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DOE OSTI2023

Validation of turbulence intensity as simulated by the Weather Research and Forecasting model off the US northeast coast

We report turbulence intensity (TI) is often used to quantify the strength of turbulence in wind energy applications and serves as the basis of standards in wind turbine design. Thus, accurately characterizing the spatiotemporal variability in TI should lead to improved predictions of power production. Nevertheless, turbulence measurements over the ocean are far less prevalent than over land due to challenges in instrumental deployment, maintenance, and operation. Atmospheric models such as mesoscale (weather prediction) and large-eddy simulation (LES) models are commonly used in the wind energy industry to assess the spatial variability of a given site. However, the TI derivation from atmospheric models has not been well examined. An algorithm is proposed in this study to realize online calculation of TI in the Weather Research and Forecasting (WRF) model. Simulated TI is divided into two components depending on scale, including sub-grid (parameterized based on turbulence kinetic energy (TKE)) and grid resolved. The sensitivity of sea surface temperature (SST) on simulated TI is also tested. An assessment is performed by using observations collected during a field campaign conducted from February to June 2020 near the Woods Hole Oceanographic Institution Martha's Vineyard Coastal Observatory. Results show that while simulated TKE is generally smaller than the lidar-observed value, wind speed bias is usually small. Overall, this leads to a slight underestimation in sub-grid-scale estimated TI. Improved SST representation subsequently reduces model biases in atmospheric stability as well as wind speed and sub-grid TI near the hub height. Large TI events in conjunction with mesoscale weather systems observed during the studied period pose a challenge to accurately estimating TI from models. Due to notable uncertainty in accurately simulating those events, this suggests summing up sub-grid and resolved TI may not be an ideal solution. Efforts in further improving skills in simulating mesoscale flow and cloud systems are necessary as the next steps.

17 WIND ENERGY↗

DOE OSTI2024

Aerosol hygroscopicity over the southeast Atlantic Ocean during the biomass burning season – Part 1: From the perspective of scattering enhancement

Aerosol hygroscopicity plays a vital role in aerosol radiative forcing. One key parameter describing hygroscopicity is the scattering enhancement factor, f(RH), defined as the ratio of the scattering coefficient at humidified relative humidity (RH) to its dry value. Here, we utilize the f(80 %) from ORACLES (ObseRvations of Aerosols above CLouds and their intEractionS) 2016 and 2018 airborne measurements to investigate the hygroscopicity of aerosols, its vertical distribution, its relationship with chemical composition, and its sensitivity to organic aerosol (OA) hygroscopicity over the southeast Atlantic (SEA) Ocean during the biomass burning (BB) season. We found that aerosol hygroscopicity remains steady above 2 km, with a mean f(80 %) of 1.40 ± 0.17. Below 2 km, aerosol hygroscopicity increases with decreasing altitude, with a mean f(80 %) of 1.51 ± 0.22, consistent with higher values of BB aerosol hygroscopicity found in the literature. The hygroscopicity parameter of OA (κ OA ) is retrieved from the Mie model with a mean value of 0.11 ± 0.08, which is in the middle to upper range compared to the literature. Higher OA hygroscopicity is related to aerosols that are more aged, oxidized, and present at lower altitudes. The enhanced biomass burning aerosol (BBA) hygroscopicity at lower altitudes is mainly due to a lower OA fraction, increased sulfate fraction, and greater κ OA at lower altitudes. We propose a parameterization that quantifies f(RH) with chemical composition and κOA based on Mie simulation of internally mixed OA–(NH 4 ) 2 SO 4 –BC mixtures. The good agreement between the predictions and the ORACLES measurements implies that the aerosols in the SEA during the BB season can be largely represented by the OA–(NH 4 ) 2 SO 4 –BC internal mixture with respect to the f(RH) prediction. The sensitivity of f(RH) to κ OA indicates that applying a constant κ OA is only suitable when the OA fraction is low and κ OA shows limited variation. However, in situations deviating these two criteria, κ OA can notably impact scattering coefficients and aerosol radiative effect; therefore, accounting for κ OA variability is recommended.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2000

Clear-Sky Closure Studies of Tropospheric Aerosol and Water Vapor During ACE-2 Using Airborne Sunphotometer, Airborne In-Situ, Space-Borne, and Ground-Based Measurements

We report on clear-sky column closure experiments (CLEARCOLUMN) performed in the Canary Islands during the second Aerosol Characterization Experiment (ACE-2) in June/July 1997. We present CLEARCOLUMN results obtained by combining airborne sunphotometer and in-situ (a differential mobility analyzer, three optical particle counters, three nephelometers, and one absorption photometer) measurements taken aboard the Pelican aircraft, space-borne NOAA/AVHRR data and ground-based lidars. A wide range of aerosol types was encountered throughout the ACE-2 area, including background Atlantic marine, European pollution-derived, and (although less frequently than expected) African mineral dust. During the two days discussed here, vertical profiles flown in cloud free air masses revealed three distinctly different layers: a marine boundary layer (MBL) with varying pollution levels, an elevated dust layer, and a very clean layer between the MBL and the dust layer. Based on size-resolved composition information we have established an aerosol model that allows us to compute optical properties of the ambient aerosol using the optical particle counter results. In the dust, the agreement in layer AOD (lambda=380-1060 nm) is 3-8%. In the MBL there is a tendency for the in-situ results to be slightly lower than the sunphotometer measurements (10-17% at lambda=525 nm), but these differences are within the combined error bars of the measurements and computations. Aerosol size-distribudon closure based on in-situ size distributions and inverted sunphotometer extinction spectra has been achieved in the MBL (total surface area and volume agree within 0.2, and 7%, respectively) but not in the dust layer. The fact that the three nephelometers operated at three different relative humidities (RH) allowed to parameterize hygroscopic growth and to therefore estimate optical properties at ambient RH. The parameters derived for different aerosol types are themselves useful for the aerosol modeling community. The fact that the nephelometers and the absorption photometer sampled the aerosol through a cyclone make those measurements less useful for thee closure study carried out here. Large corrections (especially in the dust) had to be applied. Therefore, it is not surprising that closure with the sunphotometer was not always achieved. Agreement within 0.02 in AOD was achieved in the dust layer when the airorne sunphotometer extinction or AOD was compared to ground-based lidar measurements. We found that the presence of the elevated dust layers removes the good agreement between satellite and sunphotometer AOD usually found in the absence of the dust layer. We still compare the scattering phase functions used in the satellite retrieval with those obtained from the aerosol or the sunphotometer measurements.

Schmid, Beat↗

DOE OSTI2020

Simulating the Effects of Surface Energy Partitioning on Convective Organization: Case Study and Observations in the US Southern Great Plains

Realistic cloud-resolving simulations were performed to study the effects of surface energy partitioning (surface sensible and latent heat fluxes) on the organization of isolated convection into larger mesoscale convective systems (MCSs) near the US Southern Great Plains. The role of cold pools in mediating surface-convection interactions was explored. Better organized MCSs tended to occur in the experiments with perturbed wetter soil (and more active vegetation), regardless of the effects of soil moisture on the diurnal timing of convective triggering. Wetter soil led to shallower boundary layers and more convective available potential energy than drier soil. The roles of cold pools on convection are lifecycle-stage dependent: A dry surface allows more numerous colliding cold pools, thereby aiding in convective triggering by reducing entrainment in the early stages, and providing gust-front uplift in later stages. However, horizontal propagation of the cold-pool density current can outrun the convective system, creating a slantwise updraft and thus weakening the gust front uplift in later stages. This effect calls into question previous cold pool parameterizations, in which the gust front uplift is mainly proportional to the negative buoyancy of cold air. Finally, both the model and observation show an enhancement of surface latent heat flux during the passage of a gust front at night, suggesting that a positive feedback between the surface and convection helps MCSs to persist into the nighttime.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2000

Goddard Cumulus Ensemble (GCE) Model: Application for Understanding Preciptation Processes

The global hydrological cycle is central to climate system interactions and the key to understanding their behavior. Rainfall and its associated precipitation processes are a key link in the hydrologic cycle. Fresh water provided by tropical rainfall and its variability can exert a large impact upon the structure of the upper ocean layer. In addition, approximately two-thirds of the global rain falls in the Tropics, while the associated latent heat release accounts for about three-fourths of the total heat energy for the Earth's atmosphere. Precipitation from convective cloud systems comprises a large portion of tropical heating and rainfall. Furthermore, the vertical distribution of convective latent-heat releases modulates large-scale tropical circulations (e.g., the 30-60-day intraseasonal oscillation), which, in turn, impacts midlatitude weather through teleconnection patterns such as those associated with El Nino. Shifts in these global circulations can result in prolonged periods of droughts and floods, thereby exerting a tremendous impact upon the biosphere and human habitation. And yet, monthly rainfall over the tropical oceans is still not known within a factor of two over large (5 degrees latitude by 5 degrees longitude) areas. Hence, the Tropical Rainfall Measuring Mission (TRMM), a joint U.S./Japan space project, can provide a more accurate measurement of rainfall as well as estimate the four-dimensional structure of diabatic heating over the global tropics. The distributions of rainfall and inferred heating can be used to advance our understanding of the global energy and water cycle. In addition, this information can be used for global circulation and climate models for testing and improving their parameterizations.

Tao, Wei-Kuo↗

DOE OSTI2021

Consideration of Initial Cloud Droplet Size Distribution Shapes in Quantifying Different Entrainment-Mixing Mechanisms

Entrainment-mixing process significantly affects cloud micro/macrophysics and the evaluation of aerosol indirect effects. However, it remains an open question as to how to quantify entrainment-mixing mechanism for broad cloud droplet size distributions (CDSDs). Here, CDSDs with different spectral widths are used to initialize the Explicit Mixing Parcel Model to address this problem, and microphysical properties are compared for different CDSD widths. For relatively broad CDSDs, as the number concentration and liquid water content decrease, the volume-mean radius and mean radius increase, which is opposite to the scenario for relatively narrow CDSDs and the homogeneous/inhomogeneous conceptual model. For the relatively broad CDSDs, such relationships could be mistaken as inhomogeneous mixing with subsequent ascent in analysis of the conventional microphysical mixing diagram. This then causes traditional homogenous mixing degrees to be unrealistically negative and not applicable for relatively broad CDSDs. New measures are introduced to explicitly account for the effect of CDSD spectral shapes on quantifying entrainment-mixing mechanism. The new measures yield reasonable ranges of values that conform to the dynamical measures such as the Damköhler and transitional scale numbers, and their temporal variation can be explained physically. This study extends the measures of homogeneous mixing degrees from narrow to broad CDSDs, and sheds new light on the consideration of CDSD shapes in parameterizations of entrainment-mixing mechanism.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2011

The Fate of Saharan Dust Across the Atlantic and Implications for a Central American Dust Barrier

Saharan dust was observed over the Caribbean basin during the summer 2007 NASA Tropical Composition, Cloud, and Climate Coupling (TC4) field experiment. Airborne Cloud Physics Lidar (CPL) and satellite observations from MODIS suggest a barrier to dust transport across Central America into the eastern Pacific. We use the NASA GEOS-5 atmospheric transport model with online aerosol tracers to perform simulations of the TC4 time period in order to understand the nature of this barrier. Our simulations are driven by the Modem Era Retrospective-Analysis for Research and Applications (MERRA) meteorological analyses. We evaluate our baseline simulated dust distributions using MODIS and CALIOP satellite and ground-based AERONET sun photometer observations. GEOS-5 reproduces the observed location, magnitude, and timing of major dust events, but our baseline simulation does not develop as strong a barrier to dust transport across Central America as observations suggest. Analysis of the dust transport dynamics and lost processes suggest that while both mechanisms play a role in defining the dust transport barrier, loss processes by wet removal of dust are about twice as important as transport. Sensitivity analyses with our model showed that the dust barrier would not exist without convective scavenging over the Caribbean. The best agreement between our model and the observations was obtained when dust wet removal was parameterized to be more aggressive, treating the dust as we do hydrophilic aerosols.

Nowottnick, E.↗

NASA NTRS1999

Remote Sensing of Rain

The first problem addressed concerns passive-microwave rain retrievals. Most current approaches start by building off-line a cloud-model-derived database. Given data, the retrieval algorithms search the database for the microwave temperatures "closest" to the observed data, then after some fine-tuning (performed in different ways by different implementations) the rain is estimated to be that which corresponds to the selected (and fine-tuned) set of database temperatures. These approaches have three drawbacks: they cannot properly take into account the ambiguities which arise from the fact that several rain scenarios can produce the same observed temperatures; they are quite inefficient since they require manipulating a large database along with often complex "fine-tuning" procedures; and they cannot refine their estimates if additional data is available. This past year we have derived closed formulae relating observed microwave brightness temperatures, T(sub b), and the underlying rain rates, R: average T(sub b) =f (rain) and average rain = g (T(sub b)), along with the corresponding covariance matrices. These results are sufficient to describe the conditional probabilities p(R/T(sub b)) and p(T(sub b)/R) to second order. Progress has also been made towards deriving a robust description of the rain drop size distribution (DSD). The widespread approach consisting in parameterizing the DSD as a gamma-distribution in terms of the drop diameter D suffers from the facts that, in reality, the DSD is not a smooth function of D and that the largely arbitrary Gamma model imposes unintended behavior, which has implications on any quantities derived from the DSD model. We have therefore developed a non-parametric yet practical description of the DSD, which is particularly well-suited for use in remote-sensing applications. The diagram on the left shows a comparison between an actual DSD sample and the truncated non-parametric representation. One figure shows the relation between radar reflectivity and rain rate derived using this representation. Validation of the Tropical Rainfall Measuring Mission (TRMM) radar-radiometer combined R and DSD algorithm is underway. This algorithm was designed to make optimal use of the instantaneous reflectivity profiles measured by the TRMM radar and the microwave brightness temperatures measured by the TRMM passive radiometer. So far, it appears to be the most reliable TRMM rain algorithm.

Haddad, Ziad S.↗

DOE OSTI2025

A Machine Learning Framework for Predicting Microphysical Properties of Ice Crystals From Cloud Particle Imagery

The microphysical properties of ice crystals are important because they significantly alter the radiative properties and spatiotemporal distributions of clouds, which in turn strongly affect Earth's climate. However, it is challenging to measure key properties of ice crystals, such as mass or morphological features. Here, we present a proof-of-concept framework for predicting three-dimensional (3D) microphysical properties of ice crystals from in situ two-dimensional (2D) imagery. First, we computationally generated synthetic ice crystals using 3D modeling software along with geometric parameters estimated from the 2021 Ice Cryo-Encapsulation Balloon (ICEBall) field campaign. Then, we used synthetic crystals to train machine learning (ML) models to predict effective density ($ρ_e$), effective surface area ($A_e$), and number of bullets ($N_b$) from synthetic rosette imagery. On unseen synthetic images, our ML models accurately predicted ice crystal properties. ResNet-18 performed best, achieving $R^2$ values of 0.99 and 0.98 for $ρ_e$ and $A_e$, respectively, and MAE of 0.10 for mathematical equation in single view tasks. Stereo view ResNet-18 further reduced RMSE by 40% for $ρ_e$ and $A_e$ and reduced MAE by 0.08 for $N_b$. This work provides a novel ML-driven framework for estimating ice microphysical properties from in situ imagery, which will allow for downstream constraints on microphysical parameterizations, such as the mass-size relationship.

Ko, J. [Columbia Univ., New York, NY (United State↗

NASA NTRS2023

Evaluating Greenland Surface-Mass-Balance and Firn-Densification Data Using ICESat-2 Altimetry

Surface-mass-balance (SMB) and firn-densification (FD) models are widely used in altimetry studies as a tool to separate atmospheric-driven from ice-dynamics-driven ice-sheet mass changes and to partition observed volume changes into ice-mass changes and firn-air-content changes. Until now, SMB models have been principally validated based on comparison with ice core and weather station data or comparison with widely separated flight radar-survey flight lines. Firn-densification models have been primarily validated based on their ability to match net densification over decades, as recorded in firn cores, and the short-term time-dependent component of densification has rarely been evaluated at all. The advent of systematic ice-sheet-wide repeated ice-surface-height measurements from ICESat-2 (the Ice Cloud, and land Elevation Satellite, 2) allows us to measure the net surface-height change of the Greenland ice sheet at quarterly resolution and compare the measured surface-height differences directly with those predicted by three FD–SMB models: MARv3.5.11 (Modèle Atmosphérique Régional version 3.5.11) and GSFCv1.1 and GSFCv1.2 (the Goddard Space Flight Center FD–SMB models version 1.1 and 1.2). By segregating the data by season and elevation, and based on the timing and magnitude of modelled processes in areas where we expect minimal ice-dynamics-driven height changes, we investigate the models' accuracy in predicting atmospherically driven height changes. We find that while all three models do well in predicting the large seasonal changes in the low-elevation parts of the ice sheet where melt rates are highest, two of the models (MARv3.5.11 and GSFCv1.1) systematically overpredict, by around a factor of 2, the magnitude of height changes in the high-elevation parts of the ice sheet, particularly those associated with melt events. This overprediction seems to be associated with the melt sensitivity of the models in the high-elevation part of the ice sheet. The third model, GSFCv1.2, which has an updated high-elevation melt parameterization, avoids this overprediction.

Benjamin E. Smith↗

NASA NTRS1999

Seasonal Distributions of Global Ocean Chlorophyll and Nutrients: Analysis with a Coupled Ocean General Circulation Biogeochemical, and Radiative Model

A coupled general ocean circulation, biogeochemical, and radiative model was constructed to evaluate and understand the nature of seasonal variability of chlorophyll and nutrients in the global oceans. The model is driven by climatological meteorological conditions, cloud cover, and sea surface temperature. Biogeochemical processes in the model are determined from the influences of circulation and turbulence dynamics, irradiance availability, and the interactions among three functional phytoplankton groups (diatoms, chorophytes, and picoplankton) and three nutrient groups (nitrate, ammonium, and silicate). Phytoplankton groups are initialized as homogeneous fields horizontally and vertically, and allowed to distribute themselves according to the prevailing conditions. Basin-scale model chlorophyll results are in very good agreement with CZCS pigments in virtually every global region. Seasonal variability observed in the CZCS is also well represented in the model. Synoptic scale (100-1000 km) comparisons of imagery are also in good conformance, although occasional departures are apparent. Agreement of nitrate distributions with in situ data is even better, including seasonal dynamics, except for the equatorial Atlantic. The good agreement of the model with satellite and in situ data sources indicates that the model dynamics realistically simulate phytoplankton and nutrient dynamics on synoptic scales. This is especially true given that initial conditions are homogenous chlorophyll fields. The success of the model in producing a reasonable representation of chlorophyll and nutrient distributions and seasonal variability in the global oceans is attributed to the application of a generalized, processes-driven approach as opposed to regional parameterization, and the existence of multiple phytoplankton groups with different physiological and physical properties. These factors enable the model to simultaneously represent the great diversity of physical, biological, chemical, and radiative environments encountered in the global oceans.

Gregg, Watson W.↗

NASA NTRS1994

The classification of the Arctic Sea ice types and the determination of surface temperature using advanced very high resolution radiometer data

The accurate quantification of new ice and open water areas and surface temperatures within the sea ice packs is a key to the realistic parameterization of heat, moisture, and turbulence fluxes between ocean and atmosphere in the polar regions. Multispectral NOAA advanced very high resolution radiometer/2 (AVHRR/2) satellite images are analyzed to evaluate how effectively the data can be used to characterize sea ice in the Bering and Greenland seas, both in terms of surface type and physical temperature. The basis of the classification algorithm, which is developed using a late wintertime Bering Sea ice cover data, is that frequency distributions of 10.8- micrometers radiances provide four distinct peaks, represeting open water, new ice, young ice, and thick ice with a snow cover. The results are found to be spatially and temporally consistent. Possible sources of ambiguity, especially associated with wider temporal and spatial application of the technique, are discussed. An ice surface temperature algorithm is developed for the same study area by regressing thermal infrared data from 10.8- and 12.0- micrometers channels against station air temperatures, which are assumed to approximate the skin temperatures of adjacent snow and ice. The standard deviations of the results when compared with in situ data are about 0.5 K over leads and polynyas to about 0.5-1.5 K over thick ice. This study is based upon a set of in situ data limited in scope and coverage. Cloud masks are applied using a thresholding technique that utilizes 3.74- and 10.8- micrometers channel data. The temperature maps produced show coherence with surface features like new ice and leads, and consistency with corresponding surface type maps. Further studies are needed to better understand the effects of both the spatial and temporal variability in emissivity, aerosol and precipitable atmospheric ice particle distribution, and atmospheric temperature inversions.

Massom, Robert↗

NASA NTRS2019

An Automatic Light Rain Detection Algorithm on NASA MPLNET Lidar Observations in the Frame of WMO GALION Project

The water cycle strongly influences life on Earth. In particular, the precipitation modifies the atmospheric column thermodynamics through the process of evaporation and serves as a proxy for latent heat modulation. For this reason, a correct precipitation parameterization (especially low-intensity precipitation) at global scale, bedsides improving our understanding of the hydrological cycle, it is crucial to reduce the associated uncertainty of the global climate models to correctly forecast future scenarios, i.e. to apply fast mitigation strategies. In this study we developed an algorithm to automatically detect precipitation from lidar measurements obtained by the National and Aeronautics Space Administration (NASA) Micropulse lidar network (MPLNET) permanent observational site in Goddard. The algorithm, once full operational, will deliver in Near Real Time (latency 1.5h) a new rain mask product that will be publicly available on MPLNET website as part of the new Version 3 Level 1.5 data. The methodology, based on an image processing technique, can detect only light precipitation events (defined by intensity and duration) as the morphological filters used through the detection process are applied on the lidar volume depolarization ratio range corrected composite images, i.e. heavy rain events are unusable as the lidar signal is completely extinguished after few meters in the precipitation or no signal detected because of the water accumulated on the receiver optics. Results from the algorithm, besides filling a gap in precipitation and virga detection by radars, are of particular interest for the scientific community because will help to better understand long-term aerosol-cloud interactions and aerosol atmospheric removal (scavenging effect) by rain as multi-year database being available for several MPLNET permanent observational sites across the globe. Moreover, we developed the automatic algorithm at Universitat Politecnica de Catalunya (UPC) Barcelona, the unique permanent observation station member of MPLNET and the European Aerosol Lidar Network (EARLINET) In the future the algorithm can be then easily applied to any other lidar and/or ceilometer network infrastructure in the frame of World Meteorological Organization (WMO) Global Aerosol Watch (GAW) aerosol lidar observation network (GALION)

Simone Lolli↗

DOE OSTI2025

Summer Aerosol and Trace Gas Observations in Houston, Texas Using an Adaptable Mobile Facility

An aerosol container featuring a shared inlet system was deployed to Houston, Texas in July 2022, enabling direct, high-time-resolution in situ measurements of aerosols and trace gases. The internal rack system and floorplan was designed for adaptable modularity to elucidate aerosol physicochemical processes at fine scales. The design allowed for the deployment of a core instrument suite and additional customized research grade instruments. A heterogeneous mixture of aerosols was observed during three regimes: (1) intermittent black carbon (BC) and diurnal variations in aerosol chemical composition, (2) observed particle growth associated with SO 2 , (3) transported supermicron dust. The high variability of observed particles and gases in high time resolution indicated a complex urban area with multiple local and regional sources and processes. Particle growth rates of 7–16 nm/hr were observed for submicron particles during periods when SO 2 was >0.5 ppbv. Two periods of multi-day long-range transport events of dust from the African Sahara were observed in the supermicron and submicron particle modes with total mass concentrations up to 30 μg m −3 . Aerosol scattering angstrom exponents and extinction coefficients (B ext ) increased with humidity as a function of particle composition. The measurements demonstrate collaborative capabilities that can be used to increase observations of aerosol processing, microphysical and optical properties, internal mixing state, and supermicron aerosol that are not parameterized or missing in global Earth energy system models.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2014

Application of Spectral Analysis Techniques in the Intercomparison of Aerosol Data: Part III. Using Combined PCA to Compare Spatiotemporal Variability of MODIS, MISR and OMI Aerosol Optical Depth

Satellite measurements of global aerosol properties are very useful in constraining aerosol parameterization in climate models. The reliability of different data sets in representing global and regional aerosol variability becomes an essential question. In this study, we present the results of a comparison using combined principal component analysis (CPCA), applied to monthly mean, mapped (Level 3) aerosol optical depth (AOD) product from Moderate Resolution Imaging Spectroradiometer (MODIS), Multiangle Imaging Spectroradiometer (MISR), and Ozone Monitoring Instrument (OMI). This technique effectively finds the common space-time variability in the multiple data sets by decomposing the combined AOD field. The results suggest that all of the sensors capture the globally important aerosol regimes, including dust, biomass burning, pollution, and mixed aerosol types. Nonetheless, differences are also noted. Specifically, compared with MISR and OMI, MODIS variability is significantly higher over South America, India, and the Sahel. MODIS deep blue AOD has a lower seasonal variability in North Africa, accompanied by a decreasing trend that is not found in either MISR or OMI AOD data. The narrow swath of MISR results in an underestimation of dust variability over the Taklamakan Desert. The MISR AOD data also exhibit overall lower variability in South America and the Sahel. OMI does not capture the Russian wild fire in 2010 nor the phase shift in biomass burning over East South America compared to Central South America, likely due to cloud contamination and the OMI row anomaly. OMI also indicates a much stronger (boreal) winter peak in South Africa compared with MODIS and MISR.

satellite observation↗

DOE OSTI2021

Aerosol Chemical Speciation Monitor (ACSM) Composition-Dependent Collection Efficiency (CDCE) Value-Added Product Report

Aerosol particles influence the Earth’s radiation balance directly by absorbing and scattering light and indirectly by influencing cloud formation, properties, and lifetimes. Measurements of aerosol particle optical properties, mass loading, size distributions, microphysical properties, cloud formation properties, and chemical composition are important for understanding the aerosol life cycle and for validating earth system models that predict these quantities. The U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility’s Aerosol Observing System (AOS) is a highly instrumented platform that measures many of these aerosol properties in situ. The Aerodyne aerosol chemical speciation monitor (ACSM) is a baseline instrument deployed in the AOS. The ACSM provides a quantitative measurement of aerosol particle chemical composition for non-refractory (operationally defined as components that evaporate on the 600ºC vaporizer) aerosol components in real time. Standard output is the mass concentration of particulate organics, nitrate, sulfate, chloride, and ammonium. One well-known limitation to the accuracy of the ACSM data is in evaluating the fraction of the ambient aerosol particles that are detected by the instrument. This quantity is referred to as the collection efficiency (CE) and is often less than unity. This is attributed to particles rebounding after impaction onto the heated vaporizer rather than being trapped, volatilized rapidly, and detected. Other factors, such as divergence of the aerosol particle beam, may also impact CE, but the particle rebound effect is the dominant factor. Scientists will sometimes assume CE = 0.5 (i.e., one half of sampled particles are detected) for ambient particles collected during field missions. However, parameterizations have been developed that express CE as a function of the measured chemical composition, referred to as the composition-dependent collection efficiency (CDCE). The physical explanation for a CDCE, supported by laboratory studies, is that rebound from the vaporizer is a function of the particle phase, with liquid-like particles “sticking” to the vaporizer and solid-like or crystalline particles “bouncing” from the vaporizer. Thus, particles with compositions are liquids-like under the measurement conditions (e.g., certain organics, acidic particles, particles enriched in nitrate) have CE close to unity while crystalline or solid particles (e.g., deliquesced ammonium sulfate) have lower CE. This value-added product (VAP) implements the procedure described by Middlebrook et al. (2012) to correct the ACSM data for the composition-dependent collection efficiency. Applying this parameterization improves the accuracy of the ACSM data and brings them into better agreement with other co-located aerosol measurements.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

On the Dependence of Simulated Convection on Domain Size in CRMs

Abstract We present a heuristic model to explain the suppression of deep convection in convection‐resolving models (CRMs) with a small number of grid columns, such as those used in super‐parameterized or multi‐scale modeling framework (MMF) general circulation models (GCM) of the atmosphere. Domains with few grid columns require greater instability to sustain convection because they force a large convective fraction, driving strong compensating subsidence warming. Updraft dilution, which is stronger for reduced horizontal grid spacing, enhances this effect. Thus, suppression of deep convection in CRMs with few grid columns can be reduced by increasing grid spacing. Radiative‐convective equilibrium simulations using standalone CRM simulations with the System for Atmospheric Modeling (SAM) and using GCM‐coupled CRM simulations with the Energy Exascale Earth System Model (E3SM)‐MMF confirm the heuristic model results.

CRM↗

DOE OSTI2025

Collaborative Proposal: Improving understanding of the internal structure and dynamics of deep convection using ARM observations and large eddy simulations

Recent observational and large eddy simulation (LES) modeling studies have nearly unanimously supported the view of deep cumulus convection being composed of a series of quasi-spherical bubbles of buoyant air, known as moist thermals. Despite the prevalence of moist thermals in deep convection, a comprehensive theory for the dynamics of these structures is lacking. Most current conceptual models for cumulus convection are based on canonical scaling theories for dry thermals or plumes; however, there is considerable evidence that the behavior of moist thermals differs markedly from these theories. Furthermore, the theoretical basis for most cumulus parameterizations originates from the plume conceptual model, and therefore these parameterizations are inconsistent with the real structure of moist convection. Motivated by the aforementioned knowledge gaps, this “end-to-end” research effort use theory, observations, numerical simulations, and direct improvements to the Zhang-McFarlane (ZM) convection scheme in the global climate Community Atmosphere Model (CAM) to address the following research questions: What key environmental parameters determine whether or not shallow convection will transition into deep convection, in the context of thermal-like updrafts? What factors regulate the size of thermals within cumulus updrafts? How does vertical wind shear influence thermal behavior, and as a consequence, vertical velocity and mass flux profiles and the shallow-to-deep convective transition? What are the critical processes that determine updraft vertical velocities and their connection to the vertical mass flux profile for thermal-like updrafts? Idealized LES modeling will be used in conjunction with theoretical models for the core properties of thermal-like updrafts to better understand key processes that regulate thermal ascent rates and entrainment properties. Thermal-tracking procedures will be used to characterize the behavior of thermals within the LES, and recently developed direct measures of entrainment and detrainment will be used to quantify entrainment/detrainment rates. Building from these results, we will analyze the structure of moist thermals from hemispheric range-height indicator scans taken during the Atmospheric Radiation Measurement Cloud, Aerosol, and Complex Terrain Interactions (CACTI) field campaign, and from “real case” LES of CACTI events. This combined modeling and observational analysis will provide essential validation for the existing body of research on moist thermal dynamics, which is based primarily on modeling studies. With the insight gained from the aforementioned activities, we will modify the Zhang-McFarlane convection scheme to improve its representation of updraft vertical velocity and entrainment rate profiles. These process-level changes will be tested in the Community Atmosphere Model to assess the impact on global climate simulations.

54 ENVIRONMENTAL SCIENCES↗