Microwave Radiometer Research Articles

Constrained Iterative Adaptive Algorithm for the Detection and Localization of RFI Sources Based on the SMAP L-Band Microwave Radiometer

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.

Cloud micro- and macrophysical properties from ground-based remote sensing during the MOSAiC drift experiment.

In the framework of the Multidisciplinary drifting Observatory for the Study of Arctic Climate Polarstern expedition, the Leibniz Institute for Tropospheric Research, Leipzig, Germany, operated the shipborne OCEANET-Atmosphere facility for cloud and aerosol observations throughout the whole year. OCEANET-Atmosphere comprises, amongst others, a multiwavelength Raman lidar, a microwave radiometer, and an optical disdrometer. A cloud radar was operated aboard Polarstern by the US Atmospheric Radiation Measurement program. These measurements were processed by applying the so-called Cloudnet methodology to derive cloud properties. To gain a comprehensive view of the clouds, lidar and cloud radar capabilities for low- and high-altitude observations were combined. Cloudnet offers a variety of products with a spatiotemporal resolution of 30 s and 30 m, such as the target classification, and liquid and ice microphysical properties. Additionally, a lidar-based low-level stratus retrieval was applied for cloud detection below the lowest range gate of the cloud radar. Based on the presented dataset, e.g., studies on cloud formation processes and their radiative impact, and model evaluation studies can be conducted.

Influence of Supraglacial Lakes on Accuracy of Inversion of Greenland Ice Sheet Surface Melt Data in Different Passive Microwave Bands

The occurrence of Supraglacial Lakes (SGLs) may influence the signals acquired with microwave radiometers, which may result in a degree of uncertainty when employing microwave radiometer data for the detection of surface melt. Accurate monitoring of surface melting requires a reasonable assessment of this uncertainty. However, there is a scarcity of research in this field. Therefore, in this study, we computed surface melt in the vicinity of Automatic Weather Stations (AWSs) by employing Defense Meteorological Satellite Program (DMSP) Ka-band data and Soil Moisture and Ocean Salinity (SMOS) satellite L-band data and extracted SGL pixels by utilizing Sentinel-2 data. A comparison between surface melt results derived from AWS air temperature estimates and those obtained with remote sensing inversion in the two different bands was conducted for sites below the mean snowline elevation during the summers of 2016 to 2020. Compared with sites with no SGLs, the commission error (CO) of DMSP morning and evening data at sites where these water bodies were present increased by 36% and 30%, respectively, and the number of days with CO increased by 12 and 3 days, respectively. The omission error (OM) of SMOS morning and evening data increased by 33% and 32%, respectively, and the number of days with OM increased by 17 and 21 days, respectively. Identifying the source of error is a prerequisite for the improvement of surface melt algorithms, for which this study provides a basis.

Impacts of Fengyun-4A and Ground-Based Observation Data Assimilation on the Forecast of Kaifeng’s Heavy Rainfall (2022) and Mechanism Analysis of the Event

The advancement of Numerical Weather Prediction (NWP) is pivotal for enhancing high-impact weather forecasting and warning systems. However, due to the high spatial and temporal inhomogeneity, the moisture field is difficult to describe by initial conditions in NWP models, which is the essential thermodynamic variable in the simulation of various physical processes. Data Assimilation techniques are central to addressing these challenges, integrating observational data with background fields to refine initial conditions and improve forecasting accuracy. This study evaluates the effectiveness of integrating observations from the Fengyun-4A (FY-4A) Advanced Geosynchronous Radiation Imager (AGRI) and ground-based microwave radiometer (MWR) in forecasts and mechanism analysis of a heavy rainfall event in the Kaifeng region of central China. Our findings reveal that jointly assimilating AGRI radiance and MWR data significantly enhances the model’s humidity profile accuracy across all atmospheric layers, resulting in improved heavy rainfall predictions. Analysis of the moisture sources indicates that the storm’s water vapor predominantly originates from westward air movement ahead of a high-altitude trough, with sustained channeling towards the rainfall zone, ensuring a continuous supply of moisture. The storm’s development is further facilitated by a series of atmospheric processes, including the interplay of high and low-level vorticity and divergence, vertical updrafts, the formation of a low-level jet, and the generation of unstable atmospheric energy. Additionally, this study examines the influence of Tai-hang Mountain’s terrain on precipitation patterns in the Kaifeng area. Our experiments, comparing a control setup (CTL) with varied terrain heights, demonstrate that reducing terrain height by 50–60% significantly decreases precipitation coverage and intensity. In contrast, increasing terrain height enhances precipitation, although this effect plateaus when the elevation increase exceeds 100%, closely mirroring the precipitation changes observed with a 75% terrain height increment.

Four Years of Atmospheric Boundary Layer Height Retrievals Using COSMIC-2 Satellite Data

This work aimed to study the atmospheric boundary layer height (ABLH) from COSMIC-2 refractivity data, endeavoring to refine existing ABLH detection algorithms and scrutinize the resulting spatial and seasonal distributions. Through validation analyses involving different ground-based methodologies (involving data from lidar, ceilometer, microwave radiometers, and radiosondes), the optimal ABLH determination relied on identifying the lowest refractivity gradient negative peak with a magnitude at least τ% times the minimum refractivity gradient magnitude, where τ is a fitting parameter representing the minimum peak strength relative to the absolute minimum refractivity gradient. Different τ values were derived accounting for the moment of the day (daytime, nighttime, or sunrise/sunset) and the underlying surface (land or sea). Results show discernible relations between ABLH and various features, notably, the land cover and latitude. On average, ABLH is higher over oceans (≈1.5 km), but extreme values (maximums > 2.5 km, and minimums < 1 km) are reached over intertropical lands. Variability is generally subtle over oceans, whereas seasonality and daily evolution are pronounced over continents, with higher ABLHs during daytime and local wintertime (summertime) in intertropical (middle) latitudes.

Phase external calibration in interferometric microwave radiometer

The receiver error of the interferometric microwave radiometer can be calibrated by injecting the correlated noise into the receiver’s calibration port. However, in the antenna part without the internal calibration link, the residual phase error can be calibrated by an external noise source in an anechoic chamber and the significance of phase external calibration [1][2] is also verified by imaging the anechoic chamber.

Merged and Gridded GPM and Atmospheric River Data Product

AbstractThe Global Precipitation Measurement (GPM) Mission Core Observatory satellite launched in 2014 as a joint mission between National Aeronautics and Space Administration (NASA) and JAXA. Global Precipitation Measurement (GPM) has, since that time, provided continuous, valuable dual‐frequency radar and passive microwave radiometer observations. Here, we introduce a gridded data set of collocated GPM Core Observatory observational products merged with a reanalysis‐derived Atmospheric river (AR) data set in the North Atlantic and North Pacific sectors. The three data sets that are merged and gridded are: (a) the NASA Goddard Profiling (GPROF) precipitation product, which uses GPM passive microwave radiometer observations to derive surface precipitation rates, (b) a water vapor data product derived from the GPM Core Observatory radiometer, provided by Remote Sensing Systems (RSS), and (c) the Mattingly et al. (2018, https://doi.org/10.1029/2018jd028714) AR data set that is specifically tuned to the high‐latitude regions. This novel merged data set spans from May 2014 to December 2022 with plans to update annually through 2026 at minimum. This gridded product combines RSS passive water vapor and precipitation estimates with coincident AR detection. This data product benefits the scientific community by providing (a) user‐friendly gridded satellite data compared to standard satellite data sets, while maintaining high temporal resolution, and (b) coincident satellite observations to assess the link between ARs and precipitation.

Accuracy assessment of ground-based microwave radiometer in the Mount Qomolangma region

Mount Qomolangma, known as the “Roof of the World,” holds significant importance in exploring the vertical characteristics of the atmosphere, providing insights into high-altitude atmospheric features, and improving numerical models for forecasting downstream regions. Ground-based microwave radiometers (GWR) enable continuous profiling of atmospheric conditions in the troposphere, complementing radiosonde and satellite observations. This study explores the performance of the MWP967KV GWR installed at Qomolangma Base Camp by comparison with concurrent radiosonde measurements. The assessment covers GWR's atmospheric radiative brightness temperatures (Tb) detection capability, accuracy of atmospheric parameter retrievals, and uncertainties in atmospheric parameters under different weather conditions. Using MonoRTM to calculate simulated Tb from radiosonde, the results indicate a high precision of GWR's Tb observations, showing a strong correlation (R ≈ 0.99) and a deviation of merely 2.4 K compared to simulated Tb. Moreover, GWR exhibits high reliability in temperature (R ≈ 0.98) and water vapor density (ρv) (R ≈ 0.91) observations, while the accuracy of relative humidity (RH) measurements requires improvement (R ≈ 0.55). The GWR observations are affected by clouds, precipitation, and surrounding Qomolangma mountain ridges, leading to significant uncertainties in atmospheric parameter retrievals between 2000 m to 6000 m above ground level. Additionally, GWR's sensitivity to temperature variations is limited, resulting in missed detections of temperature inversion. Despite these limitations, this study offers crucial insights into meteorological observations at high altitudes using GWR. These findings hold substantial implications for meteorological research and mountaineering activities in high-altitude regions.

Review of the Capacity to Accurately Detect the Temperature of Human Skin Tissue Using the Microwave Radiation Method.

Microwave radiometry (MWR) is instrumental in detecting thermal variations in skin tissue before anatomical changes occur, proving particularly beneficial in the early diagnosis of cancer and inflammation. This study concisely traces the evolution of microwave radiometers within the medical sector. By analyzing a plethora of pertinent studies and contrasting their strengths, weaknesses, and performance metrics, this research identifies the primary factors limiting temperature measurement accuracy. The review establishes the critical technologies necessary to overcome these limitations, examines the current state and prospective advancements of each technology, and proposes comprehensive implementation strategies. The discussion elucidates that the precise measurement of human surface and subcutaneous tissue temperatures using an MWR system is a complex challenge, necessitating an integration of antenna directionality for temperature measurement, radiometer error correction, hardware configuration, and the calibration and precision of a multilayer tissue forward and inversion method. This study delves into the pivotal technologies for non-invasive human tissue temperature monitoring in the microwave frequency range, offering an effective approach for the precise assessment of human epidermal and subcutaneous temperatures, and develops a non-contact microwave protocol for gauging subcutaneous tissue temperature distribution. It is anticipated that mass-produced measurement systems will deliver substantial economic and societal benefits.

Water vapor content prediction based on neural network model selection and optimal fusion

Accurately predicting the evolution of water vapor content has significant practical engineering implications for accurately determining the timing of artificial rain enhancement operations. However, the nonlinear, unstable, and chaotic nature of water vapor content presents considerable challenges to its precise prediction. To address this, our research proposes a combined prediction model, based on a neural network model selection mechanism and small sample water vapor content data observed by microwave radiometers in the Zhengzhou area. This combined model utilizes seven neural network models as candidate models and dynamically selects the optimal three models as the central prediction components via evaluation indicators. Additionally, we employ the maximum information coefficient method to preserve the optimal input features and use a time-varying rate wave mode decomposition algorithm as the front-end processing module. This module processes the original water vapor content sequence and extracts relevant latent information. Finally, we incorporate the African vulture intelligent optimization algorithm as the backend processing component to optimize the weighted combination coefficients, achieving the integration of prediction results. Experimental results reveal that the Mean Absolute Percentage Errors (MAPE) of the proposed model on three types of water vapor content datasets are 0.208%, 0.876%, and 0.369%, respectively. This suggests a predictive performance improvement of at least 10% compared to existing models. The prediction model proposed in this study not only establishes an accurate and reliable mechanism for predicting water vapor content but also offers more comprehensive decision support information for weather modification operations.

Assimilation of satellite swaths versus daily means of sea ice concentration in a regional coupled ocean–sea ice model

Abstract. Operational forecasting systems routinely assimilate daily means of sea ice concentration (SIC) from microwave radiometers in order to improve the accuracy of the forecasts. However, the temporal and spatial averaging of the individual satellite swaths into daily means of SIC entails two main drawbacks: (i) the spatial resolution of the original product is blurred (especially critical in periods with strong sub-daily sea ice movement), and (ii) the sub-daily frequency of passive microwave observations in the Arctic are not used, providing less temporal resolution in the data assimilation (DA) analysis and, therefore, in the forecast. Within the SIRANO (Sea Ice Retrievals and data Assimilation in NOrway) project, we investigate how challenges (i) and (ii) can be avoided by assimilating individual satellite swaths (level 3 uncollated) instead of daily means (level 3) of SIC. To do so, we use a regional configuration of the Barents Sea (2.5 km grid) based on the Regional Ocean Modeling System (ROMS) and the Los Alamos Sea Ice Model (CICE) together with the ensemble Kalman filter (EnKF) as the DA system. The assimilation of individual swaths significantly improves the EnKF analysis of SIC compared to the assimilation of daily means; the mean absolute difference (MAD) shows a 10 % improvement at the end of the assimilation period and a 7 % improvement at the end of the 7 d forecast period. This improvement is caused by better exploitation of the information provided by the SIC swath data, in terms of both spatial and temporal variance, compared to the case when the swaths are combined to form a daily mean before assimilation.

Detection of Arctic Sea Ice Using 89 GHz Microwave Radiometer Channels

Sea ice is a crucial component of the cryosphere, and extensive research has been conducted on sea ice using microwave remote sensing due to its robustness against cloud cover and illumination variations. This paper focuses on classifying Arctic sea ice based on microwave remote sensing data. Leveraging the high stability of microwave radiometers, we analyze the characteristics of different sea ice types across the Arctic region in January 2017 using high-resolution AMSR-E/AMSR2 data at the 89 GHz frequency band. Data at this frequency are less susceptible to cloud and water vapor interference, while lower frequency bands have traditionally been more commonly used in similar studies. However, our study emphasizes the significance of the 89 GHz frequency band, which offers high-resolution data for distinguishing between multi-year ice, first-year ice, and open water. The brightness temperature differences among these sea ice types are analyzed, and a method based on these differences is proposed for classification. The fine tree algorithm in MATLAB’s decision tree toolbox is employed to generate the classification results.

The preliminary measurements of water cloud microphysical properties using multiple scattering Raman lidar

Clouds play an important role in the energy balance and water cycle for the Earth-Atmosphere system, and the accurate measurement of their macro-and microphysical properties is important for understanding atmospheric physical processes, studying aerosol-cloud interactions and improving numerical model parameterization schemes. In this paper, a multiple scattering Raman lidar system is developed, which greatly simplifies the structure of the optical system and solves the complexity of the optical system in detecting the cloud characteristic parameters by adopting the dual field-of-view (FOV) technique. Based on the Quasi-Small-Angle (QSA) approximation model, the forward single or multiple scattering signals of cloud droplet particles at the bottom of the cloud layer at the dual-FOV channel and the vibrational Raman backscattering signals of nitrogen molecules are simultaneously detected. Using the correlation between the width of the forward scattering peak and the particle size of the cloud droplets, an iterative algorithm for the Raman signal is proposed for retrieving cloud parameters such as the extinction coefficient, the effective radius, and the liquid water content (LWC). To verify the feasibility of the system and retrieval algorithms, some preliminary measurements were carried out and the resulting liquid water content was compared and verified by combined observations with a co-located microwave radiometer. The experimental results show that the average deviation of the liquid water content is 0.004 g/m3, and the relative error is 20%. The system has an ability to invert cloud microphysical properties, which provides an effective method for further study of aerosol-cloud interactions.

Satellite-based atmospheric characterization for sites of interest in millimeter and sub-millimeter astronomy

Context. Water vapor is the main source of atmospheric opacity for millimeter and sub-millimeter astronomy. Hence, several studies seek to effectively characterize it for site-testing purposes. In this task, reanalysis databases are quickly becoming a popular alternative to on-site measurements due to easy accessibility and the versatility of the data they provide. Aims. In the framework of validating the use of reanalysis data as a site-testing oriented tool, we perform a statistical comparison of atmospheric water vapor values obtainable from the MERRA-2 database with ground-based microwave radiometer measurements taken at two astronomical sites in Chile: Llano de Chajnantor, Atacama, and Cerro Paranal, Antofagasta. Methods. The MERRA-2 data were interpolated both vertically (across pressure levels) and geographically (latitude-longitude). For each site, different plots were generated: a direct temporal variation plot (to visually compare the data variation over time between both sources); a PWV versus PWV plot, fitting a linear fit through robust linear regression and calculating both the Pearson (r) and Spearman (ρ) correlation coefficients in order to look for correlations between both data sources; a histogram showing the distribution of the differences between the MERRA-2 data and the water vapor measurements (defined as APWV = PWVMERRA-2 − PWVsite), along with its standard deviation (σ), mean (µ), and median values, with the aim of better appreciating the similarities of the data sources over time; and a CDF plot to compare both data distributions disregarding time stamps. Finally, millimeter and sub-millimeter transmittance curves were created through the am atmospheric modeling software, which uses ozone and temperature data along with the verified water vapor data for the two studied sites as well as three other sites of interest for the next-generation Event Horizon Telescope: Las Campanas Observatory near La Serena, Chile; Valle Nevado, located near Santiago, Chile; and the General Bernardo O’Higgins base, located in Antarctica. Results. The interpolated MERRA-2 PWV values are highly correlated with the ground-based PWV values, with a Pearson coefficient greater than 0.9 and a Spearman coefficient higher than 0.85. However, their dependence is not linear, as PWVAPEX = m * PWV, with m being higher than 0.9 in both cases. The difference histograms show an almost zero-centered distribution for Llano de Chajnantor, with a µ value of −0.021 and a median value of −0.007. On the other hand, in Cerro Paranal, the difference histogram is slightly offset toward positive values, with µ value of 0.171 and a median value of 0.256. This offset is most likely due to the strong winds present in the site’s location, close to the Pacific Ocean. The transmittance curves show different performances depending on the site studied, with Cerro Chajnantor being the highest overall transmittance and Antarctica the lowest. Additionally, the transmittance profiles estimated for Cerro Chajnantor and Cerro Paranal were scaled using the PWV measurements, providing differences of less than 12% to the model data profiles. Results obtained at the Valle Nevado site suggest promising atmospheric conditions for stronomic observations in the millimeter and sub-millimeter range. Conclusions. The results we obtained show that the atmospheric water vapor estimation using MERRA-2 data can be used for site testing of new sites by evaluating the millimeter–sub-millimeter transmittance profile through vertical pressure correction and averaging the closest grid points to the site. This new method opens the door for future site-testing studies using MERRA-2 and potentially other reanalysis databases (e.g., ERA5) as reliable sources of information.

Improvement Method of Antenna Negative Sidelobes on Cross Beam Correlation Microwave Radiometer

A weighting method is proposed for the correlation radiation measurement system based on Mills cross array. The Mills cross array achieves high spatial resolution by the product of two orthogonal fan beams. However, the product power pattern of the Mills cross array lacks a square operation, resulting in sidelobe deterioration and the presence of negative sidelobes. Therefore, a window function is necessary to improve beam performance. However, because of the negative sidelobes, the antenna array is sensitive to the weighting function, and common window functions such as Hanning and Hamming are not suitable for achieving optimal antenna performance. A weighting method is proposed to fully utilize the feature that the received energy of positive and negative sidelobes cancel each other out and to reduce the influence of antenna sidelobe errors in radiation measurement. Such a weighting function can be obtained by combining the combined cosine window function with the existing particle swarm optimization (PSO). The optimized window function is evaluated through numerical simulation and compared with the typical window function weighting results. The results show that this weighting method can minimize the impact of negative sidelobes and reduce the loss of spatial resolution.

Satellite-based time-series of sea-surface temperature since 1980 for climate applications

A 42-year climate data record of global sea surface temperature (SST) covering 1980 to 2021 has been produced from satellite observations, with a high degree of independence from in situ measurements. Observations from twenty infrared and two microwave radiometers are used, and are adjusted for their differing times of day of measurement to avoid aliasing and ensure observational stability. A total of 1.5 × 1013 locations are processed, yielding 1.4 × 1012 SST observations deemed to be suitable for climate applications. The corresponding observation density varies from less than 1 km−2 yr−1 in 1980 to over 100 km−2 yr−1 after 2007. Data are provided at their native resolution, averaged on a global 0.05° latitude-longitude grid (single-sensor with gaps), and as a daily, merged, gap-free, SST analysis at 0.05°. The data include the satellite-based SSTs, the corresponding time-and-depth standardised estimates, their standard uncertainty and quality flags. Accuracy, spatial coverage and length of record are all improved relative to a previous version, and the timeseries is routinely extended in time using consistent methods.

Concurrence of Temperature and Humidity Inversions in Winter in Qingdao, China

AbstractConcurrence of temperature inversion (TI) and humidity inversion (HI) is a particular configuration of the atmospheric boundary layer with important implications for early warning of fog formation. With a microwave radiometer device deployed in a 2‐month winter campaign at a coastal island in Qingdao, China, we here examine the relationship between TI and HI, and investigate the underneath mechanisms. Cases of temperature inversion are further divided into surface‐based temperature inversion (SBTI) and elevated temperature inversion (ETI), which show different relationship with HI. SBTI typically occurs at night with its strength significantly and positively correlated with HI. ETI also shows a high degree of temporal overlap with HI, but its strength has no obvious relationship with HI. The main explanation for this phenomenon is that ETI may block the vertical diffusion of water vapor, resulting in the formation of HI.

On the Applicability of Ground-Based Microwave Radiometers for Urban Boundary Layer Research.

Significant knowledge gaps exist in our understanding of urban boundary layer processes, particularly the hygrothermal state. The earth system community has successfully used microwave radiometers for several decades. However, the applicability in complex urban environments has never been adequately tested. Here, observations from a microwave radiometer are compared to radiosonde readings in a densely urbanized site in Houston, Texas. The site was influenced by both an urban heat island and the sea breeze phenomenon. The analysis showed significant disagreement between the virtual potential temperature predicted by the microwave radiometer and the radiosonde for all periods within the boundary layer. However, the values were reasonably comparable above the boundary layer. The microwave radiometer incorrectly predicted an inversion layer instead of a mixed layer during convective periods. The microwave radiometer measurements deviated from the radiosonde measurements throughout the lower troposphere for the relative humidity.

Quantitative analysis of the tropospheric delay differences for 1–300 GHz microwave signals with ground-based microwave radiometric profiles

Tropospheric delay is a significant source of error in various microwave measurement technologies. As the tropospheric delay differences associated with the signal frequency can be neglected for signals near the L band, the Global Navigation Satellite System (GNSS) is frequently used to assist other microwave measurements on tropospheric delay correction for its high precision and all-weather capabilities. However, when the signal frequency differences are significant, further analysis of tropospheric delay related to signal frequency is required for higher accuracy in long-distance propagation. This study quantitatively analyzed the tropospheric delay differences from L-band to millimeter band (1–300 GHz) affected by dry air, water vapor, and liquid water, based on Microwave Radiometer (MWR) measurement profiles and the Millimeter-wave Propagation Model93 (MPM93) at Shanghai. Relative to the GNSS L-band signal of 1.2 GHz, the RMS of tropospheric delay differences over 1–300 GHz varies from 0.033 mm to 9.436 mm, with the maximum tropospheric delay differences up to 18.03 mm at 300 GHz. Tropospheric delay differences strongly correlate with surface temperature and surface pressure, except for some of the atmosphere absorption bands. These quantitative analyses are helpful for the comprehensive application of GNSS to assist other microwave measurement techniques.

Lead fractions from SAR-derived sea ice divergence during MOSAiC

Abstract. Leads and fractures in sea ice play a crucial role in the heat and gas exchange between the ocean and atmosphere, impacting atmospheric, ecological, and oceanic processes. We estimated lead fractions from high-resolution divergence obtained from satellite synthetic aperture radar (SAR) data and evaluated them against existing lead products. We derived two new lead fraction products from divergence with a spatial resolution of 700 m calculated from daily Sentinel-1 images. For the first lead product, we advected and accumulated the lead fractions of individual time instances. With those accumulated divergence-derived lead fractions, we comprehensively described the presence of up to 10 d old leads and analyzed their deformation history. For the second lead product, we used only divergence pixels that were identified as part of linear kinematic features (LKFs). Both new lead products accurately captured the formation of new leads with widths of up to a few hundred meters. We presented a Lagrangian time series of the divergence-based lead fractions along the drift of the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition in the central Arctic Ocean during winter 2019–2020. Lead activity was high in fall and spring, consistent with wind forcing and ice pack consolidation. At larger scales of 50–150 km around the MOSAiC expedition, lead activity on all scales was similar, but differences emerged at smaller scales (10 km). We compared our lead products with six others from satellite and airborne sources, including classified SAR, thermal infrared, microwave radiometer, and altimeter data. We found that the mean lead fractions varied by 1 order of magnitude across different lead products due to different physical lead and sea ice properties observed by the sensors and methodological factors such as spatial resolution. Thus, the choice of lead product should align with the specific application.