High Absolute Accuracy Research Articles

An efficient method for measuring and identifying geometric and dynamic errors in dual five-axis machine tools

The dual five-axis machining machine is state-of-the-art equipment designed for producing thin-walled aerospace parts like skins and panels. It comprises two five-axis machine tools with mirrored synchronous motions: one bi-rotary machine for milling and the other machine for follow-up support. The high absolute accuracy of the five-axis machine tools themselves and the high synchronous motion accuracy between the two machines are both crucial to ensure machining accuracy. This paper proposes an efficient method to measure and identify geometric and dynamic errors of dual five-axis machine tools. Using the R-test combined with three measurement trajectories to measure the absolute motion error of two five-axis machine tools and the synchronous motion error between the two machines. The designed trajectories include the main rotary axis linkage conditions and can fully reflect the primary motion forms of the dual five-axis machine tool. The main position-independent geometric errors and rotational axis linkage dynamic errors are identified by converting quasi-static and dynamic measurement data from three different trajectories into a unified reference coordinate system. The entire process requires only one instrument installation, effectively avoiding time-consuming repetitive installations and calibration processes while reducing the impact of installation errors on results. The effectiveness of the proposed method for measuring and identifying the geometric and dynamic errors is analyzed combined with Monte Carlo simulation. Experiments were carried out on a dual five-axis mirror milling system (MMS) with a 5000 mm X-stroke. Both simulations and experiments confirmed that the proposed method is efficient in quickly identifying key error sources of the MMS.

Validation of the methane production measurement accuracy and ranking capacity of portable accumulation chambers for use with small ruminants

Using portable accumulation chambers (PAC) is an attractive approach to recording methane (CH4) production of small ruminants. Mass flow controllers (MFC), for their part, are an effective way of validating PAC measurements, as they allow to simulate methane production rates freely of extraneous factors. The present study describes a series of tests carried out to evaluate the accuracy and precision of methane mass recordings of eight PAC against known CH4 masses released by a MFC. Across the tested range, the PAC were able to recover between 67.6 and 74.5% of the true CH4 released. No significant differences were detected between the different PAC, but a statistically significant linear shift was detected over the mass range. Therefore, PAC as currently used are not well suited for applications looking at absolute production levels requiring high absolute accuracy. If recovery tests are conducted regularly across the measurement range, a calibration factor could be generated to correct for the inaccuracy. These PAC were however well suited for investigating relative differences between animals, e.g. ranking animals to compare differences in CH4 production between breeds. The methane recordings made by these chambers were highly precise, with low coefficients of variation between replicates (0.0–2.4%) and high repeatability (>0.99). Furthermore, the correlation between released and recorded CH4 masses was very strong and positive (R > 0.99). Overall, the mass recovery test presented here provides a feasible method for harmonizing methane monitoring procedures using PAC between research groups, thereby improving joint efforts aimed at mitigating greenhouse gas production in small ruminants.

High-Precision Gravimetric Monitoring Provides Insights into Subsurface Processes

Summary Monitoring of time-lapse changes in gravity has been carried out for decades, and measurement repeatability with relative spring gravimeters has in recent years improved to 0.6–2 µGal (standard deviation) for area surveys. This is better than often cited vendor specification. Gravimeters based on other physical principles offer high absolute accuracy or improved time-lapse sensitivity. All these may open possibilities for monitoring smaller and deeper subsurface changes and over shorter time intervals, with applications in hydrocarbon and geothermal production, gas and CO2 storage, hydrology, volcanology and more. Survey design and data processing are integral parts of a program. Sensor redundancy and repeat measurements provide opportunities for in-situ calibrations. An example from a gas accumulation shows a reduction in gravity of about 3 µGal and subsidence of 5–6 mm over 8 years. Both signals are clearly above the noise level and are likely to have been caused by production from a neighbouring structure. The information can quantify the amount of reservoir pore pressure drop, size of gas cap expansion and vertical movements of the gas-liquid contact to a precision well below 1 m.

A dataset of topographic factors with 12.5 m spatial resolution in the Yellow River Basin

The Yellow River Basin is a key area of soil and water conservation and ecological protection in China, due to its complex topography and great variation in climate. The study on changes of topographical factors is of great significance for the prevention and control of soil and water loss as well as vegetation restoration in this area. This dataset consists of 11 topographic factors, namely slope gradient, slope aspect, comprehensive curvature, plane curvature, sectional curvature, aspect variability, slope variability, topographic relief, surface roughness, surface cutting depth and the coefficient of variation of elevation. The data were sourced from ALOS DEMs with a resolution of 12.5m, which were then processed through four steps: preprocessing, framing, projection and topographic index calculation. Verified by GLA14 altimetry data, the ALOS 12.5 m DEM data, with a high absolute vertical accuracy, are much better in expressing terrain characteristics than data of GDEM with 30 m resolution and STRM DEM data with 90 m resolution. This dataset can provide data support for scientific research in the Yellow River Basin, such as vegetation restoration, quantitative evaluation of soil erosion, and land hydrological analysis.

In-situ and triple-collocation based assessments of CYGNSS-R soil moisture compared with satellite and merged estimates quasi-globally

Soil moisture is essential for a wide range of agricultural production and climate change. Compared with soil moisture retrieved through satellite remote sensing or merged products, soil moisture products retrieved using the Cyclone Global Navigation Satellite System observations interferometric Reflectometry (CYGNSS-R) have received extensive attention. The soil moisture products retrieved from the CYGNSS-R observation have high temporal and spatial revisit characteristics. In this study, we provide a comprehensive evaluation of CYGNSS-R-based surface soil moisture (SSM), Soil Moisture Active Passive (SMAP) SSM, and the European Space Agency’s Climate Change Initiative (ESA CCI) SSM at a quasi-global scale. An in-situ validation based on stations from International Soil Moisture Network and an evaluation strategy based on triple collocation is conducted to comprehensively assess the accuracy of heterologous SSM products. The new evaluation triplet includes the CYGNSS-R-based, merged, and in-situ SSM products TC method (CMID-TC). By analyzing the temporal and spatial distribution of the errors and the distribution in different land cover types, the accuracy of CYGNSS soil moisture product is assessed and compared. Results show that CYGNSS-R-based SSM achieve overall higher absolute accuracy than SMAP satellite-based SSM. CYGNSS-R-based SSM achieved worse accuracy indicators than SMAP SSM in grassland, savanna, shrubland, cropland, and forest land cover and had slightly better accuracy than SMAP in barren pixels. CYGNSS has demonstrated the potential to improve the accuracy of fusion products through its high accuracy and ability to capture temporal correlations in TC analyses. The complementarity in the accuracy and spatial coverage between CYGNSS-R SSM and ESA CCI SSM is shown considering diverse land cover. In general, CYGNSS-R SSM shows the potential to capture higher accuracy and temporal trends in lightly vegetated areas. The TC-based assessments proposed in this study can provide a reference for evaluating the accuracy of soil moisture products and comparing the accuracy of soil moisture from different sources. Furthermore, the error verification results can provide a reference for improving the integration of soil moisture products from different sources by comparing the accuracy in different locations and land cover.

Thermistor string corrections in data from very weakly stratified deep-ocean waters

An important parameter for observational studies on dynamical variations of the ocean is temperature (T) which determines density variations together with salinity. While the achievement of high absolute accuracy of T-sensors is a formidable task, even the maintaining of high precision is difficult for any long-term deep-ocean observations. This is because the deep ocean is very weakly stably stratified in density, in which all the dynamics is covered by a range of about 0.001 °C. One requires high-resolution T-sensors with low noise levels and precisely calibrated. However, common Negative Temperature Coefficient (NTC) thermistor T-sensors have electronic drift causing a bias of temperature with time. The drift rate varies with temperature (change) and time (age) between 0.0002 and 0.001 °C wk−1 for T-sensors used here. A string of such T-sensors moored in the open-ocean for more than a month after calibration requires a drift-correction. When moored in the deep-ocean, a secondary correction is necessary even immediately after calibration. Secondary correction uses reference layers that are homogeneous to within 0.00005 °C over 100 m vertically plus an additional polynomial correction to remove remaining bias prior or after the reference date. Secondary correction may also involve pressure information on surface tides and mooring deflection. In this paper, secondary corrections are described for two cases of data. For one case from the West-Mediterranean varying temperature-density relationship and near-homogeneous conditions exist. The other is from hadal-deep instruments on a long mooring line in the Challenger Deep, Mariana Trench, affected by tidal flow. Nearby shipborne CTD-data are always needed to establish the temperature-density relationship and the background vertical temperature slope to which the moored T-sensor data are referenced. The stability of moored T-sensors is compared with that of temperature sensors of CTD.

파라미터 중복성을 제거한 로봇 매니퓰레이터의 위치 기반 기구학 캘리브레이션

Industrial robot manipulators require high absolute position accuracy of the end effector to perform precise and complex tasks. However, manufacturing errors cause differences between nominal and actual parameters, and errors between the expected and actual positions of the end effector, resulting in undesired lower absolute position accuracy. Accordingly, to increase the absolute position accuracy of the end effector, kinematic calibration is required to correct the nominal parameters close to the actual parameters. However, in this study, redundancy of parameters may occur from the overlapping degrees of freedom of parameters in adjacent frames, which causes the problem of unnecessarily correcting many parameters in the optimization process. Thus, to solve this problem and use only the necessary parameters, this paper focuses on the linear relationship of redundant parameters and proposes a method of automatically discriminating and removing it through the Pearson Correlation Analysis. Additionally, through simulations on the two manipulator models, we verify the accuracy of redundancy of parameters determined by the proposed method, and demonstrate consistency and efficiency by comparing the results before and after redundancy removal.

Improving UAV-SfM time-series accuracy by co-alignment and contributions of ground control or RTK positioning

Unmanned Aerial Vehicle Structure from Motion (UAV-SfM) photogrammetry is increasingly applied to topographic change detection, which requires multitemporal Digital Surface Models (DSMs) with high relative accuracy. Of these tools, Ground Control Points (GCPs) and an image processing method called co-alignment have so far shown promising results for change detection studies. However, there is still insufficient research on the extent of improving 3D model accuracy by combining these tools. In our study we assess absolute and relative accuracy of 120 DSMs generated through 24 workflows of UAV-SfM photogrammetry. Surveys were acquired with two different UAVs with Real Time Kinematic (RTK) or generic Global Navigation Satellite System (GNSS) positioning, and processed with varying combinations of survey co-alignment and GCPs. We show that co-alignment reduces relative errors to below 2 cm regardless of positioning quality. A single RTK survey in a co-aligned project is sufficient to obtain high absolute xy accuracy, but GCPs for at least one survey are still required to reduce absolute z error. We demonstrate that co-aligning RTK surveys with generic GNSS surveys results in RTK class accuracy for all surveys, even when mixed sensor grades are used. Our findings enable high-accuracy change detection with lower accuracy archived images when combined with RTK surveys. For future UAV-SfM change detection studies, we recommend to apply co-alignment for all studies, and where possible to include GCPs and RTK image coordinates in one survey to optimize absolute accuracy. Collecting and digitizing GCPs in multiple surveys has shown little additional benefit when co-alignment is applied and therefore may be omitted to save time, especially in challenging field conditions.

A simple and effective way to evaluate the accuracy of digital image correlation combined with scanning electron microscopy (SEM-DIC)

Digital image correlation (DIC) is a powerful technique enabling mapping of local strain fields with the spatial resolution down to a few nanometers when combined with scanning electron microscopy. However, the accuracy of strain determination is frequently unknown due to a large number of experimental parameters influencing it. Here we present a simple and time-effective method for quantitative evaluation of uncertainties in strain values obtained by DIC. By imposing a fictive hydrostatic strain a high absolute accuracy of mean strain determination is demonstrated with deviations from the target value below 0.03%. At the same time, the precision decreases strongly with the reduction of the subset and step sizes reflecting a fundamental tradeoff between the spatial resolution and trueness of local strain values.

Gapless-REMA100: A gapless 100-m reference elevation model of Antarctica with voids filled by multi-source DEMs

The digital elevation model (DEM) of Antarctica is a fundamental dataset for a wide range of glaciological applications, from fieldwork planning to ice sheet dynamics analysis. The DEM data at high spatial resolution provides a more detailed depiction of the topography. The reference elevation model of Antarctica (REMA) mosaic is a recently released high-resolution Antarctic DEM product with a high absolute vertical accuracy of 1 m or less. The REMA mosaic was generated using an optical photogrammetry technique and can reflect the surface elevation of the ice sheet. However, the REMA mosaic has a large number of data voids of various spatial sizes, which affects its wide glaciological application. Therefore, we propose the generation of a gapless 100-m reference elevation model of Antarctica (Gapless-REMA100) with the voids filled by combining multi-source DEMs. In this study, the best-fit fill DEMs were automatically selected between almost all available circum-Antarctica or regional DEMs. Targeting numerous voids in large areas of the 100-m REMA mosaic, we propose a novel hierarchical delta surface method for interpolating the created delta surface to achieve an efficient, unbiased, and continuous filled surface. High-precision laser altimetry data were used to evaluate the vertical accuracy of the REMA mosaic. To eliminate the influence of the temporal surface elevation change and penetration depth between the DEM and laser altimetry data, we developed a new local contrast evaluation method by calculating the vertical accuracy of the equal-area buffer as a control to that of the center void. The experimental results with the Ice, Cloud and Land elevation Satellite-2 (ICESat-2) laser altimetry data validate that the filled 100-m REMA mosaic of voids can reach an elevation accuracy similar to that of the original REMA dataset for the west and east Antarctic ice sheet as well as the entire continent. In the Antarctic Peninsula area, the vertical accuracy of the filled REMA mosaic by the proposed method is superior to that of the officially released version of the Polar Geospatial Center. This is the first time that a gapless, seamless, and accurate 100-m REMA mosaic has been released which has the potential to be extensively used in glaciological applications. The proposed methods can also be used in void-filling of different types of DEM products and their elevation accuracy evaluations.

An ecological environmental carrying capacity estimation of tourist attractions based on structural equation

In order to solve the problems of high average absolute error and low accuracy of index weight calculation in traditional methods, an estimation model of ecological environment carrying capacity of tourist attractions based on structural equation was designed. Structural equation model is adopted to define the tourist scenic spot ecological environment bearing capacity estimation indicators of potential variables and show in the variable, based on the variable data to build the coupling model of ecological carrying capacity, establishing ecological environmental bearing capacity index system, index weight is obtained by using the entropy weight method, in order to build capacity estimation model, input variable data into the model of bearing capacity estimation. Experimental results show that the index system established by the model has a high goodness of fit with the model, the average absolute error of index weight calculation is low, and the calculation accuracy is up to about 95%.

Linear Feature-Based Triangulation for Large-Scale Orthophoto Generation Over Mechanized Agricultural Fields

Unmanned aerial vehicles (UAVs) equipped with imaging and ranging sensors have become an effective remote sensing data acquisition tool for digital agriculture. Among potential products derived from UAVs, high-resolution orthophotos play an important role in several phenotyping activities, such as canopy cover estimation and flowering date identification. Current structure from motion (SfM) tools for image-based 3-D reconstruction and orthophoto generation cannot perform well when working with large-scale imagery over mechanized agricultural fields. This failure is mainly due to their inability to identify enough conjugate points among overlapping images captured at low altitudes. This study addresses such limitation through a new strategy that uses plant row segments as linear features in the triangulation process. The linear features are derived in two steps. First, an automated approach is implemented to extract plant row segments from the LiDAR data which are then back-projected to the imagery using available trajectory and system calibration parameters. In the second step, a machine-assisted strategy is used to adjust the line segments in image space for deriving accurate linear features. In the proposed framework, the triangulation process is conducted by investigating two mathematical models—referred to as object-space and image-space coplanarity constraints—for incorporating linear features in the bundle adjustment (BA). The orthophoto is generated using the refined trajectory and system calibration parameters derived from the BA process. Several experimental results over an agricultural filed show that the proposed framework outperforms commonly used SfM tools, e.g., Pix4D Mapper Pro and Agisoft Metashape in terms of generating orthophotos with high visual quality and geolocation accuracy. Also, results indicate that the object-space coplanarity constraint is more robust against potential noise in line measurements when compared to the image-space coplanarity model. However, both models lead to high absolute accuracy in the range of ±2–4 cm when the noise level in the image measurements of points along the line is reasonable, i.e., ~5–10 pixels.

TriSurfaceImmersion: Computing volume fractions and signed distances from triangulated surfaces immersed in unstructured meshes

We propose a numerical method that enables the calculation of volume fractions from triangulated surfaces immersed in unstructured meshes. First, the signed distances are calculated geometrically near the triangulated surface. For this purpose, the computational complexity has been reduced by using an octree space subdivision. Second, an approximate solution of the Laplace equation is used to propagate the inside/outside information from the surface into the solution domain. Finally, volume fractions are computed from the signed distances in the vicinity of the surface. The volume fraction calculation utilizes either geometrical intersections or a polynomial approximation based on signed distances. An adaptive tetrahedral decomposition of polyhedral cells ensures a high absolute accuracy. The proposed method extends the admissible shape of the fluid interface (surface) to triangulated surfaces that can be open or closed, disjoint, and model objects of technical geometrical complexity.Current results demonstrate the effectiveness of the proposed algorithm for two-phase flow simulations of wetting phenomena, but the algorithm has broad applicability. For example, the calculation of volume fractions is crucial for achieving numerically stable simulations of surface tension-driven two-phase flows with the unstructured Volume-of-Fluid method. The method is applicable as a discrete phase-indicator model for the unstructured hybrid Level Set/Front Tracking method.The implementation is available on GitLab [27]. Program summaryProgram Title: argo/triSurfaceImmersionCPC Library link to program files:https://doi.org/10.17632/7g72xrcgjp.1Developer's repository link:https://gitlab.com/leia-methods/argoLicensing provisions: GPLv3Programming language: C++Nature of problem: Computing volume fractions and signed distances from triangulated surfaces immersed in unstructured meshes.Solution method: First, the algorithm computes minimal signed distances between mesh points (cell centers and cell corner-points) and the triangulated surface, in the close vicinity of the surface. The sign is computed with respect to the surface normal orientation. Afterwards, the sign is propagated throughout the unstructured volume mesh by an approximate solution of a diffusion equation. The bulk cells' volume fractions are set, and interface cells are identified based on the signed distances. Volume fractions in cells intersected by the triangulated surface mesh are either computed by geometric intersections between surface triangles and a cell or by an approximation of the volume fraction approximation from signed distances, coupled with tetrahedral cell decomposition and refinement.Additional comments including restrictions and unusual features: The volume mesh can consist of cells of arbitrary shape. The surface mesh normal vectors need to be oriented consistently.

Multifidelity emulation for the matter power spectrum using Gaussian processes

ABSTRACT We present methods for emulating the matter power spectrum by combining information from cosmological N-body simulations at different resolutions. An emulator allows estimation of simulation output by interpolating across the parameter space of a limited number of simulations. We present the first implementation in cosmology of multifidelity emulation, where many low-resolution simulations are combined with a few high-resolution simulations to achieve an increased emulation accuracy. The power spectrum’s dependence on cosmology is learned from the low-resolution simulations, which are in turn calibrated using high-resolution simulations. We show that our multifidelity emulator predicts high-fidelity (HF) counterparts to percent-level relative accuracy when using only three HF simulations and outperforms a single-fidelity emulator that uses 11 simulations, although we do not attempt to produce a converged emulator with high absolute accuracy. With a fixed number of HF training simulations, we show that our multifidelity emulator is ≃100 times better than a single-fidelity emulator at $k \le 2 \, h\textrm {Mpc}{^{-1}}$, and ≃20 times better at $3 \le k \lt 6.4 \, h\textrm {Mpc}{^{-1}}$. Multifidelity emulation is fast to train, using only a simple modification to standard Gaussian processes. Our proposed emulator shows a new way to predict non-linear scales by fusing simulations from different fidelities.

Intercalibration of ASCAT Scatterometer Winds from MetOp-A, -B, and -C, for a Stable Climate Data Record

Scatterometers provide very stable ocean vector wind data records. This is because they measure the ratio of backscattered to incident microwave signal over the ocean surface as opposed to an absolute quantity (e.g., emitted microwave signal). They provide an optimal source of observations for building a long ocean vector wind Climate Data Record (CDR). With this objective in mind, observations from different satellite platforms need to be assessed for high absolute accuracy versus a common ground truth and for fine cross-calibration during overlapping periods. Here we describe the methodology for developing a CDR of ocean surface winds from the C-band ASCAT scatterometers onboard MetOp-A, -B, and -C. This methodology is based on the following principles: a common Geophysical Model Function (GMF) and wind algorithm developed at Remote Sensing Systems (RSS) and the use of in situ and satellite winds to cross-calibrate the three scatterometers within the accuracy required for CDRs, about 0.1 m/s at the global monthly scale. Using multiple scatterometers and radiometers for comparison allows for the opportunity to isolate sensors that are drifting or experiencing step-changes as small as 0.05 m/s. We detected and corrected a couple of such changes in the ASCAT-A wind record. The ASCAT winds are now very stable over time and well cross-calibrated with each other. The full C-band wind CDR now covers 2007-present and can be easily extended in the next decade with the launch of the MetOp Second Generation scatterometers.

High-resolution topography of the Antarctic Peninsula combining the TanDEM-X DEM and Reference Elevation Model of Antarctica (REMA) mosaic

Abstract. The Antarctic Peninsula (AP) is one of the widely studied polar regions because of its sensitivity to climate change and potential contribution of its glaciers to global sea level rise. Precise digital elevation models (DEMs) at a high spatial resolution are much demanded for investigating the complex glacier system of the AP at fine scales. However, the two most recent circum-Antarctic DEMs, the 12 m TanDEM-X DEM (TDM DEM) from bistatic interferometric synthetic aperture radar (InSAR) data acquired between 2013 and 2014 and the Reference Elevation Model of Antarctica mosaic (REMA mosaic) at an 8 m spatial resolution derived from optical data acquired between 2011 and 2017 have specific individual limitations in this area. The TDM DEM has the advantage of good data consistency and few data voids (approx. 0.85 %), but there exist residual systematic elevation errors such as phase-unwrapping errors in the non-edited DEM version. The REMA mosaic has high absolute vertical accuracy, but on the AP it suffers from extended areas with data voids (approx. 8 %). To generate a consistent, gapless and high-resolution topography product of the AP, we fill the data voids in the TDM DEM with newly processed TDM raw DEM data acquired in austral winters of 2013 and 2014 and detect and correct the residual systematic elevation errors (i.e., elevation biases) in the TDM DEM with the support of the accurately calibrated REMA mosaic. Instead of a pixelwise replacement with REMA mosaic elevations, these provide reference values to correct the TDM elevation biases over entire regions detected through a path propagation algorithm. The procedure is applied iteratively to gradually correct the errors in the TDM DEM from a large to small scale. The proposed method maintains the characteristics of an InSAR-generated DEM and is minimally influenced by temporal or penetration differences between the TDM DEM and REMA mosaic. The performance of the correction is evaluated with laser altimetry data from Operation IceBridge and ICESat-2 missions. The overall root mean square error (RMSE) of the corrected TDM DEM has been reduced from more than 30 m to about 10 m which together with the improved absolute elevation accuracy indicates comparable values to the REMA mosaic. The generated high-resolution DEM depicts the up-to-date topography of the AP in detail and can be widely used for interferometric applications as well as for glaciological studies on individual glaciers or at regional scales.

Case Report: Optimization of Topographic Change Detection With UAV Structure-From-Motion Photogrammetry Through Survey Co-Alignment

High-quality digital surface models (DSMs) generated from structure-from-motion (SfM) based on imagery captured from unmanned aerial vehicles (UAVs), are increasingly used for topographic change detection. Classically, DSMs were generated for each survey individually and then compared to quantify topographic change, but recently it was shown that co-aligning the images of multiple surveys may enhance the accuracy of topographic change detection. Here, we use nine surveys over the Illgraben debris-flow torrent in the Swiss Alps to compare the accuracy of three approaches for UAV-SfM topographic change detection: 1) the classical approach where each survey is processed individually using ground control points (GCPs), 2) co-alignment of all surveys without GCPs, and 3) co-alignment of all surveys with GCPs. We demonstrate that compared to the classical approach co-alignment with GCPs leads to a minor and marginally significant increase in absolute accuracy. Moreover, compared to the classical approach co-alignment enhances the relative accuracy of topographic change detection by a factor 4 with GCPs and a factor 3 without GCPs, leading to xy and z offsets <0.1 m for both co-alignment approaches. We further show that co-alignment leads to particularly large improvements in the accuracy of poorly aligned surveys that have severe offsets when processed individually, by forcing them onto the more accurate common geometry set by the other surveys. Based on these results we advocate that co-alignment, preferably with GCPs to ensure a high absolute accuracy, should become common-practice in high-accuracy UAV-SfM topographic change detection studies for projects with sufficient stable areas.

FORUM: Unique Far-Infrared Satellite Observations to Better Understand How Earth Radiates Energy to Space

AbstractThe outgoing longwave radiation (OLR) emitted to space is a fundamental component of the Earth’s energy budget. There are numerous, entangled physical processes that contribute to OLR and that are responsible for driving, and responding to, climate change. Spectrally resolved observations can disentangle these processes, but technical limitations have precluded accurate space-based spectral measurements covering the far infrared (FIR) from 100 to 667 cm−1(wavelengths between 15 and 100µm). The Earth’s FIR spectrum is thus essentially unmeasured even though at least half of the OLR arises from this spectral range. The region is strongly influenced by upper-tropospheric–lower-stratospheric water vapor, temperature lapse rate, ice cloud distribution, and microphysics, all critical parameters in the climate system that are highly variable and still poorly observed and understood. To cover this uncharted territory in Earth observations, the Far-Infrared Outgoing Radiation Understanding and Monitoring (FORUM) mission has recently been selected as ESA’s ninth Earth Explorer mission for launch in 2026. The primary goal of FORUM is to measure, with high absolute accuracy, the FIR component of the spectrally resolved OLR for the first time with high spectral resolution and radiometric accuracy. The mission will provide a benchmark dataset of global observations which will significantly enhance our understanding of key forcing and feedback processes of the Earth’s atmosphere to enable more stringent evaluation of climate models. This paper describes the motivation for the mission, highlighting the scientific advances that are expected from the new measurements.

On the Accuracy of the ALMA Flux Calibration in the Time Domain and across Spectral Windows

Abstract A diverse array of science goals requires accurate flux calibration of observations with the Atacama Large Millimeter/submillimeter array (ALMA); however, this goal remains challenging due to the stochastic time-variability of the “grid” quasars ALMA uses for calibration. In this work, we use 343.5 GHz (Band 7) ALMA Atacama Compact Array observations of four bright and stable young stellar objects over seven epochs to independently assess the accuracy of the ALMA flux calibration and to refine the relative calibration across epochs. The use of these four extra calibrators allows us to achieve an unprecedented relative ALMA calibration accuracy of ∼3%. On the other hand, when the observatory calibrator catalog is not up to date, the Band 7 data calibrated by the ALMA pipeline may have a flux calibration poorer than the nominal 10%, which can be exacerbated by weather-related phase decorrelation when self-calibration of the science target is either not possible or not attempted. We also uncover a relative flux calibration uncertainty between spectral windows of 0.8%, implying that measuring spectral indices within a single ALMA band is likely highly uncertain. We thus recommend various methods for science goals requiring high flux accuracy and robust calibration, in particular, the observation of additional calibrators combined with a relative calibration strategy, and observation of solar system objects for high absolute accuracy.

Fusion of public DEMs based on sparse representation and adaptive regularization variation model

Global or quasi-global digital elevation model (DEM) datasets provide three-dimensional information on terrain surface, and they have been extremely useful in geoscience research and applications. However, the wide application of DEMs is constrained by differences in the means of observation and processing, and in the resolution of global public DEM datasets. An adaptive regularization variation model based on sparse representation is proposed to generate a high-quality DEM by fusing multi-source DEMs. First, since the sparse representation method has a powerful capability to reconstruct information based on a small amount of information, prior terrain information is extracted from the 90-m TanDEM-X DEM (TDM90) with unprecedented global accuracy using a so-called sparse representation. In this step, an intermediate DEM (termed STDM30) is first extracted from TDM90 that preserves maximum terrain details, thereby preventing the degradation of the DEM accuracy induced by resampling. Then, the designed regularization framework based on terrain slope can constrain the DEM spatial information during fusing multiple datasets. STDM30 is combined with the ALOS Global Digital Surface Model “ALOS World 3D 30 m” (AW3D30) and the 1 arc-second Shuttle Radar Topography Mission Digital Elevation Model (SRTM1) through the designed adaptive regularization variation model to generate a high-accuracy DEM product with a resolution of 30 m. The results of the proposed method were verified by a model-to-model comparison in South Dakota as well as by validation against GPS benchmarks in Southern California. The RMSE, MAE, and SD of the fused DEM are all lower than those of the existing public DEMs, especially in terms of removing topographic noise and refining terrain details. The GPS validation showed that the fused DEM has an RMSE of 3.04 m, with the highest absolute accuracy among the four studied DEMs, and its errors are almost equal to the normal distribution. These experimental results confirm that the multi-scale and multi-source DEM fusion strategy combining sparse representation and an adaptive regularization variation model can utilize existing public datasets and effectively improve the quality of global DEM products.