Neural network prediction of the topside electron content over the Euro-African sector derived from Swarm-A measurements

This study presents the first prediction results of a neural network model for the vertical total electron content of the topside ionosphere based on Swarm-A measurements. The model was trained on 5 years of Swarm-A data over the Euro-African sector spanning the period 1 January 2014 to 31 December 2018. The Swarm-A data was combined with solar and geomagnetic indices to train the NN model. The Swarm-A data of 1 January to 30 September 2019 was used to test the performance of the neural network. The data was divided into two main categories: most quiet and most disturbed days of each month. Each category was subdivided into two sub-categories according to the Swarm-A trajectory i.e. whether it was ascending or descending in order to accommodate the change in local time when the satellite traverses the poles. Four pairs of neural network models were implemented, the first of each pair having one hidden layer, and the second of each pair having two hidden layers, for the following cases: 1) quiet day-ascending, 2) quiet day-descending, 3) disturbed day-ascending, and 4) disturbed day-descending. The topside vertical total electron content predicted by the neural network models compared well with the measurements by Swarm-A. The model that performed best was the one hidden layer model in the case of quiet days for descending trajectories, with RMSE = 1.20 TECU, R = 0.76. The worst performance occurred during the disturbed descending trajectories where the one hidden layer model had the worst RMSE = 2.12 TECU, (R = 0.54), and the two hidden layer model had the worst correlation coefficient R = 0.47 (RMSE = 1.57).In all cases, the neural network models performed better than the IRI2016 model in predicting the topside total electron content. The NN models presented here is the first such attempt at comparing NN models for the topside VTEC based on Swarm-A measurements.     

Quantitative analyses of complexity and nonlinear trend of radio refractivity gradient in the troposphere

In this paper, the complexity and nonlinear trends of Radio Refractivity Gradient (RRG) in the troposphere over selected locations in Nigeria are analyzed and discussed extensively. The RRG is an important parameter in estimating path clearance and propagation effects such as ducting, surface reflection and multi-path on terrestrial line of-sights links. Also, radio wave signal propagating in the troposphere is affected by unpredictability of a weather condition which includes the variations of meteorological parameters such as temperature, pressure and relative humidity. The complex state of the atmosphere, which is the medium for the transmission of radio signals tend to have very strong influence such as scintillation and ducting on the quality of the radio signal, amplitude and phase. Variations in the meteorological parameters also induce variations in the refractive index of the atmosphere which in-turn results in the effect known as radio refractivity. For effective prediction and modeling of radio signal propagation, one should be able to characterize the nature and predictability of the computed RRG information. Chaotic Quantifiers (CQ) such as Phase Plot Reconstruction (PPR), Average Mutual Information (AMI), False Nearest Neighbor (FNN), Recurrence Plot (RP) and Recurrence Quantification Analyses (RQA) are used to assess the RRG. The information reveal, however, is based on the prediction techniques, design and frequency planning of microwave networks which may be useful for optimum performances during atmospheric turbulence.     

Comparison of GPS TEC variations with IRI-2007 TEC prediction at equatorial latitudes during a low solar activity (2009–2011) phase over the Kenyan region

This work presents an analysis of the Total Electron Content (TEC) derived from the International GNSS Service (IGS) receivers at Malindi (mal2: 2.9^oS, 40.1^oE, dip −26.813^o), Kasarani (rcmn: 36.89^oE, 1.2^oS, dip −23.970^o), Eldoret (moiu: 35.3^oE, 0.3^oN, dip −21.037^o) and GPS-SCINDA (36.8^oE, 1.3^oS, dip −24.117^o) receiver located in Nairobi for the period 2009–2011. The diurnal, monthly and seasonal variations of the GPS derived TEC (GPS-TEC) and effects of space weather on TEC are compared with TEC from the 2007 International Reference Ionosphere model (IRI-TEC) using the NeQuick option for the topside electron density. The diurnal peaks in GPS-TEC is maximum during equinoctial months (March, April, October) and in December and minimum in June solstice months (May, June, July). The variability in GPS-TEC is minimal in all seasons between 0:00 and 04:00 UT and maximum near noon between 10:00 and 14:00 UT. Significant variability in TEC at post sunset hours after 16:00 UT (19:00 LT) has been noted in all the seasons except in June solstice. The TEC variability of the post sunset hours is associated with the occurrence of the ionization anomaly crest which enhances nighttime TEC over this region. A comparison between the GPS-TEC and IRI-TEC indicates that both the model and observation depicts a similar trend in the monthly and seasonal variations. However seasonal averages show that IRI-TEC values are higher than the GPS-TEC. The IRI-TEC also depicts a double peak in diurnal values unlike the GPS-TEC. This overestimation which is primarily during daytime hours could be due to the model overestimation of the equatorial anomaly effect on levels of ionospheric ionization over the low latitude regions. The IRI-TEC also does not show any response to geomagnetic activity, despite the STORM option being selected in the model; the IRI model generally remains smooth and underestimates TEC during a storm. The GPS-TEC variability indicated by standard deviation seasonal averages has been presented as a basis for extending the IRI-model to accommodate TEC-variability.     

Improving the estimation of zenith dry tropospheric delays using regional surface meteorological data

Global Navigation Satellite Systems (GNSS) are emerging as possible tools for remote sensing high-resolution atmospheric water vapour that improves weather forecasting through numerical weather prediction models. Nowadays, the GNSS-derived tropospheric zenith total delay (ZTD), comprising zenith dry delay (ZDD) and zenith wet delay (ZWD), is achievable with sub-centimetre accuracy. However, if no representative near-site meteorological information is available, the quality of the ZDD derived from tropospheric models is degraded, leading to inaccurate estimation of the water vapour component ZWD as difference between ZTD and ZDD. On the basis of freely accessible regional surface meteorological data, this paper proposes a height-dependent linear correction model for a priori ZDD. By applying the ordinary least-squares estimation (OLSE), bootstrapping (BOOT), and leave-one-out cross-validation (CROS) methods, the model parameters are estimated and analysed with respect to outlier detection. The model validation is carried out using GNSS stations with near-site meteorological measurements. The results verify the efficiency of the proposed ZDD correction model, showing a significant reduction in the mean bias from several centimetres to about 5 mm. The OLSE method enables a fast computation, while the CROS procedure allows for outlier detection. All the three methods produce consistent results after outlier elimination, which improves the regression quality by about 20% and the model accuracy by up to 30%.     

Evaluating the predictability of eight Atmospheric-Oceanic signals affecting Iran’s Droughts,employing intelligence based and stochastic methods

The large-scale atmospheric-oceanic phenomena are among the main effective factors in the droughts in the Middle East, especially in Iran. Since these effects are usually delayed, their relevant signals can be useful for predicting droughts. As a result, the provision of a precise prediction of these signals can be efficient in increasing the drought prediction prospect. The current study predicts 8 cases of the most effective oceanic signals on the droughts which have been investigated in Iran. To do so, the problem-solving method with the time series prediction approach is based on the two model types intelligence-based (including multilayer perceptron [MLP] and support vector machine [SVM]) and stochastic (including Autoregressive Integrated Moving Average [ARIMA]) has been used. The model's input for each index included the time lags of the same index itself, which was determined by the autocorrelation function. Based on the evaluation criteria, the results were indicative of the weak predictability of the North Atlantic Oscillation (NAO) and Arctic Oscillation (AO), while the Extreme Eastern Tropical Pacific sea surface temperature (Niño [1 + 2]), East Central Tropical Pacific sea surface temperature (Niño [3 + 4]), and Oceanic Niño Index (ONI) were predicted with very good accuracy, and there is a high overlap between their predictions and observations (95.9 % < R² < 99.3 %). In the extreme events also, the rate of normalized forecasting error for Niño (1 + 2), Niño (3 + 4), and ONI were in the medium (20–30 %), good (10–20 %), and excellent (0–10 %) ranges, respectively. The comparison between the models also indicates a partial superiority of the ARIMA stochastic model over the SVM and MLP models. The overall results of the study are indicative of the applicability of the predictions of the three mentioned indices as the inputs to increase precipitation and drought forecasting prospects in Iran (as well as all regions affected by them); which have the research value for further studies in terms of drought forecasting.     

Impact of four dimensional assimilation of satellite data on long-range simulations over the Indian region during monsoon 2006

A number of experiments were conducted to study the impact of updating model basic fields by satellite data (Quick Scatterometer (QSCAT) surface winds and Atmospheric Infrared Sounder (AIRS) temperature and humidity profiles) on long-range simulation during the Indian summer monsoon 2006. The Pennsylvania State University/National Center for Atmospheric Research (PSU/NCAR) mesoscale model version5 (MM5) and its four dimensional data assimilation (FDDA) technique was used for the numerical simulations. The spatial distribution and temporal variation in model simulated basic meteorological parameters and rainfall were verified against the observed fields from National Center for Environmental Prediction (NCEP) analysis and Tropical Rainfall Measuring Mission (TRMM), respectively. The overall analysis of the results from QSCAT surface wind assimilation as compared to control simulation (CNT; without the satellite data assimilation) suggest that a better representation of a single level wind field during model integration fail to make significant improvement in the model simulation both in the basic meteorological parameters and rainfall. The assimilation of temperature and humidity profiles from the AIRS during model integration significantly improved the rainfall prediction during monsoon period. It is found that the improvement in rainfall prediction is attributed to improved thermodynamics structure due to AIRS profile assimilation and the degree of improvement is more in temperature prediction as compared to humidity prediction. It is also found that the prediction over the regions, such as south west part of India and foothills of Himalaya, where a complex orography exists, is not significantly benefited from satellite data assimilation which highlights the need of improvement in the model in addition to a better representation of atmospheric state.     

Comparing proton fluxes of central meridian SEP events with those predicted by SOLPENCO

We have developed an operational code, SOLPENCO, that can be used for space weather prediction schemes of solar energetic particle (SEP) events. SOLPENCO provides proton differential flux and cumulated fluence profiles from the onset of the event up to the arrival of the associated traveling interplanetary shock at the observer’s position (either 1.0 or 0.4 AU). SOLPENCO considers a variety of interplanetary scenarios where the SEP events develop. These scenarios include solar longitudes of the parent solar event ranging from E75 to W90, transit speeds of the associated shock ranging from 400 to 1700 km s⁻¹, proton energies ranging from 0.125 to 64 MeV, and interplanetary conditions for the energetic particle transport characterized by specific mean free paths. We compare the results of SOLPENCO with flux measurements of a set of SEP events observed at 1 AU that fulfill the following four conditions: (1) the association between the interplanetary shock observed at 1 AU and the parent solar event is well established; (2) the heliolongitude of the active region site is within 30° of the Sun–Earth line; (3) the event shows a significant proton flux increase at energies below 96 MeV; (4) the pre-event intensity background is low. The results are discussed in terms of the transit velocity of the shock and the proton energy. We draw conclusions about both the use of SOLPENCO as a prediction tool and the required improvements to make it useful for space weather purposes.     

Variability and climatology of precipitable water vapor from 12-year GPS observations in Taiwan

Because of global warming, global sea levels have risen, the frequency of drought in Taiwan is much more frequent in winter and spring, and rainfall tends to concentrate in summer. The probability of disaster-type weather has also increased significantly. Estimating precipitable water vapor (PWV) through GPS signals, related studies and analyses of weather conditions, and the effective use of meteorological forecasts have been valued by many meteorological research organizations and officials. In this study, PWV data from 2006 to 2017 and rainfall data were used for long-term harmonic analysis. PWV data calculated by ECMWF (ECMWF-PWV) and PWV data calculated by GPS (GPS-PWV) were subjected to regression analysis to verify the reliability of the GPS-PWV data. The research results show that GPS-PWV and ECMWF-PWV have extremely high correlations; however, the climatic characteristics of some regions and the high spatial resolution of GPS-PWV are able to accurately calculate the high topographic relief of small areas. It is judged that the GPS-PWV is more accurate than the ECMWF-PWV. It is worth noting that the PWV trend of the regions during the 6-year-before period has not changed very much, but the rainfall trend has changed obviously. Except for the eastern region, most of the regions show a decreasing trend year by year. More long-term observations are still needed to prove whether this phenomenon relates to global warming. Long-term rainfall analysis showed that the topography blocked water vapor to the western, southern, and mountainous regions, making them distinctly wet or dry. The harmonic curve showed great consistency with the peaks of PWV and rainfall. However, in the northern and eastern parts of the windward side, the time when maximum rainfall occurred each year may be one month later than the time when the maximum PWV value occurred each year. The reason for this difference is likely to be a decrease in the number of autumn typhoons, resulting in a nearly one-month difference in PWV peaks and rainfall peaks. Finally, we analyzed the linear trend of GPS-PWV and temperature for all regions in Taiwan, and found that annual increasing rate of GPS-PWV and temperature of all regions are within 0.4–0.5 mm/year and 0.04–0.11 C°/year, respectively.     

Investigation of ionospheric irregularities and scintillation using TEC at high latitude

The occurrence of ionospheric irregularities at high latitudes, with dimensions of several kms down to decameter scale size shows strong correlation with geomagnetic disturbance, season and solar activity. Transionospheric radio waves propagating through these irregularities experience rapid random fluctuations in phase and/or amplitude of the signal at the receiver, termed scintillation, which can degrade GNSS services. Thus, investigation and prediction of this scintillation effect is very important. To investigate such scintillation effects, a GISTM (GPS Ionospheric Scintillation and TEC Monitoring) NovAtel dual frequency (L1/L2) GPS receiver has been installed at Trondheim, Norway (63.41°$63.41°$ N, 10.4°$10.4°$ E), capable of collecting scintillation indices at a 1 min rate as well as the raw data (phase and intensity) of the satellite signals at a 50 Hz sampling rate and TEC (Total Electron Content) at a 1 Hz rate. Many researchers have reported that both phase and amplitude scintillation is closely associated with TEC fluctuations or associated with a significant developing enhancement or depletion in the TEC. In this study, a novel analogous phase index is developed which provides samples at a 1 min rate. Generally the scintillation indices can help in estimating the irregularity scintillation effect at a one minute rate, but such procedures are time consuming if DFTs of the phase and/or amplitude at a 50 Hz data are required. In this study, instead, this analogous phase index is estimated from 1 Hz rate TEC values obtained from the raw signals and is then compared for weak, moderate and strong scintillation at Trondheim for one year of data collected from the installed GPS receiver. The spectral index of the irregularities (that is the inverse power law of their spatial spectrum) is determined from the resultant phase scintillation psd. The correlations of the scintillation indices and spectral indices with the analogous phase index have been investigated under different geomagnetic conditions (represented by the Kp index) and an approximate linear correlation of phase scintillation with the analogous phase index was found. Then a principal advantage of this index is that it achieves this correlation without requiring a high sampling data rate and the need for DFTs. Thus, the index seems a good candidate for developing a simple means of ionospheric scintillation prediction which could also be utilized in the development of alerts using regional mappings.     

Performance evaluation of some rain rate conversion models for microwave propagation studies

An important characteristic of rainfall levels at a particular place is the statistical distribution of rainfall rate. In this paper, 5-min integration time rainfall data for the Northcentral region of Nigeria was obtained from the Tropospheric Data Acquisition Network (TRODAN), Anyigba, Nigeria. Also, 1-min integration time rainfall was measured at Minna, Nigeria. In order to obtain the optimal rain rate model suitable for this region, two globally recognised rain rate models were critically evaluated and compared with the 1-min measurements. These are the ITU-R P.837-7 and Lavergnat-Gole (L-G) models. The results obtained showed that the ITU-R P.837-7 and L-G models respectively underestimated the measured rain rate by 7.3 mm/h and 9 mm/h at time percentage exceedance of 0.1%, while they underestimated the measured rain rate by 23.4 mm/h and 13 mm/h respectively at 0.01%. At 0.001%, the measured rain rate was overestimated by the ITU-R P.837-7 and L-G models by 27.4 mm/h and 3 mm/h respectively. Further performance evaluation of the predefined models was carried out using different error metrics such as sum of absolute error (SAE), mean absolute error (MAE), root mean square error (RMSE), standard deviation (STDEV) and Spearman’s rank correlation. The results obtained adjudged the Lavergnat-Gole model as the best rain rate prediction model for this region.     

Comparative evaluation of five global gravity models over a part of the Bay of Bengal

Several global gravity models (GGMs) are freely available in the public domain, which can be utilised to study the earth's gravity field in almost every part of the globe. The present study compared the free-air gravity anomalies calculated from the five GGMs EGM2008, EIGEN6C4, GECO, XGM2019e_2159, and SGG-UGM-2 archived by the International Centre for Global Earth Models (ICGEM) with respect to shipborne gravity in the Bay of Bengal. The average correlation coefficient and covariance are ～ 96 % and ～ 450mGal². The mean difference between the shipborne and the modelled gravity is ? 5 mGal. Relatively higher amplitude gravity differences observed at the continental-oceanic transition, the 85°E and Ninetyeast ridges, and the western basin are possibly due to high gradient, dominant density contrasts, and rugged topography. The average standard deviation and root-mean-square-error (RMSE) of the differences are ～ 6.5 mGal and ～ 7.5 mGal. A significantly lower standard deviation and RMSE found for the models generated at higher degree/order compared to lower degree/order is due to diminishing omission error of the GGMs with increasing degrees of truncation. The spectral analysis and coherence estimation of the modelled gravity demonstrate excellent correspondence for anomalies wider than ～ 25 km. The agreement between anomaly amplitudes and shapes and calculated statistics indicates that the accuracy and resolution of the modelled gravity data are certainly good enough for regional-scale studies aiming to map Moho topography and mantle structures. However, the delineation of shorter wavelength features originating from the shallow-level basement/sedimentary might be uncertain and requires further validations. The present study confirms that all five models show promising results in terms of their accuracy and resolution. Moreover, the SGG-UGM-2 and XGM2019e_2159 models compare favourably with respect to the GECO, EIGEN6C4 and EGM2008 models in the Bay of Bengal.     

Solar activity prediction studies and services in NAOC

Solar activity prediction services started in 1960’s in National Astronomical Observatories, Chinese Academy of Sciences (NAOC). As one of the members of the International Space Environment Service (ISES), Regional Warning Center of China (RWC-China) was set up in 1990’s. Solar Activity Prediction Center (SAPC), as one of the four sub-centers of RWC-China, is located in NAOC. Solar activity prediction studies and services in NAOC cover short-term, medium-term, and long-term forecast of solar activities. Nowadays, certain prediction models, such as solar X-ray flare model, solar proton event model, solar 10 cm radio flux model, have been established for the practical prediction services. Recently, more and more physical analyses are introduced in the studies of solar activity prediction, such as the magnetic properties of solar active regions and magnetic structure of solar atmosphere. Besides traditional statistics algorithms, Machine Learning and Artificial Intelligence techniques, such as Support Vector Machine (SVM) method, are employed in the establishment of forecast models. A Web-based integrated platform for solar activity data sharing and forecast distribution is under construction.     

Prediction of geosynchronous electron fluxes using an artificial neural network driven by solar wind parameters

There are hundreds of satellites operating at the geosynchronous (GEO) orbit where relativistic electrons can cause severe damage. Thus, predicting relativistic electron fluxes is significant for spacecraft safety. In this study, using GOES satellite data during 2011–2020, we propose two neural network models with two hidden layers to predict geosynchronous relativistic electron fluxes at two energy channels (>0.8 MeV and > 2 MeV). The number of input neurons of the two channels (>0.8 MeV and > 2 MeV) are determined to be 36 and 44, respectively. The > 0.8 MeV model has 22 and 9 neurons in the hidden layers, while the > 2 MeV model has 25 and 15 neurons in the hidden layers. The input parameters include the north–south component of the interplanetary magnetic field, solar wind speed, solar wind dynamic pressure and solar wind proton density. Through the analysis of different time delays, we determine that the optimal time delays of two energy channels (>0.8 MeV and > 2 MeV) are 8 days and 10 days, respectively. The training set and validation set (Jan 2011-Dec 2018) are divided by the 10-fold cross-validation method, and the remaining data (Jan 2019-Feb 2020) is used to analyze the model performance as a test set. The prediction results of both energy channels show good agreement with satellite observations indicated by low RMSE (～0.3 cm^-2sr^-1s^?1), high PE (～0.8) and CC (～0.9). These results suggest that only using solar wind parameters is capable of obtaining reasonable predictions of geosynchronous relativistic electron fluxes.     

Characterization of GNSS scintillations over Lagos,Nigeria during the minimum and ascending phases (2009–2011) of solar cycle 24

This study characterizes equatorial scintillations at L-band frequency over Lagos, Nigeria during the minimum and ascending phases of solar cycle 24. Three years (2009–2011) of amplitude scintillation data were used for the investigation. The data were grouped on daily, monthly, seasonal, and yearly scales at three levels of scintillation (weak (0.3 ? S₄ < 0.4), moderate (0.4 ? S₄ < 0.7), and intense (S₄ ? 0.7)). To ensure reliable statistical inferences, three data cut-off criteria were adopted. Scintillations were observed to have a daily trend of occurrence during the hours of 1900–0200 LT, and higher levels of scintillations were localized within the hours of 2000–2300 LT. On monthly basis, September and October recorded the highest occurrences of scintillation, while January recorded the least. Scintillations were recorded during all the months of 2011, except January. Surprisingly, pockets of scintillation events (weak levels) were also observed during the summer months (May, June, and July). Seasonally, equinoxes recorded the highest occurrences of scintillation, while June solstice recorded the least occurrences. Scintillation activity also increases with solar and geomagnetic activity. On a scintillation active day, the number of satellites available to the receiver’s view reduces as the duration of observation reduces. These results may support the development of future models that could provide real-time predictability of African equatorial scintillations, with a view to supporting the implementation of GNSS-based navigation for aviation applications in Africa.     

Development of New Capabilities Using Machine Learning for Space Weather Prediction

With the development of space exploration and space environment measurements, the numerous observations of solar, solar wind, and near Earth space environment have been obtained in last 20 years. The accumulation of multiple data makes it possible to better use machine learning technique, which has achieved unforeseen results in industrial applications in last decades, for developing new approaches and models in space weather investigation and prediction. In this paper, the efforts on the forecasting methods for space weather indices, events, and parameters using machine learning are briefly introduced based on the study works in recent years. These investigations indicate that machine learning, especially deep learning technique can be used in automatic characteristic identification, solar eruption prediction, space weather forecasting for solar and geomagnetic indices, and modeling of space environment parameters.      

A summary of the applications of a weighted average method determining times of solar cycle extrema

This paper is a summary of our recent researches on the applications of a weighted average method determining times of solar cycle extrema in the prediction of solar activity. Some correlation coefficients among the parameters in solar cycle according to this definition are higher than those according to the conventional definition. The descending time is found to be correlated (r = −0.77) with the ascending time 3 cycles earlier. The amplitude of solar cycle is found to be correlated (r = −0.77) with the max–max solar cycle length 2 cycles earlier. The ascending time is found to be correlated (r = −0.72) with the amplitude. A newly defined parameter called effective duration is found to be well correlated (r = 0.86) with the amplitude 5 cycles later. These correlations suggest that earlier cycles should influence later ones. The next (24th) solar cycle is estimated to start in March 2007 ± 7 months, reach its maximum in January 2011 ± 14 months, with a size of 150 ± 22, larger than those from some correlations according to the conventional definition.     

Low latitude ionospheric scintillation and zonal irregularity drifts observed with GPS-SCINDA system and closely spaced VHF receivers in Kenya

In this study we have used VHF and GPS-SCINDA receivers located at Nairobi (36.8°E, 1.3°S, dip −24.1°) in Kenya, to investigate the ionospheric scintillation and zonal drift irregularities of a few hundred meter-scale irregularities associated with equatorial plasma density bubbles for the period 2011. From simultaneous observations of amplitude scintillation at VHF and L-band frequencies, it is evident that the scintillation activity is higher during the post sunset hours of the equinoctial months than at the solstice. While it is noted that there is practically no signatures of the L-band scintillation in solstice months (June, July, December, January) and after midnight, VHF scintillation does occur in the solstice months and show post midnight activity through all the seasons. VHF scintillation is characterized by long duration of activity and slow fading that lasts till early morning hours (05:00 LT). Equinoctial asymmetry in scintillation occurs with higher occurrence in March–April than in September–October. The occurrence of post midnight VHF scintillation in this region is unusual and suggests some mechanisms for the formation of scintillation structure that might not be clearly understood. Zonal drift velocities of irregularities were measured using cross-correlation analysis with time series of the VHF scintillation structure from two closely spaced antennas. Statistical analyses of the distribution of zonal drift velocities after sunset hours indicate that the range of the velocities is 30–160 m/s. This is the first analysis of the zonal plasma drift velocity over this region. Based on these results we suggest that the east–west component of the plasma drift velocity may be related to the evolution of plasma bubble irregularities caused by the prereversal enhancement of the eastward electric fields. The equinoctial asymmetry of the drift velocities and scintillation could be attributed to the asymmetry of neutral winds in the thermosphere that drives the eastward electric fields.     

Exploration and analysis of the factors influencing GNSS PWV for nowcasting applications

Precipitable water vapor (PWV) can be assimilated into a numerical weather model (NWM) to improve the prediction accuracy of numerical weather prediction. In this study, taking GNSS data for the Beijing Fangshan station (BJFS) as an example, based on the method of Pearson correlation coefficient combined with quantitative analysis, GNSS datasets are used to study the relationships between GNSS-derived PWV (GNSS PWV__Met) and its influencing factors, including the internal influencing factors zenith troposphere delay (ZTD), zenith hydrostatic delay (ZHD), zenith wet delay (ZWD), and surface temperature (T_s), and the external influencing factor haze (mainly PM_2.5). Firstly, based on the strong correlation between PWV__Met and ZTD hourly sequences from the International GNSS Service Network’s BJFS station for DOYS 182–212, 2015, the results of experiment prove that the reliability of GNSS ZTD is used to forecast PWV_Met in short-term forecasting. Secondly, based on hourly data of BJFS in 2016, the correlation between PWV__Met and ZTD, ZWD, ZHD, pressure (P) and T_s is analyzed, and then, with the rate of ZTD variation as the main factor, ZTD variation as auxiliary factor, the prediction success rate is 88.24% from hourly data of precipitation event for DOYs 183–213 in Beijing. The experiment indicates that ZTD can help forecast short-term precipitation. Thirdly, based on data from three hazy periods with relatively stable weather conditions, no heavy rainfall, and relatively continuous data in the past three years, the correlation between GNSS PWV_Met/ZTD and PM_2.5 hourly series is analyzed. The results of the experiments suggests that GNSS ZTD should be considered to assist in haze monitoring. So in the absence of radiosonde stations and meteorological elements, ZTDs on retrieval of GNSS stations have more application value in short-term forecast.     

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.