Interaction of the solar wind with non-magnetized bodies: hybrid simulations of Moon and Venus

In dieser Arbeit werden numerische Simulation der Sonnenwindwechselwirkung am Mond und an der Venus durchgeführt. Hierzu wird der 3D-Hybrid-Simulationscode A.I.K.E.F. verwendet. Die Simulation des Mondes basieren dabei auf dem ersten Vorbeiflug der Sonde ARTEMIS P1 hinter dem Mond. Im Rahmen einer dynamischen Echtzeit-Simulation werden die Anströmbedingungen des Sonnenwinds kontinuierlich anhand angepasster Daten aus der NASA-OMNI-Datenbank variiert und können so sehr genau die von der Sonde hinter dem Mond gemessenen Daten reproduzieren. Zudem zeigt die Betrachtung des Magnetfelds in der Ebene senkrecht zur Anströmrichtung eine Art Friedrichs-Diagramm hinter dem Mond mit den drei grundlegenden MHD-Moden Fast, Alfvénisch und Slow, die anhand ihrer jeweils charakteristischen Signatur identifiziert werden können. Die Simulationen der Venus zielen zunächst auf die Untersuchung der ionosphärischen Magnetisierungszustände ab, die Abhängigkeit der Höhe der magnetischen Aufstauung vor der Ionosphäre vom Anströmdruck des Sonnenwindes konnte in den Simulationen reproduziert werden. Weitere Untersuchungen zeigen die Effekte eines Sektordurchgangs des Sonnenwindmagnetfeldes: Fossile Felder erreichen auf der Tagseite wie auch im Tail jeweils nur Lebensdauern von wenigen Minuten, die Neuausprägung des Bereichs mit umgekehrter y-Komponente des Magnetfelds beansprucht jedoch etwa 30 Minuten, da dieser Effekt durch die langsamen planetaren Ionen verursacht wird. Die Wechselwirkung beider Körper weist viele Gemeinsamkeiten auf, obwohl der ionosphärenlose Mond und die mit starker Ionosphäre ausgestatte Venus prinzipiell in unterschiedliche Kategorien der Wechselwirkung eingeordnet werden. Beide zeigen jedoch eine direkte Abhängigkeit der Wechselwirkungsstrukturen von der Richtung des Sonnenwindmagnetfelds, außerdem jeweils eine sehr lange nachtseitige Struktur (Wake bzw. Tail), die aus den sehr schnellen Anströmbedingungen des Sonnenwindes resultiert.Numerical simulations of the solar wind interaction with Moon and Venus are performed in this work, applying the A.I.K.E.F. 3D hybrid simulation code. The Moon simulations are based on the first flyby of the ARTEMIS P1 probe behind the Moon. In a dynamic real time simulation, the solar wind upstream parameters are constantly adapted using shifted data from the NASA OMNI database, reproducing the data measured behind the Moon in very good agreement. Additionaly, a look at the magnetic field in the plane perpendicular to the upstream direction shows the formation of a structure resembling a Friedrichs diagram behind the Moon; the three basic MHD modes fast, Alfvénic and slow can be identified by their characteristic signatures. The simulations of Venus first focus on the reproduction of the ionospheric magnetization states. The dependency of the magnetic pile-up altitude on the solar wind upstream pressure is reproduced in the simulations. Further investigations show the effects of a solar wind magnetic field sector boundary crossing: while fossil fields on day- and nightside only show a lifetime of a few minutes, the reformation of the area of reversed magnetic field y-component requires about 30 minutes, as this effect is caused by the slow planetary ions. The interaction of both bodies shows many similarities, although the Moon without an ionosphere and Venus with a strong ionosphere belong to different categories of interaction. Common features for both include the direct dependency of the interaction structures from the solar wind magnetic field direction and a very extended nightside structure (wake or tail, respectively), caused by the very fast upstream conditions of the solar wind

SAR Satellite On-Board Ship, Wind, and Sea State Detection

This paper describes a prototype implementation of ship, wind, and sea state detection algorithms for satellite on-board SAR processing designed for Maritime Situation Awareness. Existing algorithms were adapted to run on a Multi- Processor-System-On-Chip (MPSoC) combining an FPGA and an ARM CPU and further optimized for fast runtime on the system. The achieved processing times were 20 s for ship detection and 16 s for sea state detection on a 29Mpx SAR image. SAR processing is one component of a larger prototype system being developed in the frame of the H2020 project EO-ALERT, which further comprises an optical data chain, data compression/encryption, and delivery on multiple MPSoC boards. © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

Accuracy of a Phase-Correlation Technique for Fully Automated Sea Ice Motion Retrieval based on Sequential SAR Images

In order to improve ship routing in polar waters, we present a software processor to retrieve high resolution sea ice motion fields from spaceborne Synthetic Aperture Radar (SAR) image sequences fully automatically. Sea ice is almost continually in motion. Within hours, wind and ocean currents can cause significant changes within the sea ice. When the ice is pulled apart by winds or currents from opposite directions, the ice fractures, and open water leads appear. When ice is strongly pushed together by converging wind and currents, the ice sheet will break and either pile up randomly one piece over another, forming a thick, uneven surface, or be forced upwards, creating high walls called ridges. Such obstacles are difficult or impossible even for icebreakers to overcome. SAR satellites such as TerraSAR-X or Sentinel-1 are well suitable to map different structures in the sea ice. Due to their near-polar orbit, spatially and temporally near coincident acquisitions in high latitudes are possible on a daily basis. The core of the presented software processor for sea ice motion retrieval is the well-known phase correlation technique, executed within a hierarchical motion estimation framework presented in our previous work. The output of the processor is a vector field indicating the sea ice displacement, which can be converted into sea ice velocity. Now, we investigate the accuracy of the retrieved displacement. Our test deals with a series of TerraSAR-X ScanSAR mode images acquired over drift buoys that are located in arctic waters, as well as with collocated Sentinel-1 acquisitions for comparison. We monitored the buoys during July 2017 and January 2018. In the winter sequences, an ice concentration of >90 % is predominant, while the summer acquisitions capture an ice concentration of 50 % - 80 %. Altogether, the accuracy of motion vectors estimated from TerraSAR-X image pairs amounts to 30 m (1σ-error). The motion field has a resolution of 150 m x 150 m, which gives a very detailed look into the local sea ice motion, detecting small variations. The presented processor is intended to be part of the operational data processing chain at DLR Ground Station Network sites. In ongoing work, we implement parallel processing in order to reduce computing time so vessels in ice infested waters can receive information on local sea ice motion in near real-time

Sea State from High Resolution Satellite-borne Synthetic Aperture Radar Imagery

The Sea Sate Processor (SSP) was developed for fully automatic processing of high-resolution Synthetic Aperture Radar (SAR) data from TerraSAR-X (TS-X) satellites and implemented into the processing chain for Near Real Time (NRT) services in the DLR Ground Station "Neustrelitz". The NRT chain was organised and tested to provide the processed data to the German Weather Service (DWD) in order to validate the new coastal forecast model CWAM (Coastal WAve Model) in the German Bight of the North Sea with 900 m horizontal resolution. The NRT test-runs, wherein the processed TS-X data were transferred to DWD and then incorporated into forecast products reach the best performance about 10 min for delivery of processed TS-X data to DWD server after scene acquisition. To do this, a new empirical algorithm XWAVE_C (C = coastal) for estimation of significant wave height from X-band satellite-borne SAR data has been designed for coastal applications. The algorithm is based on the spectral analysis of subscenes and the empirical model function yields an estimation of integrated sea state parameters directly from SAR image spectra without transformation into wave spectra. To provide the raster coverage analysis, the SSP intends three steps of recognising and removing the influence of non-sea-state-produced signals in the Wadden Sea areas such as ships, buoys, dry sandbars as well as nonlinear SAR image distortions produced by e.g. short and breaking waves. For the validation, more than 150 TS-X StripMap scene sequences with a coverage of ~30 km × 300 km across the German Bight since 2013 were analysed and compared with in situ Buoy measurements from 6 different locations. On this basis, the SSP autonomous processing of TS-X Stripmap images has been confirmed to have a high accuracy with an error RMSE = 25 cm for the total significant wave height

BigDataCube: Making Big Data a Commodity

The Big DataCube project aims at advancing the innovative datacube paradigm - i.e ., analysis - ready spatio - temporal raster data - from the level of a scientific prototype to precommercial Earth Observation (EO) services. To this end, the European Datacube Engine (in database lingo: ‖Array Database System ‖), rasdaman, will be installed on the public German Copernicus hub, CODE - DE , as well as in a comme rc ial c loud environment to exe mp larily offer analyt ics services and to federate both, thereby demonstrating an integrated public/private service . Started in January 2018 with a runtime of 18 months, Big DataCube will co mple ment the batch - orien ted Hadoop service already available on CODE - DE with rasdaman thereby offering important additional functionality, in particular a paradig m of -any query, any t ime, on any size‖, strictly based on open geo standards and federated with other data centers. On this platform novel, specialized services can be established by third parties in a fast, fle xible, and scalable manner. To this end, several features crucial for operational services need to be tested and/or imple mented, such as securing access (in parti cular in a distributed processing context), tuning to the specific cloud configuration of CODE - DE, and further items to be determined in the initial requirements analysis phase. The result will be the prototype of a federation of rasdaman installations on CODE-DE, cloudeo, as we ll as further (external) data centers; further, best practices on the use of array databases in operational environments will be established. This will pave the way for individual value - adding services by third parties