Geostationary Research Articles

Estimation of Earth Rotation Parameters Based on BDS-3 and Discontinuous VLBI Observations

Earth rotation parameters (ERPs) are fundamental to geodetic and astronomical studies. With its high measurement accuracy and stability, the Very Long Baseline Interferometry (VLBI) plays an irreplaceable role in estimating the ERPs and maintaining the earth reference frame. However, the imperfect global station distribution, observation discontinuity, and vast cost of the VLBI make the GNSS a more attractive technique. In 2020, the third generation of the BeiDou Navigation System (BDS), namely BDS-3, was constructed completely. In this study, we conducted a series of experiments to estimate Earth’s rotation parameters based on the continuous BDS-3 observation data, the discontinuous VLBI observation data, and the combined BDS-3 and discontinuous VLBI observation data. We used two methods, namely the weighted averaging method and the normal equation combination method, to obtain ERP combination solutions. The results are compared with the International Earth Rotation and Reference Systems Service (IERS) EOP 20C04 at 00:00:00 UTC. Final results show that (a) the estimation accuracy becomes stable when the number of BDS-3 tracking stations is more than 40. At the same time, both the number of stations and the volume of polyhedrons formed by the observing stations affect the accuracy of the ERPs estimated by the BDS-3 or VLBI. (b) Results have also shown that the inclusion of the BDS-3 IGSO and GEO satellites contributes little to the ERP estimation. (c) For the BDS-3-only MEO satellites solution, the root mean square (RMS) was 113.2 µas, 102.8 µas, and 13.1 µs/day for X-pole coordinate, Y-pole coordinate, and length of day (LOD), respectively. For the VLBI solution, the RMSs of the X-pole, Y-pole, and LOD were 100.4 µas for the X-pole, 94.2 µas for the Y-pole, and 14.1 µs/day. The RMS was 82.6 µas, 70.3 µas, and 10.5 µs/day for the combined X-pole, Y-pole, and LOD using the weighted averaging method. It was 78.2 µas, 62.6 µas, and 8.6 µs/day when the normal equation combination method was applied. This demonstrates that by taking advantage of the BDS-3 and VLBI technique combinations, accuracy in estimating the ERPs can be improved over that using either of them, in addition to enhanced stability and reliability.

Accurate nowcasting of cloud cover at solar photovoltaic plants using geostationary satellite images

Accurate nowcasting for cloud fraction is still intractable challenge for stable solar photovoltaic electricity generation. By combining continuous radiance images measured by geostationary satellite and an advanced recurrent neural network, we develop a nowcasting algorithm for predicting cloud fraction at the leading time of 0–4 h at photovoltaic plants. Based on this algorithm, a cyclically updated prediction system is also established and tested at five photovoltaic plants and several stations with cloud fraction observations in China. The results demonstrate that the cloud fraction nowcasting is efficient, high quality and adaptable. Particularly, it shows an excellent forecast performance within the first 2-hour leading time, with an average correlation coefficient close to 0.8 between the predicted clear sky ratio and actual power generation at photovoltaic plants. Our findings highlight the benefits and potential of this technique to improve the competitiveness of solar photovoltaic energy in electricity market.

Radio resource management Satellite Communication Networks MCDM Method

A communications satellite would be an artificial satellite that creates a channel here for the transmission of a communication through a transponder between a sender and a recipient located at various locations on Earth. Communications via satellite are used in telecommunications, radio, broadcast, computer, and military applications. Satellites offer three different kinds of communication services: data communication, broadcasting, and telephone. Lower Geostationary satellite, Networks for Low-Earth-orbiting (LEO) and Medium Eccentricity Orbit (MEO) are the three different types of satellite networks. A satellite system or a portion of a satellite system with only one satellite in it and a working ground station is known as a satellite network. Models 1, 2, and 3. A satellite network is a system that uses satellite technology to transmit media services to recipients. The two main components of communication systems are the ground component, which consists of stationary or mobility transmissions, receptions, and ancillary equipment, and indeed the space component, which primarily consists of the satellite. In addition to offering in-flight communications, satellites are frequently the primary voice communication method in rural areas and places where telephone connections have been disrupted during a catastrophe. Satellites are also a major source of time for cell phones and pagers. With nine operating communications satellites in geo-stationary orbit, one of the largest domestic satellite communications networks in the Asia-Pacific region is the Indian National Television (INSAT) system. Radio and cable television are provided to us. They allow us to use our mobile phones to make long distance calls. So that we can find ourselves and get directions, they offer us the Global Positioning System (GPS) our destinations. Satellites offer data on Earth's atmosphere, seas, land, and clouds. They can also see smoke, volcanoes, and forest fires. All of this data aids in weather and climate prediction. Farmers benefit from knowing what crops to plant. PROMETHEE (Priority Ranking System Method for Enrichment Assessments) About PROMETHEE methods and usage to uncover current research to classify and explain A classification scheme and a comprehensive literature review is presented. Alternative is Laser communication, optical networks, satellite optical communication, vibrations and satellite network. Evaluation parameters are Solar Radiation Pressure, Thermal Bending, Micro Meteorite impacts, Solar and Lunar Gravity and Earth oblations effects. Vibrations are got the first rank whereas is the Optical networks are having the Lowest rank. Satellite Communication Networks for using the analysis of PROMETHEE Method. Vibrations is got the first rank whereas is the Optical networks is having the Lowest rank.

An Observing System Simulation Experiment Analysis of How Well Geostationary Satellite Trace‐Gas Observations Constrain NOx Emissions in the US

AbstractWe investigate the benefit of assimilating high spatial‐temporal resolution nitrogen dioxide (NO2) measurements from a geostationary (GEO) instrument such as Tropospheric Emissions: Monitoring of Pollution (TEMPO) versus a low‐earth orbit (LEO) platform like TROPOspheric Monitoring Instrument (TROPOMI) on the inverse modeling of nitrogen oxides (NOx) emissions. We generated synthetic TEMPO and TROPOMI NO2 measurements based on emissions from the COVID‐19 lockdown period. Starting with emissions levels prior to the lockdown, we use the Weather Research and Forecasting Model coupled with Chemistry/Data Assimilation Research Testbed (WRF‐Chem/DART) to assimilate these pseudo‐observations in Observing System Simulation Experiments to adjust NOx emissions and quantify how well the assimilation of TEMPO versus TROPOMI measurements recovers the lockdown‐induced emissions changes. We find that NOx emission biases can be ameliorated using half as many simulation days when assimilating GEO observations, and the estimated NOx emissions in 23 out of 29 major urban regions in the US are more accurate. The root mean square error and coefficient of determination of posterior NOx emissions are reduced by 12.5%–41.5% and 1.5%–17.1%, respectively, across different regions. We conduct sensitivity experiments that use different data assimilation (DA) configurations to assimilate synthetic GEO observations. Results demonstrate that the temporal width of the DA window introduces −10% to −20% biases in the emissions inversion and constraining both NOx concentrations and emissions simultaneously yields the most accurate NOx emissions estimates. Our work serves as a valuable reference on how to appropriately assimilate GEO observations for constraining NOx emissions in future studies.

Atmospheric Temperature Measurements Using Microwave Hyper-Spectrum from Geostationary Satellite: Band Design, Weighting Functions and Information Content

A passive microwave instrument will be carried by China’s geostationary microwave satellite. A microwave hyper-spectral band included by the instrument ranges from 52.6 to 57.3 GHz, and totally has 89 channels in this spectral domain. The design of the hyper-spectral band is described from the aspects of scientific objectives and specifications. The weighting functions for each channel are calculated utilizing radiative transfer simulations under clear sky conditions. Then, the information content as well as the degree of freedom for signal are computed and analyzed to characterize this hyper-spectral sounding for atmospheric temperature profiling. Both the vertical distribution of the weighting functions and the width of retrieval averaging kernels indicate that the hyper-spectral band can provide more denser sampling for atmospheric temperature. The information content for the hyper-spectral band is approximately 46% higher than that of the ATMS-type channel 3 to 15, indicating that hyper-spectral measurement can improve the accuracy of retrieval. The most informative channels mainly locate near 57 GHz, having good consistency with the existing channels. The height range where the retrieval using the hyper-spectral observations is sensitive to the true profile, begins from about 800 to 1 hPa. Some channels can be considered as alternatives to each other since they have very similar information content and weighting functions. These results are expected to provide a valuable reference for future applications of the microwave hyper-spectral measurements.

Geostationary Environment Monitoring Spectrometer (GEMS) polarization characteristics and correction algorithm

Abstract. The Geostationary Environment Monitoring Spectrometer (GEMS) is the first geostationary earth orbit (GEO) environmental instrument, onboard the Geostationary Korea Multi-Purpose Satellite–2B (GEO-KOMPSAT-2B) launched on 19 February 2020, and is measuring reflected radiance from the earth's surface and atmosphere system in the range of 300–500 nm in the ultraviolet–visible (UV–Vis) region. The radiometric response of a satellite sensor that measures the UV–Vis wavelength region can depend on the polarization states of the incoming light. To reduce the sensitivity due to polarization, many current low earth orbit (LEO) satellites are equipped with a scrambler to depolarize the signals or a polarization measurement device (PMD) that simultaneously measures the polarization state of the atmosphere, then utilizes it for a polarization correction. However, a novel polarization correction algorithm is required since GEMS does not have a scrambler or a PMD. Therefore, this study aims to improve the radiometric accuracy of GEMS by developing a polarization correction algorithm optimized for GEMS that simultaneously considers the atmosphere's polarization state and the instrument's polarization sensitivity characteristics. The polarization factor and axis were derived by the preflight test on the ground as a function of wavelengths, showing a polarization sensitivity of more than 2 % at some specific wavelengths. The polarization states of the atmosphere are configured as a look-up table (LUT) using the Vector Linearized Discrete Ordinate Radiative-Transfer model (VLIDORT). Depending on the observation geometry and atmospheric conditions, the observed radiance spectrum can include a polarization error of 2 %. The performance of the proposed GEMS polarization algorithm was assessed using synthetic data, and the errors due to polarization were found to be larger in clear regions than in cloudy regions. After the polarization correction, polarization errors were reduced close to zero for almost all wavelengths, including the wavelength regions with high peaks and curvatures in the GEMS polarization factor, which sufficiently demonstrates the effectiveness of the proposed polarization correction algorithm. From the actual observation data after the launch of GEMS, the diurnal variation for the spatial distribution of polarization error was confirmed to be minimum at noon and maximum at sunrise/sunset. This can be used to improve the quality of GEMS measurements, the first geostationary environmental satellite, and then contribute to the retrieved accuracy of various Level-2 products, such as trace gases and aerosols in the atmosphere.

Intelligent Reconstruction of Radar Composite Reflectivity Based on Satellite Observations and Deep Learning

Weather radar is a useful tool for monitoring and forecasting severe weather but has limited coverage due to beam blockage from mountainous terrain or other factors. To overcome this issue, an intelligent technology called “Echo Reconstruction UNet (ER-UNet)” is proposed in this study. It reconstructs radar composite reflectivity (CREF) using observations from Fengyun-4A geostationary satellites with broad coverage. In general, ER-UNet outperforms UNet in terms of root mean square error (RMSE), mean absolute error (MAE), structural similarity index (SSIM), probability of detection (POD), false alarm rate (FAR), critical success index (CSI), and Heidke skill score (HSS). Additionally, ER-UNet provides the better reconstruction of CREF compared to the UNet model in terms of the intensity, location, and details of radar echoes (particularly, strong echoes). ER-UNet can effectively reconstruct strong echoes and provide crucial decision-making information for early warning of severe weather.

On-orbit calibration based upon star observation to correct thermal deformation of geostationary optical satellites

The thermal environment in a geostationary orbit is more complicated than a low orbit. Thermal deformation is a primary factor to deteriorate geometric quality of remote imaging of geostationary optical satellites. The on-orbit geometric quality testing of the SDLT-1 satellite of China shows that the light of sight (LOS) deformation in longitude and latitude are 26 pixels and 14 pixels, respectively, which do not meet the accuracy requirements. Therefore, a novel on-orbit geometric calibration method based on star observation is proposed to improve the geometric deformation. The imager embodied stars are employed as sources to evaluate the developed method and compared with the traditional procedure based upon landmarks. The results show that the geometric accuracy of the new approach is 3.2 pixels which is improved compared to traditional calibration method.

Mobile Observations of Ozone and Aerosols in Alabama: Southeastern US Summer Pollution and Coastal Variability

AbstractWe present a detailed analysis of measurements of ozone (O3) and aerosols collected using multiple platforms and instruments during a 5‐day mini‐campaign in Alabama, for improving our understanding of the chemistry and air quality in the Southeastern United States. Our study features the first on‐road O3 lidar profiling on highways and the coordinated airborne observations that captured O3 and aerosol structures associated with synoptic weather conditions, stratospheric intrusions, wildfire smoke transport, and enhancements in urban areas. The Doppler wind lidar data suggest that the vertical structures and variability of O3 and aerosols at Dauphin Island, a coastal site in the Gulf of Mexico, were related to both the synoptic flow and local‐scale land‐sea breeze circulation. The onshore gulf breeze, which typically started in the afternoon, helped to quickly dilute air pollutants near the surface, resulting in large gradients toward inland areas. In contrast, gradients were much smaller on days without a gulf breeze. O3 and humidity were highly anti‐correlated in the lower free troposphere, but weakly anti‐correlated in the planetary boundary layer (PBL) suggesting more complicated emissions and sink of O3 in the PBL. Our on‐road and airborne measurement strategies to quantify the vertical and horizontal chemical gradients will be useful for future geostationary satellite validation.

Synergistic observation of FY-4A&4B to estimate CO concentration in China: combining interpretable machine learning to reveal the influencing mechanisms of CO variations

Accurately estimating the concentration of carbon monoxide (CO) with high spatiotemporal resolution is crucial for assessing its meteorological-environmental-health impacts. Although machine learning models have high predictive ability in environmental research, there are relatively few explanations for model outputs. Utilizing the top-of-atmosphere radiation data of China’s new generation geostationary satellites (FY-4A and FY-4B) and interpretable machine learning models, the 24-hour near-surface CO concentrations in China was conducted (resolution: 1 hour, 0.04°). The model improved by 6.6% when using the all-sky dataset (cloud-contained model, R2 = 0.759) compared to the clear-sky dataset (cloud-removed model). The interpretability analysis of the CO estimation model used two methods, namely ante-hoc (model feature importance) and post-hoc (SHapley Additive exPlanations). The importance of daytime meteorological factors increased by 51% compared to nighttime. Combining partial dependency plots, the impact of key meteorological factors on CO was elucidated to gain a deeper understanding of the spatiotemporal variations of CO.

MT-GN: Multi-Task-Learning-Based Graph Residual Network for Tropical Cyclone Intensity Estimation

A tropical cyclone (TC) is a type of severe weather system that damages human property. Understanding TC mechanics is crucial for disaster management. In this study, we propose a multi-task learning framework named Multi-Task Graph Residual Network (MT-GN) to classify and estimate the intensity of TCs from FY-4A geostationary meteorological satellite images. And we construct a new benchmark dataset collected from the FY-4A satellite for both TC classification and intensity estimation tasks. Four different methodologies to classify TCs and estimate the intensity of TCs are fairly compared in our dataset. We discover that accurate classification and estimation of TCs, which are usually achieved separately, requires co-related knowledge from each process. Thus, we train a convolution feature extractor in a multi-task way. Furthermore, we build a task-dependency embedding module using a Graph Convolution Network (GCN) that further drives our model to reach better performance. Finally, to overcome the influence of the unbalanced distribution of TC category samples, we introduce class-balanced loss to our model. Experimental results on the dataset show that the classification and estimation performance are improved. With an overall root mean square error (RMSE) of 9.50 knots and F1-score of 0.64, our MT-GN model achieves satisfactory performance. The results demonstrate the potential of applying multi-task learning for the study of TCs.

Characterization of Scintillation Events With Basis on L1 Transmissions From Geostationary SBAS Satellites

AbstractSignals recorded by two stations in the Brazilian region: [Fortaleza (3.74°S, 38.57°W) and Inconfidentes (22.31°S, 46.32°W)], receiving L1 transmissions from different geostationary satellites, were used to evaluate the amplitude scintillation index S4 and several characteristics of scintillation events (continuous record with S4 > 0.2) during nighttime hours (18:00 LT–02:00 LT) in the years 2014–2016. The effects from solar activity, season, and local time on the number of scintillation events per night, maximum scintillation, scintillation event duration, and spacing between consecutive events will be discussed. The results indicate that: (a) scintillation occurs from September to March in both links; (b) the most likely numbers of observed scintillation events per night were two or three, particularly during the first 2 years; (c) on average, the first scintillation event usually had larger maximum S4 values when compared to those of the later ones along the night; (d) the first scintillation event had a longer mean duration than the succeeding ones in a given night; (e) the durations of scintillation events, regardless of their numbers per night and the location, decreased with local time; (f) the opposite dependence of spacings between consecutive events on local time was observed; (g) the cumulative distribution functions of the scintillation onset time indicated a strong dependence on the dip latitude of the station; and (h) early occurrences of onset times are directly related to the increased probability of the occurrence of multiple scintillation events.

Optimal Impulsive Rendezvous for Approaching Geostationary Earth Orbit Targets with Terminal Configuration Constraints

This paper explores multi-impulse trajectory design for approaching Geostationary Earth Orbit (GEO) targets, emphasizing the attainment of a 2:1 elliptical relative orbit for close inspection. The intricate optimization challenge includes dynamic and terminal constraints, as well as total mission duration limitations. Critical variables like transfer duration, impulse count, timing, and entry point are optimized using genetic algorithms to minimize velocity increment requirements. These optimal values are influenced by uncertain terminal states constrained by the 2:1 circling orbit. To address these complexities, the paper leverages a linear state transition matrix from relative orbital dynamics to formulate a multi-impulse optimization model. Additionally, a differential correction-based iterative approach mitigates nonlinear dynamics’ impact on terminal rendezvous errors. Through theoretical analysis and simulations, the study elucidates variations in optimization criteria, particularly in GEO missions with multiple impulses, and identifies optimal entry points for different impulse counts. The proposed models and methodologies offer theoretical insights for future GEO orbit approaching missions and potential applications in various space maneuver tasks.

Public Health Benefits From Improved Identification of Severe Air Pollution Events With Geostationary Satellite Data.

Despite improvements in ambient air quality in the US in recent decades, many people still experience unhealthy levels of pollution. At present, national-level alert-day identification relies predominately on surface monitor networks and forecasters. Satellite-based estimates of surface air quality have rapidly advanced and have the capability to inform exposure-reducing actions to protect public health. At present, we lack a robust framework to quantify public health benefits of these advances in applications of satellite-based atmospheric composition data. Here, we assess possible health benefits of using geostationary satellite data, over polar orbiting satellite data, for identifying particulate air quality alert days (24hr PM2.5>35μgm-3) in 2020. We find the more extensive spatiotemporal coverage of geostationary satellite data leads to a 60% increase in identification of person-alerts (alert days×population) in 2020 over polar-orbiting satellite data. We apply pre-existing estimates of PM2.5 exposure reduction by individual behavior modification and find these additional person-alerts may lead to 1,200 (800-1,500) or 54% more averted PM2.5-attributable premature deaths per year, if geostationary, instead of polar orbiting, satellite data alone are used to identify alert days. These health benefits have an associated economic value of 13 (8.8-17) billion dollars ($2019) per year. Our results highlight one of many potential applications of atmospheric composition data from geostationary satellites for improving public health. Identifying these applications has important implications for guiding use of current satellite data and planning future geostationary satellite missions.

Simulation of proton-induced primary displacement damage in GaAs under different ambient temperatures

The performance of on-orbit GaAs-based solar cells is susceptible to the displacement damage effect. The proton-induced primary displacement damage in GaAs on a geosynchronous equatorial orbit (GEO) was simulated and analyzed by combining the Monte Carlo (MC) and molecular dynamics (MD) methods. The MC simulation provided the distribution of primary knock-on atoms (PKAs) in GaAs induced by GEO-related protons to the MD simulation. In MD simulations, the effects of radiation fluence and ambient temperature on the displacement damage were considered. The simulation results showed that GEO-related protons generated a significant number of PKAs within an energy range of below 10 keV in GaAs. The high-fluence radiation emulated by the binary PKA could generate more point defects and cluster defects in GaAs than the low-fluence radiation emulated by single PKAs. As compared to room temperature (300 K), both a low (100 K) and high (500 K) ambient temperature could deteriorate the displacement damage. In addition, a high ambient temperature of 500 K could induce widespread thermal spikes in GaAs as compared to 100 and 300 K. This work can provide useful insight into the proton-induced displacement damage in GaAs and the radiation hardening of GaAs-based photoelectric devices in space.

Advances in Methodology and Generation of All-Weather Land Surface Temperature Products From Polar-Orbiting and Geostationary Satellites: A comprehensive review

Advances in Methodology and Generation of All-Weather Land Surface Temperature Products From Polar-Orbiting and Geostationary Satellites: A comprehensive review

A modeling study of ≥2 MeV electron fluxes in GEO at different prediction time scales based on LSTM and transformer networks

In this study, we develop models to predict the log10 of ≥2 MeV electron fluxes with 5-minute resolution at the geostationary orbit using the Long Short-Term Memory (LSTM) and transformer neural networks for the next 1-hour, 3-hour, 6-hour, 12-hour, and 1-day predictions. The data of the GOES-10 satellite from 2002 to 2003 are the training set, the data in 2004 are the validation set, and the data in 2005 are the test set. For different prediction time scales, different input combinations with 4 days as best offset time are tested and it is found that the transformer models perform better than the LSTM models, especially for higher flux values. The best combinations for the transformer models for next 1-hour, 3-hour, 6-hour, 12-hour, 1-day predictions are (log10 Flux, MLT), (log10 Flux, Bt, AE, SYM-H), (log10 Flux, N), (log10 Flux, N, Dst, Lm), and (log10 Flux, Pd, AE) with PE values of 0.940, 0.886, 0.828, 0.747, and 0.660 in 2005, respectively. When the low flux outliers of the ≥2 MeV electron fluxes are excluded, the prediction efficiency (PE) values for the 1-hour and 3-hour predictions increase to 0.958 and 0.900. By evaluating the prediction of ≥2 MeV electron daily and hourly fluences, the PE values of our transformer models are 0.857 and 0.961, respectively, higher than those of previous models. In addition, our models can be used to fill the data gaps of ≥2 MeV electron fluxes.

Enhancing Navigation Accuracy in a Geostationary Orbit by Utilizing a Regional Navigation Satellite System

Enhancing Navigation Accuracy in a Geostationary Orbit by Utilizing a Regional Navigation Satellite System

Application of quantum-limited optical time transfer to space-based optical clock comparisons and coherent networks

With the demonstration of quantum-limited optical time transfer capable of tolerating the losses associated with long ground-to-space links, two future applications of free-space time transfer have emerged: intercontinental clock comparisons for time dissemination and coherence transfer for future distributed sensing in the mm-wave region. In this paper, we estimated the projected performance of these two applications using quantum-limited optical time transfer and assuming existing low-size, low-weight, and low-power hardware. In both cases, we limit the discussion to the simplest case of a single geosynchronous satellite linked to either one or two ground stations. One important consideration for such future space-based operations is the choice of reference oscillator onboard the satellite. We find that with a modestly performing optical reference oscillator and low-power fiber-based frequency combs, quantum-limited time transfer could support intercontinental clock comparisons through a common-view node in geostationary orbit with a modified Allan deviation at the 10−16 level at 10-s averaging time, limited primarily by residual turbulence piston noise. In the second application of coherence transfer from ground-to-geosynchronous orbit, we find the system should support high short-term coherence with ∼10 millirad phase noise on a 300 GHz carrier at essentially unlimited integration times.

Polarization angle accuracy for future CMB experiments

The Cosmic Microwave Background (CMB) radiation offers a unique window into the early Universe, facilitating precise examinations of fundamental cosmological theories. However, the quest for detecting B-modes in the CMB, predicted by theoretical models of inflation, faces substantial challenges in terms of calibration and foreground modeling. The COSMOCal (COsmic Survey of Millimeter wavelengths Objects for CMB experiments Calibration) project aims at enhancing the accuracy of the absolute calibration of the polarization angle ψ of current and future CMB experiments. The concept includes the build of a very well known artificial source emitting in the frequency range [20-350] GHz that would act as an absolute calibrator for several polarization facilities on Earth. A feasibility study to place the artificial source in geostationary orbit, in the far field for all the telescopes on Earth, is ongoing. In the meanwhile ongoing hardware work is dedicated to build a prototype to test the technology, the precision and the stability of the polarization recovering in the 1 mm band (220-300 GHz). High-resolution experiments as the NIKA2 camera at the IRAM 30m telescope will be deployed for such use. Once carefully calibrated (Δψ < 0.1◦) it will be used to observe astrophysical sources such as the Crab nebula, which is the best candidate in the sky for the absolute calibration of CMB experiments.