Aircraft Measurements Research Articles

Derivation of the carbon dioxide total column in the atmosphere from satellite-based infrared fourier-transform spectrometer IKFS–2 measurements: analysis and application experience

The paper discusses the use of a new version of the regression technique for derivation the total content of carbon dioxide in the atmosphere XCO2 (column-averaged dry-air mole fraction) from measurements of the infrared Fourier-transform spectrometer IKFS–2 installed on board Russian meteorological satellite Meteor-M No. 2. To evaluate the accuracy of satellite-based XCO2 estimates the retrospective comparison was made with data from ground-based spectroscopic measurements at Peterhof site of St. Petersburg State University as well as with aircraft measurements of the V. E. Zuev Institute of Atmospheric Optics (IOA) in the area of the Novosibirsk Reservoir conducted in 2019-2022. A brief description of the regression technique modifications is given made to improve the accuracy of satellite – based XCO2 estimates. In particular, to compensate for the effect of changes in the IKFS-2 characteristics during a long flight, the XCO2 estimates calibration is realized using ground - based XCO2 measurements at the NOAA Observatory on Mauna Loa volcano (island of Hawaii). After calibration and cloud scenes filtering, the discrepancy between satellite estimates and ground-based / aircraft measurements is characterized by root mean square deviation of ~4 ppm or 1% of the CO2 total content. In order to accelerate the adjustment of the regression technique, used for estimating XCO2, to IKFS-2 data on new satellites, it is reasonable to use XCO2 observations at the TCCON terrestrial network in addition to conventional contact measurements of CO2 concentrations. Along with this, it seems rational to use the cryogenic film thickness on the glass of the IKFS-2 photodetector, characterizing the state of the instrument, as additional predictor in the regression model.

Isoprene nitrates drive new particle formation in Amazon’s upper troposphere

New particle formation (NPF) in the tropical upper troposphere is a globally important source of atmospheric aerosols1, 2, 3–4. It is known to occur over the Amazon basin, but the nucleation mechanism and chemical precursors have yet to be identified2. Here we present comprehensive in situ aircraft measurements showing that extremely low-volatile oxidation products of isoprene, particularly certain organonitrates, drive NPF in the Amazonian upper troposphere. The organonitrates originate from OH-initiated oxidation of isoprene from forest emissions in the presence of nitrogen oxides from lightning. Nucleation bursts start about 2 h after sunrise in the outflow of nocturnal deep convection, producing high aerosol concentrations of more than 50,000 particles cm−3. We report measurements of characteristic diurnal cycles of precursor gases and particles. Our observations show that the interplay between biogenic isoprene, deep tropical convection with associated lightning, oxidation photochemistry and the low ambient temperature uniquely promotes NPF. The particles grow over time, undergo long-range transport and descend through subsidence to the lower troposphere, in which they can serve as cloud condensation nuclei (CCN) that influence the Earth’s hydrological cycle, radiation budget and climate1,4, 5, 6, 7–8.

Early Season 2023 Wildfires Generated Record‐Breaking Surface Ozone Anomalies Across the U.S. Upper Midwest

AbstractDuring summer 2023 Canada experienced its most intense wildfire season on record. Smoke plumes from these fires advected across the United States (U.S.) Upper Midwest, producing regional scale surface enhancements of PM2.5 and ozone, as recorded by the U.S. surface monitoring network. These events are notable because they occurred early in the fire season (May 15‐June 30), and they produced the highest regional‐scale surface ozone levels ever recorded across the northern tier of the U.S. during early (May–June) or late (July‐August) summer. Specifically, the Upper Midwest 50th ozone percentile was greater than in any other year since 1995, when the ozone monitoring network had sufficient coverage to assess regional‐scale ozone levels; the 90th percentile was the highest since 2002. Satellite and aircraft measurements demonstrate the availability of ozone precursors and ozone production within the smoke plumes.

Interpreting summertime hourly variation of NO2 columns with implications for geostationary satellite applications

Abstract. Accurate representation of the hourly variation in the NO2-column-to-surface relationship is essential for interpreting geostationary observations of NO2 columns. Previous research indicated inconsistencies in this hourly variation. This study employs the high-performance configuration of the GEOS-Chem model (GCHP) to analyze daytime hourly NO2 total columns and surface concentrations during summer. We use measurements from globally distributed Pandora sun photometers and aircraft observations over the United States. We correct Pandora total NO2 vertical columns for (1) hourly variations in effective temperature driven by vertically resolved contributions to the total column and (2) changes in local solar time along the Pandora line of sight. These corrections increase the total NO2 columns by 5–6 × 1014 molec. cm−2 at 09:00 and 18:00 across all sites. Fine-scale simulations from GHCP (∼12 km) reduce the normalized bias (NB) against Pandora total NO2 columns from 19 % to 10 % and against aircraft measurements from 25 % to 13 % in Maryland, Texas, and Colorado. Similar reductions are observed in NO2 columns over the eastern US (17 % to 9 %), the western US (22 % to 14 %), Europe (24 % to 15 %), and Asia (29 % to 21 %) when compared to 55 km simulations. Our analysis attributes the weaker hourly variability in the total NO2 column to (1) hourly variations in column effective temperature, (2) local solar time changes along the Pandora line of sight, and (3) differences in hourly NO2 variability from different atmospheric layers, with the lowest 500 m exhibiting greater variability, while the dominant residual column above 500 m exhibits weaker variability.

Enhancing Fine Aerosol Simulations in the Remote Atmosphere with Machine Learning

Global aerosol models often underestimate the mass concentration of aerosols in the remote troposphere, as evidenced by aircraft measurements. This study leveraged data from the NASA Atmospheric Tomography Mission (ATom), which provides remote aerosol concentrations, to refine algorithms for simulating these concentrations. Using the GEOS-Chem model, we simulate five fine aerosol types and enhance the simulation results using five machine-learning algorithms: Random Forest, XGBoost, SVM, KNN, and LightGBM, and compare the performance of these algorithms. Additionally, we evaluate the refinement effect of algorithms based on decision trees on a validation dataset. The results demonstrate that GEOS-Chem generally underestimated aerosol mass concentration. Among the tested algorithms, algorithms based on decision trees, particularly the Random Forest algorithm and the LightGBM algorithm, exhibited a superior performance, significantly improving prediction accuracy and computational efficiency in both the training and testing phases, as well as on the validation dataset.

Age of air from in situ trace gas measurements: insights from a new technique

Abstract. The age of air is an important transport diagnostic that can be derived from trace gas measurements and compared to global chemistry climate model output. We describe a new technique to calculate the age of air, measuring transport times from the Earth's surface to any location in the atmosphere based on simultaneous in situ measurements of multiple key long-lived trace gases. The primary benefits of this new technique include (1) optimized ages of air consistent with simultaneously measured SF6 and CO2; (2) age of air from the upper troposphere through the stratosphere; (3) estimates of the second moment of age spectra that have not been well constrained from measurements; and (4) flexibility to be used with measurements across multiple instruments, platforms, and decades. We demonstrate the technique on aircraft and balloon measurements from the 1990s, the last period of extensive stratospheric in situ sampling, and several recent missions from the 2020s, and compare the results with previously published and modeled values.

Peering into Cloud Physics Using Ultra-Fine-Resolution Radar and Lidar Systems

Abstract Cloud microphysical processes, such as droplet activation, condensational growth, and collisional growth, play a central role in the evolution of clouds and precipitation. Accurate representations of these processes in numerical models are challenging partially due to incomplete understanding of them at the process level arising from limited systematic observations. Most surface-based active remote sensors, including today’s operational cloud radars and lidars, have a resolution on the order of tens of meters. This resolution is insufficient to resolve cloud microphysical processes that manifest at finer (meter and submeter) scales. A new set of ultra-high-resolution ground-based radar and lidar systems have been developed to address this observational gap. The newly developed 94-GHz cloud radar has a range resolution down to 2.8 m, or a factor of 10 finer than typical radars, using a large bandwidth and quadratic phase coding techniques. The lidar has a range resolution down to 10 cm, or a factor of 100 finer than typical lidars, using a time-gated time-correlated single-photon-counting technique. Such high-resolution observations were previously only achievable through in situ aircraft measurements. Even then, aircraft measurements do not permit continuous long-term cloud observation as is possible with ground-based remote sensing instruments. In this study, the first-light cloud observations from the new radar and lidar systems are shown to reveal detailed cloud structures that conventional sensors could only perceive in a bulk sense, thus providing new avenues to investigate cloud microphysical processes and their impact on weather and climate.

Quantifying NO x Emission Sources in Houston, Texas Using Remote Sensing Aircraft Measurements and Source Apportionment Regression Models.

Air quality managers in areas exceeding air pollution standards are motivated to understand where there are further opportunities to reduce NO x emissions to improve ozone and PM2.5 air quality. In this project, we use a combination of aircraft remote sensing (i.e., GCAS), source apportionment models (i.e., CAMx), and regression models to investigate NO x emissions from individual source-sectors in Houston, TX. In prior work, GCAS column NO2 was shown to be close to the "truth" for validating column NO2 in model simulations. Column NO2 from CAMx was substantially low biased compared to Pandora (-20%) and GCAS measurements (-31%), suggesting an underestimate of local NO x emissions. We applied a flux divergence method to the GCAS and CAMx data to distinguish the linear shape of major highways and identify NO2 underestimates at highway locations. Using a multiple linear regression (MLR) model, we isolated on-road, railyard, and "other" NO x emissions as the likeliest cause of this low bias, and simultaneously identified a potential overestimate of shipping NO x emissions. Based on the MLR, we modified on-road and shipping NO x emissions in a new CAMx simulation and increased the background NO2, and better agreement was found with GCAS measurements: bias improved from -31% to -10% and r2 improved from 0.78 to 0.80. This study outlines how remote sensing data, including fine spatial information from newer geostationary instruments, can be used in concert with chemical transport models to provide actionable information for air quality managers to identify further opportunities to reduce NO x emissions.

Inter-annual changes in transboundary air quality from KORUS-AQ 2016 to SIJAQ 2022 campaign periods and assessment of emission reduction strategies in Northeast Asia

Northeast Asia suffers from high concentrations of particulate matter (PM), prompting nations to actively implement emission reduction policies. This study evaluated the recent inter-annual changes in the chemical transformation and transboundary transport of PM over Northeast Asia, based on both ground and aircraft measurements, as well as WRF-Chem simulations, during two comprehensive campaigns: the Korea–United States Air Quality (KORUS-AQ) campaign in 2016 and the Satellite Integrated Joint Monitoring of Air Quality (SIJAQ) campaign in 2022, both conducted around the Korean Peninsula. Ground measurements in 2022 revealed significant reductions in air pollutants compared to 2016 levels. In the Beijing–Tianjin–Hebei region, PM2.5 and SO2 concentrations decreased by 47.2% and 73.9%, respectively, attributable to successful SOx and NOx emission reduction strategies. Similar trends were observed in downwind areas, including Seoul, where PM2.5 and SO2 levels declined by 30.0% and 41.4%, respectively. WRF-Chem model results indicated substantial decreases in sulfates, nitrates, and their precursors in both surface and upper atmosphere in 2022 compared to 2016. Moreover, model-calculated gas-to-particle conversion ratios, which peaked in the Yellow Sea in 2016, decreased by 15% in 2022 and shifted slightly eastward to the western Korean Peninsula. This shift suggests a potential decline in secondary PM formation processed in the Yellow Sea, coinciding with reduced long-range transport of gaseous pollutants. A comparison of model sensitivity experiments, accounting for both bottom-up emission changes and meteorological variations, revealed that while weather and climate factors such as precipitation and pressure patterns between 2016 and 2022 contributed to the overall decrease in PM concentrations, the primary driver was the reduction in emissions during this period. This study highlighted that the main driver of the substantial improvement in air quality over East Asia was the implementation of emission reduction policies targeting PM and its precursors in the main source regions in China.

Evaluation of the Aggregation Efficiency Modeling at Colder Atmospheric Temperatures in Comparison to Satellite Observations

Abstract Aggregation efficiency in the upper troposphere is highly uncertain because of the lack of laboratory experiments and aircraft measurements, especially at atmospheric temperatures below −30°C. Aggregation is physically broken down into collision and sticking. In this study, theory-based parameterizations for the collision efficiency and sticking efficiency are newly implemented into a double-moment bulk cloud microphysics scheme. Satellite observations of the global ice cloud distribution are used to evaluate the aggregation efficiency modeling. Sensitivity experiments of 9-day global simulations using a high-resolution climate model show that the use of collision efficiency parameterization causes a slight increase in the cloud ice amount above the freezing level over the tropics to midlatitudes and that the use of our new sticking efficiency parameterization causes a significant increase in the cloud ice amount and a slight decrease in the snow amount particularly in the upper troposphere over the tropics. The increase/decrease in the cloud ice/snow amount in the upper troposphere over the tropics is consistent with the vertical profile of radar echoes. Moreover, the ice fraction of the cloud optical thickness is still underestimated worldwide. Finally, the cloud radiative forcing increases over the tropics to reduce the bias in the radiation budget. These results indicate that our new aggregation efficiency modeling reasonably functions even at atmospheric temperatures below −30°C; however, further improvements in the ice cloud modeling are needed. Significance Statement Long-standing biases in the radiative budget in climate models indicate the existence of a missing mechanism to realistically represent the ice cloud growth in the upper troposphere. This study focuses on aggregation efficiency, which has been assumed to be a tuning parameter to optimize the global radiative budget. Therefore, this study employs a theory-based parameterization to calculate the aggregation efficiency. According to the parameterization, aggregation efficiency in high clouds varies by the growth stage of the individual ice particles. As a result, small ice crystals are likely to grow more slowly, and the lifetime of cirrus clouds is prolonged to enhance cloud radiative forcing, particularly over the tropics. These results are promising for reducing the biases observed in climate models.

Relationship between vertical variation of cloud microphysical properties and thickness of the entrainment interfacial layer in Physics of Stratocumulus Top stratocumulus clouds

AbstractThis study examines the vertical variations of cloud microphysics and their correlation with the thickness of the entrainment interfacial layer (EIL) in stratocumulus clouds, observed in the Physics of Stratocumulus Top (POST) aircraft measurement campaign. From the mixing fraction analysis, we identified EIL between the free atmosphere and cloud top for all 15 POST flights, and found that EIL thickness significantly influenced the vertical variation of cloud microphysics and thermodynamics. In several flights, a trend toward stronger homogeneous mixing traits with increasing depth from the cloud top was found, indicative of the vertical movement of mixed (i.e., entrainment‐affected and diluted) parcels. However, in one flight, this trend was limited to the middle part of the cloud only, with the correlation between virtual potential temperature and liquid water content being strongly negative near the cloud top, suggesting limited downward movement of mixed parcels. Another important finding is that there was a robust negative correlation between long‐wave cooling rate near the cloud top and EIL thickness, highlighting differences in radiative cooling rates between mixed and unmixed parcels due to differences in liquid water content between them. These insightful findings will be crucial for enhancing our understanding of the role of EIL in modulating entrainment and the vertical movement of mixed parcels in stratocumulus clouds.

Source Levels of In‐Cloud Air in Shallow Cumulus: Consistency Between Paluch Diagram and Lagrangian Particle Tracking

AbstractThe Paluch diagram is a widely used tool for interpreting aircraft measurements of shallow cumulus clouds. A prior study conducted by Heus et al. (2008, https://doi.org/10.1175/2008jas2572.1) concluded that the source levels of in‐cloud air inferred from the Paluch diagram exhibit biases, sometimes of several hundred meters, in comparison to those derived from Lagrangian particle tracking. In this short study we revisit this comparison. The results indicate that the upper source levels of in‐cloud air determined from the Lagrangian Particle Tracking and the Paluch diagram are consistent, and the choice of statistical methods is crucial. The significance of this research lies in confirming the reliability of the Paluch analysis, enabling its confident application to aircraft data.

Characterization of multi-rotor aircraft using truss support structure

Characterization of multi-rotor drone aircraft noise is vital to expanded use within the United States. To provide compatibility with current propagation and perception models, acoustic noise characterization focuses on the creation of equivalent lower hemispheres, and application of methods developed for manned aircraft for description of the noise directivity patterns. But a previous measurement determined that a refinement of these methods was required for the application to drone noise. One of these refinements was the use of a larger truss support rig, which was selected to provide sufficient clearance for the aircraft. The rig also provided regular holes where the microphones’ support arms could be mounted; the rigid structure possessed no flat faces and minimal cylindrical truss structures to minimize scattering of reflected signals. In addition to the lighting rig array, a number of additional microphones were placed along the flight path, and in-plane but outside of the measurement array for validation of the acoustic sources. With this improved array, another measurement of a multi-rotor aircraft was conducted, equivalent acoustic sources constructed, and validation performed. This measurement validates the use of the square array to characterize these types of aircraft.

How well can persistent contrails be predicted? An update

Abstract. The total aviation effective radiative forcing is dominated by non-CO2 effects. The largest contributors to the non-CO2 effects are contrails and contrail cirrus. There is the possibility of reducing the climate effect of aviation by avoiding flying through ice-supersaturated regions (ISSRs), where contrails can last for hours (so-called persistent contrails). Therefore, a precise prediction of the specific location and time of these regions is needed. But a prediction of the frequency and degree of ice supersaturation (ISS) on cruise altitudes is currently very challenging and associated with great uncertainties because of the strong variability in the water vapour field, the low number of humidity measurements at the air traffic altitude, and the oversimplified parameterisations of cloud physics in weather models. Since ISS is more common in some dynamical regimes than in others, the aim of this study is to find variables/proxies that are related to the formation of ISSRs and to use these in a regression method to predict persistent contrails. To find the best-suited proxies for regressions, we use various methods of information theory. These include the log-likelihood ratios, known from Bayes' theorem, a modified form of the Kullback–Leibler divergence, and mutual information. The variables (the relative humidity with respect to ice, RHiERA5; the temperature, T; the vertical velocity, ω; the divergence, DIV; the relative vorticity, ζ; the potential vorticity, PV; the normalised geopotential height, Z; and the local lapse rate, γ) come from ERA5, and RHiM/I, which we assume as the truth, comes from MOZAIC/IAGOS (Measurement of Ozone and Water Vapour on Airbus In-service Aircraft/In-service Aircraft for a Global Observing System; commercial aircraft measurements). It turns out that RHiERA5 is the most important predictor of ice supersaturation, in spite of its weaknesses, and all other variables do not help much to achieve better results. Without RHiERA5, a regression to predict ISSRs is not successful. Certain modifications of RHiERA5 before the regression (as suggested in recent papers) do not lead to improvements of ISSR prediction. Applying a sensitivity study with artificially modified RHiERA5 distributions points to the origin of the problems with the regression: the conditional distributions of RHiERA5 (conditioned on ISS and non-ISS, from RHiM/I) overlap too heavily in the range of 70 %–100 %, so for any case in that range, it is not clear whether it belongs to an ISSR or not. Evidently, this renders the prediction of contrail persistence very difficult.

Chasing parts in quadrillion: applications of dynamical downscaling in atmospheric pollutant transport modelling during field campaigns

Atmospheric transport and dispersion models (ATDMs) are widely used to study and forecast pollution events. In the frame of the “Effect of Megacities on the transport and transformation of pollutants on the regional to global scales” (EMeRGe) project, ATDM forecasts were carried out to identify potential airborne sampling areas of perfluorocarbons (PFCs) emanating from controlled PFC releases. The forecasts involved short-distance transport over small-scale topographic maxima (Manilla; Philippines), short-distance transport over large-scale topographic maxima (Taipei, Taiwan) and long-distance transport over mixed topography (Nanjing, China, sampled over Taiwan). In situ aircraft measurements of PFC mixing ratios down to a few parts per quadrillion (ppqv) provide us with a unique dataset to explore the added benefits of dynamical downscaling. Transport simulations were repeated using FLEXPART driven by ERA5 and IFS meteorological data and FLEXPART-WRF with dynamically downscaled IFS data down to 1.1 km and four PBL parametrisations. Of the three cases studied, dynamical downscaling led to significant differences for the Manilla and Taipei releases that can be interpreted through changes in the modelled orographic flow regimes. The choice of PBL scheme also significantly impacted accuracy, but there was no systematically better-performing option, highlighting the benefits of ensemble forecasting. Results show how convergence and divergence between ensemble members can be utilised to help decision-making during field campaigns. This study highlights the role that dynamical downscaling can play as an important component in campaign planning when dealing with observations over orographically complex areas.

More Frequent Spaceborne Sampling of XCO2 Improves Detectability of Carbon Cycle Seasonal Transitions in Arctic‐Boreal Ecosystems

AbstractSurface, aircraft, and satellite measurements indicate pervasive early cold season (Augut–September) CO2 emissions across Arctic regions, consistent with increased ecosystem metabolism in plants and soils. A key remaining question is whether cold season sources will become large enough to permanently shift the Arctic into a net carbon source. Polar orbiting GHG satellites provide robust estimation of regional carbon budgets but lack sufficient spatial coverage and repeat frequency to track sink‐to‐source transitions in the early cold season. Mission concepts such as the Arctic Observing Mission (AOM) advocate for flying imaging spectrometers in highly elliptical orbits (HEO) over the Arctic to address sampling limitations. We perform retrieval and flux inversion simulation experiments using the AURORA mission concept, leveraging a Panchromatic imaging Fourier Transform Spectrometer (PanFTS) in HEO. Our simulations demonstrate the potential benefits of increased CO2 sampling for detecting emissions during the early cold season.

Characterisation of particle single-scattering albedo with a modified airborne dual-wavelength CAPS monitor

Abstract. Atmospheric aerosols impact the Earth's climate system directly by scattering and absorbing solar radiation, and it is important to characterise the aerosol optical properties in detail. This study reports the development and validation of an airborne dual-wavelength cavity-attenuated phase-shift (CAPS) single monitor, named A2S2 (Aerosol Absorption Spectral Sizer), based on the commercial CAPS single-scattering albedo monitor (CAPS-PMSSA; Aerodyne), to simultaneously measure the aerosol optical scattering and extinction at both 450 and 630 nm wavelengths. Replaced pressure and temperature sensors and an additional flow control system were incorporated into the A2S2 for its utilisation on board research aircraft measuring within the troposphere. The evaluation of A2S2 characteristics was performed in the laboratory and included the investigation of the signal-to-noise ratio, validation of performance at various pressure levels, optical closure studies and intercomparing with the currently validated techniques. The chamber experiments show that the A2S2 can perform measurements at sample pressures as low as 550 hPa and at sample temperatures as high as 315 K. Based on the Allan analysis results, we have evaluated that the minimum detection limit of the measurements shows that the measurements have a limit accuracy of ∼ 2 Mm−1 at 450 nm and ∼ 1 Mm−1 at 630 nm for 1 Hz measurements of both scattering coefficients (σsca) and extinction coefficients (σext). The optical closure study with size-selected polystyrene latex (PSL) particles shows that the truncation error of the A2S2 is negligible for particles with particle volume diameter (Dp) < 200 nm, while, for the larger sub-micrometre particles, the measurement uncertainty of A2S2 increases but remains less than 20 %. The average factors to correct the truncation error are 1.13 and 1.05 for 450 and 630 nm, respectively. A simplified truncation correction, dependent on the scattering Ångström exponent (SAE), was developed to rectify truncation errors of the future A2S2 field measurement data. The σsca and σext measured by the A2S2 show good agreement with the concurrent measured results from the nephelometer and the CAPS particle extinction monitor (CAPS-PMex). The absorption coefficient (σabs) derived through the extinction-minus-scattering (EMS) method by the A2S2 also corresponds with the results obtained from the aethalometer. The A2S2 was successfully deployed during an aircraft measurement campaign (Atmospheric ChemistRy Of the Suburban foreSt – ACROSS) conducted in the vicinity of Paris and the surrounding regions. The average SSA measured during the entire ACROSS flight campaign is 0.86 and 0.88 at 450 and 630 nm, respectively, suggesting that light-absorbing organic aerosols play a significant role. The average SAE and absorption Ångström exponent (AAE) varied due to measurements in various pollution conditions. The results presented in this study indicate that the A2S2 instrument is reliable for measuring aerosol σsca and σext at both blue and red wavelengths, and it stands as a viable substitute for future airborne evaluations of aerosol optical properties.

Improving Supercooled Water Forecast Through Real‐Time Aerosol Input: A Case Study

AbstractAccurately simulating and forecasting supercooled water has been crucial for addressing the threat of aircraft icing. This study utilized the Weather Research and Forecast model and Aerosol‐Aware Thompson‐Eidhammer microphysics (TE14) scheme to simulate supercooled water properties. Three high‐resolution experiments were conducted to explore the impact of increased model resolution and different aerosol initial conditions. The synoptic‐scale characteristics and cloud extent were well‐matched between observations and the model simulation, which established a background for supercooled water comparisons. Microphysical characteristics of supercooled droplets and the formation of supercooled large drops demonstrated the TE14 scheme's reasonable simulation of supercooled water microphysics under different aerosol loadings. In situ aircraft measurements were used to validate the simulated supercooled water properties. Results highlighted the sensitivity of supercooled water simulation to aerosol number concentration via droplet activation in the scheme. The choice for the aerosol initial condition could result in a significant underestimation or overestimation of supercooled water number concentration, consequently leading to biased estimations of droplet size. Incorporating real‐time forecast aerosol data improved the simulation by increasing cloud droplet number concentration and reducing droplet size. These findings underscored the importance of aerosols via droplet activation in accurately simulating cloud water properties and emphasized their role in forecasting aircraft icing hazards.

Arctic Sea Ice Albedo Estimation from Fengyun-3C/Visible and Infra-Red Radiometer

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

Deep Convective Microphysics Experiment (DCMEX) coordinated aircraft and ground observations: microphysics, aerosol, and dynamics during cumulonimbus development

Abstract. Cloud feedbacks associated with deep convective anvils remain highly uncertain. In part, this uncertainty arises from a lack of understanding of how microphysical processes influence the cloud radiative effect. In particular, climate models have a poor representation of microphysics processes, thereby encouraging the collection and study of observation data to enable better representation of these processes in models. As such, the Deep Convective Microphysics Experiment (DCMEX) undertook an in situ aircraft and ground-based measurement campaign of New Mexico deep convective clouds during July–August 2022. The campaign coordinated a broad range of instrumentation measuring aerosol, cloud physics, radar, thermodynamics, dynamics, electric fields, and weather. This paper introduces the potential data user to DCMEX observational campaign characteristics, relevant instrument details, and references to more detailed instrument descriptions. Also included is information on the structure and important files in the dataset in order to aid the accessibility of the dataset to new users. Our overview of the campaign cases illustrates the complementary operational observations available and demonstrates the breadth of the campaign cases observed. During the campaign, a wide selection of environmental conditions occurred, ranging from dry, northerly air masses with low wind shear to moist, southerly air masses with high wind shear. This provided a wide range of different convective growth situations. Of 19 flight days, only 2 d lacked the formation of convective cloud. The dataset presented (https://doi.org/10.5285/B1211AD185E24B488D41DD98F957506C; Facility for Airborne Atmospheric Measurements et al., 2024) will help establish a new understanding of processes on the smallest cloud- and aerosol-particle scales and, once combined with operational satellite observations and modelling, can support efforts to reduce the uncertainty of anvil cloud radiative impacts on climate scales.