Abstract Measuring vertical wind using dropsondes is useful for studying dynamic and thermodynamic processes in the atmosphere, such as those in tropical cyclones. Vertical wind can be estimated from dropsondes by finding the difference between the dropsonde’s measured fall rate and terminal velocity. Determining an accurate dropsonde terminal velocity is challenging since it requires either laboratory experimentation or analyses of dropsonde observations within potentially strong convective motions. Two methods for finding the dropsonde terminal velocity and resulting vertical wind were examined. The first method determines a theoretical terminal velocity based on the dropsonde design, parachute size, and a drag coefficient. The second method estimates the terminal velocity by finding the median fall rate of a large sample of dropsondes with similar designs. The fall rates and vertical winds were calculated from dropsondes released by NOAA and NASA aircraft from 2010 to 2024 using four different dropsonde models. For three of the dropsonde models, the median fall rates were faster than the corresponding theoretical terminal velocity. This difference resulted in the vertical wind estimated using the theoretical terminal velocity having a larger percentage of downward motion than upward motion. In contrast, the vertical wind estimated using the median fall rates had a more symmetric distribution of upward and downward motions. Overall, estimating vertical wind from dropsondes using the median fall rate for the corresponding dropsonde model is recommended versus using the theoretical terminal velocity. This study focuses on observations in tropical cyclones and their surrounding environments; however, the results can be applied to observations of other atmospheric phenomena. Significance Statement Vertical wind measurements inform analyses of the dynamics and thermodynamics of tropical cyclones, and other atmospheric phenomena, to aid in research and forecasting. Various methods can be employed to estimate vertical wind from dropsondes. Dropsondes fall at different rates depending on the model and year produced, and including these parameters when determining vertical wind provides improved estimates. Using theoretical fall rates increases uncertainty since they do not reflect the variation of those parameters. Improved estimates of vertical wind can be useful for a variety of applications including studying the vertical transport of cool and dry air in tropical cyclones or how extreme vertical winds are related to tropical cyclone intensity and structure.
Abstract Ground-based radar networks have the unique ability to monitor rainfall rates close to the ground every few minutes, but in heavier flood-producing rain, when the radar observations are most useful, the radar return is attenuated by the rain. We report on rainfall derived using a consensus approach for estimating attenuation, based on three techniques: (i) A new “emission” technique to estimate this attenuation by monitoring the increased noise in “empty” gates at long range where the radar beam is above any precipitation. Attenuating rain will emit as a blackbody and raise the noise floor along the entire ray. For Met Office C-band radars, an increase of 20K is equivalent to a path integrated attenuation (PIA) of ∼1dB, but we need the distribution of attenuation over range so we appeal to: (ii) The empirical gate-by-gate correction scheme which tends to be unstable and uses a climatological relation between attenuation by rain and radar reflectivity (Z); and (iii) A polarimetric constraint: the differential phase shift between the H & V returns. The new scheme achieved reductions in the root mean square error of hourly radar based rainfall of up to 39% (compared with co-located rain gauges). These results, combined with a neutral impact on light rainfall events during both summer and winter, suggest that the new scheme is beneficial across the Met Office radar network. Emissions also have the unique potential to correct for attenuation by hail, but this is hard to evaluate in the UK.
Abstract Accurate and timely extreme precipitation data are crucial for effectively predicting and mitigating the impacts of natural phenomena. In Peru, automatic weather stations operated by the National Meteorological and Hydrological Service of Peru (SENAMHI) collected approximately 3.5 million precipitation data points between 2020 and 2021. The automated phase of the quality control (QC) system at SENAMHI flagged 4% of the data as suspect due to extreme values, but only 53% of these suspect data were validated in the manual QC phase in a timely fashion, even though 98.8% of these were ultimately classified as correct. To address this, we propose a deep learning model using satellite images and auxiliary inputs to validate extreme precipitation data more efficiently in real time, trained with human flags from the manual QC phase. We utilized a convolutional neural network (CNN)–recurrent neural network (RNN) architecture and satellite images to determine whether an extreme precipitation value is correct. The model yields a true positive rate of 95.9% considering the default threshold probability (0.5), so these suspect data could be automatically approved and published with a low false positive (error) rate of 0.329%, which would strongly reduce the workload of the human meteorologists in the manual QC. This could be optimized further by lowering the threshold and increasing the automatic approval rate while keeping the error rate at an acceptable level for SENAMHI. Additionally, an out-of-time validation using data for 2023–24, obtained after the original development and testing, showed a relatively good generalization to new climatological conditions, including the 2023–24 El Niño, albeit with a somewhat reduced performance, highlighting the need for continuous monitoring and readjusting the model.
Abstract Climate change research must ensure international data comparability over long periods, and deep-ocean temperature measurements require accurate evaluation of thermometers because temperature changes there are small. We calibrated three reference thermometers [Sea-Bird Electronics (SBE) 35] at the triple point of water and the gallium melting point, as defined by the International Temperature Scale of 1990, and confirmed that they were extremely stable over a 20-yr period; the temporal drift for two of the three was within ±0.2 mK decade −1 . We evaluated the pressure sensitivities of two conductivity–temperature–depth (CTD) thermometers in a laboratory up to 68 MPa; the results agreed with values estimated using SBE 35s in the deep ocean. We evaluated temperature hysteresis of the three SBE 35s in the laboratory: one showed no hysteresis and the other two exhibited hysteresis of 0.3–0.5 mK. Pressure hysteresis was examined in the deep ocean. Of 22 CTD thermometers, more than half showed estimated pressure hysteresis of 0.5–1 mK. The overall expanded uncertainty of the deep-ocean temperature measurement (depths greater than 20 MPa) by the CTD thermometer with small hysteresis calibrated in reference to the SBE 35 is estimated to be 0.8 mK. To eliminate the influence of systematic errors due to hysteresis, we strongly recommend aligning the temperature data 0.3 s ahead of the pressure data to account for the temperature data delay due to the sensor’s response time and applying the in situ calibration coefficients obtained from upcast data to the continuous upcast profile, thereby modifying both continuous profile data and data from discrete water sampling depths from the upcast.
Abstract We tested the capability of a high-resolution ocean general circulation model called LLC4320, with 2.3-km horizontal grid spacing at the equator, to replicate hourly measurements of currents recorded at 5-m depth intervals in the Pacific Equatorial Undercurrent (EUC) with an acoustic Doppler current profiler (ADCP) moored at 0°, 140°W. The LLC4320 simulations of the mean and variability of the horizontal currents and the vertical shear above the depth of the EUC core speed were poor compared to ADCP observations. The LLC4320 EUC core speed was slower, deeper, and less variable than ADCP measurements. The ADCP and LLC4320 currents were captured, with varying degrees of agreement: (i) meridional current oscillations with approximate 19-day period, (ii) a Kelvin wave–like pulse, and (iii) EUC surfacing. Significance Statement Currents in the interior of the ocean are very sparsely measured, and models are expected to provide information on ocean currents to analyze past and future characteristics of global ocean circulation. Ocean current models are severely challenged at the equator where current and density fields are not in balance with the rotation of Earth, unlike over the remainder of the global ocean. We examined a particular ocean general circulation model called LLC4320 because of its high spatial resolution (horizontal grid spacing of 2.3 km at the equator). The LLC4320-simulated currents in the Pacific Equatorial Undercurrent, a feature related to the El Niño and La Niña phenomena, require further refinement.
Abstract Under hydrostatic pressure, the polymer-encapsulated electrode-type conductivity cell used in Sea-Bird Scientific conductivity–temperature–depth (CTD) sensors deforms, altering the effective geometry used for conductivity measurements. To correct this pressure effect, these sensors use a pressure correction parameter CP cor . Recent observations, however, have shown that optimal CP cor values for several Sea-Bird Scientific CTD sensor models are smaller and more variable than the manufacturer-preset values, and the mechanical origin of this discrepancy has remained unclear. This study investigates pressure-induced conductivity-cell deformation using a dual-cylindrical cell model representing the inner glass cell, the outer polymer sleeve, and an adhesive layer at their interface. The adhesive layer generates shear stress between the cylinders, characterized by an effective shear stiffness (bonding factor γ ). The analysis shows that, although CP cor originates from pressure-induced deformation of the glass cell itself, both the reduction from manufacturer-preset values and the observed variability are primarily governed by the interfacial mechanical coupling between the glass cell and polymer sleeve. Models with intermediate interfacial shear stiffness reproduce observationally inferred CP cor values: approximately −11.5 × 10 −8 dbar −1 for polyurethane-sleeved sensors and −9.6 × 10 −8 dbar −1 for epoxy-sleeved sensors. Variations in interfacial stiffness, polymer mechanical properties, and cell geometry reproduce the full observed range of CP cor values, including extreme cases, providing a plausible physical explanation for the large observed scatter. These results suggest that CP cor variability may contribute significantly to uncertainty in high-accuracy deep-ocean salinity measurements, highlighting the importance of accurately characterizing pressure-induced conductivity-cell deformation in modern ocean observing systems. Significance Statement Accurate measurements of ocean salinity are essential for understanding ocean circulation and climate change. Salinity sensors used in the deep ocean are affected by high pressure, and a correction is routinely applied, but the physical basis of this correction has remained unclear. This study explains why commonly used correction values are often too large and why they vary among otherwise identical instruments. We show that the mechanical coupling between materials inside the sensor strongly influences the pressure-induced deformation. These results provide a physical framework for understanding how conductivity-cell deformation under pressure can contribute substantially to uncertainty in deep-ocean salinity measurements.
While understanding that quality control (QC) processes crucially affect the statistics of the observations compared to short-range numerical weather prediction (NWP) forecasts and the subsequent analyses, we also realize the fact that many centers implement entirely different QC methods from each other in operations. Here, we catalog the QC methods of Global Navigation Satellite System (GNSS) radio occultation (RO) observations used for data assimilation (DA) by NWP centers. The QC methods are categorized into four groups: preliminary checks which often refer to the metadata of the observed profile, background checks where the observation is compared to a short-range forecast, superrefraction checks which attempt to look for the presence of superrefraction in the profile, and miscellaneous checks such as running a one-dimensional variational scheme or using the variational QC method. This survey is the first published catalog of QC checks used in GNSS-RO observations. It also sets up the second part of this paper, where various methods for diagnosing superrefraction are compared.
Unmanned aerial vehicles (UAVs), commonly referred to as drones, offer an innovative alternative to in situ measurements performed from fixed structures or ships. Within such a general framework, this work presents the "Hyperspectral Drone-based system for above-water Radiometric Acquisitions" (HYDRA), conceived for measurements specifically supporting the validation of satellite aquatic radiometric data products. By relying on consolidated measurement methods and a class of extensively characterized hyperspectral radiometers, HYDRA comprises the UAV platform allowing to quantify the radiance from the water LT and a complementary ground-based station to measure the sky-radiance Li and the downward irradiance ES. Field measurements to evaluate the performance of the system were carried out in the northern Adriatic Sea in the proximity of the Acqua Alta Oceanographic Tower (AAOT) during almost ideal conditions determined by sun clear from clouds, sea state lower than 2 (WMO scale), cloud cover lower than 2 oktas, and wind speed below 3 m s21. During these tests, HYDRA LT measurements were complemented by independent and concurrent LT measurements performed at the AAOT using a hyperspectral radiometer belonging to the same class as that operated on the UAV. Matchups of HYDRA and AAOT LT showed mean spectral differences generally ranging between 0% and 11% in the 400-580-nm interval and within 61% in the 580-700-nm interval. The same differences characterized HYDRA and AAOT RRS spectra as a result of the application of the same Li and ES values.
Numerical weather prediction (NWP) centers around the world implement different methods from each other in the quality control (QC) of Global Navigation Satellite System radio occultation (GNSS-RO) observations. This study focused on the implementation and evaluation of the superrefraction (SR) QC methods of RO observations used for data assimilation by NWP centers. This study was conducted within the Joint Effort for Data assimilation Integration (JEDI) framework that contains generic QC filters and facilitates easy implementation of new QC methods. This intercomparison of SR QC methods is based on the same framework, i.e., the Met Office forecast model and bending angle forward operator, and a common RO observation dataset. It demonstrates that different SR QCs behave very differently. The methods used by the U.S. Naval Research Laboratory and M & eacute;t & eacute;o France remove the most observations, those used by the Met Office and the U.S. National Centers for Environmental Prediction remove the next most, and a method based on differences in impact parameter remove the least. It is noted that some NWP centers implement their QC in a conservative practice aiming at screening out potentially suspicious data. RO observations of sharp refractivity gradients may be rejected even when the observations themselves are of good quality. Comparisons of the vertical refractivity gradients from the observations and the model highlight systematic differences, with sharp refractivity gradients being largely absent from the observations, indicating that QC outcomes depend also on the upstream processing procedures. We hope this study can provide guidance for reconsidering RO QC in NWP practice. SIGNIFICANCE STATEMENT: Numerical weather prediction (NWP) centers around the world use very different quality control (QC) methods in the assimilation of Global Navigation Satellite System radio occultation (GNSS-RO) data. This study provides technical details of the GNSS-RO QC schemes used in NWP centers within the Joint Effort for Data Assimilation Integration (JEDI) framework. It presents an intercomparison of different QC methods for detecting superrefraction (SR) from multiple perspectives. The findings aim to support the GNSS-RO data assimilation community by highlighting key differences between these methods and suggesting potential improvements for optimizing data utilization.
Observationally based reference products for time-mean dynamic sea level (MDSL) are useful for evaluating the representation of sea level and ocean circulation in climate models. They also serve as constraints for ocean reanalysis products. However, in previous analyses, little attention has been paid to consistently quantifying and interpreting various sources of model-data differences. Such an effort is especially important near coastlines, where data uncertainties are expected to be relatively large, and model representation of sea level is critical. Here, we find that two new MDSL reference products exhibit differences well beyond their provided uncertainties, especially in western boundary currents and along coastlines. We thus implement a consistent approach to evaluating reference product and model errors arising from different sources, including mistimed internal variability, that can be extended to accommodate different models and/or reference datasets. In a demonstration evaluation of a GFDL climate model, we find that, along the U.S. East Coast, uncertainties in reference products are generally smaller than biases in the climate model, suggestive of misrepresented dynamics and/or the influence of coarsened bathymetry. Model errors due to internal variability play a less important but nonnegligible role.
Abstract The three-dimensional (3D) global wind field is critical for understanding the general circulation and initializing numerical weather prediction models. Although imagers on the polar and geostationary satellites provide wind information through atmospheric motion vectors (AMVs) by tracking cloud motion, they fail to adequately sample the clear-air mass prevalent in the midtroposphere. This gap can be addressed using AMVs on discrete pressure levels (3D winds) retrieved by tracking moisture from hyperspectral infrared soundings. Observing system simulation experiments (OSSEs) were conducted for AMVs derived from two satellite orbital constellations with different spatial coverage—one providing well-distributed global coverage and the other exhibiting coverage gaps at the mid- and low latitudes, with these gaps shifting longitudinally over time. Both configurations improved global forecasts, especially the wind fields, with the globally distributed constellation yielding greater benefits. Forecast improvements were more pronounced in the Southern Hemisphere, likely due to the sparse radiosonde network. Despite better wind analyses in the tropics, tropospheric temperature forecasts degraded there. This degradation may stem from climatological differences between the nature run and the Global Forecast System (GFS) or from suboptimal background error covariances for AMV assimilation. Future assimilation of real 3D wind observations will help validate these OSSE results and clarify the cause of the tropical temperature degradation. Significance Statement The results of this paper are significant because more hyperspectral sounders are being launched by various countries. Their data can be used to derive three-dimensional winds, providing broad observational coverage and enabling these observations to positively impact global forecasts.
Abstract Spectral surface albedo depends on the surface type. Broadband surface albedo is calculated as the integral of the spectral albedo weighted by the surface downward irradiance spectrum. Consequently, unlike spectral albedo, broadband albedo depends not only on surface properties but also on atmospheric conditions, such as whether the sky is clear or cloudy. The difference between clear- and cloudy-sky broadband albedos is particularly significant for snow-covered surfaces for two main reasons. First, snow exhibits a strong contrast in spectral albedo between the visible and near-infrared (NIR) regions. Second, cloud absorption is stronger in the NIR, which reduces the surface downward irradiance relative to the visible region. When clouds are present, broadband snow albedo increases nearly by 0.1 above clear-sky albedo. In the Clouds and the Earth’s Radiant Energy System (CERES) radiative transfer model, satellite-observed broadband surface albedos are used to constrain assumptions about surface spectral albedo. However, such satellite observations are available only under clear-sky conditions. The current algorithm, which derives the initial spectral albedo for the radiative transfer model, normalizes a cloudy-sky spectral albedo to a clear-sky broadband albedo derived directly from CERES observations. We find that this normalization underestimates surface broadband albedo over snow and ice under cloudy-sky conditions. We demonstrate that, even in the presence of clouds, because the numerator in the normalization is based on a clear-sky observation, assuming an initial clear-sky spectral albedo improves the representation of surface albedo for snow and ice under clouds.
Abstract To determine Earth-referenced ocean velocity from a moving vessel, the standard method is to obtain platform-relative measurements using a Doppler sonar and subsequently combine these with independent estimates of vessel motion obtained by GPS and inertial navigation systems. Here, we suggest a different approach, where navigation data are merged into the sonar processing at an initial stage of estimating velocity. Earth-referenced velocity estimates result directly from this merged processing. For sonars mounted on moving platforms, sensitivity to measurement bias is reduced. Longer transmitted acoustic codes can be employed enabling more precise velocity estimation.
Abstract Sea surface temperature (SST) is a fundamental oceanic parameter for understanding climate trends and variability. Since the early 1980s, SSTs have been retrieved globally using inverse methods from measured radiation through several satellites. One essential goal is the generation of accurate long-term satellite SST datasets for near-real-time anomaly and trend detection. However, even after reprocessing, satellite SST requires correction for residual biases in the data, which is a challenging task due to limited in situ data and potential nonlinear bias characteristics. In this study, we focus on improving MetOp-A SST bias correction using machine learning algorithms and compare several algorithms. Light Gradient Boosting Machine (LGBM), a tree-based gradient boosting model, demonstrates the best performance and significantly reduces spatial SST bias and RMSE for both daytime and nighttime. In addition, it is confirmed that LGBM improves satellite bias in terms of spatiotemporal characteristics by effectively reducing seasonal bias patterns that appear in tropical and mid-latitude regions. Although some residual errors remain in polar regions and regions with high SST fluctuations, these results show the remarkable potential of machine learning for correcting satellite SST bias.
Abstract Snowfall measurement is critical for hydrology, climate studies, and weather forecasting, yet its reliable estimation from space remains challenging. Passive microwave (PMW) sensors, such as those aboard the Global Precipitation Measurement (GPM) constellation, provide global coverage; their snowfall retrieval skill lags behind rainfall due to weak radiometric signals, complex microphysics, and limited high-quality reference data. Active sensors like the CloudSat radar provide valuable insights but are constrained by narrow sampling footprints, limiting their utility. Improving snowfall retrievals, therefore, requires an approach that maximizes the use of sparse collocations between PMW observations and active-sensor or in situ references. Machine learning (ML), proven effective at capturing nonlinear relationships in noisy datasets, offers a promising path forward. This study investigates the use of ML to classify snowfall regimes—Deep, Shallow, Other, and Dry—directly from PMW radiances. Collocated products from the GPM Core Observatory , CloudSat radar, and the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis provide a valuable training dataset. Two ML architectures, a fully connected neural network and an extreme gradient boost decision tree, are evaluated. The best models achieve weighted accuracies of ∼80% for the four- and three-class problems (Dry/Shallow/Deep/Other) and 75%–85% for two-class tasks (Snowing/Dry and Shallow/Deep). Correctly classified snowfall amounts among the three regimes exceed 90% globally, demonstrating the potential of ML to extract useful information from ambiguous PMW signals and constrain the otherwise ill-posed retrieval of snowfall rates. Results further emphasize the importance of training dataset size, data partitioning, and class balancing—factors known but often underutilized in satellite-based ML applications.
Abstract In this work, we explore methods for classifying wildland-fire-related particles (both wildfires and prescribed burns) using WSR-88D observations. The first method includes a new pyrometeor class for the operational hydrometeor classification algorithm (HCA) method, a fuzzy-logic classification algorithm. This new class is based on statistical analysis of key polarimetric variables for pyrometeor regions. Because of persistent challenges in implementing a new class with this method, a new detection and replacement algorithm (DRA) was developed. The DRA is based on the statistical analysis of areas of pyrometeors from wildfires and prescribed burns across the United States. Areas detected as pyrometeors using specific polarimetric thresholds [ ρ hv < 0.6, −2 < Z DR < 8 dB, Z h > −10 dB Z , SD( Z ) < 6 dB Z ] are assigned a new value in the original HCA output from the WSR-88D, effectively creating a new separate class while maintaining the quality of the HCA output from the operational algorithm. Algorithm performance was evaluated using receiver operating characteristic (ROC) curves and the summary metric of area under the curve (AUC). Five cases were analyzed, and AUC values ranged from 0.81 to 0.96. The DRA shows promise for further applications with continued development. Through proposed future work, the DRA could provide additional situational awareness for wildfires and hazards related to pyrometeors.
Several previously identified and well-documented polarimetric radar signatures have been related to ongoing storm-scale processes such as size sorting. Two polarimetric signatures that result from size sorting are an enhanced area of differential reflectivity (ZDR) located along the inflow edge of the forward flank, known as the ZDR arc, and an enhanced area of specific differential phase (KDP) found in the forward flank, known as the KDP foot. Recently, the orientation of the vector connecting the KDP foot and the ZDR arc centroids (termed the KDP-ZDR separation vector) relative to storm motion has been the subject of many studies and found to be useful in distinguishing between tornadic and nontornadic supercells. Two methodologies developed to quantitatively analyze the separation vector are the "dynamic thresholding" approach and the Supercell Polarimetric Observation Research Kit (SPORK). These provide powerful tools for interrogating past cases but are not properly suited for use in the operational warning decision-making process. To address this, a novel algorithm known as "w2sepvec" has been developed to identify and track the KDP-ZDR separation vector. This study compares the output of the three methodologies and evaluates the performance of w2sepvec. All three methodologies produce quantitatively similar trends between tornadic and nontornadic supercells; separation vectors tend to be more orthogonal relative to storm motion for tornadic storms and closer to parallel for nontornadic storms. However, separation vectors produced by w2sepvec demonstrate more variability than the other two methodologies. Despite the higher variability, w2sepvec shows promise as a tool for forecasters to use in warning operations.
Abstract Precipitation systems, including flash-flood-generating supercells, are key components of Earth’s atmospheric systems. A profound understanding of the microphysical structure of precipitation within these systems is pivotal for refining weather forecasting, quantitative precipitation estimation (QPE), and deciphering cloud microphysical processes. However, the real-world morphologies of severe storms, particularly high-precipitation storm cells or tornadic supercells, are seldom confined to the shapes like a vertical column defined by the columnar vertical profile (CVP) method. Many storm cells are tilted by shear instead of being vertically oriented. Moreover, the shape and size of the downdraft and updraft are not vertically uniform across different radar elevation scans. To address these challenges, this study proposes a process-oriented vertical profile (POVP) technique, which generates vertical profiles of dual-polarization radar variables within a vertically tilted column, taking into account only a selected percentile of radar variables within a broader contour at each radar scan. This technique is designed to capture user-defined processes within storms with spatial continuity. The examples shown in this study include the 2013 El Reno Tornado case, a fast-moving supercell near Dallas, Texas, and the record-breaking flood case near Fort Lauderdale, Florida. The novel POVP technique successfully captured the microphysics characteristics within the convective supercells, including updraft, downdraft/precipitation shaft, and hail shaft. In addition, using POVP-guided R ( Z ) relationship shows significant improvement on radar QPE compared with the traditional R ( Z ) relationships. Last, the hydrometeor classification algorithm is merged with POVP within the core of convection and shows new insights into convective microphysics.
Autonomous in situ radiometric observations are increasingly used to constrain bio-optical processes and validate satellite ocean-color products, such as remote sensing reflectance and diffuse attenuation coefficients. Because these observations are collected independently of weather and sea-state conditions, their application critically depends on robust quality control. Starting in 2012, the BioGeoChemical-Argo (BGC-Argo) program has measured downwelling irradiance (Ed) at three wavelengths on autonomous floats. Since 2022, a pilot array of 12 BGC-Argo floats equipped with TriOS-RAMSES hyperspectral radiometers measuring Ed and upwelling radiance (Lu) has been deployed across open-ocean regions with diverse biooptical properties. To date, these floats have acquired hundreds of hyperspectral profiles from 0 to 300 m at ;10-day intervals near local noon. This study presents an automated quality-control (QC) method for hyperspectral Ed and Lu profiles measured by BGC-Argo floats, building upon previous QC procedures designed for multispectral radiometry. The method flags perturbations in the light field caused by self-shading, large tilt angles, passing clouds, wave focusing, and spikes and corrects for dark current signals. The QC is first applied at five key wavelengths (380, 443, 490, 555, and 620 nm) to generate wavelength-specific flags along each vertical profile, which are then combined into a final global classification for each spectral profile as good, questionable, or bad. This paper, along with its Python code and data files, provides the community with a robust and computationally efficient approach for assessing hyperspectral BGC-Argo data quality, preparing it for further bio-optical applications.
The Polar Radiant Energy in the Far-Infrared Experiment (PREFIRE) is a low-cost CubeSat mission comprising two 6U CubeSats in separate sun-synchronous orbits that continuously measure spectral emissions up to 54 μm and provide ongoing, time-lapsed observations of the polar processes that modulate them. PREFIRE fulfilled its Prime Mission between August 1, 2024, and April 30, 2025, during which time each CubeSat, PREFIRE-SAT1 and PREFIRE-SAT2, regularly resampled itself (“self-intersections”) and the other satellite (“SAT1-SAT2 intersections”). Since PREFIRE intersections will reveal the spectral signatures of the processes that modulate thermal emission from the Arctic and Antarctic, this paper introduces methods to identify PREFIRE resampling and establishes the spatial and temporal record of sub-daily PREFIRE intersections from August 1, 2024, to April 30, 2025. Our results indicate that about 76% of self-intersections and over 80% of SAT1-SAT2 intersections occur poleward of 60° latitude. Self-intersections form discrete, time-invariant latitude-temporal bands with timescales that become progressively shorter toward higher latitudes. Conversely, SAT1-SAT2 intersections are dynamic, varying in step with the increasing offset in orbital altitude between satellites, and they exhibit broader time differences between crossovers than self-intersections. Our results further suggest that the second PREFIRE CubeSat nearly quadruples the number of possible daily intersections and SAT1-SAT2 intersections yield twice as many latitude-temporal bands as self-intersections, underscoring the utility of a configuration featuring multiple CubeSats.
