SEARCH · Search NASA

Results for “Cloud Processing”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 127 records · Page 7

All sources NASA NTRS DOE OSTI

NASA NTRS2010

Convective Distribution of Tropospheric Ozone and Tracers in the Central American ITCZ Region: Evidence from Observations During TC4

During the Tropical Composition, Clouds and Climate Coupling (TC4) experiment that occurred in July and August of 2007, extensive sampling of active convection in the ITCZ region near Central America was performed from multiple aircraft and satellite sensors. As part of a sampling strategy designed to study cloud processes, the NASA ER-2, WB-57 and DC-8 flew in stacked "racetrack patterns" in convective cells. On July 24, 2007, the ER-2 and DC-8 probed an actively developing storm and the DC-8 was hit by lightning. Case studies of this flight, and of convective outflow on August 5, 2007 reveal a significant anti-correlation between ozone and condensed cloud water content. With little variability in the boundary layer and a vertical gradient, low ozone in the upper troposphere indicates convective transport. Because of the large spatial and temporal variability in surface CO and other pollutants in this region, low ozone is a better convective indicator. Lower tropospheric tracers methyl hydrogen peroxide, total organic bromine and calcium substantiate the ozone results. OMI measurements of mean upper tropospheric ozone near convection show lower ozone in convective outflow. A mass balance estimation of the amount of convective turnover below the tropical tropopause transition layer (TTL) is 50%, with an altitude of maximum convective outflow located between 10 and 11 km, 4 km below the cirrus anvil tops. It appears that convective lofting in this region of the ITCZ is either a two-stage or a rapid mixing process, because undiluted boundary layer air is never sampled in the convective outflow.

Avery, Melody↗

NASA NTRS2021

Observations of Greenhouse Gases as Climate Indicators

Humans have significantly altered the energy balance of the Earth’s climate system mainly not only by extracting and burning fossil fuels but also by altering the biosphere and using halocarbons. The 3rd US National Climate Assessment pointed to a need for a system of indicators of climate and global change based on long-term data that could be used to support assessments and this led to the development of the National Climate Indicators System (NCIS). Here we identify a representative set of key atmospheric indicators of changes in atmospheric radiative forcing due to greenhouse gases (GHGs), and we evaluate atmospheric composition measurements, including non-CO2GHGs for use as climate change indicators in support of the US National Climate Assessment. GHG abundances and their changes over time can provide valuable information on the success of climate mitigation policies, as well as insights into possible carbon-climate feedback processes that may ultimately affect the success of those policies. To ensure that reliable information for assessing GHG emission changes can be provided on policy-relevant scales, expanded observational efforts are needed. Furthermore, the ability to detect trends resulting from changing emissions requires a commitment to supporting long-term observations. Long-term measurements of greenhouse gases, aerosols, and clouds and related climate indicators used with a dimming/brightening index could provide a foundation for quantifying forcing and its attribution and reducing error in existing indicators that do not account for complicated cloud processes.

greenhouse gases↗

DOE OSTI2025

NWB Sensors Infrared Cloud Imager Data Products from SGP

NWB Sensors is a company which has developed a commercially available Infrared Cloud Imager (ICI). For more information, consult the company's webpage, https://www.nwbsensors.com/infrared-cloud-imager. To validate the radiometric accuracy of the ICI, NWB Sensors deployed it to the ARM SGP User Facility in 2023. The primary motivation of this deployment was to perform an intercomparison between the ICI and the Atmospheric Emitted Radiance Interferometer (AERI). The AERI spectral radiance data product can be integrated across the response function of the ICI and directly compared to the zenith radiance observed by the ICI. In addition, the ICI uses proprietary models of the downwelling clear-sky radiance in its cloud processing algorithms. They are based on surface meteorology and precipitable water vapor (PWV). These models were validated by comparing their predicted radiances to those derived from radiative transfer models of the ARM radiosondes. Finally, PWV observations derived from the ICI's onboard GNSS-based PWV retrieval system were compared against those from the microwave radiometer. This dataset contains the ICI radiance and cloud data products.

Atmosphere↗

NASA NTRS2009

Convective and Stratiform Precipitation Processes and their Relationship to Latent Heating

The global hydrological cycle is central to the Earth's climate system, with rainfall and the physics of its formation acting as the key links in the cycle. Two-thirds of global rainfall occurs in the Tropics. Associated with this rainfall is a vast amount of heat, which is known as latent heat. It arises mainly due to the phase change of water vapor condensing into liquid droplets; three-fourths of the total heat energy available to the Earth's atmosphere comes from tropical rainfall. In addition, fresh water provided by tropical rainfall and its variability exerts a large impact upon the structure and motions of the upper ocean layer. An improved convective -stratiform heating (CSH) algorithm has been developed to obtain the 3D structure of cloud heating over the Tropics based on two sources of information: 1) rainfall information, namely its amount and the fraction due to light rain intensity, observed directly from the Precipitation Radar (PR) on board the TRMM satellite and 2) synthetic cloud physics information obtained from cloud-resolving model (CRM) simulations of cloud systems. The cloud simulations provide details on cloud processes, specifically latent heating, eddy heat flux convergence and radiative heating/cooling, that. are not directly observable by satellite. The new CSH algorithm-derived heating has a noticeably different heating structure over both ocean and land regions compared to the previous CSH algorithm. One of the major differences between new and old algorithms is that the level of maximum cloud heating occurs 1 to 1.5 km lower in the atmosphere in the new algorithm. This can effect the structure of the implied air currents associated with the general circulation of the atmosphere in the Tropics. The new CSH algorithm will be used provide retrieved heating data to other heating algorithms to supplement their performance.

Tao, Wei-Kuo↗

NASA NTRS2006

A Multi-scale Modeling System: Developments, Applications and Critical Issues

A multi-scale modeling framework (MMF), which replaces the conventional cloud parameterizations with a cloud-resolving model (CRM) in each grid column of a GCM, constitutes a new and promising approach. The MMF can provide for global coverage and two-way interactions between the CRMs and their parent GCM. The GCM allows global coverage and the CRM allows explicit simulation of cloud processes and their interactions with radiation and surface processes. A new MMF has been developed that is based the Goddard finite volume GCM (fvGCM) and the Goddard Cumulus Ensemble (GCE) model. This Goddard MMF produces many features that are similar to another MMF that was developed at Colorado State University (CSU), such as an improved .surface precipitation pattern, better cloudiness, improved diurnal variability over both oceans and continents, and a stronger, propagating Madden-Julian oscillation (MJO) compared to their parent GCMs using conventional cloud parameterizations. Both MMFs also produce a precipitation bias in the western Pacific during Northern Hemisphere summer. However, there are also notable differences between two MMFs. For example, the CSU MMF simulates less rainfall over land than its parent GCM. This is why the CSU MMF simulated less overall global rainfall than its parent GCM. The Goddard MMF overestimates global rainfall because of its oceanic component. Some critical issues associated with the Goddard MMF are presented in this paper.

Tao, Wei-Kuo↗

DOE OSTI2025

Multiscale Modeling Framework Using Element‐Based Galerkin Methods for Moist Atmospheric Limited‐Area Simulations

This paper presents a multiscale modeling framework (MMF) to model moist atmospheric limited-area weather. The MMF resolves large-scale convection using a coarse grid while simultaneously resolving local features through numerous fine local grids and coupling them seamlessly. Both large- and small-scale processes are modeled using the compressible Navier-Stokes equations within the Nonhydrostatic Unified Model of the Atmosphere (NUMA), and are discretized using a continuous element-based Galerkin method (spectral elements) with high-order basis functions. Consequently, the large-scale and small-scale models share the same dynamical core but have the flexibility to be adjusted individually. The proposed MMF method is tested in 2D and 3D idealized limited-area weather problems involving storm clouds produced by squall line and supercell simulations. Numerical results from the MMF showed enhanced representation of cloud processes compared to the coarse model.

Kang, Soonpil [Naval Postgraduate School, Monterey↗

NASA NTRS2021

On Assessing ERA5 and MERRA2 Representations of Cold-Air Outbreaks Across the Gulf Stream

The warm Gulf Stream sea surface temperatures (SSTs) strongly impact the evolution of winter clouds behind atmospheric cold fronts. Such cloud evolution remains challenging to model. The Gulf Stream is too wide within the ERA5 and MERRA2 reanalyses, affecting the turbulent surface fluxes. Known problems within the ERA5 boundary layer (too-dry and too-cool with too strong westerlies), ascertained primarily from ACTIVATE 2020 campaign aircraft dropsondes and secondarily from older buoy measurements, reinforce surface flux biases. In contrast, MERRA2 winter surface winds and air-sea temperature/humidity differences are slightly too weak, producing surface fluxes that are too low. Reanalyses boundary layer heights in the strongly-forced winter cold-air-outbreak regime are realistic, whereas late-summer quiescent stable boundary layers are too shallow. Nevertheless, the reanalysis biases are small, and reanalyses adequately support their use for initializing higher-resolution cloud process modeling studies of cold-air outbreaks.

Gulf stream↗

NASA NTRS2019

Arctic Cloud Radiative Forcing in Contemporary Atmospheric Reanalyses

Arctic clouds play an important role in modifying the surface energy balance. In the Arctic, clouds are thought to influence the underlying sea ice cover through changing downwelling longwave radiative fluxes to the surface and through the selective reflection of the shortwave flux in summer. Atmospheric reanalyses are generally thought to have a poor representation of cloud processes at high latitudes, although the representation of trends over the perennial Arctic sea ice pack is less well known. Here, atmospheric energy fluxes are examined at the top of the atmosphere from contemporary reanalyses in comparison to satellite measurements from the CERES-EBAF version 4.1 product. The principal reanalyses examined are the NASA MERRA-2, the ECMWF ERA5 and ERA-Interim, the JRA-55, and the regional Arctic System Reanalysis version 2. In agreement with previous observation-based studies, changes with time in the shortwave cloud radiative forcing in reanalyses are found to be negligible despite strong trends in the absorbed shortwave. Over the full satellite period, there is large disagreement in the seasonality of longwave cloud forcing trends. These trends are reduced during the CERES-EBAF observing period (2003-present). An examination of these trends with respect to sea ice cover changes in each of the reanalyses is conducted.

Cullather, Richard I.↗

NASA NTRS2015

How Difficult is it to Reduce Low-Level Cloud Biases With the Higher-Order Turbulence Closure Approach in Climate Models?

Low-level clouds cover nearly half of the Earth and play a critical role in regulating the energy and hydrological cycle. Despite the fact that a great effort has been put to advance the modeling and observational capability in recent years, low-level clouds remains one of the largest uncertainties in the projection of future climate change. Low-level cloud feedbacks dominate the uncertainty in the total cloud feedback in climate sensitivity and projection studies. These clouds are notoriously difficult to simulate in climate models due to its complicated interactions with aerosols, cloud microphysics, boundary-layer turbulence and cloud dynamics. The biases in both low cloud coverage/water content and cloud radiative effects (CREs) remain large. A simultaneous reduction in both cloud and CRE biases remains elusive. This presentation first reviews the effort of implementing the higher-order turbulence closure (HOC) approach to representing subgrid-scale turbulence and low-level cloud processes in climate models. There are two HOCs that have been implemented in climate models. They differ in how many three-order moments are used. The CLUBB are implemented in both CAM5 and GDFL models, which are compared with IPHOC that is implemented in CAM5 by our group. IPHOC uses three third-order moments while CLUBB only uses one third-order moment while both use a joint double-Gaussian distribution to represent the subgrid-scale variability. Despite that HOC is more physically consistent and produces more realistic low-cloud geographic distributions and transitions between cumulus and stratocumulus regimes, GCMs with traditional cloud parameterizations outperform in CREs because tuning of this type of models is more extensively performed than those with HOCs. We perform several tuning experiments with CAM5 implemented with IPHOC in an attempt to produce the nearly balanced global radiative budgets without deteriorating the low-cloud simulation. One of the issues in CAM5-IPHOC is that cloud water content is much higher than in CAM5, which is combined with higher low-cloud coverage to produce larger shortwave CREs in some low-cloud prevailing regions. Thus, the cloud-radiative feedbacks are exaggerated there. The turning exercise is focused on microphysical parameters, which are also commonly used for tuning in climate models. The results will be discussed in this presentation.

Xu, Kuan-Man↗

NASA NTRS1997

Global Troposphere Experiment Project

For the Global Troposphere Experiment project Pacific Exploratory Measurements West B (PEM West B), we made determinations of sulfur dioxide (SO2) and dimethyl sulfide (DMS) using gas chromatography-mass spectrometry with isotopically labelled internal standards. This technique provides measurements with precision of 1 part-per-trillion by volume below 20 pptv and 1% above 20 pptv. Measurement of DMS and SO2 were performed with a time cycle of 5-6 minutes with intermittent zero checks. The detection limits were about 1 pptv for SO2 and 2 pptv for DMS. Over 700 measurements of each compound were made in flight. Volcanic impacts on the upper troposphere were again found as a result of deep convection in the tropics. Extensive emission of SO2 from the Pacific Rim land masses were primarily observed in the lower well-mixed part of the boundary layer but also in the upper part of the boundary layer. Analyses of the SO2 data with aerosol sulfate, beryllium-7, and lead-210 indicated that SO2, contributed to half or more of the observed total oxidized sulfur (SO2 plus aerosol sulfate) in free tropospheric air. Cloud processing and rain appeared to be responsible for lower SO2 levels between 3 and 8.5 km than above or below this region. During both phases of PEM-West, dimethyl sulfide did not appear to be a major source of sulfur dioxide in the upper free troposphere over the western Pacific Ocean. In 1991 the sources Of SO2 at high altitude appeared to be both anthropogenic and volcanic with an estimated 1% being solely from DMS. The primary difference for the increase in the DMS source was the very low concentration of SO2 at high altitude. In the midlatitude region near the Asian land masses, DMS in the mixed layer was lower than in the tropical region of the western Pacific. Convective cloud systems near volcanoes in the tropical convergence in the western Pacific troposphere were a major source of SO2 at high altitudes during PEM-West B. High levels of SO2 were observed in several instances with large number concentrations of ultrafine CN above 9 km in the tropical convergence zone. Conversion of SO2, by OH to SO3 and subsequently to sulfuric acid may have been enhanced by lightning-produced NO levels exceeding 1 part per billion. Coupling of strong convection and volcanic sources of SO2 apparently is an important source of new particle formation at high altitude in the tropical convergence zone.

Bandy, Alan R.↗

NASA NTRS2016

A42F-03: Small-Scale Variability in Tropical Tropopause Layer Humidity

Recent advances in statistical parameterizations of cirrus cloud processes for use in global models are highlighting the need for information about small-scale fluctuations in upper tropospheric humidity and the physical processes that control the humidity variability. To address these issues, we have analyzed high-resolution airborne water vapor measurements obtained in the Airborne Tropical TRopopause EXperiment (ATTREX) over the tropical Pacific between 14 and 20 km. Using accurate and precise 1-Hz water vapor measurements along approximately-level aircraft flight legs, we calculate structure functions spanning horizontal scales ranging from about 0.2 to 50 km, and we compare the water vapor variability in the lower (about 14 km) and upper (16-19 km) Tropical Tropopause Layer (TTL). We also compare the magnitudes and scales of variability inside TTL cirrus versus in clear-sky regions. The measurements show that in the upper TTL, water vapor concentration variance is stronger inside cirrus than in clear-sky regions. Using simulations of TTL cirrus formation, we show that small variability in clear-sky humidity is amplified by the strong sensitivity of ice nucleation rate to supersaturation, which results in highly-structured clouds that subsequently drive variability in the water vapor field. In the lower TTL, humidity variability is correlated with recent detrainment from deep convection. The structure functions indicate approximately power-law scaling with spectral slopes ranging from about minus 5 divided by 3, to minus 2.

water vapor↗

NASA NTRS2013

Atmospheric Dissolved Iron Depostiion to the Global Oceans: Effects of Oxalate-Promoted Fe Dissolution, Photochemical Redox Cycling, and Dust Mineralogy

Mineral dust deposition is suggested to be a significant atmospheric supply pathway of bioavailable iron (Fe) to Fe-depleted surface oceans. In this study, mineral dust and dissolved Fe (Fed) deposition rates are predicted for March 2009 to February 2010 using the 3-D chemical transport model GEOS-Chem implemented with a comprehensive dust-Fe dissolution scheme. The model simulates Fed production during the atmospheric transport of mineral dust taking into account inorganic and organic (oxalate)-promoted Fe dissolution processes, photochemical redox cycling between ferric (Fe(III)) and ferrous (Fe(II)) forms of Fe, dissolution of three different Fe-containing minerals (hematite, goethite, and aluminosilicates), and detailed mineralogy of windblown dust from the major desert regions. Our calculations suggest that during the yearlong simulation is approximately 0.26 Tg (1 Tg = 1012 g) of Fed was deposited to global oceanic regions. Compared to simulations only taking into account proton-promoted Fe dissolution, the addition of oxalate to the dust-Fe mobilization scheme increased total annual model-predicted Fed deposition to global oceanic regions by approximately 75%. The implementation of Fe(II)/Fe(III) photochemical redox cycling in the model allows for the distinction between different oxidation states of deposited Fed. Our calculations suggest that during the daytime, large fractions of Fed deposited to the global oceans is likely to be in Fe(II) form, while nocturnal fluxes of Fed are largely in Fe(III) form. Model simulations also show that atmospheric fluxes of Fed can be strongly influenced by the mineralogy of Fe-containing compounds. This study shows that Fed deposition to the oceans is controlled by total dust-Fe mass concentrations, mineralogy, the surface area of dust particles, atmospheric chemical composition, cloud processing, and meteorological parameters and exhibits complex and spatiotemporally variable patterns. Our study suggests that the explicit model representation of individual processes leading to Fed production within mineral dust are needed to improve the understanding of the atmospheric Fe cycle, and quantify the effect of dust-Fe on ocean biological productivity, carbon cycle, and climate.

Johnson, M. S.↗

NASA NTRS2019

Evaluation of Global Simulations of Aerosol Particle and Cloud Condensation Nuclei Number, with Implications for Cloud Droplet Formation

A total of 16 global chemistry transport models and general circulation models have participated in this study; 14 models have been evaluated with regard to their ability to reproduce the near-surface observed number concentration of aerosol particles and cloud condensation nuclei (CCN), as well as derived cloud droplet number concentration (CDNC). Model results for the period 2011-2015 are compared with aerosol measurements (aerosol particle number, CCN and aerosol particle composition in the submicron fraction) from nine surface stations located in Europe and Japan. The evaluation focuses on the ability of models to simulate the average across time state in diverse environments and on the seasonal and short-term variability in the aerosol properties. There is no single model that systematically performs best across all environments represented by the observations. Models tend to underestimate the observed aerosol particle and CCN number concentrations, with average normalized mean bias (NMB) of all models and for all stations, where data are available, of -24% and -35% for particles with dry diameters > 50 and > 120nm, as well as -36% and -34% for CCN at supersaturations of 0.2% and 1.0%, respectively. However, they seem to behave differently for particles activating at very low supersaturations (< 0.1%) than at higher ones. A total of 15 models have been used to produce ensemble annual median distributions of relevant parameters. The model diversity (defined as the ratio of standard deviation to mean) is up to about 3 for simulated N3 (number concentration of particles with dry diameters larger than 3 nm) and up to about 1 for simulated CCN in the extra-polar regions. A global mean reduction of a factor of about 2 is found in the model diversity for CCN at a supersaturation of 0.2% (CCN(0.2)) compared to that for N3, maximizing over regions where new particle formation is important. An additional model has been used to investigate potential causes of model diversity in CCN and bias compared to the observations by performing a perturbed parameter ensemble (PPE) accounting for uncertainties in 26 aerosol-related model input parameters. This PPE suggests that biogenic secondary organic aerosol formation and the hygroscopic properties of the organic material are likely to be the major sources of CCN uncertainty in summer, with dry deposition and cloud processing being dominant in winter. Models capture the relative amplitude of the seasonal variability of the aerosol particle number concentration for all studied particle sizes with available observations (dry diameters larger than 50, 80 and 120nm). The short-term persistence time (on the order of a few days) of CCN concentrations, which is a measure of aerosol dynamic behavior in the models, is underestimated on average by the models by 40% during winter and 20% in summer.

cloud droplets↗

DOE OSTI2025

Global Impacts of Marine Methanethiol Emissions and Chemistry in the Atmosphere

Oceanic emissions of dimethyl sulfide (DMS) have long been known to influence aerosol particle composition, cloud condensation nuclei (CCN) concentration, and Earth’s radiative budget. However, the impact of oceanic emissions of methanethiol (MeSH), a sulfur compound produced by the same oceanic precursor as DMS, has been relatively less explored. The gas-phase oxidation of MeSH has a higher effective yield of SO 2 and a shorter oxidative lifetime compared to DMS, highlighting the relevance of this pathway for the modeled representation of particle formation, growth, and CCN abundance in the marine atmosphere. Here, we use the global chemical transport model GEOS-Chem to explore possible scenarios representative of specific environmental conditions and MeSH emission schemes based on previous experimental studies. We further implement and test previously reported chemical mechanisms for MeSH oxidation, along with additional improvements, highlighting key uncertainties and sensitivities for regional and global sulfur budgets. We place our results in the context of recent modeling updates to DMS chemistry and cloud processing, which further impact SO 2 production in the marine atmosphere in parallel with MeSH oxidation. Within the overall marine sulfur budget, our findings highlight that MeSH plays a significant role in SO 2 production in the marine atmosphere, contributing to regional surface layer concentration increases of up to 40–60%. These results point to the importance of MeSH for efforts aimed at improving the modeled representation of sulfur spatiotemporal patterns relevant to air quality predictions and climate impact assessments.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2026

Synthesis of ARM User Facility Surface Rainfall Datasets to Construct a Best Estimate Value Added Product (PrecipBE)

Surface precipitation measurements are essential for Earth system model (ESM) evaluation and understanding cloud processes. An ever-growing need for robust, temporally evolving, and easy-to-use statistical datasets provides motivation for a baseline ground-based precipitation properties data product. The U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility operates an extensive suite of precipitation instruments with various sensitivities and operating mechanisms, which render the decision of which instrument to use based on one or more fixed thresholds challenging and prone to errors and bias. Using a long-term instrument inter-comparison from a unique per-precipitation event perspective, rather than instantaneous sample comparison, we demonstrate that ARM rainfall-measuring instruments are generally consistent with each other at the statistical level. Inter-instrument deviations at the single event level can be large, especially for specific rainfall event properties such as maximum precipitation rates. A machine-learning (ML) analysis using a random forest regressor indicates that in some cases, depending on instrument, local site climatology, and/or specific deployment configuration, certain atmospheric state variables influence the measured quantities in an unpredictable manner. Thus, a-priori weighting of different instruments does not necessarily lead to more accurate and less biased synthesis of instrument data. These results motivate the design of the ARM precipitation best-estimate (PrecipBE) value-added product, which incorporates all valid precipitation data while considering data quality and other instrument limitations. PrecipBE consists of time series and tabular statistics datasets in an easy-to-use and insightful per-precipitation event format. It provides a large set of precipitation event properties supplemented with ancillary data from ARM datasets that correspond to the detected precipitation events. We describe the PrecipBE algorithm and demonstrate its use via the examination of a single-day output as well as a long-term trend analysis of precipitation events at the ARM Southern Great Plains (SGP) site, covering more than 30 years of data. The trend analysis tentatively suggests a long-term temporal tendency for mainly shorter and less intense precipitation events at the SGP site, but a long-term increase in annual rainfall by more than 36 mm (5 %) per decade. This rainfall trend is catalyzed primarily by more extreme event properties of relatively rare, intense precipitation events, with event total and 1 min maximum precipitation rate at a 1 year timeframe increasing up to 5 mm and 9 mm h −1 (several percent) per decade, respectively. While the currently available PrecipBE datasets (at https://adc.arm.gov/discovery/, last access: 8 December 2025) cover rainfall from multiple ARM deployments up to March 2025, PrecipBE is planned to be expanded to include solid-phase precipitation and will soon become an operational product with a several-day lag from real-time. We invite the ARM user community to leverage this new product and welcome user feedback to further enhance the dataset.

Silber, Israel [Pacific Northwest National Laborat↗

DOE OSTI2024

Deep-learning-driven simulations of boundary layer clouds over the Southern Great Plains

Abstract. Based on long-term observations at the Southern Great Plains site by the Atmospheric Radiation Measurement (ARM) program for training and validation, a deep-learning model is developed to simulate the daytime evolution of boundary layer clouds (BLCs) from the perspective of land–atmosphere coupling. The model takes ARM measurements (including early-morning soundings and diurnally varying surface meteorological conditions and heat fluxes) as inputs and predicts hourly estimates (including cloud occurrence, the positions of cloud boundaries, and the vertical profile of the cloud fraction) as outputs. The deep-learning model offers good agreement with the observed cloud fields, especially in the accuracy with which cloud occurrence and base height are reproduced. When the inputs are substituted by reanalysis data from ERA5 and MERRA-2, the outputs of the deep-learning model provide a better agreement with observation than the cloud fields extracted from ERA5 and MERRA-2 themselves. Thus, the deep-learning model shows great potential to serve as a diagnostic tool for the performance of physics-based models in simulating stratiform and cumulus clouds. By quantifying biases in clouds and attributing them to the simulated atmospheric state variables versus the model-parameterized cloud processes, this observation-based deep-learning model may offer insights into the directions needed to improve the simulation of BLCs in physics-based models for weather forecasting and climate prediction.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

EPCAPE-PT-LANL Measurements: NO/NOx Analyzer

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

EPCAPE-PT-LANL Measurements: Scanning Mobility Particle Sizer

Coastal cities offer a unique environment for studying aerosol-cloud interactions and the effects of urban emissions on cloud properties. As part of the Eastern Pacific Cloud Aerosol Precipitation Experiment (EPCAPE), the Partitioning Thrust by Los Alamos National Laboratory (EPCAPE-PT-LANL) was conducted. Our campaign focused on measuring the optical and chemical properties of aerosols and their interactions within marine stratocumulus clouds in La Jolla, California. EPCAPE-PT-LANL enhances the primary goals of EPCAPE through innovative observations of vapor-phase transitions between aerosols and cloud droplets, the impact of black carbon on aerosol-cloud dynamics, and the effects of cloud processing on aerosol optical properties. Instrument: Scanning Mobility Particle Sizer (TSI), DMA Classifier Model 3082 + CPC Detector Model 3752. Data Notes: The SMPS dataset is limited due to necessary instrument maintenance and subsequent extensive downtime. Data is only available from October 21 to November 8, 2023. Post November 22, 2023, the NanoScan instrument provided coverage for the latter half of the campaign. To ensure consistency, the data was resampled into uniform size bins ranging from 15 nm to 667 nm. This adjustment was required due to changes in size bins and the maximum diameter that occurred after maintenance and repairs. Multiple Charge Correction applied, Nanoparticle Agglomerate Mobility Analysis not applied, and Diffusion Correction applied. Header: - List [15 nm to 667 nm] middle of the size bin in nanometers: Data reports the concentration particles in this size bin per cubic centimeter, dN/dLog(dp). - CVI_Flag[bool]: A boolean flag indicating whether the Counterflow Virtual Impactor (CVI) was active (true) or inactive (false) during the measurement.

54 ENVIRONMENTAL SCIENCES↗