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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.

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.

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.

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.

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.

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 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.

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.

Consistency between the brightness temperatures observed with a ground-based microwave radiometer and the brightness temperatures computed by forward modeling is important in many different data applications. Using the National Centers for Environmental Prediction-Final Operational Global Analysis (NCEP-FNL) dataset as a reference, the brightness temperature was obtained through the radiation transfer model for forward calculation. The problem of segmented features in long time of observational data from ground-based microwave radiometers (the so-called “jumping problem”) was identified. By analyzing the deviation and correlation between the observational bright temperature data and the forward calculated data under clear sky conditions, a revised scheme is proposed for the bright temperature observational data. Data obtained with a ground-based microwave radiometer in Beijing from January 1, 2010 to December 31, 2011 around the date of liquid nitrogen calibration show that the correlation between the observed brightness temperatures and the forward computed brightness temperatures is better after correction, especially at 28 and 30 GHz. The “jumping” problem in the observational data for the brightness temperature is eliminated after correction and the time continuity of the observational data and its consistency with the forward calculated data based on the NCEP-FNL dataset are improved. The proposed correction scheme can be used both for real-time data quality control and to improve the accuracy of historical datasets obtained with poorly calibrated microwave radiometers or radiometers working in polluted environments.

This paper presents a built-in calibration procedure of a 10-to-20 GHz polarimeter aimed at measuring the I, Q, U Stokes parameters of cosmic microwave background (CMB) radiation. A full-band square waveguide double directional coupler, mounted in the antenna-feed system, is used to inject differently polarized reference waves. A brief description of the polarimetric microwave radiometer and the system calibration injector is also reported. A fully polarimetric calibration is also possible using the designed double directional coupler, although the presented calibration method in this paper is proposed to obtain three of the four Stokes parameters with the introduced microwave receiver, since V parameter is expected to be zero for the CMB radiation. Experimental results are presented for linearly polarized input waves in order to validate the built-in calibration system. The authors would like to thank The Spanish Ministry of Science and Innovation for financial support provided through the grants ESP2015-70646-C2-2-R, ESP2017-83921-C2-2-R and PID2019-110610RB-C22

3 pages, 4 figures, A method for internally calibrating microwave total power radiometers by using only one level of noise injection is presented. It is based on having a previous accurate characterization of the receiver noise temperature, which used de facto as a second calibration standard. The method proves to be at least equivalent to the classical two level, as demonstrated through their intercomparison using the data provided by the Microwave Imaging Radiometer using Aperture Synthesis (MIRAS) on board the European Space Agency Soil Moisture and Ocean Salinity (SMOS) Satellite. The long-term stability in terms of retrieved brightness temperature using both methods has similar trends with a small advantage for the one-point approach proposed her, With the funding support of the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S), of the Spanish Research Agency (AEI)

Calibration of microwave radiometers is a crucial task for reliable and accurate antenna temperature measurements in remote sensing applications. This paper describes a processing procedure for data calibration of noise-adding (NA) total power (TP) radiometers, without thermal stabilization, able to improve the radiometric resolution performance and keep good accuracy. This method, easily implementable, is based on the strong dependence of the radiometric gain on the internal physical temperature of the system. It provides a calibration of the output voltage measured in the TP mode exploiting the noise source power injection only every 30 min. The quality of the proposed procedure was assessed by means of three experiments carried out in different years and environmental conditions, using a low-cost NA TP radiometer operating at 12.65 GHz. The measurements show an uncertainty better than 0.7 K and, above all, a clear improvement in radiometric resolution (below 0.1 K). The radiometric resolution benefit is particularly appreciable in applications where the aim is the detection of small radiation power increments. Two experimental tests show how this method for data calibration effectively resolves the warm target counting inside the antenna footprint, while the same data measured with the standard NA procedure do not allow the same detection capability.

The self-designed HaiYang-2B (HY-2B) satellite was launched on 24 October 2018 in China at 22:57 UT in a 99.34&deg, inclination sun-synchronous orbit. The Scanning Microwave Radiometer (SMR) on the core observatory has the capability to provide near-real-time multi-channel brightness temperature (Tb) observations, which are designed mainly for improving the level of marine forecasting and monitoring, serving the development and utilization of marine resources. After internal calibration and ocean calibration, the first effort to retrieve land surface snow parameters was performed in this study, which obtained extremely low accuracy both in snow extent and snow mass. Accordingly, land inter-sensor calibration was carried out between SMR and the Advanced Microwave Scanning Radiometer 2 (AMSR2) in order to broaden the research and application of SMR data on the Earth&rsquo, s land surface. Finally, we evaluated the consistency of the snow extent and snow mass derived from the initial and land-calibrated SMR data. The results indicated that a systematic SMR cold deviation whose magnitude depends on the channel is present for all the compared channels. After intercalibration, the conformity of the snow extent and snow mass were substantially improved compared to before, the relative bias of the snow extent and snow mass decreased from &minus, 49.97% to 2.97% and from &minus, 51.71% to 3.01%, respectively.

This paper describes the technical approach to generate homogeneous warm brightness temperature scene binary mask over the earth for the intercalibration of microwave imagers. The objective is to identify homogeneous desert scenes as targets in radiometric intercalibration of multisource multisensor systems such as Global Precipitation Measurement (GPM) constellation. In this paper, a method is developed to generate mask over continental Australia using 10, 18, and 37 GHz vertical and horizontal polarized radiance observations from GPM Microwave Imager data. Major factors considered are diurnal variation, second Stokes parameter, spatial homogeneity, and surface emissivity. Major advantage of this tool is its minimal, almost negligible, dependence on any external ancillary modeled or observed data.

Vicarious cold calibration (VCC) for spaceborne microwave radiometers is analyzed and modified for application to conical scanning microwave imagers at frequencies from 6 to 90 GHz. The details of the algorithm are modified to account for additional frequencies and polarizations that were not included in the development of the original algorithm. The modified algorithm is shown to produce a more stable cold reference brightness temperature (TB) than the original algorithm. An analysis is performed of this updated algorithm to show the global regions that contribute to the derivation of the cold reference TB and to show which geophysical parameters contribute to the coldest TBs. The analysis suggests that water vapor variability has the largest impact on the TBs in the VCC algorithm. The modified VCC algorithm is applied to microwave imager data and is used as an intercalibration method. It is shown to agree well with other intercalibration methods, demonstrating that it is a valid and accurate method for calibration of microwave imagers.

The Global Precipitation Measurement (GPM) mission is a constellation-based satellite mission designed to unify and advance precipitation measurements using both research and operational microwave sensors. This requires consistency in the input brightness temperatures (Tb), which is accomplished by intercalibrating the constellation radiometers using the GPM Microwave Imager (GMI) as the calibration reference. The first step in intercalibrating the sensors involves prescreening the sensor Tb to identify and correct for calibration biases across the scan or along the orbit path. Next, multiple techniques developed by teams within the GPM Intersatellite Calibration Working Group (XCAL) are used to adjust the calibrations of the constellation radiometers to be consistent with GMI. Comparing results from multiple approaches helps identify flaws or limitations of a given technique, increase confidence in the results, and provide a measure of the residual uncertainty. The original calibration differences relative to GMI are generally within 2–3 K for channels below 92 GHz, although AMSR2 exhibits larger differences that vary with scene temperature. SSMIS calibration differences also vary with scene temperature but to a lesser degree. For SSMIS channels above 150 GHz, the differences are generally within ~2 K with the exception of SSMIS on board DMSP F19, which ranges from 7 to 11 K colder than GMI depending on frequency. The calibrations of the cross-track radiometers agree very well with GMI with values mostly within 0.5 K for the Sondeur Atmosphérique du Profil d’Humidité Intertropicale par Radiométrie (SAPHIR) and the Microwave Humidity Sounder (MHS) sensors, and within 1 K for the Advanced Technology Microwave Sounder (ATMS).

In January 2019 a Project Authorization Request was submitted to the IEEE standards association with the title "Standard for Calibration of Microwave Radiometers in the 300 MHz to 1 THz Frequency Range for Geoscience Applications". An open committee is being assembled to draft this standard with the purpose of unifying and documenting calibration procedures for a wide range of microwave radiometers. The committee includes members, collaborators, and contributors from academia, international government and private industry. We include ground-based, air-borne, and space-borne systems. The standard will also define standardized terminology, and address procedures required to obtain traceability to fundamental units or constants. The scope of the standard encompasses various radiometer geometries, Dicke switching, total power, and differential, as well as different polarization configurations including fully polarized (full Stoke's) radiometers. The standard will also separately address free-space and single-mode (e.g. transmission-line) radiometer calibration techniques.

The Advanced Microwave Sounding Unit-B (AMSU-B) and Microwave Humidity Sounder (MHS) are total power microwave radiometers operating at frequencies near the water vapor absorption line at 183 GHz. The measurements of these instruments are crucial for deriving a variety of climate and hydrological products such as water vapor, precipitation, and ice cloud parameters. However, these measurements are subject to several errors that can be classified into radiometric and geometric errors. The aim of this study is to quantify and correct the radiometric errors in these observations through intercalibration. Since the bias in the calibration of microwave instruments changes with scene temperature, a two-point intercalibration correction scheme was developed based on averages of measurements over the tropical oceans and nighttime polar regions. The intercalibration coefficients were calculated on a monthly basis using measurements averaged over each specified region and each orbit, then interpolated to estimate the daily coefficients. Since AMSU-B and MHS channels operate at different frequencies and polarizations, the measurements from the two instruments were not intercalibrated. Because of the negligible diurnal cycle of both temperature and humidity fields over the tropical oceans, the satellites with the most stable time series of brightness temperatures over the tropical oceans (NOAA-17 for AMSU-B and NOAA-18 for MHS) were selected as the reference satellites and other similar instruments were intercalibrated with respect to the reference instrument. The results show that channels 1, 3, 4, and 5 of AMSU-B on board NOAA-16 and channels 1 and 4 of AMSU-B on board NOAA-15 show a large drift over the period of operation. The MHS measurements from instruments on board NOAA-18, NOAA-19, and MetOp-A are generally consistent with each other. Because of the lack of reference measurements, radiometric correction of microwave instruments remain a challenge, as the intercalibration of these instruments largely depends on the stability of the reference instrument.

Improvements in radiometric calibration are needed to achieve the desired accuracy and stability of satellite-based microwave-radiometer observations intended for the production of climate data records. Linearity, stability and traceability of measurements to an SI-unit standard should be emphasized. We suggest radiometer design approaches to achieve these objectives in a microwave calibration-reference instrument. Multi-year stability would be verified by comparison to radio-occultation measurements. Data from such an instrument could be used for climate studies and also to transfer its calibration to weather-satellite instruments. With the suitable selection of an orbit, a climatology of the diurnal variation in the measured parameters could be compiled, which would reduce uncertainties in climate trends inferred from earlier microwave radiometers over past decades.

Radio-frequency interference (RFI) can significantly contaminate the measured radiometric signal of current spaceborne L-band passive microwave radiometers. These spaceborne radiometers operate within the protected passive remote sensing and radio-astronomy frequency allocation of 1400–1427 MHz but nonetheless are still subjected to frequent RFI intrusions. We present a unique surface-based and airborne hyperspectral 385 channel, dual polarization, L-band Fourier transform, RFI-detecting radiometer designed with a frequency range from 1400 through ≈ 1550 MHz. The extended frequency range was intended to increase the likelihood of detecting adjacent RFI-free channels to increase the signal, and therefore the thermal resolution, of the radiometer instrument. The external instrument calibration uses three targets (sky, ambient, and warm), and validation from independent stability measurements shows a mean absolute error (MAE) of 1.0 K for ambient and warm targets and 1.5 K for sky. A simple but effective RFI removal method which exploits the large number of frequency channels is also described. This method separates the desired thermal emission from RFI intrusions and was evaluated with synthetic microwave spectra generated using a Monte Carlo approach and validated with surface-based and airborne experimental measurements.

Spaceborne microwave radiometers with high frequency channels (150 – 183 GHz) are important for retrieving many geophysical parameters such as snowfall, ice water path, and atmospheric water vapor profiles. In order to obtain accurate retrievals from the brightness temperature measurements, the radiometers must be properly calibrated. Several methods have been developed to analyze the on-orbit calibration, including comparisons with radiative transfer models, comparisons with radiosonde profiles, and cross-calibration with similar radiometers. This paper introduces a new method to calibrate high frequency channels that utilizes TB histograms. This new method gives an independent approach that can be used by itself to analyze radiometer calibration or in conjunction with other methods to corroborate results.

Ground-based microwave radiometers (MWR) are becoming more and more common for remotely sensing the atmospheric temperature and humidity profile as well as path-integrated cloud liquid water content. The calibration accuracy of the state-of-the-art MWR HATPRO-G2 (Humidity And Temperature Profiler – Generation 2) was investigated during the second phase of the Radiative Heating in Underexplored Bands Campaign (RHUBC-II) in northern Chile (5320 m above mean sea level, 530 hPa) conducted by the Atmospheric Radiation Measurement (ARM) program conducted between August and October 2009. This study assesses the quality of the two frequently used liquid nitrogen and tipping curve calibrations by performing a detailed error propagation study, which exploits the unique atmospheric conditions of RHUBC-II. Both methods are known to have open issues concerning systematic offsets and calibration repeatability. For the tipping curve calibration an uncertainty of ±0.1 to ±0.2 K (K-band) and ±0.6 to ±0.7 K (V-band) is found. The uncertainty in the tipping curve calibration is mainly due to atmospheric inhomogeneities and the assumed air mass correction for the Earth curvature. For the liquid nitrogen calibration the estimated uncertainty of ±0.3 to ±1.6 K is dominated by the uncertainty of the reflectivity of the liquid nitrogen target. A direct comparison between the two calibration techniques shows that for six of the nine channels that can be calibrated with both methods, they agree within the assessed uncertainties. For the other three channels the unexplained discrepancy is below 0.5 K. Systematic offsets, which may cause the disagreement of both methods within their estimated uncertainties, are discussed.

In microwave radiometric remote sensing, undetected radio frequency interference (RFI) can adversely affect the accuracy of the science products. A method is presented to adaptively tune the parameters of an RFI detection algorithm which controls the equivalent brightness temperature of undetected RFI. The method is adaptive in the sense that it adjusts to variations in the RFI environment, e.g., from high RFI conditions near some population centers to low RFI conditions in the tropical Pacific Ocean. The RFI environment is characterized by inferring the distribution of low-level undetected RFI from that of high-level detected RFI using appropriate scaling arguments. The resulting tuned algorithm adjusts its detection threshold to equalize the brightness temperature calibration bias due to RFI at the expense of the now variable measurement precision (noise equivalent delta temperature). This tradeoff between calibration bias and measurement precision can be represented as a modified version of the classic receiver-operating-characteristic curve. The radiometer on the Aquarius/SAC-D mission is used as an example.

Here, we describe the characteristics of brightness temperature (Tb) measured by the Advanced Microwave Scanning Radiometer-2 (AMSR2) onboard the Global Change Observation Mission 1st-Water (GCOM-W1). This mission aims to achieve long-term global monitoring of Earth using two polar-orbiting satellite observation systems with three consecutive generations. GCOM-W1, the first satellite of the GCOM-W (Water) series, was launched successfully on May 18, 2012. AMSR2 is a single-mission instrument onboard the GCOM-W1 satellite. The basic characteristics of AMSR2 are similar to those of its predecessor, AMSR-E; this allows the continuation of AMSR-E observations but with several improvements, including a larger main reflector (2.0-m diameter), additional channels at C-band frequency, an improved calibration system, and increased reliability imparted by the addition of a redundant momentum wheel. Since July 3, 2012, the instrument has functioned properly and has accumulated a data set of Tb measurements. During the initial calibration and validation period, Tb values are being evaluated and characterized according to various methodologies, including intercalibration between similar microwave radiometers [e.g., the Tropical Rainfall Measuring Mission Microwave Imager (TMI)] based on radiative transfer computations. The intercalibration results for the ocean area as the cold end and the rainforest area the as warm end demonstrate that Tb measured by AMSR2 exhibits no apparent seasonal variation, with a maximum calibration difference of approximately 5 K compared to TMI and AMSR-E. This calibration difference appears to depend on the Tb of the observed object.

Stratospheric ozone is of major interest as it absorbs most harmful UV radiation from the sun, allowing life on Earth. Ground-based microwave remote sensing is the only method that allows for the measurement of ozone profiles up to the mesopause, over 24 hours and under different weather conditions with high time resolution. In this paper a novel ground-based microwave radiometer is presented. It is called GROMOS-C (GRound based Ozone MOnitoring System for Campaigns), and it has been designed to measure the vertical profile of ozone distribution in the middle atmosphere by observing ozone emission spectra at a frequency of 110.836 GHz. The instrument is designed in a compact way which makes it transportable and suitable for outdoor use in campaigns, an advantageous feature that is lacking in present day ozone radiometers. It is operated through remote control. GROMOS-C is a total power radiometer which uses a pre-amplified heterodyne receiver, and a digital fast Fourier transform spectrometer for the spectral analysis. Among its main new features, the incorporation of different calibration loads stands out; this includes a noise diode and a new type of blackbody target specifically designed for this instrument, based on Peltier elements. The calibration scheme does not depend on the use of liquid nitrogen; therefore GROMOS-C can be operated at remote places with no maintenance requirements. In addition, the instrument can be switched in frequency to observe the CO line at 115 GHz. A description of the main characteristics of GROMOS-C is included in this paper, as well as the results of a first campaign at the High Altitude Research Station at Jungfraujoch (HFSJ), Switzerland. The validation is performed by comparison of the retrieved profiles against equivalent profiles from MLS (Microwave Limb Sounding) satellite data, ECMWF (European Centre for Medium-Range Weather Forecast) model data, as well as our nearby NDACC (Network for the Detection of Atmospheric Composition Change) ozone radiometer measuring at Bern.

The Advanced Technology Microwave Sounder (ATMS) is a space-based, cross-track radiometer for operational atmospheric temperature and humidity sounding, utilizing 22 channels over a frequency range from 23 to 183 GHz. The ATMS for the Joint Polar Satellite System-1 has undergone two rounds of rework in 2014–2015 and 2016, following performance issues discovered during and following thermal vacuum chamber (TVAC) testing at the instrument and observatory level. Final shelf-level testing, including measurement of pass band characteristics and spectral response functions, was completed in December 2016. Final instrument-level TVAC testing and calibration occurred during February 2017. Here we will describe the instrument-level TVAC calibration process, and illustrate with results from the final TVAC calibration effort.

We discuss work at NIST aimed at developing a passive microwave brightness temperature standard. By reducing uncertainty, we can provide better calibrations for future weather and climate-monitoring radiometers. We discuss the calibration procedure used, measured data, and various theoretical and simulated results that have led to an improved understanding of the various uncertainty contributions in the measurement. We overview a Monte Carlo simulation to determine the uncertainty in target brightness temperature as a function of measurement distance and target size. We also discuss other future improvements including an improved blackbody design. The achievable calibration source brightness temperature uncertainty is expected to be reduced from the current 0.7 to 1.0 K from 18 to 65 GHz to less than 0.3 K.