In this work, we investigate the assimilation of InSAR-derived water vapor into the Weather Research and Forecast Data Assimilation model (WRFDA) and evaluate the impact within the InSAR footprint and also over time and space. We ingested three sequential InSAR-derived water vapor maps, estimated from Sentinel-1 images acquired over southern Portugal and Spain during Storm Barbara in October 2020, and assessed the water vapor anomaly fields thousands of kilometers downstream and their impact on rainfall. We show that the assimilation of InSAR-derived water vapor into NWP models has the potential to enhance the understanding of mesoscale meteorological events and improve the model's capability to simulate extreme weather events. This is particularly relevant for flood forecasting and timely weather predictions.

BACKGROUND: Establishing collaborations between cohort studies has been fundamental for progress in health research. However, such collaborations are hampered by heterogeneous data representations across cohorts and legal constraints to data sharing. The first arises from a lack of consensus in standards of data collection and representation across cohort studies and is usually tackled by applying data harmonization processes. The second is increasingly important due to raised awareness for privacy protection and stricter regulations, such as the GDPR. Federated learning has emerged as a privacy-preserving alternative to transferring data between institutions through analyzing data in a decentralized manner. METHODS: In this study, we set up a federated learning infrastructure for a consortium of nine Dutch cohorts with appropriate data available to the etiology of dementia, including an extract, transform, and load (ETL) pipeline for data harmonization. Additionally, we assessed the challenges of transforming and standardizing cohort data using the Observational Medical Outcomes Partnership (OMOP) common data model (CDM) and evaluated our tool in one of the cohorts employing federated algorithms. RESULTS: We successfully applied our ETL tool and observed a complete coverage of the cohorts' data by the OMOP CDM. The OMOP CDM facilitated the data representation and standardization, but we identified limitations for cohort-specific data fields and in the scope of the vocabularies available. Specific challenges arise in a multi-cohort federated collaboration due to technical constraints in local environments, data heterogeneity, and lack of direct access to the data. CONCLUSION: In this article, we describe the solutions to these challenges and limitations encountered in our study. Our study shows the potential of federated learning as a privacy-preserving solution for multi-cohort studies that enhance reproducibility and reuse of both data and analyses.

The interferometric synthetic aperture radar (InSAR) technique has demonstrated its ability to capture temporal variations in tropospheric water vapor, providing a valuable source of information for numerical weather prediction (NWP) models. Integrating InSAR data into NWP models has the potential to significantly enhance their forecasting capabilities, especially for predicting local extreme weather events. The challenge lies in extracting a single epoch from the InSAR differential observations. In this work, we introduced a method based on the least-squares approach to estimate single epochs using the ERA5 reanalysis data from the European Centre for Medium-Range Weather Forecasts (ECMWFs) as a first guess. By leveraging ERA5 data, distinct atmospheric components can be disentangled without additional assumptions or external measurements. Since ERA5 is globally available at 1-h temporal resolution, the proposed method can be applied in remote areas without in situ data, providing improved high-resolution maps at all times (day/night) and in all weather conditions.

InSAR maps of water vapor can improve the accuracy of Numerical Weather Prediction (NWP) models, especially under extreme precipitation events. However, integrating InSAR data into NWP models is challenging due to the differential nature and absolute phase ambiguity introduced by the unwrapping algorithm. To address this, we propose a new approach that uses ERA5 reanalysis data produced by the European Centre for Medium-Range Weather Forecasts (ECMWF) to estimate the absolute epoch from InSAR data. The proposed method is based on constrained least-squares estimation, and ERA5 data are used as a first guess to untangle distinct atmospheric components without applying additional assumptions or external measurements. This approach will allow us to obtain absolute maps over regions without external in situ measurements (e.g., GNSS or radiosondes). The results are validated using a large GNSS dataset, improving about 29% over the field of the original ERA5.

The availability of C-band Synthetic Aperture Radar (SAR) images by the Sentinel-1 mission every six days boosts the concept of SAR interferometry (InSAR) meteorology. The general idea involves producing integrated water vapor maps using InSAR and assimilating them into high-resolution Numerical Weather Prediction (NWP) models to improve precipitation prediction. The temporal resolution is the disadvantage of the Sentinel-1 mission compared to other techniques (e.g., GNSS). Therefore, this study focuses on the minimum inter-station distance below which a dense GNSS network can catch atmospheric information similar to InSAR maps. The study uses an InSAR water vapor map to generate synthetic GNSS measurements of PWV. Results show that the WRF model's correct prediction of observed rainfall is more frequent when the inter-distance between GNSS stations becomes smaller than 20 km. A numerical instability of the WRF results is observed at shorter distances, indicating the need to handle the spatial correlation of assimilated measurements of PWV.

Results of an experiment aiming to estimate soil moisture (SM) using indexes based on hyperspectral and multispectral data, and decorrelation phase from SAR interferometry are presented. SM estimates are compared with in-situ measurements of SM. The issue of spaceborne and in-situ datasets not overlapping in time is studied. Results are obtained using PRISMA, Sentinel-1 and Sentinel-2 data.

A methodology to generate calibrated maps of soil moisture from C-band synthetic aperture radar (SAR) images processed by SAR interferometry (InSAR) technique is presented. The proposed methodology uses atmospheric phase delay (APD) maps obtained from a time series of Sentinel-1 interferograms, to disentangle the APD and soil moisture contributions to Sentinel-1 interferograms. We show how the high spatial resolution and short temporal baseline of Sentinel-1 image can help to estimate soil moisture using a daisy chain InSAR processing. The estimated soil moisture maps are compared within situdata collected by five soil moisture sensors installed in an experimental field, characterized by bare soil, located close to Lisbon, Portugal. Results show that after removing the APD effects in SAR interferogram, there is a correction of the bias in the soil moisture estimation and an improvement in the correlation coefficient with the soil moisture measurements, from 0.38 to 0.78. Soil moisture changes were measured during a sequence of rain events in the winter season. A root-mean-square (rms) error less than 0.04 m3/m3was found over a variety of meteorological conditions.

This work presents a study on the enhancement of Numerical Weather Prediction (NWP) model simulations when assimilating SAR interferometry (InSAR) Precipitable Waver Vapor (PWV). A 3dVar assimilation scheme is used to assimilate InSAR data. The rotationally invariant spectrum of PWV maps obtained by GNSS, InSAR, and the NWP model is computed before and after the 3dVar assimilation. The average distance of GNSS stations in this region is about 100 km. The spatial resolution of NWP model simulations is 3 km. The spectra analysis shows that there are two cut frequencies, at 10−5m−1÷ 2.10−5m−1and at 2.10−4m−1÷ 3.10−4m−1. The lower and higher cut frequencies seem to be related to the average distance of GNSS stations and the spatial resolution of NWP simulations.

In this article, a methodology to retrieve the precipitable water vapor (PWV) from a differential interferometric time series is presented. We used external data provided by atmospheric weather models (e.g., ERA-Interim reanalysis) to constrain the initial state and by Global Navigation Satellite System (GNSS) to phase ambiguities elimination introduced by phase unwrapping algorithm. An iterative least-square is then used to solve the optimization problem. We applied the presented methodology to two time series of differential PWV maps estimated from synthetic aperture radar (SAR) images acquired by the Sentinel-1A, over the southwest part of the Appalachian Mountains (USA). The results were validated using an independent GNSS data set and also compared with atmospheric weather prediction data. The GNSS PWV observations show a strong correlation with the estimated PWV maps with a root-mean-square error less than 1 mm. These results are very encouraging, particularly for the meteorology community, providing crucial information to assimilate into numerical weather models and potentially improve the forecasts.

In this work, we present a methodology to estimate 3D moisture structures in atmosphere based on the use of SAR interferometry (InSAR), and a Numerical Weather Prediction model (NWP). Maps of propagation delay in atmosphere with sub-kilometer spatial resolution are obtained by assimilating high resolution Precipitable Water Vapor (PWV) maps into the Weather Research and Forecast (WRF) model using the WRF Data Assimilation (WRFDA) System. The PWV maps are obtained by interferometrically processing of Sentinel-1 images. The comparison of WRF modeling of atmosphere parameters, before and after the assimilation is used to study the response of the model to the InSAR information and assess the physical consistency of atmosphere parameters modeled by the system. The visualization of moisture structure in atmosphere is carried out in terms of the 3D spatial distribution of atmosphere refractivity, hydrometeors, thermodynamics quantities and wind patterns. The proposed procedure is applied to the visualization of Atmospheric Gravity Waves (AGW) and Convective Systems (CS), two examples of moisture structures in atmosphere.

In this work we present a methodology to estimate the 3D distribution of water vapor in atmosphere based on the use of SAR interferometry (InSAR) and Sentinel-l data. Maps of propagation delay in atmosphere are assimilated in a high resolution Numerical Weather Model to enhance the forecast of atmosphere parameters. These are used to compute the atmosphere refractivity. Furthermore, 3D maps of hydrometers in atmosphere are derived after the assimilation of InSAR data. Both refractivity and hydrometeors maps are used to map 3D Water vapor patterns in atmosphere. Examples of InSAR signatures of water vapor in atmosphere are shown. We show how the 3D maps liquid refractivity and hydrometeors can be a useful tool to map moisture in atmosphere in case of convective phenomena in atmosphere.

In this work, we study the problem of assimilating high resolution Precipitable Water Vapor (PWV) maps using the Weather Research and Forecast 3D Variational Data assimilation system (WRF-3DVar). The PWV maps are obtained using the Sentinel-1 Synthetic Aperture Radar (SAR) images and the SAR interferometry (InSAR) technique. The influence of the high resolution PWV data on the initial condition of WRF and during the next 12 hours is studied. We demonstrate that the assimilation of InSAR PWV maps increases both the water vapor concentration and temperature over areas affected by extreme weather events so correctly generating localized convection cells. The PWV forecast, after the assimilation of InSAR maps, are compared with the PWV estimates provided by a dense GNSS network. The precipitation pattern and amount are compared to meteorological radar measurements. The case study of the extreme weather event that affected the city of Adra, Spain, on 6thSeptember 2015, is used to demonstrate how the assimilation of high resolution PWV maps.

This paper presents a methodology to generate maps of atmosphere's precipitable water vapor (PWV) over large areas with a length of hundreds of kilometers and a width of about 250 km, based on the use of interferometric Sentinel-1A/B C-band synthetic aperture radar (SAR) data with a high spatial resolution of 5 × 20 m2and the revisiting time of six days. An algorithm to calibrate and merge PWV maps from different swaths of Sentinel-1 acquired along the same track, using global navigation satellite system (GNSS) measurements, is described. The proposed methodology is tested on Sentinel-1A SAR images acquired over the Iberian Peninsula, along both descending and ascending tracks. The assessment with an independent set of GNSS measurements shows a mean difference of a fraction of millimeter and a dispersion lower than 2 mm. Both the use of Sentinel-1A/B SAR images and the proposed methodology open new perspectives on the application of SAR meteorology for the high-resolution mapping of PWV over large region-spanning areas and the assimilation of interferometric SAR data into numerical weather models.

This paper studies the problem of the assimilation of precipitable water vapor (PWV), estimated by synthetic aperture radar interferometry, using the Weather Research and Forecast Data Assimilation model 3-D variational data assimilation system. The experiment is designed to assess the impact of the PWV assimilation on the hydrometers and the rainfall predictions during 12 h after the assimilation time. A methodology to obtain calibrated maps of PWV and estimated their precision is also presented. The forecasts are compared with GPS estimates of PWV and with rainfall observations from a meteorological radar. Results show that after data assimilation, there is a correction of the bias in the PWV prediction and an improvement in the prediction of the weak to moderate rainfall up to 9 h after the assimilation time.

In this paper, the uncertainty of atmospheric delay maps obtained by a numerical weather model (NWM) is assessed. These models can simulate 3-D fields of meteorological parameters at coarse scales. A few approaches, which are mainly based on zenith wet delay estimates derived by NWM data, have been recently developed to mitigate atmospheric propagation artifacts in synthetic aperture radar interferometry applications. However, an assessment of these estimates is still missing. We present a methodology based on radiosonde data to assess the uncertainty of the meteorological parameters derived from an NWM and the dry and wet delays calculated using these parameters. The methodology is applied to the data generated by the Weather Research and Forecasting model that currently is the model with the higher accuracy relative to its predecessors.

Recently, synthetic aperture radar interferometry (InSAR) has been recognized as a promising tool to generate high-resolution maps of atmospherical precipitable water vapor temporal changes (ΔPWV) from the propagation delay of radar signal in atmosphere. The relationship between ΔPWV and propagation delay mainly depends on the vertical profiles of temperature and water vapor pressure. In this letter, we present a methodology to study the spatial and temporal variations of the temperature's vertical profile and generate more accurate high-resolution ΔPWV maps by means of InSAR.

In this paper, we present the results of an experiment aiming to compare measurements of atmospheric delay by synthetic aperture radar (SAR) interferometry and GPS techniques to estimates by numerical weather prediction. Maps of the differential atmospheric delay are generated by processing a set of interferometric SAR images acquired by the ENVISAT-ASAR mission over the Lisbon region from April to November 2009. GPS measurements of the wet zenith delay are carried out over the same area, covering the time interval between the first and the last SAR acquisition. The Weather Research and Forecasting (WRF) model is used to model the atmospheric delay over the study area at about the same time of SAR acquisitions. The analysis of results gives hints to devise mitigation approaches of atmospheric artifacts in SAR interferometry applications.

In this work we investigate the use of Synthetic Aperture Radar (SAR) images acquired by the TerraSAR-X satellite operating a very high resolution X-band SAR sensor to generate maps of temporal and spatial variations of the water vapour distribution with a horizontal resolution of 27×27 m over the Lisbon region, Portugal. We present the result of a time series of delay maps obtained by processing a set of interferometric SAR images acquired by the TerraSAR-X mission. The maps are calibrated by means of GPS estimations of the delay over the same area and covering the time interval between the first and the last SAR acquisition. The availability of maps with a high spatial resolution could increase the quality of quantitative precipitation forecasting and open interesting perspectives for nowcasting applications.