The Journey of a Raindrop: How Motions Inside Marine Clouds Decide If, and How, It Rains

“The Journey of Water Drops Matters in Predicting Rain: Long- term observations of marine clouds reveal how in- cloud motions shape rain” is a Brookhaven National Laboratory (BNL) news release (published around late October 2026) about a study in Proceedings of the National Academy of Sciences.

Researchers analyzed more than 700,000 cloud samples from multiyear observations at four oceanic sites (using DOE’s Atmospheric Radiation Measurement and ARM User Facility data). They focused on warm-rain (drizzle) formation in marine low- level clouds, stratocumulus and shallow cumulus.⁠

• Marine stratocumulus (extensive low clouds over oceans) readily produce drizzle, even with relatively little liquid water. Precipitation appears beneath them in >56% of cloudy columns with liquid water path (LWP) <130 g m⁻², and >84% when LWP exceeds that threshold. Barriers to precipitation onset appear weaker than often assumed.⁠
• For the same LWP, oceanic cumulus precipitate less frequently. When they do, the drops are roughly twice as large but tens to hundreds of times fewer than those under stratocumulus.⁠

These differences align with cumulus having roughly twice as strong updrafts and lower adiabaticity than stratocumulus. The history and path of individual drops through the cloud, encountering liquid- water- rich regions (favoring collision-coalescence growth) versus drier patches (favoring evaporation), strongly shapes the number and size of drops that reach below cloud base.

Stratocumulus precipitation is broadly consistent with gravitational free- fall growth via collision -coalescence at low LWP; cumulus properties require additional dynamical processes.

Cloud- regime properties (especially small- scale in- cloud motions and structure) influence warm precipitation more than regional environmental variability.

The results support improving how models parameterize precipitation formation by more explicitly accounting for dynamical effects on drop growth histories, and they motivate further drop- tracking approaches in observations and predictions.

Lead authors and researchers associated with the work include Katia Lamer, Pavlos Kollias, and others from BNL (with collaborators).

The full paper title is “In- cloud motions shape warm- rain production in marine clouds: Evidence from multiyear observations.”

Primary sources: BNL Newsroom and related coverage on phys.org.

ARM (Atmospheric Radiation Measurement) User Facility is a U.S. Department of Energy (DOE) national scientific user facility that provides long-term, continuous atmospheric observations, especially of clouds, aerosols, radiation, and related processes, to support climate and weather research.

It has collected data for over 30 years from fixed observatories, mobile facilities, aerial platforms, and field campaigns worldwide, archiving tens of petabytes (recent figures exceed 7- 8 PB).

Key Sites Relevant to Marine Clouds

• Eastern North Atlantic (ENA): Fixed ARM observatory on Graciosa Island in the Azores (Portugal). Focuses on marine stratocumulus and related low-level clouds.
• Gan Island (GAN): Related to ARM deployments in the Maldives and Indian Ocean region.
• Marine ARM GPCI Investigation of Clouds (MAGIC): Ship- based campaign along a route between Los Angeles and Hawaii, sampling subtropical marine clouds.
• Barbados Cloud Observatory (BCO): Long- term site operated primarily by the Max Planck Institute for Meteorology (with partners); provides complementary trade- wind cumulus observations. Data are available through BCO and MPI channels and some shared archives; it is frequently used alongside ARM marine data.⁠

Other major fixed ARM sites include the Southern Great Plains (SGP, Oklahoma) and North Slope of Alaska (NSA).

How to Access and Explore ARM Data

Data are free to anyone (no fees), but a free ARM user account is required for downloads and ordering. Registration is open to users from all institutions and countries.⁠

1. Create an account: Go to arm.gov and set up a user profile.
2. Data Discovery (primary interface):
   • URL: Typically accessed via arm.gov/data → Data Discovery (or directly at adc.arm.gov/discovery/ in many references).
   • Features: Keyword and guided search, location, instrument and measurement filters, timeline views, quality reports, quick- look plots, citations (with DOIs), and ordering.
   • Recent enhancements (as of 2025– 2026): Integration of external data (e.g., ACTRIS, MODIS/VIIRS satellite), AI/ML- based recommendations (opt-in), epoch case illustrations (e.g., for mesoscale cellular convection at ENA), and improved search.⁠
3. Data formats and tools:
   • Most data are in netCDF (self-describing, with metadata). Some use HDF or GRIB.
   • Tutorials: “Introduction to Reading and Visualizing ARM Data” (Python-focused PDF), Data Discovery video tutorial, and webinars.
   • Python tools: Atmospheric Community Toolkit (ACT) for downloading and analysis (e.g., act.discovery.download_arm_data).
   • ARM Data Workbench and Data Studio: Interactive Jupyter environments, visualization, and no- code analysis options. High-performance computing resources are also available.
4. Data types:
   • Routine instrument datastreams (near real- time, typically available within ~48 hours).
   • Value- Added Products (VAPs): Higher- order, processed products (e.g., cloud properties, thermodynamic phase).
   • Field campaign data.
   • Principal investigator (PI) products and external complementary data.
   • Quality reports, plots, and documentation.
5. Usage guidelines: Acknowledge ARM in publications. Data are open and publishable upon receipt (with appropriate credit). Early access applies during some campaigns.

Getting Started Tips for Marine Cloud and Precipitation Studies

• Search Data Discovery for sites like “ENA,” instruments such as cloud radar (e.g., Ka- band Doppler), lidar, microwave radiometer, or measurements related to LWP, Doppler velocity, reflectivity, or precipitation.
• Look for VAPs related to cloud properties or warm- rain processes.
• For the specific study’s analysis, multiyear records from the sites above (plus BCO) were key for comparing stratocumulus vs. cumulus regimes.
• Complementary BCO data: Check Max Planck Institute for Meteorology resources or SAMD archives for radar and lidar products.

Official starting points:

• Main data page: https://www.arm.gov/data
• Work with data and tutorials: https://www.arm.gov/data/work-with-arm-data/
• Home: https://www.arm.gov/

Comparison of the 2026 PNAS findings (Lamer et al.) to other studies on warm- rain processes in marine clouds

The PNAS paper (“In- cloud motions shape warm- rain production in marine clouds”) uses multiyear ground- based observations (primarily ARM sites and BCO) of >700,000 cloud samples.

Its core claims are:

• Marine stratocumulus produce drizzle frequently even at relatively low liquid water path (LWP): >56% of columns with LWP <130 g m⁻² and >84% above that threshold.
• For the same LWP, shallow cumulus precipitate less often but, when they do, produce roughly twice as large and 10- 100 times fewer drops.
• These differences align with stronger updrafts and lower adiabaticity in cumulus, implying that drop growth histories (paths through heterogeneous moisture and motion fields) matter more than bulk LWP alone.
• Stratocumulus rain is broadly consistent with gravitational collision–coalescence; cumulus requires additional dynamical processes.
• Models should better represent small- scale dynamics; Lagrangian particle-tracking approaches are motivated.
• Satellite radars (especially CloudSat) underestimate light precipitation due to surface clutter and sampling limits.⁠

Below is how these findings relate to prior and concurrent work.

1. Prevalence of drizzle in low-LWP stratocumulus

The high frequency of precipitation at modest LWP challenges older assumptions of strong “barriers” to onset (often tied primarily to LWP or aerosol number).

Earlier satellite and modeling studies (e.g., using CloudSat and AMSR- E) often reported lower drizzle frequencies and suggested GCMs produce warm rain too frequently or too lightly. Ground- based Doppler spectra work (including earlier analyses by some of the same authors) already indicated higher prevalence (~65% at LWP ≈100 g m⁻²) than traditional reflectivity thresholds. The PNAS multi- site, multi- year statistics strengthen and generalize that view across oceanic regimes.⁠

Studies emphasizing effective radius (r_e) thresholds (commonly ~12- 14 µm near cloud top) for rapid coalescence onset (e.g., Rosenfeld- related work and numerical experiments) are compatible: once drops reach that size, drizzle develops quickly even at moderate LWP. The PNAS results imply the barrier is weaker in stratocumulus than often assumed, consistent with relatively high adiabaticity allowing efficient growth.

2. Stratocumulus vs. cumulus differences

The clear regime contrast (more frequent but smaller and more numerous drops under stratocumulus; rarer but larger, fewer under cumulus at fixed LWP) is a distinctive contribution.

Heuristic and bulk models (e.g., continuous- collection models for cumulus vs. steady- state autoconversion, accretion for stratocumulus) have long noted that rain sensitivity to droplet number (N_d) is stronger in weakly precipitating stratocumulus, while thicker cumulus become more controlled by liquid- water availability.

The PNAS observations provide direct statistical support for dynamical differences (updraft strength ≈2 times, lower adiabaticity in cumulus) driving divergent drop-size distributions and frequencies.

Earlier aircraft studies of mixed cumulus– stratocumulus systems (e.g., SOCEX or ASTEX- related work) documented vigorous drizzle in both but lacked the large statistical sample and systematic LWP- matched comparison across pure regimes.

3. Role of in- cloud motions, adiabaticity, and drop histories

Emphasizing drop trajectories through variable moisture and motion fields aligns with growing evidence from large- eddy simulations (LES) that incorporate Lagrangian microphysics. Turbulence, entrainment, supersaturation fluctuations, and recirculation can enhance growth beyond pure gravitational settling, especially in more heterogeneous cumulus.

Classic 1- D trajectory ideas (e.g., Bowen model with limited recirculation) and modern Lagrangian drop and particle-tracking simulations show that “lucky” parcels with high LWC and long residence times near cloud top initiate drizzle; mixing and dynamics then spread it. The PNAS call for more Lagrangian approaches in both models and observations is consistent with this literature.⁠

Adiabaticity differences are well- established: stratocumulus tend to be closer to adiabatic; cumulus experience more entrainment dilution. The paper links this quantitatively to observed precipitation contrasts.

4. Aerosol and giant CCN influences

Many studies highlight giant, ultra- giant cloud condensation nuclei (GCCN, often sea- salt) as accelerators of drizzle initiation via rapid condensational growth to sizes that trigger coalescence. Recent observational–modeling work (e.g., ACTIVATE- related analyses) shows enhanced rainwater ratios and faster onset with higher GCCN, especially in lightly precipitating marine low clouds.

The PNAS paper does not focus on aerosols as the primary control; instead it stresses dynamical regime differences at fixed LWP.

The two views are complementary: aerosols set the initial droplet spectrum and embryo availability, while in- cloud motions and structure determine subsequent growth pathways and outcomes.

In clean marine conditions, GCCN can lower barriers further, consistent with the relatively easy onset reported for stratocumulus.

5. Satellite limitations and model implications

The critique of CloudSat under-detection of light and shallow precipitation (surface clutter in the lowest ~1 km, sampling of small- scale features) is well-supported by earlier evaluations showing miss rates of ~40% or higher for oceanic warm clouds, especially those with low tops or small optical depths.

Ground- based radar and lidar combinations resolve cloud base and light drizzle far better, which is why the PNAS frequencies are higher.

On modeling: GCM autoconversion/accretion schemes that depend mainly on bulk cloud water and N_d (with simple enhancement factors for inhomogeneity) struggle with the regime dependence and history effects highlighted here.

The results support calls for improved representation of subgrid dynamics and, longer- term, Lagrangian or trajectory- aware approaches.

Summary of alignment and novelty

• Strong agreement with prior ground -based evidence of frequent low- LWP drizzle in stratocumulus, r_e thresholds for onset, adiabaticity contrasts, and the value of Lagrangian tracking.
• Extends earlier work by providing large, multi- regime statistics that isolate dynamics (updrafts, structure, drop histories) as a first- order control over bulk LWP or regional environment.
• Complements aerosol and GCCN studies: dynamics shape how (and how efficiently) the microphysical “stage” set by aerosols is used.
• Challenges purely bulk or satellite- derived pictures that understate light precipitation frequency and the role of small- scale motions.

Overall, the paper synthesizes and strengthens the view that warm- rain outcomes in marine clouds are strongly regime- and path- dependent, pushing both observational analysis and model parameterizations beyond simple LWP–N_d relationships.

In- cloud motions shape warm- rain production in marine clouds: Evidence from multiyear observations

Despite years of research, the processes controlling warm- rain formation remain uncertain. The authors used multiyear ground- based observations from four oceanic regions to systematically reassess precipitation properties just beneath cloud base.

Warm- rain characteristics are remarkably consistent within cloud regimes but differ substantially between stratocumulus and cumulus.

This indicates that cloud- regime properties exert a stronger influence on warm precipitation than regional environmental variability.

• Stratocumulus: Precipitation is observed beneath marine stratocumulus in >56% of cloudy columns with liquid water path (LWP) <130 g m⁻², rising to >84% for LWP >130 g m⁻². Marine stratocumulus routinely supports warm-rain formation; barriers to precipitation onset appear weaker than commonly assumed.
• Cumulus (same LWP): Oceanic cumulus precipitate less frequently. When they do, they produce on average two times larger and one to two orders of magnitude fewer precipitation drops.

This contrast is consistent with cumulus having roughly twice as strong updrafts and lower adiabaticity than stratocumulus, both of which affect drop growth histories.

Stratocumulus precipitation characteristics are generally consistent with growth by collision– coalescence during gravitational free fall (especially at low LWP). Cumulus properties cannot be explained without invoking additional processes.

Implications: Warm- precipitation parameterizations in models should more explicitly account for the influence of small- scale dynamical processes on precipitation growth.

The importance of drop growth histories motivates continued exploration of Lagrangian particle- tracking approaches in both models and observations.

Data come from:

• Eastern North Atlantic (ENA) observatory (Azores; stratocumulus focus, ~2015- 2018)
• MAGIC ship campaign (Los Angeles– Hawaii transect; stratocumulus)
• Barbados Cloud Observatory (BCO) (trade- wind cumulus, ~2018- 2022)
• AMIE- GAN (Gan Island, Maldives; cumulus)

Instruments included high -sensitivity Doppler radars, lidars, and microwave radiometers. Analysis focused on low -to- moderate LWP regimes (roughly 30–700 g m⁻²), with conditional sampling by cloud type based on macrophysical properties. Emphasis was placed on precipitation just below cloud base, drop size/number, updrafts, and adiabaticity.

The paper discusses limitations of satellite radar (e.g., CloudSat surface clutter and sampling issues that under-detect light precipitation in shallow clouds) and contrasts those with the advantages of ground- based observations for resolving cloud-base precipitation and microphysics.

For the full text, figures (including the site map and example radar scenes), methods, and supporting information, access the PDF via the DOI page, PubMed, or open repositories such as the one linked above.

Title: In- cloud motions shape warm- rain production in marine clouds: Evidence from multiyear observations

Journal: Proceedings of the National Academy of Sciences (PNAS)

DOI: 10.1073/pnas.2608656123

Provided: Brookhaven National Laboratory

Published: October 21, 2026 (online; print in Vol. 123, No. 43)

Authors: Katia Lamer (corresponding author, Brookhaven National Laboratory), Pavlos Kollias, Jiseob Kim, Zeen Zhu, Fan Yang, Alessandro Battaglia, and Edward Luke

License: Creative Commons Attribution-NonCommercial- NoDerivatives 4.0 (CC BY-NC-ND).

A full- text PDF is available via institutional repositories (e.g., Politecnico di Torino IRIS).

Abstract

Despite years of research, the processes controlling warm-rain formation remain uncertain. Leveraging multiple years of observations collected across four oceanic regions, we systematically reassess the properties of precipitation just beneath cloud base. The observations show that warm-rain characteristics are remarkably consistent within cloud regimes yet differ substantially between stratocumulus and cumulus, indicating that cloud-regime properties exert a stronger influence on warm precipitation than regional environmental variability. Precipitation is observed beneath marine stratocumulus in over 56% of cloudy columns with liquid water path (LWP) <130 g m⁻², rising to more than 84% in columns with LWP beyond 130 g m⁻². This indicates that marine stratocumulus routinely support warm-rain formation and suggests that barriers to precipitation onset in this regime are weaker than commonly assumed. For the same LWP, oceanic cumulus precipitate less frequently than stratocumulus but on average produce two times larger and one to two orders of magnitude fewer precipitation drops. This contrast is consistent with cumulus presenting two times stronger updrafts and lower adiabaticity than stratocumulus, both of which can affect drop growth histories. Whereas the precipitation characteristics of stratocumulus are generally consistent with growth by collision–coalescence during gravitational free fall in low LWP clouds, those of cumulus cannot be explained without invoking additional processes. Together, these findings suggest that warm-precipitation parameterizations should more explicitly account for the influence of small-scale dynamical processes on precipitation growth. The apparent importance of drop growth histories further motivates continued exploration of Lagrangian particle-tracking approaches in both models and observations.