Three-Dimensional Variational Assimilation of InSAR PWV Using the WRFDA Model

Mateus, Pedro; Tomé, Ricardo; Nico, Giovanni; Catalão, João

doi:10.1109/tgrs.2016.2599219

Cited by 36 publications

(29 citation statements)

References 22 publications

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“…While some investigations on the assimilation in NWP models of low resolution (tens of km) EO derived products (e.g., soil moisture extracted from the Soil Moisture and Ocean Salinity mission data), are available in the literature [7], few studies were conducted on the ingestion of HR EO products. Only a few papers on the assimilation of water vapour maps derived from InSAR data [8][9][10] and one paper on the ingestion of SAR derived soil moisture maps [11] are available in the literature. These works consider a small spatial domain [8,10] or grid spacing at the cloud-permitting limit (3 km [9]).…”

Section: Introductionmentioning

confidence: 99%

“…Only a few papers on the assimilation of water vapour maps derived from InSAR data [8][9][10] and one paper on the ingestion of SAR derived soil moisture maps [11] are available in the literature. These works consider a small spatial domain [8,10] or grid spacing at the cloud-permitting limit (3 km [9]). Hence, to the best of the authors knowledge, there is currently no investigation that combines a big domain with a very high grid spacing (of the order of 1 km, in the cloud resolving range) and considers more than only one EO derived variable.…”

Section: Introductionmentioning

confidence: 99%

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A Synergistic Use of a High-Resolution Numerical Weather Prediction Model and High-Resolution Earth Observation Products to Improve Precipitation Forecast

et al. 2019

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The Mediterranean region is frequently struck by severe rainfall events causing numerous casualties and several million euros of damages every year. Thus, improving the forecast accuracy is a fundamental goal to limit social and economic damages. Numerical Weather Prediction (NWP) models are currently able to produce forecasts at the km scale grid spacing but unreliable surface information and a poor knowledge of the initial state of the atmosphere may produce inaccurate simulations of weather phenomena. The STEAM (SaTellite Earth observation for Atmospheric Modelling) project aims to investigate whether Sentinel satellites constellation weather observation data, in combination with Global Navigation Satellite System (GNSS) observations, can be used to better understand and predict with a higher spatio-temporal resolution the atmospheric phenomena resulting in severe weather events. Two heavy rainfall events that occurred in Italy in the autumn of 2017 are studied—a localized and short-lived event and a long-lived one. By assimilating a wide range of Sentinel and GNSS observations in a state-of-the-art NWP model, it is found that the forecasts benefit the most when the model is provided with information on the wind field and/or the water vapor content.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Synergistic Use of a High-Resolution Numerical Weather Prediction Model and High-Resolution Earth Observation Products to Improve Precipitation Forecast

et al. 2019

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“…Pichelli et al () found improvements in hindcasting weak to moderate precipitation (<15 mm/3 hr) in a three‐dimensional variational (3D‐Var) data assimilation of InSAR‐PWV maps using the mesoscale weather prediction model Fifth‐Generation Penn State/National Center for Atmospheric Research Mesoscale Model (MM5) and ENVISAT‐Advanced Synthetic Aperture Radar (ASAR) data over the city of Rome, Italy. Mateus et al () showed a significant improvement in the atmospheric moisture content and on the formation of light precipitation up to 9 hr after the assimilation time in a 3D‐Var data assimilation experiment using InSAR‐PWV maps, estimated from the ENVISAT‐ASAR data, and the Weather Research and Forecast (WRF) model over Lisbon, Portugal. Mateus et al () showed that in a case with two successive deep convection storms, which could be both well forecasted only after InSAR data assimilation, using two PWV maps estimated from new generation SAR images obtained by Sentinel‐1 A over Adra, Spain, with the 3D‐Var method and the WRF model.…”

Section: Introductionmentioning

confidence: 99%

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

Miranda

Mateus

Nico

et al. 2019

Geophysical Research Letters

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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.

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“…This approach requires the simultaneous acquisition of the external data and of the SAR data, which is not always feasible. Mateus et al (2016) suggested the use of a reanalysis product, opportunely oversampled over an NWP model grid, to reach a finer spatial resolution. Mateus et al (2018) tried instead to obtain a relatively fine resolution master map with an NWP model run.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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Atmospheric Phase Screens (APSs) derived from Interferometric Synthetic Aperture Radar (InSAR) observations contain the difference between the tropospheric watervapor-induced delay of two acquisition epochs, i.e., the slave and the master (or reference) epochs. Using estimates of the atmospheric state coming from independent sources, for example numerical models and/or Global Navigation Satellite System (GNSS) observations, the APSs can be transformed into absolute maps of Tropospheric Delay (Zenith Total Delay or ZTD), related to the columnar atmospheric water vapor content. In this work, a systematic comparison between various APS and ZTD products aims to determine a convenient strategy to go from APSs to InSAR-derived absolute ZTD maps, highlighting the uncertainties and approximations introduced in the entire processing. The main problem to solve is the evaluation of a sufficiently accurate highresolution master delay map. Different sources of data and two different approaches to derive the master are validated and compared to define the most suitable strategy for meteorological applications. Maps of ZTD obtained by an iterative interpolation of a global atmospheric circulation model values results in being more suited than those derived from the assimilation of GNSS observations into an NWP model. A time average approach to estimate the master map is more robust than the single epoch approach with respect to the choice of the master epoch. Still, the choice of a proper master epoch in the InSAR processing chain as well as that of the maps to be averaged crucially result in the estimate of the master.

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Three-Dimensional Variational Assimilation of InSAR PWV Using the WRFDA Model

Cited by 36 publications

References 22 publications

A Synergistic Use of a High-Resolution Numerical Weather Prediction Model and High-Resolution Earth Observation Products to Improve Precipitation Forecast

A Synergistic Use of a High-Resolution Numerical Weather Prediction Model and High-Resolution Earth Observation Products to Improve Precipitation Forecast

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

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

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