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Accurate measurements of ice hydrometeors are required to improve the representation of clouds and precipitation in weather and climate models. In this study, a newly developed, synergistic retrieval algorithm that combines radar with passive millimeter and sub-millimeter observations is applied to observations of three frontally generated, mid-latitude cloud systems in order to validate the retrieval and assess its capabilities to constrain the properties of ice hydrometeors. To account for uncertainty in the assumed shapes of ice particles, the retrieval is run multiple times while the shape is varied. Good agreement with in situ measurements of ice water content and particle concentrations for particle maximum diameters larger than 200 µm is found for one of the flights for the large plate aggregate and the six-bullet rosette shapes. The variational retrieval fits the observations well, although small systematic deviations are observed for some of the sub-millimeter channels pointing towards issues with the sensor calibration or the modeling of gas absorption. For one of the flights the quality of the fit to the observations exhibits a weak dependency on the assumed ice particle shape, indicating that the employed combination of observations may provide limited information on the shape of ice particles in the observed clouds. Compared to a radar-only retrieval, the results show an improved sensitivity of the synergistic retrieval to the microphysical properties of ice hydrometeors at the base of the cloud. Our findings indicate that the synergy between active and passive microwave observations may improve remote-sensing measurements of ice hydrometeors and thus help to reduce uncertainties that affect currently available data products. Due to the increased sensitivity to their microphysical properties, the retrieval may also be a valuable tool to study ice hydrometeors in field campaigns. The good fits obtained to the observations increase confidence in the modeling of clouds in the Atmospheric Radiative Transfer Simulator and the corresponding single scattering database, which were used to implement the retrieval forward model. Our results demonstrate the suitability of these tools to produce realistic simulations for upcoming sub-millimeter sensors such as the Ice Cloud Image or the Arctic Weather Satellite.

In order to cover the warm end of Earth-scene brightness temperature (TB) range of passive microwave radiometers, intercalibration over warm scenes is necessary. This article presents a methodology to intercalibrate the microwave radiation imager (MWRI) on the Chinese second-generation meteorological satellite Fengyun 3C (FY-3C) with the Global Precipitation Measurement (GPM) Microwave Imager (GMI) over the warm scenes of dense forests using the double-difference (DD) method. Based on the microwave radiative transfer model (RTM), an intercalibration method is developed, in which a modified land surface emissivity (LSE) model for dense forests is proposed. The forests with optically thick canopy are identified in terms of polarization TB differences and normalized difference vegetation index (NDVI) extracted from the latest vegetation product of Moderate-Resolution Imaging Spectroradiometer (MODIS). The matching TBs between FY-3C MWRI and GMI over dense forest warm scenes are collected and analyzed together with the TBs over ocean surfaces obtained by Zeng and Jiang (2020). The results show that: 1) FY-3C MWRI's observations are generally underestimated, and the intercalibration biases are polynomial functions of observations; 2) the intercalibration biases at the warm end are relatively smaller than those at the cold end; and 3) the calibration in the ascending orbits (MWRIA) is relatively better than that in the descending orbits (MWRID). At the tropical rain forest scene TBs defined in this work, the intercalibration biases (mean ± standard deviation at the mean) in the FY-3C MWRI channels of 10 V, 10 H, 18 V, 18 H, 23 V, 36 V, 36 H, 89 V, and 89 H are, respectively, -1.3 ± 0.7, -1.9 ± 1.1, 1.6 ± 0.6, 2.5 ± 0.8, -0.2 ± 0.5, -2.0 ± 0.6, -2.4 ± 0.7, -0.2 ± 0.6, and -0.1 ± 0.6 K for the ascending orbits, while they are, respectively, -4.0 ± 0.8, -5.4 ± 1.2, -1.4 ± 0.7, -1.2 ± 0.8, -2.9 ± 0.5, -4.9 ± 0.7, -5.5 ± 0.7, -2.7 ± 0.8, and -2.3 ± 0.7 K for the descending orbits. The in-orbit calibration coefficients of GMI are successfully transferred to FY-3C MWRI.

The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) was launched to the International Space Station (ISS) on June 29, 2018, and currently provides the highest spatial resolution thermal infrared (TIR) data (38 m x 69 m) available from space. In this study, we validated the ECOSTRESS level-2 Land Surface Temperature (LST) and emissivity product at fourteen global sites to Stage-1 status. Two primary methods are recommended for the validation of LST data: Temperature-based (T-based) and Radiance-based (R-based) methods. The T-based method requires calibrated measurements of the ground leaving radiance concurrent with the satellite overpass. In contrast, the R-based method uses a radiative closure simulation with external atmospheric profiles and an a priori knowledge of surface emissivity. Using these standard methods, we validated 1139 ECOSTRESS clear-sky observations between August 1, 2018, and March 31, 2020. For LST, the results show good agreement with ground-based measurements with an average root mean square error (RMSE) of 1.07 K, mean absolute error (MAE) of 0.40 K, and r²>0.988 at all sites. However, a cold bias of ~0.75 K was identified for temperatures below 295 K linked to calibration issues that will be addressed in future reprocessing of the data. Retrieved emissivity comparisons with laboratory spectra had an RMSE of 0.023 (2.3%) for all bands on average. With the decommissioning of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) on Terra in 2023, the multispectral and high-spatial-resolution characteristics of ECOSTRESS data serve as a pathfinder to the National Aeronautics and Space Administration's (NASA) Surface Biology and Geology (SBG) designated observable with an expected launch in 2026.

The Temporal Experiment for Storms and Tropical Systems Technology Demonstration (TEMPEST-D) instrument is a five-frequency millimeter-wave radiometer operating from 87 to 181 GHz. The cross-track scanning radiometer has been operating on a 6U CubeSat in low Earth orbit since September 5, 2018. The direct-detection architecture of the radiometer reduces its mass and power consumption by eliminating the need for a local oscillator and mixer, also reducing system complexity. The instrument includes a scanning reflector and ambient calibration target. The reflector rotates continuously to scan the antenna beams in the cross-track direction, first across the blackbody calibration target, then toward the Earth over the full range of incidence angles, and finally to cosmic microwave background radiation at 2.73 K. This enables precision end-to-end calibration of the millimeter-wave receivers during every 2-s scan period. The TEMPEST-D millimeter-wave radiometers are based on 35-nm indium phosphide (InP) high-electron-mobility transistor (HEMT) low-noise amplifiers. This article describes the instrument and its characterization prior to launch.

FengYun-3C (FY-3C) is the second-generation polar-orbiting meteorological satellite in China. As one of the most important microwave payloads deployed onboard FY-3C, microwave radiation imager (MWRI) has been continuously observing radiance originating from land and sea surface and supports numerical weather predictions at regional and global scales. With a long-term monitoring of the cold-end reference, MWRI onboard FY-3C was found suffering from discontinuous observations due to its temporary power-OFF/power-ON in the years of 2015 and 2018, resulting in anomalous brightness temperature (TB) jump up to 2–3 K in magnitude at some channels. Analysis of the operating status of FY-3C MWRI indicated that deviation of the receiver system from its optimal operating status affects the nonlinearity characteristics significantly. A correction algorithm, which relates nonlinearity to the autogain control (AGC) operating voltage of the sensor, is thus proposed on the basis of on-ground thermal/vacuum (T/V) test results in the prelaunch phase in order to mitigate the calibration anomalies of FY-3C MWRI. Nonlinearity correction of FY-3C MWRI is further corroborated by comparisons of simultaneously overlapping observations of global precipitation measurement (GPM) microwave imager (GMI). The results demonstrated that after nonlinearity correction the performance of FY-3C MWRI has been improved at all channels except for the 36-V channel where technical failures have been detected since October 2016.

The absolute radiometric accuracy of thermal infrared channels of the Advanced Geosynchronous Radiation Imager (AGRI) onboard the FengYun-4A (FY4A) satellite was assessed with field experiment data in a vicarious calibration on 18, 2019, and August 20, 2019, in Lake Qinghai. Continuous water temperature records were collected by an unmanned surface vehicle equipped with a radiometer during the experimental period. We proposed a spectral matching method based on atmospheric transfer code to create consistent equivalent spectral radiance, and the results predicted by atmospheric transfer simulation were convolved with instrument response functions to obtain predicted brightness temperature for comparison with satellite-derived temperature. Our results indicated that AGRI had an average temperature bias of 0.12 K with an RMSE of 0.17 and 0.61 K with an RMSE of 0.22 K in band 12 and band 13 on August 18, while these biases decreased to −0.01 K with an RMSE of 0.13 and −0.48 K with an RMSE of 0.17 K on August 20, respectively. This radiometric accuracy indicated that AGRI TIR bands are well functional compared with their preflight requirement of less than 1 K. The uncertainty analysis also suggested that atmospheric conditions would alter the calibration accuracy by influencing the transmittance and path thermal radiance. Meanwhile, cloud cover also requires more attention to select the appropriate concurrent satellite pixels to reduce the possible cloud edge effects. It is important and necessary for FY4A AGRI to conduct more intense automatic observations and obtain more data to improve the accuracy of radiometric calibration and monitor the operational status of satellite instruments.

International audience; The electrical-substitution cryogenic detector BOLometer for Use in the range of X-rays (BOLUX), which was developed some years ago at CEA/DAM, has been set up and restarted now at LNHB. It has been used for the primary measurement of the intensity (total energy per unit time) of monochromatic synchrotron beams in the energy range from 3 to 30 keV. These well-determined photon beams have been employed for the efficiency calibration of two photodiodes in terms of current induced per unit optical power at different photon energies. In a final step, we explored the possibility to use these primary calibrated photodiodes to determine the efficiency curve of an energy dispersive spectrometer based on a semiconductor detector (Silicon Drift Detector) using less intense monochromatic photon fluxes. The characteristics of the radiometer BOLUX and its principle of operation are described, and the measurements carried out at the synchrotron beamline are presented, including the determination of the beams' intensities, the direct calibration of photodiodes with respect to BOLUX and the use of one of those photodiodes as a standard transfer for the calibration of the SDD.

Temporal Experiment for Storms and Tropical Systems—Demonstration (TEMPEST-D) is a 6U CubeSat satellite with a cross-track scanning millimeter-wave radiometer measuring at five frequencies from 87 to 181 GHz. It employs a direct-detection architecture with InP HEMT monolithic microwave integrated circuit (MMIC) low-noise amplifiers and related new technologies. An end-to-end two-point external calibration is performed every 2-s rotation of the scanning mirror, based on observations of the cosmic microwave background and an internal blackbody calibration target, with three thermistors to monitor the target physical temperature. Corrections for antenna pattern effects and cross-scan biases based on prelaunch measured values were updated using data from an on-orbit calibration pitch maneuver. Validation of the observed brightness temperatures ( $T_{\text {B}}$ ) is performed by comparing to coincident nonprecipitating ocean observations from five well-calibrated on-orbit instruments, including Global Precipitation Measurement (GPM) mission Microwave Imager (GMI) and four Microwave Humidity Sounder (MHS) sensors on board NOAA-19, MetOp-A, MetOp-B, and MetOp-C satellites. Absolute calibration accuracy is within 1 K for all channels, well within the 4-K requirement. Calibration precision, or stability over time, is within 0.6 K for all channels, also well within the 2-K requirement. The intrinsic noise of TEMPEST-D is lower than MHS, resulting in similar on-orbit noise equivalent differential temperatures (NEDTs), even though TEMPEST-D has a much shorter integration time of 5 ms as compared to 18 ms for MHS. As a result, although the TEMPEST-D radiometer is substantially smaller, lower power, and lower cost than similar current operational radiometers, it has comparable or better performance in terms of instrument noise, calibration accuracy, and calibration stability or precision.

A persistent challenge in the measurement of soil moisture from satellites stems from the inherently low spatial resolution using active or passive system. The lobe differencing correlation radiometer (LDCR) on a small unmanned aerial system (sUAS) is shown to provide a capability to measure soil moisture at high spatial resolution for a range of scientific and operational purposes. Flight tests of LDCR on a fixed wing sUAS were performed at the Canton, Oklahoma Soilscape site in September 2015, and Irrigation Research Foundation (IRF) in Yuma, Colorado, in June 2016. The LDCR design and performance are discussed, and the calibration using both preflight lab test data and in-flight data over a calm pond was performed to calibrate the radiometer. Radio frequency interference (RFI) from the sUAS platform was observed and mitigated. The LDCR sampling processes are detailed and an implementation of the $\tau -\omega $ vegetation correction model along with a semiempirical surface roughness correction model incorporating a full-domain soil moisture mapping algorithm is presented. The algorithm uses a weakly nonlinear observation operator suitable for irregular sUAS flight trajectories that maps volumetric soil moisture (VSM) on a user-defined product grid from the sUAS sampling grid. Using LDCR radiometric and thermal measurements, along with Landsat-based vegetation water content (VWC) and soil texture information, soil moisture was mapped at decameter spatial resolution. The retrieved VSM data are favorably compared with in situ VSM measurements and irrigation records. A method for determining LDCR VSM estimation errors is developed to quantify the mapping algorithm accuracy and assess the impact of error sources.

This article describes a comprehensive testing method for the thermal calibration of the Thermal InfraRed Sensor (TIRS) onboard NASA's Perseverance rover. Ground-based IR detectors operating at the surface of Mars are subjected to inaccuracies caused by the inclusion of thermal gradients in their packages. To reduce such uncertainties, it is necessary to compensate for their effects. Here, details of the TIRS thermo-mechanical design and a simplified thermal mathematical model (TMM) that accounts for the presence of thermal gradients in the detector's package are provided. Then, a set of equations for the estimation and compensation of thermal gradients are proposed based on the results of the TMM. Thermal calibration tests to identify the mathematical estimators are analyzed, providing details of the test setups and highlighting their limitations and restrictions. Finally, experimental results of the calibration tests are presented, along with the uncertainty sources and potential systematic errors associated with the estimation of the gradients. The results presented here show a significant improvement in the accuracy of TIRS versus previous work, thus fulfilling of the radiometer scientific requirements set by the Mars 2020 science team.

The Physikalisch-Technische Bundesanstalt (PTB) has set up an additional measurement approach for the absolute calibration of the spectral responsivity of detectors in the near-infrared (NIR) and mid-infrared (MIR) spectral range. This alternative method uses the radiation of a blackbody operating at about 1200 K with a precision aperture. The blackbody radiation can be calculated by Planck's law and is additionally spectrally selected by accurately characterized optical bandpass filters. Thus, a calibration of the spectral responsivity of a detector with respect to irradiance can be achieved at the bandpass wavelength of the applied transmission filters. If the aperture of the detector is known, the spectral responsivity can also be calculated with respect to radiant power. Thermopile detectors with known aperture size were calibrated in terms of their spectral responsivity with several bandpass filters in the spectral range between 1.5 µm up to 14 µm with relative standard measurement uncertainties between 5 % and 19 %. The obtained results are consistent with previous calibrations at PTB's national primary detector standard. Therefore, this additional measurement approach is a further validation of the existing primary method which is based on a cryogenic radiometer and extends the usable wavelength range.

The visible-shortwave infrared Advanced Hyperspectral Imager (AHSI) is a payload onboard the Gaofen-5 satellite, which is China’s first hyperspectral satellite and is part of the Chinese High-Resolution Earth Observation System. As a supplement to the onboard radiometric calibration of the AHSI instrument, vicarious calibration is also required, which is independent of the instrument-based calibration. In this article, a reflectance-based vicarious calibration approach is presented, which takes surface reflectance data, aerosol data, and atmospheric water vapor data into account. The Dunhuang test site, which is one of the China Radiometric Calibration Sites (CRCS) for the vicarious calibration of spaceborne sensors, possesses stable, uniform, and measurable surface objects, so it was chosen as the radiation source to replace the laboratory and onboard calibrators. A Spectra Vista Corporation (SVC) spectral radiometer and a CE318 sun photometer were utilized for the measurement of the surface reflectance and the condition of the aerosol, respectively. The radiance at the entrance pupil at the top of atmosphere was then obtained through the MODerate resolution atmospheric TRANsmission (MODTRAN) atmospheric transmission model. The surface reflectance was obtained using the Fast Line-of-sight Atmospheric Analysis of Hypercubes (FLAASH) atmospheric model for validation. The results show that, with regard to the calibration coefficients, the calibrated AHSI instrument presents a stable radiometric performance among different land-cover types. The ratios on all the bands are between 0.8 and 1.2 and are consistent with the reflectance data from the Dunhuang test site. The ${R} ^{{2}}$ values are all greater than 0.95 and the spectral angle is all less than 2°. The standard deviations of the ratios are less than 3% for each chosen band, which proves that the calibrated data have a high consistency with the in situ measurements. When compared with Landsat 8 and Sentinel-2, the mean errors of the surface reflectance are all under 0.06, which further demonstrates that the calibrated reflectance has a high accuracy.

In this contribution, we present a comparison of three digital real-time spectrometers used in passive remote sensing of ozone and other trace gases in the middle atmosphere. During a period of six months, we connected the spectrometers to the same radiometric front-end to perform parallel observations of the ozone emission line at 110.836 GHz. This allowed us to better characterize a bias previously observed on the integrated spectra of the Acqiris AC240, a widely used digital spectrometer which has been used for more than a decade in many operational microwave radiometers. We investigated the bias under different atmospheric conditions and found that it is caused by multiple sources. Nonlinearities in the calibration are responsible for part of the bias, but a larger contribution stems from a second effect in the AC240. Although this error source is still partly unexplained, we found that a simple correction scheme simulating a spectral leakage can be applied to the integrated spectra of the AC240 and worked well on our range of observations. We also show that by applying our bias correction to the spectra, we can correct the bias in the ozone retrievals. There is still a need for further measurements to validate this approximate correction, but it could help to correct the numerous time series of ozone and other atmospheric constituents recorded by the AC240.

This study presents a correction algorithm to remove the backlobe intrusion from the hot-load reflector of microwave radiation imager (MWRI) on-board China FengYun-3C (FY-3C) meteorological satellite. Discontinuities in the radiometric gain of MWRI, due to inaccurate backlobe spillover of the hot-load reflector, have been diagnosed. A physical correction method, relating the radiometric gain difference between two distinct backlobe scenes to the spillover of the hot-load reflector, is thus established to estimate the in-orbit spillover factor of the MWRI hot-load reflector. Meanwhile, it is found that brightness temperature (TB) at each 1° × 1° grid could not address the backlobe pattern appropriately, leading to abrupt changes in the radiometric gain occurring near the coastlines. To better represent the backlobe spillover, the effective backlobe TB is now averaged over a 4° × 4° area from advanced microwave scanning radiometer-observed TBs, which is now in a finer resolution of 0.25° × 0.25°. With the adjusted spillover for the hot-load reflector, anomalous behaviors of the radiometric gain are mitigated effectively and the improvement at the coastlines is pronounced, inducing TB variation up to 2 K in magnitude at the 10.65 GHz channel. Although the algorithm presented in this study is only restricted to channel frequencies up to 23.8 GHz due to its accuracy limitation, it could tackle similar issues for those microwave radiometers not capable of in-orbit backlobe maneuvers and receiving the radiances inevitably from the outer edge of the hot-load or cold-sky reflectors.

The amount of radiation given from the phototherapy lamp (Blue Light) who not right for neonates with hyperbilirubin is feared to cause the bilirubin levels in not decrease accordance with the calculated dose. The purpose of this study is to make a Blue Light calibration device with a stable measurement. The contribution of this research is by determine a sensor who able to measure the irradiation value more accurately between TCS3200 and AS7262 sensor. TCS3200 sensor measures the wavelengths of 470nm, 524nm and 640nm and AS7262 sensor can measure wavelengths of 430-670nm. The results of both sensors are stored in the Electrically Erasable Programmable Read-Only Memory, with the amount of data and the length of measurement can be adjusted according to user needs. Measurement the irradiation value of two sensors is done simultaneously using 3 Watt Light Emitting Diode lamp as a Blue Light simulation where the lamp is placed directly above the sensor and distance of the lamp to the sensor is 10cm, 20cm, 30cm, and 40cm. The average uncertainty value with TCS3200 sensor is 14.65 and the average uncertainty value with AS7262 sensor is 2.17. Type A uncertainty value is based on results of repeated measurements that show how close the measurement results are to the actual value (stable measurement results). The results showed that the average uncertainty value on AS7262 sensor is relatively small, so its mean the measurement results of AS7262 sensor are stable. The author suggests using sensors who capable of reading the value of light radiation without conversion. The results of this study can be implemented to measure the intensity of the lamp and be used as a reference to determining the time of lamp replacement.

The soil moisture active passive (SMAP) microwave radiometer is a fully-polarimetric L -band radiometer flown on the SMAP satellite in a 6 AM /6 PM sun-synchronous orbit at 685-km altitude. After the SMAP L1B_TB data product version 4 was released in 2018, the radiometer has undergone further calibration and validation. The goal is to reduce the difference between antenna temperature of ascending and descending orbits during the eclipse, and to reduce the dips in the calibration drift over the cold sky (CS) during the eclipse seasons in 2017 and 2018. The postlaunch calibration algorithm has been revisited by retrieving all of the calibration parameters simultaneously with two different options for the hot calibration source (the global ocean, or the radiometer internal reference load). The performance of the two options are compared here. The option with the radiometer internal reference load has been chosen by the SMAP science team for data release version 5. In addition, a correction offset is applied to the input signal to account for offsets during the early-mission stages with the SMAP synthetic aperture radar transmitter operating alongside the radiometer.

Sea surface salinity (SSS) in coastal zones with high temporal and spatial resolutions is also needed for scientific research and marine economy. However, one of the limitations of airborne L -band real-aperture microwave radiometers and synthesis aperture interferometric radiometers is high mass and volume or the high complexity of the system. To overcome the limitations, an L -band phased array radiometer is proposed as an alternative solution to monitor SSS in coastal zones from a small unmanned aerial vehicle (UAV). The L -band phased array radiometer is introduced, which only comprises a phased array and a single receiver. In addition, the calibration methods of the L -band phased array, radiometer that the external calibration method and the internal calibration method, are also introduced. A series of experiments was performed to assess the performance of the L -band phased array radiometer. The results indicate that the L -band phased array radiometer show a good performance in low pointing angles and low scanning angles. As a conclusion, the L -band phased array radiometer can be an alternative solution to monitor SSS in coastal zones with high temporal and spatial resolutions from a small UAV.

Microwave remote sensing can be used to monitor the time evolution of some key parameters over land, such as land surface temperature or surface water extent. Observations are made with instruments, such as the Scanning Microwave Multichannel Radiometer (SMMR) before 1987, the Special Sensor Microwave/Imager (SSM/I) and the subsequent Special Sensor Microwave Imager/Sounder (SSMIS) from 1987 and still operating, and the more recent Global Precipitation Measurement Microwave Imager (GMI). As these instruments differ on some of their characteristics and use different calibration schemes, they need to be inter-calibrated before long-time-series products can be derived from the observations. Here an inter-calibration method is designed to remove major inconsistencies between the SMMR and other microwave radiometers for the 18 and 37 GHz channels over continental surfaces. Because of a small overlap in observations and a ∼6 h difference in overpassing times between SMMR and SSM/I, GMI was chosen as a reference despite the lack of a common observing period. The diurnal cycles from 3 years of GMI brightness temperatures are first calculated and then used to evaluate SMMR differences. Based on a statistical analysis of the differences, a simple linear correction is implemented to calibrate SMMR on GMI. This correction is shown to also reduce the biases between SMMR and SSM/I, and can then be applied to SMMR observations to make them more coherent with existing data records of microwave brightness temperatures over continental surfaces.

Microwave humidity and temperature sounder (MWHTS) is an important payload of FENGYUN-3D (FY-3D) satellite. MWHTS is designed as a total power radiometer because of its high radiometric resolution (NEDT) and simple configuration. NEDT is one of the most important indicators for MWHTS. However, the traditional NEDT analysis results have a large error compared with the measured results. To estimate the overall radiometer performance, this article presents a numerical system simulation model that can be used to estimate NEDT. The model considers two of three main components that degrade the NEDT: thermal noise and calibration error. The third component $1/f$ noise is calculated by analyzing the power spectrum of the actual output voltage. Based on digital signal processing, this article presents a method for accurate system modeling, including radiation source modeling, frontend modeling, and backend modeling. The simulation model is validated by comparing the dynamic range, linearity, and standard deviation in output signals of the simulation system and actual instrument. In this article, the model has been applied to NEDT estimate of 15 MWHTS channels operating from 89 to 183 GHz. The comparison of the estimate results between the simulation model and actual instrument shows they are in good agreement and the maximum error is no more than 18%.

The relative phase and amplitude calibration between individual receiving elements are one of the key challenges in interferometric microwave radiometry. For monolithic interferometers, for example the Microwave Imaging Radiometer with Aperture Synthesis (MIRAS) on board the Soil Moisture and Ocean Salinity (SMOS) mission, the calibration of these two parameters is accomplished by correlated noise injection, where a noise signal from a central noise source is distributed to multiple elements in phase, and any measured phase and amplitude differences can be measured and compensated. As new multisatellite interferometers are being proposed, a new strategy capable of calibrating these errors between the free-flying antennas as well as any antenna position errors is required. In this article, a new calibration scheme is proposed which uses a set of microwave beacons in place of the central noise source, placed at known locations within the interferometer’s field of view. The visibility function produced by these point-sources can be precalculated, and any errors between the calculated and the measured visibility samples can be attributed to the errors to be determined. Since the phase of the measured visibility is a function of phase and antenna position errors, this technique is capable of calibrating these two parameters simultaneously. Calibration equations for far- and near-field beacons are presented. Using these expressions, five interferometric calibration routines are proposed and examined for geostationary formation flight microwave radiometers. Although this technique is ideal for multisatellite interferometers with variable antenna positions, it is also applicable to monolithic interferometers that can undergo substantial array deformation.

The Prede sky radiometer measures direct solar irradiance and the angular distribution of diffuse radiances at the ultraviolet, visible, and near-infrared wavelengths. These data are utilized for the remote sensing of aerosols, water vapor, ozone, and clouds, but the calibration constant, which is the sensor output current of the extraterrestrial solar irradiance at the mean distance between Earth and the Sun, is needed. The aerosol channels, which are the weak gas absorption wavelengths of 340, 380, 400, 500, 675, 870, and 1020 nm, can be calibrated by an on-site self-calibration method, the Improved Langley method. This on-site self-calibration method is useful for the continuous long-term observation of aerosol properties. However, the continuous long-term observation of precipitable water vapor (PWV) by the sky radiometer remains challenging because calibrating the water vapor absorption channel of 940 nm generally relies on the standard Langley (SL) method at limited observation sites (e.g., the Mauna Loa Observatory) and the transfer of the calibration constant by a side-by-side comparison with the reference sky radiometer calibrated by the SL method. In this study, we developed the SKYMAP algorithm, a new on-site method of self-calibrating the water vapor channel of the sky radiometer using diffuse radiances normalized by direct solar irradiance (normalized radiances). Because the sky radiometer measures direct solar irradiance and diffuse radiance using the same sensor, the normalization cancels the calibration constant included in the measurements. The SKYMAP algorithm consists of three steps. First, aerosol optical and microphysical properties are retrieved using direct solar irradiances and normalized radiances at aerosol channels. The aerosol optical properties at the water vapor channel are interpolated from those at aerosol channels. Second, PWV is retrieved using the angular distribution of the normalized radiances at the water vapor channel. Third, the calibration constant at the water vapor channel is estimated from the transmittance of PWV and aerosol optical properties. Intensive sensitivity tests of the SKYMAP algorithm using simulated data of the sky radiometer showed that the calibration constant is retrieved reasonably well for PWV cm, which indicates that the SKYMAP algorithm can calibrate the water vapor channel on-site in dry conditions. Next, the SKYMAP algorithm was applied to actual measurements under the clear-sky and low-PWV ( cm) conditions at two sites, Tsukuba and Chiba, Japan, and the annual mean calibration constants at the two sites were determined. The SKYMAP-derived calibration constants were 10.1 % and 3.2 % lower, respectively, than those determined by a side-by-side comparison with the reference sky radiometer. After determining the calibration constant, we obtained PWV from the direct solar irradiances in both the dry and wet seasons. The retrieved PWV values corresponded well to those derived from a global-navigation-satellite-system–global-positioning-system receiver, a microwave radiometer, and an AERONET (Aerosol Robotic Network) sun–sky radiometer at both sites. The correlation coefficients were greater than 0.96. We calculated the bias errors and the root mean square errors by comparing PWV between the DSRAD (direct solar irradiance) algorithm and other instruments. The magnitude of the bias error and the root mean square error were and  cm for PWV cm, respectively. However, our method tended to underestimate PWV in the wet conditions, and the magnitude of the bias error and the root mean square error became large, and  cm for PWV>3 cm, respectively. This problem was mainly due to the overestimation of the aerosol optical thickness before the retrieval of PWV. These results show that the SKYMAP algorithm enables us to observe PWV over the long term, based on its unique on-site self-calibration method.

Calibration plays an important role in improving the accuracy of the microwave and millimeter-wave radiometric measurements. Several calibration techniques have been used in radiometers including external calibration targets, vicarious sources, and internal calibrators such as noise diodes or matched reference load. A new calibration technique based on deep learning has recently been developed to calibrate microwave and millimeter-wave radiometers. The deep-learning calibrator has been previously demonstrated on a computer noise-wave modeled Dicke-switching radiometer. This article applies the new deep-learning calibration technique for the calibration of the high-frequency airborne microwave and millimeter-wave radiometer (HAMMR) instrument. A deep-learning neural network model is built to calibrate the 2014 West Coast Flight Campaign antenna temperature measurements of the HAMMR. The deep-learning calibrator antenna temperature estimates are obtained from the radiometric measurements. The deep-learning calibration results are compared with the existing conventional calibration techniques used in HAMMR 2014 field campaign. The results have shown that the deep-learning calibrator is in agreement with the conventional calibration techniques. In this article, it is demonstrated that the deep-learning calibrator can be employed for calibrating the radiometers with high accuracy.

This work aims to determine the radiance responsivity to be used in the calibration of polychromatic radiation sources with low uncertainty. To realize the radiance, Ar-ion, He-Ne, and Nd-YAG lasers as well as an integrating sphere with a 0.15 m diameter are used to obtain radiation sources having Lambertian distributions. Then, a silicon photodiode-based reflection-type trap detector with calibrated precision aperture, which is traceable to a liquid helium cooled laser-based cryogenic radiometer, is used to measure the photocurrent corresponding to each wavelength and thereby to obtain radiance. The proposed system, which measures the spectral current response of this laser-based radiance, is a double-grating monochromator with a 2 × 300 mm focal length and triple gratings in each of its turrets. First, the radiance of the laser beam that emerged from the integrating sphere is calculated, and then the radiance responsivity of the system is obtained by measuring the photocurrent outputted from the exit slit of the monochromator at each laser wavelength. Finally, the spectral radiance values of the polychromatic lamps are obtained using the radiance responsivity of the system. Consequently, the study aims to develop the derivation and better understand traceability of the other radiometric and photometric quantities with low uncertainty from the fundamental radiometric radiance unit. Measurement results obtained in the expanded measurement uncertainty scale are determined using both classical and Monte Carlo methods.

The Fundamental Climate Data Record (FCDR) of Microwave Imager Radiances from the Satellite Application Facility on Climate Monitoring (CM SAF) comprises inter-calibrated and homogenized brightness temperatures from the Scanning Multichannel Microwave Radiometer (SMMR), the Special Sensor Microwave/Imager (SSM/I), and the Special Sensor Microwave Imager/Sounder SSMIS radiometers. It covers the time period from October 1978 to December 2015 including all available data from the SMMR radiometer aboard Nimbus-7 and all SSM/I and SSMIS radiometers aboard the Defense Meteorological Satellite Program (DMSP) platforms. SMMR, SSM/I, and SSMIS data are used for a variety of applications, such as analyses of the hydrological cycle, remote sensing of sea ice, or as input into reanalysis projects. The improved homogenization and inter-calibration procedure ensures the long-term stability of the FCDR for climate-related applications. All available raw data records from different sources have been reprocessed to a common standard, starting with the calibration of the raw Earth counts, to ensure a completely homogenized data record. The data processing accounts for several known issues with the instruments and corrects calibration anomalies due to along-scan inhomogeneity, moonlight intrusions, sunlight intrusions, and emissive reflector. Corrections for SMMR are limited because the SMMR raw data records were not available. Furthermore, the inter-calibration model incorporates a scene dependent inter-satellite bias correction and a non-linearity correction in the instrument calibration. The data files contain all available original sensor data (SMMR: Pathfinder level 1b) and metadata to provide a completely traceable climate data record. Inter-calibration and Earth incidence angle normalization offsets are available as additional layers within the data files in order to keep this information transparent to the users. The data record is complemented with noise-equivalent temperatures (NeΔT), quality flags, surface types, and Earth incidence angles. The FCDR together with its full documentation, including evaluation results, is freely available at: https://doi.org/10.5676/EUM_SAF_CM/FCDR_MWI/V003 (Fennig et al., 2017).

The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) was launched on June 29, 2018, to the International Space Station (ISS). Landsats 7 and 8 were launched on April 15, 1999 and February 11, 2013, respectively. The thermal channels of all three instruments have been validated at the Lake Tahoe, CA/NV, USA, and Salton Sea, CA, USA, automated validation sites. These sites have been used to validate a large number of thermal infrared radiometers including ASTER, MODIS, and VIIRS. We have validated 41 cloud-free ECOSTRESS scenes acquired between July 29, 2018 and June 23, 2019; 625 cloud-free Landsat 7 scenes acquired between June 30, 1999 and February 7, 2019; and 375 cloud-free Landsat 8 scenes acquired between March 10, 2013 and February 8, 2019. Validation involved propagating ground measurements to equivalent at-sensor (vicarious) values and comparing them to the measurements obtained from the sensor using its on-board calibration (OBC). The overall correlation between the in situ measurements and at-sensor radiance for the thermal channels from all three instruments was excellent with $R^{2}$ of 0.98–0.99 in all the spectral channels. All three instruments were shown to meet or improve on their preflight absolute radiometric accuracy requirement with absolute radiometric values of better than ±1 K at 300 K. All three instruments were also shown to have in-flight noise equivalent delta temperatures which were similar to their preflight values and between 0.1 and 0.3 K depending on the spectral channel.

This work addresses the intercalibration of the Microwave Radiation Imager (MWRI) on the Chinese second-generation polar-orbiting meteorological satellite Fengyun 3C (FY-3C) against the Microwave Imager (GMI) on the Global Precipitation Measurement (GPM) Core Observatory, in which a modified Double Difference (DD) method is developed and the Brightness Temperatures (TBs) in FY-3C MWRI and GMI channels are simulated using the ocean microwave Radiative Transfer Model (RTM) fed with the fifth generation of European Centre for Medium-range Weather Forecast (ECMWF) atmospheric reanalysis (ERA5) data. With the modified DD method, the intercalibration of FY-3C MWRI in 2017 are obtained. The results show that the MWRI observations are underestimated, especially for the low frequency channels. The calibration biases (mean of DDs) in FY-3C MWRI channels are temperature dependent, and decrease with the frequency increment. The in-orbit calibration of the MWRI descending (MWRID) data is 1~2 K worse than that of the MWRI ascending (MWRIA) data. At the TBs of standard scene defined by the Global Space-based Inter-Calibration System (GSICS), the calibration errors (mean of DDs ± standard deviation at the mean) of MWRIA data in January 2017 are -6.7±0.4 K, -8.3±0.8 K, -3.0±0.7 K, -1.9±1.0 K, -2.5±1.1 K, -3.9±0.8 K, -2.1±1.5 K, -1.5±1.0 K and -0.4±2.3 K in the 10V/H, 18V/H, 23V, 36V/H and 89V/H channels, respectively, while the calibration errors of MWRID data in January 2017 are -7.6±0.8 K, -9.1±1.2 K, -4.4±0.8 K, -2.9±1.3 K, -3.6±1.2 K, -5.1±0.8 K, -2.6±1.4 K, -2.5±1.1 K and -1.2±2.5 K in the nine channels, respectively. Although calibration biases exist, the in-orbit calibration of FY-3C MWRI is generally stable in 2017. The results of the modified DD method are consistent with that of the DD method. The modified DD method is promising to be applied to the intercalibration with both target and reference radiometers on polar-orbiting satellites.

The advanced microwave radiometer-climate quality (AMR-C) is a part of the European Sentinel-6A/B series, a collaboration between ESA and NASA, of two Earth-observing satellites, which will be launched in 2020 and 2025. Compared to its predecessor, Jason-3, the two AMR-C radiometer instruments have an external calibration system which enables higher radiometric stability accomplished by moving the secondary mirror between well-defined targets. Sentinel-6 allows continuing the study of the ocean circulation, climate change, and sea-level rise for at least another decade. Besides the external calibration for the AMR heritage radiometer (18.7, 23.8, and 34 GHz channels), the AMR-C contains a high-resolution microwave radiometer (HRMR) with radiometer channels at 90, 130, and 168 GHz. This subsystem allows for a factor of 5× higher spatial resolution at coastal transitions. This article presents a brief description of the instrument and the measured performance of the completed AMR-C-A and AMR-C-B instruments.

Recently, polarimetry has been used to enhance classical photometry to infer aerosol optical properties, as polarized radiation contains additional information about the particles. Therefore, we have equipped the Sun–sky automatic radiometer (SSARA) with polarizer filters to measure linearly polarized light at 501.5 nm. We describe an improved radiometric and polarimetric calibration method, which allows us to simultaneously determine the linear polarizers' diattenuation and relative orientation with high accuracy (0.002 and 0.1∘, respectively). Furthermore, we employed a new calibration method for the alt-azimuthal mount capable of correcting the instrument's pointing to within 32 arcmin. So far, this is limited by the accuracy of the Sun tracker. Both these methods are applicable to other Sun and sky radiometers, such as the Cimel CE318-DP instruments used in the AErosol RObotic NETwork (AERONET). During the A-LIFE (Absorbing aerosol layers in a changing climate: aging, LIFEtime and dynamics) field campaign in April 2017, SSARA collected 22 d of data. Here, we present two case studies. The first demonstrates the performance of an aerosol retrieval from SSARA observations under partially cloudy conditions. In the other case, a high aerosol load due to a Saharan dust layer was present during otherwise clear-sky conditions.

The instrument cross-calibration is an effective way to assess the quality of satellite data. In this study, a new me-thod is proposed to cross-calibrate the sensors among satellite instruments by using a RObotic Lunar Observatory (ROLO) model and Apollo sample reflectance in reflective solar bands (RSBs). The ROLO model acts as a transfer radiometer to bridge between the instruments. The reflective spectrum of the Apollo sample is used to compensate for the difference in the instrument’s relative spectral responses (RSRs). In addition, the double ratio between the observed lunar irradiance and the simulated lunar irradiance is used to reduce the difference in instrument lunar viewing and illumining geometry. This approach is applied to the Moderate Resolution Imaging Spectroradiometer (MODIS), the Sea-Viewing Wide Field-of-View Sensor (SeaWiFS), and the Advanced Land Imager (ALI) on board three satellites, respectively. The mean difference between MODIS and SeaWiFS is less than 3.14%, and the difference between MODIS and ALI is less than 4.75%. These results indicate that the proposed cross-calibration method not only compensates for the RSR mismatches but also reduces the differences in lunar observation geometry. Thus, radiance calibration of any satellite instrument can be validated with a reference instrument bridged by the moon.

In this paper, we present the design and implementation tests of a water vapor radiometer (WVR) suitable for very long baseline interferometry (VLBI) observation. We describe the calibration method with an analysis of the sources of measurement errors. The experimental results show that the long-term measurement accuracy of the brightness temperature of the water vapor radiometer can reach 0.2 K under arbitrary ambient conditions by absolute calibration, receiver gain error calibration, and antenna feeder system temperature noise error calibration. Furthermore, we present a method for measurements of the calibration error of the oblique path measurement. This results in an oblique path wet delay measurement accuracy of the water vapor radiometer reaching 20 mm (within one month).

International audience; Vegetation optical depth (VOD) retrieved from microwave remote sensing techniques has been used as an important proxy for monitoring the vegetation dynamics at large scales. In a first study we developed a new X-VOD indice from inversion of the X-MEB model from the Microwave Scanning Radiometer 2 (AMSR2, 2012-present) observations. The Advanced Microwave Scanning Radiometer for EOS (AMSR-E, 2002–2011) and AMSR2 share many characteristics, such as local crossing time, incidence angle, frequency at X-band, etc, which makes it possible to produce a consistent long-term X-VOD product over a long period spanning from 2002 to present. The objective of this study was to merge the AMSR-E and AMSR2 observations in order, in a second step, to produce a consistent long-term X-VOD product spanning over the combined periods of AMSR-E and AMSR2.The main challenges in retrieving a consistent X-VOD data set from AMSR-E and AMSR2 are that (i) there is a bias between observations from the two sensors; and (ii) the lack of overlapping observations between the two sensors makes it impossible to achieve a direct inter-calibration. Here, to overcome this problem, we used AMSR-E slow rotation data (AMSR-E L1S), which has similar characteristics as AMSR-E and provided observations concurrently with AMSR2, as a bridge to calibrate the AMSR-E brightness temperature (TB) observations with AMSR2 TB.As our main objective in this TB calibration study was to produce a consistent long-term AMSR-E/AMSR2 X-VOD product, we evaluated the retrieved X-VOD product based on different inter-calibration, either global-based or pixel-based, methods. Both AMSR-E and AMSR2 X-VOD were evaluated against the Aboveground Biomass (AGB), Leaf Area Index (LAI) and the Normalized Difference Vegetation Index (NDVI). The results suggest that global-based inter-calibration methods using homogeneous and temporally stable land covers (Evergreen Broadleaf Forests and Snow and Ice) as reference calibration data sets provided the best results. For instance, the spatial relationships between X-VOD and AGB/LAI/NDVI are highly consistent over the AMSR-E and AMSR2 periods after the calibration work. This study laid a solid foundation for monitoring the dynamics of X-VOD, as a proxy of AGB, over the combined periods of AMSR-E and AMSR2 sensors (almost 20 years).

Continuous observation and quantitative retrieval of aerosol backscatter coefficients are important in the study of air quality and climate in metropolitan areas such as New York City. Ceilometers are ideal for this application, but aerosol backscatter coefficient retrievals from ceilometers are challenging and require proper calibration. In this study, we calibrate the ceilometer (Lufft CHM15k, 1064 nm) system constant with the molecular backscatter coefficient and evaluate the calibrated profiles with other independent methods, including the water-phase cloud method and comparison with the NASA Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) attenuated backscatter coefficient profile. Multiple-day calibration results show a stable system constant with a relative uncertainty of about 7%. We also evaluate the overlap correction for the CHM15k ceilometer (provided by Lufft) with a Vaisala CL-31 ceilometer, and the results show good consistency between two ceilometers’ range-corrected signal (RCS) profiles above 200 m. Next, we implement a forward iterative method to retrieve aerosol backscatter coefficients from continuous ceilometer measurements. In the retrieval, the lidar ratio is constrained by the co-located NASA AERONET radiometer aerosol optical depth (AOD) retrieval and agrees with the AERONET lidar-ratio products, derived from aerosol microphysical parameters. The aerosol backscatter coefficient retrievals are validated with co-located elastic-Raman lidar retrievals and indicate a good correlation (R2≥0.95) in the planetary boundary layer (PBL). Furthermore, a case study shows that the ceilometer retrieved aerosol extinction coefficient profiles can be used to estimate the AOD of the PBL and the aloft plumes. Finally, simulations of the uncertainty of aerosol backscatter coefficient retrieval show that a calibration error of 10% results in 10–20% of relative error in the aerosol backscatter coefficient retrievals, while relative error caused by a lidar-ratio error of 10% is less than 4% in the PBL.

In order to achieve high precision from non-contact temperature measurement, the hardware structure of a broadband correlative microwave radiometer, calibration algorithm, and temperature inversion algorithm are innovatively designed in this paper. The correlative radiometer is much more sensitive than a full power radiometer, but its accuracy is challenging to improve due to relatively large phase error. In this study, an error correction algorithm is designed, which reduces the phase error from 69.08° to 4.02°. Based on integral calibration on the microwave temperature measuring system with a known radiation source, the linear relationship between the output voltage and the brightness temperature of the object is obtained. Since the metal aluminum plate, antenna, and transmission line will have a non-linear influence on the receiver system, their temperature characteristics and the brightness temperature of the object are used as the inputs of the neural network to obtain a higher accuracy of inversion temperature. The temperature prediction mean square error of a back propagation (BP) neural network is 0.629 °C, and its maximum error is 3.351 °C. This paper innovatively proposed the high-precision PSO-LM-BP temperature inversion algorithm. According to the global search ability of the particle swarm optimization (PSO) algorithm, the initial weight of the network can be determined effectively, and the Levenberg–Marquardt (LM) algorithm makes use of the second derivative information, which has higher convergence accuracy and iteration efficiency. The mean square error of the PSO-LM-BP temperature inversion algorithm is 0.002 °C, and its maximum error is 0.209 °C.

METimage is an advanced multispectral radiometer for weather and climate forecasting developed by Airbus Defence & Space under the auspices of the German Space Administration (DLR) for the EUMETSAT Polar System –Second Generation (EPS-SG). The instrument is equipped with a continuously rotating scan mirror with a 1.7s period followed by a static telescope. The scan mirror permits an extended Earth view of 108° per revolution and regular views to on-board calibration sources. A derotator assembly, which is half-speed synchronised with the scanner, is inserted in the optical beam after the telescope to compensate the image rotation in the focal plane. The derotator optical arrangement is a fivemirror concept that minimises the polarisation sensitivity. The derotator design is constrained by optical performance, mass and compactness, which led to the selection of a full silicon carbide (SiC) concept. This paper is presented as a continuation of the ICSO 2018 paper to describe the detailed design and early physical model results of the derotator optics.

The first five years of operation of the Deep Space Climate Observatory (DSCOVR) Earth Polychromatic Imaging Camera (EPIC) at the Lagrange one point have produced results that uniquely complement the data from currently operating low orbit Earth-observing instruments. In this paper we describe an updated unified approach to EPIC calibration. In this approach, calibration coefficients and their trends were obtained by comparing EPIC observations to the measurements from polar orbiting radiometers. In this study L1B reflectances from Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the Aqua and Terra satellites, Multi-angle Imaging Spectroradiometer (MISR) onboard Terra and Visible Infrared Imaging Radiometer (VIIRS) onboard the Suomi National Polar-orbiting Partnership (Suomi NPP) spacecraft were used to infer calibration coefficients for four EPIC visible and near-infrared channels: 443 nm, 551 nm, 680 nm, and 780 nm. EPIC Version three measurements made between June 2015 and August 2020 were used for comparison. The calibration procedure identifies the most homogeneous low Earth orbit radiometer scenes matching scattering angles that are temporarily and spatially collocated with EPIC observations. These scenes are used to determine reflectance to count (R/C) ratios in spectrally analogous channels. Seasonal average R/C ratios were analyzed to obtain EPIC calibration gains and trends. The trends for the full dataset period are not statistically significant except in the 443 nm channel. No significant changes in calibration were found after the instrument’s exit from safe hold in March 2020. The R/C ratios were also used to determine the differences in EPIC gains resulting from separate calibrations: against MODIS Aqua or Terra, as well as against forward or aftward MISR cameras. Statistical tests indicate that the differences between the two datasets are not significant except in the 780 nm channels where Aqua-derived coefficients may be around 2% lower compared to Terra. The dependence of EPIC calibration gains on the instrument scattering angle and on DSCOVR-Earth distance were investigated. Lastly, model Low Earth Orbit (LEO) reflectances calculated to match the EPIC viewing geometry were employed to study how EPIC calibration coefficients depend on EPIC-LEO viewing geometry differences. The effect of LEO and EPIC angular mismatch on calibration was shown to be small.

The expediency of using a compensation radiometer (CR) with periodic absolute calibration as a sensor for preprocessing the information of correlation-extreme navigation systems (CENS) of unmanned aerial vehicles (UAV) was shown. This is determined by the possibility of obtaining and using the estimates of gain fluctuations obtained in previous frames which will provide an increase in the radiometer sensitivity. In addition, due to the accumulation of information, an increase in accuracy of measurement of the elements of the current image formed by the CENS will be provided. The algorithm of processing the obtained calibration estimates during linear processing corresponds to a certain digital filter (DF). By defining a set of the DF weight coefficients, it is possible to improve the CR fluctuation sensitivity by reducing the gain fluctuations. Up to 1.8-time gain in sensitivity can be reached for typical frequency and time parameters of the compensation radiometer of UAV CENS. The problem of synthesis of a digital filter was set. A solution to the problem of synthesizing an optimal digital filter was proposed. Its use in a CR will improve the fluctuation sensitivity. In its turn, this will make it possible to improve the quality of a current image generated by the system when siting by means of sighting surfaces with low-contrast objects taking into account fluctuations in radio-brightness temperature. It was found that the gain in sensitivity when using the optimal digital filter increases with an increase in the operating period of the radiometer and an increase in the digital filter order. Improvement of fluctuation sensitivity of the CENS data preprocessing system is important for UAV location in low-contrast areas

After we replaced the argon mini-arc with a laser-driven light source in the Ultraviolet Spectral Comparator Facility at the National Institute of Standards and Technology (NIST), we realized that the optical power should be sufficient to use the comparator system for absolute-cryogenic radiometry. Calibrating working standard detectors directly against an absolute-cryogenic radiometer in the system used for calibrations would eliminate all uncertainties resulting from the use of transfer standards, which were calibrated in a separate system using a different light source and optics. The transfer standards are the middlemen we refer to in the title. Any uncertainty caused by differences in bandpass, out-off-band radiation, spectral purity, collimation, or data interpolation would be removed. In the end, we successfully set up a twin system resembling the Ultraviolet Spectral Comparator Facility and used this system to perform a primary calibration of several photodiodes, based on an absolute-cryogenic radiometer. Using this system, we were able to reduce relative standard uncertainties at wavelengths below 220 nm from above 1 % ( k = 1 ) to below 0.5%. We refer to this system as the Ultraviolet Scale Realization Facility or UV-SRF.

A comprehensive comparison of more than 70 000 synchronous 1 min aerosol optical depth (AOD) data from three Global Atmosphere Watch precision-filter radiometers (GAW-PFR), traceable to the World AOD reference, and 15 Aerosol Robotic Network Cimel radiometers (AERONET-Cimel), calibrated individually with the Langley plot technique, was performed for four common or “near” wavelengths, 380, 440, 500 and 870 nm, in the period 2005–2015. The goal of this study is to assess whether, despite the marked technical differences between both networks (AERONET, GAW-PFR) and the number of instruments used, their long-term AOD data are comparable and consistent. The percentage of data meeting the World Meteorological Organization (WMO) traceability requirements (95 % of the AOD differences of an instrument compared to the WMO standards lie within specific limits) is >92 % at 380 nm, >95 % at 440 nm and 500 nm, and 98 % at 870 nm, with the results being quite similar for both AERONET version 2 (V2) and version 3 (V3). For the data outside these limits, the contribution of calibration and differences in the calculation of the optical depth contribution due to Rayleigh scattering and O3 and NO2 absorption have a negligible impact. For AOD >0.1, a small but non-negligible percentage (∼1.9 %) of the AOD data outside the WMO limits at 380 nm can be partly assigned to the impact of dust aerosol forward scattering on the AOD calculation due to the different field of view of the instruments. Due to this effect the GAW-PFR provides AOD values, which are ∼3 % lower at 380 nm and ∼2 % lower at 500 nm compared with AERONET-Cimel. The comparison of the Ångström exponent (AE) shows that under non-pristine conditions (AOD >0.03 and AE ) the AE differences remain . This long-term comparison shows an excellent traceability of AERONET-Cimel AOD with the World AOD reference at 440, 500 and 870 nm channels and a fairly good agreement at 380 nm, although AOD should be improved in the UV range.

Deep learning artificial neural network techniques can be applied for on-orbit calibration of microwave and millimeter-wave radiometer spaceborne instruments, including those for small satellites. The noise-wave model has been employed for noise characterization and validation of the proposed deep learning calibration technique for a synthetically generated Dicke-switching radiometer. The developed deep learning neural network radiometer calibrator produces high accuracy estimates of antenna temperatures from the measurements of radiometer output voltage and thermistor readings. Tests with noise-free and noisy samples of the developed model have shown that the proposed calibration method does not add any significant noise to the radiometer calibration. The performance of the proposed method does not degrade with increased nonlinearity for a radiometer, while nonlinearity is a challenging issue for conventional calibration techniques. The deep learning calibration model learns the radiometer noise characteristics from radiometer prelaunch measurements during thermal vacuum chamber testing. The neural network calibrator proposed in this paper has self-learning capability during the on-orbit operation of a radiometer that can be used to improve the performance of on-orbit calibration. The proposed technique is demonstrated by comparing the residual uncertainty of the deep learning calibration with the theoretical value. No numerical study is presented to compare the performance with conventional calibration techniques. The new method may be solely applied to calibrate the radiometer or applied along with conventional calibration techniques.