Highlights about the performances of storm-time TEC modelling techniques for low/equatorial and mid-latitude locations

A statistical evaluation of storm-time total electron content (TEC) modelling techniques over various latitudes of the African sector and surrounding areas is presented. The source of observational TEC data used in this study is the Global Navigation Satellite Systems (GNSS), specifically the Global Positioning Systems (GPS) receiver networks. For each selected receiver station, three different storm-time models based on empirical orthogonal functions (EOF) analysis, non-linear regression analysis (NLRA) and Artificial neural networks (ANN), were implemented. Storm-time GPS TEC data used for both development and validation of the models was selected based on the storm criterion of $\mathit{Dst}?-50$ nT or $K_p?4$ to take into account both coronal mass ejections (CMEs) and co-rotating interaction regions (CIRs) driven storms, respectively. To make an independent test of the models, storm periods considered for validation were excluded from datasets used during the implementation of the models and results are compared with observations, monthly median values, and International Reference Ionosphere (IRI-2016) predictions. Considering GPS TEC as reference, a statistical analysis performed over six storm periods reserved for validation revealed that ANN model is about 10%, 26%, and 58% more accurate than EOF, NLRA, and IRI models, respectively. It was further found that, EOF model performs 15%, and 44% better than NLRA, and IRI models, respectively, while NLRA is 25% better than IRI. On the other hand, results are also discussed referring to the background ionosphere represented by monthly median TEC (MM TEC) and statistics are provided. Moreover, strengths and weaknesses of each model are highlighted.     

An Artificial Neural Network based approach for estimation of rain intensity from spectral moments of a Doppler Weather Radar

By using a Doppler Weather Radar (DWR) at Shriharikota (13.66°N & 80.23°E), an Artificial Neural Network (ANN) based technique is proposed to improve the accuracy of rain intensity estimation. Three spectral moments of a Doppler spectra are utilized as an input data to an ANN. Rain intensity, as measured by the tipping bucket rain gauges around the DWR station, are considered as a target values for the given inputs. Rain intensity as estimated by the developed ANN model is validated by the rain gauges measurements. With the help of a developed technique, reasonable improvement in the estimation of rain intensity is observed. By using the developed technique, root mean square error and bias are reduced in the range of 34–18% and 17–3% respectively, compared to Z–R approach.     

Robust extension of the simple sea-surface irradiance model to handle cloudy conditions for the global ocean using satellite remote sensing data

Sea-surface solar radiation (abbreviated as photosynthetically available radiation, PAR) in the visible wavelength (400–700 nm) is an essential parameter to estimate marine primary productivity and understanding phytoplankton dynamics, upper ocean physics and biogeochemical processes. Although many remote-sensing models were developed to estimate daily PAR (DPAR) from ocean colour data, these models often produce biases in the DPAR products under cloudy-sky and complex atmospheric conditions due to the lack of parameterization to deal with the cloud cover conditions and insufficient in-situ DPAR data. This study presents an Extended Sea-surface Solar Irradiance Model (ESSIM) for estimating DPAR over the global ocean. The ESSIM uses the direct and diffuse components from the Simple sea-surface Solar Irradiance Model (SSIM) along with a new parameter to handle cloudy conditions. The ESSIM produced DPAR products with greater accuracy under both clear and cloudy conditions. Its performance was tested on the time-series MODIS-Aqua images and compared with the concurrent in-situ data and the results from two global models. Results showed that the DPAR values produced by ESSIM agree with in-situ data better than the global models for all-sky conditions (with a mean relative error of 11.267 %; a root mean square error of 5.563 Em^?2day^?1; and a mean net bias of 2.917 Em^?2day^?1). The ESSIM performed slightly better than the SSIM for clear conditions and the Frouin's Operational Algorithm (FOA) for all-sky conditions. As the new parameterization accounts for cloudy conditions, the ESSIM produced more accurate results for cloud cover conditions across latitudes (up to 60°). The time-series Level-3 binned MODIS-Aqua data (global gridded) also demonstrated that the ESSIM improved the accuracy of DPAR products and produced spatially and temporally consistent DPAR products over the global ocean regardless of the seasons and sky conditions.     

Investigation of GPS-TEC measurements using ANN method indicating seismo-ionospheric anomalies around the time of the Chile (Mw = 8.2) earthquake of 01 April 2014

In 94 km NW of Iquique in Chile (19.610°S, 70.776°W) a powerful earthquake of M_w = 8.2 took place at 23:46:47 UTC (20:46:47 LT) on April 01, 2014. Using GPS-TEC (Total Electron Content) measurements, potential unusual variations around the time and location of the Chile earthquake have been detected based on the median and Artificial Neural Network (ANN) methods. The indices D_st, K_p, A_p and F10.7 were used to distinguish pre-earthquake anomalies from the other anomalies related to the solar-geomagnetic activities. Using the median method, striking anomalies in time series of TEC data are observed 4 days before the earthquake at 14:00 and 16:00 UTC. The ANN method detected a number of anomalies, 4 (02:00 and 16:00 UTC) and 13 (24:00 UTC) days preceding the earthquake. The results indicate that the ANN method due to its capability of non linear learning is quite promising and deserves serious attention as a robust predictor tool for seismo-ionospheric anomalies detection.     

Thermal and TEC anomalies detection using an intelligent hybrid system around the time of the Saravan,Iran, (Mw = 7.7) earthquake of 16 April 2013

A powerful earthquake of M_w = 7.7 struck the Saravan region (28.107° N, 62.053° E) in Iran on 16 April 2013. Up to now nomination of an automated anomaly detection method in a non linear time series of earthquake precursor has been an attractive and challenging task. Artificial Neural Network (ANN) and Particle Swarm Optimization (PSO) have revealed strong potentials in accurate time series prediction. This paper presents the first study of an integration of ANN and PSO method in the research of earthquake precursors to detect the unusual variations of the thermal and total electron content (TEC) seismo-ionospheric anomalies induced by the strong earthquake of Saravan. In this study, to overcome the stagnation in local minimum during the ANN training, PSO as an optimization method is used instead of traditional algorithms for training the ANN method. The proposed hybrid method detected a considerable number of anomalies 4 and 8 days preceding the earthquake. Since, in this case study, ionospheric TEC anomalies induced by seismic activity is confused with background fluctuations due to solar activity, a multi-resolution time series processing technique based on wavelet transform has been applied on TEC signal variations. In view of the fact that the accordance in the final results deduced from some robust methods is a convincing indication for the efficiency of the method, therefore the detected thermal and TEC anomalies using the ANN + PSO method were compared to the results with regard to the observed anomalies by implementing the mean, median, Wavelet, Kalman filter, Auto-Regressive Integrated Moving Average (ARIMA), Support Vector Machine (SVM) and Genetic Algorithm (GA) methods. The results indicate that the ANN + PSO method is quite promising and deserves serious attention as a new tool for thermal and TEC seismo anomalies detection.     

Uncertainties and effects on geocenter motion estimates from global GPS observations

Global positioning system (GPS) observations can be used to estimate the geocenter motion, but are subjected to large uncertainties and effects due to uneven distribution of GPS stations and high-degree aliasing errors. In this paper, uncertainties and effects on geocenter motion estimates from global GPS observations are investigated and assessed with different truncated degrees and selected GPS network distributions based on different plate motion models, including NUVEL-1A, MORVEL56 and ITRF08. Results show that the selected GPS stations have no big effects on geocenter motion estimates based on different plate motion models, while large uncertainties are found at annual and semi-annual components when using different truncated degrees. Correlations of geocenter motion estimates from selected GPS networks with GRACE and SLR are better with truncated degree 3, and higher truncated degrees will degrade geocenter estimates. Smaller RMS also shows better results with the truncated degree 3 and the NUVEL1A has the worse results because more GPS sites are eliminated. For annual signal with truncated degree 3, four GPS strategies can reduce annual amplitudes by about 29.2% in X, 5.6% in Y, and 27.9% in Z with respect to truncated degree 1. Annual phases of all GPS solutions from MORVEL56 and ITRF08 are almost close to the GRACE solution with truncated degrees from 3 to 10, while the semi-annual signals are relatively weaker for all cases.     

Imaging Global Electron Content backwards in time more than 160 years ago

The Global Electron Content, GEC, represents the total number of electrons in the spherical layer over the Earth restricted by orbit of Global Positioning Satellite system (20,200 km). GEC is produced from Global Ionospheric Map of Total Electron Content, GIM-TEC, transformed to the electron density varying with height using the International Reference Ionosphere and Plasmasphere model, IRI-Plas. The climatologic GEC model is developed from GIM-TEC maps for a period 1999–2012 including the solar activity, annual and semiannual cycles as the most important factors affecting daily GEC variation. The proxy R_zp of the international sunspot numbers, R_i, is used as a measure of solar activity composed of 3 day smoothed R_i, 7 day and 81 day backwards mean of R_i scaled to the range of 1–40 proxy units, p.u. The root mean square error of the GEC climatologic model is found to vary from 8% to 13% of GEC. Taking advantage of a long history of sunspot numbers, the climatologic GEC model is applied for GEC reconstruction backwards in time for more than 160 years ago since 1850. The extended set of GEC values provides the numerical representation of the ionosphere and plasmasphere electron content coherent with variations of solar activity as a potential proxy index driving the ionosphere models.     

An alternative Shell inversion technique - Analysis and validation based on COSMIC and ionosonde data

Multi-channel Global Positioning System (GPS) carrier phase signals, received by the six low Earth orbiting (LEO) satellites from the Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) program, were used to undertake active limb sounding of the Earth’s atmosphere and ionosphere via radio occultation. In the ionospheric radio occultation (IRO) data processing, the standard Shell inversion technique (SIT), transformed from the traditional Abel inversion technique (AIT), is widely used, and can retrieve good electron density profiles. In this paper, an alternative SIT method is proposed. The comparison between different inversion techniques will be discussed, taking advantage of the availability of COSMIC datasets. Moreover, the occultation results obtained from the SIT and alternative SIT at 500 km and 800 km, are compared with ionosonde measurements. The electron densities from the alternative SIT show excellent consistency to those from the SIT, with strong correlations over 0.996 and 0.999 at altitudes of 500 km and 800 km, respectively, and the peak electron densities (N_mF₂) from the alternative SIT are equivalent to the SIT, with 0.839 vs. 0.844, and 0.907 vs. 0.909 correlation coefficients when comparing to those by the ionosondes. These results show that: (1) the N_mF₂ and h_mF₂ retrieved from the SIT and alternative SIT are highly consistent, and in a good agreement with those measured by ionosondes, (2) no matter which inversion technique is used, the occultation results at the higher orbits (∼800 km) are better than those at the lower orbits (∼500 km).     

TEC derived from some GPS stations in Nigeria and comparison with the IRI and NeQuick models

Total electron content (TEC) measured simultaneously using Global Positioning System (GPS) ionospheric monitors installed at some locations in Nigeria during the year 2011 (Rz = 55.7) was used to study the diurnal, seasonal, and annual TEC variations. The TEC exhibits daytime maximum, seasonal variation and semiannual variations. Measured TEC were compared with those predicted by the improved versions of the International Reference Ionosphere (IRI) and NeQuick models. The models followed the diurnal and seasonal variation patterns of the observed values of TEC. However, IRI model produced better estimates of TEC than NeQuick at all locations.     

Estimation of sediment settling velocity in estuarine and coastal waters using optical remote sensing data

A robust method has been developed for estimating sediment settling velocity (w_s) from high resolution optical remote sensing data in estuarine, coastal and harbor waters. This method estimates settling velocity as a function of the drag coefficient (C_d), Reynolds number (Re), grain size (D₅₀), specific gravity (Δ_SG) and grain shape (in terms of the Corey Shape Factor – CSF). These parameters were derived from the particulate inherent optical properties such as backscattering (b_bp), beam attenuation (c_p), suspended sediment concentration and turbidity using Landsat 8 OLI and HICO data. Preliminary results for the Gulf of Cambay in the eastern Arabian Sea and Yangtze river estuary in the East China Sea, showed that satellite-retrieved settling velocities (m?s^?1) varied from very low values in clear oceanic waters, intermediate values in coastal waters, to very high values in river plumes and sediment-laden coastal waters. The remote sensing retrievals of sediment properties and their settling velocities were generally consistent with the field and laboratory results, which indicate that the proposed methodology will have important implications in various coastal engineering, environmental and management studies.     

Zonal and meridional wind components derived from intercosmos-19 hmF2 measurements

The technique for the derivation of the meridional, V, and zonal, U, components of neutral wind from the longitudinal variations of vertical plasma drift, W, is developed. (Longitudinal variations of W were calculated by means of servo-model from Intercosmos-19 hmF2 data carefully selected for the ±40° invariant and geographical latitudes in the Northern and Southern hemispheres.) The technique is based on expansion of longitudinal variations of W, V, U and parameters of the geomagnetic field into finite Fourier series and on solution of the obtained equations set. The best solution of this problem is obtained by means of the Tikhonov regularization method. The most precise solution is derived for average value of meridional wind, the least precise one - for the longitudinal variations of zonal wind. The comparison with HWM and MWM wind models is carried out. The contributions of the different factors in the longitudinal variations of hmF2 are estimated.     

A new method for improving the performance of an ionospheric model developed by multi-instrument measurements based on artificial neural network

There are remarkable ionospheric discrepancies between space-borne (COSMIC) measurements and ground-based (ionosonde) observations, the discrepancies could decrease the accuracies of the ionospheric model developed by multi-source data seriously. To reduce the discrepancies between two observational systems, the peak frequency (foF2) and peak height (hmF2) derived from the COSMIC and ionosonde data are used to develop the ionospheric models by an artificial neural network (ANN) method, respectively. The averaged root-mean-square errors (RMSEs) of COSPF (COSMIC peak frequency model), COSPH (COSMIC peak height model), IONOPF (Ionosonde peak frequency model) and IONOPH (Ionosonde peak height model) are 0.58 MHz, 19.59 km, 0.92 MHz and 23.40 km, respectively. The results indicate that the discrepancies between these models are dependent on universal time, geographic latitude and seasons. The peak frequencies measured by COSMIC are generally larger than ionosonde’s observations in the nighttime or middle-latitudes with the amplitude of lower than 25%, while the averaged peak height derived from COSMIC is smaller than ionosonde’s data in the polar regions. The differences between ANN-based maps and references show that the discrepancies between two ionospheric detecting techniques are proportional to the intensity of solar radiation. Besides, a new method based on the ANN technique is proposed to reduce the discrepancies for improving ionospheric models developed by multiple measurements, the results indicate that the RMSEs of ANN models optimized by the method are 14–25% lower than the models without the application of the method. Furthermore, the ionospheric model built by the multiple measurements with the application of the method is more powerful in capturing the ionospheric dynamic physics features, such as equatorial ionization, Weddell Sea, mid-latitude summer nighttime and winter anomalies. In conclusion, the new method is significant in improving the accuracy and physical characteristics of an ionospheric model based on multi-source observations.     

Case study of inclined sporadic E layers in the Earth’s ionosphere observed by CHAMP/GPS radio occultations: Coupling between the tilted plasma layers and internal waves

We have used the radio occultation (RO) satellite data CHAMP/GPS (Challenging Minisatellite Payload/Global Positioning System) for studying the ionosphere of the Earth. A method for deriving the parameters of ionospheric structures is based upon an analysis of the RO signal variations in the phase path and intensity. This method allows one to estimate the spatial displacement of a plasma layer with respect to the ray perigee, and to determine the layer inclination and height correction values. In this paper, we focus on the case study of inclined sporadic E (E_s) layers in the high-latitude ionosphere based on available CHAMP RO data. Assuming that the internal gravity waves (IGWs) with the phase-fronts parallel to the ionization layer surfaces are responsible for the tilt angles of sporadic plasma layers, we have developed a new technique for determining the parameters of IGWs linked with the inclined E_s structures. A small-scale internal wave may be modulating initially horizontal E_s layer in height and causing a direction of the plasma density gradient to be rotated and aligned with that of the wave propagation vector k. The results of determination of the intrinsic wave frequency and period, vertical and horizontal wavelengths, intrinsic vertical and horizontal phase speeds, and other characteristics of IGWs under study are presented and discussed.     

Temporal extrapolation of TEC using WNN during 2007–2018 and comparison against GIM,IRI2016 and Kriging

In this paper, a new method of temporal extrapolation of the ionosphere total electron content (TEC) is proposed. Using 3-layer wavelet neural networks (WNNs) and particle swarm optimization (PSO) training algorithm, TEC time series are modeled. The TEC temporal variations for next times are extrapolated with the help of training model. To evaluate the proposed model, observations of Tehran GNSS station (35.69°N, 51.33°E) from 2007 to 2018 are used. The efficiency of the proposed model has been evaluated in both low and high solar activity periods. All observations of the 2015 and 2018 have been removed from the training step to test the proposed model. On the other hand, observations of these 2 years are not used in network training. According to the F10.7, the 2015 has high solar activity and the 2018 has quiet conditions. The results of the proposed model are compared with the global ionosphere maps (GIMs) as a traditional ionosphere model, international reference ionosphere 2016 (IRI2016), Kriging and artificial neural network (ANN) models. The root mean square error (RMSE), bias, dVTEC = |VTEC_GPS ? VTEC_Model| and correlation coefficient are used to assess the accuracy of the proposed method. Also, for more accurate evaluation, a single-frequency precise point positioning (PPP) approach is used. According to the results of 2015, the maximum values of the RMSE for the WNN, ANN, Kriging, GIM and IRI2016 models are 5.49, 6.02, 6.34, 6.19 and 13.60 TECU, respectively. Also, the maximum values of the RMSE at 2018 for the WNN, ANN, Kriging, GIM and IRI2016 models are 2.47, 2.49, 2.50, 4.36 and 6.01 TECU, respectively. Comparing the results of the bias and correlation coefficient shows the higher accuracy of the proposed model in quiet and severe solar activity periods. The PPP analysis with the WNN model also shows an improvement of 1 to 12 mm in coordinate components. The results of the analyzes of this paper show that the WNN is a reliable, accurate and fast model for predicting the behavior of the ionosphere in different solar conditions.     

Preliminary results of fair-weather atmospheric electric field in the proximity of Main Boundary Thrust,Northern Pakistan

The atmospheric electric Potential Gradient (PG) variation of fair-weather days at Muzaffarabad (MZF, Northern Pakistan) station is presented for the period of January 2015–October 2017. The present investigations focus on the diurnal variation of atmospheric PG on the seasonal and annual time scale. The fair-weather seasonal and annual average PG variation has revealed two peaks, i.e. a primary maximum peak and a secondary maximum peak. The average maximum PG value found is ～410?Vm^?1. The results are then compared with the well-known standard oceanic Carnegie curve. The diurnal curve of PG is found to deviate from the Carnegie curve. The seasonal PG variation at MZF shows lower values during the summer (monsoon) and autumn (post-monsoon) as compared to the winter and spring (pre-monsoon) which could be due to local aerosol concentration. Global comparison of PG (%) values of the annual mean at different longitude, with MZF observatory, shows local morning and evening peaks depicting local sunrise and sunset effects on the PG. The overall outcomes will certainly contribute to further investigate the Global Electric Circuit (GEC).     

Assessing the relationship between solar activity and some large scale climatic phenomena

The nature of the climatic response to solar variability is assessed over a long-time scale. The wavelet analysis applied to paleoclimatic proxy data of large scale atmospheric phenomena (North Atlantic Oscillation, Atlantic Multidecadal Oscillation, Pacific Decadal Oscillation and Southern Oscillation Index) has revealed coherence between the climatic oscillations and the solar phenomena (the cosmogenic isotope ¹⁰Be and the Total Solar Irradiance) preferentially with periods of Schwabe, Hale and Yoshimura–Gleissberg cycles that may reflect a modulation of solar activity.     

The photometric study of light scattering from the surface of alumina powder and interpretations by Hapke formula

A laboratory experiment helps to understand the light scattering property of regolith like samples with known compositions and other physical parameters. The laboratory data so obtained can be compared with the existing in situ data on celestial objects like asteroids. Further, it may be analyzed with the help of various theoretical models to understand the light scattering processes from regolith more clearly. In this work we have performed laboratory based photometry of the light scattered from the surfaces of powdered alumina (Al₂O₃) at various tilt angles of the sample and at large phase angles, with the particles having diameter 0.3 μm. The wavelength of observation was 632.8 nm. These data have been fitted by a surface scattering model originally suggested by Hapke. Instead of using empirical Henyey–Greenstein phase function to fix the values of albedo and phase function to be used within Hapke formula, we have used Mie theory for the same. This approach helped us to determine the single particle properties such as particle diameter and complex refractive index from surface scattering phase curve alone. Mie theory depends only on the size parameter X(=2π(radius/wavelength)) and complex refractive index (n, k) of the material. Since the absorption coefficient (k) for alumina is known to be very low but not exactly zero, the best fit to the experimental data was obtained by least square technique with k as a free parameter, as the other parameters are known. Finally, we compare our results with other published results and discuss the scope of application of the method we adopted.     

Development of a regional rain retrieval algorithm for exclusive mesoscale convective systems over peninsular India

The present study emphasize the development of a region specific rain retrieval algorithm by taking into accounts the cloud features. Brightness temperatures (T_bs) from various TRMM Microwave Imager (TMI) channels are calibrated with near surface rain intensity as observed from the TRMM – Precipitation Radar. It shows that T_b–R relations during exclusive-Mesoscale Convective System (MCS) events have greater dynamical range compared to combined events of non-MCS and MCS. Increased dynamical range of T_b–R relations for exclusive-MCS events have led to the development of an Artificial Neural Network (ANN) based regional algorithm for rain intensity estimation. By using the exclusive MCSs algorithm, reasonably good improvement in the accuracy of rain intensity estimation is observed. A case study of a comparison of rain intensity estimation by the exclusive-MCS regional algorithm and the global TRMM 2A12 rain product with a Doppler Weather Radar shows significant improvement in rain intensity estimation by the developed regional algorithm.     

Comparing IRI and a regional model with ionosonde measurements in Pakistan

We have used the technique of expansion in Empirical Orthogonal Functions (EOFs) to develop regional models of the critical frequencies of E and F₂ layers (f_oE, f_oF₂), peak height (h_mF₂), and semi-thickness of F₂ layer (Y_mF₂) over Pakistan. In the present study levels of solar activity specified by Smoothed Sunspot Number (R) from 10 to 200 are taken into account. The magnetic dip angle for the model ranges from 30° to 60°. We have compared the regional model and the International Reference Ionosphere (IRI) with measurements of three ionosondes in Pakistan. The model parameters f_oE and f_oF₂ are found overall comparable to the observed hourly median values during daytime at Karachi (geographic latitude = 24.95°N, longitude = 67.13°E, magnetic inclination = 37°), Multan (30.18°N, 71.48°E, 45°) and Islamabad (33.75°N, 73.13°E, 51.5°) during the years 1988, 1996 and 2000. For h_mF₂ the computed values by regional and IRI model for the year 1995 are found close to each other. However, for Y_mF₂the results are better during daytime as compared to nighttime.     

Optical properties of dust and the opacity of the Martian atmosphere

Particulate component of the Mars atmosphere composed by micron-sized products of soil weathering and water ice clouds strongly affects the current climate of the planet. In the absence of a dust storm so-called permanent dust haze with τ ≈ 0.2 in the atmosphere of Mars determines its thermal structure. Dust loading varies substantially with the season and geographic location, and only the data of mapping instruments are adequate to characterize it, such as TES/MGS and IRTM/Viking. In spite of vast domain of collected data, no model is now capable to explain all observed spectral features of dust aerosol. Several mineralogical and microphysical models of the atmospheric dust have been proposed but they cannot explain the pronounced systematic differences between the IR data (τ = 0.05–0.2) and measurements from the surface (Viking landers, Pathfinder) which give the typical “clear” optical depth of τ ≈ 0.5 from one side, and ground-based observations in the UV–visible range showing much more transparent atmosphere, on the other side. Also the relationship between τ₉ and the visible optical depth is not well constrained experimentally so far. Future focused measurements are therefore necessary to study Martian aerosol.