ALBERT

Description: In 2018 we celebrated 25 years of development of radar altimetry, and the progress achieved by this methodology in the fields of global and coastal oceanography, hydrology, geodesy and cryospheric sciences. Many symbolic major events have celebrated these developments, e.g., in Venice, Italy, the 15th (2006) and 20th (2012) years of progress and more recently, in 2018, in Ponta Delgada, Portugal, 25 Years of Progress in Radar Altimetry. On this latter occasion it was decided to collect contributions of scientists, engineers and managers involved in the worldwide altimetry community to depict the state of altimetry and propose recommendations for the altimetry of the future. This paper summarizes contributions and recommendations that were collected and provides guidance for future mission design, research activities, and sustainable operational radar altimetry data exploitation. Recommendations provided are fundamental for optimizing further scientific and operational advances of oceanographic observations by altimetry, including requirements for spatial and temporal resolution of altimetric measurements, their accuracy and continuity. There are also new challenges and new openings mentioned in the paper that are particularly crucial for observations at higher latitudes, for coastal oceanography, for cryospheric studies and for hydrology. The paper starts with a general introduction followed by a section on Earth System Science including Ocean Dynamics, Sea Level, the Coastal Ocean, Hydrology, the Cryosphere and Polar Oceans and the “Green” Ocean, extending the frontier from biogeochemistry to marine ecology. Applications are described in a subsequent section, which covers Operational Oceanography, Weather, Hurricane Wave and Wind Forecasting, Climate projection. Instruments’ development and satellite missions’ evolutions are described in a fourth section. A fifth section covers the key observations that altimeters provide and their potential complements, from other Earth observation measurements to in situ data. Section 6 identifies the data and methods and provides some accuracy and resolution requirements for the wet tropospheric correction, the orbit and other geodetic requirements, the Mean Sea Surface, Geoid and Mean Dynamic Topography, Calibration and Validation, data accuracy, data access and handling (including the DUACS system). Section 7 brings a transversal view on scales, integration, artificial intelligence, and capacity building (education and training). Section 8 reviews the programmatic issues followed by a conclusion.

Description: Interannual to decadal sea level trends are indicators of climate variability and change. A major source of global and regional sea level data is satellite radar altimetry, which relies on precise knowledge of the satellite's orbit. Here, we assess the error budget of the radial orbit component for the TOPEX/Poseidon mission for the period 1993 to 2004 from a set of different orbit solutions. Upper bound errors for seasonal, interannual (5 years), and decadal periods are estimated on global and regional scales based on radial orbit differences from three state-of-the-art orbit solutions provided by different research teams (GFZ, GSFC, and GRGS). The global mean sea level error related to the orbit is of the order of 7 mm (more than 10 % of the sea level variability) with negligible contributions on the annual and decadal time scale. In contrast, the orbit related error of the interannual trend is 0.1 mm/year (18 % of the corresponding sea level variability) and might hamper the estimation of an acceleration of the global mean sea level rise. For regional scales, the gridded orbit related error is up to 11 mm and for about half the ocean the orbit error accounts for at least 10 % of the observed sea level variability. The seasonal orbit error amounts to 10 % of the observed seasonal sea level signal in the Southern Ocean. At interannual and decadal time scales, the orbit related trend uncertainties reach regionally more than 1 mm/year. The interannual trend errors account for 10 % of the observed sea level signal in the Tropical Atlantic and the south-eastern Pacific. For decadal scales, the orbit related trend errors are prominent in a couple of regions including: South Atlantic, western North Atlantic, central Pacific, South Australian Basin, and Mediterranean Sea. Based on a set of test orbits calculated at GFZ, the sources of the observed orbit related errors are further investigated. Main contributors on all time scales are uncertainties in Earth’s time variable gravity field models and on annual to interannual time scales discrepancies of the tracking station sub-networks, i.e., SLR and DORIS.

Description: The data set provides GFZ VER11 orbits of altimetry satellites ERS-1 (August 1, 1991 - July 5, 1996), ERS-2 (May 13, 1995 - February 27, 2006), Envisat (April 12, 2002 - April 8, 2012), Jason-1 (January 13, 2002 - July 5, 2013) and Jason-2 (July 5, 2008 - April 5, 2015) TOPEX/Poseidon (September 23, 1992 - October 8, 2005), derived at the time spans given at Helmholtz Centre Potsdam GFZ German Research Centre for Geosciences within the Sea Level phase 2 project of the European Space Agency (ESA) Climate Change Initiative using "Earth Parameter and Orbit System - Orbit Computation (EPOS-OC)" software and the Altimeter Database and processing System (ADS, http://adsc.gfz-potsdam.de/ads/) developed at GFZ. The orbits were computed in the same (ITRF2008) terrestrial reference frame for all satellites using common, most precise models and standards available and described below. The ERS-1 orbit is computed using satellite laser ranging (SLR) and altimeter crossover data, while the ERS-2 orbit is derived using additionally Precise Range And Range-rate Equipment (PRARE) measurements. The Envisat, TOPEX/Poseidon, Jason-1 and Jason-2 orbits are based on Doppler Orbitography and Radiopositioning Integrated by Satellite (DORIS) and SLR observations. The orbit files are available in the Extended Standard Product 3 Orbit Format (SP3-c, ftp://igscb.jpl.nasa.gov/igscb/data/format/sp3c.txt) Files are gzip-compressed. File names are given as sate_YYYYMMDD_SP3C.gz, where "sate" is the abbreviation (ENVI, ERS1, ERS2, JAS1, JAS2, TOPX) of the satellite name, YYYY stands for 4-digit year, MM stands for month and DD stands for day of the beginning of the file. More details on these orbits are provided in Rudenko et al. (2017)

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: New, precise, consistent orbits (VER11) of altimetry satellites ERS-1, ERS-2, TOPEX/Poseidon, Envisat, Jason-1, and Jason-2 have been recently derived at the GFZ German Research Centre for Geosciences in the extended ITRF2008 terrestrial reference frame using improved models and covering the time span 1991–2015. These orbits show improved quality, as compared with GFZ previous (VER6) orbits derived in 2013. Improved macromodels reduce root mean square (RMS) fits of satellite laser ranging (SLR) observations by 2.6%, 6.9%, and 7% for TOPEX/Poseidon, Jason-1, and Jason-2, respectively. Applying Vienna Mapping Functions 1 instead of Hopfield model for tropospheric correction of Doppler Orbitography and Radiopositioning Integrated by Satellite (DORIS) observations reduces RMS fits of SLR observations by 2%–2.4% and those of DORIS observations by 2.6% for Envisat and Jason satellites. Using satellite true attitude instead of models improves Jason-1 SLR RMS fits by 41% from July 2012 until July 2013. The VER11 orbits indicate the mean values of the SLR RMS fits between 1.2 and 2.1 cm for the different missions. The internal orbit consistency in the radial direction is between 0.5 and 1.9 cm. The global mean sea level trend for the period 1993–2014 from TOPEX, Jason-1, and Jason-2 is 2.8 and 3.0 mm/year using GFZ VER6 and VER11 orbits, respectively. Regionally, the decadal trends from GFZ VER11 and external orbits vary in the order of 1 mm/year.

Description: This study makes use of current GFZ monthly and daily gravity field products from 2002 to 2014 based on radial basis functions (RBF) instead of time variable gravity field modeling for precise orbit determination of altimetry satellites. Since some monthly solutions are missing in the GFZ GRACE RL05a solution and in order to reach a better quality for the precise orbit determination, daily generated RBF solutions obtained from Kalman filtered GRACE data processing and interpolated in case of gaps have been used. Moreover, since the geopotential coefficients of low degrees are better determined using SLR observations to geodetic satellites like Lageos, Stella, Starlette and Ajisai than from GRACE observations, these terms are co-estimated in the RBF solutions by using apriori SLR-derived values up to degree and order 4. Precise orbits for altimetry satellites Envisat (2002-2012), Jason-1 (2002-2013) and Jason-2 (2008-2014) are then computed over the given time intervals using this approach and compared with the orbits obtained when using other models such as EIGEN-6S4. An analysis of the rootmean- square values of the observation fits of SLR and DORIS observations and the orbit arcs overlaps will allow us to draw a conclusion on the quality of the RBF solution and to use these new trajectories for sea level trend estimates and geophysical application.

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: In early 2023, the DTRF2020 solution of DGFI-TUM was finalized. In this presentation, we discuss the results of the analysis of the DTRF2020 station coordinates and datum parameters. Based on time series analyses, we investigate the impact of the reduction of non-tidal loading displacements from the daily GNSS normal equations on the derived station position time series and discuss possible (local/regional) differences with respect to the loading models. The datum parameter time series derived in DTRF2020 computation are also analyzed in terms of their sensitivity to the considered non-tidal loading displacements. We further analyze the noise evolution of the time series and their long-term stability. In connection with the investigations of the DTRF2020 scale, we also discuss scale changes compared to DTRF2014 which are mainly caused by updates in modeling and parameterization of the individual techniques.