A. Rius

Estimation of the transmitter and receiver differential biases and the ionospheric total electron content from Global Positioning System observations

In the estimation of the ionospheric total electron content from the Global Positioning System (GPS) observables, various instrumental systematic effects such as the biases in the GPS satellites and receivers must be modeled. This paper describes a procedure, based on a Kalman filtering approach, for estimating these instrumental biases as well as the total electron content at each GPS station, using dual GPS data. The method is applied to six data sets, of 48 hours each, spanning one year, from the Deep Space Network with GPS stations in Australia, Spain, and the United States. The formal errors for the estimated satellite biases and for the total electron content at each station are about 0.07 ns and 0.2×1016 el/m2, respectively. The variation in time of the satellite biases (relative to the mean of all of them) estimated in different epochs during 1-year period, is below 1 ns.

4D tropospheric tomography using GPS slant wet delays

Tomographic techniques are successfully applied to obtain 4D images of the tropospheric refractivity in a local dense network of global positioning system (GPS) receivers. We show here how GPS data are processed to obtain the tropospheric slant wet delays and discuss the validity of the processing. These slant wet delays are the observables in the tomographic processing. We then discuss the inverse problem in 4D tropospheric tomography making extensive use of simulations to test the system and define the resolution and the impact of noise. Finally, we use data from the Kilauea network in Hawaii for February 1, 1997, and a local 4 × 4 × 40 voxel grid on a region of 400 km2 and 15 km in height to produce the corresponding 4D wet refractivity fields, which are then validated using forecast analysis from the European Center for Medium Range Weather Forecast (ECMWF). We conclude that tomographic techniques can be used to monitor the troposphere in time and space.

Altimetric Analysis of the Sea-Surface GPS-Reflected Signals

We describe the development and implementation of a method for extracting altimetric information using the Passive Reflectometry and Interferometry System (PARIS), i.e., from GPS signals after their reflection off the sea surface. We have formalized one idea laid out in the description of a bistatic system for ocean altimetry using the GPS signal, by Hajj and Zuffada (Jet Propulsion Laboratory), and have extended it to real situations encountered in PARIS aircraft experiments. Second, we have developed the corresponding algorithms to produce real-time altimetric observables to be used in dedicated digital signal processors. Finally, we have applied this method to estimate sea-surface height from one flight experiment in the North Sea off the coast of Norway.

Improving the vertical resolution of ionospheric tomography with GPS Occultations

We combine GPS/MET data from 29 occultations and IGS ground data collected from 160 stations around the world to perform stochastic tomography of the ionosphere with a 4×20×20 global grid of voxels extending from 200 to 650 km above the mean surface of the Earth. A correlation functional approach that limits the spatial high frequency content of the images is used, and a Kalman filter is applied in the time direction. The combination of ground and occultation data and the use of smoothing techniques is robust enough for vertical resolution in this four-layer model analysis. We discuss the role of noise on the choice of the correct range of eigenvalues in the inversion problem, and the impact of occultation data, showing that ground data alone is insufficient for vertical resolution even in a three-layer, noise-less simulation.

Sea surface state measured using GPS reflected signals

We discuss an airborne experiment aimed to establish the potential of the PARIS concept (PAssive Reflectometry and Interferometry System) to retrieve small features in the sea surface topography. The date and location were chosen to coincide with a TOPEX/POSEIDON (T/P) overflight. The signals of the Global Positioning System (GPS) reflected off the sea surface are tracked and compared to the directly received ones, to compute the relative delays. The features detected in the peak tracking are likely caused by topographic and sea roughness variations. While very promising, these results open the challenge to use additional information to appropriately separate both contributions.

A two‐layer model of the ionosphere using Global Positioning System data

We present a new approach to model the Ionosphere based on GPS data. Previous authors have used models with an unique shell. In this case we have included a second shell to account for the distribution of the electrons in the outer part of the Ionosphere. We have analyzed the ionospheric electron content of a region above 30 degrees in declination in different conditions of ionospheric activity using the Kalman filter. The data used has been obtained from the International GPS Service for Geodynamics (IGS) network. Simultaneously we have studied the receiver and transmitter differential biases showing the effects of neglecting the outer part of the Ionosphere in the model. It appears a systematic variations for the receivers—depending on its latitude—not for the satellites.

A GPS-Reflections Receiver That Computes Doppler/Delay Maps in Real Time

This paper describes a new instrument that was specially designed and developed to gather Global Positioning System (GPS) signals after they have been reflected from suitable surfaces (sea, ice, and ground), for Earth remote sensing. The device has been called the GPS open-loop differential real-time receiver (GOLD-RTR). Its main and most innovative feature is its computation and storage, in real time, of complex-valued (I and Q) cross correlations (waveforms) between GPS L1-C/A signals - received directly and after reflection - and the corresponding models of these signals. Particularly, the GOLD-RTR schedules consecutive coherent integration time slots of 1 ms over which ten parallel correlation channels, with 64 lags each, work simultaneously and continuously with the input raw data sampled at 40 MHz. The total throughput is 10 000 waveforms per second, each waveform being 64 lags long. These real-time correlation resources can be flexibly distributed in several configurations according to the observational requirements, for instance: Doppler/delay maps or up to ten simultaneous reflected waveforms for ten different GPS satellites are examples of what can be done. The further processing of the real-time computed 1-ms waveforms in a flight campaign over the ocean, ice, or ground can be used to obtain geophysical parameters such as sea level and tides, sea surface mean-square slopes, ice roughness and thickness, soil moisture and biomass, or future applications. This paper covers the GOLD-RTR architecture and hardware, signal processing and data storage issues, machine-user interface, laboratory readiness tests, and waveform data samples from the first two jet aircraft campaigns at 9300 m over the sea

Mediterranean Balloon Experiment: ocean wind speed sensing from the stratosphere, using GPS reflections

Abstract The MEditerranean Balloon EXperiment (MEBEX), conducted in August 99 from the middle–up stratosphere, was designed to assess the wind retrieval sensitivity of Global Navigation Satellite Systems Reflections (GNSSR) technology from high altitudes. Global Positioning System reflected signals (GPSR) collected at altitudes around 37 km with a dedicated receiver have been inverted to mean square slopes (MSS) of the sea surface and wind speeds. The theoretical tool to interpret the geophysical parameters was a bistatic model, which also depends on geometrical parameters. The results have been analyzed in terms of internal consistency, repeatability and geometry-dependent performance. In addition, wind velocities have been compared to independent measurements by QuikSCAT, TOPEX, ERS/RA and a Radio Sonde, with an agreement better than 2 m/s. A Numerical Weather Prediction Model (NWPM, the MM5 mesoscale forecast model) has also been used for comparison with varying results during the experiment. The conclusion of this study confirms the capability of high altitude GPSR/Delay-map receivers with low gain antennas to infer surface winds.

Consolidating the Precision of Interferometric GNSS-R Ocean Altimetry Using Airborne Experimental Data

This paper revises the precision of altimetric measurements made with signals of the Global Navigation Satellite Systems (GNSS) reflected (GNSS-R) off the sea surface. In particular, we investigate the performance of two different GNSS-R techniques, referred to here as the clean-replica and interferometric approaches. The former has been used in GNSS-R campaigns since the late 1990s, while the latter has only been tested once, in 2010, from an 18-m-high bridge in static conditions and estuary waters. In 2011, we conducted an airborne experiment over the Baltic Sea at 3-km altitude to test the interferometric concept in dynamic and rougher conditions. The campaign also flew a clean-replica GNSS-R instrument with the purpose of comparing both approaches. We have analyzed with detail the data sets to extract and validate models of the noise present in both techniques. After predicting the noise models and verifying these with aircraft data, we used them to obtain the precision of altimetric measurements and to extrapolate the performance analysis to spaceborne scenarios. The main conclusions are that the suggested noise model agrees with measured data and that the GNSS-R interferometric technique is at least two times better in precision than a technique based on using a clean replica of the publicly available GPS code. This represents a factor of at least four times finer along-track resolution. A precision of 22 cm in 65-km along-track averaging should be achievable using near-nadir interferometric GNSS-R observations from a low earth orbiter.

GPS tomography of the ionospheric electron content with a correlation functional

The authors develop a minimization functional in order to regularize the inverse problem associated with three-dimensional (3D) ionospheric stochastic tomography. This functional is designed to yield, upon minimization, a solution which maximizes the frequency content of the solution below a certain cutoff, while keeping /spl chi//sup 2/ constant. The authors show how this functional can be rewritten in terms of the correlation function of the image, thereby facilitating the algorithmic implementation of the method. They then implement this functional in a Kalman filter and obtain a smoothing algorithm that acts in both space and time. Finally, they use this technique to perform global scale Global Positioning System (GPS) tomography of the ionospheric electron content.