Vertical TEC representation by IRI 2012 and IRI Plas models for European midlatitudes

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ScienceDirect Advances in Space Research 55 (2015) 2070–2076 www.elsevier.com/locate/asr

Vertical TEC representation by IRI 2012 and IRI Plas models for European midlatitudes I.E. Zakharenkova a,b,⇑, Iu.V. Cherniak a,b, A. Krankowski a, I.I. Shagimuratov b a

Geodynamics Research Laboratory, University of Warmia and Mazury, 1 Oczapowskiego st., 10-719 Olsztyn, Poland b West Department of IZMIRAN, 41 Pobeda Av., 236010 Kaliningrad, Russia Received 22 March 2014; received in revised form 21 June 2014; accepted 23 July 2014 Available online 1 August 2014

Abstract Vertical total electron content (vTEC) values computed using IRI-2012 and IRI Plas models have been compared with diurnal GPS vTEC data derived from European mid-latitude GPS station Potsdam. Comparative data-model analysis does not reveal good performance in vTEC representation. It was found that new extension of IRI model – IRI Plas – cannot represent correctly the vTEC variations over European midlatitudes and mainly overestimates GPS vTEC especially for low and moderate solar activity. In order to estimate the source of the data-model discrepancies, the case-study with detailed analysis of the model simulated electron density profiles was done. It was obtained that all models do not represent correctly the topside profile part and tend to overestimate the electron density higher than F2 peak. So, the main problem of the IRI vTEC representation is not situated in the plasmaspheric part, its absence in IRI model or its presence in IRI Plas model, the main source of the resulted discrepancies is still in the IRI topside ionosphere representation. Ó 2014 COSPAR. Published by Elsevier Ltd. All rights reserved.

Keywords: Ionosphere; Total electron content; GPS; IRI; IRI Plas

1. Introduction During last several decades dual frequency GPS measuring technique is well proved and widely used for studies of the near-Earth plasma environment (Jakowski, 1996; Davies and Hartmann, 1997; Garner et al., 2008). With the rapid growth of the global and a number of regional ground-based GPS receiver networks, measurements of the total columnar electron content (TEC) along the ray path from a GPS satellite to the receiver can be used as unprecedented large database for the ionosphere monitoring and research. The ionized atmosphere surrounding

⇑ Corresponding author at: West Department of IZMIRAN, 41 Pobeda Av., 236010 Kaliningrad, Russia. Tel./fax: +7 4012 215606. E-mail addresses: [email protected] (I.E. Zakharenkova), [email protected] (Iu.V. Cherniak), [email protected] (A. Krankowski), [email protected] (I.I. Shagimuratov).

http://dx.doi.org/10.1016/j.asr.2014.07.027 0273-1177/Ó 2014 COSPAR. Published by Elsevier Ltd. All rights reserved.

the Earth at altitudes’ range of about 80–100 km up to 3–5 RE represents a dispersive medium for the GPS signals which propagate through the oxygen-dominated plasma of the ionosphere and the tenuous hydrogen-dominated plasma of the plasmasphere on their way to the groundbased GPS receivers. As GPS technique presents opportunity to be used in real-time or near real-time ionosphere services for space weather monitoring and the high popularity of GPS TEC observations for the ionosphere research, including generation of global and regional ionospheric maps with high temporal and spatial resolution, there is a great demand in a proper model for GPS TEC specification. Nowadays the International Reference Ionosphere (IRI) provides one of the better model specifications for the main ionospheric parameters (Bilitza, 2001). However, the IRI model specifies the ionosphere only up to 2000 km, which is a problem because the GPS satellites are located at

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20,200 km. While the plasma density above 2000 km is at least two orders of magnitude less than the F-region peak density (e.g. Gallagher et al., 2000), the length above the IRI model (18,200 km) is about two orders of magnitude greater than the thickness of the F layer (Garner et al., 2008). It is necessary to extrapolate the ionosphere to higher altitudes for use with GPS measurements. The International Standardization Organization, ISO, recommends the IRI model for the specification of ionosphere plasma densities and temperatures and lists several plasmasphere models for extending IRI to plasmaspheric altitudes. IRI Plas model, the International Reference Ionosphere extended to Plasmasphere (Gulyaeva et al., 2002), has been proposed as one of the possible candidate model for the plasmasphere extension of the IRI model (Gulyaeva and Bilitza, 2012). The present work analyzes results coming from comparison of a long-time series of the GPS vertical TEC data recorded at mid-latitudinal GPS station with simulated vTEC results derived by IRI 2012 and IRI Plas models. As a specific location we chose the GPS station POTS (Potsdam, Germany) as a representative of the European mid-latitude station, besides it is one of the most closely located IGS station to the Juliusruh ionosonde. 2. Database 2.1. GPS vTEC data The GPS vTEC data are obtained from the GPS receiver of IGS network: POTS (Potsdam, Germany). The station has geographical coordinates (52.4N; 13.1E). The vTEC values were computed from the raw GPS data in RINEX format available at NASA CDDIS archive (CDDIS, 2014) using our algorithm of TEC determination and calibration. The well-known formulas of TEC estimation from the frequency-differenced GPS phase delay were used from (Hofmann-Wellenhof, 2001). The slant TEC (sTEC), defined as the line integral of the electron density from a GPS satellite to a receiver, can be estimated from the difference between the pseudoranges (P1 and P2) or the difference between the carrier phase (L1 and L2) of two frequencies (Blewitt, 1990). During TEC processing there was done detection and correction of cycle slips and lossof-lock, resolving of the phase ambiguity. To convert sTEC to vTEC, the common procedure is used. The ionosphere is assumed to be a thin layer and sTEC is converted to vTEC at the pierce point (Eq. (1)). Eq. (1) should be corrected taking into account satellites and receiver differential code biases (Eq. (2)). VTEC ¼ STEC  cos v

ð1Þ

VTEC ¼ STEC  cos v  bs  br   RE cos a v ¼ arcsin RE þ h

ð2Þ ð3Þ

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where v is the zenith angle, a is the elevation angle, RE is the mean radius of the Earth, h is the height of the ionospheric layer (here, h = 400 km), bs and br are satellites and receiver biases. vTEC is calculated in TEC units, 1 TECU = 1016 el/m2. In fact, the GPS TEC estimation technique has a basic assumption of the ionosphere as a thin shell layer at the fixed altitude. This shell height should not be considered as height of F2 layer peak and should not trace dynamics of F2 layer. Usually the shell height is accepted to be chosen higher than F2 peak and most commonly used values are 350, 400 or 450 km. Komjathy (1997) studied the effects of these different ionospheric shell heights to the TEC output and concluded that using 400 km shell height, especially with cutoff angle of 25 deg, provides the best to the “truth” result with smallest rms residuals. Up to date, the most optimal shell height is used to be 400 km for both single GPS station analysis and GPS TEC mapping techniques. In order to obtain diurnal vTEC variation from the retrieved set of sTEC data, there is applied algorithm of trigonometrical polynomial expansion of degree 6. Unknown satellites and receiver differential code biases are obtained during solution of overdetermined system of polynomial expansion equations with use of least-squares fitting technique and singular value decomposition. As a result of this technique application to the RINEX database, we derive diurnal vTEC values for POTS station for each day of three years: 2000, 2003 and 2008 that represent years with high, moderate and low solar activity level respectively. From analysis we remove all days with geomagnetic disturbances according to the following criteria: (1) sum of Kp > 25; (2) Dst < 50 nT. 2.2. Model calculations To analyze the model-derived estimates of vTEC we used the IRI-2012 version of the International Reference Ionosphere (IRI) model. The IRI vTEC values were calculated for each hour of the quiet middle day of each month for considered years. These hourly values are taken to be representative of the ionospheric average behavior during concrete month. The foF2 STORM model option was turned off because this study deals with quiet geomagnetic conditions. For the input parameters of solar activity level, we used the final (actual) version of “ig_rz.dat” file, that contains monthly values of IG12 (a 12-month-running mean of the global ionosphere index) and Rz12 (a 12month-running mean of the sunspot number) indices for IRI model. The CCIR model option was used for F2 peak density calculations. As a topside electron density profile we used NeQuick option. Also it should be noted, that IRI vTEC is a result of electron density profile integration from 60 km to 2000 km, i.e. without plasmaspheric part. In the given research we also use IRI Plas model, the International Reference Ionosphere extended to Plasmasphere (Gulyaeva et al., 2002, 2013). This model has been proposed as a candidate model for the plasmasphere

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extension of the IRI model (Gulyaeva and Bilitza, 2012). Fortran code of the model is available at the IZMIRAN web service (http://ftp.izmiran.ru/pub/izmiran/SPIM/). IRI Plas presents the combination of the IRI model with the Russian Standard Model of the Ionosphere and Plasmasphere, SMI (Chasovitin et al., 1998). The models of distribution of Ne and Te are developed directly using experimental data. COSPAR Reference Atmosphere Model is used for producing the neutral temperature. Geomagnetic Reference Field models are used to produce the ionosphere– plasmasphere driving parameters including modified dip latitude and corrected magnetic latitudes and longitudes. CCIR predictions of the F2 layer peak parameters (CCIR 1986) are used by the both IRI and SMI models. For the smooth fitting of the two models, the shape of the IRI topside electron density profile was changed based on ISIS 1, ISIS 2 and IK19 satellite inputs (Gulyaeva, 2003). IRI-based IG_RZ.dat, ap.dat, CCIR.asc data files are used as input parameters. IRI Plas model was used to derive simulated vTEC values within altitudinal limits of 80–20,000 km for all months of the years 2000, 2003 and 2008. 3. Results As known, IRI model reproduces vTEC estimate as a result of integration of electron density profile within altitudinal limits of 60–2000 km. According to Gallagher et al. (2000) the electron densities in plasmasphere are several orders of magnitude less than in ionosphere and in fact the plasmasphere is often ignored in analysis of GPS vTEC data, assuming that it is the question of several TECU only. Based on such assumptions one can expect that IRI vTEC should be always lower than observed GPS vTEC. Is it so? Here we analyze differences between standard version of IRI model and extended version of the model – IRI Plas. Diurnal variation of GPS vTEC values was retrieved with 10 min resolution. On a monthly basis we calculated mean and standard deviation values for each moment of time. Fig. 1 illustrates a comparison of the observed monthly mean values of the GPS vTEC with model-derived results for solstice and equinox months in the years 2000, 2003 and 2008. Each graph contains a monthly mean and standard deviation of observed GPS vTEC values at the midlatitude station POTS (thin grey lines) and for comparison the IRI-2012 vTEC results (thin dotted lines) and IRI Plas vTEC results (thick black lines). It is seen that IRI Plas vTEC demonstrates the same behavior as IRI vTEC with some upward offset, that should be due to the presence of plasmaspheric part in IRI Plas model and changes in the topside ionospheric part that was done for smooth fitting of the ionosphere and plasmasphere models. IRI Plas generally overestimates GPS vTEC especially for low and moderate solar activity years. Comparison between IRI2012 vTEC and IRI Plas vTEC revealed that IRI vTEC prediction was much closer to the observed GPS vTEC than IRI Plas vTEC. However IRI vTEC representation

is not reliable enough, sometimes it is lower than GPS vTEC, sometimes it is much higher. For March equinox conditions IRI-2012 tends to have lesser vTEC values vs. GPS vTEC in 2000, then higher values in 2003 and again lesser values for solar minimum in 2008. Then we select two specific intervals: day-time and night-time related to the intervals of 10-16 LT and 2204 LT correspondingly. For each month of the considered years we calculate differences (data-model) between GPS vTEC values for each quiet day and model-derived vTEC, and obtain monthly estimates for such differences and day/ night conditions. Fig. 2 represents mean and standard deviations of the data-model vTEC differences calculated for IRI-2012 and IRI Plas models. Mean estimates of datamodel differences are varied in the same way with some offset between models. The most significant differences are observed during day time and high level of solar activity (year 2000). Here we should note that IRI-2012 overestimates GPS vTEC data during October–March, the same tendency exhibits also during moderate solar activity (year 2003). Night-time estimates for differences are characterized by lower values. During solar minimum conditions differences become much smaller, but due to considerably smaller ionospheric electron density and vTEC values. Data-model differences in TEC units and percent differences for June and December conditions are summarized in Table 1. IRI-2012 model shows better agreement with experimental data during summer solstice in comparison with winter conditions. IRI Plas model demonstrates more pronounced divergence from vTEC data, maximal values are observed for night-time conditions and winter season, when difference in several TECU gives more than 100% in relative ratio.

4. Discussion and conclusion So, we found discrepancies between GPS vTEC and model-derived vTEC and we can estimate them for particular geographical location and time, but is it informative enough to make any conclusion about source of the problem? Probably not. Let us consider one case from the obtained results in more details. We analyze event for 1200 LT of December 2000, when both models represent considerable overestimation over vTEC observations. Fig. 3(a) presents comparison of the electron density profiles (EDPs) generated by IRI-2012 and IRI Plas models in the altitudinal range 80–2000 km. We can note evident difference in F2 peak electron density, as well as differences in the shape of the topside part of the profiles. Interesting thing that in spite of the shape differences these profiles have the same integration result, value of ionospheric TEC is about 48 TECU. And here is one of the problem with integration origin of vTEC, that there could be a number of profiles with different shapes that have profile integration result in the same value of TECU. But what profile is more

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Fig. 1. Monthly mean values of the GPS vTEC and model vTEC calculations for solstice and equinox months in the years 2000, 2003 and 2008.

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Fig. 2. Mean and standard deviations of the data-model vTEC differences for all months of the years 2000, 2003 and 2008.

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Table 1 Mean and standard deviation of data-model differences for summer and winter solstices of the years 2000, 2003 and 2008. June

December

Diff (TECU)

2000 IRI IRI Plas

2003 IRI IRI Plas

2008 IRI IRI Plas

Diff (%)

Mean

Std

Day Night Day Night

5.7 3.0 3.1 6.5

3.6 3.6 3.7 3.8

Day Night Day Night

3.3 0.2 4.4 6.9

Day Night Day Night

0.9 0.2 5.3 4.6

Mean

Diff (TECU)

Diff (%)

Std

Mean

Std

19.4 12.3 12.7 45.4

11.2 19.5 16.1 33.4

12.5 1.2 13.4 5.2

6.3 1.5 6.3 1.8

51.4 18.0 54.4 98.7

36.7 20.8 36.5 52.4

2.8 1.9 2.9 2.1

16.7 1.8 25.8 78.8

12.2 18.9 18.8 36.0

3.7 0.7 13.3 5.2

3.6 0.9 4.0 1.0

42.5 13.5 125.5 180.0

40.5 25.6 68.9 78.5

0.9 1.3 1.2 1.4

10.2 3.3 63.7 108.1

9.3 26.0 18.2 43.5

1.6 2.7 5.2 0.9

0.7 0.7 1.3 0.6

28.8 64.3 90.1 25.6

13.4 8.6 20.9 20.2

reliable and proper represents the ionospheric density in this case? We decide to consider additional measurements to the Fig. 3(a). Firstly, we calculate electron density profile for 1200 LT of December 2000 using independent ionospheric model NeQuick 2 (Nava et al., 2008). It can be done by running the Fortran code or via NeQuick2 Web Model service (http://t-ict4d.ictp.it/nequick2/nequick-2-web-model). NeQuick model uses also CCIR peak parameters maps. Fig. 3(b) shows EDPs produced by NeQuick and IRIbased models. NeQuick EDP, shown by thick black line, demonstrates best agreement with IRI Plas in NmF2 value and at the same time rather good coincidence with part of the topside profile of IRI-2012, that can be explained by use of the NeQuick option for topside. Maybe NeQuick EDP is the best model-derived solution for proper representation of the ionospheric electron density distribution and vTEC representation? Moreover, integration of NeQuick EDP gives us vTEC value of about 38 TECU, that is much closer to the observed mean GPS vTEC value. Fig. 3(c) illustrates comparison of vTEC estimates for conditions of 1200 LT December 2000: mean GPS vTEC observations during this month and model-derived vTEC values. Results of EDP integration up to 2000 km are shown by light grey bars, up to 20,000 km by dark grey bars. The next question is why NmF2 value in IRI-2012 EDP differs from IRI Plas and NeQuick EDPs if all models use CCIR peak maps? As GPS station POTS was selected for our analysis mainly due to its close location to the Juliusruh ionosonde station, we decide to check ionosonde data. The manually validated foF2 values for Juliusruh ionosonde were obtained from the Australian Ionosphere Prediction Service (IPS) of the World Data Centre for Solar-Terrestrial Science in Australia. The Juliusruh data are kindly provided to IPS by the Leibniz-Institute of

Mean

Std

Atmospheric Physics e.V. at University of Rostock (IAP) of Germany. The ionospheric F region peak electron density, NmF2, was calculated from F2 layer critical frequency via relation NmF2 [m3] = 1.24  1010(foF2[MHz])2. We consider NmF2 values for the moment of 1200 LT during all days of December 2000, remove days with geomagnetic disturbances and calculate mean and standard deviation for NmF2 value. The obtained characteristics were plotted at Fig. 3(b). It is evident that IRI-derived NmF2 perfectly coincides with mean NmF2 from real ionosonde’s observation. NmF2 estimates calculated by IRI Plas and NeQuick models are even outside of standard deviation limits for observed NmF2 values. We can conclude that in this case IRI-2012 has shown a remarkably better performance for the ionospheric F2 peak parameters. In addition to model simulations and the ground-based measurements we analyze in-situ data provided by CHAMP (Challenging Mini-Satellite Payload) satellite. GFZ (http://isdc.gfz-potsdam.de/) had managed this mission and provides access to the observations and post-processed products. In order to determine the electron density at the CHAMP satellite location there is used the Planar Langmuir Probe (PLP). The CHAMP PLP measurements have been validated by McNamara et al. (2007), through comparison with plasma frequencies derived from Digisonde ionograms at Jicamarca, Peru. We check CHAMP PLP data for our case study, for December 2000 there were available measurements for 11 last days of the month, orbit altitude was 450 km. CHAMP satellite moved in the meridional direction at the topside ionospheric altitude. CHAMP passes were filtered in the longitudinal range of ±15° and latitudinal range of ±5° relative to the POTS station coordinates. For the state of December 2000, all satellite passes were corresponded to the specific local time of approx. 00 and 12 LT. All selected measurements were processed and estimates of mean and standard deviation for

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Fig. 3. Comparison of IRI-2012 and IRI Plas electron density profiles for 1200 LT of December 2000 (a). Comparison of IRI-2012, IRI Plas, NeQuick electron density profiles, ionosondes NmF2 and in situ CHAMP measurements for the same time (b). Comparison of GPS vTEC with vTEC derived by IRI-2012, IRI Plas, NeQuick models with different upper limit for EDP integration (c).

electron density at the altitude of 450 km and for the moment of about 12 LT were derived. These values were also plotted at Fig. 3(b). It is found that all three modelderived EDPs do not match the CHAMP observations, and practically twice overestimate them. These results cause more questions than at the beginning. Yes, it seems that IRI Plas mainly overestimates GPS vTEC observations, especially during low and moderate solar activity. Besides, IRI Plas discrepancies have similar behavior as IRI discrepancies with some offset, maybe due to the plasmasphere adding or maybe due to topside + plasmasphere estimate. Yes, further investigations are needed to estimate the plasmaspheric contribution for different geographic locations and conditions in order to improve the plasmaspheric vTEC representation by this model. Most promising data sources for this purpose can be measurements provided by space-based GPS receivers, installed at the majority of modern Low Earth Orbit (LEO) satellites for precise positioning and provided upward measurements along rays LEO-GPS from various

bottom altitudes (e.g. Heise et al., 2002; Garner et al., 2008). However, we think that main problem is not in the need to add some plasmasphere model to IRI or smooth junction of both models, but the most important demand is the further improvement of the IRI topside profile. During last years the improvement of topside electron density profile is one of the most actual task in the IRI Working Group, in particular great number of reprocessed topside profiles from Alouette 1,2 and ISIS 1,2 was analyzed for topside correction (Bilitza, 2004), as well as the adjunction of the NeQuick topside model. As a result now there are three options in IRI to model topside part of the profile (Bilitza, 2009). But it does not solve the problem of topside in the IRI model. Based on CHAMP and GRACE in situ measurements, Lu¨hr and Xiong (2010) report that from 2005 onward the solar minimum the overestimation of the topside electron density by the IRI model was progressively increasing and annual averages reached more than 60% for the year 2009. They used NeQuick option for the topside profile in the IRI model. Future

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improvements for the representation of topside electron density profiles in IRI are related with continued research on the choice of the proper function for the topside profile shape and use of the varying scale height on the base of aChapman profiler (Reinisch et al., 2007; Nsumei et al., 2012), or combination of different profilers (Verhulst and Stankov, 2014). Angling and Jackson-Booth (2011) used IRI-2007 as a background model for Electron Density Assimilative Model (EDAM) and reported about test assimilation of foF2 and GPS TEC into EDAM for the case of 14 February 2007. They found that assimilation of only one type of data (foF2 or TEC) into EDAM leads to the large post assimilation residuals of the second type of data and only simultaneous assimilation of both foF2 and GPS TEC gives better results with the lowest RMS errors in post assimilated foF2 and TEC. It was explained by more effective modification of the F layer slab thickness and the whole shape of the electron density profile. We should say that today it is difficult to use IRI model as a climatological model to represent vertical TEC, as a model background for comparison with GPS vTEC. From the other hand, it is practically impossible to use reports about observed discrepancies between IRI vTEC and GPS vTEC, as usually these reports have punctual character, limited in time and do not provide any new information where can be the problem of such discrepancies. We think that there is need in a joint and simultaneous analysis of measurements provided by different ground-based and space born facilities on a global scale. For this aim there should be used GPS networks, LEO GPS receivers and in situ observations provided by present-day LEO missions like CHAMP, C/NOFS. Acknowledgments We are grateful to International GNSS Service (IGS) for GPS data and products. We acknowledge the Information System and Data Center, GFZ, Potsdam for providing CHAMP data. The Juliusruh ionosonde data are kindly provided to IPS by the Leibniz-Institute of Atmospheric Physics e.V. at University of Rostock (IAP) of Germany. We acknowledge the IRI Working group for providing and evaluating the IRI model FORTRAN code and Dr. Tamara Gulyaeva for IRI Plas code. We thank the Telecommunications/ICT for Development (T/ICT4D) Laboratory of the Abdus Salam International Centre for Theoretical Physics, Trieste, Italy for NeQuick model data. References Angling, M.J., Jackson-Booth, N.K., 2011. A short note on the assimilation of collocated and concurrent GPS and ionosonde data into the electron density assimilative model. Radio Sci. 46, RS0D13. http:// dx.doi.org/10.1029/2010RS004566. Bilitza, D., 2001. International reference ionosphere 2000. Radio Sci. 36 (2), 261–275. http://dx.doi.org/10.1029/2000RS002432.

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