The ACM-CAP product provides a synergistic retrieval of clouds, aerosols, and precipitation from the combination of EarthCARE's active and passive instruments, ATLID, CPR and MSI. It uses the CAPTIVATE optimal estimation retrieval framework and delivers a comprehensive set of products including profiles of liquid clouds and rain . ACM-CAP is unique in that it provides a unified retrieval of all hydrometeors and aerosols. This has the advantage of facilitating retrievals even in complex atmospheric situations and ensures vertical consistency for hydrometeor properties. The products are provided at 1 km horizontal resolution along track and 100 m vertical resolution.

To focus on stratocumulus regimes, we retain only single-layer, low-level clouds by selecting clouds with cloud-top height (CTH) <3 km, consistent with the typical depth of marine stratocumulus decks . Only clouds over the ocean are considered.

Cloud geometric properties (CTH and cloud-base height, CBH) are extracted from the ACM-CAP liquid cloud mask (“liquid_classification”). For cloud microphysics, we use ACM-CAP retrievals of liquid water content, cloud droplet effective radius (re), droplet number concentration (Nd), rain water content, and rain median volume diameter (D0). In this study, we focus on column-integrated or averaged values: Liquid water content, and rain water content are vertically integrated to obtain liquid water path (LWP) and rain water path (RWP). The re and D0 values are averaged per profile, and Nd is for liquid clouds already reported as a profile-constant value, following the ACM-CAP retrieval design . Error estimates from ACM-CAP are used to filter unreliable liquid water content and Nd retrievals.

MSI-based products (M-NOM and M-COP) provide the two-dimensional spatial context required for classifying mesoscale cloud patterns. The M-NOM product contains the calibrated MSI Level 1b reflectances and brightness temperatures . The M-COP product provides cloud optical and physical retrievals, notably cloud optical thickness (COT), effective radius, and cloud-top height . All products are available at a spatial resolution of 500 m. For the detection of closed and open cells, we use COT from M-COP and the three thermal infrared channel brightness temperatures centred at wavelengths 8.8, 10.8 and 12.0 µm, from M-NOM as inputs for the CNN (described in detail below in Sect. ). Additionally, we compute cloud cover from the M-COP cloud mask (i.e. COT > 0).

We compute trajectories, initialized at EarthCARE overpasses over the SEP region in regular distances of 64 km, as this is the distance between CNN scenes (see Sect. ). The trajectories are computed using ERA5 boundary-layer wind fields and the python package backtrajectory-calculator , which is based on the Lagrangian method proposed by . Trajectories are driven by ERA5 winds at 975 hPa (last data access in January 2026), with a temporal resolution of 6 h. We compute wind trajectories 30 h backward and forward from each EarthCARE overpass to follow the evolution of the observed cloud scene over a 60 h period.

Along the trajectories, we use data from the GOES-East Advanced Baseline Imager (ABI) to determine the cloud mesoscale structure, more specifically GOES-16 from August 2024 until 7 April 2025, and GOES-19 since then (data access between 5–14 January 2026). We use the optical thickness product COD2KMF (ABI-L2), and radiances of the three infrared window channels C11, C14 and C15 centred at wavelengths 8.4, 11.2, 12.3 µm (ABI-L1b), which are converted into brightness temperatures, as inputs for the CNN (described in detail below in Sect. ). All four products have a spatial resolution of 2 km at nadir.

We use fine-tuned versions of the CNN developed by (last access on 5 September 2025) to classify mesoscale cloud structure. The CNN was originally developed for MODIS and takes as input COT together with three combinations of infrared window brightness temperatures. Each input for the CNN spans 128 km × 128 km. In the following, we refer to these 128 km × 128 km windows in GOES, MSI, and the corresponding ACM-CAP section along-track, as “cloud scenes”. The CNN assigns each scene to one of six mesoscale structure categories following the classification scheme in : solid stratus, closed mesoscale convective cells (MCC), open MCC, disorganized MCC, clustered cumulus, and suppressed cumulus. In this study, we focus on the closed- and open-MCC categories.

To apply the CNN to MSI and GOES, the input scene data must have a resolution of 1 km, i.e. the original MODIS resolution. To this end, the resolution of the MSI input scenes is reduced from 500 m to 1 km by a 2×2 average-pooling operation, and the GOES input scene data is interpolated from an original resolution of approximately 2 to 1 km by a weighted mean of nearby original values. The CNN is applied only during daytime. For MSI, this restriction arises because COT is unavailable at night. For GOES, nighttime COT proved incompatible with the network, likely due to fundamental limitations of infrared-only retrievals in capturing high COT values. We only consider scenes without overlying ice clouds. To this end, we check for each scene if there are brightness temperature values below 273 K, and disregard the scene if more than 5 % of the pixels within a scene have these low brightness temperatures. Besides the classification into cloud structures, the CNN provides a certainty value between 0 and 1 (see for details). We filter for cloud scenes that were classified with a certainty value >0.75, which is the case for about 67 % of both closed and open cell scenes.

To adapt the network to MSI and GOES, we produce two fine-tuned CNN versions, one for each imager. The fine-tuning is done for the final network layer using manually labeled cloud scenes. Approximately 500 scenes per instrument were labeled according to the six cloud structure classes. Parts of this dataset are withheld from training to evaluate model performance. After fine-tuning, the CNN shows substantially improved classification skill with an F1 score (a performance metric balancing precision and recall) of 0.86 and 0.9 for closed and open cells respectively for the CNN fine-tuned to MSI, and an F1 score of 0.88 and 0.94 for closed and open cells respectively for the CNN fine-tuned to GOES. A detailed evaluation of the fine-tuned CNNs for MSI and GOES is provided in the Appendix . Examples of the resulting mesoscale cloud-structure classifications for MSI and GOES are shown in Figs.  and .

The CNN fine-tuned to MSI is applied to MSI swaths in all four stratocumulus regions. Scene midpoints are spaced 64 km apart, resulting in overlapping scenes of 128 km (see Fig. ). This overlap provides a finer structure detection, particularly near cloud structure boundaries. Due to this scene overlap, individual profiles are typically assigned to two neighbouring MSI scenes, and therefore sampled twice. For ACM-CAP, we compute the mean and coefficient of variation (CV = standard deviation / mean) for different cloud and precipitation properties for each of these along-track 128 km scenes. For LWP and RWP, means and CVs are computed over all cloudy profiles; thus, they represent values conditional on cloud occurrence. For CTH, CBH, Nd, and re, scene-statistics are likewise computed over cloudy profiles, whereas D0 scene-statistics are calculated only for raining profiles. Additionally, we define a binary rain occurrence for each scene, counting a scene as rainy if any of its profiles show rain. We also compute a rain fraction as the relative frequency of rainy profiles compared to cloudy profiles within a scene.

The resulting dataset contains scenes classified into one of the six mesoscale structure classes, including open and closed cells, and cloud property information from ACM-CAP per scene for all four stratocumulus regions. Overall, the dataset contains about 5000 scenes of closed cells, and about 2500 scenes of open cells. Figure  shows the geographic distribution of closed and open cells in the four stratocumulus regions, computed from this dataset. Closed cells occur most frequently in near-coastal regions, whereas open cells typically occur more offshore, consistent with previous findings .

For the SEP region, we retrieve additional information about the temporal development of cloud structures around the EarthCARE overpasses. To this end, we apply the CNN to GOES imagery along the computed wind trajectories. More specifically, we interpolate the trajectories to a temporal resolution of two hours and use the corresponding GOES data for 128 km × 128 km scenes around the position of each trajectory. We then apply the fine-tuned CNN to these scenes to determine the cloud structure. Figure  illustrates this approach for a single EarthCARE overpass. An animation of this example, showing all time steps, is provided in the Video Supplement 10.5446/73703.

For each tracked cloud scene, we identify whether a transition from closed to open cells occurs and record the timing of this transition. In the example in Fig. , a pocket of open cells has formed by the time of the EarthCARE overpass at latitudes -15 to -20° (Fig. c). Using the trajectories, we trace the observed open cells back to the moment when they formed. Figure a, b shows, for example, that the pocket of open cells had not yet formed 20 h before the EarthCARE overpass and was smaller in size 4 h before the overpass. Conversely, some scenes are classified as closed cells at the time of the EarthCARE overpass, and transition to open cells later (in Fig. d due to the increasing size of the pocket of open cells). Again, we identify whether a transition occurs in these cloud scenes and determine its timing. As described in Sect. , the CNN classification for GOES is only available during daytime. For transitions occurring during nighttime – i.e. when a scene is classified as closed cells at the last available time of day 1 and as open cells at the first available time of day 2 – we pinpoint the time of transition using the brightness temperature of the 11.2 µm GOES channel: As cloud fraction is expected to decrease rapidly during a transition from closed to open cells, we define the time during the night when the change in brightness temperature is maximal as the transition time.

Overall, we find 803 trajectories for which transitions from closed to open cells occur; 330 of which are closed cells at the time of the EarthCARE overpass and transition to open cells at a later point in time, and 473 are open cells at the EarthCARE overpass and could be traced back to their previous closed-cell state. Figure  shows all of these trajectories. It shows that trajectories go, as expected in the SEP region, typically from South-East to North-West. Transitions were found throughout the studied region, with the majority being located relatively central to it. Figure  shows that transitions were mainly found during the time period from June to October, consistent with previous satellite observations that show that the frequency of occurrence of both closed and open cells is highest during these months .

Note that it is not feasible to estimate the time evolution of cloud scenes by collecting several EarthCARE overpasses along a trajectory (as was done e.g. by , for MODIS data), since the active instrument swath is narrow and very rarely coincides with the position of a trajectory more than once. Therefore, the approach of combining EarthCARE with GOES was chosen, which has the additional advantage that, due to the high temporal resolution of GOES, it is possible to pinpoint the timing of the transition to a narrow time range.

In this section, we analyse the similarities and differences between closed and open cells in terms of their cloud properties, as determined by ACM-CAP. To illustrate the cloud and rain products, and the variability withing cloud scenes, Fig.  shows example scenes of typical closed and open cells from ACM-CAP and M-COP. As described in Sect. , we compute the mean and CV of cloud and rain properties for each 128 km along-track cloud scene in ACM-CAP (see Fig. ). The statistics of scene-mean and CV for all scenes from all stratocumulus regions are shown in in Fig. . In Appendix , we additionally present statistics of the median, and 5th, 25th, 75th and 95th percentiles of all closed and open cell scenes, since mean and CV alone cannot capture all aspects of a distribution.

We start with analysing the vertical structure of clouds. EarthCARE's high vertical resolution and reduced surface clutter enable satellite-based measurements of the vertical structure that were difficult to obtain with previous missions. Figure  shows that the vertical extent in terms of average cloud base height (CBH) and cloud top height (CTH) is similar for the closed and open cell examples. It also shows that the CTH and CBH of the closed cells remain stable across the scene, whereas the open cells exhibit greater variability, with thin cloud layers existing between thicker cells. These thin cloud layers are usually found at the minimum CBH or maximum CTH level. This indicates that they may be new cells forming or remnants of old cells, respectively. The vertical structure features in the example scenes are typical of the overall statistics over all data in the four stratocumulus regions (see Fig. ). The distributions of mean CTH and CBH show only small differences. Open cell CTHs are slightly higher (about 200 m on average), and this relatively small difference reduces even to 60 m, when stratifying by longitude and season for each stratocumulus region (not shown). The mean CBH difference between open and closed cells, when stratified by longitude and season, is only 10 m (not shown). The higher CV of both CBH and CTH reflects the highly variable vertical structure of open cells, which consists of thin layers interspersed with thicker cells. These findings on vertical structure broadly agree with previous findings from aircraft case studies .

Next, we analyse the cloud microphysical properties. The examples in Fig.  illustrate that the LWP is spatially more homogeneous for closed cells. In open cells it is much more variable, with LWP values significantly higher in some regions than in others. In the overall statistics (see Fig. ), the mean LWP per scene is similar or slightly smaller for open compared to closed cells, but open cells have a higher CV, i.e. they have more “extreme” values of LWP, both smaller and larger (see also the low and high percentile distribution in Appendix ). A similar pattern can be seen with re: closed cells exhibit highly homogeneous re, whereas open cells demonstrate greater variability. While the mean re is similar for both cloud types, as with LWP, open cells exhibit greater variability and “extreme” re values. Nd on the other hand shows a large difference between closed and open cells in mean values, with open cells having very low Nd values of 10 to maximally 100 cm⁻³. The findings of similar LWP and lower Nd for open cells fit to previous satellite studies using passive imagers . However, previous studies have often found larger average re in open cells, (e.g. ), whereas we find that the average re value does not change but the variability of re gets larger with more large particles, but also more small particle sizes. A comparison with collocated M-COP retrievals shows that also EarthCARE's passive imager retrieves larger re in open cells, and that small re values in ACM-CAP occur mainly in optically thin parts of the open cells (Appendix ). These optically thin cloud layers are more readily detected by the ACM-CAP active-sensor synergy than by passive imagers, which preferentially sample the optically thicker parts of open cells and therefore retrieve larger average re.

A plausible physical explanation for the microphysical differences between closed and open cells comes from LES simulations e.g.: In closed cells, there are typically many CCN available (as confirmed by aircraft measurements; ). Updrafts and high supersaturation lead to the activation of many droplets, which compete for water vapor. These droplets stay small in size and with a narrow droplet size distribution. In open cells, there are fewer CCN available due to rain scavenging. This leads to fewer cloud droplets, as well as to broader size distributions.

An interesting feature, that we see in many M-COP examples of non-precipitating closed cells, is the horizontal distribution of re within individual cloud cells: re is largest at the edges of the cells and smallest at the centre, where the updraft is located – even though the difference is small, it is consistent through many examples we analysed (see M-COP re in Fig. a as an example). An explanation might be that droplets are older in downdrafts than updrafts who showed larger droplet ages in downdrafts for open cells and might therefore have more time to grow to (slightly) larger sizes. For precipitating closed cells and open cells, the horizontal re distribution is reversed, with larger re at the positions of updrafts (see M-COP re in Fig. b–d).

Lastly, we analyse the rain properties. Open cells show a higher frequency of rain occurrence and higher RWP compared to closed cells, which fits well with smaller Nd, higher variability of droplet sizes and greater extremes in LWP (see Fig. ). However, note that closed cells also drizzle in about 60 % of scenes, albeit often only lightly. More heavy rain (RWP > 20 g m⁻²) is rarely found in closed cells, but often occurs in open cells (see 95th percentile distribution in Appendix ). The rain fraction in Fig.  shows a broad distribution for both closed and open cells. Especially for open cells, the sampling nature of active instruments may be partly responsible for this broad distribution, since in some cases the satellite passes directly over the centres of raining cells; in other cases it may sample mainly cloud edges or optically thinner areas. For closed cells, the most common cases are only few raining profiles, i.e. rain fraction close to zero, and rain fractions close to one. Lastly, rain drops are larger in open cells (see Fig. ). The size of raindrops has implications for virga depth and surface rain rates, since larger droplets are more likely to reach the surface. This, in turn, implies stronger aerosol scavenging. The higher CV of rain drop sizes is due to a higher number of large raindrops in open cells; small raindrop sizes are equally frequent in closed and open cells (see 5th and 95th percentile distribution in Appendix ).

Higher rain rates in open cells compared to closed cells have also been found in previous studies using CloudSat and AMSR/E , as well as ground based measurements and aircraft campaigns . The bigger surprise is the high rain occurrence in closed cells, which have in the past often been considered as non-raining. This notion may stem from the fact that most field studies have sampled clouds relatively close to the coast with small CTHs and consequently lower probability of rain, as shown by . EarthCARE's high sensitivity enables the rain properties of low-level clouds to be quantified and studied in great detail for the first time from a satellite. As mentioned in Sect. , EarthCAREs drizzle detection is highly improved and able to detect even light drizzle, that might have gone undetected before. Our finding that (light) drizzle is prevalent in closed cells, is to a large extent thanks to this improved sensitivity . It should be noted here that, as the other variables, the frequency of rain is computed on a scene basis, i.e. a scene is counted as raining if at least one profile within the 128 km scene shows rain. This may lead to higher values for rain occurrence in closed cells with sparse precipitation than if we were to count rain profiles in the original 1 km resolution. However, Fig.  shows that high rain fractions are also relatively common in closed cells, and we believe that scene-based rain occurrence is a more relevant measure for capturing mesoscale cloud properties. The measurement of rain drop sizes, D0, are also strongly improved for EarthCARE: The Doppler measurements are strongly dependent on particle sizes, and therefore help to determine both rain rates and drop sizes .

As described above, closed and open cells exhibit similar mean LWP, yet open cells show substantially larger RWP. This indicates that the LWP-RWP relationship differs between the two cloud regimes, as examined in Fig. . For a given LWP, open cells consistently produce higher RWP than closed cells, implying a markedly greater efficiency in rain formation. This behavior is robust across all four stratocumulus regions analysed. The enhanced RWP in open cells is primarily driven by larger raindrop sizes rather than by increased raindrop number concentrations. These statistics align with observations of individual cases: we find open-cell examples exhibiting intense precipitation even at low LWP, including instances where RWP exceeds LWP (e.g., the left-most and central cells in Fig. c). Such cases likely represent decaying cells in which a substantial fraction of cloud water has already been converted into rain. Similar phenomena have been reported in airborne observations by .

Summarizing, EarthCARE provides detailed observations on vertical structure and microphysics of cloud and rain for closed and open cells. The two cloud structures show large differences in their microphysics, most notably lower Nd and stronger rain in open cells.

A polar-orbiting satellite such as EarthCARE provides only instantaneous measurements of cloud properties at the time of overpass. As described in Sect. , we combine these instantaneous observations with backward and forward trajectories, as well as GOES/ABI geostationary imagery, to reconstruct the temporal evolution of individual cloud scenes, especially their time to transition (= time of EarthCARE overpass - time of transition from closed to open cells). This analysis focuses on the SEP region, where most transitions between open and closed cells occur.

To illustrate clouds and their properties around the transition time with an example, Fig.  shows M-COP and ACM-CAP variables for an overpass section consisting of four neighboring, overlapping cloud scenes (grey lines in uppermost panel). Clouds in the left two scenes have (mostly) transitioned to open cells shortly before (1 h) the overpass, and the clouds in the right two scenes transition to open cells shortly after (1 and 3 h respectively) the overpass, as determined by tracking them in GOES. Note the large re, low Nd and high RWC in the closed cells, compared to typical non-transitioning closed cells as shown in Fig. . More example scenes, ordered by their time to transition, can be found in Appendix .

Figures  and  present all cloud scenes in which closed-to-open cell transitions were identified. The figures show cloud and rain properties retrieved from EarthCARE, plotted as a function of their time to transition. Figure a shows the number of cloud scenes that were found for different time-to-transition values. The EarthCARE overpass, and therefore the time of measurement, is always around 13:00 LT (local time) in the SEP region. Different time-to-transition values therefore mean that the transition happened at different times of the day. A first observation to note in Fig. a is that transitions were found at almost all time-to-transition values, hence transitions can happen at any time of the day. A transition occurring between 5 to 19 h before or after the overpass, means local nighttime. Adding all transition counts during daytime and nighttime in Fig. a shows that the majority of transitions occur during nighttime (around 60 %), consistent with . Furthermore, the number of transitions show an interesting pattern with peaks at dawn (e.g. a time to transition of -19 means that a transition to open cells happened 19 h after the EarthCARE overpass, hence around 08:00 LT. A time to transition of 5 means that the transition to open cells happened 5 h before the EarthCARE overpass, i.e. again around 08:00 LT). Figure b shows cloud cover, derived from M-COP. As expected, cloud cover is near unity for closed cells and drops to much lower values, around 0.6, after clouds transition to open cells. Both CTH and CBH remain relatively constant and do not show notable deviations from their climatological values around the transition time. Their CV remain stable until the transition and increase around the transition time to higher values typical for open cells (dashed line in Fig. b).

In contrast, LWP, re and Nd show a distinct temporal evolution already long before the transition (see Fig. a–f). LWP begins to increase approximately 25 h before the transition, peaks near the transition time, and subsequently decreases as open cells form – consistent with the emergence of thinner cloud layers characteristic of open-cell structure. In parallel to LWP, re increases with a peak shortly before the transition and decreases thereafter. As for LWP, the decrease is consistent with the emergence of thin cloud layers with small droplet sizes between the thicker open cell clouds. Nd steadily decreases toward the low climatological values of open cells. For both re and Nd, the deviations from their climatological closed-cell values seem to begin slightly later than for LWP, around 20 h before the transition. However, since the number of observed cases at these time-to-transition values are few (see Fig. a), it is difficult to draw a definite conclusion about the exact timing. The CV for LWP, re and Nd is relatively stable around the climatological values for closed cells and increases only shortly before the time of the transition. Hence, although the mean values of cloud microphysical parameters begin to change well before a transition occurs, they remain relatively homogeneous, i.e. at CV values typical of closed cells. It is only around the time of the transition that they begin to show stronger variations.

As with cloud microphysics, changes in rain properties occur well before the transition. RWP is already slightly elevated approximately 25 h before transition and increases to near open-cell climatological values approximately 10 h before the transition. In contrast to re and Nd, which show the strongest rates of change shortly before the transition, the increase in RWP slows down from around 10 h before the transition. D0 steadily increases toward the higher climatological values of open cells. This increase of D0 seems to begin a few hours later than for RWP, around 20 h prior to the transition, following a similar timeline as changes in re and Nd. However, as mentioned above, the precise timing should be viewed with a degree of caution and is more speculative than the overall quite clear trend.

Because increases in RWP (defined as mean_128 km(RWP), where the mean is taken over all cloudy profiles, as explained in Sect.  and shown in Fig. g) may have multiple origins, we decompose it in Fig.  into rain fraction (i.e. relative frequency of raining profiles compared to cloudy profiles) and mean128kmraining(RWP), where the scene-mean is only taken only over raining profiles, i.e. capturing the rain intensity. Both quantities show increased values before transition. This means that the increase in RWP is both due to higher rain intensities and more clouds producing rain. This decomposition further shows that the early increase of RWP (from around 25 until 15 h before transition) is mainly due to increasing rain fractions (Fig. a), whereas the subsequent increase in RWP (from around 15 h before transition) is mainly due to increasing rain intensity (Fig. b).

From the sequence of deviations from climatological values, we propose a timeline of physical processes leading to closed-to-open cell transitions (see Fig. ). We hypothesise that the early increase in LWP and, as a consequence, RWP is due to increased moisture in the boundary layer – consistent with , who found that transitions are preceded (for lead times of ∼3 d) by strong surface winds that increase boundary layer moisture. Once rain intensifies and reaches the surface, aerosols in the boundary layer are scavenged, reducing Nd and increasing re as a consequence. This promotes further precipitation, sustaining a rain-aerosol-scavenging feedback. Increasing re also leads to larger D0, which further intensify the rain-aerosol-scavenging feedback. After several hours, the rain and consequently the evaporative cooling below cloud base are strong enough to reorganise the mesoscale cloud structure into open cells through stabilization of the sub-cloud layer and colliding cold-pools. In this interpretation, enhanced moisture acts as a possible initial trigger, while aerosol scavenging provides an important positive feedback that intensifies precipitation and promotes breakup. Note that the the whole transition process following this timeline takes a long time; around 25 h.

LES studies often use aerosol number concentration as a control parameter and show that low aerosol availability can favour open-cell conditions. Our observations are consistent with aerosol playing an important role, but suggest that low aerosol availability need not be the initial trigger of the transition: Nd is initially close to climatological values and decreases only after LWP and precipitation have already increased. This timing is consistent with aerosol depletion developing as part of the precipitation feedback rather than necessarily preceding it. Other upstream environmental controls, including SST and surface forcing, may contribute to the initial increase in boundary-layer moisture and precipitation , but quantifying their relative roles is beyond the scope of the present study.

The diurnal cycle of the number of transitions (see Fig. a) provides additional insight into the underlying processes. The observed peak in transitions around dawn may be linked to the diurnal evolution of LWP. In closed-cell stratocumulus, radiative cooling at cloud top is the primary driver of boundary-layer circulation. During nighttime, the absence of solar radiation enhances this cooling, leading to a gradual increase in LWP with a maximum near dawn. Given the close relationship between LWP and RWP, precipitation is also expected to peak at this time. This behaviour is consistent with our interpretation of a precipitation-linked transition pathway.

An open question is whether the onset of incoming radiation at sunrise also actively contributes to the transition. One possible mechanism is that shortwave radiation weakens the radiatively driven circulation of closed cells, thereby facilitating a shift toward open-cell dynamics. Assessing the relative roles of nocturnal cooling and daytime radiative effects in triggering transitions will require further dedicated investigation.

Our results, supported by previous work by , suggest that increased moisture in the boundary layer is a potential trigger for increased rain leading to a transition. This implies that changes in environmental conditions that increase boundary-layer moisture, for instance stronger surface winds and higher sea-surface temperatures, could lead to more open cell occurrences, decreasing the albedo and cooling effect of the stratocumulus decks.

Another implication is that high amounts of aerosols could delay or suppress rain formation, and consequently closed-to-open cell transition . This suggests that the recent trend of decreasing aerosol amounts may lead to a positive forcing (i.e. warming) through earlier transitions from closed to open cells along their trajectories.

Our mesoscale cloud typing is based on a CNN applied to GOES and MSI imagers, and uncertainties in these classifications propagate into both the inferred contrasts between open and closed cells and the estimated time-to-transition. Manual inspection suggests a failure mode is the potential misclassification of dissipating or fragmented cloud scenes as open cells, which would bias the retrieved LWP and RWP low for the open-cell category. Another difficulty lies in scenes with clouds that are at the edge of transitions or breaking up (see Fig. ). Here, the CNN usually classifies scenes as closed cells if more than half of the scene is covered by them, and vice versa for open cells. Therefore, the cloud and rain properties around the transition time may be partially derived from the other cloud regime covering part of the scene.

Note also that neural networks trained on different regions, seasons, or cloud scene sizes can encode systematically different notions of “open” and “closed” cells. In our case, the fixed scene size of 128×128 km limits detectability of very large cell sizes and may preferentially capture smaller-scale open cells. This scale dependence should be considered when interpreting differences with prior work.

In addition to classification uncertainties, uncertainties in the EarthCARE retrievals must also be acknowledged. These products are still maturing, and forthcoming retrieval updates may affect some variables, particularly the distribution of cloud and rain particles within the vertical profiles. For this reason, we focus on column-integrated cloud and rain properties that are more robust and additionally constrained by the passive MSI imager.

Please note that our study focuses on daytime retrievals only, since the current version of the fine-tuned CNN only operates during the day. Cloud properties may exhibit diurnal cycles, which could impact transition processes . However, this aspect is not addressed in our study. As mentioned above in Sect. , we observe a diurnal pattern in the number of transitions, with the majority occurring around dawn. Although the timing of transitions is more challenging at night, introducing an element of uncertainty into their distribution (see Sect. ), this observation suggests that the diurnal cycle plays an important role in the transition process. A possible next step would be to incorporate night-time overpasses and study the effect of the diurnal cycle on closed and open cell properties and their transitions directly.

Further, the nature of active remote-sensing measurements may enhance the spread in our results. Depending on the exact satellite-ground track position relative to the mesoscale cloud structure, EarthCARE may sample different parts of the same cloud regime. In some cases, the satellite passes directly over the centres of cells or over regions of strong precipitation; in other cases – particularly for open-cellular scenes – it may sample mainly cloud edges or optically thinner areas. These geometric and structural differences naturally produce variability in the measured quantities. Such scene-dependent sampling differences are therefore expected to contribute to the overall dispersion in the data.

Finally, we would like to comment on the spread in many of the variables in the time series plots (Figs. ,  and ). This spread is often substantial relative to the magnitude of the detected trends. Therefore, one might argue that caution should be exercised when interpreting the observed trends. However, it should be noted that transition detection as well as time-to-transition estimation are performed independently for each cloud scene. The fact that smooth and coherent trends emerge despite this independence strengthens our confidence that the observed signals are physically meaningful.

Our analysis targets clouds in the subtropics and focuses on cloud and rain microphysics, since observations and theory suggest that the closed-to-open cell transitions are mainly driven by microphysical processes instead of large-scale meteorological conditions . While we expect the microphysical processes as discussed in this work to hold in every situation, meteorological factors can still be of crucial importance in changing the initial cloud conditions or the timeline of transitions, for example by facilitating or hindering the development of rain. found that increased wind speeds are associated with the development of open cells, which is consistent with our interpretation of increased moisture as a trigger for transitions. found that a stronger instability, i.e. weaker boundary layer inversion strength, is associated with an increasing frequency of open cells. They show that this effect is strongest in mid and high latitudes, suggesting that the relative importance of precipitation feedbacks versus large-scale forcing is likely regime-dependent. Nevertheless, they still find a pronounced effect also in the subtropics. In future work it would be interesting to check the dependence of the transition timeline studied here on meteorological factors, like the inversion strength or sea surface temperature, to better understand how closed-to-open cell transitions might change in a changing climate.