Pedro Benevides

Bridging InSAR and GPS Tomography: A New Differential Geometrical Constraint

The integration of interferometric synthetic aperture radar (InSAR) and GPS tomography techniques for the estimation of the 3-D distribution of atmosphere refractivity is discussed. A methodology to use the maps of the temporal changes of precipitable water vapor (PWV) provided by InSAR as a further constraint in the GPS tomography is described. The aim of the methodology is to increase the accuracy of the GPS tomography reconstruction of the atmospheres refractivity. The results, which are obtained with SAR and GPS data acquired over the Lisbon area, Portugal, are presented and assessed. It has been found that the reconstruction of the atmospheric refractivity is closer to the real atmospheric state with a mitigation of the smoothing effects due to the usual geometrical constraints of the GPS tomography.

Merging SAR interferometry and GPS tomography for high-resolution mapping of 3D tropospheric water vapour

Microwave sensing of the atmosphere with GPS data, particularly using GPS tomography, provides a unique opportunity to measure the 3D state of the atmospheric water vapor, since it acquires data in a high temporal sampling. However, this technique requires a full domain coverage which is not fulfilled by GPS data, leading to an ill-posed conditioning only solved by constraints. SAR interferometry can be used to get integrated water vapor maps with high spatial resolution, but with a temporal frequency depending on the SAR acquisitions. In this work we describe a methodology to include integrated water vapor maps provided by SAR interferometry into the GPS tomography processing scheme. The differential water vapor spatial distribution occurred between the acquisition time of master and slave images is used to constrain the GPS tomography solution. Results are obtained over Lisbon area using GPS and ENVISAT-ASAR data and validated with regional radiosonde data.

Analysis of Galileo and GPS Integration for GNSS Tomography

Global Navigation Satellite System (GNSS) tomography provides 3-D reconstructions of atmosphere wet refractivity, related to water vapor. A simulated analysis of the integration of Global Positioning System and future Galileo data is presented. Atmospheric refractivity is derived from radiosonde data acquired over the Lisbon area. The impact of Galileo data on the tomographic reconstruction is assessed. Furthermore, horizontal anomalies are added to a reference vertical profile of atmospheric refractivity to reproduce low-level dry or wet air intrusions, a phenomenon commonly observed in meteorological data acquired by both radiosonde and satellites. The dependence of tomographic solution on the GNSS network density is also analyzed. Better reconstruction capabilities in the lower layers are observed when increasing the network density.

Inclusion of high resolution MODIS maps on a 3D tropospheric water vapor GPS tomography model

Observing the water vapor distribution on the troposphere remains a challenge for the weather forecast. Radiosondes provide precise water vapor profiles of the troposphere, but lack geographical and temporal coverage, while satellite meteorological maps have good spatial resolution but even poorer temporal resolution. GPS has proved its capacity to measure the integrated water vapor in all weather conditions with high temporal sampling frequency. However these measurements lack a vertical water vapor discretization. Reconstruction of the slant path GPS observation to the satellite allows oblique water vapor measurements. Implementation of a 3D grid of voxels along the troposphere over an area where GPS stations are available enables the observation ray tracing. A relation between the water vapor density and the distanced traveled inside the voxels is established, defining GPS tomography. An inverse problem formulation is needed to obtain a water vapor solution. The combination of precipitable water vapor (PWV) maps obtained from MODIS satellite data with the GPS tomography is performed in this work. The MODIS PWV maps can have 1 or 5 km pixel resolution, being obtained 2 times per day in the same location at most. The inclusion of MODIS PWV maps provides an enhanced horizontal resolution for the tomographic solution and benefits the stability of the inversion problem. A 3D tomographic grid was adjusted over a regional area covering Lisbon, Portugal, where a GNSS network of 9 receivers is available. Radiosonde measurements in the area are used to evaluate the 3D water vapor tomography maps.

Analysis of the relation between GPS tropospheric delay and intense precipitation

The electromagnetic signal transmitted by the global navigation and positioning systems (GNSS) suffers a delay which is mainly caused by the water vapor in the atmosphere. Estimating the delay affecting the signal propagation, it is possible to estimate the water vapor column on the troposphere above each station. The aim of this study is to characterize the water vapor field on the troposphere over time by GNSS techniques. It is expected that can also come to assist in the Nowcasting particularly in the prediction of severe meteorological phenomena. Several events of strong, intense and short precipitation, observed in the Lisbon region throughout 2012 were analyzed. The choice of these events was based on the analysis of hourly precipitation given by a meteorological station located on Lisbon center. This region is monitored by a network of 15 GNSS stations covering about 100 square kilometers. The relationship between the GPS precipitable water vapor (PWV) and the hourly accumulated precipitation was evaluated over time (1D closest GPSmeteorological station plots) and spatially (2D maps) interpolated over the GNSS and meteorological stations. It was verified that there were a high and sudden increment of the GPS PWV prior to severe precipitation events. The PWV increment starts 6 to 10 hours before the rain and the value has increased between 57% and 75% relatively to the PWV value observed previously. In this study is shown that GPS data has good potential for forecasting severe rain events and high moisture flux situations.

Can Galileo increase the accuracy and spatial resolution of the 3D tropospheric water vapour reconstruction by GPS tomography

GPS tomography provides a unique opportunity to sense the 3D state of the atmosphere. However, the setting of the model grid can affect the water vapor solution since GPS data can be insufficient to cover all the domain, leading to an ill-posed conditioning, usually solved by constraints. In this work we present the result of a simulation analysis to study the impact of Galileo data on the reconstruction of 3D atmospheric water vapor. GPS tomography results are compared with those provided by merging GPS and Galileo data in order to ascertain the enhancement of the 3D water vapor refractivity reconstruction.

Evaluation of rainfall forecasts combining GNSS precipitable water vapor with ground and remote sensing meteorological variables in a neural network approach

In this study, an experiment aimed to integrate Global Navigation Satellite System (GNSS) atmospheric data with meteorological data into a neural network system is performed. Precipitable Water Vapor (PWV) estimates derived from GNSS are combined with surface pressure, surface temperature and relative humidity obtained continuously from ground-based meteorological stations. The work aims to develop a methodology to forecast short-term intense rainfall. Hence, all the data is sampled at one hour interval. A continuous time series of 3 years of GNSS data from one station in Lisbon, Portugal, is processed. Meteorological data from a nearby meteorological station are collected. Remote sensing data of cloud top from SEVIRI is used, providing collocated data also on an hourly basis. A 3 year time series of hourly accumulated precipitation data are also available for evaluation of the neural network results. In previous studies, it was found that time varying PWV is correlated with rainfall, with a strong increase of PWV peaking just before intense rainfall, and with a strong decrease afterwards. However, a significant amount of false positives was found, meaning that the evolution of PWV does not contain enough information to infer future rain. In this work a multilayer fitting network is used to process the GNSS and meteorological data inputs in order to estimate the target outputs, given by the hourly precipitation. It is found that the combination of GNSS data and meteorological variables processed by neural network improves the detection of heavy rainfall events and reduces the number of false positives.

High resolution mapping of soil moisture in agriculture based on Sentinel-1 interferometric data

In this work we study the problem of mapping soil moisture by means of Synthetic Aperture Radar (SAR) images. A test site has been set in Companhia das Lezirias, close to Lisbon, Portugal. The main advantage of using SAR images is their capability to map soil moisture at a very high spatial resolution. This opens interesting perspectives for agricultural applications, where soil moisture can abruptly change across field boundaries depending on the agricultural practices. The study area is characterized by flat topography, large agricultural areas and sparse vegetation. Five sensors have been deployed in a test area to measure soil moisture with a sampling time of one hour for a period of seven months. In-situ measurements are compared with the results obtained by processing 33 C-band Sentinel-1 images using the SAR interferometry technique. The aim of the study is to analyze the relation between the interferometric phase and time varying soil moisture. The main advantage of SAR interferometry with respect to the use of radar cross-section is that the information about soil moisture can be recovered using a reduced number of in-situ measurements. In particular, we combine three interferograms obtained from three SAR images, acquired over the same area at different times, to derive maps of bi-coherence and phase triplet. This last quantity allows to disentangle the phase contribution due to soil moisture from those related to microwave propagation in atmosphere and terrain displacements. Results are compared to those obtained using the interferometric phase and coherence to emphasize the importance to split the effects due to propagation (e.g. atmosphere) from those related to volume scattering.