The combination and contribution of different gravity measurements in regional quasi-geoid determination based on spherical radial basis functions

<p>In this study, we investigate the optimal combination of local gravity observations and their contributions to the regional quasi-geoid model. The study area is located in Colorado, USA, with two types of regional data sets, namely terrestrial gravity data and airborne gravity data, available within the “1 cm geoid experiment”. The approach based on series expansions in terms of spherical radial basis functions (SRBF) is applied, which has been developed at DGFI-TUM in the last two decades. We use two different types of basis functions covering the same spectral domain separately for the terrestrial and the airborne measurements. The Shannon function is applied to the terrestrial data, and the Cubic Polynomial (CuP) function which has smoothing features is applied to the airborne data for filtering their high-frequency noise.</p><p>To assess the contributions of the regional terrestrial and airborne gravity data to the final quasi-geoid model, four solutions are compared, namely the combined solution, the terrestrial only, the airborne only, and finally the model only solution, i.e., only the global gravity model and the topographic model are used without any gravity data from regional measurements. By adding the terrestrial data to the GGM and the topographic model, the RMS error of the quasi-geoid model w.r.t the validation data (the mean solution of independent computations delivered by fourteen institutions from all over the world) drops from 4 to 1.8 cm, and it is further reduced to 1 cm by including the airborne data.</p>

Regional gravity field refinement for (quasi-) geoid determination based on spherical radial basis functions in Colorado

This study presents a solution of the ‘1 cm Geoid Experiment’ (Colorado Experiment) using spherical radial basis functions (SRBFs). As the only group using SRBFs among the fourteen participated institutions from all over the world, we highlight the methodology of SRBFs in this paper. Detailed explanations are given regarding the settings of the four most important factors that influence the performance of SRBFs in gravity field modeling, namely (1) the choosing bandwidth, (2) the locations of the SRBFs, (3) the type of the SRBFs as well as (4) the extensions of the data zone for reducing the edge effect. Two types of basis functions covering the same spectral range are used for the terrestrial and the airborne measurements, respectively. The non-smoothing Shannon function is applied to the terrestrial data to avoid the loss of spectral information. The cubic polynomial (CuP) function which has smoothing features is applied to the airborne data as a low-pass filter for filtering the high-frequency noise. Although the idea of combining different SRBFs for different observations was proven in theory to be possible, it is applied to real data for the first time, in this study. The RMS error of our height anomaly result along the GSVS17 benchmarks w.r.t the validation data (which is the mean results of the other contributions in the ‘Colorado Experiment’) drops by 5% when combining the Shannon function for the terrestrial data and the CuP function for the airborne data, compared to those obtained by using the Shannon function for both the two data sets. This improvement indicates the validity and benefits of using different SRBFs for different observation types. Global gravity model (GGM), topographic model, the terrestrial gravity data, as well as the airborne gravity data are combined, and the contribution of each data set to the final solution is discussed. By adding the terrestrial data to the GGM and the topographic model, the RMS error of the height anomaly result w.r.t the validation data drops from 4 to 1.8 cm, and it is further reduced to 1 cm by including the airborne data. Comparisons with the mean results of all the contributions show that our height anomaly and geoid height solutions at the GSVS17 benchmarks have an RMS error of 1.0 cm and 1.3 cm, respectively; and our height anomaly results give an RMS value of 1.6 cm in the whole study area, which are all the smallest among the participants.

Space Habitat Data Centers—For Future Computing

Data from sensor-bearing satellites requires processing aboard terrestrial data centres that use water for cooling at the expense of high data-transfer latency. The reliance of terrestrial data centres on water increases their water footprint and limits the availability of water for other applications. Therefore, data centres with low data-transfer latency and reduced reliance on Earth’s water resources are required. This paper proposes space habitat data centres (SHDCs) with low latency data transfer and that use asteroid water to address these challenges. The paper investigates the feasibility of accessing asteroid water and the reduction in computing platform access latency. Results show that the mean asteroid water access period is 319.39 days. The use of SHDCs instead of non-space computing platforms reduces access latency and increases accessible computing resources by 11.9–33.6% and 46.7–77% on average, respectively.

Space Habitat Data Centers – For Future Computing

Data from sensor bearing satellites requires processing aboard terrestrial data centers that use water for cooling at the expense of high data transfer latency. The reliance of terrestrial data centers on water increases their water footprint and limits the availability of water for other applications. Therefore, data centers with low data transfer latency and reduced reliance on earth’s water resources are required. This paper proposes space habitat data centers (SHDCs) with low latency data transfer and that use asteroid water to address these challenges. The paper investigates the feasibility of accessing asteroid water and the reduction in computing platform access latency. Results show that the mean asteroid water access period is 319.39 days. The use of SHDCs instead of non-space computing platforms reduces access latency and increases accessible computing resources by (11.9% – 33.6%) and (46.7% – 77%) on average respectively.

Delayed maximum and recession of an East Antarctic outlet glacier

During the last glaciation, East Antarctic outlet glaciers contributed to a grounded ice sheet in the Ross Embayment. The timing of maximum ice extent, as well as of subsequent deglaciation of these outlets, has implications for the behavior of the Antarctic Ice Sheet (AIS) and its impact on global sea level. We present 45 radiocarbon ages of lacustrine cyanobacteria from the Lake Wellman region alongside Hatherton Glacier, which are the first terrestrial data to both record advance of an Antarctic glacier to its maximum position as well as document a high-resolution chronology of subsequent retreat. Seventeen new exposure ages are widely scattered, but the youngest four are in broad agreement with the radiocarbon data. Hatherton Glacier slowly thickened from 13,000 to 9500 yr B.P. and then thinned steadily until at least ca. 2800 yr B.P. Our work affords evidence of both a delayed maximum and recession of an East Antarctic outlet glacier compared to the global Last Glacial Maximum (LGM) and supports growing evidence of a time-transgressive local LGM within the Ross Sea sector of the ice sheet. Both observations are consistent with the idea that the timing of outlet glacier expansion and timing of recession are controlled by the balance between dynamic thinning from ocean forcing and increased accumulation due to atmospheric warming.

The use of different spherical radial basis functions to combine terrestrial and airborne measurements for regional gravity field refinement

<p>The objective of this study is the combination of different types of basis functions applied separately to different kinds of gravity observations. We use two types of regional data sets: terrestrial gravity data and airborne gravity data, covering an area of about 500 km × 800 km in Colorado, USA. These data are available within the “1 cm geoid experiment” (also known as the “Colorado Experiment”). We apply an approach for regional gravity modeling based on series expansions in terms of spherical radial basis functions (SRBF). Two types of basis functions covering the same spectral domain are used, one for the terrestrial data and another one for the airborne measurements. To be more specific, the non-smoothing Shannon function is applied to the terrestrial data to avoid the loss of spectral information. The Cubic Polynomial (CuP) function is applied to the airborne data as a low-pass filter, and the smoothing features of this type of SRBF are used for filtering the high-frequency noise in the airborne data. In the parameter estimation procedure, these two modeling parts are combined to calculate the quasi-geoid.</p><p>The performance of our regional quasi-geoid model is validated by comparing the results with the mean solution of independent computations delivered by fourteen institutions from all over the world. The comparison shows that the low-pass filtering of the airborne gravity data by the CuP function improves the model accuracy by 5% compared to that using the Shannon function. This result also makes evident the advantage of combining different SRBFs covering the same spectral domain for different types of observations.</p>