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

Abstract

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.

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

“…These results show a strong case for the assimilation of InSAR data in weather forecasts, bolstering a few specific case studies of some severe deep‐convection events (Mateus et al, ). The relatively large sample of cases evaluated here suggests that such gains were not fortuitous, but a consistent response to an improved initial state, valid in different weather conditions.…”

“…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. The latter result suggested the high potential added value of the new InSAR data, and the need for a systematic evaluation of its value in a representative range of weather conditions, which is the objective of the current study.…”

“…Mateus et al (2016) 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 (2018) showed that in a ©2019. The Authors.…”

Mitigation of Tropospheric Delay in SAR and InSAR Using NWP Data: Its Validation and Application Examples

“…After compensation of the tropospheric differential phases, the height-dependent phase has been significantly mitigated (see right column of Figure 9). Other atmospheric effects with lower magnitude are thus visible, such as the turbulent atmosphere, which is not contained in the ECMWF data but could be potentially modeled using meso-scale weather assimilation techniques [49,50].…”

Neural Network Approach to Forecast Hourly Intense Rainfall Using GNSS Precipitable Water Vapor and Meteorological Sensors

“…The spatial and temporal rapid variations of the tropospheric water vapor in the low atmosphere poses one of the main challenges to the numerical weather models (NWMs) forecast accuracy [1]. Nowadays, the atmospheric measurement techniques are still insufficient to measure the tropospheric water vapor with the temporal and spatial resolution needed for the NWM precise forecasts [2]. The model's uncertainty regarding the water vapor distribution limits its capability to identify instability situations, which can trigger heavy rain occurrences [3].…”