Satellite retrievals are derived from the GOES-18's Advanced Baseline Imager (ABI) using a family of algorithms referred to as the Satellite Cloud and Radiation Property Retrieval System (SatCORPS). These algorithms are adapted from those developed by the Clouds and Earth's Radiant Energy System (CERES) to ingest radiances from geostationary sensors (Minnis et al., 2008, 2011, 2023). For this study, the native resolution pixel retrievals at ∼ 2 km are produced half hourly and gridded to 0.20° × 0.20° grid over the EPCAPE domain, defined here as the region covering the box of 28–43° N and 112.5–130° W. These gridded data are averaged according to their cloud height into low (height < 2 km), middle (2 km < height < 6 km), and high (height > 6 km) clouds. The cloud products include cloud fraction (CF), cloud optical depth (τ), droplet effective radius (re), LWP, temperature, and height. Given the large uncertainties and limitations of optical retrievals during nighttime (optical depth and droplet effective radius) we only retain daytime observations with solar zenith angle (SZA) less than 70°. To minimize retrieval artifacts from thin or broken clouds, we apply additional screening criteria in our analysis: CF > 10 %, τ > 2 (Grosvenor et al., 2018). Also, we filter-out grid boxes with cloud top temperature (CTT) lower than 273.15 K and specifically select for this analysis averaged retrievals corresponding to the low height category (< 2 km) with LWP range of 5–250 g m⁻² to limit the analysis to stratus and stratocumulus clouds and minimize retrieval uncertainties. On average these filters reduce the data availability by approximately 60 %, however, provides a cleaner and more physically consistent sample for evaluating regime-dependent aerosol–cloud relationships. The SatCORPS algorithm computes LWP from the retrieved cloud optical thickness (τ) and droplet effective radius (re) using the relation (Nakajima and King, 1990; Nakajima et al., 1991): 1LWP=(4/3)⋅ρw⋅τ⋅re/Q where ρw is the density of liquid water and Q is the cloud extinction efficiency, approximately 2.0. The algorithm follows the bispectral approach and simultaneously retrieves τ and re using the 0.64 and 3.9 µm reflectances under the assumption of a 1-D, plane-parallel, homogeneous cloud layer. Because passive satellite sensors are most sensitive to the upper few optical depths of the cloud, the retrieved re corresponds to values near cloud top rather than the vertically averaged droplet radius, and the LWP computed following Eq. (1) would be an overestimate in marine stratiform clouds where typically re increases from cloud base to cloud top. To account for this bias, an adiabatic correction following Wood and Hartmann (2006) is applied to the LWP computation. The adiabatic LWP is computed using Eq. (2) given below, and accounting for the adiabaticity reduces the LWP derived from Eq. (1) by a factor of (5/6) or 17 %. 2LWPadb=(5/9)⋅τ⋅re The cloud droplet number concentration (Nd) is estimated following the approximation for subtropical clouds in Painemal and Zuidema (2011) based on the adiabatic approximation. This approach assumes that liquid water content increases linearly with height and incorporates an adiabatic factor of 1.0. 3Nd=1.4067×10-6[cm-1/2]⋅τ1/2/re5/2 Since numerous studies have reported a systematic positive bias in re derived from geostationary and sun-synchronous imagers of about 20 % relative to in-situ data (e.g. Minnis et al., 2021; Painemal et al., 2023, 2025), we apply a 0.8 correction factor to the re values before computing Nd, to minimize error propagations in Eq. (3).

Self-Organizing Maps (SOMs; Kohonen, 1982; Kohonen, 1990; Bishop, 1995; Vesanto and Alhoniemi, 2000; Kohonen, 2001; Haykin, 2009) is an unsupervised machine learning technique designed to cluster and visualize high-dimensional data by projecting it onto a two-dimensional grid while preserving the topological relationships inherent in the data. The SOM algorithm groups similar data patterns into neighboring neurons on the grid, with each neuron i represented by a weight vector wi. This weight vector is iteratively adjusted during training to reflect the distribution of the input data, ensuring that patterns with similar features are positioned close to each other on the grid. Recent studies emphasize the effectiveness of SOMs in synoptic climatology and environmental analysis (Hewitson and Crane, 2002; Sheridan and Lee, 2011; Jiang et al., 2013; Pearce et al., 2014; Juliano and Lebo, 2020; Painemal et al., 2023). The training process of SOMs involves several steps as described in Haykin (1999, 2009):

Initialization: The weighting vectors of the neurons are initialized randomly for better convergence. Each neuron in the map is represented by a weighting vector with the same dimensionality as the input data.

The first step in the synoptic clustering is to select the meteorological fields that will be used for the pattern classification. To this end, we consider a variable that represents the large-scale features over the region of study and a second parameter that captures local features along the coast. The clear choice that satisfies these conditions over the coastal NEP are, respectively, sea level pressure (SLP, e.g., Juliano and Lebo, 2020) and boundary-layer wind fields at 975 hPa (e.g., Abel and Hall, 2010). We also experimented with adding the 500 hPa geopotential and 850 hPa winds fields to SOM, which resulted in negligible changes in the clustering (not shown). Next, 11 years (2012–2022) of daily April through September SLP and winds are normalized by subtracting the 30 d moving average at each 0.25° grid point and divided by the 30 d running standard deviation. This normalization ensures that the algorithm primarily discerns meaningful meteorological patterns rather than seasonal changes and extreme modes of variability. Alternative anomaly normalizations, such as spatial averaging methods used in previous studies (e.g., Juliano and Lebo, 2020), were tested but were found to disproportionately weigh land-ocean differences in SLP variability. Secondly, to determine the spatial domain extent best suited for capturing meteorological variability relevant to the NE California coast, we tested three domains of varying sizes: a large domain (10–50° N, 160–100° W), a medium-size domain (20–50° N, 140–110° W), and a small domain (30–40° N, 125–115° W). We found that the clustering for the large domain was dominated by large-scale changes in SLP far offshore, showing modest differences in meteorological conditions for the coastal region near Scripps Pier. In contrast, the small domain captured many more details that tended to yield spatially noisy patterns that lacked consistent features. The medium-sized domain (30° × 30°) provided the most representative patterns for the Southern California region, effectively capturing the dominant synoptic-scale features. Finally, we tested several objective methods to determine the optimal number of SOM nodes. First, we used the silhouette score to evaluate the uniqueness of patterns across different node selections. This metric informs about how close each point in one cluster is to the points in the neighboring cluster, with score ranging between -1 and 1. A higher score (> 0.5) suggests the clusters are well-separated, whereas a negative score suggests wrong cluster assignment. In our case, the scores ranged from 0.065 for six clusters to 0.021 for nine clusters, degrading further with higher node counts, which rendered this method unsuitable. Second, we applied a two-step clustering approach: initially generated an SOM classification with 20 clusters (4 × 5 nodes) and then regrouped with K-means to reduce the number of patterns. However, this approach produced slightly noisier and less coherent patterns. Ultimately, the optimal number of nodes/clusters was chosen visually by testing several configurations (4 × 5, 4 × 4, 3 × 4, 3 × 3, etc.) to ensure clear classification of regional synoptic features. A 3 × 3 SOM (nine nodes) provided distinct, coherent synoptic regimes, striking a balance between capturing variability and maintaining interpretability. Moreover, this 3 × 3 SOM configuration effectively captures the evolution of the SLP discerned by the 4 × 5 SOM, as corroborated by comparing the normalized sea level pressure fields in Fig. 1 and Fig. S1 in the Supplement.

The 9 patterns (regimes) determined by the SOM classification is shown in Fig. 1. These patterns capture the dominant synoptic features over the NEP and coastal Southern California and are arranged to reflect a progression in anticyclone/cyclone movement. It is interesting to observe a coherent transition of the anticyclone-cyclone system that resembles the evolution of weather disturbances. Pattern 1 displays pronounced high-pressure anomaly centered northwest of the Scripps Pier with anticyclonic offshore wind anomalies, and occur 14.46 % of the time (the highest frequency among all regimes). Patterns 2–8 feature the development, eastward advancement, and southeastward dissipation of a cyclonic system. More specifically, pattern 2 features an anticyclone strengthening with its center positioned about 10° W of Scripps Pier, and with intensified offshore anticyclonic anomalous flow, whereas a cyclonic system develops at the northwest corner. Pattern 3 is characterized by an anomalous anticyclone closer to the Pier, as the anomalous cyclonic pattern strengthens. Pattern 4 depicts a diminished anomalous anticyclonic signature now centered even closer to the Pier with modest winds, with the cyclonic center shifting further south relative to pattern 3. Pattern 5 displays a predominant cyclonic feature centered directly west of Southern California, producing broad anomalous onshore winds across the region. Pattern 6 captures a moderate anomalous low-pressure system advancing southeastward covering much of the domain, accompanied by enhanced anomalous onshore flow. The anomalous low-pressure pattern further advances along the direction in pattern 7, generating strong anomalous onshore flow. Pattern 8 shows a modest anomalous low-pressure feature over Southern California while anomalous anticyclone displaced further south in pattern 7. Pattern 9 presents minimal changes in SLP and winds and, thus, represents climatological features.

We use SLP and wind fields in the boundary layer (975 hPa, ∼ 300 m a.s.l.) and the free troposphere (750 hPa, ∼ 2.5 km a.s.l.) for describing the circulation and transport pathways during April–September 2023 of EPCAPE campaign. Table S1 summarizes the monthly occurrence frequency of the 9 regimes for the period of study. In addition, low-cloud control variables examined here include vertical velocity in pressure coordinates at 500 hPa (ω500), 750 hPa relative humidity (RH₇₅₀), and lower tropospheric stability (LTS). LTS is defined as the difference in potential temperature (θ) between 750 hPa and the surface (similar to Klein and Hartmann, 1993). The meteorological fields for each regime are depicted as mean composites in Fig. 2, and as anomalies relative to the monthly running mean in Fig. 3. While the discussion is mainly guided by Fig. 2, Fig. 3 provides quantitative information of magnitude changes of the deseasonalized fields. The SLP patterns in Fig. 2 features a dominant anticyclone with varying values of magnitude and extent anticipated by Fig. 1. In contrast, regime 1 depicts the strongest development of the anticyclone, with boundary layer winds exceeding 10 m s⁻¹ near the Scripps Pier. Similarly, the high-pressure system is well developed in regimes 7 and 8, but with their core displaced farther northwest than in regime 1, and with wind speeds greater than 10 m s⁻¹. Regime 2 shows a moderately developed anticyclone but with relatively modest deseasonalized values (Fig. 3a), with its center aligned with the latitude of Scripps Pier. Regimes 1, 2, 7, and 8 feature wide areas of subsidence (ω500 > 0) and consistent free tropospheric drying (RH₇₅₀ < 30 %) over the ocean. For these regimes with intense mean and deseasonalized anticyclone, we observe a strengthening of the coastal winds parallel to the coast south of 40° N. Notably, these coastal winds tends to be stronger in the boundary layer (Fig. 2a, arrows) than in the lower free troposphere (Fig. 2b, arrows), which is a common feature of the coastal low-level jet around Cape Mendocino (40.4° N, Parish, 2000). Regimes 3, 4, and 5 are unique in that they feature a weak cyclonic circulation north of 40° N, with westerly winds north of 35° N, and concomitant with 500 hPa ascents (Fig. 2a, ω500 < 0). Regime 9 features the climatological mean pattern, as discussed in Sect. 3.1.

In terms of 750 hPa winds, consistent with the strengthening of the anticyclonic and cyclonic circulation, regimes 3–5 exhibit a dominant zonal component, suggesting transport of marine air masses inland. For 1, 2, and 8 (anticyclones), winds run parallel to the coast, potentially contributing with a mixture of continental and marine air masses to Scripps Pier. For regimes 6–7, the wind direction shows dominant meridional component. There is an increase in LTS for all the regimes with values > 20 K for the broad coastal area south of 40°, except that regimes 4 and 5 show slightly lower LTS. It is, nevertheless, surprising that deseasonalized anomalies in Fig. 3b show modest variability for the same coastal area, especially near Scripps Pier, with inter-regimes differences smaller than 3 K, and with unclear connections with the atmospheric circulation depicted in Fig. 3a. This implies that the patterns of the mean LTS in Fig. 2b primarily reflect seasonal changes rather than synoptic-scale processes. As we will see in the following sections, the modest variability in LTS cannot explain changes in the cloud fields, confirming the poor correlation between LTS and cloud cover at synoptic scales reported by a number of studies (e.g. Klein et al., 1997).

After providing a regional description of the atmospheric variability over the coastal NEP, we center our attention on the thermodynamical properties of the lower troposphere derived from radiosondes observations collected during EPCAPE at Scripps Pier. Figures 4 and 5 provide, respectively, median and deseasonalized atmospheric profiles for each regime. The boundary layer for all regimes is statically stable, with mean LTS values ranging between 15–20 K (Fig. 4a). Regime specific features are more easily observed in the θ anomaly (deseasonalized) profiles in Fig. 5a. Regimes experiencing an anomalous anticyclonic (e.g., regimes 3, 4, 5) or cyclonic circulation (e.g., regimes 7, 8) around the Pier depict negative θ anomalies (about -1 K) within the boundary layer, while the other regimes generally show positive θ anomalies. In terms of relative humidity, most regimes maintain high RH (> 80 %) in the boundary layer with the expected sharp decrease in the free troposphere (Fig. 4b). Median RH profiles suggest shallow MBL tops (< 1 km) in most regimes, while regimes 7 and 8 feature slightly deeper boundary layers extending beyond 1 km. These regimes are associated with anomalous cyclonic dominant synoptic conditions that promote upward moisture transport, particularly regimes 7 and 8, which maintains elevated RH throughout the lowest 2 km, consistent with moist northwesterly flow and enhanced ascent (Zhou et al., 2015). In addition, stronger boundary-layer turbulence from intensified winds and upper level wind reversal enhancing entrainment under relatively weaker static stability, likely contribute to the boundary-layer deepening observed in regimes 7 and 8. Consistently, these regimes also exhibit positive RH anomalies. Regimes 1 and 2 are typically associated with anomalous high-pressure and associated subsidence in coastal regions and exhibit weak negative RH anomalies.

Wind profiles in Fig. 4c generally feature a westerly component with magnitude of less than ∼ 2 m s⁻¹ in the boundary layer. Meridional wind (V) profiles are more revealing of the position and strength of the anticyclone: Most regimes are dominated by a modest but persistent northerly flow (negative V), consistent with cold air advection and coastal subsidence typical of summertime marine stratus regimes (Parish, 2000). On the other hand, regimes 7 and, to a lesser extent, 8 display southerly flow (positive V), indicative of warm, moist advection into the coastal boundary layer, likely enhancing turbulence and transport of aerosol from southern shore to the Pier (Painemal et al., 2013).

Specific properties that characterize the boundary layer are presented in Fig. 6. Boundary-layer stability is quantified from radiosonde observations using a MBL stability metric, defined as the potential temperature difference between 700 m and the surface. Regime-mean values range from 5 to 10 K, with larger values indicating a more stable MBL and smaller values reflecting enhanced turbulence. Although the medians are below 1 K, regimes 1–4 exhibit larger mean values (∼ 2 to 3 K) and wider upper-quartile range (> 6 K), representing the stable boundary layer. Conversely, regimes 5-8 exhibit weakest stability, with mean values (≤ 2 K), consistent with a less stratified boundary layer and enhanced turbulence mixing. The boundary layer height (BLH) is simply estimated as the altitude for which RH drops below 45 % (Bretherton et al., 2010). BLH exhibits moderate variability across regimes (Fig. 6b). Consistently, regimes 1-4 exhibit shallowest boundary layers with mean BLH ∼ 750 m, consistent with stronger subsidence that inhibits vertical development of the MBL. In contrast, regimes 5–8 are characterized by substantially deeper boundary layer. Deeper MBL is typically conducive to enhanced vertical cloud extent and increased LWP, a trend consistent with the regime-specific cloud structures to be discussed in Sect. 3.4 and 3.5.

Figure 6c shows the distribution of TKE across the synoptic regimes, highlighting substantial regime-dependent modulation of boundary-layer turbulence. TKE is highest in regimes 5–8 with mean values of approximately 0.3–0.35 m² s⁻², indicating the favoring of strong boundary layer mixing. In contrast, regimes 1–4 (with the exception of regime 2) exhibit lower mean TKE of about 0.20–0.25 m² s⁻², representing a weakly turbulent boundary layer. Regime 9 shows intermediate behavior, with turbulence levels ranging between those of the shallow, stable regimes and the deeper, less stable regimes. Lastly, regime 7 depicts moderate mean TKE, which is also regime with deeper and a weakly stratified BL.

The difference between cloud base height (CBH) and the lifting condensation level (LCL) provides insight into the degree of cloud-boundary layer coupling (Fig. 6d). A CBH–LCL separation exceeding approximately 150 m (highlighted by the dashed line) typically signifies decoupled conditions, wherein the cloud layers are no longer directly connected to the surface moisture supply and below-cloud turbulence (Wood and Bretherton, 2004; Jones et al., 2011). Regimes 5–8 frequently exhibit upper quartiles with CBH–LCL values that exceed the 150 m threshold, indicating frequent decoupling. Conversely, regimes 1–4 maintain tightly clustered CBH–LCL distributions near or below 150 m, indicative of well-coupled marine boundary layers. In such regimes, cloud bases closely follow the lifting condensation level, enhancing aerosol–cloud interactions and generally supporting higher Nd as discussed in the Sect. 3.6, assuming sufficient aerosol availability (Wood, 2012).

To characterize the link between synoptic-scale meteorology and low-level clouds along the Southern California coast during the EPCAPE, we create regime composites of SatCORPS GOES-18 cloud properties. Figure 7 presents the spatial mean distributions of key low-level cloud properties: low-level cloud fraction (LCF), Nd, LWP, and CTH. Figure 8 shows the corresponding spatial anomalies of these variables, calculated relative to the 30 d running mean climatology. Figures 7a and 8a show that low-level clouds are less frequent when the subtropical anticyclone center is closer to the coastal Southern California or if it is weaker (regimes 1–5). In these regimes, the fractional low-cloud amount is below 50 % along the shoreline and increased to 70 %–80 % over the southern offshore domain. About 10 %–20 % of mid+high cloud incidence (Fig. S2) in these regimes along the coastline indicates that the corresponding low-cloud reduction is partially attributed to obscuration of higher clouds in the satellite sensor field of view. Notably, regime 5 is partially linked to synoptic storm systems in midlatitudes, indicating that deeper convection and frontal systems may play a dominant role in shaping the clouds in this regime. In contrast, regimes characterized by an offshore high pressure and a developing low pressure over land (regimes 6–8) display greater low cloud amount exceeding 70 % along the southern California shoreline and wider offshore. Indeed, the positive deseasonalized anomalies in LCF were above 10 % in these regimes and this increase in low-cloud coverage is consistent with wide regions with subsidence and a reinforcement of the northerly winds (Fig. 2a), two factors that favor the strengthening of the thermal inversion (Myers and Norris, 2013; Wood, 2012). Regime 9, the regime closest to climatological neutrality, displays average LCF values of ∼ 70 %, but shows negative anomalies (∼ 5 % to 10 %) around the EPCAPE site.

Figures 7b and 8b show the spatial mean and anomaly fields of SatCORPS GOES-18 Nd across the nine synoptic regimes. All regimes exhibit a strong coastal gradient in Nd with local maxima (> 200 cm⁻³) occurring nearshore and a systematically westward decrease. This well-known pattern is consistent with an expected continental aerosol outflow, dilution and precipitation scavenging offshore. Interestingly, under well-developed high-pressure conditions with stronger boundary layer winds (e.g., regimes 1–8 with the exception of regime 6), Nd enhancement is limited to a narrow coastal band and decreases abruptly to ∼ 100 cm⁻³ offshore. This suggests that intense anticyclonic circulations hinder the transport of continental aerosol over the open ocean, confining CCN sources to coastal regions. Notably, regime 6 features the largest extent of positive Nd anomalies and mean values greater than 175 cm⁻³, associated with a northwest displacement of the anticyclone and winds that run parallel to the coast north of 35° N. This unique synoptic configuration was also identified in Painemal et al. (2015) as the circulation that drives the highest Nd during MAGIC over the NEP.

The LWP variation in 9 synoptic regimes are featured in Figs. 7c and 8c. Broadly, the regimes of anomalous high-pressure areas around the coastal Southern California (e.g., regimes 2, 3, 4) with developing cyclone in the northwest relate well with the regimes of lower LWP below 60 g m⁻²along the coast, and anomalies reaching as much as 20 g m⁻² less than the mean climatology. These regimes are often characterized by shallower marine boundary layers with CTH below 800 m. Regimes 6–9 exhibit enhanced LWP frequently exceeding 75 g m⁻² across large spatial extents. These regimes also show strong positive LWP anomalies, in some cases surpassing 20 g m⁻² compared to climatological means. High values of LWP in these regimes appear to coincide with regimes of anomalous cyclone to the coast and a boundary layer deepening (Fig. 8d). Regionally, satellite CTH (Fig. 7d) shows shallower clouds along the coast (a region with cooler sea surface), with cloud top deepening farther offshore over warmer waters. Most synoptic regimes exhibit mean CTH values within 800 m along coastal California, except regimes 5–7, which reach ∼ 1 km. Deseasonalized CTH patterns highlight distinct variations: most anomalous anticyclone dominant regimes (1–4) display systematically lower cloud tops, while the other regimes except regime 8 show higher cloud tops, particularly over the Southern California.

Regime-specific comparison of SatCORPS satellite retrievals and EPCAPE ground-based observations of key cloud properties are shown in Fig. 9. The SatCORPS satellite data here correspond to the 0.2° grid box around Scripps Pier. Both GOES-18 and KAZR-derived CTHs exhibit similar variability across regimes however satellite derived CTHs shows consistently higher range up to an overestimate of ∼ 250 m. A key difference is the smaller lower quartile magnitude for the KAZR, likely attributed to the ability of the radar to detect small shallow cumulus clouds that are undetected at the satellite pixel resolution. Both the ground based and remotely derived CTH show the shallowest median cloud tops (< 1000 m) in regimes 1–4, where the anticyclone is centered closer to Scripps Pier, as indicated by positive SLP anomalies over the pier and adjacent land areas (Figs. 7d and 8d). In contrast, deeper cloud tops are observed in regimes with an anticyclone accompanied by a weak coastal low (regimes 5–8, Fig. 2a), reflected in negative anomalous SLP around Scripps Pier (Fig. 3a). To note, highest surface precipitation events were noticed in regimes 6 and 7 (0.45 % and 0.95 % occurrence frequency) although which is < 0.1 % occurrence frequency and the datasets are removed from the analysis. The ceilometer derived CBH in Fig. 9b shows that regime 6 often develops cloud bases closer to the surface (median ∼ 250 m), under the most stable boundary layer conditions (Fig. 6a). The mean ceilometer derived CBH in Fig. 9b is within 450 m in regimes with an anticyclone centered close to Scripps Pier (regimes 1–4). Regimes 5–8, on the other hand, show the highest mean cloud base (> 600 m) in accordance with increased TKE, deeper, and decoupled BL. Figure 9c shows LWP variation across the regimes from both the ground-based MWR and GOES-18 SatCORPS retrievals, both exhibit broadly consistent regime-to-regime variability except that the MWR LWPs are slightly higher. At the Scripps Pier site, regime 2, characterized by moderate synoptic forcing, shows the lowest mean LWP. Regimes 5–8 consistently exhibit reduced LWP, whereas the climatologically closer depicting regime 9 displays largest LWP.

To assess how synoptic regimes modulate aerosol variability and transport at Scripps Pier, we analyze number concentrations of smaller particles (Na ≤ 0.1 µm, Na≤0.1) and larger accumulation-mode particles (0.1 < Na < 1 µm; Na>0.1), derived from merged SMPS and APS measurements (Fig. 10a). Smaller particles are typically associated with fresh local emissions and pollution, whereas larger particles reflect aging-driven growth or contributions from marine and continental sources such as sea spray or dust. Both size modes exhibit substantial variability across synoptic regimes. As inferred from Fig. 10a, upper quartile concentrations of Na≤0.1 exceed 600 cm⁻³ for all the regimes, but with the lowest medians for regimes 6 and 9. While Na>0.1 generally follow a similar regime evolution with the median values > 300 cm⁻³, regimes 3, 4, and 9 exhibit a narrow range of these particles. Overall, Na≤0.1 are significantly higher than Na>0.1 in most regimes.

In terms of CCN at 0.2 % of supersaturation (CCN_0.2 %, Fig. 10b, blue), regime changes are somewhat similar to Na>0.1, with concentrations comparable to those for Na≤0.1. CCN at 0.1 % supersaturation (CCN_0.1 %) is, as expected, smaller than CCN_0.2 % but with qualitatively similar regime changes. The accumulation mode particle concentration Na≤0.1 is higher than CCN concentrations at 0.1 % SS but lower than CCN concentrations at 0.2 % SS. This finding is consistent with Dedrick et al. (2026) that found SS below 0.2 % at Scripps Pier. Changes in CCN with supersaturation can be further analyzed in the context of clouds by examining variation in Nd derived from GOES-18 and from the remote sensors at the pier (Fig. 10c). Nd values are similar to the concentrations for CCN_0.1 %, suggesting small magnitudes of supersaturations present in this region, limiting the number of particles activated despite relatively large aerosol concentrations (Fig. 10a). Both satellite and the ground-based remote sensors show similar changes in Nd with regimes, but with larger interquartile ranges for the ground-based observations. The discrepancies between ground-based and GOES-18 Nd cannot be easily explained as we do not count on independent in-situ observations to validate these computations. Therefore, the data interpretation will be based on features common to both satellite and ground-based Nd. Regimes 2 as well as 6-8 consistently exhibit higher Nd with the mean Nd ranging ∼ 250–400 cm⁻³, whereas regimes 1, 3, 4, and 9 show lower mean Nd (∼ 200–250 cm⁻³). This variability in Nd aligns with TKE pattern in regimes (Fig. 6c); as regimes with stronger turbulence tend to exhibit higher Nd, even when particle number concentrations and CCN concentrations are comparable to, or lower than, those in the weekly turbulent regimes.

While aerosol loading is controlled by synoptic and local variability, Nd responses are likely influenced by BL turbulence and cloud dynamics. For assessing the impact of turbulence in aerosol and cloud droplet concentration, we bin Nd as function of accumulation mode Na (Na>0.1) and TKE bins (Fig. 11). As TKE increases from 0.1 to 0.5 m²s⁻², Na>0.1 increase from 320 to 520 cm⁻³, suggesting that enhanced turbulence is associated with higher concentration of accumulation mode aerosols. In addition, we observe an increase of the ground-based Nd with TKE, with changes that exceed 200 cm⁻³, reinforcing the role of turbulence in strengthening aerosol activation into CCN. The averaged Nd as a function of TKE and Na indicates that an increase in Na combined with strong TKE leads to a higher Nd. It appears that the relationship between TKE and Nd are tighter, only when there is a sufficiently higher concentration of accumulation mode aerosol particles (Na>0.1 >400 cm⁻³).

Backtrajectories in Fig. 12 are dominated by a flow that runs parallel to the coast (Han et al., 2025; Maneenoi et al., 2025). A departure from this pattern is observed for regimes 1–3, which feature a significant number (> 30 %) of trajectories that originate from or intersect land areas. Given the modest changes in the backtrajectory pattern per regime, it is not possible to establish a relationship between long-range transport, backtrajectories, and aerosol variability. This suggests that for this coastal site, local aerosol sources and transport (e.g., land-sea breeze), are, at least, as important as the synoptic meteorology for explaining aerosol variability. In terms of aerosol composition, non-refractive organics is the dominant species at Scripps Pier, followed by sulfate and ammonium (Fig. 13). A synoptic-scale maximum in all species (organics, sulphate, Ammonium and Nitrate) is identified for regime 4. Regimes 3 and 5 exhibit a minimum in organics. With greater percentage of land intersecting trajectories, regime 2 depict higher concentration of organics, however, the species concentrations are non-correlative nature to the land intersecting trajectories likely due to the seasonally dominant marine sources of these species.