Sentinel-1 Interferometric SAR Mapping of Precipitable Water Vapor Over a Country-Spanning Area

Mateus, Pedro; Catalão, João; Nico, Giovanni

doi:10.1109/tgrs.2017.2658342

Cited by 45 publications

(44 citation statements)

References 29 publications

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“…The absolute PWV maps on 6 and 7 September, used for the assimilation study, were obtained by adding the WRF forecast on 25 and 26 August at the same times, that is, PWV 6 =ΔPWV InSAR +PWV WRF(day 25) and PWV 7 =ΔPWV InSAR +PWV WRF(day 26) . An assessment of both maps was made using all GNSS stations within the map area (not shown), a root‐mean‐square of about 1.5 mm was found (in agreement with what has been found in Mateus et al, , ). The errors introduced in the final maps by this approach are half of the error assumed in the covariance matrix R (equation .…”

Section: Model Configuration Insar Data Set and Analysis Methodssupporting

confidence: 88%

“…Since the hydrostatic component is related only with spatially smooth variables (i.e., partial pressure due to dry air and temperature), we used the forecast of the WRF model, at the acquisition times of SAR images to calculate the hydrostatic phase delay maps. Details on the determination, accuracy, and application of these corrections can be found in Mateus et al (). The liquid phase contribution (Δϕ l i q ) is due to the liquid water mass in clouds and falling droplets (hydrometeors) and is given by

Δ ϕ_{liq} = 1 0^{- 6} \cdot \frac{4 π}{λ} \cdot Δ \int W normald s,

where W is the total liquid water content, including precipitation, in g/m 3 (Hanssen, ).…”

Section: Model Configuration Insar Data Set and Analysis Methodsmentioning

confidence: 99%

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Assimilating InSAR Maps of Water Vapor to Improve Heavy Rainfall Forecasts: A Case Study With Two Successive Storms

et al. 2018

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Very high resolution precipitable water vapor maps obtained by the Sentinel‐1 A synthetic aperture radar (SAR), using the SAR interferometry (InSAR) technique, are here shown to have a positive impact on the performance of severe weather forecasts. A case study of deep convection which affected the city of Adra, Spain, on 6–7 September 2015, is successfully forecasted by the Weather Research and Forecasting model initialized with InSAR data assimilated by the three‐dimensional variational technique, with improved space and time distributions of precipitation, as observed by the local weather radar and rain gauge. This case study is exceptional because it consisted of two severe events 12 hr apart, with a timing that allows for the assimilation of both the ascending and descending satellite images, each for the initialization of each event. The same methodology applied to the network of Global Navigation Satellite System observations in Iberia, at the same times, failed to reproduce observed precipitation, although it also improved, in a more modest way, the forecast skill. The impact of precipitable water vapor data is shown to result from a direct increment of convective available potential energy, associated with important adjustments in the low‐level wind field, favoring its release in deep convection. It is suggested that InSAR images, complemented by dense Global Navigation Satellite System data, may provide a new source of water vapor data for weather forecasting, since their sampling frequency could reach the subdaily scale by merging different SAR platforms, or when future geosynchronous radar missions become operational.

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Section: Model Configuration Insar Data Set and Analysis Methodssupporting

confidence: 88%

Δ ϕ_{liq} = 1 0^{- 6} \cdot \frac{4 π}{λ} \cdot Δ \int W normald s,

where W is the total liquid water content, including precipitation, in g/m 3 (Hanssen, ).…”

Section: Model Configuration Insar Data Set and Analysis Methodsmentioning

confidence: 99%

Assimilating InSAR Maps of Water Vapor to Improve Heavy Rainfall Forecasts: A Case Study With Two Successive Storms

et al. 2018

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“…Each Single Look Complex image produced by the SAR covers a rectangle of about 170 × 255 km 2 in the chosen region. Two adjacent images along the flight pass were merged in a single interferogram for the present study following the method described in Mateus et al (), covering a rectangle of about 340 × 255 km 2 as shown in Figure . Overall, 51 interferograms (maps of phase difference) were computed whenever possible from SAR images 12 days apart or in the case of a missing image (19 cases) from images separated by 24 days by means of the Sentinel Application Platform (https://step.esa.int/main/toolboxes/snap/).…”

Section: Methodsmentioning

confidence: 99%

“…In the absence of surface deformation, and after the mitigation of the ionospheric and hydrostatic phase signals (Hanssen, ; Kinoshita et al, ; Mateus, Nico, & Catalão, ; Mateus, Nico, Tomé, et al, ; Mateus et al, ; Remy et al, ; Wadge et al, ), InSAR images can be used to compute the spatial distribution of PWV, offering one of the few data sources with a horizontal resolution relevant for the atmospheric mesoscale. Its impact in short‐range numerical weather prediction was recently assessed in three studies.…”

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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“…InSAR can be generated by subtracting two single look complex images (SLCs) acquired at nearly the same satellite position but at a different time, and its phase signal contains not only surface deformation, but also atmospheric contributions due to the ionosphere and water vapor in the troposphere as well as GNSS observations. Advantages of InSAR for the meteorological application are that (1) InSAR has an ability to observe the spatial water vapor distribution with an unprecedented spatial resolution when there are no other contributions in the image such as surface displacements and ionospheric phase disturbances (e.g., Mateus et al 2017); (2) InSAR requires no observation instruments on the ground; and (3) InSAR microwave penetrates clouds and volcanic plumes that consist of relatively small particles compared to the microwave wavelength. The phase modification due to atmospheric phenomena has been regarded as a "noise" for higher-precision InSAR observation for geophysical research such as earthquake and volcanic activity.…”

Section: Introductionmentioning

confidence: 99%

Detections and simulations of tropospheric water vapor fluctuations due to trapped lee waves by ALOS-2/PALSAR-2 ScanSAR interferometry

Kinoshita

Morishita

Hirabayashi

2017

Earth Planets Space

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Detailed wave-like spatial patterns of atmospheric propagation delay signals associated with mountain lee waves were detected in Hokkaido and Tohoku by synthetic aperture radar (SAR) interferometry (InSAR) with the ScanSAR mode observation data of a Phased Array-type L-band Synthetic Aperture Radar 2 on board the Advanced Land Observing Satellite 2. Both cases occurred under stable atmosphere conditions. The InSAR-observed peak-to-trough line of sight changes in the mountain wave signals was 4 and 5 cm with the horizontal wavelengths of 9 and 15 km in Hokkaido and Tohoku, respectively. Locations of positive phase maxima in the mountain wave signals coincides with locations of cloud streets observed by visible satellite imagery, indicating that crests of mountain waves contain relatively much water vapor compared with wave troughs. Numerical weather simulations with the horizontal grid spacing of 1 km were performed to reproduce InSAR phase variations, and as a result those simulations could reasonably reproduce observed wave amplitudes and wavelengths in both cases. On the other hand, numerical simulations tended to overestimate wave attenuation rates: simulated mountain waves decreased as the wave propagated faster than those of observed signals. Because the simulated wave attenuation rate is sensitive to physics in the planetary boundary layer (PBL), we investigated the reproducibility of five PBL schemes implemented in the WRF model. As a result, all the PBL schemes showed little attenuation except for the Yonsei University scheme (YSU), while the wavelength in the YSU was most close to the observation. Our study demonstrated the uniqueness and usefulness of InSAR for meteorological application as the ability to map the detailed water vapor distribution regardless of cloud cover. In addition, the reasonable reproducibility of the water vapor delay signal due to lee waves by the numerical weather model encourages researchers who tackle the correction of the tropospheric propagation delay, increasing the accuracy in detecting surface deformations.

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Sentinel-1 Interferometric SAR Mapping of Precipitable Water Vapor Over a Country-Spanning Area

Cited by 45 publications

References 29 publications

Assimilating InSAR Maps of Water Vapor to Improve Heavy Rainfall Forecasts: A Case Study With Two Successive Storms

Assimilating InSAR Maps of Water Vapor to Improve Heavy Rainfall Forecasts: A Case Study With Two Successive Storms

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

Detections and simulations of tropospheric water vapor fluctuations due to trapped lee waves by ALOS-2/PALSAR-2 ScanSAR interferometry

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