The Global Ozone Monitoring Experiment-2 (GOME-2) instrument continues the long-term monitoring of atmospheric trace gas constituents started with GOME / ERS-2 and SCIAMACHY / Envisat. Currently, there are three GOME-2 instruments operating on board EUMETSAT's Meteorological Operational satellites MetOp-A, -B, and -C, launched in October 2006, September 2012, and November 2018, respectively. GOME-2 can measure a range of atmospheric trace constituents, with the emphasis on global ozone distributions. Furthermore, cloud properties and intensities of ultraviolet radiation are retrieved. These data are crucial for monitoring the atmospheric composition and the detection of pollutants. DLR generates operational GOME-2 / MetOp level 2 products in the framework of EUMETSAT's Satellite Application Facility on Atmospheric Chemistry Monitoring (AC-SAF). GOME-2 near-real-time products are available already two hours after sensing. The operational ozone total column products are generated using the algorithm GDP (GOME Data Processor) version 4.x integrated into the UPAS (Universal Processor for UV / VIS Atmospheric Spectrometers) processor for generating level 2 trace gas and cloud products. The new improved DOAS-style (Differential Optical Absorption Spectroscopy) algorithm called GDOAS, was selected as the basis for GDP version 4.0 in the framework of an ESA ITT. GDP 4.x performs a DOAS fit for ozone slant column and effective temperature followed by an iterative AMF / VCD computation using a single wavelength. For more details please refer to relevant peer-review papers listed on the GOME and GOME-2 documentation pages: https://atmos.eoc.dlr.de/app/docs/
In recent years, electricity production from distributed renewable energy generators in Germany increased significantly due to the German Renewable Energy Sources Act. Photovoltaic power plants have shown the highest growths rates in 2009 and 2010. About two thirds of photovoltaic power plants in Continental Europe are connected to low voltage networks. Related grid codes allow for distributed generation only to operate within frequency ranges that are in many cases extremely close to nominal frequency. At an abnormal system condition the frequency of a region may increase above those thresholds and distributed generators would disconnect within immediately. The paper investigates the related potential frequency stability problem and analyses mitigation measures.
<p>Abfälle können in haushaltsüblichen Mengen an diese Wertstoff-Center gebracht werden.</p> <p>Wir nehmen an:</p> <ul> <li>Sperrmüll, Elektroaltgeräte, Metalle, Papier/Pappe, Schadstoffe, Bauschutt</li> <li>Kostenlose Annahme von haushaltsüblichen Mengen an Altkleidern, CDs/DVDs, Elektro-Altgeräten, Grünschnitt, Leichtverpackungen, Metall, Papier, Pappe/Kartonagen, Schadstoffen und Sperrmüll</li> <li>Kostenpflichtige Annahme von Bauschutt in Kleinmengen (Gewerbeschadstoffe nur in Ossendorf)</li> </ul> <p>Wir nehmen nicht an:</p> <p>Asbest, Dämmmaterial, Außenhölzer, Teerpappe</p> <ul> <li>Sprengstoff, Munition</li> <li>Gasflaschen</li> <li> Infektiöses Material, Tierkadaver</li> <li> Motoren, Getriebeöle</li> <li>Gewerbeschadstoffe</li> </ul>
Cellulose stellt den am häufigsten vorkommenden Naturstoff unseres Planeten dar. Mit einer pflanzlichen Weltjahresproduktion von ca. 180 Milliarden Tonnen (Engelhardt, j. Carbohydr. Eur. 12, 5-14 (1995)) ist Cellulose der bedeutendste nachwachsende Rohstoff. Dieses Biopolymer findet außer in der Papier-, Pharma- und Textilindustrie in vielen anderen Bereichen (z.B. Medizin, Kosmetik, Kunststoff-Industrie) reichliche Verwendung. Trotz der großen wirtschaftlichen Bedeutung und über drei Jahrzehnten intensiver Forschung ist bisher nicht bekannt, wie Cellulose in der Pflanze gebildet wird. Informationen über die Gene und die dazugehörigen Enzyme, die die Cellulose synthetisieren, würden neue Möglichkeiten eröffnet bis hin zu transgenen Pflanzen mit erhöhtem Cellulosegehalt, einer verbesserten Qualität, aber auch der Entwicklung ganz neuer Herbizide, die gezielt die Cellulosebiosynthese z. B. von Unkräutern inhibieren können. Die Zielsetzung dieses Projektes ist es, die Proteine die an der Cellulosesynthese beteiligt sind, unter Aktivitätserhalt zu isolieren und zu charakterisieren sowie die entsprechenden Gene zu identifizieren, um so erstmals den molekularen Mechanismus der pflanzlichen Cellulosebiosynthese aufzuklären.
We investigate the biology of the economically important and wood colonizing fungus Stereum sanguinolentum. The basidiomycete Stereum sanguinolentum is a primary coloniser of fresh wounds of conifers where it causes white rot. Population structure, genetics and ecology of S. sanguinolentumare studied the ultimate goal beeing biological control of this pathogen. So far, the spatial population structure has been recorded in a windthrow and amphitallsim has been detected this reproductively very versatile species. For the future we envisage to characterize metabolites which are involved in wood discolouration and to compare strains from heartrot with wound colonizeres. Moreover, interactions with the mycoparasite Tremella encephala will be studied. The study is performed as a series of diploma/master and term papers.
The Global Ozone Monitoring Experiment-2 (GOME-2) instrument continues the long-term monitoring of atmospheric trace gas constituents started with GOME / ERS-2 and SCIAMACHY / Envisat. Currently, there are three GOME-2 instruments operating on board EUMETSAT's Meteorological Operational satellites MetOp-A, -B, and -C, launched in October 2006, September 2012, and November 2018, respectively. GOME-2 can measure a range of atmospheric trace constituents, with the emphasis on global ozone distributions. Furthermore, cloud properties and intensities of ultraviolet radiation are retrieved. These data are crucial for monitoring the atmospheric composition and the detection of pollutants. DLR generates operational GOME-2 / MetOp level 2 products in the framework of EUMETSAT's Satellite Application Facility on Atmospheric Chemistry Monitoring (AC-SAF). GOME-2 near-real-time products are available already two hours after sensing. The operational NO2 total column products are generated using the algorithm GDP (GOME Data Processor) version 4.x integrated into the UPAS (Universal Processor for UV / VIS Atmospheric Spectrometers) processor for generating level 2 trace gas and cloud products. The operational NO2 tropospheric column products are generated using the algorithm GDP (GOME Data Processor) version 4.x for NO2 [Valks et al. (2011)] integrated into the UPAS (Universal Processor for UV / VIS Atmospheric Spectrometers) processor for generating level 2 trace gas and cloud products. The total NO2 column is retrieved from GOME solar back-scattered measurements in the visible wavelength region using the DOAS method. An additional algorithm is applied to derive the tropospheric NO2 column: after subtracting the estimated stratospheric component from the total column, the tropospheric NO2 column is determined using an air mass factor based on monthly climatological NO2 profiles from the MOZART-2 model. For more details please refer to relevant peer-review papers listed on the GOME and GOME-2 documentation pages: https://atmos.eoc.dlr.de/app/docs/
The Global Ozone Monitoring Experiment-2 (GOME-2) instrument continues the long-term monitoring of atmospheric trace gas constituents started with GOME / ERS-2 and SCIAMACHY / Envisat. Currently, there are three GOME-2 instruments operating on board EUMETSAT's Meteorological Operational satellites MetOp-A, -B and -C, launched in October 2006, September 2012, and November 2018, respectively. GOME-2 can measure a range of atmospheric trace constituents, with the emphasis on global ozone distributions. Furthermore, cloud properties and intensities of ultraviolet radiation are retrieved. These data are crucial for monitoring the atmospheric composition and the detection of pollutants. DLR generates operational GOME-2 / MetOp level 2 products in the framework of EUMETSAT's Satellite Application Facility on Atmospheric Chemistry Monitoring (AC-SAF). GOME-2 near-real-time products are available already two hours after sensing. OCRA (Optical Cloud Recognition Algorithm) and ROCINN (Retrieval of Cloud Information using Neural Networks) are used for retrieving the following geophysical cloud properties from GOME and GOME-2 data: cloud fraction (cloud cover), cloud-top pressure (cloud-top height), and cloud optical thickness (cloud-top albedo). OCRA is an optical sensor cloud detection algorithm that uses the PMD devices on GOME / GOME-2 to deliver cloud fractions for GOME / GOME-2 scenes. ROCINN takes the OCRA cloud fraction as input and uses a neural network training scheme to invert GOME / GOME-2 reflectivities in and around the O2-A band. VLIDORT [Spurr (2006)] templates of reflectances based on full polarization scattering of light are used to train the neural network. ROCINN retrieves cloud-top pressure and cloud-top albedo. The cloud-top pressure for GOME scenes is derived from the cloud-top height provided by ROCINN and an appropriate pressure profile. For more details please refer to relevant peer-review papers listed on the GOME and GOME-2 documentation pages: https://atmos.eoc.dlr.de/app/docs/
The Global Ozone Monitoring Experiment-2 (GOME-2) instrument continues the long-term monitoring of atmospheric trace gas constituents started with GOME / ERS-2 and SCIAMACHY / Envisat. Currently, there are three GOME-2 instruments operating on board EUMETSAT's Meteorological Operational satellites MetOp-A, -B and -C, launched in October 2006, September 2012, and November 2018, respectively. GOME-2 can measure a range of atmospheric trace constituents, with the emphasis on global ozone distributions. Furthermore, cloud properties and intensities of ultraviolet radiation are retrieved. These data are crucial for monitoring the atmospheric composition and the detection of pollutants. DLR generates operational GOME-2 / MetOp level 2 products in the framework of EUMETSAT's Satellite Application Facility on Atmospheric Chemistry Monitoring (AC-SAF). GOME-2 near-real-time products are available already two hours after sensing. OCRA (Optical Cloud Recognition Algorithm) and ROCINN (Retrieval of Cloud Information using Neural Networks) are used for retrieving the following geophysical cloud properties from GOME and GOME-2 data: cloud fraction (cloud cover), cloud-top pressure (cloud-top height), and cloud optical thickness (cloud-top albedo). OCRA is an optical sensor cloud detection algorithm that uses the PMD devices on GOME / GOME-2 to deliver cloud fractions for GOME / GOME-2 scenes. ROCINN takes the OCRA cloud fraction as input and uses a neural network training scheme to invert GOME / GOME-2 reflectivities in and around the O2-A band. VLIDORT [Spurr (2006)] templates of reflectances based on full polarization scattering of light are used to train the neural network. ROCINN retrieves cloud-top pressure and cloud-top albedo. The cloud optical thickness is computed using libRadtran [Mayer and Kylling (2005)] radiative transfer simulations taking as input the cloud-top albedo retrieved with ROCINN. For more details please refer to relevant peer-review papers listed on the GOME and GOME-2 documentation pages: https://atmos.eoc.dlr.de/app/docs/
The Global Ozone Monitoring Experiment-2 (GOME-2) instrument continues the long-term monitoring of atmospheric trace gas constituents started with GOME / ERS-2 and SCIAMACHY / Envisat. Currently, there are three GOME-2 instruments operating on board EUMETSAT's Meteorological Operational satellites MetOp-A, -B and -C, launched in October 2006, September 2012, and November 2018, respectively. GOME-2 can measure a range of atmospheric trace constituents, with the emphasis on global ozone distributions. Furthermore, cloud properties and intensities of ultraviolet radiation are retrieved. These data are crucial for monitoring the atmospheric composition and the detection of pollutants. DLR generates operational GOME-2 / MetOp level 2 products in the framework of EUMETSAT's Satellite Application Facility on Atmospheric Chemistry Monitoring (AC-SAF). GOME-2 near-real-time products are available already two hours after sensing. OCRA (Optical Cloud Recognition Algorithm) and ROCINN (Retrieval of Cloud Information using Neural Networks) are used for retrieving the following geophysical cloud properties from GOME and GOME-2 data: cloud fraction (cloud cover), cloud-top pressure (cloud-top height), and cloud optical thickness (cloud-top albedo). OCRA is an optical sensor cloud detection algorithm that uses the PMD devices on GOME / GOME-2 to deliver cloud fractions for GOME / GOME-2 scenes. For more details please refer to relevant peer-review papers listed on the GOME and GOME-2 documentation pages: https://atmos.eoc.dlr.de/app/docs/
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