Monitoring the 3 Dimensional Ionospheric Electron Distribution based on GPS Measurements

Transcription

1 Monitoring the 3 Dimensional Ionospheric Electron Distribution based on GPS Measurements Stefan Schlüter 1, Claudia Stolle 2, Norbert Jakowski 1, and Christoph Jacobi 2 1 DLR Institute of Communications and Navigation stefan.schlueter@dlr.de 2 Institute for Meteorology University of Leipzig stolle@uni-leipzig.de Summary. In the last decade GPS has become a valuable tool for determining various ionospheric and neutral atmospheric parameters. The presently existing global and regional networks of GPS ground stations (e.g. regional surveying offices, International GPS service IGS) open the opportunity to establish monitoring systems to estimate ionospheric and tropospheric/stratospheric key-parameters (e.g. electron content, temperature, water vapour) on a regular base. Here first results of a 3 dimensional ionospheric electron density monitoring system applied for the European region are reported. The system is generally based on continuous GPS derived TEC measurements from the dense European IGS network and uses model assisted (IRI, Gallagher model) iterative algebraic methods to solve the inversion problem. Nevertheless, even in dense GPS networks the available number of measurements are inevitably sparse, and the combination of ground-based measurements gives not fully satisfying vertical resolution. Therefore ionosonde and radio occultation data, as those of CHAMP, have been used to improve the image reconstruction. Key words: Ionosphere, 3D-Tomography, GPS, CHAMP, MART 1 Introduction The already existing permanent operating GPS networks give the opportunity to install continuous ionospheric and tropospheric monitoring systems. Succesful examples for such monitoring systems are, e.g., the estimation of integrated water vapor in the atmosphere by the GFZ Potsdam, based on the GPS reference stations of the SAPOS network [1] and the permanent estimation of the vertical total electron content of the ionosphere (TEC) on base of the network of the International GPS Service (IGS) by the DLR Neustrelitz [3], [5]. One step to extend the observation capability of these systems is the combination of the integrated measurements with a tomographic approach to estimate 3 dimensional distributions of the selected parameter, here the ionospheric electron density applied for the European region. The idea of this approach is to permanently operate a GPS ground-based monitoring of the ionospheric electron density supported by ionosonde data

2 522 Stefan Schlüter et al. Fig. 1. Distribution of European IGS stations and ionosondes and radio occultation measurements, which improve the vertical resolution but are not permanently available for the selected region. A description of the data processing and first results will be given in the following sections. 2 Data Processing The monitored region selected for this approach extends from -E to W and from 32.5N to 7N. GPS data from 47 selected European IGS stations as shown in Fig. 1 form the database for the tomographic reconstrution of the ionosphere. In a first step the collected GPS code and carrier phase data are preprocessed to estimate the TEC for every single ray path [6]. In order to remove the plasmaspheric contribution of the given GPS measured TEC, a plasmaspheric correction factor is applied (In principle it possible to include the plasmaspheric region in the tomography, but for this approach we concentrate on the ionosphere). The factor describes the proportion of the TEC fraction above and below 1km altitude for each ray path. It is computed using a combination of the ionospheric model IRI and the plasmaspheric Gallagher model [2]. As signal bending effects can be ignored in ionospheric tomography, the ray paths are assumed as straight lines between satellite and receiver. For tomographic reconstruction the ionosphere in the selected region is represented as a three-dimensional array of voxels. Here the voxels are defined at a height range from 8 km up to 1 km, with a resolution of 5 in longitude, 2.5 in latitude and 1 km in height. The inversion problem can

3 be expressed as a set of linear equations: GPS based Ionospheric Tomography 523 Ax = g, (1) where g represents the vector of the given TEC measurements, x the unknown electron density in the voxels and A is a large sparse matrix containing the voxel path lengths for each ray. To solve this illposed problem (1656 unkowns and only about measurements) mulitiplicative algebraic reconstruction technique (MART) is used. In MART, each measurement is iteratively compared to a predicted (forward projection) value computed from the current reconstructed data estimate. A multiplicative correction factor is then applied to each voxel covered by the measurement set. This causes the forward projection to approach the measurement. The algorithm is given by x new j ( ) λk a = x old g ij/a i i j (x old, (2), a i ) where a ij are the elements of the matrix A and a i is the row vector of the matrix A. As intitial guess for the iteration process the IRI model is used. After all measured TEC values have been used during the iteration, NmF2 data from selected ionosonde measurements (here Rome and Juliusruh) are compared with the tomographic result for these particular locations. The iteration is repeated until no essential improvements are obtained by the algorithm. In order to fill the data gaps in the voxels which are not intersected by rays and also to stabilize the solution a simple three-dimensional distance weighted gaussian-like boxcar average of the specific width w is applied after the iteration. Therefore we rewrite the vector x to a three-dimensional matrix B with the dimension L, M, N and apply R l,m,n = w 1 w 1 w 1 i= j= k= ω i,j,k B l+i w/2,m+i w/2,n+i w/2 w 1 w 1 w 1 i= j= k= B l,m,n, ω i,j,k otherwise (3) where l = w/2,, L w m = w/2,, M w n = w/2,, N w (4) and ω represents a weighting factor obtaind by ω i,j,k = e (i w/2) 2 c (j w/2)2 c 2 + (k w/2)2 c (5)

4 524 Stefan Schlüter et al. 1 1 standard deviation:26.96 standard deviation: NmF2 residual between ionosonde and IRI data / e/(1 1 m 2 ) NmF2 residual between ionosonde and tomograpy data / e/(1 1 m 2 ) Fig. 2. Residual between NmF2 data retrieved from selected ionosonde and the IRI model(left panel) and tomography (right panel) The values of the coefficients c 1, c 2, c 3 and w, have been estimated by serveral runs, cross-checking the tomographic result with ionosonde and TEC data from the Neustrelitz maps. After the smoothing has been applied, B is rewritten to Ax and the iteration is restarted. The whole process is repeated until no significant changes appear in the iteration process. 3 Evaluation An important task in ionospheric tomography is the evaluation of the results. In principle there are no sources of continuous data covering the whole European region and giving vertical information about the internal structure of the ionosphere up to 1 km. Therefore we have used NmF2 and hmf2, obtained by ionosonde measurements, as a key parameter for the evaluation of vertical resolution of the tomographic reconstruction. In particular, 3-hourly NmF2 and hmf2 estimates from April 1 st to April 21 th, of 11 European ionosondes (see Fig. 1) were used for the comparison (the ionosonde stations Juliusruh and Rome where not used in the comparison, because their data have been taken for the tomogram). This period was chosen, because a geomagnetic storm event occured at April 6 th,. Such events, causing effects which are not reproduced by the inital model, give good opportunity to prove the potential of this very model dependent approach. The results for NmF2 and hmf2 residuals between the ionosonde data and the data obtained by tomography are given in Fig. 2 and 3. For comparison residuals under the same conditions but using the initial model instead of tomography are computed ( see also 2 and 3). The results show that the tomographic approach has led to an overall improvement of the NmF2 model estimate of about 14% (comparing the

5 GPS based Ionospheric Tomography standard deviation: standard deviation: HmF2 residual between ionosonde and IRI data / km) HmF2 residual between ionosonde and tomography data / km) Fig. 3. Residual between hmf2 data retrieved from selected ionosondes and the IRI model (left panel) and tomography (right panel) dispersion and the bias to the ionosondes in both statistics), whereas no significant improvement can be found in the hmf2 data. 4 Including Radio Occultation Data As shown, pure ground-based GPS measurements contain hardly any information on the vertical ionospheric profile. An opportunity to improve the vertical resolution in tomography is the use of GPS radio-occultation data as those obtained by the CHAMP satellite. To prove the potential improvement obtained by such data, a set of calibrated horizontal TEC data from the CHAMP satellite [4] where included in the tomography and compared with ionosonde data of the Juliusruh station, which was close to the ray path of the occultation links. As an example Fig. 4 shows vertical profiles over Juliusruh, obtained by the IRI model, ionosonde data (with a model extension above the electron density peak) and ground based GPS tomography. Fig. 4 shows the corresponding results with CHAMP data included in the tomographic algorithm. The improvement of the model estimate obtained by the use of ground-based GPS data is shown in Fig. 4, as it has already been indicated by the statistical investigations. Model correction by real data is significantly better by combining ground-based GPS and radio occultation data as demonstrated in Fig.4. 5 Conclusion So far GPS based radio tomography is still in a developing state. However, it was shown that the use of GPS networks as that of IGS can be the base for a

6 526 Stefan Schlüter et al. Fig. 4. Height profile of electron density at Juliusruh (Germany), obtain by IRI, ionosonde (the upper part is extended by a model) and GPS-tomography using IGS data (left panel) and using combined IGS and CHAMP data (right panel). continous three-dimensional ionospheric monitoring system. As exemplarily shown, tomographic combination of ground-based GPS and radio-occultation data can lead to promising results. Regardless of the inversion method, to obtain tomographic results with a resolution that allows the investigation of even smaller structures and phenomena in the ionosphere, more and especially horizontal measurements are needed. Future navigation systems as the European Galileo and further radiooccultation missions may improve such data base. Nevertheless, even under better data conditions, satellite radiotomography has to include additional information. In our case, using the recursive MART algorithm, data of the IRI model were used as a priori information and ionosonde data to obtain a stop criteria for the iteration process. Acknowledgements The authors are grateful to the CHAMP team, co-ordinated by the GFZ Potsdam, for the given support and data. We also thank all members of the IGS community for providing their data service. Ionosonde data have kindly been provided by the IAP Kühlungsborn. References 1. Dick G, Gent G, Reigber C (1) First experience with near real-time water vapor estimation in a German network. J of Atmos and Solar-Terr Phys, 63,

7 GPS based Ionospheric Tomography Gallagher DL, Craven PD, Comfort RH () Global core plasma model. J Geophys Res,15(A8), Jakowski N, Sárdon E, Engler E, Jungstand A, Klähn D (1996) Relationships between GPS signal propagation errors and EISCAT observations. Annal Geophysicae, 14, Jakowski N, Wehrenpfennig A, Heise S, Reigber Ch, Lühr H, Grunwaldt L, Meehan TK (2) GPS Radio Occultation Measurements of the Ionosphere from CHAMP: Early results. Geophys Res Lett, (in press). 5. Jakowski N, Schlüter S (1999) Ionospheric storms detected by GPS based TEC monitoring., In: Hanbaba R, de la Morena BA (eds) Proc 3rd COST251 Workshop, Sardón E, Rius A, Zarraoa N (1994) Estimation of the transmitter and receiver differential biases and the ionospheric total electron content from Global Positioning System observations. Radio Sci, 29(3),