ALBERT

Description: The thermosphere causes by far the largest non-gravitational perturbing acceleration of near-Earth orbiting satellites. Especially between 80 km and 1,000 km, the thermospheric density distribution and variations are required to model accurately this acceleration for precise orbit determination (POD), ephemeris computation and re-entry prediction of the Low-Earth Orbiting (LEO) satellites. So far, mostly on-board accelerometers are used to measure the thermospheric density. However, such type of satellite is usually of complex shape and any error or mismodelling in the satellite drag coefficient and satellite effective cross-sectional area will directly propagate into the derived thermospheric density values. At GFZ, an empirical model of the thermospheric mass density denoted as “CH-Therm-2018” has been developed by using 9 years (2001–2009) of CHAMP observations. A completely different approach for thermospheric density determination is based on using satellite laser ranging (SLR) measurements to LEO satellites equipped with retro-reflectors to determine an accurate satellite orbit. These measurements are sensitive to small perturbations acting on the satellite. In order to minimize the error induced by imprecise satellite macro-models, we use in our investigation SLR observations to satellites with a simple spherical shape and thus, relate estimated scaling factors to the thermospheric density. In this paper, we use SLR observations to two ANDE-2 satellites – ANDE-Castor and ANDE-Pollux – as well as SpinSat with altitudes between 248 km and 425 km to calibrate the CH-Therm-2018 model, as well as four other empirical models of thermospheric density, namely CIRA86, NRLMSISE00, JB2008 and DTM2013. For our tests, we chose a period from 16 August 2009 to 26 March 2010 of low solar activity and a period from 29 December 2014 to 29 March 2015 of high solar activity. Using data of a few geodetic satellites obtained at the same and different time intervals allows us to investigate the reliability of the scaling factors of the thermospheric densities provided by the models. We have found that CIRA86 and NRLMSISE00 most significantly overestimate the thermospheric density at the period of low solar activity among the models tested. The JB2008 model is the least scaled model and provides reliable values of the thermospheric density for the periods of both low and high solar activity. The GFZ CH-Therm-2018 model, on the contrary, underestimates the thermospheric density at the time interval of low solar activity. Using SLR observations at longer time intervals should allow to investigate temporal evolution of the scaling factors of these models more precisely.

Description: In this study, we present an empirical model, named CH-Therm-2018, of the thermospheric mass density derived from 9-year (from August 2000 to July 2009) accelerometer measurements from the CHAllenging Mini-satellite Payload (CHAMP) satellite at altitudes from 460 to 310km. The CHAMP dataset is divided into two 5-year periods with 1-year overlap (from August 2000 to July 2005 and from August 2004 to July 2009) to represent the high-to-moderate and moderate-to-low solar activity conditions, respectively. The CH-Therm-2018 model describes the thermospheric density as a function of seven key parameters, namely the height, solar flux index, season (day of year), magnetic local time, geographic latitude and longitude, as well as magnetic activity represented by the solar wind merging electric field. Predictions of the CH-Therm-2018 model agree well with CHAMP observations (within 20%) and show different features of thermospheric mass density during the two solar activity levels, e.g., the March–September equinox asymmetry and the longitudinal wave pattern. From the analysis of satellite laser ranging (SLR) observations of the ANDE-Pollux satellite during August–September 2009, we estimate 6h scaling factors of the thermospheric mass density provided by our model and obtain the median value equal to 1.267±0.60. Subsequently, we scale up our CH-Therm-2018 mass density predictions by a scale factor of 1.267. We further compare the CH-Therm-2018 predictions with the Naval Research Laboratory Mass Spectrometer Incoherent Scatter Radar Extended (NRLMSISE-00) model. The result shows that our model better predicts the density evolution during the last solar minimum (2008–2009) than the NRLMSISE-00 model.

Description: Integrated observation platforms have been set up to investigate consequences of global change within a terrestrial network of observatories (TERENO) in Germany. The aim of TERENO is to foster the understanding of water, energy, and matter fluxes in terrestrial systems, as well as their biological and physical drivers. Part of the Lower Rhine Valley-Eifel observatory of TERENO is located within the Eifel National Park. Recently, the National Park forest management started to promote the natural regeneration of near-natural beech forest by removing a significant proportion of the spruce forest that was established for timber production after World War II. Within this context, the effects of such a disturbance on forest ecosystem functioning are currently investigated in a deforestation experiment in the Wustebach catchment, which is one of the key experimental research sites within the Lower Rhine Valley-Eifel observatory. Here, we present the integrated observation system of the Wustebach test site to exemplarily demonstrate the terrestrial observatory concept of TERENO that allows for a detailed monitoring of changes in hydrological and biogeochemical states and fluxes triggered by environmental disturbances. We present the observation platforms and the soil sampling campaign, as well as preliminary results including an analysis of data consistency. We specifically highlight the capability of integrated datasets to enable improved process understanding of the post-deforestation changes in ecosystem functioning.