DWD’s fully automatic MOSMIX product optimizes and interprets the forecast calculations of the NWP models ICON (DWD) and IFS (ECMWF), combines these and calculates statistically optimized weather forecasts in terms of point forecasts (PFCs). Thus, statistically corrected, updated forecasts for the next ten days are calculated for about 5400 locations around the world. Most forecasting locations are spread over Germany and Europe. MOSMIX forecasts (PFCs) include nearly all common meteorological parameters measured by weather stations. For further information please refer to: [in German: https://www.dwd.de/DE/leistungen/met_verfahren_mosmix/met_verfahren_mosmix.html ] [in English: https://www.dwd.de/EN/ourservices/met_application_mosmix/met_application_mosmix.html ]
DWD’s fully automatic MOSMIX product optimizes and interprets the forecast calculations of the NWP models ICON (DWD) and IFS (ECMWF), combines these and calculates statistically optimized weather forecasts in terms of point forecasts (PFCs). Thus, statistically corrected, updated forecasts for the next ten days are calculated for about 5400 locations around the world. Most forecasting locations are spread over Germany and Europe. MOSMIX forecasts (PFCs) include nearly all common meteorological parameters measured by weather stations. For further information please refer to: [in German: https://www.dwd.de/DE/leistungen/met_verfahren_mosmix/met_verfahren_mosmix.html ] [in English: https://www.dwd.de/EN/ourservices/met_application_mosmix/met_application_mosmix.html ]
DWD’s fully automatic MOSMIX product optimizes and interprets the forecast calculations of the NWP models ICON (DWD) and IFS (ECMWF), combines these and calculates statistically optimized weather forecasts in terms of point forecasts (PFCs). Thus, statistically corrected, updated forecasts for the next ten days are calculated for about 5400 locations around the world. Most forecasting locations are spread over Germany and Europe. MOSMIX forecasts (PFCs) include nearly all common meteorological parameters measured by weather stations. For further information please refer to: [in German: https://www.dwd.de/DE/leistungen/met_verfahren_mosmix/met_verfahren_mosmix.html ] [in English: https://www.dwd.de/EN/ourservices/met_application_mosmix/met_application_mosmix.html ]
DWD’s fully automatic MOSMIX product optimizes and interprets the forecast calculations of the NWP models ICON (DWD) and IFS (ECMWF), combines these and calculates statistically optimized weather forecasts in terms of point forecasts (PFCs). Thus, statistically corrected, updated forecasts for the next ten days are calculated for about 5400 locations around the world. Most forecasting locations are spread over Germany and Europe. MOSMIX forecasts (PFCs) include nearly all common meteorological parameters measured by weather stations. For further information please refer to: [in German: https://www.dwd.de/DE/leistungen/met_verfahren_mosmix/met_verfahren_mosmix.html ] [in English: https://www.dwd.de/EN/ourservices/met_application_mosmix/met_application_mosmix.html ]
Mehrmals täglich ermitteln Fachleute an Messstationen der Bundesländer und des Umweltbundesamtes die Qualität unserer Luft. Schon kurz nach der Messung können Sie sich über die Luftdaten-API die aktuellen Messwerte abrufen. Zurzeit sind Daten ab dem Jahr 2016 abrufbar. Bitte beachten Sie, dass es sich bei den Daten des laufenden Jahres um noch nicht endgültig geprüfte Daten handelt. Erst im Juni des Folgejahres werden die finalen Daten bereitgestellt. Die aktuellen Daten können Lücken aufgrund Übertragungsproblemen enthalten. Das UBA kann keine Vollständigkeit garantieren. Unterjährig erfolgen Updates mit vorläufig geprüften Daten.
Das Projekt 'Bäume als Indikatoren für die urbane Wärmeinsel (BIWi)' ist eine Vorstudie, in der an 12 stadtökologisch unterschiedlichen Standorten Berlins, der Stadt Deutschlands mit dem größten innerstädtischen Wärmeinseleffekt (urban heat island, UHI), mittels dendroklimatologischer Methoden Chronologien zu verschiedenen Jahrringparametern (Jahrringbreite JRB, Weiserjahrkataloge, holzanatomische Merkmale) erzeugt und analysiert werden. Das Ziel ist es zu untersuchen, mit welcher Güte und Sicherheit welche Wuchsmerkmale auf Einflüsse des UHI-Effektes zurückzuführen sind. Ausgehend von in Dendroklimatologie und Zeitreihenanalytik anerkannten und häufig erfolgreich angewandten Methoden zur Messtechnik, Datenaufbereitung und Datenanalyse soll ein Methodenverbund aus Korrelations-, Regressions-, Hauptkomponenten- und Extremjahranalysen für urbane Räume entwickelt werden, um an verschiedenen Standorten die Wachstumsfaktoren für die im Mittel herrschenden Klimabedingungen, wie auch für extreme Wetterlagen (Trocken- oder Hitzeperioden, Smoglagen) zu bestimmen und zu hierarchisieren. Dazu werden an 12 stadtökologisch unterschiedlichen Standorten an insgesamt ca. 150 Bäumen Chronologien zur Jahrringbreite wie auch Kataloge zu extremen Wuchsreaktionen und holzanatomischen Merkmalen (Frostringe, Dichteschwankungen, u.a.) generiert. Im Vergleich mit Standorten aus dem Berliner Umland werden die Effekte der UHI abschließend von den allgemeinen klimatischen Wachstumsfaktoren getrennt. Insbesondere für diesen Teilschritt ist neben der Analyse spezifischer Stadtbaumarten (Platane, Ahorn, Winterlinde oder ähnlichen) auch die von sogenannten waldbildenden Baumarten wie etwa Eiche, Buche oder Kiefer in der Stadt von Bedeutung, um die gefundenen Stadt-Umland-Diversitäten nicht durch artspezifische Unterschiede zu verwischen. Die in der Vorstudie gewonnenen Ergebnisse werden im Rahmen zweier Masterarbeiten ausgewertet und interpretiert und überdies in einem international anerkannten Fachjournal veröffentlicht. Bisher vorliegende Studien setzen die Dendroklimatologie erfolgreich ein, um das Wachstum urbaner Bäume zu analysieren. Die Innovation des Projektes BIWi beruht auf der erstmaligen Nutzung der Bäume und der dendroklimatologischen Techniken für die Analyse stadtklimatologischer Fragestellungen, insbesondere der räumlichen und zeitlichen Entwicklung der UHI. Im Erfolgsfalle dient diese Vorstudie dazu, in einem größer angelegten Folgeprojekt das übergeordnete Ziel zu verfolgen, ein Verfahren zur Untersuchung der räumlichen Verbreitung und raumzeitlichen Entwicklung von UHIs mit Hilfe dendroökologischer Datensätze zu entwickeln. Perspektivisch kann so dazu beizutragen werden retrospektiv und projektiv Aussagen zur Entwicklung von UHIs vor dem Hintergrund sich ändernder klimatischer, demographischer und städteplanerischer Entwicklungen zu treffen.
Introduction: In Malaysia, excessive nutrients from livestock waste management systems are currently released to the environment. Particularly, large amounts of manure from intensive pig production areas are being excreted daily and are not being fully utilised. Alternatively, the excess manure can be applied as an organic fertiliser source in neighbouring cropping systems on the small landholdings of the pig farms to improve soil fertility so that its nutrients will be available for crop uptake instead of being discharged into water streams. Thus, there is a need for better tools to analyse the present situation, to evaluate and monitor alternative livestock production systems and manure management scenarios, and to support farmers in the proper management of manure and fertiliser application. Such tools are essential to quantify, and assess nutrient fluxes, manure quality and content, manure storage and application rate to the land as well as its environmental effects. Several computer models of animal waste management systems to assist producers and authorities are now available. However, it is felt that more development is needed to adopt such models to the humid tropics and conditions of Malaysia and other developing countries in the region. Objectives: The aim is to develop a novel model to evaluate nutrient emission scenarios and the impact of livestock waste at the landscape or regional level in humid tropics. The study will link and improve existing models to evaluate emission of N to the atmosphere, and leaching of nutrients to groundwater and surface water. The simulation outputs of the models will be integrated with a GIS spatial analysis to model the distribution of nutrient emission, leaching and appropriate manure application on neighbouring crop lands and as an information and decision support tool for the relevant users.
Within phase 2 of the CAOS research unit we will work towards a holistic framework to explore how spatial organization alongside with spatial heterogeneity controls terrestrial water and energy cycles in intermediate scale catchments. 'Holistic' means for us to link the 'how' to the 'why' by drawing from generic understanding of landscape formation and biotic controls on processes and structures as well as to rely on exemplary experimental learning in a hypothesis and theory based manner. This also implies treatment of soil, vegetation and atmosphere as coupled system rather than a linear combination of different compartments. To jointly work towards this goal we propose 7 projects which will closely cooperate within two overarching work packages:WP1: Linking hydrological similarity with landscape structure across scalesWP2: Searching for appropriate catchment models and organizing principles. Within WP1 we will further refine the existing stratified multi-method and multi-sensor setup to search for functional entities in the Attert and, if they exist, to learn in an exemplary manner which structural features control functional characteristics. This essentially includes identification of suitable metrics to discriminate functional and structural similarity from data as well as identification of useful quantitative descriptors for the rather fuzzy term 'hydrological function'. Overall we aim to synthesize a protocol to decide 'where to assess which data for what reasons' for characterizing hydrological functioning across a scale range of four orders of magnitude.Within WP2 we will foster our distillery of parsimonious and nevertheless physically consistent model structures which rely on observable quantities and make use of symmetries in the landscape to simplify the governing model equations in a hypothesis based manner. To this end we will compare concurring model structures (among those the CAOS model) and work towards a framework for an objective model inter comparison with special emphasis on a) the added value of different data/information sources and b) on consistency of predictions with respect to distributed dynamics and integral flows. Additionally, we aim in WP2 at linking the 'how' to the 'why' by synthesizing testable hypotheses that could explain whether spatial organization has evolved in accordance with candidate organizing principles. Ecology, fluvial geomorphology and thermodynamics offer a large set of candidate organizing principles for this issue. Based on our recent work we will focus especially on thermodynamic limits and optimality principles like maximum entropy production, explore their value for uncalibrated hydrological predictions and work out the necessary requirements on data and models for testing these principles. We put special emphasis on a possible experimental falsification of these candidate principles; also in close collaboration with the B2-Landscape Evolution Observatory in Tucson, Arizona.
DWD’s fully automatic MOSMIX product optimizes and interprets the forecast calculations of the NWP models ICON (DWD) and IFS (ECMWF), combines these and calculates statistically optimized weather forecasts in terms of point forecasts (PFCs). Thus, statistically corrected, updated forecasts for the next ten days are calculated for about 5400 locations around the world. Most forecasting locations are spread over Germany and Europe. MOSMIX forecasts (PFCs) include nearly all common meteorological parameters measured by weather stations. For further information please refer to: [in German: https://www.dwd.de/DE/leistungen/met_verfahren_mosmix/met_verfahren_mosmix.html ] [in English: https://www.dwd.de/EN/ourservices/met_application_mosmix/met_application_mosmix.html ]
Aims: Floods in small and medium-sized river catchments have often been a focus of attention in the past. In contrast to large rivers like the Rhine, the Elbe or the Danube, discharge can increase very rapidly in such catchments; we are thus confronted with a high damage potential combined with almost no time for advance warning. Since the heavy precipitation events causing such floods are often spatially very limited, they are difficult to forecast; long-term provision is therefore an important task, which makes it necessary to identify vulnerable regions and to develop prevention measures. For that purpose, one needs to know how the frequency and the intensity of floods will develop in the future, especially in the near future, i.e. the next few decades. Besides providing such prognoses, an important goal of this project was also to quantify their uncertainty. Method: These questions were studied by a team of meteorologists and hydrologists from KIT and GFZ. They simulated the natural chain 'large-scale weather - regional precipitation - catchment discharge' by a model chain 'global climate model (GCM) - regional climate model (RCM) - hydrological model (HM)'. As a novel feature, we performed so-called ensemble simulations in order to estimate the range of possible results, i.e. the uncertainty: we used two GCMs with different realizations, two RCMs and three HMs. The ensemble method, which is quite standard in physics, engineering and recently also in weather forecasting has hitherto rarely been used in regional climate modeling due to the very high computational demands. In our study, the demand was even higher due to the high spatial resolution (7 km by 7 km) we used; presently, regional studies use considerably larger grid boxes of about 100 km2. However, our study shows that a high resolution is necessary for a realistic simulation of the small-scale rainfall patterns and intensities. This combination of high resolution and an ensemble using results from global, regional and hydrological models is unique. Results: By way of example, we considered the low-mountain range rivers Mulde and Ruhr and the more alpine Ammer river in this study, all of which had severe flood events in the past. Our study confirms that heavy precipitation events will occur more frequently in the future. Does this also entail an increased flood risk? Our results indicate that in any case, the risk will not decrease. However, each catchment reacts differently, and different models may produce different precipitation and runoff regimes, emphasizing the need of ensemble studies. A statistically significant increase of floods is expected for the river Ruhr in winter and in summer. For the river Mulde, we observe a slight increase of floods during summer and autumn, and for the river Ammer a slight decrease in summer and a slight increase in winter.
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