote Sensing.

[33]  Philip Whittaker,et al.  System Design for Geosynchronous Synthetic Aperture Radar Missions , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[34]  Giorgio Franceschetti,et al.  Global and local phase-unwrapping techniques: a comparison , 1997 .

[35]  Antonio Pepe,et al.  SBAS-Based Satellite Orbit Correction for the Generation of DInSAR Time-Series: Application to RADARSAT-1 Data , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[36]  Giovanna Venuti,et al.  Detection of water vapor time variations associated with heavy rain in northern Italy by geodetic and low-cost GNSS receivers , 2018, Earth, Planets and Space.

[37]  J. Dudhia,et al.  Examining Two-Way Grid Nesting for Large Eddy Simulation of the PBL Using the WRF Model , 2007 .

[38]  F. Meyer,et al.  Constructing accurate maps of atmospheric water vapor by combining interferometric synthetic aperture radar and GNSS observations , 2015 .

[39]  R. Rotunno,et al.  Effects of the Alps and Apennines on forecasts for Po Valley convection in two HyMeX cases , 2017 .