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In recent decades, theoretical studies and numerical models of thermal tides have gained attention. It has been recognized that tides have a significant influence on the dynamics of the middle and upper atmosphere; as they grow in amplitude and propagate upward, they transport energy and momentum from the lower to the upper atmosphere, contributing to the vertical coupling between atmospheric layers. The superposition of tides with other atmospheric waves leads to non-linear wave–wave interactions. However, direct measurements of thermal tides in the middle atmosphere are challenging and are often limited to satellite measurements in the tropics and at low latitudes. Due to orbit geometry, such observations provide only a reduced insight into the short-term variability in atmospheric tides. In this paper, we present tidal analysis from 5 years of continuous observations of middle-atmospheric temperatures. The measurements were performed with the ground-based temperature radiometer TEMPERA (TEMPErature RAdiometer), which was developed at the University of Bern in 2013 and was located in Bern (46.95° N, 7.45° E) and Payerne (46.82° N, 6.94° E). TEMPERA achieves a temporal resolution of 1–3 h and covers the altitude range between 25–50 km. Using an adaptive spectral filter with a vertical regularization (ASF2D) for the tidal analysis, we found maximum amplitudes for the diurnal tide of approximately 2.4 K, accompanied by seasonal variability. The maximum amplitude was reached on average at an altitude of 43 km, which also reflected some seasonal characteristics. We demonstrate that TEMPERA is suitable for providing continuous temperature soundings in the stratosphere and lower mesosphere with a sufficient cadence to infer tidal amplitudes and phases for the dominating tidal modes. Furthermore, our measurements exhibit a dominating diurnal tide and smaller amplitudes for the semidiurnal and terdiurnal tides in the stratosphere.

Passive microwave remote sensing is a vital tool for acquiring valuable information regarding the Earth's surface, with significant applications in agriculture, water management, forestry, and various environmental disciplines. Precision agricultural (PA) practices necessitate the availability of field-scale, high-resolution remote sensing data products. This study focuses on the design and development of a cost-effective, portable L-band microwave radiometer capable of operating from an unmanned aircraft system platform to measure high-resolution surface brightness temperature ($T_{B}$). This radiometer consists of a dual-polarized (Horizontal polarized, H-pol and Vertical polarized, V-pol) antenna and a software-defined radio-based receiver system with a 30 MHz sampling rate. The post-processing methodology encompasses the conversion of raw in-phase and quadratic (I&Q) surface emissions to radiation $T_{B}$ through internal and external calibrations. Radiometric measurements were conducted over an experimental site covering both bare soil within an agricultural field and a large water body. The results yielded a high-resolution $T_{B}$ map that effectively delineated the boundaries between land and water, and identified land surface features. The radiometric temperature measurements of the sky and blackbody demonstrated a standard deviation of 0.95 K for H-pol and 0.57 K for V-pol in the case of the sky and 0.39 K for both H-pol and V-pol in the case of the blackbody observations. The utilization of I&Q samples acquired via the radiometer digital back-end facilitates the generation of different time–frequency (TF) analyses through short-time Fourier transform and power spectral density (PSD). The transformation of radiometer samples into TF representations aids in the identification and mitigation of radio frequency interference originating from the instrument itself and external sources.

Abstract Exploring the Venusian lower atmosphere is crucial for studying the atmospheric circulation, surface‐atmosphere interactions, and origin and evolution of the Venusian atmosphere and climate. In this study, we investigate the theoretical capabilities of a downward‐looking passive microwave sounder placed in low Venus orbit to measure the temperature, sulfur dioxide (SO2) and gaseous sulfuric acid (H2SO4(g)) profiles. A nonlinear iterative retrieval algorithm combining a radiative transfer (RT) model and an optimal‐estimation‐based inversion algorithm is established. With the RT model adapted to the Venusian atmosphere, simulations under different atmospheric conditions are performed to optimize the selection of frequency channels. The achievable altitude coverage, vertical resolution and corresponding expected precision of the temperature, SO2 and H2SO4(g) retrievals from the multi‐channel brightness temperature measurements are quantified via retrieval simulations. The temperature can be retrieved from the surface of Venus to an altitude of ∼61 km with a precision of 1–3.5 K and a vertical resolution of 6–15 km. A precision of 10%–35% is expected for SO2 in the ∼12–64 km altitude range and with a vertical resolution of 8–19 km. H2SO4(g) can be retrieved in the ∼36–54 km altitude range with a precision of 10%–30% and a vertical resolution of 6–13 km.

The existence of radio frequency interference (RFI) has a great influence on the measurement of synthetic aperture interferometric radiometer (SAIR). The strong RFI signal energy picked up by the sidelobes of synthetic beam (or array factor) of SAIR easily results in false positives, deteriorating the RFI detection performance, especially when considering the representative scenes where RFIs have a large dynamic range of intensity. In this article, we propose a novel RFI source detection method by exploiting the array factor property (AFP) of SAIR. The AFP-based RFI detection method mainly consists of three steps. First, RFI sources are recovered from visibility function samples through sparse reconstruction (SR). In the recovered RFI map, the background noise is reduced and the potential RFI target regions could be extracted well. Second, the AFP of SAIR is analyzed concerning distribution characteristics of main beam and sidelobes. Based on the AFP analysis, we present a new spatial weight indicator (SWI) describing the probability of one potential RFI being a false positive. Then, an SWI-based probability map (SPM) is generated to discriminate true sources with the false positive sources. Third, the SPM and the SR-based map are combined to reconstruct the RFI image, where false positives are filtered out while true RFI sources are retained. The experiments using synthetic data and real SMOS satellite data demonstrate the effectiveness and superiority of the proposed method in RFI detection tasks for SAIR.

Surface melt on ice shelves has been linked to hydrofracture and subsequent ice shelf breakup. Since the 1990s, scientists have been using microwave radiometers to detect melt on ice shelves and ice sheets by applying various statistical thresholding techniques to identify significant increases in brightness temperature that are associated with melt. In this study, instead of using a fixed threshold, we force the Snow Microwave Radiative Transfer model (SMRT) with outputs from the Community Firn Model (CFM) to create a dynamic, physics-based threshold for melt. In the process, we also combine our method with statistical thresholding techniques and produce microwave grain-size information. We run this “hybrid method” across the Larsen C ice shelf as well as 13 sites on the Antarctic Ice Sheet. Melt and non-melt days from the hybrid method and three statistical thresholding techniques match with the surface energy balance within 94 ± 1 %; the effect of melt on the passive microwaves is mostly binary and thus largely detectable by statistical thresholding techniques as well as physics-based techniques. Rather than always replacing statistical thresholding techniques with the hybrid method, we recommend using the hybrid method in studies where the melt volume or grain size is of interest. In this study, we show that the hybrid method can be used to (a) model dry-snow brightness temperatures of Antarctic snow and (b) derive a measure of grain size; therefore, it is an important step forwards towards using firn and radiative-transfer modeling to quantify melt rather than to simply detect melt days.

The Peak Design Ltd hyperspectral radiometer (HSR1) was tested at the Atmospheric Radiation Measurement (ARM) user facility Southern Great Plains (SGP) site in Lamont, Oklahoma, for 2 months from May to July 2022. The HSR1 is a prototype instrument that measures total (Ftotal) and diffuse (Fdiffuse) spectral irradiance from 360 to 1100 nm with a spectral resolution of 3 nm. The HSR1 spectral irradiance measurements are compared to nearby collocated spectral radiometers, including two multifilter rotating shadowband radiometers (MFRSRs) and the Shortwave Array Spectroradiometer–Hemispheric (SASHe) radiometer. The Ftotal at 500 nm for the HSR1 compared to the MFRSRs has a mean (relative) difference of 0.01 W m−2 nm−1 (1 %–2 %). The HSR1 mean Fdiffuse at 500 nm is smaller than the MFRSRs' by 0.03–0.04 (10 %) W m−2 nm−1. The HSR1 clear-sky aerosol optical depth (AOD) is also retrieved by considering Langley regressions and compared to collocated instruments such as the Cimel sunphotometer (CSPHOT), MFRSRs, and SASHe. The mean HSR1 AOD at 500 nm is larger than the CSPHOT's by 0.010 (8 %) and larger than the MFRSRs' by 0.007–0.017 (6 %–18 %). In general, good agreement between the HSR1 and other instruments is found in terms of the Ftotal, Fdiffuse, and AODs at 500 nm. The HSR1 quantities are also compared at other wavelengths to the collocated instruments. The comparisons are within ∼ 10 % for the Ftotal and Fdiffuse, except for 940 nm, where there is relatively larger disagreement. The AOD comparisons are within ∼ 10 % at 415 and 440 nm; however, a relatively larger disagreement in the AOD comparison is found for higher wavelengths.

This study focuses on the characterization of aerosol hygroscopicity using remote sensing techniques. We employ a Mie–Raman–fluorescence lidar (Lille Lidar for Atmospheric Study, LILAS), developed at the ATOLL platform, Laboratoire d'Optique Atmosphérique, Lille, France, in combination with the RPG-HATPRO-G5 microwave radiometer to enable continuous aerosol and water vapor monitoring. We identify hygroscopic growth cases when an aerosol layer exhibits an increase in both aerosol backscattering coefficient and relative humidity. By examining the fluorescence backscattering coefficient, which remains unaffected by the presence of water vapor, the potential temperature, and the absolute humidity, we verify the homogeneity of the aerosol layer. Consequently, the change in the backscattering coefficient is solely attributed to water uptake. The Hänel theory is employed to describe the evolution of the backscattering coefficient with relative humidity and introduces a hygroscopic coefficient, γ, which depends on the aerosol type. The particularity of this method revolves around the use of the fluorescence which is employed to take into account and correct the aerosol concentration variations in the layer. Case studies conducted on 29 July and 9 March 2021 examine, respectively, an urban and a smoke aerosol layer. For the urban case, γ is estimated as 0.47 ± 0.03 at 532 nm; as for the smoke case, the estimation of γ is 0.5 ± 0.3. These values align with those reported in the literature for urban and smoke particles. Our findings highlight the efficiency of the Mie–Raman–fluorescence lidar and microwave radiometer synergy in characterizing aerosol hygroscopicity. The results contribute to advance our understanding of atmospheric processes, aerosol–cloud interactions, and climate modeling.

Wheat (Triticum aestivum) yield predictions can be improved by using multispectral remote sensing to identify different genotypes and crop growth stages. We propose an innovative machine learning technique aimed at classifying diverse wheat crop genotypes and providing accurate estimations of plant age. Multispectral reflectance data was obtained from different sites where various wheat genotypes were cultivated. This approach involved analyzing incoming radiation and canopy light reflectance across five distinct spectral bands using a multispectral radiometer. The newly collected remote sensing data was utilized as input for the machine learning algorithm. Impressively, the random forest model achieved an accuracy rate of 98.77% in wheat crop genotype classification. Furthermore, the proposed approach's effectiveness was confirmed through a 10‐fold cross‐validation mechanism. Moreover, a multiple linear regression model for predicting the age of wheat genotypes explained 91% of the observed variation. These findings signify significant progress in wheat crop genotype and age prediction, ultimately leading to enhanced wheat yield. Core Ideas: This research addresses a significant challenge in wheat crop genotype and age prediction.We propose an innovative machine learning methodology to classify different wheat crop genotypes.We collected different wheat seed genotype samples using the multispectral radiometer. [ABSTRACT FROM AUTHOR]

The methodology to determine the unfiltered solar and thermal radiances from the measured EarthCARE Broadband Radiometer (BBR) shortwave (SW) and total-wave (TW) filtered radiances is presented. Within the EarthCARE ground processing, the correction for the effect of the BBR spectral responses, the unfiltering, is performed by the so-called BM-RAD processor which produces the level-2 BM-RAD product. The BM-RAD product refers to unfiltered broadband radiances that are derived from the BBR and the Multi-Spectral Imager (MSI) instruments on board the forthcoming EarthCARE satellite. The method is based on theoretical regressions between filtered and unfiltered radiances, as is done for the Clouds and the Earth's Radiant Energy System (CERES) and the Geostationary Earth Radiation Budget (GERB) instruments. The regressions are derived from a large geophysical database of spectral radiance curves simulated using radiative transfer models. Based on the radiative transfer computations, the unfiltering error, i.e., the error introduced by the small spectral variations of the BBR instrument response, is expected to remain well below 0.5 % in the shortwave (SW) and 0.1 % in the longwave (LW), at 1 standard deviation. These excellent performances are permitted by the very simple optics used in the BBR instrument: a telescope with a single paraboloid mirror. End-to-end verification of the unfiltering algorithm has been performed by running the BM-RAD processor on modelled level-1 BBR radiances obtained for three EarthCARE orbits simulated by an integrated forecasting and data assimilation system. The resulting unfiltered radiances are eventually compared to the solar and thermal radiances derived by radiative transfer simulations over the three EarthCARE orbits. In addition, this end-to-end verification has provided further evidence on the high accuracy of the unfiltered radiance process, with accuracies better than 0.5 % for SW and better than 0.1 % for LW.

This study investigates the impacts of the joint assimilation of ground-based microwave radiometer (MWR) and FY-3D microwave sounder (MWTS-2/MWHS-2) observations on the analyses and forecasts for precipitation forecast. Based on the weather research and forecasting data assimilation (WRFDA) system, four experiments are conducted in this study, concerning a heavy precipitation event in Beijing on 2 July 2021, and 10-day batch experiments were also conducted. The key study findings include the following: (1) Both ground-based microwave radiometer and MWTS-2/MWHS-2 data contribute to improvements in the initial fields of the model, leading to appropriate adjustments in the thermal structure of the model. (2) The forecast fields of the experiments assimilating ground-based microwave radiometer and MWTS-2/MWHS-2 data show temperature and humidity performances closer to the true fields compared with the control experiment. (3) Separate assimilation of two types of microwave radiometer data can improve precipitation forecasts, while joint assimilation provides the most accurate forecasts among all the experiments. In the single-case, compared with the control experiment, the individual and combined assimilation of MWR and MWTS-2/MWHS-2 improves the six-hour cumulative precipitation threat score (TS) at the 25 mm level by 57.1%, 28.9%, and 38.2%, respectively. The combined assimilation also improves the scores at the 50 mm level by 54.4%, whereas individual assimilations show a decrease in performance. In the batch experiments, the MWR_FY experiment’s TS of 24 h precipitation forecast improves 28.5% at 10 mm and 330% at 25 mm based on the CTRL.

The non-scanning radiometer with short-wavelength (SW: 0.2–5.0 μm) and total-wavelength (TW: 0.2–50.0 μm) channels is the primary payload of the Integrated Earth–Moon Radiation Observation System (IEMROS), which is designed to provide comprehensive Earth radiation measurements and lunar calibrations at the L1 Lagrange point of the Earth–Moon system from a global perspective. This manuscript introduces a radiometer preflight calibration methodology, which involves background removal and is validated using accurate and traceable reference sources. Simulated Earth view tests are performed to evaluate repeatability, linearity, and gain coefficients over the operating range. Both channels demonstrate repeatability uncertainties better than 0.34%, indicating consistent and reliable measuring performance. Comparative polynomial regression analysis confirms significant linear response characteristics with two-channel nonlinearity less than 0.20%. Gain coefficients are efficiently determined using a two-point calibration approach. Uncertainty analysis reveals an absolute radiometric calibration accuracy of 0.97% for the SW channel and 0.92% for the TW channel, underscoring the non-scanning radiometer’s capability to provide dependable global Earth radiation budget data crucial to environmental and climate studies.

A new cost-effective radiometer has been designed, built, and tested to measure direct normal solar irradiance (DNI). The proposed instrument for solar irradiance measurement is based on an optical fiber as the light beam collector, a semiconductor photodiode to measure the optical power, and a calibration algorithm to convert the optical power into solar irradiance. The proposed radiometer offers the advantage of separating the measurement point, where the optical fiber collects the solar irradiation, from the place where the optical power is measured. A calibration factor is mandatory because the semiconductor photodiode is only spectrally responsive to a limited part of the spectral irradiance. Experimental tests have been conducted under different conditions to evaluate the performance of the proposed device. The measurements confirm that the proposed instrument performs similarly to the expensive high-accuracy pyrheliometer used as a reference.

The Soil Moisture Active Passive (SMAP) satellite carries an L-band microwave radiometer. This sensor can be used to observe global soil moisture (SM) and sea surface salinity (SSS) within the protected L-band spectrum (1400–1427 MHz). Owing to the complex effects of radio frequency interference (RFI), the SM and SSS data are missing or have low accuracy. In this paper, a constrained iterative adaptive algorithm for the detection, identification, and localization of RFI sources is designed, named MICA-BEID. The algorithm synthesizes antenna temperatures for the third and fourth Stokes parameters before RFI filtering, creating a new polarization parameter called WSPDA, designed to approximate the level of RFI interference on the L-band microwave radiometer. The algorithm then utilizes the WSPDA intensity and distribution density of RFI detection samples to enhance the identification and classification of RFI sources across various intensity levels. By utilizing statistical methods such as the probability density function (PDF) and the cumulative distribution function (CDF), the algorithm dynamically adjusts adaptive parameters, including the RFI detection threshold and the maximum effective radius of RFI sources. Through the application of multiple iterative clustering methods, the algorithm can adaptively detect and identify RFI sources at various satellite orbits and intensity levels. Through extensive comparative analysis with other localization results and known RFI sources, the MICA-BEID algorithm can achieve optimal localization accuracy of approximately 1.2 km. The localization of RFI sources provides important guidance for identifying and turning off illegal RFI sources. Moreover, the localization and long-time-series characteristic analysis of RFI sources that cannot be turned off is of significant value for simulating the spatial distribution characteristics of localized RFI source intensity in local areas.

The Ku band microwave scatterometer (SCA) and scanning microwave radiometer (SMR) onboard HaiYang-2B (HY-2B) can simultaneously supply active and passive microwave observations over the polar region. In this paper, a polar ice water discrimination model and Arctic sea-ice-type classification model based on the support vector machine (SVM) method were established and used to produce a daily sea ice extent dataset from 2019 to 2021 with data from SCA and SMR. First, suitable scattering and radiation parameters are chosen as input data for the discriminant model. Then, the sea ice extent was obtained based on the monthly ice water discrimination model, and finally, the ice over the Arctic was classified into multiyear ice (MYI) and first-year ice (FYI). The 3-year ice extent and MYI extent products were consistent with the similar results of the National Snow and Ice Data Center (NSIDC) and Ocean and Sea Ice Satellite Application Facility (OSISAF). Using the OSISAF similar product as validation data, the overall accuracies (OAs) of ice/water discrimination and FYI/MYI discrimination are 99% and 97%, respectively. Compared with the high spatial resolution classification results of the Moderate Resolution Imaging Spectroradiometer (MODIS) and SAR, the OAs of ice/water discrimination and FYI/MYI discrimination are 96% and 86%, respectively. In conclusion, the SAC and SMR of HY-2B have been verified for monitoring polar sea ice, and the sea ice extent and sea-ice-type products are promising for integration into long-term sea ice records.

The statistical characteristics of combined lidar and radiometric measurements obtained from satellite lidar CALIOP and ground-based sun-radiometer stations were used as input datasets to retrieve the altitude profiles of aerosol parameters (LRS-C technique). The signal-to-noise ratio of the input satellite lidar signals increased when averaging over a large array of measured data. An algorithm and software package for processing the input dataset of the LRS-C sounding of atmospheric aerosol in regions with medium and low aerosol loads was developed. This paper presents the results of studying long-term changes in the concentration profiles of aerosol modes in regions of East Europe (AERONET site Minsk, 53.92° N, 27.60° E) and East Antarctic (AERONET site Vechernaya Hill, 67.66° S, 46.16° E).

The soil moisture at the medium spatial scale is strongly desired in the context of satellite remote sensing data validation. The use of a ground-installed passive L-band radiometer ELBARA at the Bubnów-Sęków test site in the east of Poland gave a possibility to provide reference soil moisture data from the area with a radius of 100 m. In addition, the test site comprised three different land cover types that could be investigated continuously with one day resolution. The studies were focused on the evaluation of the ω-τ model coefficients for three types of land cover, including meadow, wetland, and cropland, to allow for the assessment of the soil moisture retrievals at a medium scale. Consequently, a set of reference time-dependent coefficients of effective scattering albedo, optical depth, and constant-in-time roughness parameters were estimated. The mean annual values of the effective scattering albedo including two polarisations were 0.45, 0.26, 0.14, and 0.54 for the meadow with lower organic matter, the meadow with higher organic matter, the wetland, and the cropland, respectively. The values of optical depth were in the range from 0.30 to 0.80 for the cropland, from 0.40 to 0.52 for the meadows (including the two investigated meadows), and from 0.60 to 0.70 for the wetland. Time-constant values of roughness parameters at the level of 0.45 were obtained.

The sea ice albedo can amplify global climate change and affect the surface energy in the Arctic. In this paper, the data from Visible and Infra-Red Radiometer (VIRR) onboard Fengyun-3C satellite are applied to derive the Arctic sea ice albedo. Two radiative transfer models, namely, 6S and FluxNet, are used to simulate the reflectance and albedo in the shortwave band. Clear sky sea ice albedo in the Arctic region (60°~90°N) from 2016 to 2019 is derived through the physical process, including data preprocessing, narrowband to broadband conversion, anisotropy correction, and atmospheric correction. The results are compared with aircraft measurements and AVHRR Polar Pathfinder-Extended (APP-x) albedo product and OLCI MPF product. The bias and standard deviation of the difference between VIRR albedo and aircraft measurements are −0.040 and 0.071, respectively. Compared with APP-x product and OLCI MPF product, a good consistency of albedo is shown. And analyzed together with melt pond fraction, an obvious negative relationship can be seen. After processing the 4-year data, an obvious annual trend can be observed. Due to the influence of snow on the ice surface, the average surface albedo of the Arctic in March and April can reach more than 0.8. Starting in May, with the ice and snow melting and melt ponds forming, the albedo drops rapidly to 0.5–0.6. Into August, the melt ponds begin to freeze and the surface albedo increases.

The Electrically Scanning Microwave Radiometer (ESMR) instrument onboard the NIMBUS 5 satellite was a one-channel microwave radiometer that measured the 19.35 GHz horizontally polarized brightness temperature (TB) from 11 December 1972 to 16 May 1977. The original tape archive data in swath projection have recently been made available online by the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC). Even though the ESMR was a predecessor of modern multi-frequency radiometers, there are still parts of modern processing methodologies which can be applied to the data to derive the sea ice extent globally. Here, we have reprocessed the entire dataset using a modern processing methodology that includes the implementation of pre-processing filtering, dynamical tie points, and a radiative transfer model (RTM) together with numerical weather prediction (NWP) for atmospheric correction. We present the one-channel sea ice concentration (SIC) algorithm and the model for computing temporally and spatially varying SIC uncertainty estimates. Post-processing steps include resampling to daily grids, land-spillover correction, the application of climatological masks, the setting of processing flags, and the estimation of sea ice extent, monthly means, and trends. This sea ice dataset derived from the NIMBUS 5 ESMR extends the sea ice record with an important reference from the mid-1970s. To make it easier to perform a consistent analysis of sea ice development over time, the same grid and land mask as used for EUMETSAT's OSI-SAF SMMR-based sea-ice climate data record (CDR) were used for our ESMR dataset. SIC uncertainties were included to further ease comparison to other datasets and time periods. We find that our sea ice extent in the Arctic and Antarctic in the 1970s is generally higher than those available from the National Snow and Ice Data Center (NSIDC) Distributed Active Archive Center (DAAC), which were derived from the same ESMR dataset, with mean differences of 240 000 and 590 000 km2, respectively. When comparing monthly sea ice extents, the largest differences reach up to 2 million km2. Such large differences cannot be explained by the different grids and land masks of the datasets alone and must therefore also result from the differences in data filtering and algorithms, such as the dynamical tie points and atmospheric correction. The new ESMR SIC dataset has been released as part of the ESA Climate Change Initiative (ESA CCI) program and is publicly available at https://doi.org/10.5285/34a15b96f1134d9e95b9e486d74e49cf (Tonboe et al., 2023).

Abstract Since its first launching, the ability of satellite Altimetry in providing reliable and accurate ocean geophysical information of the sea surface height (SSH), significant wave height (SWH), and wind speed has been proven by numerous researchers, as it was designed for observing the ocean dynamics through nadir range measurement between satellite and the sea surface. However, to achieve high level accuracy, environmental and geophysical effects on the range measurement must be accurately determined and corrected, particularly the effects from the atmospheric water vapor which can divert altimeter range up to 3–45 cm. Thus, satellite Altimetry is originally equipped with the on-board microwave radiometer to measure the water vapour content for correcting the range measurement. To our knowledge, no one has attempted to apply the on-board radiometer for atmospheric studies. In this present work, we attempt to optimize the on-board radiometer data for studying the atmosphere variability due to the El Niño–Southern Oscillation (ENSO) phenomena. We convert the on-board water vapor data into the precipitable water vapour (PWV), and we then investigate whether the derived PWV can capture the variability of ocean–atmosphere phenomena due to ENSO as accurate as the conventional Altimetry-derived sea level anomaly (SLA). Based on our analysis using the empirical orthogonal function (EOF), the results show convincing argument that Altimetry-derived PWV are reliable in examining the atmospheric fluctuation as the correlation of its primary principal component time series (PC1) with Oceanic Nino Index (ONI) is higher (0.87) than SLA (0.80). These results may reinforce the confidence in the ability of satellite Altimetry for ocean–atmospheric studies. Graphical Abstract

The ultraviolet multi-filter rotating shadow-band radiometer (UV-MFRSR) is a seven-channel radiometer with narrowband filters centered between wavelengths 300 and 368 nm. Four of the middle wavelengths in this device are near those used in the Dobson spectrometer to retrieve ozone column abundance. In this paper measurements from Mauna Loa Observatory (MLO) were used first to calibrate the instrument using the Langley plot method and subsequently to derive column ozone and aerosol optical depths. The ozone derived from the UV-MFRSR was compared to the ozone measured by a Dobson spectrophotometer that operates daily at the MLO, resulting in column values within about 1 DU on average for 43 d in 2018. The aerosol optical depth (AOD) retrievals are more challenging. Generally, the AOD increases with wavelength between 305 and 332 nm, not what is expected given the typical AOD wavelength dependence at visible wavelengths. An example of this behavior is discussed, and research by others is cited that indicates similar behavior at these wavelengths, at least for the low-aerosol-optical-depth conditions encountered at high-altitude sites.

Abstract Background Vegetation health (VH) is a powerful characteristic for forecasting malaria incidence in regions where the disease is prevalent. This study aims to determine how vegetation health affects the prevalence of malaria and create seasonal weather forecasts using NOAA/AVHRR environmental satellite data that can be substituted for malaria epidemic forecasts. Methods Weekly advanced very high-resolution radiometer (AVHRR) data were retrieved from the NOAA satellite website from 2009 to 2021. The monthly number of malaria cases was collected from the Ministry of Health of Benin from 2009 to 2021 and matched with AVHRR data. Pearson correlation was calculated to investigate the impact of vegetation health on malaria transmission. Ordinary least squares (OLS), support vector machine (SVM) and principal component regression (PCR) were applied to forecast the monthly number of cases of malaria in Northern Benin. A random sample of proposed models was used to assess accuracy and bias. Results Estimates place the annual percentage rise in malaria cases at 9.07% over 2009–2021 period. Moisture (VCI) for weeks 19–21 predicts 75% of the number of malaria cases in the month of the start of high mosquito activities. Soil temperature (TCI) and vegetation health index (VHI) predicted one month earlier than the start of mosquito activities through transmission, 78% of monthly malaria incidence. Conclusions SVM model D is more effective than OLS model A in the prediction of malaria incidence in Northern Benin. These models are a very useful tool for stakeholders looking to lessen the impact of malaria in Benin.

This paper presents a comprehensive intercomparison study investigating the radiometric performance of and concurrence among the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), Landsat 8 Operational Land Imager (L8 OLI), and Landsat 9 OLI (L9 OLI) instruments. This study leverages data sourced from the Radiometric Calibration Network (RadCalNet) and focuses on spectral bands relevant for vegetation analysis and land cover classification, encompassing a thorough assessment of data quality, uncertainties, and underlying influencing factors. This study's outcomes underscore the efficacy of RadCalNet in evaluating the precision and reliability of remote sensing data, offering valuable insights into the strengths and limitations of ASTER, L8 OLI, and L9 OLI. These insights serve as a foundation for informed decision making in environmental monitoring and resource management, highlighting the pivotal role of RadCalNet in gauging the radiometric performance of remote sensing sensors. Results from RadCalNet sites, namely Railroad Valley Playa and Gobabeb, show their possible suitability for sensors with spatial resolutions down to 15 m. The results indicate that the measurements from both ASTER and OLI closely align with the data from RadCalNet, and the observed agreement falls comfortably within the total range of potential errors associated with the sensors and the test site information. [ABSTRACT FROM AUTHOR]

This study developed a random forest approach for downscaling the coarse-resolution (36 km) soil moisture measured by The National Aeronautics and Space Administration (NASA) Soil Moisture Active Passive (SMAP) mission to 1 km spatial resolution, utilizing airborne remotely sensed data (radar backscatter and radiometer retrieved soil moisture), vegetation characteristics (normalized difference vegetation index), soil properties, topography, and ground soil moisture measurements from before the launch of SMAP for training a random forest model. The 36 km SMAP soil moisture product was then downscaled by the trained model to 1 km resolution using the information from SMAP. The downscaled soil moisture was evaluated using airborne retrieved soil moisture observations and ground soil moisture measurements. Considering the airborne retrieved soil moisture as a reference, the results demonstrated that the proposed random forest model could downscale the SMAP radiometer product to 1 km resolution with a correlation coefficient of 0.97, unbiased Root Mean Square Error of 0.048 m3 m−3 and bias of 0.016 m3 m−3. Accordingly, the downscaled soil moisture captured the spatial and temporal heterogeneity and demonstrated the potential of the proposed machine learning model for soil moisture downscaling.

Higher spatial resolution can improve the application ability of spaceborne microwave radiometer data. To investigate the capability to enhance the spatial resolution of the radiometer measurements, we analyzed the effect of enhanced field of view (FOV) and sampling strategy on improving spatial resolution using the Backus–Gilbert (BG) method, with the scanning parameters of FengYun-3D Microwave Radiation Imager (MWRI) as an example. The results suggest that selecting an appropriate enhanced FOV and increasing the sampling overlap rate can yield better spatial resolution and effectively reduce the brightness temperature (BT) error that arises from resolution enhancement. As an example, the MWRI 18.7 GHz channel with a spatial resolution of 30 × 50 km is taken. When the spatial resolution of the enhanced FOV is set to 25 × 35 km, compared with other options, the average BT error caused by resolution enhancement is reduced to 0.38 K, and the spatial resolution is improved to 27.3 × 40.5 km. In addition, when the sampling overlap rate of cross-track and along-track directions is set to 91.4% × 92.4%, the highest in the experimental conditions, compared with the original sampling setting, the average BT error caused by resolution enhancement is reduced from 0.72 to 0.4 K, and the spatial resolution is improved to 22.8 × 35.7 km. These experimental findings may provide insights into designing future radiometers and applying the BG method.

The non-scanning radiometer with short-wavelength (SW: 0.2–5.0 μm) and total-wavelength (TW: 0.2–50.0 μm) channels is the primary payload of the Integrated Earth–Moon Radiation Observation System (IEMROS), which is designed to provide comprehensive Earth radiation measurements and lunar calibrations at the L1 Lagrange point of the Earth–Moon system from a global perspective. This manuscript introduces a radiometer preflight calibration methodology, which involves background removal and is validated using accurate and traceable reference sources. Simulated Earth view tests are performed to evaluate repeatability, linearity, and gain coefficients over the operating range. Both channels demonstrate repeatability uncertainties better than 0.34%, indicating consistent and reliable measuring performance. Comparative polynomial regression analysis confirms significant linear response characteristics with two-channel nonlinearity less than 0.20%. Gain coefficients are efficiently determined using a two-point calibration approach. Uncertainty analysis reveals an absolute radiometric calibration accuracy of 0.97% for the SW channel and 0.92% for the TW channel, underscoring the non-scanning radiometer's capability to provide dependable global Earth radiation budget data crucial to environmental and climate studies. [ABSTRACT FROM AUTHOR]

Exploring the Venusian lower atmosphere is crucial for studying the atmospheric circulation, surface‐atmosphere interactions, and origin and evolution of the Venusian atmosphere and climate. In this study, we investigate the theoretical capabilities of a downward‐looking passive microwave sounder placed in low Venus orbit to measure the temperature, sulfur dioxide (SO2) and gaseous sulfuric acid (H2SO4(g)) profiles. A nonlinear iterative retrieval algorithm combining a radiative transfer (RT) model and an optimal‐estimation‐based inversion algorithm is established. With the RT model adapted to the Venusian atmosphere, simulations under different atmospheric conditions are performed to optimize the selection of frequency channels. The achievable altitude coverage, vertical resolution and corresponding expected precision of the temperature, SO2 and H2SO4(g) retrievals from the multi‐channel brightness temperature measurements are quantified via retrieval simulations. The temperature can be retrieved from the surface of Venus to an altitude of ∼61 km with a precision of 1–3.5 K and a vertical resolution of 6–15 km. A precision of 10%–35% is expected for SO2 in the ∼12–64 km altitude range and with a vertical resolution of 8–19 km. H2SO4(g) can be retrieved in the ∼36–54 km altitude range with a precision of 10%–30% and a vertical resolution of 6–13 km. Plain Language Summary: The hostile environment and thick sulfuric acid clouds make it difficult to measure the lower atmosphere of Venus. Although many attempts have been made, the existing measurements of the lower atmosphere have provided little information about the spatiotemporal distribution and variation in the temperature and sulfur compound profiles. This limits our understanding of the atmospheric circulation, surface‐atmosphere interactions, and origin and evolution of the Venusian atmosphere and climate. In this study, we investigate the theoretical capabilities of a passive microwave sounder placed in low Venus orbit to measure the temperature, sulfur dioxide (SO2) and gaseous sulfuric acid (H2SO4(g)) profiles. By model simulations, the frequency channel selection and performance simulation for the sounder are completed. Simulation results demonstrate that the sounder could provide the required high‐precision, high‐resolution and vertically resolved observations of the temperature, SO2 and H2SO4(g) in the Venusian lower atmosphere. Key Points: The ability of a microwave radiometer to measure the temperature, SO2 and H2SO4(g) profiles in the Venusian lower atmosphere is verifiedThe measurement channels of the radiometer are determined based on radiative transfer simulationsThe expected precision, vertical resolution and achievable altitude coverage are quantified via retrieval simulations [ABSTRACT FROM AUTHOR]

The Soil Moisture Active Passive (SMAP) satellite carries an L-band microwave radiometer. This sensor can be used to observe global soil moisture (SM) and sea surface salinity (SSS) within the protected L-band spectrum (1400–1427 MHz). Owing to the complex effects of radio frequency interference (RFI), the SM and SSS data are missing or have low accuracy. In this paper, a constrained iterative adaptive algorithm for the detection, identification, and localization of RFI sources is designed, named MICA-BEID. The algorithm synthesizes antenna temperatures for the third and fourth Stokes parameters before RFI filtering, creating a new polarization parameter called WSPDA, designed to approximate the level of RFI interference on the L-band microwave radiometer. The algorithm then utilizes the WSPDA intensity and distribution density of RFI detection samples to enhance the identification and classification of RFI sources across various intensity levels. By utilizing statistical methods such as the probability density function (PDF) and the cumulative distribution function (CDF), the algorithm dynamically adjusts adaptive parameters, including the RFI detection threshold and the maximum effective radius of RFI sources. Through the application of multiple iterative clustering methods, the algorithm can adaptively detect and identify RFI sources at various satellite orbits and intensity levels. Through extensive comparative analysis with other localization results and known RFI sources, the MICA-BEID algorithm can achieve optimal localization accuracy of approximately 1.2 km. The localization of RFI sources provides important guidance for identifying and turning off illegal RFI sources. Moreover, the localization and long-time-series characteristic analysis of RFI sources that cannot be turned off is of significant value for simulating the spatial distribution characteristics of localized RFI source intensity in local areas. [ABSTRACT FROM AUTHOR]

During the EUREC4A field study, microwave radiometric measurements were performed at Barbados Cloud Observatory (BCO) and aboard RV Meteor and RV Maria S Merian in the downstream winter trades of the North Atlantic. We present retrieved integrated water vapor (IWV), liquid water path (LWP), and temperature and humidity profiles as a unified, quality-controlled, multi-site data set on a 3 s temporal resolution for a core period between 19 January and 14 February 2020 in which all instruments were operational. Multi-channel radiometric measurements were performed at BCO and aboard RV Meteor between 22 and 31 GHz (K-band) and from 51 to 58 GHz (V-band). Combined radar–radiometer measurements of a W-band Doppler radar with a single-channel radiometer instrument were conducted at 89 GHz aboard RV Meteor and RV Maria S Merian. We present a novel retrieval method to retrieve LWP from single-channel 89 GHz measurements, evaluate retrieved quantities with independent measurements, and analyze retrieval uncertainties by site and instrument intercomparison. Mean IWV conditions of 31.8 kg m−2 match independent radiosoundings at BCO with a root-mean-square difference of 1.1 kg m−2. Mean LWP conditions in confidently liquid cloudy, non-precipitating conditions ranged between 63.1 g m−2 at BCO and 46.8 g m−2 aboard RV Maria S Merian. Aboard the ships, 90 % of LWP was below 120 g m−2 with a 30 % uncertainty for LWP of 50 g m−2. Up to 20 % of confidently liquid cloudy profiles ranged below the LWP detection limit due to optically thin clouds. The data set comprises of processed raw data (Level 1), full quality-controlled post-processed instrument data (Level 2), a unified temporal resolution (Level 3), and a ready-to-use multi-site time series of IWV and LWP (Level 4), available to the public via AERIS (https://doi.org/10.25326/454##v2.0; Schnitt et al., 2023a). The data set complements the airborne LWP measurements conducted during EUREC4A and provides a unique benchmark tool for satellite evaluation and model–observation studies.