The AVHRR Mulitchannel Sea Surface Temperature Map (MCSST) was the first result of DLR's AVHRR pathfinder activities. The goal of the product is to provide the user with actual Sea Surface Temperature (SST) maps in a defined format easy to access with the highest possible reliability on the thematic quality. After a phase of definition, the operational production chain was launched in March 1993 covering the entire Mediterranean Sea and the Black Sea. Since then, daily, weekly, and monthly data sets have been available until September 13, 1994, when the AVHRR on board the NOAA-11 spacecraft failed. The production of daily, weekly and monthly SST maps was resumed in February, 1995, based on NOAA-14 AVHRR data. The NOAA-14 AVHRR sensor became some technical difficulties, so the generation was stopped on October 3, 2001. Since March 2002, NOAA-16 AVHRR SST maps are available again. With the beginning of January 2004, the data of AVHRR on board of NOAA-16 exhibited some anormal features showing strips in the scenes. Facing the “bar coded” images of NOAA16-AVHRR which occurred first in September 2003, continued in January 2004 for the second time and appeared in April 2004 again, DFD has decided to stop the reception of NOAA16 data on April 6th, 2004, and to start the reception of NOAA-17 data on this day. On April 7th, 2004, the production of all former NOAA16-AVHRR products as e.g. the SST composites was successully established. NOAA-17 is an AM sensor which passes central Europe about 2 hours earlier than NOAA-16 (about 10:00 UTC instead of 12:00 UTC for NOAA-16). In spring 2007, the communication system of NOAA-17 has degraded or is operating with limitations. Therefore, DFD has decided to shift the production of higher level products (NDVI, LST and SST) from NOAA-17 to NOAA-18 in April 2007. In order to test the performance of our processing chains, we processed simultaneously all NOAA-17 and NOAA-18 data from January 1st, 2007 till March 29th, 2007. All products are be available via EOWEB. Please remember that NOAA-18 is a PM sensor which passes central Europe about 1.5 hours later than NOAA-17 (about 11:30 UTC instead of 10:00 UTC for NOAA17). The SST product is intended for climate modelers, oceanographers, and all geo science-related disciplines dealing with ocean surface parameters. In addition, SST maps covering the North Atlantic, the Baltic Sea, the North Sea and the Western Atlantic equivalent to the Mediterranean MCSST maps are available since August 1994. The most important aspects of the MCSST maps are a) correct image registration and b) reasonable cloud screening to ensure that only cloud free pixels are taken for the later processing and compositing c) for deriving MCSST, only channel 4 and 5 are used.. The SST product consists of one 8 bit channel. For additional information, please see: https://wdc.dlr.de/sensors/avhrr/
Atomarer Sauerstoff (O) ist eine der Hauptkomponenten der Mesopausenregion der terrestrischen Atmosphäre. Er spielt für die Energiebilanz der Mesopausenregion eine entscheidende Rolle, da er aufgrund seiner langen Lebensdauer chemische potentielle Energie über große Distanzen transportieren kann und indirekt an der Strahlungskühlung dieser Höhenregion beteiligt ist. Darüber hinaus steht er in direktem Zusammenhang mit Ozon, was wiederum für die diabatische solare Heizung von großer Bedeutung ist. Die Zahl der O Messungen in der Mesopausenregion ist ziemlich begrenzt, insbesondere was Zeitserien über Zeiträume von mehr als einigen Jahren betrifft. Die üblicherweise verwendeten Methoden zur Messung von O in der Mesopausenregion basieren auf Airglow-Emissionen der Spezies O, O2 und OH und erfordern die Kenntnis zahlreicher chemischer Ratenkonstanten. Bisherige Studien zeigen klare Hinweise darauf, dass die existierenden Modelle zur Beschreibung der O2 A-Banden-Emission, der grünen Sauerstofflinie und der OH* Meinel-Emissionen nicht konsistent sind, und O Konzentrationsprofile liefern, die sich signifikant unterscheiden. Im Rahmen dieses Projektes soll die Konsistenz der existierenden photochemischen Modelle für die drei genannten Airglow-Emissionen untersucht werden und unter Verwendung von simultanen Satellitenmessunen aller drei Emissionen, sowie dedizierter Modellrechnungen die Übereinstimmung der Modelle verbessert werden. Bei den Messungen handelt es sich um Nightglow Messungen des SCIAMACHY (Scanning Imaging Absorption spectroMeter for Atmospheric CHArtographY) Instruments, das von 2002 - 2012 auf dem Umweltforschungssatelliten Envisat operierte. SCIAMACHY bietet aufgrund seines breiten Spektralbereichs die einmalige Möglichkeit alle für dieses Projekt relevanten Airglow Emissionen gleichzeitig und spektral aufgelöst zu messen. Die geplanten Modellrechnungen sollen mit einer etablierten Suite an photochemischen und globalen Modellen durchgeführt werden. Mittels eines Inversionsverfahrens sollen photochemische Modellparameter derart optimiert werden, dass die Differenzen zwischen Modellergebnissen und SCIAMACHY Messungen für alle relevanten Emissionen simultan minimiert werden. Darüber hinaus soll im Rahmen des Projekts die räumliche und zeitliche Variabilität von O in der Mesopausenregion charakterisiert werden, insbesondere hinsichtlich solarere Einflüsse und möglicher Langzeittrends über den Zeitraum von 2002 - 2012. Es ist außerdem geplant, die existierende - und bekannte Weise unzureichendem - klimatologischen Modelle (z.B. MSIS) von O in der Mesopausenregion zu verbessern. Die Antragsteller sind anerkannte Experten auf Ihren jeweiligen Hauptarbeitsgebieten und besitzen langjährige Erfahrung im Bereich der Satellitenfernerkundung mittels Airglow-Emissionen, beziehungsweise der atmosphärischen Modellierung.
Entwicklung und Verbesserung von Verfahren der Fernerkundung (Photogrammetrie, Photointerpretation) fuer die Herstellung von topographischen und thematischen Karten sowie fuer die Anwendung in anderen geowissenschaftlichen Bereichen (Geographie, Geologie usw.), in der Land- und Forstwirtschaft, in der Landesplanung und Raumordnung, im Umweltschutz (Gewaesserueberwachung, Vegetationsschaeden usw.) und in aehnlichen Bereichen. Durchfuehrung grundlegender und experimenteller Untersuchungen zur Verfahrenstechnik, einschliesslich Genauigkeit und Wirtschaftlichkeit.
The magnetosphere of a planet is controlled by a number of factors such as the intrinsic magnetic field, the atmosphere and ionosphere, and the solar wind. Different combinations of these control factors are at work at the terrestrial planets Mercury, Venus, Earth, and Mars, hence they form a very suitable set for quantitative comparative studies. A significant intrinsic dipolar magnetic field is present only on Earth and on Mercury. However, the configuration at Mercury differs considerably from that at Earth because Mercury does not support an atmosphere and ionosphere, the dipolar field is much weaker, the solar wind denser, and the interplanetary magnetic field stronger. Both Mars and Venus have atmospheres but lack a global planetary magnetic field, with regional crustal magnetization being present on Mars. This proposal aims at investigating and comparing electrical current systems in the space environments of terrestrial planets using magnetic vector data collected by orbiting spacecraft such as Venus Express, Mars Global Surveyor, CHAMP (Earth), and MESSENGER (Mercury). We propose to construct data-driven and physically meaningful representations that reveal and quantify the influence of various control factors. To achieve this, we will tailor Empirical Orthogonal Function (EOF) analysis and other multivariate methods to the specifics of planetary magnetic field observations. In contrast to representations that build on predefined functions like spherical harmonics, basis functions in the EOF approach are derived directly from the data. EOFs are designed to extract dominant coherent variations for further interpretation in terms of known physical phenomena, and then, in a regression step, for modeling using suitable control variables. The EOF methodology thus allows quantifying the relative importance of control factors for each planet individually, and thus contributes to the solution of topical science questions. The resulting empirical models will facilitate comparative studies of current systems at the terrestrial planets.
Bei der Haupttätigkeit der MTU Maintenance Hannover GmbH , Inspire-ID: https://registry.gdi-de.org/id/de.ni.mu/06293538610-3260) handelt es sich um Oberflächenbehandlung d. elektrolytische od. chem. Verf. (NACE-Code: 33.16 - Reparatur und Instandhaltung von Luft- und Raumfahrzeugen). Es wurden keine Freisetzungen oder Verbringungen nach PRTR berichtet zu: Freisetzung in die Luft, Freisetzung in das Wasser, Freisetzung in den Boden, Verbringung von Schadstoffen mit dem Abwasser, Verbringung gefährlicher Abfälle im Ausland, Verbringung nicht gefährlicher Abfälle.
Bei der Haupttätigkeit der Lufthansa Technik AG , Inspire-ID: https://registry.gdi-de.org/id/de.hh/pf.bube-eureg_/4249) handelt es sich um Oberflächenbehandlung d. elektrolytische od. chem. Verf. (NACE-Code: 33.16 - Reparatur und Instandhaltung von Luft- und Raumfahrzeugen). Es wurden keine Freisetzungen oder Verbringungen nach PRTR berichtet zu: Freisetzung in die Luft, Freisetzung in das Wasser, Freisetzung in den Boden, Verbringung von Schadstoffen mit dem Abwasser, Verbringung gefährlicher Abfälle im Ausland.
The GRACE (Gravity Recovery and Climate Experiment) satellites, which comprises two spacecraft, GRACE-A and GRACE-B, were launched on 17 March 2002 into a near-circular, polar (inclination = 89◦ ) orbit with an initial altitude of about 490 km. The two satellites follow each other at a distance of about 200 km. The primary objective of the GRACE mission is to provide global high-resolution models of the Earth’s gravity field. The instruments supporting our study are the K-Band Ranging System (KBR), and the GPS Space Receiver (GPS). The K-Band Ranging System (KBR) system is the key science instrument of GRACE which measures the dual one-way range change between both satellites with a precision of about 1 μm per second. From the KBR1B data we can get the change of Total Electron Content (TEC). In addition the GPS Navigation Data (GNV1B) can provide us the position of the two satellites. From these data we can derive the average electron density between the two satellites. The data are stored as daily ASCII files using the file naming convention 'KBRNE_YYYY_MM_DD.dat'. Headers in each data file contain a short name for each column. A more detailed description is provided in the readme file.
Here we present a photogrammetric dataset on the 2018-2019 eruption episode at Shiveluch Volcano, one of the most active volcanoes in Kamchatka Peninsula. The data were acquired by optical sensors and complemented by thermal sensors. The optical satellite images were tri-stereo panchromatic 1-m resolution imagery acquired on 18 July 2018 with Pléiades satellite PHR1B sensor. We processed the data in Erdas Imagine 2015 v15.1. For the relative orientation of the images, 37 tie points were calculated automatically with further manual correction, and for the interior and exterior orientation, Rational Polynomial Coefficients block adjustment, which is a transformation between pixels to latitude, longitude, and height information, was automatically employed. After the image orientation, we obtained a photogrammetric model with a total root mean square error (RMSE) of 0.2 m. By using the Enhanced Automatic Terrain Extraction module (eATE) with normalized cross correlation algorithm as implemented in the Erdas Imagine software, we were able to extract a 2 m resolution point cloud (PC) referenced to the WGS84 coordinate system UTM57 zone. This PC was filtered with the CloudCompare v2.9.1 noise filter and then manually cleaned with the CloudCompare segmentation tool. As strong volcanic steam emissions caused a large gap in the PC at the NE part of the dome, we used a 5 m resolution DEM constructed from TanDEM-X data to fill the gap and obtain the missing topography. TanDEM-X is a bistatic SAR mission, formed by adding a second, almost identical spacecraft, to TerraSAR-X. Therefore, it allows the acquisition of two simultaneous SAR imageries over the same area, eliminating possible temporal decorrelations between them and maintaining a normal baseline between 250 and 500 m, which is suitable for SAR interferometry for DEM generation. We used the interferometric module in ENVI SARscape to build the interferogram, perform the unwrapping step and finally convert it into height information using forward transformation from radar to geographic coordinates. The RMSE of the generated DEM is evaluated based on the coherence value, i.e. quality of the interferogram, and is estimated to be approximately 5 m.
Data assimilation aims to blend incomplete and inaccurate data with physics-based dynamical models. In the Earth's radiation belts, it is used to reconstruct electron phase space density, and it has become an increasingly important tool for validating our current understanding of radiation belt dynamics, identifying new physical processes, and predicting the near-Earth hazardous radiation environment. The dataset presents the electron flux reconstructed by assimilating electron flux measurements of the following spacecraft into the 3D Versatile Electron Radiation Belt model (VERB; Shprits et al., 2008, Subbotin and Shprits, 2009): 1. Van Allen Probes Magnetic Electron Ion Spectrometer (MagEIS; Blake et al., 2013) and Relativistic Electron Proton Telescope (REPT; Baker et al., 2013), and 2. Geostationary Operational Environmental Satellites (GOES) Magnetospheric Electron Detector (MAGED; Hanser, 2011), and Energetic Proton, Electron, and Alpha Detector (EPEAD; Onsager et al., 1996, Hanser, 2011). The method employs a split-operator Kalman filter (Shprits et al., 2013). The dataset contains electron flux for the period from 01 October 2012 00:00 UT to 01 October 2016 00:00 UT, organized in monthly files for selected values of electron energies (0.5 MeV, 1 MeV, and 2 MeV) and equatorial pitch angles (20 degree, 50 degree, and 70 degree).
This dataset comprises global upper thermospheric cross-track neutral wind measurements obtained from accelerometer data of the CHAMP satellite during its almost ten year’s lifetime from 2001 to 2009. One key scientific instrument on-board CHAMP was a sensitive triaxial accelerometer. It was located at the spacecraft's centre of mass and sampled effectively accelerations due to non-gravitational forces with an accuracy of ~3×10^-9 ms^-2 (Doornbos et al., 2010). The along-track air drag measurements resulted in thermospheric mass density estimations, while the instrument was sensitive enough to deduce also the horizontal neutral wind component from the cross-track accelerations.The CHAllenging Minisatellite Payload (CHAMP) spacecraft circled the Earth from July 2000 to September 2010 on a near-polar orbit (inclination 87.3°). Each orbit period took about 93 minutes at an altitude of initially 455 km, and decaying to about 320 km in 2009. Due to CHAMP's precession, the satellite achieved full coverage of all local times within about 131 days in each case.This work was part of a study in 2007-2009 (Doornbos et al., 2009) funded by the European Space Agency’s General Studies Program which aimed at a more precise estimation of the non-gravitational forces, considering the precise satellite geometry and its optical and mechanical surface properties. To obtain the actual air drag forces, the modelled accelerations due to radiation pressure forces from the sun, the Earth's albedo, and the Earth's infrared radiation had to be computed and removed from the calibrated and edited accelerometer data to get the observed aerodynamic acceleration vector. The modelling of the radiation pressure forces comprised several nontrivial components like the modelling of eclipse and semi-shadow conditions for solar radiation pressure, values for the reflectivity and infrared emissivity of Earth surface elements, and models of the geometry and optical properties of the satellite surfaces (Doornbos et al., 2010).The detailed description of supersonic flow of the neutral gas particles across the satellite's surface and its reflection requires a model of the gas–surface interaction, which specifies the angular distribution and energy flux of the reflected particles. One has to make assumptions and educated guesses, because information on the gas–surface interaction, as well as in situ observations of aerodynamic model parameters like air temperature and neutral gas species' concentrations should be measured by independent instruments on the accelerometer-carrying satellite.Here, we relied on the empirical atmosphere model NRLMSISE-00 (Picone et al., 2002) and the rarefied aerodynamic equations for flat panels, derived by Sentman (1961). These equations take into account the random thermal motion of the incident particles and assume a completely diffuse distribution of the reflected particle flux. The energy flux accommodation coefficient alpha (Moe et al., 2004), which determines whether the particles retain their mean kinetic energy (alpha = 0) or acquire the temperature of the spacecraft surface wall (alpha = 1), was found to be optimally chosen with alpha = 0.8 for this data set.This thermospheric cross-track neutral wind data set consists of a series of annual CDF data files for both CHAMP wind measurements (subfolder: CH_PN_R03_denswind_iter2_Sentman_alpha08) and CHAMP orbital data (subfolder: CH_orbit_GEO_RSO). The CDF data files are documented in the header. The complete dataset contains more than 25 million data points with a temporal cadence of 10 sec.In addition to the data, we are providing supplementary Figures to Aruliah et al. (2019, subfolder: 2019-001_Foerster-Doornbos_Figures). They are complementary, in particular, to Figs. 1-4 of this paper, but additionally show the original data as “cloud” of data points in the background of the statistical averages. Each figure plot (png-format) has an accompanying txt-file of the same name (except the extension) with ASCII tables of the hourly statistical averages and their standard deviations.The data were used in various previous publications mainly with respect to high-latitude upper thermosphere studies (Förster et al., 2008, 2011) and investigations of the interhemispheric coupling processes of the magnetosphere, ionosphere, and thermosphere (Förster et al., 2017). Actually, this data publication serves as supplement to Aruliah et al. (2019).
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