This article describes the principles used to generate global gap-free Leaf Area Index (LAI) time series from 2002–2012, based on MERIS (MEdium Resolution Imaging Spectrometer) full-resolution Level1B data. It is produced as a series of 10-day composites in geographic projection at 300-m spatial resolution. The processing chain comprises geometric correction, radiometric correction, pixel identification, LAI calculation with the BEAM (Basic ERS & Envisat (A)ATSR and MERIS Toolbox) MERIS vegetation processor, re-projection to a global grid and temporal aggregation selecting the measurement closest to the mean value. After the LAI pre-processing, we applied time series analysis to fill data gaps and to filter outliers using the technique of harmonic analysis (HA) in combination with mean annual and multiannual phenological data. Data gaps are caused by clouds, sensor limitations due to the solar zenith angle (<10°), topography and intermittent data reception. We applied our technique for the whole period of observation (July 2002–March 2012). Validation, carried out with VALERI (Validation of Land European Remote Sensing Instruments) and BigFoot data, revealed a high degree (R² : 0.88) of agreement on a global scale.

Many geo-hazards such as earthquakes, tsunamis, volcanic eruptions, severe weather, etc., produce acoustic waves with sub-audible frequency, so called infrasound. This sound propagates from the surface to the middle and upper atmosphere causing pressure and temperature perturbations. Temperature fluctuations connected with the above mentioned events usually are very weak at the surface, but the amplitude increases with height because of the exponential decrease of atmospheric pressure with increasing altitude. At the mesopause region (80–100 km height) signal amplitudes are about two to three orders of magnitude larger than on the ground. The GRIPS (GRound-based Infrared P-branch Spectrometer) measurement system operated by the German Remote Sensing Data Center of the German Aerospace Center (DLR-DFD) derives temperatures of the mesopause region by observing hydroxyl (OH) airglow emissions in the near infrared atmospheric emission spectrum originating from a thin layer at approximately 87 km height. The GRIPS instrument is in principle suited for the detection of infrasonic signals generated by e.g. tsunamis and other geo-hazards. This is due to the fact that the infrasound caused by such events should induce observable short-period fluctuations in the OH airglow temperatures. First results obtained during a field campaign performed at the Environmental Research Station "Schneefernerhaus", Zugspitze (47.4° N, 11.0° E) from October to December 2008 are presented regarding potential sources of meteorological and orographical origin. An adequate distinction of the overlapping infrasonic signatures caused by different infrasound sources in the OH temperature record is needed for the ascription to the proper source. The approach presented here could form a contribution to a hazard monitoring and early warning system.

We demonstrate how machine learning can be easily applied to support the analysis of large quantities of excited hydroxyl (OH^*) airglow imager data. We use a TCN (temporal convolutional network) classification algorithm to automatically pre-sort images into the three categories “dynamic” (images where small-scale motions like turbulence are likely to be found), “calm” (clear-sky images with weak airglow variations) and “cloudy” (cloudy images where no airglow analyses can be performed). The proposed approach is demonstrated using image data of FAIM 3 (Fast Airglow IMager), acquired at Oberpfaffenhofen, Germany, between 11 June 2019 and 25 February 2020, achieving a mean average precision of 0.82 in image classification. The attached video sequence demonstrates the classification abilities of the learned TCN. Within the dynamic category, we find a subset of 13 episodes of image series showing turbulence. As FAIM 3 exhibits a high spatial (23 m per pixel) and temporal (2.8 s per image) resolution, turbulence parameters can be derived to estimate the energy diffusion rate. Similarly to the results the authors found for another FAIM station (Sedlak et al., 2021), the values of the energy dissipation rate range from 0.03 to 3.18 W kg⁻¹.

The ionosphere plays an important role for the propagation of radio signals. The majority of ionospheric disturbances is caused by magnetospheric and solar processes. However, a significant number of disturbances cannot be explained by these external forcing mechanisms. It is suspected that internal atmospheric dynamics, including small- and large-scale waves propagating from the lower atmosphere into the ionosphere, are the cause of much of the remaining variability (e.g., the formation of sporadic E-layers).These ionospheric disturbances are a significant and highly variable source of positioning errors of global navigation satellite system (GNSS) signals. The relation between middle atmospheric dynamics and GNSS signal integrity is studied by utilizing several years of OH airglow observations in the vicinity of European Geostationary Navigation Overlay Service (EGNOS) grid points.OH airglow observations provide neutral atmospheric temperatures at the upper mesosphere lower thermosphere (UMLT), i.e. at approximately 80 to 100 kilometers height, so in the ionospheric D region. Ground-based airglow observations with high temporal resolution are performed at the reference site of the Network for the Detection of Mesospheric Change (NDMC) at the Environmental Research Station Schneefernerhaus (UFS, 11.0° N, 47.0° E) since 2009. These data allow precise observations of acoustic, gravity, tidal and planetary wave disturbances in the UMLT; at least some of these atmospheric waves can propagate from the D region higher up into the E region or maybe F region.While the EGNOS provides integrated information on the ionospheric state at a given time, the OH airglow at the lower edge of the ionosphere is influenced amongst others by upward propagating phenomena. Therefore, we investigate the relationship between EGNOS-broadcasted ionospheric delays and observed UMLT-variability addressing the question whether airglow observations can be used to perform short-term predictions of GNSS signal deterioration by atmospheric variability. Emphasize is placed on the role of semi-diurnal tides and a potential connection to a semi-annual oscillation observed in EGNOS-delay information.

The “Alpine Environmental Data Analysis Centre” (AlpEnDAC) is a research data management and analysis platform for research facilities around the Alps and similar mountain ranges. It provides the computational infrastructure for the Virtual Alpine Observatory (VAO), which is a research network of European high-altitude research stations (http://www.vao.bayern.de). Within the scope of previous work, the platform was developed with the focus on research data and metadata management as well as analysis and simulation tools. It offers the possibility to store and retrieve data securely (data-on-demand), to share it with other scientists and to interpret it with the help of computing-on-demand solutions via a user friendly web-based graphical user interface. The AlpEnDAC allows the analysis and consolidation of heterogeneous data sets from ground-based to satellite instruments. In a further development phase, launched on 1 August 2019, the existing services of the AlpEnDAC will be supplemented by new components in the fields of user support and quality assurance. Furthermore, the modelling and analysis software portfolio will be extended, focusing on the development of innovative services in the fields of service-on-demand and operating-on-demand as well as the integration of new data sources and measurement instruments. The AlpEnDAC helps environmental scientists to benefit from modern data management, data analysis, and simulation techniques. The VAO network, now including ten countries (Austria, France, Germany, Georgia, Italy, Norway, Slovenia, Switzerland, Bulgaria, and the Czech Republic) is an ideal and exciting context for developing the AlpEnDAC with researchers. This project receives funding from the Bavarian State Ministry of the Environment and Consumer Protection.