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The Microwave Radiation Imager (MWRI) sensors aboard on the Chinese FengYun-3 (FY-3) series satellites have a great potential for long-term study of sea ice distribution. This study corrected the newly released FY-3D MWRI brightness temperature (TB) data with the Advanced Microwave Scanning Radiometer 2 (AMSR2) TB data and used an Arctic Radiation and Turbulence Interaction Study Sea Ice (ASI) dynamic tie points algorithm to derive the sea ice concentration (SIC) from these corrected MWRI TB data. We assessed the accuracy of our MWRI-ASI SIC product by comparing with the published SIC products at different spatial resolution and by validating with the Moderate Resolution Imaging Spectroradiometer (MODIS) and Sentinel-1 data. We find that MWRI-ASI SIC has the smallest difference with AMSR2-ASI SIC with the mean absolute difference (MAD) of 6.8%, and the differences mainly occur at the marginal ice zone regions. The MWRI-ASI displays the highest difference with Sea Ice Index and OSI-430-b at 25 km with the mean MAD of 11.8%. MADs between MWRI-ASI SIC and SIC products from other sensors are below 10% except for late June to early October, when those are higher. Compared with the MODIS SIC, MWRI-ASI SIC outperforms other SIC products at the same resolution, with the mean bias of -2.1% and mean RMSD of 13.5%. Although MWRI-ASI tends to under-estimate high SIC, it can capture details of large leads, ice edge, and fragmented ice area. Our MWRI-ASI SIC product at 12.5 km is promising to integrate into long-term sea ice records.

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.

Across spatial and temporal scales, sea-ice motion has implications on ship navigation, the sea-ice thickness distribution, sea ice export to lower latitudes and re-circulation in the polar seas, among others. Satellite remote sensing is an effective way to monitor sea-ice drift globally and daily, especially using the wide swaths of passive microwave missions. Since the late 1990s, many algorithms and products have been developed for this task. Here, we investigate how processing sea-ice drift vectors from the intersection of individual swaths of the Advanced Microwave Scanning Radiometer 2 (AMSR2) mission compares to today's status-quo (processing from daily averaged maps of brightness temperature). We document that the swath-to-swath (S2S) approach results in many more (two orders of magnitude) sea-ice drift vectors than the daily-maps (DM) approach. These S2S vectors also validate better when compared to trajectories of on-ice drifters. For example, the RMSE of the 24 hour Arctic sea-ice drift is 0.9 km for S2S vectors, and 1.3 km for DM vectors from the 36.5 GHz imagery of AMSR2. Through a series of experiments with actual AMSR2 data and simulated Copernicus Imaging Microwave Radiometer (CIMR) data, we study the impact that geo-location uncertainty and imaging resolution have on the accuracy of the sea-ice drift vectors. We conclude by recommending that a swath-to-swath approach is adopted for the future operational Level-2 sea-ice drift product of the CIMR mission. We outline some potential next steps towards further improving the algorithms, and making the user community ready to fully take advantage of such a product.

Objectives: To predict meteorological phenomena such as rain events from sky status during rainy and non-rainy periods. Methods: The method used here is based on brightness temperature ratio measurement at two different frequencies, namely 23.8 GHz and 30 GHz respectively, using ground based dual frequency radiometric data. The ratios of brightness temperature readings obtained by the dual-frequency radiometer at the two above mentioned frequencies are calculated for each simultaneously-taken pair of measurements. Data obtained by the authors for the year 2009 at Cachoeira Paulista in Brazil has been used for analysis. Findings: The major results obtained from the analysis of data collected over a continuous period of seven months are used to construct corresponding histograms and cumulative count graphs of brightness temperature ratios. The histograms and graphs clearly show three peak values that could be interpreted as thresholds between clear sky, cloudy sky and rainy sky conditions respectively. Novelty: The study implements detection of rain events from sky status during rainy and non-rainy periods using peak brightness temperature values obtained from graphs generated using the observation data. The outlined technique can therefore be used to clearly determine sky conditions and accurately predict rain phenomena. The ratio of brightness temperatures at the two frequencies is a unique parameter which is critical to the successful estimation of rain from sky status. The results agree well with multi-channel radiometric data obtained by other researchers at lower frequencies. Keywords: sky status; microwave; millimeter wave; dual frequency radiometer; brightness temperature; rainfall

Compared with traditional real aperture microwave radiometers, one-dimensional synthetic aperture microwave radiometers have higher spatial resolution. In this paper, we proposed to retrieve sea surface temperature using a one-dimensional synthetic aperture microwave radiometer that operates at frequencies of 6.9 GHz, 10.65 GHz, 18.7 GHz and 23.8 GHz at multiple incidence angles. We used the ERA5 reanalysis data provided by the European Centre for Medium-Range Weather Forecasts and a radiation transmission forward model to calculate the model brightness temperature. The brightness temperature measured by the spaceborne one-dimensional synthetic aperture microwave radiometer was simulated by adding Gaussian noise to the model brightness temperature. Then, a backpropagation (BP) neural network algorithm, a random forest (RF) algorithm and two multiple linear regression algorithms (RE1 and RE2) were developed to retrieve sea surface temperature from the measured brightness temperature within the incidence angle range of 0°–65°. The results show that the retrieval errors of the four algorithms increase with the increasing Gaussian noise. The BP achieves the lowest retrieval errors at all incidence angles. The retrieval error of the RE1 and RE2 decrease first and then increase with the incidence angle and the retrieval error of the RF is contrary to that of RE1 and RE2.

Calibration is a key issue for quantitative application of meteorological satellite data. The complex space environment may cause many uncertainties in data calibration. A highly stable and reliable calibrator in flight is needed. Because the Moon has no atmosphere and no environmental variation, the physical and chemical properties of its surface are stable in the long term. The Moon might be an ideal candidate for in-flight thermal calibration. In advanced satellite-borne microwave remote sensing such as NOAA-18, the deep space view (DSV) of the microwave humidity sounder (MHS) has viewed the Moon many times every year. Using the thermal-physical properties of the lunar regolith derived from the Diviner infrared (IR) brightness temperature (TB) data, we solve the one-dimensional heat conduction equation to obtain the temperature profile of the near side of the lunar regolith medium. The loss tangents of the regolith medium are retrieved from microwave TB data of the Chinese satellite Chang’e-2. The integrated radiative transfer equation is used to simulate the weighted disk-average TB of the lunar surface for the MHS channels at 89, 157, and 183 GHz for the year 2011. The Moon is taken as an extended circular target. The simulated TBs are used to correct the full width at half maximum (FWHM) fitted with the MHS counts. We analyze the influences of the distance between the satellite and the Moon, the lunar phase angle, and the FWHM of the radiometer on the inverted FWHM. The corrected TB data are compared with the simulation. This paper presents a new method for thermal calibration of spaceborne in-flight microwave and millimeter-wave radiometers with the weighted disk-average TB of the lunar surface.

This study proposes the use of a Spectral Linear Mixing Model (SLMM) on passive microwave data for mapping the concentration and area of young and/or first-year ice in the oceanic region located in...

Land surface temperature (LST) is a key parameter and plays an important role in hydrology, ecology, environment, and biogeochemistry. It is difficult for the existing satellites to acquire global LST with spatial and temporal consistency. The Moon-based Earth observation platform with a long life, large coverage can observe continuously the Earth, and obtain the global-scale LST. At present, various approaches for retrieving LST from passive microwave remote sensing data have been developed for the satellite remote sensing data with small and constant viewing zenith angle, however, the Moon-based Earth observation platform located outside the Earth's ionosphere has the viewing zenith angle of 0-90°. In this study, a modified physical-based method of LST retrieval from passive microwave data was developed for wide viewing zenith angles, and the LST and emissivity can be simultaneously estimated through the analysis of various atmospheric and ionosphere parameters. Three types of data, including the FengYun-3B satellite microwave radiation imager data, the multichannel Advanced Microwave Scanning Radiometer data, and Moon-based microwave radiation simulation data with the viewing zenith angles of 52-53°, 55°, and 0-90°, respectively, were used to retrieve LST. Results show that the estimation accuracy of LST decreases with the increase of viewing zenith angle. The 23.8 and 36.5 GHz brightness temperature is optimum for the LST estimation under a large-scale viewing zenith angle, and the root mean square errors of the LST are 5.18, 5.44, and 4.79 K, respectively.

Chinese FY-4M is a new-generation geosynchronous satellite, which will be launched in the near future. It is equipped with multichannel millimeter wave radiometers, which operate in the range of 50–430 GHz. This satellite will increase revisit time and significantly improve the monitoring and weather variation forecasting capabilities. Calibration is an important issue for the quantitative analysis and application of brightness temperature observations. Calibration is first conducted in a thermal/vacuum chamber prior to the launch. A hot target onboard the satellite is used to provide relative irradiance for onboard calibration, and the cold cosmos is used as a cold target. However, complicated space conditions may result in many uncertainties and instabilities during calibration. A new method is required to evaluate the performance of the instruments during long-term operation. Because of the non-existence of atmospheric and other environmental interferences, the lunar surface maintains its physical and chemical stability. Thus, it is an ideal source for thermal calibration. The earliest ground-based observation of thermal microwave emission of the Moon dates back to the early 1940s. At that time, the temperature profile and dielectric constant of the lunar surface had not been studied in detail. Thus, interpreting the observed microwave brightness temperature was difficult. In recent years, many studies of the lunar surface have been performed owing to the advances in satellite-borne remote sensing programs. The Diviner Lunar Radiometer Experiment onboard the Lunar Reconnaissance Orbiter investigated the global solar reflectance and mid-IR irradiance. These infrared measurements contribute to the quantitative description of the thermophysical properties of the superficial lunar regolith media. Chinese probes, Chang’e-1 and Chang’e-2, carried four-channel microwave radiometers to observe the microwave thermal emission of the lunar regolith media, which may provide information on superficial dielectric properties of the lunar surface. These advances created a foundation for the millimeter channel observation of the lunar surface. In this study, a real-time model of the sub-solar position and total irradiance of the lunar surface was developed. The altitude of FY-4M is 35800 km. The distance between the Moon and the Earth is between 363400 km (perigee) and 405400 km (apogee). This large distance enables nadir observation from the FY-4M radiometer toward the center region of the lunar equator. The observation beam angle ranges from 0.097° to 0.027°. The diameters of the observation regions at different channels are 750 km (55 GHz), 480 km (89, 118 GHz), 300 km (166, 183 GHz), and 210 km (425 GHz) at apogee. Based on the surface albedo derived from the Clementine data and the constrained thermophysical parameters of the lunar regolith media obtained from the Diviner infrared brightness temperature, the one-dimensional thermal transfer equation is solved to obtain the physical temperature profile of the lunar regolith media. Using the 37-GHz brightness temperature from Chang’e-2 observations, the loss tangent of the regolith media is inverted. Using the fluctuation dissipation theorem, the brightness temperature of the lunar surface in FY-4M millimeter channels is calculated. The thermophysical profile of the regolith can be validated using the Diviner infrared brightness temperature data. The calculated temperature at a depth of 1.3 m is consistent with the measurements obtained in the Apollo heat flow experiment. The surface temperature and millimeter brightness temperature on the lunar equator (0°N, 0°E) during the entire year of 2010 are simulated. The peak value of the brightness temperature is attained after that of the surface physical temperature peak value. In the low frequency channel, the penetration depth increases, its peak value is typically small, and the peak appears later. This study thus presents an effective model for FY-4M calibration and data analysis.

An ultrawideband radiometer was used to measure microwave brightness temperature spectra over Arctic sea ice in the Lincoln Sea near the north coast of Greenland. Spectra over the range of 0.5–2 GHz were compared to thermal infrared images collected during the airborne campaign and also compared to nearly concurrent Sentinel-1 C-band synthetic aperture radar (SAR) data. Based on those comparisons, spectral signatures were associated with thick multiyear ice and thin ice. A radiative transfer (RT) model consisting of a homogeneous slab of sea ice bounded by sea water and air was then used to invert the spectra for sea ice thickness and salinity. Inferred thicknesses were consistent with ice thickness climatology for ice floes in the Lincoln Sea. Salinities are higher than expected which may be a consequence of neglecting surface and volume scattering contributions in the models.

Recent lunar observations have concluded that significant water ice deposits may exists in the regolith, especially in the permanently shadowed regions near the lunar poles. Detection and utilization of these reserves is very important to sustain the continuous human presence on the Moon. This paper, using analytical models and numerical simulations, demonstrates that wideband microwave radiometers are promising instruments for exploring underground water ice deposits on the Moon as their brightness temperature spectra are highly sensitive to water presence in the regolith.

Microwave Imager Combined Active and Passive (MICAP), which is a package of active and passive microwave instruments including L/C/K-band radiometers and L-band scatterometer, has been approved to be taken onbord the Chinese Ocean Salinity Mission. The L-band one-dimensional synthetic aperture radiometer (L-Rad) is the key part of MICAP to measure sea surface salinity (SSS). Since radio frequency interference (RFI) is reported as a serious threat to L-band radiometry, the RFI detection and mitigation techniques must be carefully designed before launch. However, these techniques need to be developed based on the knowledge of how RFI affects complex correlation, visibility function, and reconstructed brightness temperature. This paper presents a time-domain signal modeling method for the simulation of interferometric measurement under RFI’s presences, and a simulation system for L-Rad is established accordingly. Several RFI cases are simulated with different RFI types, parameters, and positions, and the RFI characteristics upon L-Rad’s measurement are discussed. The proposed simulation system will be further dedicated to the design of RFI processing strategy onboard MICAP.

Modern microwave radiometers have demonstrated the feasibility of monitoring surface salinity from space and also the need for better accuracy in cold water. Accuracy could be improved by adding measurements at lower frequencies (lower than the measurement at 1.4 GHz currently used) and closer to the peak in sensitivity of brightness temperature to changes in salinity. Proposals to accomplish this have focused on wide bandwidth receivers which include at the low end frequencies close to the peak in sensitivity. This strategy involves trade-offs, some obvious such as radio frequency interference (RFI) when operating outside the protected band at 1.4 GHz and the loss of spatial resolution at lower frequencies. Others stemming from the interdependence of the retrieval of salinity on water temperature and surface roughness are more subtle. The objective of this manuscript is to examine this interdependence and its implications to future wide bandwidth instruments for remote sensing of salinity from space.

Passive microwave radiometers provide brightness temperature (TB) measurements in a large spectral range with relatively high temporal resolution. Because of their significant role in environmental studies, it is imperative to have a higher temporal resolution and more consistent datasets. This research aims to build a TB diurnal cycle at the Global scale. The shape (amplitude and phase) of the diurnal cycle for each month is obtained by merging several days of Global Precipitation Measurement (GPM) Microwave Imager (GMI) TB measurements because the acquisition times of GMI vary from day to day. This preliminary shape will later be improved by using the other sensors with daily fixed acquisition times such as three Special Sensor Microwave Imager/Sounder (SSMIS) and Advanced Microwave Scanning Radiometer 2 (AMSR2). The final product is a highly frequent diurnal cycle that can help prediction of the time of the freeze thaw transition states.

The water cycle observation mission (WCOM) will build upon previous L and C band passive microwave soil moisture satellite missions. WCOM will consist of a passive microwave synthetic aperture radiometer operating at L , S , and C bands. The WCOM requirements for passive soil moisture are to estimate soil moisture in the top 5 cm of soil layer with an error less than 0.04 m3/m3, at 15-km resolution and with a 3-day revisit. A new set of algorithms for these multi-frequency platforms will need to be developed for estimating the data products at the desired resolution. To accomplish this, a brightness temperature (TB) downscaling methodology is developed that uses passive S -band TB (30 km) to downscale L -band TB (50 km) and to estimate soil moisture at a 30-km resolution, based on the linear relationships between the passive signals of L band and S band. To test this downscaling method, analysis was performed using PALS data from the Soil Moisture Experiments in 2002 (SMEX02). For this study, 4-km L -band observations were downscaled to 800 m. The root mean square errors between the downscaled TBL at 800 m with the observed TBL at 800 m are 2.63 and 1.60 K for H and V polarizations, respectively. The results also showed that it was possible to use these disaggregated TB to estimate soil moisture to meet the mission requirement of 0.04 m3/m3. These results showed that we can obtain higher resolution soil moisture from the L -band passive TB with a high accuracy ( S -band information.

The NASA-sponsored Calibrated Passive Micro- wave Daily Equal-Area Scalable Earth Grid 2.0 Brightness Temperature (CETB) Earth System Data Record Project team has generated a multisensor, multidecadal time series of high-resolution radiometer products designed to support climate studies. This project uses image reconstruction techniques to generate conventional and enhanced-resolution daily brightness temperature images on a standard set of map projections. Sensors included in CETB are the Aqua Advanced Microwave Scanning Radiometer–Earth Observing System (AMSR-E), Scanning Multichannel Microwave Radiometer, and all Special Sensor Microwave/Imager and Special Sensor Microwave Imager/Sounder radiometers. These span frequencies between 6 and 89 GHz. This paper considers the issues of adding the L-band (1.6 GHz) Soil Moisture Active Passive (SMAP) radiometer measurements to the CETB climate record, with emphasis on optimizing the reconstruction to provide the highest possible spatial resolution at the lowest noise level. SMAP radiometer reconstruction on SMAP-standard grids is also considered. Simulation is used to optimize the reconstruction, and the results confirmed using actual data. A comparison of the performance of the Backus–Gilbert approach and the radiometer form of the Scatterometer Image Reconstruction algorithm is provided. These are compared to the conventional drop-in-the-bucket gridded imaging.

Collocated CloudSat rain rates and Advanced Microwave Scanning Radiometer for EOS (AMSR-E) 89-GHz brightness temperature Tb retrievals allow for the development of an algorithm to estimate light, warm rain statistics as a function of AMSR-E 89-GHz Tb for shallow marine clouds. Four statistics are calculated from CloudSat rainfall rate estimates within each 4 km × 6 km Tb pixel sampled by both sensors: the probability of rainfall, the mean rain rate, the mean rate when raining, and the maximum rain rate. Observations with overlying cold clouds are removed from the analysis. To account for confounding variables that modify Tb, curves are fit to the mean relationships between Tb and these four statistics within bins of constant column-integrated water vapor from AMSR-E, and sea surface temperature and wind speed from reanalysis grids. The coefficients that define these curves are then applied to all available AMSR-E Tb retrievals to estimate rain rate throughout the eastern subtropical oceans. A preliminary analysis shows strong agreement between AMSR-E rain rates and the CloudSat training dataset. Comparison with an existing microwave precipitation product shows that the new statistical product has an improved sensitivity to light rain. A climatology for the year 2007 shows that precipitation rates tend to be heavier where the sea surface is warmer and that rain is most frequent where stratocumulus transitions to trade cumulus in the subtropics.

A physically based Bayesian passive microwave precipitation retrieval requires an accurate forward radiative transfer model along with realistic database representation of hydrometeors, atmospheric properties, and surface emission. NASA’s Global Precipitation Measurement (GPM) Mission provides an unprecedented opportunity for the development of such databases, matching a well-calibrated radiometer with dual-frequency radar. Early versions of passive microwave products from GPM utilized a physically constructed database in a Bayesian retrieval scheme, assumed ice particles to be spheres, and used Mie radiative transfer. A large body of recent work demonstrates that this is insufficient for retrieval at the GPM radiometer frequencies. In this paper, the retrieval is updated to use nonspherical particles. Simulated brightness temperature (Tb) agreement with observations is shown to be significantly improved across the high frequencies, decreasing biases significantly and increasing correlations to observed Tb. This is compared with a second identical retrieval performed with the assumption of spherical ice particles, and retrieval results are compared globally, seasonally, and instantaneously for a case study at the rain rate level. While not at the high level of improvement shown in Tb space, the precipitation retrieval is improved as compared to one using observed Tb in correlation, bias, and root-mean-square error. Reported improvements, while modest in magnitude, advance the retrieval to more physical consistency which allows for deeper insight into ice particle properties associated with precipitation.

Sea-ice monitoring using long-term data from satellite passive microwave instruments allows one to make quantitative estimates of climatic trends. These numerical estimates depend on the methods used for sea-ice parameter retrievals. This work presents a review of methods to retrieve sea-ice parameters from the data of satellite microwave radiometers. An analysis of the physics of the formation of microwave radiation over sea ice and its transport in the atmosphere makes it possible to determine the main sources of errors and classify methods. This paper considers the basic principles underlying the methods, assumptions, and approximations used and it analyzes the verification data. Weather filters are considered to identify the areas of open water. A comparative analysis of the advantages and limitations of the main sea-ice concentration retrieval methods is provided by measurements of satellite microwave radiometers such as the Radiometers of the Special Sensor Microwave/Imager series (SSM/I) and the Advanced Microwave Scanning Radiometer (AMSR). A review of satellite products based on SSM/I, AMSR-E, and AMSR2 data, as well as available Internet resources with operational and historical sea-ice data, is presented.

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 an analysis of the experimental measurements of the azimuthal anisotropy of the microwave emissions of a rough water surface. The experimental data were obtained using microwave radiometers with operating frequencies of ~37 GHz (a wavelength of ~8 mm) on the oceanographic platform of the Black Sea Hydrophysical Experimental Facility of the Russian Academy of Sciences in the period from 2005 to 2016. This paper describes the azimuthal anisotropy effect, experimental studies, measurement methods, and data processing. A qualitative comparison of the measurement results with the model calculations and other experimental studies of anisotropy was performed. It was shown that there is a strong sensitivity of the variation in the radio emissions of a rough water surface, both to the wind speed and to its direction at different incidence observation angles.

形態: カラー図版あり, Physical characteristics: Original contains color illustrations, Accepted: 2020-11-01, 資料番号: PA2110066000

Passive microwave brightness temperature (TB) radiometers are widely used to retrieve several atmospheric and surface parameters such as precipitation, soil moisture, freeze and thaw, water vapor, air temperature profile, and land surface emissivity. Since TBs are measured at different microwave frequencies with various instruments, incident angles, footprints, spatial resolutions, and radiometric characteristics, a combination of data from different microwave sensors could be inconsistent. For this reason, this study primarily uses the nonsynchronous Global Precipitation Measurement (GPM) Microwave Imager (GMI) measurements to construct the diurnal cycle of TBs for each month. This diurnal pattern could be used as a point of reference to validate and calibrate the diurnal cycle of TBs from fusing the measurements of other sensors with daily fixed acquisition times (SSM/I, SSMIS, AMSR2, and etc.). The data from these sensors should also be merged and harmonized in order to build a comprehensive global diurnal cycle of passive microwave TBs. This highly frequent diurnal cycle will eventually help predict the emissivity estimation and potentially further advance the accurate prediction of the estimated time of the freeze/thaw (FT) transition states. Global comparison of TBs obtained from different sensors shows a moderate variance with a significant dependence on land cover type. The results of this study can enhance the temporal detection of freeze and thaw which is more helpful during the transition times when multiple FT changes may occur within a day.

Chinese ocean salinity satellite is a mission designed for global ocean salinity monitoring. Two main payloads equipped onboard the satellite are the Interferometric Microwave Radiometer (IMR) and the Microwave Imager Combined Active and Passive (MICAP) which both have L-band radiometer and adopt aperture synthesis technology. A simulation is designed according to the payloads' configurations and consists of three parts: the brightness temperature generator, the radiometer system and the parameters retrieval. Preliminary results, which contain IMR and MICAP's brightness temperature resolution, spatial resolution, and parameter retrieval accuracy, are presented.

Early in the 1990s, the Goddard Profiling Algorithm (GPROF) was created to retrieve both surface rainfall and hydrometeor vertical profiles from satellite passive microwave sensors. Over the last 25 years it has been the primary algorithm for the TRMM Microwave Imager (TMI) and the follow-on sensor – the Global Precipitation Measurement (GPM) Microwave Imager (GMI). To meet the objectives of these missions, GPROF has been designed not just for single sensors, but to consistently retrieve rainfall from the full suite of passive sensors in the GPM constellation. These include Advanced Microwave Scanning Radiometer-2 (AMSR2), Special Sensor Microwave Imager/Sounder (SSMIS), Microwave Humidity Sounder (MHS), Advanced Technology Microwave Sounder (ATMS), and the historical sensors, Special Sensor Microwave Imager (SSM/I), and the Advanced Technology Microwave Sounder (AMSR-E).

Land emissivity sensed within a suitable range of wavelengths by microwave radiometer is useful to deduce land surface temperature and land surface emissivity. Brightness temperature measurements by space-borne microwave sensors have been utilized to determine land surface emissivity for the study of climatology, hydrological and agricultural applications. Currently, application of sounder data is gaining attention, in analyzing land surface characteristic features especially due to the higher spatial resolution, in general. In the present work, an attempt is made to obtain land surface emissivity from the six channels of SAPHIR (Sondeur Atmospherique du Profil d Humidit Intertropicale par Radiometrie), sensor with special reference to bare land and south Western Ghats of India during pre-monsoon and post-monsoon seasons. The innovation of the present analysis is to demonstrate the possibility of estimating and retrieving surface parameters from emissivity values retrieved from brightness temperature measured from SAPHIR sounder with channel 6 of 183.31 ± 11.0 GHz over selected study area with various surface conditions. Further, the present analysis relates to application of emissivity values retrieved from brightness temperature measured from SAPHIR sounder with channel 6, i.e., 183.31 ± 11.0 GHz for the study of vegetation, climatology and agricultural applications.

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.

The Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), launched in late November 1997, has provided a > 17-year record of tropical precipitation measurements, and prior to the launch of the global precipitation measurements (GPM) satellite in February 2014, the TMI served as the radiometric transfer standard for the TRMM constellation radiometers. Thus, the radiometric calibration of the TRMM constellation radiometers depends upon that of TMI. Therefore, the level-1 TMI brightness temperatures have been reprocessed to produce the TMI legacy brightness temperature product namely 1B11 version 8 (also known as GPM05), which has been released by NASA in late 2017. During this data reprocessing, a radiometric calibration anomaly, associated with the hot load, was discovered. This error was caused by an occasional transient solar intrusion, which introduced a systematic error in the brightness temperature. To remove this error, an independent hot load correction algorithm was implemented. In this paper, the process of identifying the issue, developing the solution, and evaluating the results is described.

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.

Accurate estimation of passive microwave land surface emissivity (LSE) is crucial for numerical weather prediction model data assimilation, for microwave retrievals of land precipitation and atmospheric profiles, and for a better understanding of land surface and subsurface characteristics. In this study, global instantaneous LSE is estimated for a 9-yr period from the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) and for a 5-yr period from the Advanced Microwave Scanning Radiometer 2 (AMSR2) sensors. Estimates of LSE from both sensors were obtained by using an updated algorithm that minimizes the discrepancy between the differences in penetration depths from microwave and infrared remote sensing observations. Concurrent ancillary datasets such as skin temperature from the Moderate Resolution Imaging Spectroradiometer (MODIS) and profiles of air temperature and humidity from the Atmospheric Infrared Sounder are used. The latest collection 6 of MODIS skin temperature is used for the LSE estimation, and the differences between collections 6 and 5 are also comprehensively assessed. Analyses reveal that the differences between these two versions of infrared-based skin temperatures could lead to approximately a 0.015 difference in passive microwave LSE values, especially in arid regions. The comparison of global mean LSE features from the combined use of AMSR-E and AMSR2 with an independent product—Tool to Estimate Land Surface Emissivity from Microwave to Submillimeter Waves (TELSEM2)—shows spatial pattern correlations of order 0.92 at all frequencies. However, there are considerable differences in magnitude between these two LSE estimates, possibly because of differences in incidence angles, frequencies, observation times, and ancillary datasets.

The Microwave Radiation Imager (MWRI) on board Chinese Fengyun-3 (FY-3) satellites provides measurements at 10.65, 18.7, 23.8, 36.5, and 89.0 GHz with both horizontal and vertical polarization channels. Brightness temperature measurements of those channels with their central frequencies higher than 19 GHz from satellite-based microwave imager radiometers had traditionally been used to retrieve cloud liquid water path (LWP) over ocean. The results show that the lowest frequency channels are the most appropriate for retrieving LWP when its values are large. Therefore, a modified LWP retrieval algorithm is developed for retrieving LWP of different magnitudes involving not only the high frequency channels but also the lowest frequency channels of FY-3 MWRI. The theoretical estimates of the LWP retrieval errors are between 0.11 and 0.06 mm for 10.65- and 18.7-GHz channels and between 0.02 and 0.04 mm for 36.5- and 89.0-GHz channels. It is also shown that the brightness temperature observations at 10.65 GHz can be utilized to better retrieve the LWP greater than 3 mm in the eyewall region of Super Typhoon Neoguri (2014). The spiral structure of clouds within and around Typhoon Neoguri can be well captured by combining the LWP retrievals from different frequency channels.

Satellite and ground-based microwave radiometers are routinely used for the retrieval of liquid water path (LWP) under all atmospheric conditions. The retrieval of water vapor and LWP from ground-based radiometers during rain has proved to be a difficult challenge for two principal reasons: the inadequacy of the nonscattering approximation in precipitating clouds and the deposition of rain drops on the instrument’s radome. In this paper, we combine model computations and real ground-based, zenith-viewing passive microwave radiometer brightness temperature measurements to investigate how total, cloud, and rain LWP retrievals are affected by assumptions on the cloud drop size distribution (DSD) and under which conditions a nonscattering approximation can be considered reasonably accurate. Results show that until the drop effective diameter is larger than ~200 $\mu \text{m}$ , a nonscattering approximation yields results that are still accurate at frequencies less than 90 GHz. For larger drop sizes, it is shown that higher microwave frequencies contain useful information that can be used to separate cloud and rain LWP provided that the vertical distribution of hydrometeors, as well as the DSD, is reasonably known. The choice of the DSD parameters becomes important to ensure retrievals that are consistent with the measurements. A physical retrieval is tested on a synthetic data set and is then used to retrieve total, cloud, and rain LWP from radiometric measurements during two drizzling cases at the atmospheric radiation measurement Eastern North Atlantic site.

Most polar-orbit-satellite based passive microwave radiometers, e.g. AMSR-2 and the FY-3B Microwave Radiation Imager (FY3B-MWRI), have the revisit cycles of ~2 days in low latitudes. This indicates that requirement of complete global coverage on the daily basis has not been met. On the other hand, using synergistic observations from different radiometers seems promising to enhance the temporal resolution of observations in such areas, whereas cross-radiometer inter-calibration is essential prior to their synergism. For this purpose, conventional inter-calibration models are usually built based on linear relations of brightness temperature (TB) between different radiometers. However, the linear model has relatively poor performance in low latitudes primarily due to wet surface conditions. In this study, we propose an improved non-linear model for calibrating MWRI TB against AMSR-2 TB at 18.7 GHz and 23.8 GHz channels. Global land surface temperature (LST) is estimated using the post-calibration MWRI TB data and then validated against AMSR-2 based LST, MODIS based LST, and in situ near-surface air temperature datasets in low latitudes respectively. The validation results show that LST outcome based on the improved non-linear inter-calibration method has smaller global Root Mean Square Errors (RMSEs) below 3.5 K, compared with the RMSEs of ~6 K obtained from the conventional linear inter-calibration method and from the results without inter-calibration. Further analysis also shows that synergistic observations between AMSR-2 and MWRI are capable of shortening the maximum global revisit cycle of AMSR-2 LST from ~2 days to less than 1.2 days. These results indicate that the improved inter-calibration method is effective for obtaining daily LST datasets with quasi-complete global coverage and reliable accuracy.

In previous studies, remote sensing properties of hailstorms have been discussed using various spaceborne sensors. Relationships between hail occurrence and strong passive microwave brightness temperature depressions have been established. Using a 16-yr precipitation-feature database derived from the Tropical Rainfall Measuring Mission (TRMM) satellite, the performance of the TRMM Precipitation Radar and TRMM Microwave Imager is further investigated for hail detection. Detection criteria for hail larger than 19 mm are separately developed from Ku-band radar reflectivity and microwave brightness temperature properties of precipitation features that are collocated with surface hail reports over the southeastern and south-central United States. A threshold of 44 dBZ at −22°C is found to have the highest critical success index and Heidke skill score. The threshold of 230 K at 37 GHz yields the best scores among passive microwave properties. Using these two thresholds, global distributions of possible hail events are generated over 65°S–65°N using two years of observations from the Global Precipitation Measurement Core Observatory satellite. Differences in the derived hail geographical distributions are found between radar and passive microwave methods over tropical South America, the “Maritime Continent,” west-central Africa, Argentina, and South Africa. These discrepancies result from different vertical structures of the maximum radar reflectivity profiles over these regions relative to the southeastern and south-central United States, where the thresholds were established. Those differences generally led to overestimates in the tropics from the passive microwave methods, relative to the radar-based methods.

International audience; L-band radiometer measurements collected over the Dome C area from 2010 to 2015 indicated that the brightness temperature (T B) was relatively stable at vertical (V) polarization (standard deviation lower than 1 K at annual scale), while it was slightly more variable at horizontal (H) polarization. During the 2014-2015 austral summer, an exceptional situation was recorded by both the DOMEX ground radiometer and the European Space Agency (ESA)'s Soil Moisture and Ocean Salinity (SMOS) satellite. From November 2014 to March 2015, T B H showed a progressive and significant increase until 20 March 2015 when it sharply decreased by about 5 K (at 52.5 o incidence angle) within a few days. In parallel to the increase in T B H, glaciological and meteorological in situ measurements showed a wind speed that was lower than usual and a low-density snow layer being progressively set up on the surface. This was consistent with the exceptional hoar event observed, as well as with snow accumulation on the surface. On the other hand, the decrease in T B H was related to the passing over Dome C of a storm that removed or compacted the layer of light snow on the surface. The WALOMIS (Wave Approach for LOw-frequency MIcrowave emission in Snow) snow-emission model was used with in situ measurements of the snowpack as inputs for evaluating the effect of changes observed on the snow surface in T B H. The simulations indicated that the surface snow density variations were sufficient for predicting the increasing and decreasing trends of the T B H. However, the thickness variations of the superficial layer were essential so as to obtain a better agreement with the SMOS observations. This result confirmed that the L-band T B H was affected by the snow properties of the top centimeters of the snowpack, in spite of the large penetration depth (hundreds of meters). Both the surface snow density and the thickness of the superficial layer were relevant, due to coherent interference effects.

Scattering differences induced by frozen particle microphysical properties are investigated, using the vertically (V) and horizontally (H) polarized radiances from the Global Precipitation Measurement (GPM) Microwave Imager (GMI) 89 and 166 GHz channels. It is the first study on frozen particle microphysical properties on a global scale that uses the dual-frequency microwave polarimetric signals.From the ice cloud scenes identified by the 183.3 ± 3 GHz channel brightness temperature (Tb), we find that the scattering by frozen particles is highly polarized, with V–H polarimetric differences (PDs) being positive throughout the tropics and the winter hemisphere mid-latitude jet regions, including PDs from the GMI 89 and 166 GHz TBs, as well as the PD at 640 GHz from the ER-2 Compact Scanning Submillimeter-wave Imaging Radiometer (CoSSIR) during the TC4 campaign. Large polarization dominantly occurs mostly near convective outflow regions (i.e., anvils or stratiform precipitation), while the polarization signal is small inside deep convective cores as well as at the remote cirrus region. Neglecting the polarimetric signal would easily result in as large as 30 % error in ice water path retrievals. There is a universal bell curve in the PD–TBV relationship, where the PD amplitude peaks at ∼ 10 K for all three channels in the tropics and increases slightly with latitude (2–4 K). Moreover, the 166 GHz PD tends to increase in the case where a melting layer is beneath the frozen particles aloft in the atmosphere, while 89 GHz PD is less sensitive than 166 GHz to the melting layer. This property creates a unique PD feature for the identification of the melting layer and stratiform rain with passive sensors.Horizontally oriented non-spherical frozen particles are thought to produce the observed PD because of different ice scattering properties in the V and H polarizations. On the other hand, turbulent mixing within deep convective cores inevitably promotes the random orientation of these particles, a mechanism that works effectively in reducing the PD. The current GMI polarimetric measurements themselves cannot fully disentangle the possible mechanisms.

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.

It is proposed and substantiated that, in the ocean‒atmosphere system, the microwave self-radiation intensity (brightness temperature), which is measured by means of satellites in the millimeter and centimeter wave bands, can be used as the direct characteristic of the thermal and dynamic parameters of the system. The vertical turbulent fluxes of sensible and latent heat and momentum are determined at the ocean‒atmosphere interface according to the data measured by the special sensor microwave imagers located on weather satellites created in the scope of the defense meteorological satellite program and the advanced microwave scanning radiometer installed aboard the EOS Aqua oceanographic satellite. For the first time, the integrated (annual) characteristics of heat exchange between the ocean and atmosphere are investigated in the energy-active zones of the North Atlantic with the help of long-term satellite microwave radiometric measurements.

L-band microwave radiometers (1.4 GHz) are relied in the field of remote sensing to retrieve and analyze land surface characteristics. However, the existing ground based L-band microwave radiometers are not portable and requires heavy maintenance. This paper focuses on introducing a new terrestrial hand held L-band microwave radiometer to measure the passive microwave data over bare ground surface. Soil temperature, brightness temperature (TB), emissivity and soil moisture of the bare ground is measured and the relationships between the measured values are evaluated for the present study.

The application of passive microwave soil moisture products is limited by the poor spatial resolution. In this study, a spatially-based regression method was tested using the PLMR(Polarimetric L-band Multibeam Radiometer) observation data acquired in the Heihe Watershed Allied Telemetry Experimental Research (HiWATER). The land surface temperature data were used as the auxiliary data to perform downscaling soil moisture and brightness temperature. The root-mean-square errors (RMSEs) of brightness temperature and soil moisture are 5.5 K and 0.021 cm3/cm3 between the calculated values and refenced values, respectively. This method provided a potential way for the downscaling of passive microwave brightness temperature and soil moisture.

Radio Frequency Interference (RFI) at 18.7 GHz in and around the continental United States observed by passive microwave radiometers such as the Global Precipitation Measurement (GPM) Microwave Imager (GMI) has been noted in multiple publications. A large part of this RFI arises from reflected geosynchronous direct broadcast satellite transmissions. The interference yields surprisingly high artificial brightness temperature values, ranging from a few Kelvin up to over 1000 Kelvin. Some of the highest levels and most direct reflections occur over land from specular reflections from smooth surfaces such as lakes and rivers. Over the ocean surrounding the United States, the reflections are generally less directional, occurring at higher glint angles and often with lower peak levels than over land. The interference corrupts the GMI measurements about 13% of the time over the ocean around the US, and over 5% of the time over the affected land areas.

Global Satellite Mapping of Precipitation (GSMaP) is a system to produce global surface precipitation field with 0.1 degree and 1 hour resolution from geostationary infrared imager and microwave radiometer in low earth orbit (Ushio et al. 2019, Mega et al. 2018). On this system, the input data sets are precipitation rate retrieved from the microwave radiometers and infrared images to compute the moving vector fields. Based on the moving vector fields calculated from the successive IR images, precipitation fields are propagated and refined on the Kalman filter model which uses the relationship between the infrared brightness temperature and surface precipitation rate. Because of this methodology, basically the resolution of the GSMaP products is limited on the resolution of the geostationary infrared images.

Typhoon (TC) is one of the most powerful and destructive natural disasters. The analysis and determination of TC intensity is of great importance for disaster prevention. Satellite remote sensing has become an effective means of monitoring TCs based on its high temporal and spatial resolution and large coverage. It is possible to estimate TC intensity using these satellite measurements when direct measurements are not available. Microwave observations from polar-orbiting satellites can play a crucial role in revealing convective organization and eyewall structure that would otherwise be obscured by cloud tops. Passive microwave sensors, such as SSM/I, TRMM/TMI, have been used to estimate TC intensity. Besides, scatterometer also has allowed for continuous observation of ocean surface vector winds. Thus, scatterometer is also a very useful tool for monitoring TCs. However, scatterometer measured wind speed range 2-24 m/s, which make it very difficult to directly obtain the intensity of TCs. Both radiometer and scatterometer have their own advantages. However, the intensity of TCs has not been estimated simultaneously by microwave radiometer and scatterometer. In this study, a new TC intensity estimation model based on multivariate linear is proposed, which synergistic used brightness temperature (TB) and wind speed retrieval as model inputs. We evaluated our estimated method using both TC best track data and airborne observations. The RMSE between model outputs and test data is 5.94 m/s and their BIAS is -0.43 m/s. The estimated results of our model are therefore slightly better than other existing algorithms. More detailed results and discussions on the analysis accuracy will be presented in the full-length paper.

The Canadian Arctic Archipelago (CAA) presents unique challenges to the determination of melt onset (MO) using remote sensing data. High spatial resolution data is required to discern melt onset among the islands and narrow waterways of the region. Current passive microwave retrievals use daily averaged 19 GHz and 37 GHz data from the multi-channel microwave radiometer (SMMR) and/or the special sensor microwave/imager (SSM/I). The development of a new passive microwave melt onset method capable of using higher resolution data is desirable. The new passive microwave melt onset method described here, named the Dynamic Threshold Variability Method (DTVM), uses higher resolution data from the 37 GHz vertically-polarized channel from the advanced microwave scanning radiometers (AMSR-E and AMSR-2). The DTVM MO detection methodology differs from previously presented passive microwave Arctic MO methods in that it does not use a fixed threshold of a brightness temperature parameter. Instead, the DTVM determines MO dates based on the distribution of dates corresponding to the exceedance of a range of brightness temperature variability thresholds. The method also uses swath data instead of daily averaged brightness temperatures, which is found to lead to improved melt detection. Two current passive microwave MO methods are compared and evaluated for applicability in the CAA alongside the DTVM. The DTVM provides MO dates at a higher spatial resolution than earlier methods in addition to higher correlation with MO dates from surface air temperature (SAT) reanalyses. It is found that, for some years, MO dates in the CAA exhibit a latitudinal dependence, while in other years the MO dates in the CAA are relatively uniform across the domain.

The method of flood monitoring based on Soil Moisture Active Passive (SMAP) radiometer data is put forward. The SMAP L1C enhanced brightness temperature product with 9 km spatial resolution is higher than others passive microwave data, such as AMSR-E, ATMS and SSM/I. In order to study the SMAP’s potential ability, the case of flood induced by Hurricane Harvey occurred in 2017 in United State was selected to apply the SMAP data to monitor the inundated water. The result showed that the flood area can be reflected based on SWAP data, and H polarization data is more sensitive to the V polarization data. The mixed pixel separation method is applied to estimate the water fraction and provide more detailed sub-pixel scale water information. The SMAP L1C enhanced brightness temperature product with 9 km spatial resolution can provide more detailed flood information than previous passive microwave data. By compare the result with Fengyun-3 Microwave Radiation Imager (FY-3/MWRI) data and NASA's Earth Observing System Moderate Resolution Imaging Spectroradiometer (EOS/MODIS) data, there are better accordance with the SMAP data, which prove the effect of SMAP data for flood monitoring.

Miniaturized microwave radiometers deployed on nanosatellites in Low Earth Orbit (LEO) are now demonstrating the ability to provide science-quality weather measurements, such as the 3U Micro-sized Microwave Atmospheric Satellite-2A (MicroMAS-2A). The goal of having cost-effective miniature instruments distributed in LEO constellations is to improve temporal and geospatial coverage. The Time-Resolved Observations of Precipitations structure and storm Intensity with a Constellation of Smallsats (TROPICS) is a constellation of six 3U CubeSats, based on MicroMAS-2A, scheduled to launch no earlier than 2020. Each CubeSat hosts a scanning 12-channel passive microwave radiometer. TROPICS will improve temporal resolution to less than 60 minutes compared to larger satellites in polar orbit, such as NOAA-20 which hosts the Advanced Technology Microwave Sounder (ATMS) and has a revisit rate of 7.6 hours. [1] The improved refresh rate will provide high value observations of inner-core conditions for tropical cyclones [2]. In order to effectively use miniaturized microwave radiometers on small satellites such as MicroMAS-2A and TROPICS operationally, new approaches to calibration are needed to achieve state-of-the-art performance. Calibration on nanosatellite platforms present new challenges, as standard blackbody targets are too bulky to fit on 3U CubeSats. Instead, internal noise diodes are used for calibration, with long-term drifts measured on the order of 0.2% to 3.0% (approximately 0.4 K to 6.0 K) [4]. Blackbody calibration targets such as used on ATMS have 0.14 K error or better for warm calibration [3]. In order to provide state of the art calibration for CubeSats, methods must be developed to trend and correct noise diode drift. We develop a new way to continuously calibrate CubeSat constellations, such as TROPICS, by incorporating frequent and periodic solar and lunar intrusions as an additional source of information to counter noise diode drift. These solar and lunar intrusions also occur for existing satellites hosting microwave radiometers in polar orbits, but are much more infrequent than for the scanning payload on the TROPICS constellation, and are typically treated as an observational and calibration limiting constraint. The higher occurrence rate of intrusions for TROPICS motivates the novel idea of using the intrusions to support calibration. An algorithm is developed to compare expected effective brightness temperature from solar and lunar measurements to actual measured brightness temperatures. Future work includes developing a detailed error budget for the algorithm and testing the algorithm using actual sun and moon measurements taken by MicroMAS-2A and ATMS. In this paper we discuss background information, provide our approach, and show initial results.

The integral of lightning current squared over time, named as the “lightning action integral”, is an indicator of Joule heat generated by lightning discharge. The temperature of air molecules is thus increased, which can be observed by a ground-based microwave radiometer for atmospheric temperature remote sensing. Observational experiments were performed in early summer in 2017 with a ground-based, MWP967KV type microwave radiometer, which is commonly used for atmospheric temperature profile remote sensing but has been properly-configured this study in order to observe the artificially triggered lightning events at Guangzhou field experiment site for lightning research and test (GFESL). Data from the radiometer and the instrument for recording lightning current have been analyzed and the results from 7 effective events show that a relationship like ΔTB=exp(aX) may exist between the brightness temperature increment, ΔTB, in unit of K, observed in 30-50GHz band and the lightning action integral, X, in unit of A2s, calculated from high temporal resolution lightning current records. The correlation coefficient is as high as 0.8863 while the coefficient, a, is equal to 6.25381*10−5 (sample size = 7).