Atmospheric Radiation Measurement Research Articles

Extending the wind profile beyond the surface layer by combining physical and machine learning approaches

Abstract. Accurate estimation of the wind profile, especially in the lowest few hundred meters of the atmosphere, is of great significance for the weather, climate, and renewable energy sector. Nevertheless, the Monin–Obukhov similarity theory fails above the surface layer over a heterogeneous underlying surface, causing an unreliable wind profile to be obtained from conventional extrapolation methods. To solve this problem, we propose a novel method called the PLM-RF method that combines the power-law method (PLM) with the random forest (RF) algorithm to extend wind profiles beyond the surface layer. The underlying principle is to treat the wind profile as a power-law distribution in the vertical direction, with the power-law exponent (α) determined by the PLM-RF model. First, the PLM-RF model is constructed based on the atmospheric sounding data from 119 radiosonde (RS) stations across China and in conjunction with other data such as surface wind speed, land cover type, surface roughness, friction velocity, geographical location, and meteorological parameters from June 2020 to May 2021. Afterwards, the performance of the PLM-RF, PLM, and RF methods over China is evaluated by comparing them with RS observations. Overall, the wind speed at 100 m from the PLM-RF model exhibits high consistency with RS measurements, with a determination coefficient (R2) of 0.87 and a root mean squared error (RMSE) of 0.92 m s−1. By contrast, the R2 and RMSE of wind speed results from the PLM (RF) method are 0.75 (0.83) and 1.37 (1.04) m s−1, respectively. This indicates that the estimates from the PLM-RF method are much closer to observations than those from the PLM and RF methods. Moreover, the RMSE of the wind profiles estimated by the PLM-RF model is relatively large for highlands, while it is small for plains. This result indicates that the performance of the PLM-RF model is affected by the terrain factor. Finally, the PLM-RF model is applied to three atmospheric radiation measurement sites for independent validation, and the wind profiles estimated by the PLM-RF model are found to be consistent with Doppler wind lidar observations. This confirms that the PLM-RF model has good applicability. These findings have great implications for the weather, climate, and renewable energy sector.

Chemical properties and single-particle mixing state of soot aerosol in Houston during the TRACER campaign

Abstract. A high-resolution soot particle aerosol mass spectrometer (SP-AMS) was used to selectively measure refractory black carbon (rBC) and its associated coating material using both the ensemble size-resolved mass spectral mode and the event trigger single particle (ETSP) mode in Houston, Texas, in summer 2022. This study was conducted as part of the Department of Energy Atmospheric Radiation Measurement (ARM) program's TRacking Aerosol Convection interactions ExpeRiment (TRACER) field campaign. The study revealed an average (±1σ) rBC concentration of 103 ± 176 ng m−3. Additionally, the coatings on the BC particles were primarily composed of organics (59 %; 219 ± 260 ng m−3) and sulfate (26 %; 94 ± 55 ng m−3). Positive matrix factorization (PMF) analysis of the ensemble mass spectra of BC-containing particles resolved four distinct types of soot aerosol, including an oxidized organic aerosol (OOABC,PMF) factor associated with processed primary organic aerosol, an inorganic sulfate factor (SO4,BC,PMF), an oxidized rBC factor (O-BCPMF), and a mixed mineral dust–biomass burning aerosol factor with significant contribution from potassium (K-BBBC,PMF). Additionally, K-means clustering analysis of the single-particle mass spectra identified eight different clusters, including soot particles enriched in hydrocarbon-like organic aerosol (HOABC,ETSP), sulfate (SO4,BC,ETSP), two types of rBC, OOA (OOABC,ETSP), chloride (ClBC,ETSP), and nitrate (NO3,BC,ETSP). The single-particle measurements demonstrate substantial variation in BC coating thickness with coating-to-rBC mass ratios ranging from 0.1 to 100. The mixing state index (χ), which denotes the degree of homogeneity of the soot aerosol, varied from 4 % to 94 % with a median of 40 %, indicating that the aerosol population lies in between internal and external mixing but has large temporal and source type variability. In addition, a significant fraction of BC-containing particles, a majority enriched with oxidized organics and sulfate, exhibit sufficiently high κ values and diameters conducive to activation as cloud nuclei under atmospherically relevant supersaturation conditions. This finding bears significance in comprehending the aging processes of rBC-containing particles and their activation into cloud droplets. Our analysis highlights the complex nature of soot aerosol and underscores the need to comprehend its variability across different environments for accurate assessment of climate change.

Assessing Radiative Impacts of African Smoke Aerosols Over the Southeastern Atlantic Ocean

AbstractBiomass burning smoke aerosols are efficient at attenuating incoming solar radiation. The Layered Atlantic Smoke Interactions with Clouds campaign was conducted from June 2016 to October 2017. The U. S. Department of Energy mobile Atmospheric Radiation Measurement site located on Ascension Island (AMF‐ASI) identified several instances of smoke plume intrusions. Increases in surface and column measurements of aerosol loading were directly related to increases in fine mode fraction, number concentrations of aerosols (Na), and cloud condensation nuclei (NCCN). During periods of weak lower tropospheric stability, smoke particles were more likely to be advected downward either by boundary layer turbulence or cloud top entrainment under non‐overcast sky conditions. Backward trajectory analysis illustrated that smoke aerosols reaching the AMF‐ASI site were fine mode, less aged, strongly absorbing, and had shorter boundary layer trajectories while longer boundary layer trajectories denoted mixtures of weakly absorbing smoke and coarse mode marine aerosols. The most polluted smoke cases of August 2016 and 2017 revealed a notable contrast in radiative forcing per unit aerosol optical depth or radiative forcing efficiency (ΔFeff) at the top of the atmosphere (TOA) and near‐surface (BOA). The weakly (strongly) absorbing 2016 cases exhibited weaker (stronger) ΔFeff at the TOA and BOA suggesting a warming (cooling) effect within the boundary layer. The 2017 cases featured the strongest ΔFeff suggesting more of a cooling effect at the TOA and BOA due to mixing of fresh smoke with marine aerosols during transport.

The Physics behind Precipitation Onset Bias in CMIP6 Models: The Pseudo-Entrainment Diagnostic and Trade-Offs between Lapse Rate and Humidity

Abstract Conditional instability and the buoyancy of plumes drive moist convection but have a variety of representations in model convective schemes. Vertical thermodynamic structure information from Atmospheric Radiation Measurement (ARM) sites and reanalysis (ERA5), satellite-derived precipitation (TRMM3b42), and diagnostics relevant for plume buoyancy are used to assess climate models. Previous work has shown that CMIP6 models represent moist convective processes more accurately than their CMIP5 counterparts. However, certain biases in convective onset remain pervasive among generations of CMIP modeling efforts. We diagnose these biases in a cohort of nine CMIP6 models with subdaily output, assessing conditional instability in profiles of equivalent potential temperature, θe, and saturation equivalent potential temperature, θes, in comparison to a plume model with different mixing assumptions. Most models capture qualitative aspects of the θes vertical structure, including a substantial decrease with height in the lower free troposphere associated with the entrainment of subsaturated air. We define a “pseudo-entrainment” diagnostic that combines subsaturation and a θes measure of conditional instability similar to what entrainment would produce under the small-buoyancy approximation. This captures the trade-off between larger θes lapse rates (entrainment of dry air) and small subsaturation (permits positive buoyancy despite high entrainment). This pseudo-entrainment diagnostic is also a reasonable indicator of the critical value of integrated buoyancy for precipitation onset. Models with poor θe/θes structure (those using variants of the Tiedtke scheme) or low entrainment runs of CAM5, and models with low subsaturation, such as NASA-GISS, lie outside the observational range in this diagnostic.

Case study of a bore wind-ramp event from lidar measurements and HRRR simulations over ARM Southern Great Plains

The rapid change of wind speed and direction on 21 August 2017 is studied using Doppler lidar measurements at five sites of the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) facility in north-central Oklahoma. The Doppler lidar data were investigated along with meteorological variables such as temperature, humidity, and turbulence available from the large suite of instrumentation deployed at the SGP Central Facility (C1) during the Land-Atmosphere Feedback Experiment in August 2017. Lidar measurements at five sites, separated by 55–70 km, allowed us to document the development and evolution of the wind flow over the SGP area, examine synoptic conditions to understand the mechanism that leads to the ramp event, and estimate the ability of the High-Resolution Rapid Refresh model to reproduce this event. The flow feature in question is an atmospheric bore, a small-scale phenomenon that is challenging to represent in models, that was generated by a thunderstorm outflow northwest of the ARM SGP area. The small-scale nature of bores, its impact on power generation, and the modeling challenges associated with representing bores are discussed in this paper. The results also provide information about model errors between sites of different surface and vegetation types.

Influences of Cloud Microphysics on the Components of Solar Irradiance in the WRF-Solar Model

An accurate forecast of Global Horizontal solar Irradiance (GHI) and Direct Normal Irradiance (DNI) in cloudy conditions remains a major challenge in the solar energy industry. This study focuses on the impact of cloud microphysics on GHI and its partition into DNI and Diffuse Horizontal Irradiance (DHI) using the Weather Research and Forecasting model specifically designed for solar radiation applications (WRF-Solar) and seven microphysical schemes. Three stratocumulus (Sc) and five shallow cumulus (Cu) cases are simulated and evaluated against measurements at the US Department of Energy’s Atmospheric Radiation Measurement (ARM) user facility, Southern Great Plains (SGP) site. Results show that different microphysical schemes lead to spreads in simulated solar irradiance components up to 75% and 350% from their ensemble means in the Cu and Sc cases, respectively. The Cu cases have smaller microphysical sensitivity due to a limited cloud fraction and smaller domain-averaged cloud water mixing ratio compared to Sc cases. Cloud properties also influence the partition of GHI into DNI and DHI, and the model simulates better GHI than DNI and DHI due to a non-physical error compensation between DNI and DHI. The microphysical schemes that produce more accurate liquid water paths and effective radii of cloud droplets have a better overall performance.

Using Downwelling Far- and Thermal-Infrared Hyperspectral Radiance for Cloud Phase Classification in the Antarctic

The cloud phase is one of the most important parameters of clouds. In this paper, we propose a method for cloud phase classification that synergistically utilizes the far- and thermal-infrared bands based on the Atmospheric Emitted Radiance Interferometer (AERI) at the Atmospheric Radiation Measurement West Antarctic Radiation Experiment (AWARE) observatory in 2016. The possible features in the far- and thermal-infrared bands are analyzed based on the differences in the simulated cloud brightness temperature (BT) spectra with different cloud phases. Using the support vector machine (SVM) algorithm, four features are determined to identify the cloud phase, which include the BT at 900 cm−1, the slope of the fitted function of BT in the 900–1000 cm−1 interval, the BT difference (BTD) between 512 cm−1 and 726 cm−1, and the BTD between 550 cm−1 and 726 cm−1. Here, the performance of the proposed method is evaluated with Shupe’s and Turner’s method. The monthly average accuracy of the proposed method, the method without the two far-infrared features, and Turner’s method are about 76%, 36%, and 49%, respectively, which infer the good performance of the proposed method and also indicate that the far-infrared band features can effectively enhance cloud phase classification. It is notable that, compared to Shupe’s method, the accuracy for the proposed method is only 61% during the Antarctic summer, which results from the definitions of cloud phase and radiative effect. In addition, the accuracy is only 44% for Turner’s method in seasons with a low frequency of mixed clouds due to the significant effect of water vapor.

Examining the vertical heterogeneity of aerosols over the Southern Great Plains

Abstract. Atmospheric aerosols affect the global energy budget by scattering and absorbing sunlight (direct effects) and by changing the microphysical structure, lifetime, and coverage of clouds (indirect effects). Both aerosol direct and indirect effects are affected by the vertical distribution of aerosols in the atmosphere, which is further influenced by a range of processes, such as aerosol dynamics, long-range transport, and entrainment. However, many observations of these processes are based on ground measurements, limiting our ability to understand the vertical distribution of aerosols and simulate their impact on clouds and climate. In this work, we examined the vertical heterogeneity of aerosols over the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) using data collected from the Holistic Interactions of Shallow Clouds, Aerosols and Land Ecosystems (HI-SCALE) campaign. The vertical profiles of meteorological and aerosol physiochemical properties up to 2500 m above are examined based on the 38 flights conducted during the HI-SCALE campaign. The aerosol properties over the SGP show strong vertical heterogeneity and seasonal variabilities. The aerosol concentrations at the surface are the highest due to strong emissions at ground level. In general, the mode diameter of aerosols during summer (∼ 100 nm) is larger than that during spring (∼ 30 nm), a result of enhanced condensational growth due to enriched volatile organic compounds in summer. The concentration of aerosols below 30 nm in the boundary layer (BL) (e.g., below 1000 m) during spring is higher than that during summer, a result of the stronger new particle formation (NPF) events due to the reduced condensation sink in spring. In the BL, the size of the aerosols gradually increases with altitude due to condensational growth and cloud processing. However, the chemical composition of the aerosols remained similar, with organics and sulfates representing 59.8 ± 2.2 % and 22.7 ± 2.1 %, respectively, of the total mass in the BL. Through the vertical profiles of aerosol properties, we observed NPF events in the upper BL during 7 out of 38 research flights, where the newly formed particles continue to grow as they are mixed down to the surface. There is also an indication that deep convection brings aerosols from the free troposphere (FT) to the surface, where they grow to contribute to the cloud condensation nuclei (CCN). Overall, the vertical heterogeneity of aerosols over the SGP is influenced by aerosol dynamics (new particle formation, growth, and cloud processing) and transport processes (mixing in the BL, long-range transport, entrainment, and convective downward transport). Case studies showing the influence of these factors are discussed.

Evaluation of four ground-based retrievals of cloud droplet number concentration in marine stratocumulus with aircraft in situ measurements

Abstract. Cloud droplet number concentration (Nd) is crucial for understanding aerosol–cloud interactions (ACI) and associated radiative effects. We present evaluations of four ground-based Nd retrievals based on comprehensive datasets from the Atmospheric Radiation Measurement (ARM) Aerosol and Cloud Experiments in the Eastern North Atlantic (ACE-ENA) field campaign. The Nd retrieval methods use ARM ENA observatory ground-based remote sensing observations from a micropulse lidar, Raman lidar, cloud radar, and the ARM NDROP (Droplet Number Concentration) value-added product (VAP), all of which also retrieve cloud effective radius (re). The retrievals are compared against aircraft measurements from the fast cloud droplet probe (FCDP) and the cloud and aerosol spectrometer (CAS) obtained from low-level marine boundary layer clouds on 12 flight days during summer and winter seasons. Additionally, the in situ measurements are used to validate the assumptions and characterizations used in the retrieval algorithms. Statistical comparisons of the probability distribution function (PDF) of the Nd and cloud re retrievals with aircraft measurements demonstrate that these retrievals align well with in situ measurements for overcast clouds, but they may substantially differ for broken clouds or clouds with low liquid water path (LWP). The retrievals are applied to 4 years of ground-based remote sensing measurements of overcast marine boundary layer clouds at the ARM ENA observatory to find that Nd (re) values exhibit seasonal variations, with higher (lower) values during the summer season and lower (higher) values during the winter season. The ensemble of various retrievals using different measurements and retrieval algorithms such as those in this paper can help to quantify Nd retrieval uncertainties and identify reliable Nd retrieval scenarios. Of the retrieval methods, we recommend using the micropulse lidar-based method. This method has good agreement with in situ measurements, less sensitivity to issues arising from precipitation and low cloud LWP and/or optical depth, and broad applicability by functioning for both daytime and nighttime conditions.

The Surface Atmosphere Integrated Field Laboratory (SAIL) Campaign

Abstract The science of mountainous hydrology spans the atmosphere through the bedrock and inherently crosses physical and disciplinary boundaries: land–atmosphere interactions in complex terrain enhance clouds and precipitation, while watersheds retain and release water over a large range of spatial and temporal scales. Limited observations in complex terrain challenge efforts to improve predictive models of the hydrology in the face of rapid changes. The Upper Colorado River exemplifies these challenges, especially with ongoing mismatches between precipitation, snowpack, and discharge. Consequently, the U.S. Department of Energy’s (DOE) Atmospheric Radiation Measurement (ARM) user facility has deployed an observatory to the East River Watershed near Crested Butte, Colorado, between September 2021 and June 2023 to measure the main atmospheric drivers of water resources, including precipitation, clouds, winds, aerosols, radiation, temperature, and humidity. This effort, called the Surface Atmosphere Integrated Field Laboratory (SAIL), is also working in tandem with DOE-sponsored surface and subsurface hydrologists and other federal, state, and local partners. SAIL data can be benchmarks for model development by producing a wide range of observational information on precipitation and its associated processes, including those processes that impact snowpack sublimation and redistribution, aerosol direct radiative effects in the atmosphere and in the snowpack, aerosol impacts on clouds and precipitation, and processes controlling surface fluxes of energy and mass. Preliminary data from SAIL’s first year showcase the rich information content in SAIL’s many datastreams and support testing hypotheses that will ultimately improve scientific understanding and predictability of Upper Colorado River hydrology in 2023 and beyond.

Observations of Boundary Layer Convergence Lines and Associated Updrafts in the U.S. Southern Great Plains

Abstract Boundary layer convergence lines (CLs) are highly effective at deep-convection initiation (DCI), suggesting that their associated updraft properties differ from those of more widespread turbulent updrafts in the planetary boundary layer (PBL). This study exploits observations at the Atmospheric Radiation Measurement Southern Great Plains (ARM SGP) observatory in Oklahoma from 2011 to 2016 to quantify CL properties and their relation to turbulent PBL eddies preceding CL arrival. Two independent methods for estimating CL properties are developed at two locations in the SGP region, both relying on the assumption of a 2D circulation in the CL-normal plane but using different combinations of instruments. The first (the radar method) relies mainly on scanning radar data and is applied to 61 CLs passing near a high-resolution scanning radar based in Nardin, Oklahoma, while the second (the surface method) relies mainly on surface wind data and is applied to 68 CLs crossing the SGP facility in nearby Lamont, Oklahoma. Mean daytime (1000–1900 LST) CL width (∼2 km) and convergence magnitude (∼0.003 s−1) are similar for both methods, and mean daytime CL depth is ∼0.75 km. The two methods disagree at night (0000–1000 and 1900–2400 LST), where the surface method estimates wider and weaker CLs than the radar method. This difference may stem from the radar beam overshooting the shallow, highly stable nocturnal PBL. The largest CL updrafts are slightly wider (∼20%) and stronger (∼40%) than the largest PBL updrafts in the pre-CL period, generating 50%–100% larger updraft mass fluxes over most of the PBL depth. Significance Statement Deep convection is commonly initiated by boundary layer convergence lines (CLs), which are associated with intense surface-based wind convergence and strong updrafts that may lift air to saturation. Although CLs form regularly, they are far less common than ordinary, short-lived turbulent thermals in the daytime boundary layer. To better understand why CLs are so effective at deep-convection initiation, we observationally quantify their morphologies and strengths and compare these properties to those of surrounding turbulent updrafts. Perhaps surprisingly, the CLs are found to exhibit only slightly larger scales and strengths as the turbulent updrafts. Although these marginal increases help to explain the preference for storms to initiate along CLs, they likely are not the whole story.

Drivers of Cloud Condensation Nuclei in the Eastern North Atlantic as Observed at the ARM Site

AbstractSeven years of data collected at the Atmospheric Radiation Measurement's Eastern North Atlantic (ENA) site are analyzed to understand the controls of Cloud Condensation Nuclei (CCN) concentrations in the region. Day‐night differences in the aerosol data as segregated by wind direction demonstrate the aerosol observations to be impacted by local emissions when the wind direction (wdir) is between 90° and 310° (measured clockwise from the North where air is coming from). Data collected during marine conditions (wdir <90° or wdir >310°) show the CCN concentrations to be higher in the summer months as compared to the winter months. CCN budget analysis revealed advection and precipitation scavenging being primarily responsible for modulating the CCN concentrations at the site on monthly timescales, with rain rates driving the precipitation scavenging term. High (greater than 75th percentile) and low (lower than 25th percentile) CCN events were identified for each month to characterize the sub‐monthly variability of CCN concentrations. Low CCN events had thicker clouds, stronger rain rates, and lower reanalysis reported free‐tropospheric aerosol pseudo number concentration at the ENA site as compared to the high CCN events. Analysis of satellite data of air‐parcels 48 hr prior to their arrival at the ENA site demonstrated the air parcels during low CCN events to encounter higher cloudiness, stronger rain rates, and higher cloud top heights as compared to the high CCN events. The results presented herein provide key constraints for model evaluation studies and climatological studies conducted at the ENA site.

Machine-learning-based investigation of the variables affecting summertime lightning occurrence over the Southern Great Plains

Abstract. Lightning is affected by many factors, many of which are not routinely measured, well understood, or accounted for in physical models. Several commonly used machine learning (ML) models have been applied to analyze the relationship between Atmospheric Radiation Measurement (ARM) data and lightning data from the Earth Networks Total Lightning Network (ENTLN) in order to identify important variables affecting lightning occurrence in the vicinity of the Southern Great Plains (SGP) ARM site during the summer months (June, July, August and September) of 2012 to 2020. Testing various ML models, we found that the random forest model is the best predictor among common classifiers. When convective clouds were detected, it predicts lightning occurrence with an accuracy of 76.9 % and an area under the curve (AUC) of 0.850. Using this model, we further ranked the variables in terms of their effectiveness in nowcasting lightning and identified geometric cloud thickness, rain rate and convective available potential energy (CAPE) as the most effective predictors. The contrast in meteorological variables between no-lightning and frequent-lightning periods was examined for hours with CAPE values conducive to thunderstorm formation. Besides the variables considered for the ML models, surface variables and mid-altitude variables (e.g., equivalent potential temperature and minimum equivalent potential temperature, respectively) have statistically significant contrasts between no-lightning and frequent-lightning hours. For example, the minimum equivalent potential temperature from 700 to 500 hPa is significantly lower during frequent-lightning hours compared with no-lightning hours. Finally, a notable positive relationship between the intracloud (IC) flash fraction and the square root of CAPE (CAPE) was found, suggesting that stronger updrafts increase the height of the electrification zone, resulting in fewer flashes reaching the surface and consequently a greater IC flash fraction.

Integration of a physics-based direct normal irradiance (DNI) model to enhance the National Solar Radiation Database (NSRDB)

The National Solar Radiation Database (NSRDB) is an extensively used dataset that furnishes satellite-retrieved solar resource data across the United States and an expanding list of other countries. Although the NSRDB uses a physical model to compute global horizontal irradiance (GHI), it currently employs an empirical approach based on surface observations to estimate cloudy-sky direct normal irradiance (DNI). Recently, a new physics-based approach, known as the Fast All-sky Radiation Model for Solar applications with DNI (FARMS-DNI), was developed to improve DNI forecasting. FARMS-DNI integrates direct and scattered solar radiances within the circumsolar region, resulting in improved day-ahead forecasting of DNI by incorporating it into the Weather Research and Forecasting model with Solar extensions (WRF-Solar). This study incorporates FARMS-DNI into the NSRDB algorithm to produce high-spatiotemporal-resolution DNI data from satellite data. The accuracy of the NSRDB based on FARMS-DNI is analyzed using surface observations from 19 sites situated within the National Oceanic and Atmospheric Administration (NOAA) Surface Radiation Budget (SURFRAD) and Solar Radiation (SOLRAD) networks, the University of Oregon (UO) network, the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) network, and at the National Renewable Energy Laboratory (NREL). The results demonstrate that FARMS-DNI reduces the significant overestimation of DNI in the conventional NSRDB at all surface sites, particularly in cloud overcast conditions classified using both satellite retrievals and surface observations. Consequently, this new model can effectively improve the overall accuracy of the NSRDB. The results also suggest that further improvement of DNI estimates at individual time steps, however, requires advanced satellite techniques and precise identification of clouds and retrieval of cloud properties.

Earth System Model Aerosol–Cloud Diagnostics (ESMAC Diags) package, version 2: assessing aerosols, clouds, and aerosol–cloud interactions via field campaign and long-term observations

Abstract. Poor representations of aerosols, clouds, and aerosol–cloud interactions (ACIs) in Earth system models (ESMs) have long been the largest uncertainties in predicting global climate change. Huge efforts have been made to improve the representation of these processes in ESMs, and the key to these efforts is the evaluation of ESM simulations with observations. Most well-established ESM diagnostics packages focus on the climatological features; however, they lack process-level understanding and representations of aerosols, clouds, and ACIs. In this study, we developed the Earth System Model Aerosol–Cloud Diagnostics (ESMAC Diags) package to facilitate the routine evaluation of aerosols, clouds, and ACIs simulated the Energy Exascale Earth System Model (E3SM) from the US Department of Energy (DOE). This paper documents its version 2 functionality (ESMAC Diags v2), which has substantial updates compared with version 1 (Tang et al., 2022a). The simulated aerosol and cloud properties have been extensively compared with in situ and remote-sensing measurements from aircraft, ship, surface, and satellite platforms in ESMAC Diags v2. It currently includes six field campaigns and two permanent sites covering four geographical regions: the eastern North Atlantic, the central US, the northeastern Pacific, and the Southern Ocean. These regions produce frequent liquid- or mixed-phase clouds, with extensive measurements available from the DOE Atmospheric Radiation Measurement user facility and other agencies. ESMAC Diags v2 generates various types of single-variable and multivariable diagnostics, including percentiles, histograms, joint histograms, and heatmaps, to evaluate the model representation of aerosols, clouds, and ACIs. Select examples highlighting the capabilities of ESMAC Diags are shown using E3SM version 2 (E3SMv2). In general, E3SMv2 can reasonably reproduce many observed aerosol and cloud properties, with biases in some variables such as aerosol particle and cloud droplet sizes and number concentrations. The coupling of aerosol and cloud number concentrations may be too strong in E3SMv2, possibly indicating a bias in processes that control aerosol activation. Furthermore, the liquid water path response to a perturbed cloud droplet number concentration behaves differently in E3SMv2 and observations, which warrants further study to improve the cloud microphysics parameterizations in E3SMv2.

Evaluation of liquid cloud albedo susceptibility in E3SM using coupled eastern North Atlantic surface and satellite retrievals

Abstract. The impact of aerosol number concentration on cloud albedo is a persistent source of spread in global climate predictions due to multi-scale, interactive atmospheric processes that remain difficult to quantify. We use 5 years of geostationary satellite and surface retrievals at the US Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) eastern North Atlantic (ENA) site in the Azores to evaluate the representation of liquid cloud albedo susceptibility for overcast cloud scenes in the DOE Energy Exascale Earth System Model version 1 (E3SMv1) and provide possible reasons for model–observation discrepancies. The overall distribution of surface 0.2 % CCN concentration values is reasonably simulated, but simulated liquid water path (LWP) is lower than observed and layer mean droplet concentration (Nd) comparisons are highly variable depending on the Nd retrieval technique. E3SMv1's cloud albedo is greater than observed for given LWP and Nd values due to a lower cloud effective radius than observed. However, the simulated albedo response to Nd is suppressed due to a correlation between the solar zenith angle (SZA) and Nd created by the seasonal cycle that is not observed. Controlling for this effect by examining the cloud optical depth (COD) shows that E3SMv1's COD response to CCN concentration is greater than observed. For surface-based retrievals, this is only true after controlling for cloud adiabaticity because E3SMv1's adiabaticities are much lower than observed. Assuming a constant adiabaticity in surface retrievals as done in top-of-atmosphere (TOA) retrievals narrows the retrieved ln Nd distribution, which increases the cloud albedo sensitivity to ln Nd to match the TOA sensitivity. The greater sensitivity of COD to CCN is caused by a greater Twomey effect in which the sensitivity of Nd to CCN is greater than observed for TOA-retrieved Nd, and once model–observation cloud adiabaticity differences are removed, this is also true for surface-retrieved Nd. The LWP response to Nd in E3SMv1 is overall negative as observed. Despite reproducing the observed LWP–Nd relationship, observed clouds become much more adiabatic as Nd increases, while E3SMv1 clouds do not, associated with more heavily precipitating clouds that are partially but not completely caused by deeper clouds and weaker inversions in E3SMv1. These cloud property differences indicate that the negative LWP–Nd relationship is likely not caused by the same mechanisms in E3SMv1 and observations. The negative simulated LWP response also fails to mute the excessively strong Twomey effect, highlighting potentially important confounding factor effects that likely render the LWP–Nd relationship non-causal. Nd retrieval scales and assumptions, particularly related to cloud adiabaticity, contribute to substantial spreads in the model–observation comparisons, though enough consistency exists to suggest that aerosol activation, drizzle, and entrainment processes are critical areas to focus E3SMv1 development for improving the fidelity of aerosol–cloud interactions in E3SM.

Machine Learning Downscaling of SoilMERGE in the United States Southern Great Plains

SoilMERGE (SMERGE) is a root-zone soil moisture (RZSM) product that covers the entire continental United States and spans 1978 to 2019. Machine learning techniques, Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and Gradient Boost (GBoost) downscaled SMERGE to spatial resolutions straddling the field scale domain (100 to 3000 m). Study area was northern Oklahoma and southern Kansas. The coarse resolution of SMERGE (0.125 degree) limits this product’s utility. To validate downscaled results in situ data from four sources were used that included: United States Department of Energy Atmospheric Radiation Measurement (ARM) observatory, United States Climate Reference Network (USCRN), Soil Climate Analysis Network (SCAN), and Soil moisture Sensing Controller and oPtimal Estimator (SoilSCAPE). In addition, RZSM retrievals from NASA’s Airborne Microwave Observatory of Subcanopy and Surface (AirMOSS) campaign provided a nearly spatially continuous comparison. Three periods were examined: era 1 (2016 to 2019), era 2 (2012 to 2015), and era 3 (2003 to 2007). During eras 1 and 2, RF outperformed XGBoost and GBoost, whereas during era 3 no model dominated. Performance was better during eras 1 and 2 as opposed to the pre-L band era 3. Improvements across all eras, regions, and models realized from downscaling included an increase in correlation from 0.03 to 0.42 and a decrease in ubRMSE from −0.0005 to −0.0118 m3/m3. This study demonstrates the feasibility of SMERGE downscaling opening the prospect for the development of a long-term RZSM dataset at a more desirable field-scale resolution with the potential to support diverse hydrometeorological and agricultural applications.

ARMing the Edge: Designing Edge Computing–Capable Machine Learning Algorithms to Target ARM Doppler Lidar Processing

Abstract There is a need for long-term observations of cloud and precipitation fall speeds in validating and improving rainfall forecasts from climate models. To this end, the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility Southern Great Plains (SGP) site at Lamont, Oklahoma, hosts five ARM Doppler lidars that can measure cloud and aerosol properties. In particular, the ARM Doppler lidars record Doppler spectra that contain information about the fall speeds of cloud and precipitation particles. However, due to bandwidth and storage constraints, the Doppler spectra are not routinely stored. This calls for the automation of cloud and rain detection in ARM Doppler lidar data so that the spectral data in clouds can be selectively saved and further analyzed. During the ARMing the Edge field experiment, a Waggle node capable of performing machine learning applications in situ was deployed at the ARM SGP site for this purpose. In this paper, we develop and test four algorithms for the Waggle node to automatically classify ARM Doppler lidar data. We demonstrate that supervised learning using a ResNet50-based classifier will classify 97.6% of the clear-air images and 94.7% of cloudy images correctly, outperforming traditional peak detection methods. We also show that a convolutional autoencoder paired with k-means clustering identifies 10 clusters in the ARM Doppler lidar data. Three clusters correspond to mostly clear conditions with scattered high clouds, and seven others correspond to cloudy conditions with varying cloud-base heights.

Using an Absolute Cavity Pyrgeometer to Calibrate Pyrgeometers Outdoors with Respect to the International System of Units

Accurate measurement of the atmospheric longwave irradiance is important for renewable energy and atmospheric science applications. Pyrgeometers are deployed outdoors all over the world to measure the atmospheric longwave irradiance and presently are calibrated with traceability to the interim standards for atmospheric longwave radiation measurement, the standards are based on four pyrgeometers and their average irradiance is the World InfraRed Standard Group (WISG) which is developed and maintained by The Physikalisch-Meteorologisches Observatorium Davos/World Radiation Center (PMOD/WRC). Since 2013 the InfraRed Integrating Sphere (IRIS) developed by PMOD/WRC and the Absolute Cavity Pyrgeometer (ACP) developed by the National Renewable Energy Laboratory (NREL) have been compared outdoors six times at different locations and the difference between the measured atmospheric longwave irradiance by ACP and IRIS was less than 2 W/m<sup>2</sup> with traceability to the International System of Units (SI). During the six comparisons the irradiance measured by the interim WISG was 5 W/m<sup>2</sup> lower than the irradiance measured by the average irradiance measured by the ACP and IRIS [1]. Based on this discrepancy, the World Meteorological Organization’s Commission for Instruments and Methods of Observation (CIMO) recommended that the interim WISG should be adjusted to be traceable to SI units [2]. In anticipation of CIMO’s expert team agreement on establishing the world reference using the average irradiance measured by ACP and IRIS in this article we describe a procedure to calibrate pyrgeometers with traceability to SI. One Absolute Cavity Pyrgeometer (ACP95F3) was used to calibrate four pyrgeometers traceable to SI units. Three Eppley PIRs and one Kipp&Zonen CG4 were originally calibrated with traceability to the interim WISG. Using the described procedure below, the responsivity of each pyrgeometer was then adjusted to match the irradiance measured by ACP. Outdoor data was collected during one clear sky nights monitored by the output thermopile voltage of ACP95F3. The irradiance measured by the PIRs was calculated using NREL equation and the CG4 using NREL equation and PMOD/WRC equation. Using the NREL equation, the calculated uncertainty (<i>U</i><sup>95</sup>) of the PIRs varied from 2.43 W/m<sup>2</sup> to 2.67 W/m<sup>2</sup>, and for the CG4 using the NREL equation <i>U</i><sub>95</sub> equals 1.97 W/m<sup>2</sup>, and using the PMOD equation <i>U</i><sub>95</sub> equals 2.88 W/m<sup>2</sup> with respect to SI.

Assessment of Warm and Dry Bias over ARM SGP Site in E3SMv2 and E3SM-MMF

Abstract Many climate models exhibit a dry and warm bias over the central United States during the summer months, including the Energy Exascale Earth System Model (E3SM) and its Multiscale Modeling Framework (MMF) configuration. Understanding the causes of this bias is important to shine a light on this common model error and reduce the uncertainty in future projections. In this study, we use E3SMv2 and E3SM-MMF to assess how parameterized and resolved convection affect temperature and precipitation biases over the Southern Great Plains site of the Atmospheric Radiation Measurement program. Both configurations overestimate near-surface temperature and underestimate precipitation at the ARM SGP site. The bias is associated with a lack of low-level clouds during days without precipitation and too much incoming solar radiation causing the surface to warm. Low-level cloud fraction in E3SM-MMF during the nonprecipitating days is lower in comparison to E3SMv2 and observation, consistent with the larger warm bias. We also find that the underestimated precipitation can be characterized as “too frequent, too weak” in E3SMv2 and “too rare, too intense” in E3SM-MMF. These deficiencies conspire to sustain the warm and dry bias over the central United States.