Abstract Airborne multi-frequency radars provide valuable information regarding the size, density, and shape of ice hydrometeors. Higher values of the dual-frequency reflectivity ratio (DFR), for instance, are often associated with the presence of larger ice crystals with potential implications for snowfall at the surface. The three-year Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign aimed to address the linkage between airborne remote sensing and in situ microphysics by collecting data obtained through the close coordination of a “satellite-simulating” ER-2 and “storm-penetrating” P-3 aircraft. In regions of prominently higher Ku- and Ka-band DFR along the P-3 flight track, the solid-phase mass-weighted mean diameter (D m ) was 66% larger, the liquid-equivalent normalized intercept parameter (N w ) 123% lower, the effective density (ρ e ) 73% smaller, and the ice water content (IWC) 46% higher for the 21 events studied. Use of a pre-existing neural network radar retrieval allowed for the vertical structure of microphysical properties to be compared to the larger DFR signatures, with similar conclusions valid for D m and N w at all altitudes studied. Analysis of the in-situ microphysics data and the radar retrieval complement the relationship between bulk microphysical properties and multi-frequency radar signatures that allow for microphysical processes such as aggregation and deposition to be inferred for different environments (e.g., at particular temperature ranges, or depths below cloud top).

Abstract. Recent aircraft measurements in stratocumulus clouds suggest that entrainment mixing is inhomogeneous (IM) near cloud top and homogeneous (HM) within the cloud. However, this proposed height-dependence of mixing transition is uncertain because of artifacts involved in the aircraft measurements. In this study, we use the Explicit Mixing Parcel Model to simulate mixing scenarios in stratocumulus clouds and reconstruct the virtual aircraft measurements to investigate the mixing signature. Results show that, from the aircraft-measurement perspective, the mixing signature always exhibits IM characteristic near cloud top and HM characteristic within cloud, independent of the types of the local entrainment-mixing process. The appearance of the vertical IM-to-HM transition is essentially a collective behavior of multiple parcels sampled at the same height, experiencing distinct entrainment-mixing-evaporation histories. This bulk view of mixing process, which is widely used for aircraft measurements, could lead to misinterpretations of the true mixing mechanism occurring in clouds. Our result underscores the limitations of using aircraft measurements to identify the entrainment-mixing mechanism at the process level.

Abstract Understanding Earth's climate sensitivity remains a critical challenge for improving climate projections. Discrepancies between recent and previous generations of climate models highlight the need for better representation of low‐level clouds over the Southern Ocean (SO)—a region with unique modeling challenges due to complex interactions among dynamics, microphysics, and radiation, compounded by a historical lack of in situ observations. This study evaluates the performance of a convection‐permitting WRF model configuration during post‐frontal conditions, using recent field campaign data and satellite observations for validation. Our results show that the simulation effectively reproduces the key morphological distinctions between closed and open mesoscale cellular convective clouds (MCC), aligning with both in situ and remote sensing data. The simulation captures the higher cloud cover in closed MCC regions (65%) with lower cloud top heights (1.3 km), compared to open MCC regions (46%) with higher cloud tops (2.3 km), consistent with field measurements. Additionally, the simulation reproduces qualitative differences in ice production and precipitation frequency between closed and open MCCs. However, particularly at higher latitudes, the simulation underestimates cloud cover and significantly underrepresents precipitation and ice production, likely due to limitations in the representation of ice production mechanisms. This research also sets the stage for further analysis of the processes driving the mesoscale organization and transitions between open and closed MCC states (Part II). The combined use of field observations, satellite data, and convection‐permitting simulations provides a comprehensive framework for advancing our understanding of these cloud systems and their role in climate sensitivity.

Cloud optical properties and precipitation, which are crucial to weather and climate, are strongly influenced by cloud microphysical properties that are still poorly understood. Here, we develop a high-resolution time-correlated single-photon-counting lidar and apply it to observe cloud microphysical properties at one-centimeter range resolution in a convection chamber under well-controlled conditions. Together with concurrent in-situ measurements and theoretical analysis, our lidar observations indicate that although turbulent mixing tends to homogenize the cloud in the bulk region, entrainment and sedimentation cause inhomogeneities in droplet concentrations near the cloud top. Specifically, the topmost region is directly affected by entrainment, and lidar profiles show clear evidence of entrained air and detrained cloud filament. The transition region below exhibits vertical size sorting of cloud droplets caused by sedimentation. Our results suggest that using a single sedimentation velocity for all cloud droplets, as is done in many atmospheric models, overlooks key physics relevant to the microphysical structure near the cloud top. Our conceptual model used to describe these measurements can serve as a step toward improving the current modeling of processes in the cloud top region.

Abstract Microphysical measurements within winter storms are commonly analyzed using two-dimensional radar cross sections from airborne vertically pointing radars or ground-based scanning radars. While these radars offer valuable insights, they provide limited insights into the storm’s microphysical characteristics within the context of the storm’s three-dimensional structure. To address this limitation, this analysis uses conically scanning X-band radar data to investigate the three-dimensional structure of a shallow generating cell (GC) driven snowstorm (<6 km deep) sampled over central Illinois and Indiana on 25 February 2020 during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. The observed microphysical properties and reflectivity structures along the nadir-pointing radar cross section represent the superposition of 3D trajectories of fall streaks originating in GCs upwind of the aircraft’s flight track. GCs formed in a potentially unstable layer near cloud top based on HRRR analysis, where supercooled water formed and created a droplet-rich environment for ice crystal nucleation, growth, and fallout. In situ microphysics measurements beneath cloud top allowed for the assessment of particle aspect ratios within and outside of GC fall streaks. When sampled 2–3 km below cloud top, fall streaks typically contained larger ice crystals and aggregates with higher aspect ratios compared to the surrounding cloud, and increased reflectivity in nadir and plan-view scans. The southern end of the storm lacked GCs, was supercooled, and contained smaller, low-aspect-ratio ice crystals in high concentrations. Significance Statement Microphysical characteristics of winter storms are typically analyzed using ground-based plan-position indicator scans from operational radars or two-dimensional radar cross sections from research airborne vertically pointing radars or ground-based scanning radars. While informative, these approaches offer limited insight into a storm’s observed microphysical properties within the context of its three-dimensional structure. To address this limitation, this study examines the three-dimensional structure of a shallow snowstorm driven by cloud-top generating cells using airborne scanning radar data. The analysis reveals that generating cell fall streaks, not evident in the vertical two-dimensional cross section, precipitate through the cross section impacting the observed microphysical characteristics.

Context . Stationary waves play a crucial role in vertically transporting momentum and energy in Venus’s atmosphere. Their global contributions (approximately −0.1 m s −1 day −1 at the upper cloud) are smaller than those of planetary-scale waves and meridional circulation (approximately ±1.0 m s −1 day −1 ), but stationary waves exert strong regional control, shaping the longitudinal structure of the super-rotating flow above highlands. Observations have linked wave signatures near the cloud top (~70 km) to underlying highland regions. However, their vertical propagation characteristics and contributions to the morphology of super-rotation remain poorly understood. Aims . This study aims to characterize the structure, variability, and propagation of stationary waves in Venus’s atmosphere and to evaluate their role in modulating the longitudinal structure of cloud-top super-rotation. Methods . We analyzed eight years of thermal emission data from Akatsuki/LIR to isolate stationary wave components. Simulations were performed using the high-resolution Venus planetary climate model, which incorporates a realistic topography and a hybrid vertical coordinate system. Results . Stationary wave signatures in the brightness temperature and horizontal winds are consistently observed and simulated above highland regions, with a notable local time dependence and long-term variability. The vertical transport of angular momentum and heat dominates the wave-induced momentum and energy budget, leading to zonal wind deceleration and adiabatic heating in the upper cloud layer. Despite filtering by two weak static stability layers in the deep atmosphere, stationary waves can propagate upward and impact cloud-top dynamics. Conclusions . Stationary waves exert a measurable influence on Venus’s upper-cloud super-rotation by vertically redistributing momentum and heat in longitude. Their effects are modulated by both vertical static stability and diurnal variations. These results highlight the crucial role of stationary waves in maintaining the observed longitudinal structure of the super-rotating atmosphere.

Abstract Hailstorms exhibit distinct signatures in spaceborne remote sensing datasets, namely, overshooting cloud tops (OTs) in infrared (IR) imagery or large brightness temperature depressions in passive-microwave radiometer (MWR) imagery. Approaches that leverage these signatures are prone to several issues, e.g., nonuniform beamfilling in MWR datasets or insensitivity to processes below cloud top in IR imagery. Upwelling MWR can also be scattered to extremely low brightness temperatures by high concentrations of graupel-sized ice scatterers. To address this, we investigate Aqua IR, and MWR observations paired with ground-based radar maximum expected size of hail (MESH), and reanalysis environmental parameters over the continental United States. We observe that storms with low (<250 K) 19-GHz brightness temperatures tend to exhibit larger, deeper OTs and a decreasing probability of large MESH with very low (<180 K) 37-GHz brightness temperature. To capture the complex MWR, IR, and environmental parameter interactions, we train a deep neural network (DNN) to estimate severe hail likelihood for a given suite of Aqua observations and reanalysis variables. Leveraging these datasets in combination produces a critical success index of 0.593 and a Heidke skill score of 0.401. The satellite observation analysis and performance assessment using the DNN suggest the relationships between likelihood of large hail (vs smaller hail/graupel) and satellite parameters are complex and nonlinear. The analysis suggests that passive-microwave channels with shorter wavelengths (<1 cm) are vulnerable to scattering from smaller particles, and that hail retrievals mistakenly flag these occurrences as hail, thereby pointing to error sources in previously documented climatologies. Significance Statement Satellites provide the most uniform way to map the distribution of severe weather like hail: They can observe across the globe and provide data in otherwise unreachable locations, like remote areas or over the ocean. For decades, satellite datasets have been used to estimate the global climatology of hail. Two of the most prominent datasets used are infrared (IR) cloud-top products and passive-microwave radiometer (MWR). These approaches are standard but suffer from biases, and this can result in hail being overestimated in the tropics. In this paper, we explore some of those issues to understand where the satellite datasets’ vulnerabilities are and how we might use them together to produce a more accurate result. The relationships we discover turn out to be very complex, so we explore a deep neural network machine learning model to quantify the performance of IR and MWR datasets in detecting likely severe hail over the United States.

Abstract Precipitation occurs frequently in observed shallow convection across the Northern Atlantic Trades. Despite its central role in shaping the water cycle, it remains challenging to quantify how much and when precipitation occurs, and how precipitation shapes its immediate thermodynamic and dynamic environment. Here we make use of the synergy of active profiling remote sensing aboard the RV Maria S. Merian during the Elucidating the Role of Clouds–Circulation Coupling in Climate (A) field study in 2020. We investigate the thermodynamic and dynamic conditions before, during, and after precipitation using a statistical approach. By distinguishing between shallow and congestus profiles according to observed cloud geometrical thickness, we find that congestus clouds occur more frequently than shallow (21% and 9% of profiles), contain three times more liquid water (60 vs. ), and precipitate more often (71% vs. 7%). Shallow clouds, as a precursor stage to congestus, show little variability during the day, while congestus clouds maximize at night. Convection is initiated in shallow clouds located in patches of humidity moister than the clear‐sky state, sometimes coupled with sea‐surface induced changes in the sub‐cloud layer. At the congestus stage, microphysical and thermodynamic processes trigger further cloud and precipitation growth in the cloud layer. In shallow conditions, virga induce a cooling and moistening anomaly in the sub‐cloud layer that fosters cold‐pool development. In precipitating congestus clouds, dry, cold air is found in sub‐cloud and cloud layers. By analyzing a case study with multiple datasets, we recorded a variation in the vertical velocity field at cloud top associated with the development of precipitation and connected with a change in mesoscale cloud patterns. The statistics presented, as well as the case study, can serve as a benchmark dataset for studying the precipitation life cycle in high‐resolution modeling.

Abstract Marine stratocumulus clouds (MSC) strongly influence Earth's radiation budget, yet the mechanisms governing the descent of entrainment‐affected (diluted) parcels, and the relative roles of cloud‐top entrainment instability (CTEI) and longwave radiative cooling (RC), remain debated. Using helicopter‐borne observations from the ACORES campaign that combine high‐resolution in situ vertical profiling with co‐located remote sensing, we examine vertical variations of microphysics, thermodynamics, and the entrainment interfacial layer (EIL). When CTEI conditions were strongly met, inhomogeneous mixing (IM) traits appeared near cloud top and transitioned to homogeneous mixing (HM) traits deeper in the cloud layer, accompanied by localized elevations of cloud base, signatures consistent with enhanced descent of diluted parcels. We argue that these apparent HM traits arise from adiabatic warming and evaporation during descent rather than true HM. When CTEI was weakly met or not met, IM traits near the top were weaker, HM traits emerged deeper in the cloud, and cloud base elevation was not observed; these differences are explained by RC‐driven buoyancy contrasts modulated by turbulence and EIL thickness. Even in such cases, diluted parcels descended, but weakly. Integrating these results with prior field studies, we provide observational evidence that sufficiently strong CTEI can dominate RC and drive diluted‐parcel descent, clarifying how CTEI, RC, and EIL thickness jointly shape MSC structure and offering guidance for improved representation in weather and climate models.

Abstract. Stratocumulus clouds, with their low cloud base and top, affect the atmospheric boundary layer wind and turbulence profile, thereby modulating wind energy resources. GOES satellite data reveal an abundance of stratocumulus clouds in the late spring and summer months off the coast of northern and central California, where there are active plans to deploy floating offshore wind farms at two lease areas (near Morro Bay and Humboldt). Since the fall of 2020, two buoys equipped with multiple instruments, including Doppler lidar, have been deployed for about 1 year in these wind farm lease areas to assess the rotor layer wind conditions in these locations. The objective of this study is to evaluate how well the High-Resolution Rapid Refresh (HRRR) model represents stratocumulus cloud characteristics and turbine-relevant rotor layer winds (surface to 300 m) by comparing HRRR simulations with buoy and satellite observations. We first find that the HRRR model reproduces the seasonal cycle of cloud top height reasonably well in these regions. However, during the warm season – especially at Morro Bay – the HRRR-simulated stratocumulus clouds tend to have lower tops by about 150 m and exhibit weaker diurnal cycles than satellite observations. Our analysis also shows that rotor layer wind speeds and vertical shear are stronger at Humboldt than at Morro Bay, and both are generally stronger under clear-sky conditions. Finally, the HRRR model bias in rotor layer wind speed is small under cloudy conditions but larger and dependent on observed wind speed under clear skies. Specifically, HRRR underestimates wind speeds at Morro Bay and overestimates them at Humboldt under clear-sky conditions.

Context . Sulfur dioxide and water are two key minor species of Venus’ atmosphere which drive its chemical evolution. However, the long-term variations in the SO 2 abundance at the cloud top, measured since 1978, remain unexplained. Aims . In order to address this question, since 2012, we have performed a ground-based campaign to monitor the SO 2 and H 2 O abundances in the region of the Venus upper cloud, using the TEXES (Texas Echelon Cross-Echelle Spectrograph) imaging spectrometer at the NASA InfraRed Telescope Facility (IRTF, Mauna Kea Observatory). Methods . Observations were recorded in three spectral ranges: (1) the 1342–1348 cm −1 (7.4 µm) spectral range, where SO 2 , CO 2 and HDO (used as a proxy for H 2 O) transitions are observed at an altitude of about 62 km, defined in our model as the cloud top; (2) the 529–530 cm −1 range (18.9 µm), where SO 2 and CO 2 are probed within the clouds a few kilometers below the cloud top; (3) the 1160–1165 cm −1 range (8.6 µm) where the weak SO 2 v 1 band is used to probe a few kilometers above the cloud top. Results . We present here the data recorded from July 2023 to February 2025. As was reported in our previous analyses, the SO 2 maps show evidence for the formation of SO 2 plumes, mostly located around the equator, with a typical lifetime of a few hours; large variations in the SO 2 disk-integrated abundance also appear on a timescale of a few months. In contrast, the H 2 O abundance is remarkably uniform over the disk and shows moderate variations as a function of time. The present dataset shows for the first time the detection of the SO 2 v 1 band above the cloud top. The simultaneous analysis of the three SO 2 bands allows us to constrain the SO 2 volume mixing ratio within and above the cloud. In addition, we have re-analyzed the long-term evolution of the SO 2 and H 2 O mixing ratios at the cloud top. Between 2012 and 2023, the SO 2 abundance is clearly anti-correlated with the solar activity, which suggests that photochemistry, activated by the solar UV flux, is the main mechanism driving the SO 2 abundance. After 2023, SO 2 exhibits strong variations with a timescale as short as two months, which suggest that other mechanisms are involved. Finally, we have re-analyzed the distribution of SO 2 at the cloud top as a function of the local time, using three different data subsets (2012–2017, 2021–2022, 2023–2025). In spite of the differences, there is a general trend toward a minimum value around 12:00 and maxima around 5:00 and 17:00. Conclusions . Our data suggest that the solar cycle plays an important role in the long-term evolution of SO 2 and H 2 O at the cloud top. In addition, in some cases, other mechanisms are probably also involved, possibly associated with dynamical motions, implying sublimation/condensation processes and/or gas-aerosol conversion.

Abstract Close orbits by the Uranus Orbiter and Probe (UOP) could be used to deduce Uranus’s multipolar gravity field to a higher precision and angular degree than the J 2 and J 4 currently measured from ground-based ring occultations and the Voyager 2 flyby. We examine J n sensitivity limits obtained from simulations of candidate UOP trajectories, pairing these with Uranus interior and wind models to perform retrievals from the gravity moments. We consider zonal wind profiles derived from recent feature-tracking data, assuming that zonal winds extend into the planet along cylinders, with a radial decay function similar to those that explain Jupiter and Saturn gravity. Present knowledge of J 2 and J 4 permits a fairly wide range of possible wind depths in Uranus, up to 1800 km or 7% of the planet’s radius. Measuring additional gravity moments is essential to separate this unknown wind depth from other interior properties of interest, but J 6 is found to be too dominated by bulk rotation to be a useful probe of the wind depth. Odd moments arising from Uranus’s observed north–south asymmetric flow are strong functions of the wind depth, but the usefulness of J 3 is hindered by its sensitivity to present uncertainties in the wind profile. The even moment J 8 or the odd moments J 5 and J 7 are the best probes of the depth of Uranus’s winds. J 8 and, most likely, J 5 and J 7 are measurable in a highly inclined orbit making ≳10 pericenter passages inward of the ζ ring, approximately 1000–2500 km above Uranus’s cloud tops.

Abstract Turbulence in the upper troposphere and lower stratosphere (UTLS) plays an important role in total energy dissipation and in the transport and mixing of heat, momentum, and atmospheric constituents while also posing significant hazards to aviation. In this study, we leverage high‐altitude in situ observations from the NASA Dynamics and Chemistry of the Summer Stratosphere (DCOTSS) field campaign—including data acquired near convection and at altitudes rarely covered by commercial aircraft—to investigate turbulence and its potential link to UTLS composition. We find that strong turbulence occurs frequently within 2 km above the tropopause, which corresponds to 1–2 km above deep convective cloud tops, particularly within moist convective plumes characterized by enhanced water vapor. The occurrence of strong turbulence increases with proximity to convection with elevated frequencies persisting out to 100 km from convective cores. In particular, enhanced turbulence potential occurs near UTLS convective outflows, which is supported by favorable environmental factors such as strong vertical wind shear, flow deformation, and large divergence tendencies. Furthermore, although turbulence intensity and water vapor are generally weakly correlated, positive correlations occur more frequently near the tropopause and under strong vertical wind shear, suggesting that turbulent mixing may contribute to localized stratospheric moistening under certain atmospheric conditions. The findings demonstrate the utility of high‐altitude in situ observations for advancing our understanding of UTLS turbulence and its influence on atmospheric composition, with implications not only for climate but also for future high‐altitude aviation applications.

Abstract. With the coming launch of the Climate Absolute Radiance and Refractivity Earth Observatory (CLARREO) Pathfinder (CPF) comes an opportunity to develop a new retrieval for warm, non-precipitating clouds from spectral reflectance measurements. With continuous coverage across the shortwave spectrum and a factor of 5 to 10 lower radiometric uncertainty than the Moderate Resolution Imaging Spectroradiometer (MODIS), CPF facilitates the retrieval of a vertical profile of droplet size, providing insight into the internal structure of a cloud. Measurements from MODIS coincident with in situ observations provide the foundation for developing an optimal estimation technique. Solution constraints were required to ensure consistency with forward model assumptions. The limited unique information in the MODIS bands used in this analysis resulted in a non-unique solution, with many droplet profiles leading to convergence. Droplet size at cloud bottom is difficult to constrain because visible and shortwave infrared reflectances have an average penetration depth near cloud top. The region of convergence within the solution space decreased along the cloud bottom radius dimension by 1 µm when increasing the number of wavelengths used in the retrieval from 7 to 35 and by 3.75 µm when reducing the total uncertainty from 3 % to 1 %. The enhanced accuracy and, to a lesser degree, the enhanced spectral sampling provided by CPF measurements are essential to extracting vertically resolved droplet size information from moderately thick, warm clouds.

Abstract. Understanding the characteristics of the rain cell, the most basic unit in the natural precipitation system, is helpful in improving the cognition of the precipitation system. In this study, based on the merged precipitation profile data, reflectance and infrared data, and microwave brightness temperature data observed by the Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR), visible and infrared scanner (VIRS) and TRMM microwave imager (TMI), rain cells were identified in the PR swath. For the identified valid rain cells, two fitting methods (the minimum bounding rectangle (MBR) and the best fit ellipse (BFE)) were applied to fit the external frame. Then, the geometric and physical parameters of rain cells were also calculated. By analyzing the geometric parameters (length, width, height, and so on) and physical parameters (rain rate, visible reflectance and thermal infrared brightness temperature from cloud top, and microwave brightness temperature from cloud column) of two rain cells (weak rain cell and strong rain cell), the results indicate that the strong rain cell is filled with deep convective precipitation and has low thermal infrared brightness temperature at the cloud top, while the weak rain cell is mainly characterized by stratiform precipitation with low rain rate. Compared to the BFE method, the area of the external frame calculated by the MBR method is generally larger. The filling ratio of the BFE method is slightly higher than that of the MBR method. In general, the results indicate that the rain cell definition parameters using the two fitting methods are reasonable and intuitive. The data used in this paper are freely available at https://doi.org/10.5281/zenodo.15387988 (Wu and Fu, 2025).

Zonal-flow variability associated with meridional circulation at the cloud top in a Venus atmospheric general circulation model

Abstract Venus's cloud‐layer superrotation, characterized by equatorial zonal winds of ∼100 m/s, is sustained by the atmospheric angular momentum (AM) transport induced by atmospheric waves, especially thermal tides, and meridional circulation. However, the overall patterns of thermal tides and their individual components' contribution to superrotation remain poorly understood. Using a 16‐year radio occultation data set observed by Venus Express and Akatsuki, we have, for the first time, revealed the thermal tide structure from the cloud base to mesopause (50–90 km) in the southern hemisphere. The tidal patterns are equatorially symmetric and validated by simulations with the Venus Planetary Climate Model, extending tidal insights beyond the northern hemisphere focus of previous studies. The simulation indicates that diurnal tide‐induced AM flux divergence is the primary driving force for equatorial cloud‐top superrotation, with its meridional and vertical AM flux divergence dominating in the region of ∼5 km above and below the cloud top, respectively.

Abstract. Cloud drop number concentration (Nd) can be retrieved through passive satellite observation. These retrievals are useful due to their wide spatial and temporal coverage. However, the accuracy of the retrieved values is not well understood. In this study, we seek to understand why the retrievals agree or disagree with in situ measurements by examining the various cloud properties that underlie the retrievals. To do so, we compare satellite Nd derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument with in situ aircraft measurements made using a phase Doppler interferometer on board three flight campaigns sampling marine stratocumulus clouds. Intercomparison of Nd values shows that the discrepancy between retrieved and in situ Nd can be ± 50 % or more. In the mean, there is evidence of an overestimation bias by MODIS retrievals, although the sample size is insufficient for statistical certainty. We find that MODIS Nd is best interpreted as representative of the mid-cloud region, as there is almost always a greater discrepancy from in situ values near the cloud top and cloud base. We also find evidence of cases where Nd is accurately retrieved but the effective radius is not, presumably due to offsetting errors in other retrieval parameters. Vertical profiles of the extinction coefficient β, liquid water content L, and effective radius re measured during sawtooth-pattern flight legs through the cloud top are also compared to implicit MODIS retrieval profiles. For the two cases with Nd agreement, all profiles match well. For the six cases with significant disagreement, there is no consistent underlying cause. The discrepancy originates from one of the following: (a) discrepancy in the re profile, (b) discrepancy in the β and L profiles, or (c) discrepancy in both.

Abstract. The long series of multispectral measurements from the Advanced Very High Resolution Radiometer (AVHRR), which began in 1979, is now approaching its end, with the last remaining AVHRR sensor currently operating aboard EUMETSAT's Metop-C satellite. Several climate data records (CDRs) built on AVHRR data now face the end of their observational record. However, since many modern imagers contain AVHRR-heritage spectral channels, the potential for an extension of these AVHRR-based climate data records exists. This study investigates the possibility of simulating original National Oceanic and Atmospheric Administration-19 (NOAA-19) AVHRR channels from the Suomi National Polar-orbiting Platform (NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) radiances using collocated AVHRR–VIIRS datasets from 2012–2013. Spectral band adjustments (SBAs) were derived using linear regression and neural networks (NNs). The NN approach produced the best results, and separating daytime from night-time conditions when simulating AVHRR channel 3B at 3.7 µm was key. Furthermore, daytime radiance corrections in this channel must depend on actual surface and cloud reflectances to be realistic, which was achieved only through the NN approach. The cloud mask, cloud top height, and cloud phase products were produced from the simulated AVHRR radiances using the same retrieval methods for NOAA-19 data used to compile the CLARA-A3 CDR. CLARA-A3 is the third edition of the EUMETSAT Climate Monitoring Satellite Application Facility (CM SAF) CDR, with cloud parameters, surface albedo, surface radiation, and top of atmosphere (TOA) radiation products from AVHRR. Products were validated using Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations–Cloud-Aerosol Lidar with Orthogonal Polarization (CALIPSO–CALIOP) cloud products and agreed well with the original CLARA-A3 products, with the best results provided by the NN simulation approach. The NN-based approach best reproduced the corresponding products for cloud optical thickness (COT), cloud effective radius (CRE), liquid water path (LWP), and ice water path (IWP). The CLARA-A3 CDR will be complemented and extended with VIIRS-based products to cover the period 1979–2024 (46 years). This edition will be known as CLARA-A3.5. Future extensions and editions can follow a similar approach by applying the same radiance simulation method to collocated data from the Metop-C AVHRR and the Metop Second Generation (SG) METimage sensors, with the first satellite of the latter scheduled for launch in August 2025. The successful simulation of AVHRR radiances from METimage and VIIRS data enables the CLARA CDR extension for several decades.

Abstract. Cloud water adjustments are a part of aerosol–cloud interactions, affecting the ability of clouds to reflect solar shortwave radiation through processes altering the vertically integrated cloud water content L in response to changes in the droplet concentration N. In this study, we utilize a simple entrainment parameterization used in mixed-layer models to determine entrainment-mediated cloud water adjustments in weakly and non-precipitating stratocumulus. At lower N, L decreases due to an increase in entrainment in response to an increase in N suppressing the stabilizing effect of evaporating precipitation (virga) on boundary layer dynamics. At higher N, the decrease in cloud droplet sedimentation sustains more liquid water at the cloud top, and hence stronger preconditioning of free-tropospheric air, which increases entrainment with N. Using this idealized framework that neglects interactive surface fluxes, changes in boundary layer depth, and the diurnal cycle of solar radiation, we are able to show that cloud water adjustments weaken distinctly from dln⁡(L)/dln⁡(N)=-0.48 at N=100cm-3 to −0.03 at N=1000cm-3, indicating that a single value to describe cloud water adjustments in weakly and non-precipitating clouds is insufficient. Based on these results, we speculate that cloud water adjustments at lower N are associated with slow changes in boundary layer dynamics, while a faster response is associated with the preconditioning of free-tropospheric air at higher N.