Field Time Series Research Articles

HUST-Grace2024: a new GRACE-only gravity field time series based on more than 20 years of satellite geodesy data and a hybrid processing chain

Abstract. To improve the accuracy of monthly temporal gravity field models for the Gravity Recovery and Climate Experiment (GRACE) and the GRACE Follow-On (GRACE-FO) missions, a new series named HUST-Grace2024 is determined based on the updated L1B datasets (GRACE L1B RL03 and GRACE-FO L1B RL04) and the newest atmosphere and ocean de-aliasing product (AOD1B RL07). Compared to the previous HUST temporal gravity field model releases, we have made the following improvements related to updating the background models and the processing chain: (1) during the satellite onboard events, the inter-satellite pointing angles are calculated to pinpoint outliers in the K-band ranging (KBR) range-rate and accelerometer observations. To exclude outliers, the advisable threshold is 50 mrad for KBR range rates and 20 mrad for accelerations. (2) To relieve the impacts of KBR range-rate noise at different frequencies, a hybrid data-weighting method is proposed. Kinematic empirical parameters are used to reduce the low-frequency noise, while a stochastic model is designed to relieve the impacts of random noise above 10 mHz. (3) A fully populated scale factor matrix is used to improve the quality of accelerometer calibration. Analyses in the spectral and spatial domains are then implemented, which demonstrate that HUST-Grace2024 yields a noticeable reduction of 10 % to 30 % in noise level and retains consistent amplitudes of signal content over 48 river basins compared with the official GRACE and GRACE-FO solutions. These evaluations confirm that our aforementioned efforts lead to a better temporal gravity field series. This data set is identified with the following DOI: https://doi.org/10.5880/ICGEM.2024.001 (Zhou et al., 2024).

NDVI estimation using Sentinel-1 data over wheat fields in a semiarid Mediterranean region

ABSTRACT Annual crop monitoring is a key parameter for managing agricultural strategies. Several studies have relied on remote sensing products such as the normalized difference vegetation index (NDVI) as a vegetation dynamic metric. However, the dependence of optical data on weather conditions limits its availability. In this study, we reconstruct the NDVI time series of wheat fields using the moving averages of the Sentinel-1 normalized VH/VV cross-polarization ratio (IN) and the interferometric coherence in VV polarization over wheat selected fields in a semiarid site in Tunisia during two seasons, from 2018 to 2020. The crop cycle is divided into two periods: before and after the heading phase, which occurs in approximately the middle of March. Due to the volume-scattering impact, the second phase is divided into the ripening and maturation phase (NDVI ≥0.4) and senescence phase (NDVI <0.4). To estimate the NDVI values, different methods are used: curve-fitting equations and machine learning regressors such as the random forest (RF) and the support vector regressor (SVR). Low root mean square error (RMSE) values characterize NDVI estimation during the first period. In the second period, the RMSE values reach 0.06 when the NDVI is lower than 0.4. When the NDVI values exceed 0.4 in the second period, lower accuracy marks the NDVI estimation using the curve-fitting equations as a function of IN or coherence. Relative low accuracy characterizes the regression algorithms’ estimations when NDVI ≥ 0.4 compared to their performance during the aforementioned periods. The proposed approach was tested on different wheat fields. The NDVI estimations are characterized by RMSE values varying between 0.12 and 0.19. The use of RF and SVR outperformed the curve-fitting methods with an RMSE equal to 0.12. The present findings revealed the high accuracy of the proposed approach to estimate the missing values of wheat fields NDVI values during the vegetation development period until heading and the senescence phase. The presence of the mutual effect of the vegetation water content and its volume complicated the NDVI estimation using the C-band data.

Machine learning techniques for estimation of Pc5 geomagnetic pulsations observed at geostationary orbits during solar cycle 23

Pc5 geomagnetic pulsations can accelerate electrons in the radiation belts, which can pose adverse threats to both astronauts and satellites in space. The estimation of Pc5 waves in space is crucial to radiation belt dynamics studies and will help mitigate these challenges. Here, we explore the advantages of the Feed-forward Neural Network (FFNN) and Random Forest (RF) algorithm for effective estimation of Pc5 geomagnetic pulsations observed in space at geostationary orbit during solar cycle 23. The dataset used in this study is the vector magnetic field measurements retrieved from the Geostationary Operational Environmental Satellite-10 (GOES-10) and the solar wind parameters: Bz and Vx component of the solar wind in the Geocentric Solar Ecliptic (GSE) coordinate system, proton density, flow pressure, and plasma beta obtained from the OMNI Web database during part of solar cycle 23. Pc5 geomagnetic pulsations were extracted from the toroidal component of the magnetic field time series using a bandpass Butterworth filter. The continuous wavelet transform (CWT) was utilized to study the characteristics of the extracted wave in the time-frequency domain for its validation. The validated Pc5 events were used as the target in the model's development, with the solar wind parameters as the inputs. In addition to the solar wind parameters, we included an attribute of the magnetic field time series as an input variable in the model. The dataset is carefully divided to ensure effective training and testing of the models. Finally, we trained both models using the same inputs and targets and explored their estimation abilities. The model was tested during the maximum, descending, and minimum phases of solar cycle 23. Both the FFNN and RF models have a similar estimation, with average cross-correlation score (R) values of 0.74 and 0.73 and corresponding average root mean squared error (RMSEs) of 0.16 nT and 0.67 nT, respectively. The model was deployed to investigate the response of Pc5 waves during three storm days in each testing year. The machine learning (ML) model outputs showed good coherence with the observed Pc5 waves. To validate the models, we studied the correlation between the estimated Pc5 events with the Kp index, and a good correlation was seen to exist between both events. This validates the good performance of the developed models. This work will aid in the study of radiation belt dynamics and the construction of electron depletion regions in the radiation belt.

Mapping Geoelectric Field Hazards in Ireland

AbstractGeoelectric fields are generated at the Earth's surface and can lead to the induction of hazardous geomagnetically induced currents (GIC) in infrastructure like power grids, railways and pipelines during geomagnetic storms. Magnitude and orientation of the geoelectric fields, in relation to the infrastructure, are key features needed to determine the intensity of GIC. Here, we developed the first geoelectric hazard map for the island of Ireland, with the aim of providing detailed information that can help stakeholders mitigate the impact of GICs. The hazard map was developed by modeling and mapping the geoelectric field across Ireland for 28 years (1991–2018) using magnetic field data with magnetotelluric transfer functions. The approach for developing the hazard map calculates the probability of exceeding a hazardous geoelectric field threshold (500 mV/km) during large geomagnetic storms, taking directionality and amplitude into account. We found hazardous geoelectric fields to be mostly localized in areas in the west, south‐west and northern coast. We observed that the geoelectric field have a stronger dominant orientation than the orientation of the geomagnetic field, often constraining the hazardous geoelectric field in particular directions only. We demonstrate a seasonal/diurnal effect is present in the geoelectric field time series. The impact of galvanic distortion was also assessed, and we demonstrate that there is a significant difference in terms of amplitude and direction between both models.

Earth's core variability from magnetic and gravity field observations

Abstract. The motions of the liquid within the Earth's outer core lead to magnetic field variations together with mass distribution changes. As the core is not accessible for direct observation, our knowledge of the Earth’s liquid core dynamics only relies on indirect information sources. Mainly generated by the core dynamics, the surface geomagnetic field provides information about the variations of the fluid motion at the top of the core. The dynamic of the fluid core is also associated with mass distribution changes inside the core and produces gravitational field time fluctuations. By applying several statistical blind source separation methods to both the gravity and magnetic field time series, we investigate the common space–time variabilities. We report several robust interannual oscillations shared by the two observation sets. Among those, a common mode of around 7 years looks very significant. Whereas the nature of the driving mechanism of the coupled variability remains unclear, the spatial and temporal properties of the common signal are compatible with a core origin.

A machine learning approach combined with wavelet analysis for automatic detection of Pc5 geomagnetic pulsations observed at geostationary orbits

Pc5 geomagnetic pulsations are ultra-low frequency (ULF) waves whose period lies within 150–600 s. Their detection is crucial for accurate space weather modelling, monitoring, and analysis. Identification of these pulsations using manual approaches is difficult and time-consuming because of the smaller magnitudes and the limited durations within which they occur. To overcome this challenge, we propose a robust Cascade Forward Neural Network (CFNN) model combined with the wavelet technique for automatically detecting Pc5 events from satellite datasets observed at geostationary orbits. The dataset used in this study is the magnetic field vector measurements retrieved from the Geostationary Operational Environmental Satellite-10 (GOES-10) from 2000 to 2009. Pc5 geomagnetic pulsations were extracted from the toroidal component of the field perturbation using a bandpass Butterworth filter. Continuous wavelet transform (CWT) analysis using the mother Morlet wavelet was utilized to validate the integrity of the extracted signal in the time–frequency domain. The extracted Pc5 signal was decomposed into details and approximations using Daubechies wavelet transform to separate the intelligible signal from the incoherent noise that left their trace on the magnetic field time series. The detail signal was subjected to denoising using the heuristic Stein Unbiased Risk Estimate (SURE) approach with soft thresholding to obtain the denoised Pc5 events. The denoised Pc5 events were utilized as the target in the machine learning whereas the inputs were obtained from 5-dimensional space transformation of the toroidal component of the time series. The developed algorithm performed well and demonstrated a detection accuracy of 80 % and a Root Mean Square Error (RMSE) of 0.13 nT indicating a high performance in detecting Pc5 events. For effective validation of the model, Pc5 events detected by our model were correlated with the Kp index and the amplitude of the Pc5 events observed at geostationary orbit. A good correlation was obtained in both cases, making our model a practical and preferred choice for Pc5 pulsation detection in contrast to conventional frequency analysis tools which take much time for signal processing on large data sets.

Adjoint-based data assimilation for reconstruction of thermal convection in a highly viscous fluid from surface velocity and temperature snapshots

SUMMARY It is a general problem in geoscience to estimate the time-series of velocity and temperature fields for a fluid based on limited observations, such as the flow velocity at the fluid surface and/or a temperature snapshot after flow. In this study, an adjoint-based data assimilation method (also known as 4-D variational data assimilation) was used to reconstruct the thermal convection in a highly viscous fluid (e.g. Earth’s mantle) to investigate which observations constrain the thermal convection and how accurately the convection can be reconstructed for different wavelengths. The data assimilated to the adjoint-based model were generated synthetically from forward models with convecting cells of different length-scales. Based on the surface velocity and temperature snapshot, our simulations successfully reconstructed thermal convection over 50 Myr in the case that the wavelength of the convective cells is sufficiently large. We obtained two main results from this parametric study. (1) When we only considered instantaneous thermal structure fitting in the cost function, the convection reconstruction tended to fail. However, there are some cases where the laminar thermal convection can be reconstructed by assimilating only the velocity along the fluid surface. (2) There is a limit to the reconstruction of thermal convection in the case that the convecting cells are small (∼1000 km for a 50 Myr reconstruction). We propose that (1) is related to the balance of forces due to the thermal buoyancy and viscous stress around the thermal anomalies and (2) is related to how information is preserved (i.e. how the previous thermal structure is maintained in the observable state throughout the convection process). The results enable the use of geological records to estimate time-series of velocity and temperature in Earth’s deep interior, even though the records may only contain information from shallow parts of Earth.

Deep learning framework for neurological diseases diagnosis through near-infrared eye video and time series imaging algorithms

Internet of Things (IoT) solutions have greatly evolved recently with the application of Artificial Intelligence (AI). Indeed, AI enriches the IoT with intelligence capabilities. In particular, AI methods are highly effective in the scope of the Internet of Medical Things (IoMT) for the applications requiring decision support for doctors. In this work, we propose a Deep Learning (DL) framework for the classification of Parkinson’s disease (PD) and Progressive Supranuclear Palsy (PSP). In contrast to the state-of-the-art solutions relying on only the saccade test for the classification of these neurodegenerative diseases, we collect a dataset while the subjects perform five exercises including saccade, spontaneous nystagmus, optokinetic nystagmus, pursuit, and gaze test. We then extract the pupil features (coordinates, area, minor axis, and major axis) using the image segmentation DL model and represent them as images using Gramian Angular Difference Field (GADF) time series imagining algorithm. The resultant images are supplied to the proposed disease-detection model for running the classification procedure. The best classification results were obtained for the optokinetic exercise with the accuracy of 96.9%, 90.8%, and 96.9% for the left, right, and both eyes, respectively.

Small satellite formations and constellations for observing sub-daily mass changes in the Earth system

SUMMARY Satellite gravity missions so far are medium size satellites consisting of one or a pair of satellites flying in near-polar or sun-synchronous orbital planes. Due to the limited observation geometry and the related space–time sampling, high-frequency non-tidal mass variation signals from atmosphere and ocean cannot be observed and cause temporal aliasing. For current single-pair satellite gravimetry missions as GRACE and GRACE Follow-On (GRACE-FO) temporal aliasing is the limiting factor and represents the major error source in the gravity field time-series. Adding a second inclined satellite pair to a GRACE-like polar pair (Bender constellation) currently is the most promising solution to increase the spatio-temporal resolution and to significantly reduce the temporal aliasing error. This shall be implemented with the MAGIC mission in future. With the ongoing developments in miniaturization of satellites and gravity-relevant instruments (accelerometers and intersatellite ranging), in future constellations of multiple small satellite pairs may solve this problem even beyond the capabilities of a Bender constellation. Therefore, in this study the capabilities of such constellations flying in specific formations are investigated in order to enable a retrieval of the temporal gravity field on short time scales. We assess the performance of up to 18 satellite pairs. The satellite configurations cover satellite pairs in polar and inclined orbits flying in pair-wise or pearl-string formation with varying mean anomalies and right ascensions of the ascending node (RAAN). As future potential miniaturized instruments optomechanical accelerometers with similar performance as those flying on GRACE-FO are a candidate, while for the intersatellite ranging instrument still some technological development is required. Therefore, in this study a microwave ranging system equivalent to the GRACE and GRACE-FO instruments performance is taken as baseline assuming that such instruments can be miniaturized in future as well. In numerical closed-loop simulations, up to nine different satellite configurations with up to 18 satellite pairs are evaluated based on the retrieval of the non-tidal temporal gravity field on a monthly basis. From the simulation results it is concluded that the best-performing satellite constellation of 18 polar satellite pairs already is outperformed by a typical Bender-like constellation of 1 polar and 1 inclined pair. In general, we identify that increasing the number of satellite pairs leads to an improved gravity field retrieval, either at low spherical harmonic degree and order (d/o) by the shift in RAAN or at high d/o by the shift in mean anomaly. By a two-step simulation approach, co-estimating also (sub-)daily gravity fields for selected configurations with a large number of satellite pairs distributed equally over the globe, it is possible to retrieve stand-alone gravity fields at 24, 12 and 6 hr temporal resolution. Ultimately it is concluded that a network of miniaturized satellites with instrument performances similar to GRACE-FO and flying in a well-defined constellation has the potential to observe (sub-)daily mass variations and therefore could drastically reduce the problem of temporal aliasing due to high frequency mass variations in the Earth system.

Characterizing the Solar Activity Using the Visibility Graph Method

In this paper, the Sun and its behavior are studied by means of complex networks. The complex network was built using the Visibility Graph algorithm. This method maps time series into graphs in which every element of the time series is considered as a node and a visibility criterion is defined in order to connect them. Using this method, we construct complex networks for magnetic field and sunspots time series encompassing four solar cycles, and various measures such as degree, clustering coefficient, mean path length, betweenness centrality, eigenvector centrality and decay exponents were calculated. In order to study the system in several time scales, we perform both a global, where the network contains information on the four solar cycles, and a local analysis, involving moving windows. Some metrics correlate with solar activity, while others do not. Interestingly, those metric which seem to respond to varying levels of solar activity in the global analysis, also do in the moving windows analysis. Our results suggest that complex networks can provide a useful way to follow solar activity, and reveal new features on solar cycles.

Nowcasting geoelectric fields in Ireland using magnetotelluric transfer functions

Geomagnetically induced currents (GIC) driven by geoelectric fields pose a hazard to ground-based infrastructure, such as power grids and pipelines. Here, a new method is presented for modelling geoelectric fields in near real time, to provide valuable information to help mitigate the impact of GIC. The method uses magnetic field measurements from the Magnetometer Network of Ireland (MagIE; https://www.magie.ie), interpolates the geomagnetic field variations between magnetometers using spherical elementary current systems (SECS), and estimates the local electric field using a high-density (&lt; 40 km) network of magnetotelluric transfer functions (MT-TF) encompassing the island. The model was optimised to work in near real time, with a correction curve applied to the geoelectric field time series. This approach was successfully validated with measured electric fields at four sites for a number of geomagnetic storms, providing accurate electric fields up to a 1-minute delay from real time, with high coherence (0.70 – 0.85) and signal-to-noise ratio (SNR; 3.2 – 6.5) relative to measured electric field validation time series. This was comparable to a standard non-real-time geoelectric field model (coherence = 0.80 − 0.89 and SNR = 4.0 − 7.0). The impact of galvanic distortion on the model was also briefly evaluated, with a galvanic distortion correction leading to a more homogeneous representation of the direction of the electric field, at a regional scale.

Origin of multifractality in solar wind turbulence: the role of current sheets

ABSTRACT In this work, a multifractal framework is proposed to investigate the effects of current sheets in solar wind turbulence. By using multifractal detrended fluctuation analysis coupled with surrogate methods and volatility, two solar wind magnetic field time series are investigated, one with current sheets and one without current sheets. Despite the lack of extreme-events intermittent bursts in the current sheet-free series, both series are shown to be strongly multifractal, although the current sheet-free series displays an almost linear behaviour for the scaling exponent of structure functions. Long-range correlations are shown to be the main source of multifractality for the series without current sheets, while a combination of heavy-tail distribution and non-linear correlations is responsible for multifractality in the series with current sheets. The multifractality in both time series is formally shown to be associated with an energy-cascade process using the p-model.

Automated bow shock and magnetopause boundary detection with Cassini using threshold and deep learning methods

Two algorithms set for automatic detection of bow shock (BS) and magnetopause (MP) boundaries at Saturn using in situ magnetic field and plasma data acquired by the Cassini spacecraft are presented. Traditional threshold-based and modern deep learning algorithms were investigated for the task of boundary detection. Sections of Cassini’s orbits were pre-selected based on empirical BS and MP boundary models, and from outlier detection in magnetic field data using an autoencoder neural network. The threshold method was applied to pre-selected magnetic field and plasma data independently to compute parameters to which a threshold was applied to determine the presence of a boundary. The deep learning method used a type of convolutional neural network (CNN) called ResNet on images of magnetic field time series data and electron energy-time spectrograms to classify the presence of boundaries. 2012 data were held out of the training data to test and compare the algorithms on unseen data. The comparison showed that the CNN method applied to plasma data outperformed the threshold method. A final multiclass CNN classifier trained on plasma data obtained F1 scores of 92.1% ± 1.4% for BS crossings and 84.7% ± 1.9% for MP crossings on a corrected test dataset (from use of a bootstrap method). Reliable automated detection of boundary crossings could enable future spacecraft experiments like the PEP instrument on the upcoming JUICE spacecraft mission to dynamically adapt the best observing mode based on rapid classification of the boundary crossings as soon as it appears, thus yielding higher quality data and improved potential for scientific discovery.

Volume scattering of sound signals in gassy bottom and estimation of bubbles concentration in Lake Kinneret

In the paper, results of experiments in shallow Lake Kinneret (Israel) and the corresponding data processing are presented. Wideband LFM sound signals (300 Hz–3.50 kHz and 300 Hz–15 kHz) were radiated by the source mounted directly on vertical line array (VLA) of the length 20 m placed at several locations with different water depths (from 20 to 35 m). Received sound field timeseries constitutes a sequence of pulse arrivals comprised of specular reflections from interfaces followed by reverberation codas caused by non-specular scattering from the interface roughness and volume inhomogeneity. Properties of received signals are analyzed assuming that the sediment can be modeled as a thin layer containing bubbles over a homogeneous fluid half-space. Using a set of hydrophones of VLA allowed isolation of the volume scattering component, estimating frequency-angle dependences of scattered field, and hypothesizing some properties of the gassy sediment, such as concentration of bubbles and their effective size. [Work was supported by BSF grant 2018150.].

Automatic detection of auroral Pc5 geomagnetic pulsation using machine learning approach guided with discrete wavelet transform

Geomagnetic micropulsations are ultra-low frequency (ULF) signals observed in the magnetosphere as well as on the ground. These signals offer us an effective means to study the coupling of the magnetosphere-ionosphere processes in the space field. The most prominent observed type of such ULF waves is the Pc5 pulsations (with a frequency range of 1–7 mHz), known to have their maximum amplitude in the auroral oval. The low magnitude of Pc5 signals withstands against distinguishing them from the background noise. This study presents a machine learning approach for the automatic detection of geomagnetic Pc5 pulsations in the auroral zone using artificial neural networks (ANN) guided by discrete wavelet transform. Our ANN algorithm is validated and tested using a huge amount of datasets of auroral ground-based geomagnetic records from the Svalbard network during the two solar cycles 23 and 24. The wavelet-based coherence was used to determine the signal’s consistency detected by the magnetometer stations of the Svalbard network; since they are sensitive to all sorts of space wave-related phenomena that left their footprint on the magnetic field time series. The Daubechies wavelet transform was utilized to classify and extract Pc5 signals from the artificial noise and the results are correlated with the geomagnetic pulsation records as detected by our ANN-based model. The ANN-based model showed a good correlation of an average of 98% for the different phases of the two studied solar cycles. The statistical regression analysis of the post-processed results yielded a high coefficient of determination of R2 = 0.9 and norms of residuals of 8–21 nT. The Pc5 events detected by the ANN-based algorithm during the two solar cycles showed a good correlation with the Kp index, which enables our model for space weather forecasting.

Transport in turbulent, recurrent flows: Time-extrapolation and statistical symmetrization

We investigate bubble dynamics in a submerged double-jet as an example of weakly coupled, Lagrangian transport. The setup resembles continuous casting of steel at Reynolds numbers Re=136000 to Re=272000. Using short time series of flow fields obtained from large-eddy simulations (LES) with a discrete bubble model, we calculate a database of transport patterns and time-extrapolate them to long durations in the framework of recurrence CFD (rCFD). A dedicated averaging procedure along bubble trajectories allows us to study their transport with large steps at little numerical costs and monitor their spatial distribution. Besides time-extrapolation for fixed Re with a single database, we demonstrate how several time series can be combined to (i) enforce symmetries and (ii) approximately model conditions in between. We compare bubble volume fraction fields and total hold-up obtained from LES and from rCFD and find very good to satisfying agreement with speed up factors of more than 500.

From emission scenarios to spatially resolved projections with a chain of computationally efficient emulators: coupling of MAGICC (v7.5.1) and MESMER (v0.8.3)

Abstract. Producing targeted climate information at the local scale, including major sources of climate change projection uncertainty for diverse emissions scenarios, is essential to support climate change mitigation and adaptation efforts. Here, we present the first chain of computationally efficient Earth system model (ESM) emulators that allow for the translation of any greenhouse gas emission pathway into spatially resolved annual mean temperature anomaly field time series, accounting for both forced climate response and natural variability uncertainty at the local scale. By combining the global mean, emissions-driven emulator MAGICC with the spatially resolved emulator MESMER, ESM-specific and constrained probabilistic emulated ensembles can be derived. This emulator chain can hence build on and extend large multi-ESM ensembles such as the ones produced within the sixth phase of the Coupled Model Intercomparison Project (CMIP6). The main extensions are threefold. (i) A more thorough sampling of the forced climate response and the natural variability uncertainty is possible, with millions of emulated realizations being readily created. (ii) The same uncertainty space can be sampled for any emission pathway, which is not the case in CMIP6, where only a limited number of scenarios have been explored and some of the most societally relevant strong mitigation scenarios have been run by only a small number of ESMs. (iii) Other lines of evidence to constrain future projections, including observational constraints, can be introduced, which helps to refine projected ranges beyond the multi-ESM ensembles' estimates. In addition to presenting results from the coupled MAGICC–MESMER emulator chain, we carry out an extensive validation of MESMER, which is trained on and applied to multiple emission pathways for the first time in this study. By coupling MAGICC and MESMER, we pave the way for rapid assessments of any emission pathway's regional climate change consequences and the associated uncertainties.

Continuous monitoring of ice dynamics in Iceland with Sentinel-1 satellite radar images

In recent years, satellite remote sensing has revolutionized observations of glacier dynamics enabling for the first time the generation of detailed ice-velocity fields at regular intervals for Icelandic glaciers. We generated dense time series of ice-velocity fields from 2014 to 2020 exploiting the continuous acquisition of Sentinel-1 SAR using the offset-tracking technique. The fastest ice flow, with velocities up to 400–800 metres per year, is observed in the middle and lower part of the main outlet glaciers of the ice caps that span a large elevation range in the areas of high precipitation in the South and Southeast of Iceland. Several outlet glaciers of Vatnajökull, such as Skeiðarárjökull and Breiðamerkurjökull, draining towards the South and Southeast, show high-ice-speed channels with pronounced shearing zones where the ice speed increases by an order of magnitude within a distance of only a few ice thicknesses. Velocities on the order of a few tens of metres per year, and up to 50–100 metres per year, are observed on the large surge-type outlet glaciers of N- and W-Vatnajökull and generally on glaciers in the central Icelandic highland and in the northern and western part of the country. Slow-moving ice is observed along the main ice divides and near the glacier margins. The velocity data set is affected by gaps due to decorrelation, particularly during summer, because of temporal variations in the radar-image texture. The ice-velocity fields derived in this study from Sentinel-1 data agree well with other data sets, although these are affected by a larger number of outliers and data gaps, particularly in the accumulation areas. The generated velocity time series can be used for monitoring long-term dynamic trends, seasonal variations and for studying glaciological events such as surges or jökulhlaups.

OBSERVATION OF ULF ELECTROMAGNETIC EMISSIONS BEFORE THE M 7.8 NEW ZEALAND EARTHQUAKE OF NOVEMBER 13, 2016

We analyzed the ground geomagnetic data obtained from a 3-component fluxgate magnetometer at the Eyrewell Geomagnetic Observatory (New Zealand) (43.474 °S, 172.393 °E) from October 1 to December 31, 2016. The study aimed to investigate electromagnetic precursors associated with the M 7.8 New Zealand earthquake of November 13, 2016. This earthquake occurred 54 km northeast of Amberley (New Zealand). Its epicenter was located 158 km from the Eyrewell Observatory. We used three methods focused on the polarization ratio, fractal dimension and principal component analysis to identify anomalies in the geomagnetic data. The time series showed an enhanced polarization ratio at two times, October 20 and October 30, 2016, i.e. before the occurrence of the New Zealand earthquake, and a value ~1 or more during these instances. Since the global geomagnetic indices Kp and Dst were normal in these cases, the enhanced polarization ratio may be related to the preparation phase of the New Zealand earthquake. To further classify them, we applied the principal component analysis to the magnetic data on component H. The first three principal components showed more than 90 % of the variance of the original ultra-low frequency (ULF) magnetic field time series. The first principal component was found to be well correlated with the storm index (Dst) recorded during this period. Again, the second principal component was dominated by daily variations, which were the periodic component of the recorded ULF magnetic field. The temporal variation of the third principal component was analyzed to verify a possible correlation between the ULF emissions and the occurrence of the earthquake. The fractal dimension of components D and Z of the magnetic data decreased initially and sharply increased three days before the New Zealand earthquake.

Spatiotemporal patterns of vegetation conversion under the Grain for Green Program in southwest China

AbstractChina's Grain for Green Program (GGP) is the largest reforestation program in the world. Previous studies lacked targeted assessments regarding its effectiveness in vegetation conversion. Using the time series of Vegetation Continuous Fields in southwest China, we derived the spatiotemporal variations in total vegetation cover, short vegetation (SV) cover and tree canopy (TC) cover during the period from 2000 to 2016. By conducting residual trend analyses independently on the SV and TC cover time series, the trends in human‐induced SV cover (SVH) and TC cover (TCH) were detected. Then, we performed overlay analyses to derive the human‐induced SV–TC conversion in cropland. The study found that the SV in southwest China showed a net browning trend whereas the TC presented a net greening trend. Approximately 69.5% of the pixels with a significant browning trend in SVH showed a significant greening trend in TCH. Furthermore, 50.6% of the human‐induced SV–TC conversion in southwest China occurred in cropland, and the proportion was even larger for the eastern provinces. Our study provides a targeted evaluation of the performance of the GGP and highlights that the implementation of the GGP has caused widespread SV–TC conversion that potentially mitigates global climate change.