Much of the uncertainty in projections of future climate is directly or indirectly related to clouds and their associated processes (IPCC, 2013), including shallow marine cumuliform clouds (Bony and Dufrense, 2005). The low-cloud–climate feedback is generally regarded to be positive (e.g., Clement et al., 2009). Many studies, however, suggest that the sign and magnitude of the feedback are cloud-type-dependent (e.g., Caldwell et al. 2013; Bretherton et al., 2013; Dal Gesso et al., 2015; Stephens, 2005; Yue et al., 2017; Zelinka et al., 2012).

Using large eddy simulation (LES) experiments forced with doubled CO2, Bretherton et al. (2013) show that the gradient of relative humidity (RH) from the marine boundary layer (MBL) to the free troposphere is a key factor that controls the shortwave cloud radiative feedback. Rieck et al. (2012) used LES forced by perturbed lower-tropospheric temperature profiles with fixed RH to show that an increase in surface moisture fluxes leads to a drying of the trade-cumulus-topped MBL. The drying overwhelms the increased shortwave reflection from the liquid water lapse rate feedback, thus leading to reduced cloudiness and a positive shortwave cloud feedback. These mechanisms are also discussed by Nuijens and Stevens (2012) in the context of bulk theory and clearly demonstrate that free-tropospheric temperature and moisture gradients act as constraints for climate-change-induced surface flux changes.

While the constant RH framework is a useful concept to investigate cloud–climate feedback in simplified modeling experiments, an overall reduction of RH in the subtropical free troposphere was found in the Coupled Model Intercomparison Project Phase 3 (CMIP3; Sherwood et al., 2010; Fasullo and Trenberth, 2012) and Phase 5 (CMIP5; Lau and Kim, 2015) archives with a non-negligible spread in the changing magnitude and vertical structure of RH among the models. Therefore, the assumption that constant RH might hold across the diversity of subtropical cloud regimes with a changing climate is likely not valid. Medeiros and Nuijens (2016) showed that the RH gradient between the MBL and lower free troposphere is widely variable among the CMIP5 models within the trade cumulus regime. Therefore, further examination of cloud variability and the vertical structure of RH with present-day satellite and reanalysis observations is warranted.

A strong linkage between cloud amount and estimated inversion strength (EIS; Wood and Bretherton, 2004), lower-tropospheric stability (LTS) (Klein and Hartmann, 1993), and moist static energy differences (dMSE) between the free troposphere and surface (Kawai and Teixeira, 2010; Chung et al., 2012; Kubar et al., 2015) is well understood. Satellite observations of the MBL have revealed prodigious variations of cloud organization that span orders of magnitude over spatial and temporal scales (Cahalan et al., 1994; Wood and Hartmann, 2006; Muhlbauer et al., 2014). Even for a fixed value of cloud fraction, a large diversity of statistical variability may be observed (Kawai and Teixeira, 2012). Correlations of cloud fraction to other environmental variables are highly dependent on the timescale of comparison (e.g., Brueck et al., 2015). At present, the relationships of cloud fraction and spatial variability to larger-scale properties other than EIS/LTS remain poorly understood. Furthermore, previous work has emphasized correlations of MBL cloud properties to 500 hPa vertical velocity and RH that are averaged over monthly, seasonal, or annual timescales. Kawai and Teixeira (2010) found significant correlations for instantaneous observations of cloud inhomogeneity and the skewness of liquid water path (LWP) to thermodynamic structure changes between 850 and 1000 hPa; the correlations are larger for LWP than with cloud fraction.

Modeling and observational studies have demonstrated that the vertical structures of moments of conserved thermodynamic variables depend on the cloud regime (e.g., Suselj et al., 2013; Ghate et al., 2016; Zhu and Zuidema, 2009). Substantial differences exist between stratocumulus and trade cumulus in the mean, variance, skewness, and kurtosis of equivalent potential temperature θe, liquid water potential temperature θl, and vertical velocity profiles, and they point to the importance of a global perspective uniquely provided by satellite and reanalysis data. The NASA A-train (Stephens et al., 2002) provides a wealth of remote-sensing data about the microphysics and thermodynamics of the cloudy MBL. Reanalysis data such as the Modern Era Retrospective-Analysis for Research and Applications (MERRA; Rienecker et al., 2011) offer a complementary set of thermodynamic and dynamic variables that help establish a larger-scale perspective for coincident remote-sensing observations.

Our primary purpose is to investigate instantaneous relationships between cloud microphysical and optical properties, dynamics, and thermodynamic variables from the A-train and MERRA at the native temporal and spatial resolution of the observations. The satellite and reanalysis data each provide unique information that should ideally be combined together at the native resolution rather than relying on one instrument or reanalysis alone, or combining over time and space averages. The geophysical fields are retained at the native spatial and temporal resolution such that the instantaneous spatial “snapshots” of the cloud probability density function (PDF) are preserved and are then conditioned by available thermodynamic and dynamic variables. This approach removes the temporal variability in order to focus on the spatial variability and covariances. The statistical behavior of cloud properties, and how the thermodynamic and dynamic state variables are related to them, is thus inferred using the finest temporal and spatial resolutions available. The different instruments and reanalysis data sets are treated as “building blocks” that construct a simultaneous view of the MBL, playing on the strengths of each data set. This holistic synthesis of multivariate and multimoment data sets may highlight aspects of MBL structure that are otherwise overlooked. The MBL structures of interest are summarized in Nuijens et al. (2009) using surface-based observations and demonstrate testable relationships between clouds, wind, humidity, and precipitation. Lastly, the approach taken herein may ultimately enhance our ability to quantify the complex time, space, and cloud regime coupling of clouds and circulation (Bony et al., 2015).

Section 2 describes the data sets used, while Sect. 3 details the methodological approach taken in this investigation. Section 4 details the regional maps, while Sect. 5 examines the joint PDFs. We conclude in Sect. 6.

The Atmospheric Infrared Sounder (AIRS)/Advanced Microwave Sounding Unit (AMSU) sounding suite located on board NASA's Earth Observing System (EOS) Aqua satellite has obtained vertical profiles of temperature and water vapor at approximately 45 km horizontal resolution since September 2002 (Chahine et al., 2006). While AIRS cannot capture the sharpness of the temperature and water vapor mixing ratio gradients across the top of the MBL (Maddy and Barnet, 2008; Yue et al., 2011), the coarse-resolution vertical gradients from the surface to the lower free troposphere are obtained with high fidelity as shown in validation studies using numerical weather prediction model data or radiosondes (Yue et al., 2013; Kalmus et al., 2015). The AIRS operational products also provide numerous cloud variables that include effective cloud fraction (ECF), cloud thermodynamic phase (liquid, ice, and unknown categories), and others (Kahn et al., 2014). A MBL depth estimate inferred from the height/pressure of maximum RH gradient is described and validated with radiosondes launched during the Rain in Shallow Cumulus over the Ocean (RICO) campaign in Martins et al. (2010).

The AIRS version 5 channel 4 visible spectral radiance (0.49–0.94 µm) (Gautier et al., 2003; Aumann et al., 2006) with a nadir spatial resolution of 2.28 km is used and has units of W m-2 µm-1 sr-1. AIRS visible-band data are co-registered to the AIRS IR footprint such that 72 visible pixels are aligned within every footprint. A prototype AIRS visible cloud mask (Gautier et al., 2003) that was developed to support earlier algorithm development efforts is also used. Although the cloud mask has not been compared directly against benchmarks such as the Moderate-Resolution Imaging Spectroradiometer (MODIS) cloud mask, manual inspection suggests that this cloud mask tends towards clear-sky conservative and captures many shallow, broken subpixel cumulus clouds.

The MODIS instrument on EOS Aqua is capable of observing a wide variety of land, ocean, and atmospheric variables (Platnick et al., 2017) that are collocated to the AIRS field of view (FOV). We use the Collection 6 liquid phase cloud optical thickness τ and effective radius re retrievals from the MYD06_L2 swath product and the 1 km cloud mask from the MYD035_L2 swath product. Platnick et al. (2017) show that the re change between C5.1 and C6 is ±1–2 µm. We have tested the differences in the PDFs between C5.1 and C6 for a subset of the data investigated, and very little change in the PDFs was observed (not shown). The MODIS liquid cloud re is used as a proxy for precipitation and is verified with the CloudSat 2C-RAIN-PROFILE (Release 4) precipitation product (L'Ecuyer and Stephens, 2002).

The MERRA instantaneous, 6-hourly, native-resolution, gridded data sets at 1/2∘ × 2/3∘ (Rienecker et al., 2011) are used to assess the thermodynamic profiles derived from AIRS, assign vertical profiles of horizontal u and v wind components, and assign vertical profiles of pressure velocity ω in the MBL and lower free troposphere. All of the instantaneous MERRA data are spatially and temporally matched to the A-train orbit using a nearest-neighbor matching approach with no time interpolation.

The matching approach uses a nearest-neighbor technique weighted by the sensor spatial response function (Schreier et al., 2010). The mean, variance, and skewness of MODIS cloud properties at 1 or 5 km resolution is retained within a larger 45 km resolution AIRS/AMSU field of regard (FOR), while MERRA's 1/2∘ × 2/3∘ resolution thermodynamic and dynamic variables are matched to the nearest AIRS/AMSU FOR.

Four subtropical oceanic regions that capture transitions from stratocumulus to trade cumulus are investigated. The four regions are greatly expanded in scale from those used in Klein and Hartmann (1993) to investigate the stratocumulus-topped MBL and are listed in Table 1. While all available daytime (ascending) orbits from 1 January 2009 to 31 December 2009 were used, the remaining discussion is limited to the seasons that contain the observed peak in cloud frequency listed in Table 1 (Klein and Hartmann, 1993).

Figure 1a is an example visible image for a 6 min AIRS granule within the southeast Atlantic Ocean (SEA). The visible band captures various spatial structures of clouds. The cloud mask derived from AIRS visible bands for the same granule is shown in Fig. 1b. The cloud mask is used to narrow down the spatial sampling for the following analysis. The cloud mask likely includes instances of clear sky, but the approach only requires a coarse masking approach to filter out a majority of the clear-sky pixels. We will discuss implications regarding the filtering process in Sect. 4.

Removal of pixels containing mid- and high-level clouds helps to reduce ambiguities introduced by free-tropospheric clouds and also a portion of the thermodynamic and dynamic variability associated with cloudy areas of synoptic-scale waves. Figure 2 shows the AIRS infrared Tb within a clean atmospheric window at 1231 cm-1, the cloud thermodynamic phase mask, three constant pressure levels of AIRS RH (700, 850, and 925 hPa), and the skewness of visible radiance for the same granule shown in Fig. 1. The cloud thermodynamic phase identifies some scattered ice in the northern portion of the granule. All pixels identified with ice are removed in the following analysis. Jin and Nasiri (2014) showed that AIRS successfully identifies the presence of ice within the AIRS FOV in excess of 90 % of the time when compared to Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) thermodynamic phase estimates at the pixel scale. A similar approach is taken in Nam et al. (2012) and Myers and Norris (2015) to minimize impacts from convection and synoptic-scale weather systems. Additional occurrences of Tb,1231 < 273 K that potentially contain supercooled liquid phase mid-level clouds are also removed.

As the AIRS cloud phase algorithm is based on a channel selection that exploits differences in the index of refraction for liquid and ice, the cloud amount observed in the AIRS pixel is frequently small enough that the spectral signature does not trigger a positive liquid test (e.g., Jin and Nasiri, 2014). The ECF for these unknown phase cases can simultaneously be well above the sensitivity of cloud detection (validated using CALIPSO lidar; see Kahn et al., 2014). As a result, none of the phase tests are triggered even though cloud is observed within the AIRS pixel. These unknown cases line up very well with the frequency of trade cumulus in the four regions selected based on inspection of individual granule data (e.g., Figs. 1 and 2) and gridded seasonal averages (Sect. 4).

The AIRS liquid detections coincide with uniform stratocumulus (Fig. 1) with close to normally distributed visible radiances (lower right, Fig. 2), while unknown detections correspond well to shallow cumulus with a distinctive positively skewed visible radiance, very similar to previous results obtained using LWP (Wood and Hartmann, 2006; Kawai and Teixeira, 2010). Previous investigations have used free-tropospheric vertical velocity to separate cloud regime types (e.g., Bony and Dufresne, 2005; Medeiros and Stevens, 2011; Nam et al., 2012). Henceforth, the two regimes defined exclusively by liquid and unknown phase detections will be generically referred to as stratocumulus and cumulus regimes, respectively. An advantage of this instantaneous approach is that the temporal and spatial variations of cumulus and stratocumulus cloud areas are more precisely separated from each other.

For the AIRS/AMSU FORs containing MBL clouds, the coincident AIRS and MODIS geophysical fields are collocated. The AIRS ECF is averaged over the entire AIRS/AMSU FOR where clear sky is equal to a value of 0. The AIRS thermodynamic phase is averaged over cloudy AIRS FOVs only. The individual phase tests are summed and liquid is defined for values < -0.8, unknown between -0.8 and +0.8, and ice for values > +0.8. The MODIS cloud mask and τ are averaged over the entire AIRS/AMSU FOR. The MODIS re is averaged only over the successful retrievals that are a subset of MODIS pixels identified as containing cloud. The nearest neighbor is matched for MERRA geophysical fields at a similarly sized spatial resolution. The mean, standard deviation, and skewness of MODIS and AIRS FOV properties are then calculated for each AIRS/AMSU FOR separately. Therefore, multiple satellite instrument and reanalysis observations at multiple spatial scales can be linked together through joint PDFs for a large combination of statistical moments. These data serve as the basis of the following investigation.

Regional-scale, seasonal averages are calculated from the pixel-scale data described in Sect. 3 for 90 daytime (130 pm Equator-crossing time) snapshots and are then re-gridded to 1∘ × 1∘ spatial resolution and help facilitate comparisons to previous studies. Figure 3 shows the visible radiance skewness for JJA in the northeast Pacific (NEP) and northeast Atlantic (NEA) regions, and SON in the southeast Pacific (SEP) and SEA regions, with an overlay of AIRS total ECF. The coastal stratocumulus radiances are distributed approximately normally, while the radiances are positively skewed away from the coast, where disorganized cumulus dominates (e.g., Wood and Hartmann, 2006). Contours of the magnitude of radiance skewness closely align with the magnitude of ECF in cumulus but much less so in proximity to the coast within stratocumulus. Very poor spatial correspondence between radiance skewness and the mean value of MODIS cloud fraction was found (not shown) and is consistent with low correlations between Geostationary Operational Environmental Satellite (GOES)-derived cloud fraction and LWP noted by Kawai and Teixeira (2010) in the SEP region. Interestingly, the average radiance skewness is larger and ECF is smaller in the NEA than the other three regions, which is consistent with other satellite observations (Klein and Hartmann, 1993; Rossow and Schiffer, 1999) and ship-based observations (Wood, 2012). The patterns of radiance skewness shown in Fig. 3 also resemble typical climatological patterns of cloud sizes reported in Wood and Field (2011) and cloud texture as viewed from the Multi-angle Imaging SpectroRadiometer (MISR) (Zhao et al., 2016).

Values of MODIS total water path (TWP) skewness do not show a clear transition from normally distributed to positively skewed values in Weber et al. (2011). This further motivates the removal of mid- and high-level cloud occurrences using the AIRS phase mask that comprise anywhere from 4 to 18 % of the total number of FOVs depending on the region of study (Table 1). The total number of collocated data points within each region is roughly ∼ 180,000. However, the AIRS and MODIS cloud fields have smaller spatial resolutions that are aggregated to the AIRS/AMSU field of regard, and the raw counts for these fields number in the millions. Oreopoulos and Cahalan (2005) show that the inhomogeneity parameter calculated from MODIS LWP, rather than TWP, is most homogeneous near the coast and indicates increasing heterogeneity that extends into the cumulus regimes. We argue that the results of Oreopoulos and Cahalan (2005) are more definitive than those shown in Weber et al. (2011) and more closely resemble the gradients and magnitudes contained within Fig. 3.

There are several factors that contribute to relationships between ECF and the various moments of radiance. A reduced ECF and increased radiance skewness (Fig. 3) may indicate smaller cloud sizes, but this is probably not universally true. If the cloud optical thickness is decreased, the ECF is also decreased from reductions in cloud emissivity even though cloud coverage itself may remain constant. (Recall that the ECF is a convolution of emissivity and cloud fraction.) If the cloud optical thickness is fixed, the cloud emissivity remains fixed even though the cloud coverage itself and ECF could be decreased. The ECF could also be decreased (increased) if small cloud elements become more widely spaced (packed together), assuming the cloud sizes of the individual cumulus elements remain the same. With respect to the visible radiances, the radiance is decreased if cloud elements become smaller than the nominal 2.2 km pixel size, assuming the optical thickness of the cloud elements does not change. Therefore, if an increased proportion of a cloud population with normally distributed radiances becomes subpixel in size, one would expect a shift towards positive skewness. If cloud distributions are spatially resolved, an increased skewness radiance is still entirely possible if the optical thickness of cloud distributions is skewed itself. However, in this investigation, the skewness of the MODIS optical thickness is less skewed at low ECF than visible radiance (not shown). This suggests that the skewness in the visible radiance at low ECF at least partially arises from smaller cloud sizes.

The mean MBL depth (Fig. 4) reaffirms a characteristic transition from shallow MBLs (920–970 hPa) near the coast to deeper MBLs (830–880 hPa) to the west and is a well-observed feature of the stratocumulus-to-cumulus transition previously observed by Karlsson et al. (2010), Teixeira et al. (2011), and others. Closest to the coast, the MBL is shallowest in the NEA and slightly deeper in the NEP. The SEA and SEP are deeper than their Northern Hemisphere (NH) counterparts, with SEP the deepest. The SEP MBL depths agree with VAMOS Ocean–Cloud–Atmosphere–Land Study Regional Experiment (VOCALS-REx) in situ radiosonde-derived temperature inversion base heights described by Bretherton et al. (2010). Furthermore, the inter-regional differences in MBL depth show consistency with Global Positioning System–radio occultation (GPS-RO) data described by Chan and Wood (2013).

dMSE between 700 and 1000 hPa are calculated following the approach outlined by Kubar et al. (2012) and are also shown in Fig. 4. The dMSE is calculated from quality-controlled AIRS soundings (PGood ≥ 1000 hPa) and is nearly identical to estimates from ERA-Interim shown by Kubar et al. (2012). The magnitude of dMSE is larger and positive near the coast in the Southern Hemisphere (SH) compared to the NH and is somewhat reduced in the NEA region. Yue et al. (2011) showed that values of EIS and LTS obtained from AIRS soundings are lower in the NEA compared to the other three regions and are also consistent with Fig. 4.

Seasonal averages of AIRS RH700 with an overlay of the corresponding MERRA–AIRS RH700 differences are shown in Fig. 5. Wind vectors depict the mean horizontal flow. Overall, RH700 in the SH is lower than the NH, while the NEA is the moistest of the four regions, and SEP the driest. MERRA is on average moister than AIRS by ∼ 5 % in the NH, nearly identical to AIRS in the SEA, and a much more spatially heterogeneous difference is observed in the SEP from the coastal proximity westward between 8 and 12∘ S.

Bretherton et al. (2010) demonstrate that the free troposphere in the SEP westward of 75∘ W is characteristically very dry (0.1 g kg-1) with sporadic filaments of moist air (as high as 3–6 g kg-1) up to an altitude of 2.5 km. In addition, these moist filaments have been observed with GPS-RO refractivity profiles by von Engeln et al. (2007). The vertical structure of RH obtained from VOCALS-REx radiosondes implies a well-mixed MBL near the coast with MBL decoupling west of 80∘ W. Myers and Norris (2015) showed that 700 hPa is drier in the SH subtropics than in the NH using ERA-Interim data. When general circulation models are sampled for RICO-like conditions using representative mid-tropospheric large-scale vertical velocities as in Medeiros and Stevens (2011), a dry bias is obtained above the MBL in comparison to a composite of RICO radiosondes.

The seasonal averages of AIRS RH850, and the corresponding MERRA–AIRS RH850 differences, are larger than those found for RH700 (Fig. 6) and are due to temperature and water vapor weighting function widths on the order of 2–3 km (Maddy and Barnet, 2008).

In summary, the seasonal averages exhibit realistic three-dimensional spatial morphologies and gradients and show consistency with MERRA RH in the subtropical MBL. The MBL depth and seasonal variations (not shown) agree with GPS-RO (Chan and Wood, 2013). The AIRS-derived dMSE between 700 and 1000 hPa agrees with ERA-Interim (Kubar et al., 2012). The radiance skewness is strongly related to dMSE (Kawai and Teixeira, 2012). The AIRS ECF distributions closely correspond to well-established climatologies of cloud amount (e.g., Klein and Hartmann, 1993; Rossow and Schiffer, 1999; Wood, 2012). The vertical structure of the horizontal wind flow well represents known climatological patterns in the MBL and lower free troposphere. While the variability within each region and between the four regions is consistent with previous studies, the physical reasons for these differences are beyond the scope of the current investigation.

The global-scale relationships and coupling of cloud fraction and spatial variability to thermodynamic and dynamic properties of the atmosphere remain poorly understood (Bony et al., 2015). The NASA A-train (Stephens et al., 2002) provides a wealth of remote-sensing data about the microphysics and thermodynamics of the cloudy MBL. MERRA (Rienecker et al., 2011) offers a complementary set of thermodynamic and dynamic variables that helps establish context for coincident remote-sensing observations. The synergy between satellite and reanalysis data at the native spatial and temporal resolutions available has not been fully exploited to date. We describe a new approach that leverages coincident reanalysis and remote-sensing data at the native resolution of the observations. The spatial variability of clouds, and the relationship to thermodynamic and dynamic state variables, is thus inferred using the finest temporal and spatial resolutions available.

Four subtropical oceanic regions that capture transitions from stratocumulus to trade cumulus are investigated. We define two regimes based exclusively on liquid and unknown cloud thermodynamic phase detections with the AIRS instrument, and we generically refer to them as stratocumulus and cumulus regimes, respectively. The mean, standard deviation, and skewness of MODIS and AIRS FOV cloud properties and visible radiances are calculated for each AIRS and MERRA temperature and humidity observation.

As with previous findings, coastal stratocumulus radiances are approximately normally distributed, while the radiances are positively skewed away from the coast, where disorganized cumulus dominates. The radiance skewness closely aligns with the magnitude of AIRS ECF in cumulus with less correspondence in stratocumulus. Strong (poor) spatial correspondence between radiance skewness and AIRS ECF (MODIS cloud fraction) was found, suggesting infrared-based ECF is a potentially valuable and unappreciated diagnostic for MBL cloud characterization. The mean MBL depth derived from AIRS (Martins et al., 2010) shows a characteristic transition from shallow MBLs (920–970 hPa) near the coast to deeper MBLs (830–880 hPa) away from the coast and is a well-observed feature of the stratocumulus-to-cumulus transition (e.g., Teixeira et al., 2011). The AIRS-derived dMSE between 700 and 1000 hPa agree very well with ERA-Interim (Kubar et al., 2012). We find that the radiance skewness is strongly related to the magnitude of dMSE as previously found by Kawai and Teixeira (2012). The MBL depth is shallower for stratocumulus than cumulus for almost all values of visible radiance and ECF.

The change in MBL depth is somewhat greater for an identical increase in radiance skewness when compared to τ skewness. The population of cumulus occurrences at low ECF for positive skewness > 1 is mostly absent in the τ data but are very common in radiance data. This highlights the importance of understanding the sampling from derived Level 2 products compared to Level 1 radiances that may capture a fuller range of the geophysical state in different cloud regimes.

The re in stratocumulus is about 11 to 12 µm for most values of radiance and ECF in all four regions of study. For cumulus, re ranges anywhere from 12 to 20 µm, with larger re for increasing positive skewness especially when ECF is small. The values of re appear to be tightly coupled to cloud microphysical processes that respond to changing MBL wind speed and a deepening MBL. We argue that for these successful MODIS retrievals the increase in re is consistent with increased droplet growth and hence precipitation occurrence. This may be caused by larger re in the cloud itself or by the association of precipitating clouds with an increased subpixel inhomogeneity that leads to the plane-parallel homogeneous bias; this topic warrants further investigation. In the SEP region, the elevated values of re that correspond with the increased u925 are more frequently precipitating according to CloudSat.

The RICO observations provide an important multiparameter testing benchmark (Nuijens et al., 2009). These results are generalized into three types of shallow precipitating cumulus regimes observed during RICO. The joint PDFs imply simultaneous increases in θe,700, θe,925, u925, and ECF in three of the four regions investigated (NEP, SEP, and SEA) with a strong correspondence between u925 and re. The NEA less clearly follows these behaviors and is an outlier, although this is based on one season's worth of data during 2009.

Future work will expand to other cloud regimes, additional data sets, and multiple years of data. A similar approach with numerical model output should also be attempted using temporal snapshots of similar geophysical fields. We expect that this approach will be especially useful for linking cloud microphysics together with the thermodynamic and dynamic state of the atmosphere at the process scale.