Description: We describe the ocean general circulation model Icosahedral Nonhydrostatic Weather and Climate Model (ICON‐O) of the Max Planck Institute for Meteorology, which forms the ocean‐sea ice component of the Earth system model ICON‐ESM. ICON‐O relies on innovative structure‐preserving finite volume numerics. We demonstrate the fundamental ability of ICON‐O to simulate key features of global ocean dynamics at both uniform and non‐uniform resolution. Two experiments are analyzed and compared with observations, one with a nearly uniform and eddy‐rich resolution of ∼10 km and another with a telescoping configuration whose resolution varies smoothly from globally ∼80 to ∼10 km in a focal region in the North Atlantic. Our results show first, that ICON‐O on the nearly uniform grid simulates an ocean circulation that compares well with observations and second, that ICON‐O in its telescope configuration is capable of reproducing the dynamics in the focal region over decadal time scales at a fraction of the computational cost of the uniform‐grid simulation. The telescopic technique offers an alternative to the established regionalization approaches. It can be used either to resolve local circulation more accurately or to represent local scales that cannot be simulated globally while remaining within a global modeling framework.

Description: Plain Language Summary: Icosahedral Nonhydrostatic Weather and Climate Model (ICON‐O) is a global ocean general circulation model that works on unstructured grids. It rests on novel numerical techniques that belong to the class of structure‐preserving finite Volume methods. Unstructured grids allow on the one hand a uniform coverage of the sphere without resolution clustering, and on the other hand they provide the freedom to intentionally cluster grid points in some region of interest. In this work we run ICON‐O on an uniform grid of approximately 10 km resolution and on a grid with four times less degrees of freedom that is stretched such that in the resulting telescoping grid within the North Atlantic the two resolutions are similar, while outside the focal area the grid approaches smoothly ∼80 km resolution. By comparison with observations and reanalysis data we show first, that the simulation on the uniform 10 km grid provides a decent mesoscale eddy rich simulation and second, that the telescoping grid is able to reproduce the mesoscale rich circulation locally in the North Atlantic and on decadal time scales. This telescoping technique of unstructured grids opens new research directions.

Description: Key Points: We describe Icosahedral Nonhydrostatic Weather and Climate Model (ICON‐O) the ocean component of ICON‐ESM 1.0, based on the ICON modeling framework. ICON‐O is analyzed in a globally mesoscale‐rich simulation and in a telescoping configuration. In telescoping configuration ICON‐O reproduces locally the eddy dynamics with less computational costs than the uniform configuration.

Description: https://swiftbrowser.dkrz.de/public/dkrz_07387162e5cd4c81b1376bd7c648bb60/kornetal2021

Description: https://mpimet.mpg.de/en/science/modeling-with-icon/code-availability

Description: TecVolSA (Tectonics and Volcanoes in South America) is a project dedicated to the development of an intelligent Earth Observation (EO) data exploitation system for monitoring various geophysical activities in South America. Three partners from the German Aerospace Center (DLR) and the German Research Centre for Geosciences (GFZ) are involved to combine their expertise in signal processing, geophysics and Artificial Intelligence (AI). The first milestone of the project is to perform interferometric processing on tens of terabytes of SAR data to generate deformation products. Efficient algorithms have been designed to accommodate big data processing. Employing these algorithms, five-year data archives of Sentinel-1 have been processed thus far. The data archives span an area of over 770,000 km² surrounding the central volcanic zone of the Andes. Products in the form of surface deformation velocity and displacement time series are generated as point-wise measurements. To ensure highly accurate deformation estimates, two novel techniques have been utilized: large-scale atmospheric correction and covariance-based phase estimation for distributed scatterers. The second milestone is automatic mining of the wealth of the deformation products to gain insights about anthropogenic and geophysical signals in the region. Here two challenges are faced: the variety of crustal deformation processes as well as the sheer volume of the data. A closer analysis of the estimated deformation velocity verifies the presence of various signals including tectonic movements, volcanic unrest and slope-induced deformations. Such variety requires the classification of the observed signals. Furthermore, the dataset includes displacement time series and velocity estimates of over 750 million data points. This data volume necessitates the incorporation of AI for efficient mining of the products. The aforementioned challenges are met by combining geophysical and signal processing expertise of the project partners, and translating them to the AI algorithms. The use of AI in EO is a growing topic with numerous successful applications. However, compared to the well-established AI applications of cartography and ground cover classification, there is not enough training data available for the analysis of tectonic and volcanic signals. Therefore, there is a need for synthetic data generation. GFZ produces geophysical models for the simulation of a diverse database that is used for the training of neural networks to autonomously discover significant events in deformation products. DLR employs supervised machine learning techniques based on simulated data to automatically detect volcanic deformation from InSAR products. Apart from this application, signals which are not attributed to volcanic deformation are automatically clustered for further studies by expert geologists. For this approach, we depend on InSAR and geometrical feature engineering as well as advanced unsupervised learning algorithms. In the presentation, examples of clustering similar points in terms of temporal progression and a prototype system for the automatic detection of volcanic deformations will be illustrated. Our system is being developed with scalability and transferability in mind. South America serves as a generic and challenging case for this development, as it reveals manifold geophysical and anthropogenic signals. Our ultimate goal is to apply the developed AI-assisted system for global processing.

Description: Providing accurate measurement of the magnetic field intensity and its vector components is one of the primary objectives of the China Seismo‐Electromagnetic Satellite (CSES). The high precision magnetometer (HPM) payload assembled on CSES is designed to achieve this goal. In this study, the data format, naming convention, and content of the CSES HPM Level 2 scientific data products are introduced, as a reference for users who are interested in this data set. In particular, flags for potential magnetic field disturbances from the platform and payloads are discussed. Possible scientific applications are also outlined. A preliminary validation of the data is conducted through comparison with magnetic data from the ESA’s Swarm constellation, and the result demonstrates that the HPM data of CSES are of good quality. Taking the intense geomagnetic storm that occurred on August 25–26, 2018 as an example, the magnetic field variations and the expansion of the field‐aligned currents (FACs) during this storm are discussed. We finally show that the CSES HPM data can be used to derive a satellite‐derived index equivalent to the Dst index, which agrees well to the index during this event. Our analysis thus suggests a high scientific potential of the HPM data.

Description: In this paper we provide a comprehensive comparison of in‐situ electron density (Ne) and temperature (Te) measured by Langmuir probe (LAP) on board the China Seismo‐Electromagnetic Satellite (CSES), with nearly simultaneous measurements from the Swarm B satellite, incoherent scatter radar (ISR) at Millstone Hill, as well as predictions from empirical models including the Local Empirical Model (LEM) of Millstone Hill ISR and International Reference Ionosphere model (IRI‐2016). Results reveal that the global distributions and their relative variations of Ne/Te from CSES and Swarm are quite consistent during conjunction periods of the two satellites, although the absolute values of Swarm Ne are proportionally larger than that of CSES. The large‐scale ionospheric structures, such as the equatorial ionization anomaly (EIA), the longitudinal wave number (WN3/4), the Weddell Sea anomaly (WSA), the northern mid‐latitude summer nighttime anomaly (MSNA) and the mid‐latitude ionospheric trough (MIT), are well represented by the CSES measurements. For the temporal variation over Millstone Hill station, CSES Ne at nightside shows some different characteristics from the predictions of IRI and LEM, possibly due to the influences of MIT and mid‐latitude arc (MLA) that are often observed at latitudes of Millstone Hill. Our results suggest that the CSES in‐situ plasma parameters are reliable with a high scientific potential for investigation of geophysics and space physics.

Description: Temporally and spatially comprehensive data products of calving front variation are essential for a better understanding and modelling of tidewater glaciers. However, most current calving front records are limited in temporal resolution as they rely on manual delineation which is a laborious and time-consuming, hence ineffective process. In this contribution, we address this issue by applying an automated method to delineate calving front positions from optical satellite imagery. The technique is based on recent developments in deep learning while taking full advantage of multi-spectral input data. After evaluating the method utilizing three independent test datasets, we apply it to Landsat imagery generating 9243 calving front positions across 23 Greenland outlet glaciers from 2013 to 2021. Resulting time series are analysed using a rectilinear box method. In this way we are able to resolve not only long-term and seasonal terminus changes but also sub-seasonal fluctuations. This allows us to classify different calving patterns and accurately identify pattern changes within our time series. We discuss different glaciological applications of our results, in particular their implications for associated glacier modelling efforts.Our method and inferred results form a significant advancement towards establishing intelligent processing strategies for glacier monitoring tasks. We create new opportunities to study and model the dynamics of tidewater glaciers. These include the advance towards constructing a digital twin of the Greenland ice sheet, which will enhance our understanding of its evolution and its role within the broader Earth’s climate system.

Description: In the Global Navigation Satellite System (GNSS), the satellite clock bias (SCB) is one of the sources of ranging error, and the prediction capability directly affects the users navigation and positioning accuracy. The establishment of a reliable SCB predicting model is important for real-time precise point positioning, precise orbit determination and optimization of navigation message parameters. In this report, we apply a Long Short-Term Memory (LSTM) model for predicting BDS-3 SCB, which uses a multiple single-step predicting method to avoid error accumulation in the process. Short- (0 to 6 hours), medium- (6 hours to 3 days) and long-term (3 days to 7 days) predicting is performed, and the results are compared with those of two traditional models to verify the reliability and accuracy of the LSTM method. In the long-term prediction of BDS-3 SCB, LSTM improves the accuracy about 70% and 60% compared to the autoregressive integrated moving average (ARIMA) and quadratic polynomial (QP) model, respectively. This report also presents the results of predicting GPS and Galileo SCB using the LSTM method.

Description: Global navigation satellite system reflectometry (GNSS-R) has shown a capability in recent years to be applied as a novel remote sensing technique to retrieve ocean wind speeds. The combination of GNSS-R observable delay-Doppler maps (DDMs) and deep learning algorithms provides the possibility to build an end-to-end pipeline for improving wind speed estimations. Recent studies have proven that data-driven approaches can be applied to generate enhanced estimation products. However, these are usually trained with convolutional neural networks (CNNs), which include inductive bias throughout the models. The inbuilt translation equivariance in CNNs represents an inexactitude for the feature extraction on DDMs. To address this issue, we propose Transformer-based models, named DDM-Former and DDM-Sequence-Former (DDM-Seq-Former), to exploit delay-Doppler correlation within and between DDMs, respectively. The advantages of our methods over conventional retrieval algorithms and other deep learning-based approaches are presented based on the Cyclone GNSS (CYGNSS) version 3.0 dataset. In addition, a comprehensive study on the attention mechanism for our models is demonstrated. The proposed DDM-Former yields the best overall performance with a root mean square error (RMSE) of and a bias of over the nine months test period. Moreover, with an RMSE of and a bias of , the proposed DDM-Seq-Former can promisingly improve the estimations in the cases with wind speeds higher than . There are still opportunities for further enhancements in creating more robust models that could perform well in all wind regimes given a non-uniform wind distribution. We will make our code publicly available.

Description: Based on the data of Mingantu Spectral Radioheliograph (MUSER) at 0.4-2.0GHz, the EUV images of the Atmospheric Imaging Assembly (AIA) and the magnetic field data of Helioseismic Magnetic Imager (HMI), we carried out a detailed study of solar bursts with a fan-spine structure that occurred on December 17, 2014. The results show that during the low active level of the active region, there are usually two radio sources, one is around zero point of the fan-spine structure and the other is related to the spine. However, when the M7.8 flare occurred, the location of the radio sources changed dramatically. The high frequency source became one source and moves to the left side of the fan, but the low frequency sources usually displays two sources. The stronger one of which is located on the left side of the fan whereas the weaker one settled at the far end of the spine. These results might shine light on understanding the morphology of three-dimensional magnetic reconnection as well as the acceleration of electrons and the radio emissions caused by them.

Description: As a novel remote sensing technique, GNSS reflectometry (GNSS-R) opens a new era of retrieving Earth surface param- eters. Several studies employ the combination of deep learn- ing and GNSS-R observable delay-Doppler maps (DDMs) to generate ocean wind speed estimation. Unlike these methods that often use convolutional neural networks (CNNs) with in- ductive bias, we proposed a Transformer-based model, named DDM-Former, to exploit fine-grained delay-Doppler correla- tion independently. Our model is evaluated on the Cyclone GNSS (CYGNSS) version 3.0 dataset and shown to outper- form the other retrieval methods.

Description: GNSS Reflectometry (GNSS-R), referring to exploiting the GNSS signal of opportunity reflected off the Earth surface, has emerged as a novel remote sensing technique for monitoring geophysical parameters. The Cyclone GNSS (CYGNSS), launched on December 15th, 2016, is a constellation of eight microsatellites with cost-effected receivers, fully dedicated to the GNSS-R applications, and can track reflected signals from multiple GNSS satellites. Compared with traditional optical and radar remote sensing, GNSS-R can provide massive datasets with global coverage and improved temporal resolution, which offers unique potential for characterizing the complex Earth system.With the increase of GNSS-R observation data volume, deep learning techniques show their strong capability in retrieving ocean surface wind speed by extracting features from the Delay-Doppler Maps (DDMs). Furthermore, it is shown that deep learning models significantly improve the quality of existing GNSS-R wind speed products. The model achieves an overall RMSE of 1.31 m/s compared with the ERA5 reanalysis data and leads to an improvement of 28% in comparison to the operational retrieval algorithm based on the empirical geophysical model functions (GMFs).However, some known geophysical parameters, such as precipitation, are theorized to be impacting the reflected signals, altering the pattern of the DDMs, and consequently biasing the retrievals. The correction of such bias is not trivial because of its nonlinear dependency on various environmental and technical parameters. Therefore, we explore how deep learning-based fusion on additional precipitation data can correct the bias and further investigate the potential of deep learning models to retrieve precipitation.