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A 5-year, 1-minute resolution observational dataset of clouds and solar radiation was produced that includes two metrics of the variability in surface solar irradiance due to cloud type and fractional sky cover. Multiple regression models were trained to fit observations of surface solar irradiance variability from those two cloud property predictors. We found that ensemble tree-based methods, Random Forest and Gradient Boosting Machine, have the least overfitting issues and showed the best performance with an R2 of 0.42. While the observational data trained in this study was only from one site, the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site in Oklahoma, initial comparisons of the seasonality of the statistics suggest that these results are relatively weather regime independent; the generality of such a finding across sites will be tested in future work. The observational data and developed machine learning model are being used to create a numerical weather prediction model parameterization to enable day-ahead solar variability prediction in a computationally efficient way. This is a first step towards creating a new paradigm of predicting day-ahead variability with the potential to provide a new tool to improve grid operation, planning, and resilience.

Various methods have been developed to characterize cloud type, otherwise referred to as cloud regime. These include manual sky observations, combining radiative and cloud vertical properties observed from satellite, surface-based remote sensing, and digital processing of sky imagers. While each method has inherent advantages and disadvantages, none of these cloud-typing methods actually includes measurements of surface shortwave or longwave radiative fluxes. Here, a method that relies upon detailed, surface-based radiation and cloud measurements and derived data products to train a random-forest machine-learning cloud classification model is introduced. Measurements from five years of data from the ARM Southern Great Plains site were compiled to train and independently evaluate the model classification performance. A cloud-type accuracy of approximately 80% using the random-forest classifier reveals that the model is well suited to predict climatological cloud properties. Furthermore, an analysis of the cloud-type misclassifications is performed. While physical cloud types may be misreported, the shortwave radiative signatures are similar between misclassified cloud types. From this, we assert that the cloud-regime model has the capacity to successfully differentiate clouds with comparable cloud–radiative interactions. Therefore, we conclude that the model can provide useful cloud-property information for fundamental cloud studies, inform renewable energy studies, and be a tool for numerical model evaluation and parameterization improvement, among many other applications.

Industry advances have greatly reduced the cost and size of ground-based shortwave (SW) sensors for the ultraviolet, visible, and near-infrared spectral ranges that make up the solar spectrum, while simultaneously increasing their ruggedness, reliability, and calibration accuracy needed for outdoor operation. These sensors and collocated meteorological equipment are an important part of the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) User Facility, which has supported parallel integrated measurements of atmospheric and surface properties for more than two decades at fixed and mobile sites around the world. The versatile capability of these ground-based measurements includes 1) rich spectral information required for retrieving cloud and aerosol microphysical properties, such as cloud phase, cloud particle size, and aerosol size distributions, and 2) high temporal resolution needed for capturing fast evolution of cloud microphysical properties in response to rapid changes in meteorological conditions. Here we describe examples of how ARM’s spectral radiation measurements are being used to improve understanding of the complex processes governing microphysical, optical, and radiative properties of clouds and aerosol.

During the Chequamegon Heterogeneous Ecosystem Energy-balance Study Enabled by a High-density Extensive Array of Detectors 2019 (CHEESEHEAD19) field campaign, held in the summer of 2019 in northern Wisconsin, USA, active and passive ground-based remote sensing instruments were deployed to understand the response of the planetary boundary layer to heterogeneous land surface forcing. These instruments include radar wind profilers, microwave radiometers, atmospheric emitted radiance interferometers, ceilometers, high spectral resolution lidars, Doppler lidars, and collaborative lower-atmospheric mobile profiling systems that combine several of these instruments. In this study, these ground-based remote sensing instruments are used to estimate the height of the daytime planetary boundary layer, and their performance is compared against independent boundary layer depth estimates obtained from radiosondes launched as part of the field campaign. The impact of clouds (in particular boundary layer clouds) on boundary layer depth estimations is also investigated. We found that while all instruments are overall able to provide reasonable boundary layer depth estimates, each of them shows strengths and weaknesses under certain conditions. For example, radar wind profilers perform well during cloud-free conditions, and microwave radiometers and atmospheric emitted radiance interferometers have a very good agreement during all conditions but are limited by the smoothness of the retrieved thermodynamic profiles. The estimates from ceilometers and high spectral resolution lidars can be hindered by the presence of elevated aerosol layers or clouds, and the multi-instrument retrieval from the collaborative lower atmospheric mobile profiling systems can be constricted to a limited height range in low-aerosol conditions.

Cloud cover estimates of single-layer shallow cumuli obtained from narrow field-of-view (FOV) lidar–radar and wide-FOV total sky imager (TSI) data are compared over an extended period (2000–2017 summers) at the established United States Atmospheric Radiation Measurement mid-continental Southern Great Plains site. We quantify the impacts of two factors on hourly and sub-hourly cloud cover estimates: (1) instrument-dependent cloud detection and data merging criteria and (2) FOV configuration. Enhanced observations at this site combine the advantages of the ceilometer, micropulse lidar (MPL) and cloud radar in merged data products. Data collected by these three instruments are used to calculate narrow-FOV cloud fraction (CF) as a temporal fraction of cloudy returns within a given period. Sky images provided by TSI are used to calculate the wide-FOV fractional sky cover (FSC) as a fraction of cloudy pixels within a given image. To assess the impact of the first factor on CF obtained from the merged data products, we consider two additional subperiods (2000–2010 and 2011–2017 summers) that mark significant instrumentation and algorithmic advances in the cloud detection and data merging. We demonstrate that CF obtained from ceilometer data alone and FSC obtained from sky images provide the most similar and consistent cloud cover estimates; hourly bias and root-mean-square difference (RMSD) are within 0.04 and 0.12, respectively. However, CF from merged MPL–ceilometer data provides the largest estimates of the multiyear mean cloud cover, about 0.12 (35 %) and 0.08 (24 %) greater than FSC for the first and second subperiods, respectively. CF from merged ceilometer–MPL–radar data has the strongest subperiod dependence with a bias of 0.08 (24 %) compared to FSC for the first subperiod and shows no bias for the second subperiod. The strong period dependence of CF obtained from the combined ceilometer–MPL–radar data is likely results from a change in what sensors are relied on to detect clouds below 3 km. After 2011, the MPL stopped being used for cloud top height detection below 3 km, leaving the radar as the only sensor used in cloud top height retrievals. To quantify the FOV impact, a narrow-FOV FSC is derived from the TSI images. We demonstrate that FOV configuration does not modify the bias but impacts the RMSD (0.1 hourly, 0.15 sub-hourly). In particular, the FOV impact is significant for sub-hourly observations, where 41 % of narrow- and wide-FOV FSC differ by more than 0.1. A new “quick-look” tool is introduced to visualize impacts of these two factors through integration of CF and FSC data with novel TSI-based images of the spatial variability in cloud cover. The influence of cloud field organization, such cloud streets parallel to the wind direction, on narrow- and wide-FOV cloud cover estimates can be visually assessed.

During the Chequamegon Heterogeneous Ecosystem Energy-balance Study Enabled by a High-density Extensive Array of Detectors 2019 (CHEESEHEAD19) field campaign, held in the summer of 2019 in northern Wisconsin, U.S.A., active and passive ground-based remote sensing instruments were deployed to understand the response of the planetary boundary layer to heterogeneous land surface forcing. These instruments include Radar Wind Profilers, Microwave Radiometers, Atmospheric Emitted Radiance Interferometers, Ceilometers, High Spectral Resolution Lidars, Doppler Lidars, and Collaborative Lower Atmospheric Modelling Profiling Systems that combine several of these instruments. In this study, these ground-based remote sensing instruments are used to estimate the height of the daytime planetary boundary layer, and their performance is compared against independent boundary-layer depth estimates obtained from radiosondes launched as part of the field campaign. The impact of clouds (in particular boundary layer clouds) on boundary-layer depth is also investigated. We found that while overall all instruments are able to provide reasonable boundary-layer depth estimates, each of them shows strengths and weaknesses under certain conditions. For example, Radar Wind Profilers perform well during cloud free conditions, and Microwave Radiometers and Atmospheric Emitted Radiance Interferometers have a very good agreement during all conditions, but are limited by the smoothness of the retrieved thermodynamic profiles. The estimates from Ceilometers and High Spectral Resolution Lidars can be hindered by the presence of elevated aerosol layers or clouds, and the multi-instrument retrieval from the Collaborative Lower Atmospheric Modelling Profiling Systems can be constricted to a limited height range in low aerosol conditions.

Long-term records of aerosol optical depth (AOD) with high quality, suitable temporal continuity and spatial coverage are of immense interest to climate-related research activities. Both satellite- and ground-based measurements of AOD are typically provided by instruments with different designs, and distinct data acquisition and processing schemes. Thus, the corresponding AOD records likely have different accuracy, spatial coverage, and temporal resolution. Several studies have been focused on the synergy of multi-sensor satellite AOD products. Here we combine multi-year (1997-2018) AOD records available from four collocated ground-based instruments deployed at the mid-continental Southern Great Plains (SGP) Central Facility supported by the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) Program. We demonstrate how to minimize drawbacks (patchy spots) and to maintain benefits (high quality) of these records. Our demonstration finds a combined AOD obtained at two wavelengths (500 and 870 nm), with high temporal resolution (1-min), and provides the user with an estimate of the AOD uncertainty. Finally, we highlight expected applications of the merged dataset and its future extensions.

A long-term climatology of classified cloud types has been generated for 13 years (1997–2009) over the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site for seven cloud categories: low clouds, congestus, deep convection, altocumulus, altostratus, cirrostratus/anvil, and cirrus. The classification was based on the cloud macrophysical quantities of cloud top, cloud base, and physical thickness of cloud layers, as measured by active sensors such as the millimeter-wavelength cloud radar (MMCR) and micropulse lidar (MPL). Climate variability of cloud characteristics has been examined using the 13-yr cloud-type retrieval. Low clouds and cirrus showed distinct diurnal and seasonal cycles. Total cloud occurrence followed the variation of low clouds, with a diurnal peak in early afternoon and a seasonal maximum in late winter. Additionally, further work has been done to identify fair-weather shallow cumulus (FWSC) events for 9 years (2000–08). Periods containing FWSC, a subcategory of clouds classified as low clouds, were produced using cloud fraction information from a total-sky imager and ceilometer. The identified FWSC periods in our study show good agreement with manually identified FWSC, missing only 6 cases out of 70 possible events during the spring to summer seasons (May–August).

Climate simulations with more accurate process-level representation at finer resolutions ( km) are a pressing need in order to provide more detailed actionable information to policy makers regarding extreme events in a changing climate. Computational limitation is a major obstacle for building and running high-resolution (HR, here 0.25∘ average grid spacing at the Equator) models (HRMs). A more affordable path to HRMs is to use a global regionally refined model (RRM), which only simulates a portion of the globe at HR while the remaining is at low resolution (LR, 1∘). In this study, we compare the Energy Exascale Earth System Model (E3SM) atmosphere model version 1 (EAMv1) RRM with the HR mesh over the contiguous United States (CONUS) to its corresponding globally uniform LR and HR configurations as well as to observations and reanalysis data. The RRM has a significantly reduced computational cost (roughly proportional to the HR mesh size) relative to the globally uniform HRM. Over the CONUS, we evaluate the simulation of important dynamical and physical quantities as well as various precipitation measures. Differences between the RRM and HRM over the HR region are predominantly small, demonstrating that the RRM reproduces the precipitation metrics of the HRM over the CONUS. Further analysis based on RRM simulations with the LR vs. HR model parameters reveals that RRM performance is greatly influenced by the different parameter choices used in the LR and HR EAMv1. This is a result of the poor scale-aware behavior of physical parameterizations, especially for variables influencing sub-grid-scale physical processes. RRMs can serve as a useful framework to test physics schemes across a range of scales, leading to improved consistency in future E3SM versions. Applying nudging-to-observations techniques within the RRM framework also demonstrates significant advantages over a free-running configuration for use as a test bed and as such represents an efficient and more robust physics test bed capability. Our results provide additional confirmatory evidence that the RRM is an efficient and effective test bed for HRM development.

The ndrop_mfrsr value-added product (VAP) provides an estimate of the cloud droplet number concentration of overcast water clouds retrieved from cloud optical depth from the multi-filter rotating shadowband radiometer (MFRSR) instrument and liquid water path (LWP) retrieved from the microwave radiometer (MWR). When cloud layer information is available from vertically pointing lidar and radars in the Active Remote Sensing of Clouds (ARSCL) product, the VAP also provides estimates of the adiabatic LWP and an adiabatic parameter (beta) that indicates how divergent the LWP is from the adiabatic case. quality control (QC) flags (qc_drop_number_conc), an uncertainty estimate (drop_number_conc_toterr), and a cloud layer type flag (cloud_base_type) are useful indicators of the quality and accuracy of any given value of the retrieval. Examples of these major input and output variables are given in sample plots in section 6.0.

Aerosol optical depth (AOD) characterizes the aerosol burden in the atmosphere, while its wavelength dependence is a sign of particle size. Long-term records of wavelength-resolved AOD with high quality and suitable continuity are required for climate change assessment. Typically, climate-related studies use AOD products provided by several, and perhaps different, ground-based instruments. The measurements from these instruments often have different accuracy and temporal resolution. To preserve the advantages of these products (high quality) and to reduce their disadvantages (patchy records), we generate a merged dataset obtained from four instruments deployed at a US continental site in which a nearly-continuous AOD record is found at two wavelengths (500 and 870 nm) with high quality and high temporal resolution (1-min) for a 21-yr period (1997–2018). The combined dataset addresses: (1) varying data quality and resolution mismatch of the individual AOD records, and (2) the uncertainty of the merged AOD and its relevance for user-specified needs. The generated dataset will be beneficial for a wide range of applications including aerosol-radiation interactions., Measurement(s) aerosol optical depth Technology Type(s) atmospheric observatory system Factor Type(s) year of data collection Sample Characteristic - Environment atmosphere Sample Characteristic - Location State of Oklahoma Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.13256699

The Chequamegon Heterogeneous Ecosystem Energy-Balance Study Enabled by a High-Density Extensive Array of Detectors 2019 (CHEESEHEAD19) is an ongoing National Science Foundation project based on an intensive field campaign that occurred from June to October 2019. The purpose of the study is to examine how the atmospheric boundary layer (ABL) responds to spatial heterogeneity in surface energy fluxes. One of the main objectives is to test whether lack of energy balance closure measured by eddy covariance (EC) towers is related to mesoscale atmospheric processes. Finally, the project evaluates data-driven methods for scaling surface energy fluxes, with the aim to improve model–data comparison and integration. To address these questions, an extensive suite of ground, tower, profiling, and airborne instrumentation was deployed over a 10 km × 10 km domain of a heterogeneous forest ecosystem in the Chequamegon–Nicolet National Forest in northern Wisconsin, United States, centered on an existing 447-m tower that anchors an AmeriFlux/NOAA supersite (US-PFa/WLEF). The project deployed one of the world’s highest-density networks of above-canopy EC measurements of surface energy fluxes. This tower EC network was coupled with spatial measurements of EC fluxes from aircraft; maps of leaf and canopy properties derived from airborne spectroscopy, ground-based measurements of plant productivity, phenology, and physiology; and atmospheric profiles of wind, water vapor, and temperature using radar, sodar, lidar, microwave radiometers, infrared interferometers, and radiosondes. These observations are being used with large-eddy simulation and scaling experiments to better understand submesoscale processes and improve formulations of subgrid-scale processes in numerical weather and climate models.

Heterogeneity in warm-season (May-August) land-atmosphere (LA) coupling is quantified with the long-time, multiple-station measurements from the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) program and the moderate-resolution imaging spectroradiometer (MODIS) satellite remote sensing at the Southern Great Plains (SGP). We examine the coupling strength at 7 additional locations with the same surface type (i.e., pasture/grassland) as the ARM SGP central facility (CF). To simultaneously consider multiple factors and consistently quantify their relative contributions, we apply a multiple linear regression method to correlate the surface evaporative fraction (EF) with near-surface soil moisture (SM) and leaf area index (LAI). The observations show moderate to weak terrestrial segment LA coupling with large heterogeneity across the ARM SGP domain in warm-season. Large spatial variabilities in the contributions from SM and LAI to the EF changes are also found. The coupling heterogeneities appear to be associated with differences in land use, anthropogenic activities, rooting depth, and soil type at different stations. Therefore, the complex LA interactions at the SGP cannot be well represented by those at the CF/E13 based on the metrics applied here. Overall, the LAI exerts more influence on the EF than does the SM due to its overwhelming impacts on the latent heat flux. This study complements previous studies based on measurements only from the CF and has important implications for modeling LA coupling in weather and climate models. The multiple linear regression provides a more comprehensive measure of the integrated impacts on LA coupling from several different factors.

Surface broadband shortwave and longwave irradiance are key components of the surface energy budget and give insight on atmospheric constituents like clouds and aerosols as well as provide useful i...

To understand the microphysical processes that impact diabatic heating and cloud lifetimes in convection, we need to characterize the spatial distribution of supercooled liquid water. To address this observational challenge, vertically pointing active sensors at the Darwin Atmospheric Radiation Measurement (ARM) site are used to classify cloud phase within a deep convective cloud in a shallow to deep convection transitional case. The cloud cannot be fully observed by a lidar due to signal attenuation. Thus we develop an objective method for identifying hydrometeor classes, including mixed-phase conditions, using k-means clustering on parameters that describe the shape of the Doppler spectra from vertically pointing Ka band cloud radar. This approach shows that multiple, overlapping mixed-phase layers exist within the cloud, rather than a single region of supercooled liquid, indicating complexity to how ice growth and diabatic heating occurs in the vertical structure of the cloud.

This work is motivated by previous studies of transatlantic transport of Saharan dust and the observed quasi-static nature of coarse mode aerosol with a volume median diameter (VMD) of approximately 3.5 μm. The authors examine coarse mode contributions from transpacific transport of dust to North American aerosol properties using a dataset collected at the high-elevation Storm Peak Laboratory (SPL) and the nearby Atmospheric Radiation Measurement (ARM) Mobile Facility. Collected ground-based data are complemented by quasi-global model simulations and satellite and ground-based observations. The authors identify a major dust event associated mostly with a transpacific plume (about 65% of near-surface aerosol mass) in which the coarse mode with moderate (~3 μm) VMD is distinct and contributes substantially to total aerosol volume (up to 70%) and scattering (up to 40%). The results demonstrate that the identified plume at the SPL site has a considerable fraction of supermicron particles (VMD ~3 μm) and, thus, suggest that these particles have a fairly invariant behavior despite transpacific transport. If confirmed in additional studies, this invariant behavior may simplify considerably parameterizations for size-dependent processes associated with dust transport and removal.

We develop a new climatology of the macrophysical properties of single-layer shallow cumuli (ShCu), such as cloud amount and cloud base/top heights, observed during 19 summers (2000-2018) at the Atmospheric Radiation Measurement (ARM) Program’s Southern Great Plains (SGP) Central Facility in northern Oklahoma, USA. Similar to the established datasets, the climatology incorporates the well-known advantages of the narrow field-of-view (FOV) lidar-radar measurements to resolve the vertical structure of clouds along the wind direction. In contrast to these datasets, the climatology combines the well-known advantages of the wide-FOV sky images to describe the horizontal changes of cloud amount across the wind direction. The recent update includes (1) a new tool for visualization of these across-wind changes with user-selected spatial and temporal resolutions, (2) an additional macrophysical property, the so-called cloud equivalent diameter (CED), estimated over a wide range of cloud sizes (about 0.01–3.5 km) with high temporal resolution (30s) and (3) environmental parameters. Our development of the extended climatology is aimed to enhance understanding of the environmental impact on the diurnal evolution of the cloud macrophysical properties and thus to improve performance of ShCu parameterizations.

Accurate height-resolved measurements of higher-order statistical moments of vertical velocity fluctuations are crucial for improved understanding of turbulent mixing and diffusion, convective initiation, and cloud life cycles. The Atmospheric Radiation Measurement (ARM) Climate Research Facility operates coherent Doppler lidar systems at several sites around the globe. These instruments provide measurements of clear-air vertical velocity profiles in the lower troposphere with a nominal temporal resolution of 1 sec and height resolution of 30 m. The purpose of the Doppler lidar vertical velocity statistics (DLWSTATS) value-added product (VAP) is to produce height- and time-resolved estimates of vertical velocity variance, skewness, and kurtosis from these raw measurements. The VAP also produces estimates of cloud properties, including cloud-base height (CBH), cloud frequency, cloud-base vertical velocity, and cloud-base updraft fraction.

Pacific Northwest National Laboratory and the University of Washington team up to teach students about state-of-the-art research instrumentation.

Mesoscale convective systems (MCSs) are an important component of our hydrologic cycle as they produce prolific rainfall in the tropics and mid-latitudes. Recent advancements in high-resolution mod...

A new cloud-droplet number concentration (NDROP) value added product (VAP) has been produced at the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site for the 13 years from January 1998 to January 2011. The retrieval is based on surface radiometer measurements of cloud optical depth from the multi-filter rotating shadow-band radiometer (MFRSR) and liquid water path from the microwave radiometer (MWR). It is only applicable for single-layered warm clouds. Validation with in situ aircraft measurements during the extended-term aircraft field campaign, Routine ARM Aerial Facility (AAF) CLOWD Optical Radiative Observations (RACORO), shows that the NDROP VAP robustly reproduces the primary mode of the in situ measured probability density function (PDF), but produces a too wide distribution, primarily caused by frequent high cloud-droplet number concentration. Our analysis shows that the error in the MWR retrievals at low liquid water paths is one possible reason for this deficiency. Modification through the diagnosed liquid water path from the coordinate solution improves not only the PDF of the NDROP VAP but also the relationship between the cloud-droplet number concentration and cloud-droplet effective radius. Consideration of entrainment effects rather than assuming an adiabatic cloud improves the values of the NDROP retrieval by reducing themore » magnitude of cloud-droplet number concentration. Aircraft measurements and retrieval comparisons suggest that retrieving the vertical distribution of cloud-droplet number concentration and effective radius is feasible with an improvement of the parameter representing the mixing effects between environment and clouds and with a better understanding of the effect of mixing degree on cloud properties.« less

Substantial difference between cloud amounts obtained from active and passive remote sensing has been documented by previous studies. The difference is typically attributed to two main factors: the different field-of-view (FOV) (first factor) and different sensitivity to cloud properties (second factor) of the active and passive ground-based instruments. The relative impact of these two main factors on shallow cumuli cloud amount is demonstrated in this study. The demonstration involves a new multi-year (2000-2017) product, which integrates both the active and passive remote sensing data collected at the mid-continental Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) Central Facility. Cloud fraction (CF) obtained from the narrow-FOV lidar-radar observations and wide-FOV fractional sky cover (FSC) acquired from sky images are key components of the integrated product. Results of this study indicate that (1) CF tends to overestimate FSC and this overestimation can be large (~40% on average) even at extended temporal scales (several years) and (2) the observed overestimate is primarily due to different sensitivity of the active and passive remote sensing instruments to shallow cumuli, while the limited FOV of active remote sensing instruments plays a minor role in such overestimation.

The total aerosol burden in the atmosphere is typically represented by aerosol optical depth (AOD). To capture important and climate-relevant signatures of the aerosol burden, such as year-to-year and seasonal variability, continuous multi-year AOD observations are required. For more than two decades, these observations have been performed at the mid-continental Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) Central Facility (CF) using ground-based passive remote sensing. The partially overlapping and fragmentary AOD records at the ARM SGP CF have been provided by four individual instruments, namely two co-located (C1 and E13) Multifilter Rotating Shadowband Radiometers (MFRSRs), a Normal Incidence Multifilter Radiometer (NIMFR), and a Cimel Sunphotometer (CSPHOT). Since these individual records are sporadic with instrument- and time-dependent data quality, development of a continuous multi-year high-quality AOD dataset is a challenging task. In this work, an initial development of a continuous 20-year (1997-2017) high-quality AOD product is introduced. The development involves (1) incorporation of the available data quality information and delivery of the historical time series of AOD with high quality from four individual instruments, (2) comparison of multiple AOD retrievals to identify potential instrument-related issues and/or retrieval problems, and (3) merging these individual time series, generation of a two-decade continuous climatology of high-quality AOD and reporting of the uncertainty estimations of the merged product.

Knowledge of cloud phase (liquid, ice, mixed, etc.) is necessary to describe the radiative impact of clouds and their lifetimes, but is a property that is difficult to simulate correctly in climate models. One step towards improving those simulations is to make observations of cloud phase with sufficient accuracy to help constrain model representations of cloud processes. In this study, we outline a methodology using a basic Bayesian classifier to estimate the probabilities of cloud-phase class from Atmospheric Radiation Measurement (ARM) vertically pointing active remote sensors. The advantage of this method over previous ones is that it provides uncertainty information on the phase classification. We also test the value of including higher moments of the cloud radar Doppler spectrum than are traditionally used operationally. Using training data of known phase from the Mixed-Phase Arctic Cloud Experiment (M-PACE) field campaign, we demonstrate a proof of concept for how the method can be used to train an algorithm that identifies ice, liquid, mixed phase, and snow. Over 95 % of data are identified correctly for pure ice and liquid cases used in this study. Mixed-phase and snow cases are more problematic to identify correctly. When lidar data are not available, including additional information from the Doppler spectrum provides substantial improvement to the algorithm. This is a first step towards an operational algorithm and can be expanded to include additional categories such as drizzle with additional training data.

This study utilizes six commonly used reanalysis products, including the NCEP–Department of Energy Reanalysis 2 (NCEP2), NCEP Climate Forecast System Reanalysis (CFSR), ECMWF interim reanalysis (ERA-Interim), Japanese 25-year Reanalysis Project (JRA-25), Modern-Era Retrospective Analysis for Research and Applications (MERRA), and North American Regional Reanalysis (NARR), to evaluate features of the southern Great Plains low-level jet (LLJ) above the U.S. Department of Energy’s Atmospheric Radiation Measurement Program (ARM) Climate Research Facility (ACRF) Southern Great Plains site. Two sets of radiosonde data are utilized: the six-week Midlatitude Continental Convective Clouds Experiment (MC3E) and a 10-yr period spanning 2001 through 2010. All six reanalyses are compared to MC3E data, while only the NARR, MERRA, and CFSR are compared to the 10-yr data. The reanalyses are able to represent most aspects of the composite LLJ profile, although there is a tendency for each reanalysis to overestimate the wind speed between the nose of the LLJ (at approximately 900 mb) and a pressure level of 700 mb. There are large discrepancies in the number of LLJs observed and derived from the reanalysis, particularly for strong LLJs, leading to an underestimate of the moisture transport associated with LLJs. When the 10-yr period is considered, the NARR and CFSR overestimate and MERRA underestimates the total moisture transport, but all three underestimate the transport associated with strong LLJs by factors of 1.4, 2.0, and 2.7 for CFSR, NARR, and MERRA, respectively. During MC3E there were differences in the patterns of moisture convergence and divergence, but the patterns are more consistent during the 10-yr period.

Cloud amount is an essential and extensively used macrophysical parameter of cumulus clouds. It is commonly defined as a cloud fraction (CF) from zenith-pointing ground-based active and passive remote sensing. However, conventional retrievals of CF from the remote sensing data with very narrow field-of-view (FOV) may not be representative of the surrounding area. Here we assess its representativeness using an integrated dataset collected at the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) program's Southern Great Plains (SGP) site in Oklahoma, USA. For our assessment with focus on selected days with single-layer cumulus clouds (2005-2016), we include the narrow-FOV ARM Active Remotely Sensed Clouds Locations (ARSCL) and large-FOV Total Sky Imager (TSI) cloud products, the 915-MHz Radar Wind Profiler (RWP) measurements of wind speed and direction, and also high-resolution satellite images from Landsat and the Moderate Resolution Imaging Spectroradiometer (MODIS). We demonstrate that the root-mean-square difference (RMSD) between the 15-min averaged ARSCL cloud fraction (CF) and the 15-min averaged TSI fractional sky cover (FSC) is large (up to 0.3). We also discuss how the horizontal distribution of clouds can modify the obtained large RMSD using a new uniformity metric. The latter utilizes the spatial distribution of the FSC over the 100° FOV TSI images obtained with high temporal resolution (30 sec sampling). We demonstrate that cases with more uniform spatial distribution of FSC show better agreement between the narrow-FOV CF and large-FOV FSC, reducing the RMSD by up to a factor of 2.

Tower-based data combined with high-resolution satellite products have been used to produce surface albedo at various spatial scales over land. Because tower-based albedo data are available at only a few sites, surface albedos using these combined data are spatially limited. Moreover, tower-based albedo data are not representative of highly heterogeneous regions. To produce areal-averaged and spectrally-resolved surface albedo for regions with various degrees of surface heterogeneity, we have developed a transmission-based retrieval and demonstrated its feasibility for relatively homogeneous land surfaces. Here, we demonstrate its feasibility for a highly heterogeneous coastal region. We use the atmospheric transmission measured during a 19-month period (June 2009–December 2010) by a ground-based Multi-Filter Rotating Shadowband Radiometer (MFRSR) at five wavelengths (0.415, 0.5, 0.615, 0.673 and 0.87 µm) at the Department of Energy’s Atmospheric Radiation Measurement (ARM) Mobile Facility (AMF) site located on Graciosa Island. We compare the MFRSR-retrieved areal-averaged surface albedo with albedo derived from Moderate Resolution Imaging Spectroradiometer (MODIS) observations, and also a composite-based albedo. We demonstrate that these three methods produce similar spectral signatures of surface albedo; however, the MFRSR-retrieved albedo, is higher on average (≤0.04) than the MODIS-based areal-averaged surface albedo and the largest difference occurs in winter.

We present here a simple retrieval of the areal-averaged spectral surface albedo using only ground-based measurements of atmospheric transmission under fully overcast conditions. Our retrieval is based on a one-line equation. The feasibility of our retrieval for routine determinations of albedo is demonstrated for different landscapes with various degrees of heterogeneity using three sets of measurements: (1) spectral atmospheric transmission from the Multi-Filter Rotating Shadowband Radiometer (MFRSR) at five wavelengths (415, 500, 615, 673, and 870 nm); (2) tower-based measurements of local surface albedo at the same wavelengths; and (3) areal-averaged surface albedo at four wavelengths (470, 560, 670 and 860 nm) from collocated and coincident Moderate Resolution Imaging Spectroradiometer (MODIS) observations. These integrated datasets cover both temporally long (2008–2013) and short (April–May 2010) periods at the Atmospheric Radiation Measurement (ARM) Southern Great Plains site and the National Oceanic and Atmospheric Administration (NOAA) Table Mountain site, respectively. The calculated root mean square error (RMSE), defined here as the root mean squared difference between the MODIS-derived surface albedo and the retrieved areal-averaged albedo, is quite small (RMSE ≤ 0.015) and comparable with that obtained previously by other investigators for the shortwave broadband albedo. Good agreement between tower-based measurements of daily-averaged surface albedo for completely overcast and non-overcast conditions is also demonstrated.

The location of the Atmospheric Radiation Measurement (ARM) site on Manus island was chosen because it is very close to the coast, in a flat, near–sea level area of the island, hopefully minimizing the impact of local island effects on the meteorology of the measurements. In this study, we confirm that the Manus site is indeed less impacted by the island meteorology than slightly inland by comparing over a year of broadband surface irradiance and ceilometer measurements and derived quantities at the standard Manus site and a second location 7 km away as part of the ARM Madden Julian Oscillation Investigation Experiment (AMIE)-Manus campaign. The two sites show statistically similar distributions of irradiance and other derived quantities for all wind directions except easterly winds, when the inland site is downwind from the standard Manus site. Under easterly wind conditions, which occur 17% of the time, there is a higher occurrence of cloudiness at the downwind site likely due to land heating and orographic effects. This increased cloudiness is caused by scattered clouds often with bases around 700 m in altitude. While the central Manus site consistently measures a frequency of occurrence of low clouds (cloud base height less than 1200 m) about 25% of the time regardless of wind direction, the AMIE site has higher frequencies of low clouds (38%) when winds are from the east. This increase in low, locally produced clouds causes an additional −20W/m2shortwave surface cloud radiative effect at the AMIE site than the Manus site.

A 4-yr climatology of midlevel clouds is presented from vertically pointing cloud lidar and radar measurements at the Atmospheric Radiation Measurement Program (ARM) site at Darwin, Australia. Few studies exist of tropical midlevel clouds using a dataset of this length. Seventy percent of clouds with top heights between 4 and 8 km are less than 2 km thick. These thin layer clouds have a peak in cloud-top temperature around the melting level (0°C) and also a second peak around −12.5°C. The diurnal frequency of thin clouds is highest during the night and reaches a minimum around noon, consistent with variation caused by solar heating. Using a 1.5-yr subset of the observations, the authors found that thin clouds have a high probability of containing supercooled liquid water at low temperatures: ~20% of clouds at −30°C, ~50% of clouds at −20°C, and ~65% of clouds at −10°C contain supercooled liquid water. The authors hypothesize that thin midlevel clouds formed at the melting level are formed differently during active and break monsoon periods and test this over three monsoon seasons. A greater frequency of thin midlevel clouds are likely formed by increased condensation following the latent cooling of melting during active monsoon periods when stratiform precipitation is most frequent. This is supported by the high percentage (65%) of midlevel clouds with preceding stratiform precipitation and the high frequency of stable layers slightly warmer than 0°C. In the break monsoon, a distinct peak in the frequency of stable layers at 0°C matches the peak in thin midlevel cloudiness, consistent with detrainment from convection.

Areal-averaged albedos are particularly difficult to measure in coastal regions, because the surface is not homogenous, consisting of a sharp demarcation between land and water. With this difficulty in mind, we evaluate a simple retrieval of areal-averaged surface albedo using ground-based measurements of atmospheric transmission alone under fully overcast conditions. To illustrate the performance of our retrieval, we find the areal-averaged albedo using measurements from the Multi-Filter Rotating Shadowband Radiometer (MFRSR) at five wavelengths (415, 500, 615, 673, and 870 nm). These MFRSR data are collected at a coastal site in Graciosa Island, Azores supported by the U.S. Department of Energy’s (DOE’s) Atmospheric Radiation Measurement (ARM) Program. The areal-averaged albedos obtained from the MFRSR are compared with collocated and coincident Moderate Resolution Imaging Spectroradiometer (MODIS) whitesky albedo at four nominal wavelengths (470, 560, 670 and 860 nm). These comparisons are made during a 19-month period (June 2009 - December 2010). We also calculate composite-based spectral values of surface albedo by a weighted-average approach using estimated fractions of major surface types observed in an area surrounding this coastal site. Taken as a whole, these three methods of finding albedo show spectral and temporal similarities, and suggest that our simple, transmission-based technique holds promise, but with estimated errors of about ±0.03. Additional work is needed to reduce this uncertainty in areas with inhomogeneous surfaces.

We introduce and evaluate a simple retrieval of areal-averaged surface albedo using ground-based measurements of atmospheric transmission alone at five wavelengths (415, 500, 615, 673 and 870nm), under fully overcast conditions. Our retrieval is based on a one-line semi-analytical equation and widely accepted assumptions regarding the weak spectral dependence of cloud optical properties, such as cloud optical depth and asymmetry parameter, in the visible and near-infrared spectral range. To illustrate the performance of our retrieval, we use as input measurements of spectral atmospheric transmission from the Multi-Filter Rotating Shadowband Radiometer (MFRSR). These MFRSR data are collected at two well-established continental sites in the United States supported by the U.S. Department of Energy’s (DOE’s) Atmospheric Radiation Measurement (ARM) Program and National Oceanic and Atmospheric Administration (NOAA). The areal-averaged albedos obtained from the MFRSR are compared with collocated and coincident Moderate Resolution Imaging Spectroradiometer (MODIS) white-sky albedo. In particular, these comparisons are made at four MFRSR wavelengths (500, 615, 673 and 870nm) and for four seasons (winter, spring, summer and fall) at the ARM site using multi-year (2008-2013) MFRSR and MODIS data. Good agreement, on average, for these wavelengths results in small values (≤0.015) of the corresponding root mean square errors (RMSEs) for these two sites. The obtained RMSEs are comparable with those obtained previously for the shortwave albedos (MODIS-derived versus tower-measured) for these sites during growing seasons. We also demonstrate good agreement between tower-based daily-averaged surface albedos measured for “nearby” overcast and non-overcast days. Thus, our retrieval originally developed for overcast conditions likely can be extended for non-overcast days by interpolating between overcast retrievals.

[1] A new method of estimating Tropical Tropopause Layer Cirrus (TTLC) formation mechanism (object method) is compared to interpretations of formation from previous studies using back trajectory calculations matched to convection identified from satellites and statistical relationships of TTLC with temperature and water vapor. The object method groups neighboring Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) TTLC cloud profiles into cloud objects and classifies them as convective (35% of TTLC) if they are directly attached to a convective cloud along the CALIPSO track. The percentage of back trajectory calculations that intersect convection (39–95% of TTLC within 5 days) depends strongly on the spatial and temporal resolution of the convection data set, and the manner in which deep convection is identified. Using minimum brightness temperature in 3 hourly, 1° resolution grid boxes to define convection, 46% of non-convective TTLC (37% of all TTLC) did not trace back to convection within 24 h. Back trajectory calculations of thin cirrus identified by the High Resolution Dynamics Limb Sounder (HIRDLS) gave similar results. Temperature is not a good proxy for formation mechanism as convective and non-convective TTLC frequencies both increase monotonically with decreasing temperature at approximately the same rate. Non-convective TTLC frequencies have a stronger relationship with relative humidity than convective TTLC frequencies, though are not sufficiently different to distinguish object method categories. A decrease in TTL cirrus frequency found at low temperatures in previous studies is caused by insufficient variability in reanalysis temperature data and is not indicative of TTLC formation mechanism.

[1] Tropical tropopause layer cirrus (TTLC) profiles identified from CALIPSO lidar measurements are grouped into cloud objects and classified according to whether or not they are directly connected to deep convection. TTLC objects directly connected to deep convection are optically and physically thicker than isolated objects, consistent with what would be expected if convective objects were formed from convective detrainment and isolated objects formed in situ. The frequency of occurrence of these different classifications of TTLC varies depending on height and region. Thirty-six percent of TTLC profiles contain convective TTLC when TTLC is defined with cloud base height greater than 14 km (±20° Latitude). This estimate of convective TTLC is thought to be a lower limit on the percentage of TTLC formed by convective detrainment. Aged anvil that has persisted longer than the thick regions of the convective cloud would not be classified as convective in our classification. Regions with higher occurrence of deep convection also have higher occurrence of TTLC, and a greater percentage of those TTLC are classified as convective. Definitions of TTLC using higher cloud base height thresholds contain a smaller percentage of TTLC that are classified as convective. Cloud top heights of both isolated and convective TTLC are distributed similarly with respect to the height of the cold point tropopause. Cloud optical depths were found to be higher in TTLC over thick clouds than in TTLC over clear sky during the day but not in night measurements. It is not clear whether this has a physical cause or is due to daytime measurement uncertainties. Both cloud base and top heights are higher over deep convection than over clear sky.

[1] Annual average total irradiance increases by 1–2% per decade at three monitoring stations in Oregon over the period from 1980 to 2007. Direct normal irradiance measurements increase by 5% per decade over the same time period. The measurements show no sign of a dimming before 1990. The impact of high concentrations of stratospheric aerosols following the volcanic eruptions of El Chichon and Mount Pinatubo are clearly seen in the measurements. Removing these years from the annual average all-sky time series reduces the trends in both total and direct normal irradiance. Clear-sky periods from this long direct normal time series are used in conjunction with radiative transfer calculations to test whether part of the increase could be caused by anthropogenic aerosols. All three sites show relatively low clear-sky measurements before the eruption of El Chichon in 1982, suggesting higher aerosol loads during this period. After removing the periods most strongly impacted by volcanic eruptions, two of the sites show statistically significant increases in clear-sky direct normal irradiance from 1987 to 2007. Radiative transfer calculations of the impact of volcanic aerosols and tropospheric water vapor indicate that only about 20% of that clear-sky increase between background aerosol periods before and after the eruption of Mount Pinatubo can be explained by these two factors. Thus a statistically significant clear-sky trend remains between 1987 and 2007 that is consistent with the hypothesis that at least some of the increase in surface irradiance could be caused by a reduction of anthropogenic aerosols.

[1] Long-term pyrheliometer measurements of direct normal irradiance at three sites in Oregon are compared to irradiance calculated from data of the International Satellite Cloud Climatology Project. An increase of direct normal irradiance of up to 1 W/m2 per year between 1980 and 2004 is found in the measured data. The increase seems to be partially caused by stratospheric aerosols from two major volcano eruptions. After removing data for the years most affected by volcanic aerosol, two of the three sites still show a significant increase in direct normal irradiance, indicating other atmospheric changes may contribute to the observed trends. From 1984–2004, for which satellite data are available, both data sets correlate very well and show an increase in direct normal irradiance of 0.3 W/m2 per year on average. In both data sets trends vary seasonally. A strong increase is observed during summer and fall. At two sites a significant decrease during winter, which is consistent in measured and satellite derived values, counteracts part of the summer increase.

If we move beyond the call for more scientific research, what do we do? Society's choices regarding climate change will depend upon our values and how we speak about them, not on whether climate change science reaches greater degrees of certainty Since the driving force that will shape our world is the value-based decision-making we engage in as individuals and communities, we should frame our discussion about climate change accordingly.