Radar mosaic data represent a critical and widely utilized resource in weather forecasting. Nevertheless, the frequent occurrence of regional radar echo gaps, caused by factors including radar hardware malfunctions, data delivery delays, and software processing errors – each contributing to substantial spatial uncertainty in the missing areas – significantly constrains its quantitative application. To address this issue, we propose BiConvLSTM-UNet, a sequence reconstruction model designed to restore missing radar echoes. The model operates without relying on a missing-value mask during both training and inference, learning the inherent spatiotemporal variation patterns of radar echoes to reconstruct complete sequences. A post-processing procedure is implemented to minimize the impact of reconstruction on areas without missing data. Furthermore, multiple missing scenarios are synthetically generated to improve the model's robustness and repair performance across diverse missing-data conditions. Comparative assessments against traditional and other deep learning approaches demonstrate the superior inpainting performance of the proposed BiConvLSTM-UNet across multiple missing-data scenarios. The method introduces minimal artifact to non-missing regions, and subsequent post-processing further diminishes reconstruction errors. Moreover, the model sustains consistent performance across varying missing data lengths and continuity patterns, indicating robust generalization capabilities. Consequently, the BiConvLSTM-UNet is more adept at addressing the intricate and varied scenarios of incomplete radar mosaic data encountered in practical applications.
Accurate beam pointing is essential for vertically pointing cloud radars, as even small tilts away from zenith introduce projections of horizontal wind into Doppler velocity measurements. These effects can bias the interpretation of vertical air motion and hydrometeor fall speeds, leading to systematic errors in cloud and precipitation retrievals.We present the U-normalized velocity–direction display (UN-VDD) method to estimate and validate radar beam pointing angle using collocated radiosonde observations. When the radar beam is tilted, the observed Doppler velocity exhibits a cosine dependence on wind direction due to projection of horizontal wind onto the beam, with an amplitude proportional to wind speed. By normalizing Doppler velocity with horizontal wind speed, this geometric dependence can be isolated, enabling quantitative estimation of off-zenith beam pointing.The method is applied to the Ka-band ARM Zenith Radar (KAZR) and the Marine W-band ARM Cloud Radar (MWACR) during the Cloud and Precipitation Experiment at kennaook (CAPE-k). Results show a clear wind-direction dependence consistent with small but measurable beam pointing offsets. Differences in Doppler velocity between KAZR and MWACR further reduce the influence of hydrometeor fall velocity and vertical air motion, providing an independent constraint on relative pointing errors. Application to KAZR observations at ARM fixed sites and recent field campaigns further demonstrates that the method is robust across a range of atmospheric conditions. This approach provides a practical and scalable tool for evaluating radar beam pointing using routinely available radiosonde data, with direct implications for improving the accuracy of ARM cloud and precipitation products.
Atmospheric new particle formation is driven by condensable vapors such as sulfuric acid and highly oxygenated organic molecules (HOMs). Measuring these gases is challenging because they are present at trace concentrations, they are easily lost through condensation onto surfaces, and they include a large number of chemically diverse species. Chemical Ionization Mass Spectrometry (CIMS) has been extensively used for their detection; however, results obtained in different studies are not always directly comparable. This limitation reflects differences in instrument designs, operating configurations, and reagent ion schemes employed. To investigate these factors, the Aerosol, Clouds and Trace Gases Research Infrastructure (ACTRIS) organized its first CIMS field intercomparison campaign (CI-FI1) during summer 2024 at a Finnish boreal forest site, the SMEAR II (Station for Measuring Forest Ecosystem-Atmosphere Relations) station. Six instruments employing different inlet designs, mass analyzers, and reagent ions were operated using their routine configurations and calibrated according to standard procedures. For sulfuric acid measured in nitrate mode, the conventional sulfuric acid calibration enabled moderately good agreement among instruments, although larger discrepancies were observed at the lower concentrations, typically observed during nighttime. When targeting higher-mass compounds, such as monoterpene-derived HOM monomers (
Radiative transfer in strongly scattering media remains computationally challenging, particularly for systems with highly asymmetric phase functions and large optical depths, where conventional discrete-ordinate approaches may suffer from numerical instability, slow convergence, or loss of accuracy. We present EigenFlux, a multistream radiative transfer framework based on eigenmode decomposition, natural-reflectance stabilization, and flexible mesh-based angular discretization. Unlike previous approaches relying primarily on global polynomial expansions, EigenFlux permits localized resolution of phase functions with strongly forward scattering or significant backward-scattering components while preserving flux conservation and numerical stability. The method is evaluated across 957 test cases spanning asymmetry factors from
Nitrous oxide (N2O) is a potent greenhouse gas, and emissions from wastewater treatment plants (WWTPs) represent a significant and highly variable source. Understanding the dynamics in microbial pathways of N2O formation and reduction during biological nitrogen removal is essential for targeted mitigation strategies. Stable isotope analysis of N2O (δ15Nα, δ15Nβ, δ18O, and 15N site preference) provides a powerful tool to disentangle and quantify N2O production and reduction processes, yet conventional analytical approaches lack temporal resolution. Here, we present the first long-term application of an off-axis integrated cavity output spectrometer for real-time N2O isotopic analysis at a pilot-scale WWTP over one year of operation. We developed a dynamic dilution system and implemented correction protocols for drift, N2O mole fraction dependence, and gas matrix effects on isotopic results, achieving uncertainties of 0.8 ‰ (δ15Nα), 1.1 ‰ (δ15Nβ), 0.8 ‰ (δ15Nbulk), 0.5 ‰ (δ18O) and 1.1 ‰ (15N site preference). Representative datasets demonstrate the system's capability to (i) identify dominant N2O production pathways under standard WWTP operation, (ii) quantify N2O reduction in relation to dissolved oxygen concentration, and (iii) trace nitrogen transformation during low-level 15N-labelling experiments. Our results indicate nitrifier or heterotrophic denitrification as the main source of N2O, and that N2O reduction efficiency is strongly controlled by oxygen availability. This study highlights the potential of laser spectroscopy for continuous isotopic monitoring in real-world engineered systems and provides practical guidelines for uncertainty reduction and data interpretation. More specifically, our work forms a foundation for further investigations of the operational factors controlling N2O formation and N2O reduction in biological WWTPs and other complex anthropogenically-perturbed settings.
Vertical momentum entrainment is driven by vertical shear and turbulent mixing and plays a key role in the recovery of wind turbine and wind-farm wakes, but remains poorly documented by field measurements. The LOLland offshore Lidar EXperiment (LOLLEX) campaign introduced a novel measurement approach to address this knowledge gap. The primary objective of this campaign was to develop a new atmospheric measurement strategy using Doppler wind lidar technology to better understand atmospheric conditions favourable to enhanced momentum entrainment inside and outside an offshore wind farm. LOLLEX was conducted from September 2022 to August 2023 in Denmark, in and around the Rødsand II wind farm, just south of the island of Lolland. During this campaign, two pulsed Doppler wind lidars, a scanning lidar (WindCube100S) and a lidar wind profiler (WindCubeV2), were deployed onboard a crew transfer vessel (CTV) commuting daily between the harbour and the Rødsand II offshore wind farm. Additionally, a scanning pulsed Doppler wind lidar (Halo Photonics) was mounted on a transformer platform north of the wind farm to perform range height indicator scans across the farm. Horizontal wind speed data were collected up to 300 m above the sea surface by the lidar wind profiler. The scanning lidar on the CTV collected data up to 2.5 km, alternating between the wind profiling mode and vertical stare mode. The latter scan operated at a sampling frequency of 1 Hz and along-beam spatial resolution of 10 m, allowing for the study of the turbulent vertical wind velocity component. The dataset includes several thousand hours of vertical scans. As a result of the moving vessel, many of the observations occurred inside or near the wind farm, providing insight into the near and far wakes of individual and multiple turbines. The potential and limitations of the new measurement strategy are illustrated using four case studies: (1) the observation of a Kelvin-Helmholtz instability above the wind farm, examined further in a companion paper; (2) turbulent mixing propagating downward from the top of the boundary layer; (3) internal atmospheric waves; and (4) wake characterisation inside the wind farm using the range-height indicator scans from the lidar deployed on the platform. This work demonstrates a novel methodology integrating remote sensing with a mobile offshore platform to measure turbulence at unprecedented altitudes. The dataset offers valuable data for wind energy research, boundary-layer meteorology, and further development of atmospheric measurement techniques.
In a two-part series of papers, a climatology of quasi-vertical profiles (QVPs) of polarimetric variables and ice-microphysical retrievals such as ice water content, total number concentration and mean volume diameter is presented. QVPs are generated from plan position indicator scans at 18° elevation angle measured with the X-band radar located in the city of Bonn in western Germany between 2013 and 2023. They have been statistically analysed including error analysis with special emphasis on the characteristics of the melting layer and the dendritic growth layer. This long-term climatology improves the understanding of microphysical processes in stratiform cloud regimes and provides a reference for numerical weather prediction modellers to e.g. advance existing microphysical bulk paramerisation schemes.While part two analyses and discusses the climatology, this first part of the series describes the prior thorough calibration of the radar reflectivity factor (ZH) and the differential reflectivity (ZDR). One method uses the relation between ZHZDRZH. Best fits are determined from simulated ZHZDRZHZDRZDR. Obtained ZHKDP). The successful calibration of ZH−0.50 dB) compared to the offsets obtained from satellite information. Offsets calculated by applying self-consistency relations show larger discrepancies, which favours the suitability of the novel method.
This study investigates hydrometeor fall speeds using a dual-frequency profiling radar operating during the 2005–2006 monsoon season near Darwin, Australia. Our focus is on tropical deep convection where the observations provide a new perspective on hydrometeor fall speeds within and near intense drafts having mixed-phase media. The techniques we employ avoid undue assumptions on the air motion or media distributions, offering a convenient path to estimate bulk radar reflectivity(Z)-weighted hydrometeor fall speed (Vt). While these mixed-phase media estimates are not specific to size or density, they may be replicated by models and are practical for radar-based retrievals that necessitate VtTests performed under rain and snow conditions show comparable performance to disdrometer and other references. The standard deviation of residuals for rain and snow relationships are ≅1≅0.4 m s−1, respectively. In convective core regions aloft, Darwin observations align with existing graupel Vt-Z>1–2 m s−1) for Z<35 dBZ than prior relationships. Breakdowns suggest that Active and Break monsoon conditions favor a similar Vt-ZZ<35 dBZ. Break events also show higher variability in Vt-ZVtZ
Earth's radiation budget (ERB) is an essential climate variable, and its continuous observation from space is critical to understanding long-term climate change. The Clouds and Earth's Radiant Energy System (CERES) has maintained the longest continuous ERB record since 2000, with its follow-on mission, Libera, launching in 2027 with a 5-year prime mission lifetime. Beyond Libera, plans for ERB continuity remain uncertain, increasing the possibility of future gaps in the record. The Compact Total Irradiance Monitor (CTIM) was a 6U CubeSat developed under the NASA In-Space Validation of Earth Science Technologies (InVEST) program to measure total solar irradiance (TSI). Launched in July 2022 and operating until December 2023, CTIM collected climate-quality science data at an uncertainty of 0.017 %. During orbital eclipse, CTIM was pointed in the nadir direction to measure Earth's outgoing longwave emission, exploring the Earth-observing potential of an instrument designed for TSI. These measurements were compared to coincident CERES observations aboard Terra, Aqua, and NOAA-20. Additional adjustment factors for limb darkening were derived from radiative transfer simulations over a variety of scene types and atmospheric conditions and applied to CERES non-nadir viewing observations to better match the CTIM nadir observations and explain some of the variance exhibited between measurements. The resulting comparisons show an overall relative agreement of 1.2 %, within the respective instrument uncertainties. This study demonstrates that leveraging CubeSat technology could complement heritage ERB missions and reduce the risk of future observation gaps. The routine inclusion of Earth-viewing capabilities in future TSI instrument designs represents a natural extension of this work, with the potential to meaningfully contribute to ERB observation continuity and reduce the risk of gaps in future ERB observations from space.
Accurate estimation of deep convective cloud (DCC) top height is essential for reliable parallax correction of geostationary satellite imagery and for constructing homogeneous long-term climatologies from multi-generation meteorological satellite records (e.g. Meteosat MVIRI, SEVIRI, and FCI). This study validates the cloud-top height (CTH) retrieval method of Šoljan et al. (2024), which estimates cloud-top pressure in deep moist convection from satellite-derived 11 µmValidation was conducted in two configurations reflecting polar-orbiting and geostationary viewing geometries. First, approximately 1.7 million DCC collocations from 2007 were used to compare CTH estimates with MODIS retrievals and lidar–radar profiles from CloudSat and CALIPSO. Second, a SEVIRI time series from 2004 to 2024 was evaluated against the operational CLAAS-3 CTH product. Overall, the method underestimated CTH by 2.7 km (18 %) relative to lidar–radar data and by 1.1 km (10 %) relative to CLAAS, with mean absolute errors of 1.5–2.8 km and correlations up to 0.9. Following a simple regression-based bias adjustment, normalized errors decreased to below 7 % and absolute errors fell below 1.2 km.The method was subsequently applied to SEVIRI observations to assess its suitability for parallax correction over the European domain. Despite a ∼10 % underestimation of CTH, the impact on parallax correction was minimal: 84 % of pixel geolocations coincided with those derived from CLAAS-based corrections, increasing to 97 % when CLAAS retrieval uncertainty was accounted for. Differences in DCC frequency remained below 7 % on a monthly scale and below 5 % seasonally. These results demonstrate that the method provides operationally sufficient accuracy for parallax correction of DCCs and supports the development of a homogeneous DCC climatology across all generations of Meteosat satellites and, potentially, other geostationary platforms.
Quantifying and understanding methane emissions of cities are of great importance given their role in current and future mitigation efforts to reduce climate-forcing emissions. However, it remains challenging to routinely and accurately characterize and verify city-scale methane emission inventories. Although previous studies of urban methane emissions have employed a range of emission quantification methods, a simple, efficient, and widely applicable framework for quantifying urban-scale emissions remains lacking. In this study, we describe our development of an advanced mass balance emissions accounting using TROPOspheric Monitoring Instrument (TROPOMI) satellite observations to estimate the orbit-level net bulk (city-level) methane emissions and corresponding emissions uncertainties due to the method. We have tested and demonstrated that the novel integration of hourly-resolved wind data under the planetary boundary layer (PBL) enables a more conceptually-accurate assessment of methane emissions from urban areas than methods that do not consider thermodynamic variability. In addition, we have introduced an upwind-based approach for background determination, in which background methane concentrations were defined using city-adjacent regions along the PBL-pressure-weighted-mean upwind direction. Initial assessments with this approach were tested for three megacities (London, Los Angeles and New York) between 2021 and 2023. Results indicate that existing emission inventories generally underestimate urban methane emissions across all three cities, but with significant inter-annual and inter-city variability. Satellite-derived emissions from 2021 to 2023 range from 5.99 to 11.90 t h−1−1−1
Flux measurements have started to play an important role outside academia in assessing carbon sinks of different ecosystems and land-use types. If natural carbon solutions are to be deployed and monetized in carbon markets, more low-powered and low-cost flux systems should be deployed. There is a growing need for low-cost sensors that still fulfil the requirements for scientific applications. We present a case study that aimed to develop an inexpensive yet precise fast-response carbon dioxide (CO2) and water vapour (H2O) sensor. A working prototype was field-tested against a scientific reference eddy covariance (EC) setup. Special attention was paid to response time, lowered sampling frequency, and auto-calibration related to the temperature. The enclosed-path EC prototype achieved a CO222R2=0.96
We present the first global, data-driven analysis of power plant NO2 plume detectability from space. Using TROPOspheric Monitoring Instrument (TROPOMI) observations (nadir pixel size 3.5–7 km) over 6000 of the world's highest-emitting power plants and hourly Continuous Emissions Monitoring Systems (CEMS) data for 500 US plants, we develop an automated algorithm that labels plumes and attributes them to their sources with 98 % accuracy. For the subsequent detectability analysis, we restrict to plants outside interference zones (at least 20 kmkmNOx emissions in our datasets. We then train a machine learning model to predict plume detectability (the probability of detection given the observation conditions) from meteorological, environmental, sensor, and power-plant variables sampled at the single TROPOMI pixel over each plant (F1 score>0.66, AUC>0.8). Out of 25 variables, we find that NOx emission rate, surface altitude, surface albedo (NO2 window), sensor zenith angle, primary fuel type, and wind speed jointly explain much of the variability in detectability. For US power plants, an hourly NOx emission rate of ≈ 400 kg h−1∼ 50 % detectability, but detectability varies from < 20 % to > 60 % under different combinations of these conditions. These results provide the first empirical quantification of the physical and environmental factors that govern NO2 plume visibility in TROPOMI data, establishing a foundation for models to use similar predictors as auxiliary variables when quantifying emission rates from plume appearance.
Cloud screening algorithms have always been a critical component of Aerosol Robotic Network (AERONET) aerosol optical depth (AOD) Level 1.5 and 2.0 product. The initial cloud screening algorithm in the Version 1 and 2 database was semi-automatic and required involvement of a human analyst to finalize the results. It became fully automatic in Version 3 (V3) due to employing information on the angular shape of sky radiances measured in the aureole (curvature algorithm). Although efficient, the curvature algorithm is threshold based and fails to detect clouds when its parameters are beyond the corresponding pre-determined thresholds. This is especially noticeable at high latitudes where the size of ice crystals in cirrus clouds are sometimes relatively small and therefore comparable in size to aerosols. It is shown that additional information can be extracted from analysis of the smoothness of diurnal variability of sky radiances measured at the 3.3° scattering angle. This measurement is a part of the so-called curvature scan (CCS), which takes measurements from 3 to 7.5° scattering angle with 0.3° steps after each measurement of AOD. The analysis of the diurnal variability of CCS (3.3) for cloud-free conditions shows relatively smooth temporal dependencies, which can be fitted by polynomials with high correlation coefficients while in conditions almost completely dominated by clouds, the temporal variability is completely random. For partially cloudy days, the two main features are observed: relatively smooth aerosol signatures and irregular spikes due to clouds. The new technique is proposed that employs the smoothness of the diurnal variability of the CCS(3.3) scan as a criterion of the cloud free conditions. In the case when both features are present, the idea of the new algorithm is to remove irregular spikes due to clouds while keeping the smooth part due to aerosols intact. The new algorithm detects spikes associated with clouds by comparing magnitudes of CCS(3.3) at neighboring time stamps through calculating their first differences (FD). This algorithm was applied to the CCS(3.3) measurements taken at several AERONET sites. The results were analyzed in terms of net change in Angstrom exponent (AE) as well as number of AOD measurements. The analysis showed the algorithm performs satisfactorily at AERONET sites dominated by fine mode aerosols, however at sites dominated by dust, the algorithm removes a big fraction of cloud-free observations. The issue was corrected by introducing an additional cloud screening parameter. It is based on observation of the different rates in changing of AE with iterations for cloud-free and cloudy conditions with a much higher rate in the former case. The new parameter was selected as a slope of the linear regression between integration number and the value of AE after the corresponding iteration. The algorithm disregards FD algorithm results if the slope is smaller than certain threshold value. Finalizing the FD algorithm threshold setting as well as evaluation of the algorithm performance is done by using independent cloud detection information available from Micro-Pulse Lidar Network (MPLNET) data. The AERONET and MPLNET data were time and space collocated with additional averaging over a one-hour period. The comparison showed that, on average, the FD algorithm outperformed V3 L1.5 by about 0.02 in Matthews Correlation Coefficient (MCC), suggesting
Monitoring volcanic emissions is essential for understanding volcanic processes and predicting eruption dynamics. Remote sensing is the only method that allows safe measurements right before, during, and after eruptions. Current monitoring relies on scattered sunlight, whose essentially unconstrained viewing geometry permits continuous and automated observation. It is, however, mostly limited to the ultraviolet and visible (UV-VIS) spectral ranges by the available sky brightness, restricting observations largely to SO2.Here, we assess the feasibility of constraining volcanic emissions by passive Fourier transform infrared (FTIR) spectroscopy of sky-scattered sunlight in the near-infrared (NIR), where more gases of interest have absorption features. Combining an instrument model for the spectral signal-to-noise ratio (SNR) with an information-content analysis, and incorporating actual measurements to capture the systematic uncertainties inherent to atmospheric total column retrievals, we estimate detection limits for individual trace gas columns. The instrument model accurately reproduces the results of laboratory validation experiments. We use Mount Etna as a representative high-emission volcano. We find that CO2HClHF, whose atmospheric background is low, are detectable within seconds under bright skies and within a few minutes under dark conditions. For these species, a multi-instrument approach makes plume-composition measurements practical: pairing the FTIR with co-aligned UV observations of SO2SO2CO2
The Earth Cloud, Aerosol, and Radiation Explorer (EarthCARE) satellite carries a cloud profiling radar (CPR) designed to observe global cloud properties. In this study, we assess the calibration of CPR reflectivity profiles by comparing them with seven calibrated ground-based cloud radars from the European Aerosol, Clouds and Trace Gases Research Infrastructure (ACTRIS).We compare the statistics of ice cloud reflectivities observed from space and from each ground site. The CPR dataset includes all observations within a 200 km radius of each site, while the ground-based dataset comprises vertical profiles collected during the same time period. For this study, the CPR and ACTRIS data were compared on a period spanning from July 2024 to November 2025. By analysing the differences in reflectivity statistics, we estimate the calibration bias between CPR and each site. To ensure robustness, we implement a method to select height bins with comparable reflectivity statistics, excluding uncorrelated observations that could contaminate the results. The reliability of our bias estimates is validated through closure: each ground radar has been calibrated using the same reference, and the independently derived space-versus-ground biases obtained across sites are consistent. Our methodology also provides uncertainty estimates for the reflectivity biases and explores the time sampling required for reliable comparisons.Based on the comparisons from the seven ground-sites, we find that the bias in the EarthCARE L2a reflectivity product is of
Accurate correction of high-frequency attenuation remains a critical challenge in eddy-covariance measurements of turbulent gas fluxes. Experimental correction approaches based on either power spectra or co-spectra are widely used, yet their relative behaviour and implications for flux estimates have not been systematically assessed across sites and atmospheric conditions.This study presents a multi-site comparison of these two approaches using one year of CO2H2OOverall, the ICOS setup exhibited limited high-frequency attenuation, with correction factors generally below 1.2 for CO2H2OCO2, differences between methods remained small after standard turbulence filtering, with cumulative flux differences typically below 2 %. In contrast, H2OCO2H2OThe comparison highlights methodological limitations of both approaches. Both methods rely on assumptions regarding spectral similarity, but departures from these assumptions were found to affect gas spectra much more strongly than co-spectra, particularly for H2OA comparison with fluxes produced by the ICOS Ecosystem Thematic Centre revealed systematic differences that were often larger than those observed between the spectral and co-spectral approaches themselves, especially for H2O. These results demonstrate the effectiveness of setup standardisation across the ICOS network while identifying high-frequency attenuation correction as a remaining source of uncertainty. They also highlight the need for a broader reassessment of this processing step within the ICOS flux-processing pipeline.
This study presents a newly developed balloon-borne AeroSonde system equipped with calibrated low-cost optical particulate matter sensors (SPS30) for vertically resolved aerosol optical measurements. Calibration of the sensors against reference instruments (Aurora 4000 nephelometer and Laser Aerosol Spectrometer (LAS) 3340) enabled the estimation of scattering coefficients (ASC), scattering Ångström exponent (SAE), and effective particle radius (Reff), with Pearson correlation coefficients exceeding 0.88. The best agreement was obtained for ASC, while SAE and ReffThe AeroSondes were deployed for the first time during measurement campaigns in June and August 2025 over southeastern Poland. Soundings with vertical resolution below 10 m reached altitudes of 10–27 km a.g.l. during episodes of long-range transport, including North American biomass-burning (BB) plumes and Saharan dust intrusions. The vertical distribution of aerosol layers observed by the AeroSonde is consistent with active remote sensing measurements, showing good agreement in both the optical layering structure and the absolute values of extensive aerosol properties, with correlation coefficients ranging from 0.74 to 0.81. The extended vertical sounding range allows the estimation of column-integrated quantities, such as aerosol optical depth, as well as their contributions from individual layers, for example, within the planetary boundary layer.These results demonstrate that low-cost, balloon-borne AeroSonde sensors can provide reliable vertical profiles of aerosol properties, supporting enhanced spatio-temporal coverage and improved evaluation of atmospheric aerosol models.
Accurate retrieval of land surface temperature (Ts) from broadband longwave radiometric measurements is limited by the nonlinear coupling between surface emissivity (ϵ) and temperature, particularly when the available radiative contrast provides insufficient information to distinguish their contributions. A conditioning-controlled framework is presented for jointly retrieving broadband ϵTsL) and downwelling (l) longwave irradiance measurements, without requiring externally prescribed emissivity as a retrieval input. The framework constructs a local population of candidate temporal pairs and combines quasi-steady thermal-state screening, numerical and physical admissibility, explicit irradiance-identifiability testing, uncertainty- and information-aware progressive pair selection, physical-state-dependent reciprocal emissivity treatment, fixed eight-update Newton–Raphson refinement, and correlated uncertainty propagation. Field evaluation used 637 datasets from nine SURFRAD and BSRN stations spanning vegetated, mixed, and bare/desert environments. Five datasets lacked the required finite initial irradiances, leaving 632 usable cases, all of which produced final finite retrievals; 628 reached the stable-centre selection criterion and four required the prescribed fallback pathway. Across the 632 field cases, retrieved Ts−0.162 K, mean absolute error (MAE) of 0.432 K, root-mean-square error (RMSE) of 0.558 K, and R2=0.9984ϵtrueTs,true
The surface moisture flux is a large term in the surface energy balance and difficult to estimate remotely. The main difficulty for its remote estimation is a poor ability to measure near-surface humidity. Current methods to retrieve near-surface specific humidity approach the problem statistically and have errors of approximately 1 g kg−14A field campaign (ElUcidating the RolE of Cloud-Circulation Coupling in Climate), we demonstrate that remote sensing measurements of cloud base height can provide useful estimates of near-surface humidity over convective oceanic regions where optically-thick clouds do not prevent lidar sampling. First applying the method to 171 coincident radiosonde and ceilometer pairings collected from a research vessel from 18 January to 14 February 2020 yields skillful predictions of near-surface specific humidity regarding the mean (mean bias 0.43 g kg−1r=0.76). We then apply this method using an airborne lidar to estimate cloud base height from above. In two representative case studies, we find similar skill in the predicted humidity, with low mean biases (0.04 and −0.06 g kg−1r=0.61r=0.57, respectively). Besides estimates of cloud base height, we highlight two main error sources: (i) the relative humidity lapse rate below cloud base and (ii) the temperature difference between the sea surface and near-surface air, which would need to be calibrated if using this method to develop an operational product to estimate the near-surface specific humidity from downward-looking spaceborne lidar. This proof of concept raises the potential for application over convective oceanic regions where lidar sampling of cloud base is possible. This method could provide a physics-based augmentation to existing, more empirical approaches and therefore provide an additional observational constraint on the surface energy budget.