Atmospheric Radiation Measurement Southern Great Plains Site Research Articles

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.

Evaluation of the Near-Surface Variables in the HRRR Weather Model Using Observations from the ARM SGP Site

Abstract The performance of version 4 of the NOAA High-Resolution Rapid Refresh (HRRR) numerical weather prediction model for near-surface variables, including wind, humidity, temperature, surface latent and sensible fluxes, and longwave and shortwave radiative fluxes, is examined over the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) region. The study evaluated the model’s bias and bias-corrected mean absolute error relative to the observations on different time scales. Forecasts of near-surface geophysical variables at five SGP sites (HRRR at 3-km scale) were found to agree well with observations, but some consistent observation–forecast differences also occurred. Sensible and latent heat fluxes are the most challenging variables to be reproduced. The diurnal cycle is the main temporal scale affecting observation–forecast differences of the near-surface variables, and almost all of the variables showed different biases throughout the diurnal cycle. Results show that the overestimation of downward shortwave and the underestimation of downward longwave radiative flux are the two major biases found in this study. The timing and magnitude of downward longwave flux, wind speed, and sensible and latent heat fluxes are also different with contributions from model representations, data assimilation limitations, and differences in scales between HRRR and SGP sites. The positive bias in downward shortwave and negative bias in longwave radiation suggests that the model is underestimating cloud fraction in the study domain. The study concludes by showing a brief comparison with version 3 of the HRRR and shows that version 4 has better performance in almost all near-surface variables. Significance Statement A correct representation of the near-surface variables is important for numerical weather prediction models. This study investigates the capability of the latest NOAA High-Resolution Rapid Refresh (HRRRv4) model in simulating the near-surface variables by comparing against the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) in situ observations. Among others, we find that the surface heat fluxes, such as sensible and latent heat fluxes, are the most difficult variables to be reproduced. This study also shows that the diurnal cycle has the dominant impact on the model’s performance, which means the majority of the outputted near-surface variables have the strong diurnal cycle in their bias errors.

Long-Term Retrievals of Cloud Type and Fair-Weather Shallow Cumulus Events at the ARM SGP Site

AbstractA 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).

Regional Moisture Budget and Land‐Atmosphere Coupling Over the U.S. Southern Great Plains Inferred From the ARM Long‐Term Observations

AbstractTen‐year warm‐season (May–August) observations at the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site are used to examine the large‐scale atmospheric moisture budget and the strength of land‐atmosphere coupling. Atmospheric moisture budget components, computed from the ARM variational analysis data, are compared between different wet/dry scenarios and convection regimes. Consistent with previous studies, large‐scale moisture flux convergence dominates the SGP regional moisture budget on the daily time scale, but surface evaporation does play a more prominent role on late‐afternoon deep convection days. Moreover, the relationship between moisture flux convergence and precipitation increases significantly from dry year to wet years. The land‐atmosphere coupling strength is quantified for local convective events within the “Local L‐A Coupling Process Chain” (Santanello et al., 2018, https://doi.org/10.1175/BAMS‐D‐17‐0001.1). The impact of land surface on the evolution of planetary boundary layer is highly dependent on the vegetation leaf area index. A significantly larger surface sensible heat flux is found over the SGP forest region in midsummer, which is accompanied by a much higher planetary boundary layer development and a large increase in shallow cumulus cloud fraction. The significant relationship between afternoon precipitation and surface turbulent flux only exists in the northeast part of the ARM SGP site, which is mainly covered by grassland/pasture.

Differences in Eddy‐Correlation and Energy‐Balance Surface Turbulent Heat Flux Measurements and Their Impacts on the Large‐Scale Forcing Fields at the ARM SGP Site

AbstractDifferences in the surface turbulent heat fluxes measured by the eddy correlation flux measurement system (ECOR) and the energy balance Bowen ratio system (EBBR) are examined using 12 years of continuous measurements collected at multiple stations in the Atmospheric Radiation Measurement (ARM) program Southern Great Plains (SGP) surface observational network. The flux measurements are found strongly impacted by their upwind surface types, which vary with wind direction. For collocated ECOR and EBBR at the central facility, the fluxes measured by ECOR and EBBR agree much better when their upwind fetches are over the same surface type. Among all stations at SGP, ECOR measures more over winter wheat fields while EBBR measures mostly over grassland. The different seasonality of growth cycles between winter wheat and grass causes systematic differences in measured fluxes between ECOR and EBBR. These differences impact the derived large‐scale forcing as illustrated in a constrained variational analysis, in which the state variables have to be adjusted according to different fluxes to keep the column‐integrated energy and moisture budgets in balance. This impact prevails in summertime on nonprecipitating days. A single‐column model test shows that model‐simulated boundary layer development is impacted by using the large‐scale forcing data of different surface turbulent fluxes. It is recommended to include both ECOR and EBBR measurements to better represent the domain‐mean turbulent fluxes and atmospheric budgets of energy and water vapor at the SGP.

Comparison of Daytime Low‐Level Cloud Properties Derived From GOES and ARM SGP Measurements

AbstractLarge‐scale satellite data are critical for both verifying and improving general circulation model parameterizations of clouds and radiation for climate prediction. For reliable application of satellite data sets in cloud processes and climate models, it is important to have a reasonable estimate of the errors in the derived cloud properties. The daytime single‐layered low‐level cloud properties retrieved by the Geostationary Operational Environmental Satellite system (GOES) are compared with ground‐based observations and retrievals over the Department of Energy Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) Central Facility from June 1998 to December 2006. The GOES retrievals are made via the Visible‐Infrared Solar‐infrared Split‐window Technique. They are spatially averaged within a 0.15° × 0.15° box centered on the ARM SGP site, and the ARM surface observations are temporally averaged ±15 min around the GOES scans to produce collocated pairs. Comparisons are made for monthly means, diurnal means, and one‐to‐one GOES and ARM collocated pairs. GOES Teff is highly correlated with ARM Ttop cloud temperature, having an R2 value of 0.75, though GOES exhibits a cold bias. GOES‐retrieved τ and liquid water path have very good agreement with ARM retrievals with R2s of 0.45 and 0.47, while re (GOES), on average, is about 2 μm greater than ARM re. An examination of solar and viewing geometry has shown that GOES‐retrieved mean re and τ values are impacted by solar zenith angle and especially scattering angle, which is not unexpected and needs to be accounted for by users.

Aerosol properties and their impacts on surface CCN at the ARM Southern Great Plains site during the 2011 Midlatitude Continental Convective Clouds Experiment

Aerosol particles are of particular importance because of their impacts on cloud development and precipitation processes over land and ocean. Aerosol properties as well as meteorological observations from the Department of Energy Atmospheric Radiation Measurement (ARM) platform situated in the Southern Great Plains (SGP) are utilized in this study to illustrate the dependence of continental cloud condensation nuclei (CCN) number concentration (NCCN) on aerosol type and transport pathways. ARM-SGP observations from the 2011 Midlatitude Continental Convective Clouds Experiment field campaign are presented in this study and compared with our previous work during the 2009–10 Clouds, Aerosol, and Precipitation in the Marine Boundary Layer field campaign over the current ARM Eastern North Atlantic site. Northerly winds over the SGP reflect clean, continental conditions with aerosol scattering coefficient (σsp) values less than 20 Mm−1 and NCCN values less than 100 cm−3. However, southerly winds over the SGP are responsible for the observed moderate to high correlation (R) among aerosol loading (σsp < 60 Mm−1) and NCCN, carbonaceous chemical species (biomass burning smoke), and precipitable water vapor. This suggests a common transport mechanism for smoke aerosols and moisture via the Gulf of Mexico, indicating a strong dependence on air mass type. NASA MERRA-2 reanalysis aerosol and chemical data are moderately to highly correlated with surface ARM-SGP data, suggesting that this facility can represent surface aerosol conditions in the SGP, especially during strong aerosol loading events that transport via the Gulf of Mexico. Future long-term investigations will help to understand the seasonal influences of air masses on aerosol, CCN, and cloud properties over land in comparison to over ocean.

The Diurnal Cycle of Clouds and Precipitation at the ARM SGP Site: An Atmospheric State‐Based Analysis and Error Decomposition of a Multiscale Modeling Framework Simulation

AbstractLong‐term reflectivity data collected by a millimeter cloud radar at the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site are used to examine the diurnal cycle of clouds and precipitation and are compared with the diurnal cycle simulated by a Multiscale Modeling Framework (MMF) climate model. The study uses a set of atmospheric states that were created specifically for the SGP and for the purpose of investigating under what synoptic conditions models compare well with observations on a statistical basis (rather than using case studies or seasonal or longer time scale averaging). Differences in the annual mean diurnal cycle between observations and the MMF are decomposed into differences due to the relative frequency of states, the daily mean vertical profile of hydrometeor occurrence, and the (normalized) diurnal variation of hydrometeors in each state. Here the hydrometeors are classified as cloud or precipitation based solely on the reflectivity observed by a millimeter radar or generated by a radar simulator. The results show that the MMF does not capture the diurnal variation of low clouds well in any of the states but does a reasonable job capturing the diurnal variations of high clouds and precipitation in some states. In particular, the diurnal variations in states that occur during summer are reasonably captured by the MMF, while the diurnal variations in states that occur during the transition seasons (spring and fall) are not well captured. Overall, the errors in the annual composite are due primarily to errors in the daily mean of hydrometeor occurrence (rather than diurnal variations), but errors in the state frequency (that is, the distribution of weather states in the model) also play a significant role.

The diurnal cycle of clouds and precipitation at the ARM SGP site: Cloud radar observations and simulations from the multiscale modeling framework

AbstractMillimeter Wavelength Cloud Radar (MMCR) data from December 1996 to December 2010, collected at the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) program Southern Great Plains (SGP) site, are used to examine the diurnal cycle of hydrometeor occurrence. These data are categorized into clouds (−40 dBZe ≤ reflectivity &lt; −10 dBZe), drizzle and light precipitation (−10 dBZe ≤ reflectivity &lt; 10 dBZe), and heavy precipitation (reflectivity ≥ 10 dBZe). The same criteria are implemented for the observation‐equivalent reflectivity calculated by feeding outputs from a Multiscale Modeling Framework (MMF) climate model into a radar simulator. The MMF model consists of the National Center for Atmospheric Research Community Atmosphere Model with conventional cloud parameterizations replaced by a cloud‐resolving model. We find that a radar simulator combined with the simple reflectivity categories can be an effective approach for evaluating diurnal variations in model hydrometeor occurrence. It is shown that the MMF only marginally captures observed increases in the occurrence of boundary layer clouds after sunrise in spring and autumn and does not capture diurnal changes in boundary layer clouds during the summer. Above the boundary layer, the MMF captures reasonably well diurnal variations in the vertical structure of clouds and light and heavy precipitation in the summer but not in the spring.

Remote sensing of PM2.5 during cloudy and nighttime periods using ceilometer backscatter

Abstract. Monitoring PM2.5 (particulate matter with aerodynamic diameter d ≤ 2.5 µm) mass concentration has become of more importance recently because of the negative impacts of fine particles on human health. However, monitoring PM2.5 during cloudy and nighttime periods is difficult since nearly all the passive instruments used for aerosol remote sensing are not able to measure aerosol optical depth (AOD) under either cloudy or nighttime conditions. In this study, an empirical model based on the regression between PM2.5 and the near-surface backscatter measured by ceilometers was developed and tested using 6 years of data (2006 to 2011) from the Howard University Beltsville Campus (HUBC) site. The empirical model can explain ∼ 56, ∼ 34 and ∼ 42 % of the variability in the hourly average PM2.5 during daytime clear, daytime cloudy and nighttime periods, respectively. Meteorological conditions and seasons were found to influence the relationship between PM2.5 mass concentration and the surface backscatter. Overall the model can explain ∼ 48 % of the variability in the hourly average PM2.5 at the HUBC site when considering the seasonal variation. The model also was tested using 4 years of data (2012 to 2015) from the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site, which was geographically and climatologically different from the HUBC site. The results show that the empirical model can explain ∼ 66 and ∼ 82 % of the variability in the daily average PM2.5 at the ARM SGP site and HUBC site, respectively. The findings of this study illustrate the strong need for ceilometer data in air quality monitoring under cloudy and nighttime conditions. Since ceilometers are used broadly over the world, they may provide an important supplemental source of information of aerosols to determine surface PM2.5 concentrations.

Improving Parameterization of Entrainment Rate for Shallow Convection with Aircraft Measurements and Large-Eddy Simulation

AbstractThis work examines the relationships of entrainment rate to vertical velocity, buoyancy, and turbulent dissipation rate by applying stepwise principal component regression to observational data from shallow cumulus clouds collected during the Routine Atmospheric Radiation Measurement (ARM) Aerial Facility (AAF) Clouds with Low Optical Water Depths (CLOWD) Optical Radiative Observations (RACORO) field campaign over the ARM Southern Great Plains (SGP) site near Lamont, Oklahoma. The cumulus clouds during the RACORO campaign simulated using a large-eddy simulation (LES) model are also examined with the same approach. The analysis shows that a combination of multiple variables can better represent entrainment rate in both the observations and LES than any single-variable fitting. Three commonly used parameterizations are also tested on the individual cloud scale. A new parameterization is thus presented that relates entrainment rate to vertical velocity, buoyancy, and dissipation rate; the effects of treating clouds as ensembles and humid shells surrounding cumulus clouds on the new parameterization are discussed. Physical mechanisms underlying the relationships of entrainment rate to vertical velocity, buoyancy, and dissipation rate are also explored.

Investigation of ice cloud microphysical properties of DCSs using aircraft in situ measurements during MC3E over the ARM SGP site

AbstractSix deep convective systems (DCSs) with a total of 5589 five‐second samples and a range of temperatures from −41°C to 0°C during the Midlatitude Continental Convective Clouds Experiment (MC3E) were selected to investigate the ice cloud microphysical properties of DCSs over the Department of Energy Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site. The ice cloud measurements of the DCS cases were made by the University of North Dakota Citation II research aircraft, and the ice cloud properties were derived through the following processes. First, the instances of supercooled liquid water in the ice‐dominated cloud layers of DCSs have been eliminated using multisensor detection, including the Rosemount Icing Detector, King and Cloud Droplet Probes, as well as 2DC and Cloud Imaging Probe images. Then the Nevzorov‐measured ice water contents (IWCs) at maximum diameter Dmax &lt; 4000 µm are used as the best estimation to determine a new mass‐dimensional relationship. Finally, the newly derived mass‐dimensional relationship (a = 0.00365, b = 2.1) has been applied to a full spectrum of particle size distributions (PSDs, 120–30,000 µm) constructed from both 2DC and High‐Volume Precipitation Spectrometer measurements to calculate the best‐estimated IWCs of DCSs during MC3E. The averages of the total number concentrations (Nt), median mass diameter (Dm), maximum diameter (Dmax), and IWC from six selected cases are 0.035 cm−3, 1666 µm, 8841 µm, and 0.45 g m−3, respectively. The gamma‐type‐size distributions are then generated matching the observed PSDs (120–30,000 µm), and the fitted gamma parameters are compared with the observed PSDs through multimoment assessments including first moment (Dm), third moment (IWC), and sixth moment (equivalent radar reflectivity, Ze). For application of observed PSDs to the remote sensing community, a series of empirical relationships between fitted parameters and Ze values has been derived, and the bullet rosette ice crystal backscattering relationship has been suggested for ground‐based remote sensing.

Cloud fraction at the ARM SGP site: reducing uncertainty with self-organizing maps.

Instrument downtime leads to uncertainty in the monthly and annual record of cloud fraction (CF), making it difficult to perform time series analyses of cloud properties and perform detailed evaluations of model simulations. As cloud occurrence is partially controlled by the large-scale atmospheric environment, this knowledge is used to reduce uncertainties in the instrument record. Synoptic patterns diagnosed from the North American Regional Reanalysis (NARR) during the period 1997–2010 are classified using a competitive neural network known as the self-organizing map (SOM). The classified synoptic states are then compared to the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) instrument record to determine the expected CF. A number of SOMs are tested to understand how the number of classes and the period of classifications impact the relationship between classified states and CFs. Bootstrapping is utilized to quantify the uncertainty of the instrument record when statistical information from the SOM is included. Although all SOMs significantly reduce the uncertainty of the CF record calculated in Kennedy et al. (Theor Appl Climatol 115:91–105, 2014), SOMs with a large number of classes and separated by month are required to produce the lowest uncertainty and best agreement with the annual cycle of CF. This result may be due to a manifestation of seasonally dependent biases in NARR. With use of the SOMs, the average uncertainty in monthly CF is reduced in half from the values calculated in Kennedy et al. (Theor Appl Climatol 115:91–105, 2014).

Water vapor turbulence profiles in stationary continental convective mixed layers

AbstractThe U.S. Department of Energy Atmospheric Radiation Measurement (ARM) program's Raman lidar at the ARM Southern Great Plains site in north central Oklahoma has collected water vapor mixing ratio (q) profile data more than 90% of the time since October 2004. Three hundred (300) cases were identified where the convective boundary layer was quasi‐stationary and well mixed for a 2 h period, and q mean, variance, third‐order moment, and skewness profiles were derived from the 10 s, 75 m resolution data. These cases span the entire calendar year, and demonstrate that the q variance profiles at the mixed layer (ML) top changes seasonally and is strongly related to the gradient of q across the interfacial layer. The q variance at the top of the ML shows only weak correlations (r &amp;#60; 0.3) with sensible heat flux, Deardorff convective velocity scale, and turbulence kinetic energy measured at the surface. The median q skewness profile is most negative at 0.85 zi, zero at approximately zi, and positive above zi, where zi is the depth of the convective ML. The spread in the q skewness profiles is smallest between 0.95 zi and zi. The q skewness at altitudes between 0.6 zi and 1.2 zi is correlated with the magnitude of the q variance at zi, with increasingly negative values of skewness observed lower down in the ML as the variance at zi increases, suggesting that in cases with larger variance at zi there is deeper penetration of the warm, dry free tropospheric air into the ML.

Aircraft Evaluation of Ground-Based Raman Lidar Water Vapor Turbulence Profiles in Convective Mixed Layers

AbstractHigh temporal and vertical resolution water vapor measurements by Raman and differential absorption lidar systems have been used to characterize the turbulent fluctuations in the water vapor mixing ratio field in convective mixed layers. Since daytime Raman lidar measurements are inherently noisy (due to solar background and weak signal strengths), the analysis approach needs to quantify and remove the contribution of the instrument noise in order to derive the desired atmospheric water vapor mixing ratio variance and skewness profiles. This is done using the approach outlined by Lenschow et al.; however, an intercomparison with in situ observations was not performed.Water vapor measurements were made by a diode laser hygrometer flown on a Twin Otter aircraft during the Routine Atmospheric Radiation Measurement (ARM) Program Aerial Facility Clouds with Low Optical Water Depths Optical Radiative Observations (RACORO) field campaign over the ARM Southern Great Plains (SGP) site in 2009. Two days with Twin Otter flights were identified where the convective mixed layer was quasi stationary, and hence the 10-s, 75-m data from the SGP Raman lidar could be analyzed to provide profiles of water vapor mixing ratio variance and skewness. Airborne water vapor observations measured during level flight legs were compared to the Raman lidar data, demonstrating good agreement in both variance and skewness. The results also illustrate the challenges of comparing a point sensor making measurements over time to a moving platform making similar measurements horizontally.

Impact of large‐scale dynamics on the microphysical properties of midlatitude cirrus

In situ microphysical observations of midlatitude cirrus collected during the Department of Energy Small Particles in Cirrus (SPARTICUS) field campaign are combined with an atmospheric state classification for the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site to understand statistical relationships between cirrus microphysics and the large‐scale meteorology. The atmospheric state classification is informed about the large‐scale meteorology and state of cloudiness at the ARM SGP site by combining ECMWF ERA‐Interim reanalysis data with 14 years of continuous observations from the millimeter‐wavelength cloud radar. Almost half of the cirrus cloud occurrences in the vicinity of the ARM SGP site during SPARTICUS can be explained by three distinct synoptic conditions, namely, upper level ridges, midlatitude cyclones with frontal systems, and subtropical flows. Probability density functions (PDFs) of cirrus microphysical properties such as particle size distributions (PSDs), ice number concentrations, and ice water content (IWC) are examined and exhibit striking differences among the different synoptic regimes. Generally, narrower PSDs with lower IWC but higher ice number concentrations are found in cirrus sampled in upper level ridges, whereas cirrus sampled in subtropical flows, fronts, and aged anvils show broader PSDs with considerably lower ice number concentrations but higher IWC. Despite striking contrasts in the cirrus microphysics for different large‐scale environments, the PDFs of vertical velocity are not different, suggesting that vertical velocity PDFs are a poor predictor for explaining the microphysical variability in cirrus. Instead, cirrus microphysical contrasts may be driven by differences in ice supersaturations or aerosols.

Improving the Representation of Low Clouds and Drizzle in the ECMWF Model Based on ARM Observations from the Azores

AbstractIn this study, the representation of marine boundary layer clouds is investigated in the ECMWF model using observations from the Atmospheric Radiation Measurement (ARM) mobile facility deployment to Graciosa Island in the North Atlantic. Systematic errors in the occurrence of clouds, liquid water path, precipitation, and surface radiation are assessed in the operational model for a 19-month-long period. Boundary layer clouds were the most frequently observed cloud type but were underestimated by 10% in the model. Systematic but partially compensating surface radiation errors exist and can be linked to opposing cloud cover and liquid water path errors in broken (shallow cumulus) and overcast (stratocumulus) low-cloud regimes, consistent with previously reported results from the continental ARM Southern Great Plains (SGP) site. Occurrence of precipitation is overestimated by a factor of 1.5 at cloud base and by a factor of 2 at the surface, suggesting deficiencies in both the warm-rain formation and subcloud evaporation parameterizations. A single-column version of the ECMWF model is used to test combined changes to the parameterizations of boundary layer, autoconversion/accretion, and rain evaporation processes at Graciosa. Low-cloud occurrence, liquid water path, radiation biases, and precipitation occurrence are all significantly improved when compared to the ARM observations. Initial results from the modified parameterizations in the full model show improvement in the global top-of-the-atmosphere shortwave radiation, suggesting the reduced errors in the comparison at Graciosa are more widely applicable to boundary layer cloud around the globe.

Signal Postprocessing and Reflectivity Calibration of the Atmospheric Radiation Measurement Program 915-MHz Wind Profilers

Abstract The Department of Energy Atmospheric Radiation Measurement (ARM) Program has recently initiated a new research avenue toward a better characterization of the transition from cloud to precipitation. Dual-wavelength techniques applied to millimeter-wavelength radars and a Rayleigh reference have a great potential for rain-rate retrievals directly from dual-wavelength ratio measurements. In this context, the recent reconfiguration of the ARM 915-MHz wind profilers in a vertically pointing mode makes these instruments the ideal candidate for providing the Rayleigh reflectivity/Doppler velocity reference. Prior to any scientific study, the wind profiler data must be carefully quality checked. This work describes the signal postprocessing steps that are essential for the delivery of high-quality reflectivity and mean Doppler velocity products—that is, the estimation of the noise floor from clear-air echoes, the absolute calibration with a collocated disdrometer, the dealiasing of Doppler velocities, and the merging of the different modes of the wind profiler. The improvement added by the proposed postprocessing is confirmed by comparison with a high-quality S-band profiler deployed at the ARM Southern Great Plains site during the Midlatitude Continental Convective Clouds Experiment. With the addition of a vertically pointing mode and with the postprocessing described in this work in place, besides being a key asset for wind research wind profilers observations may therefore become a centerpiece for rain studies in the years to come.

Statistical decision analysis for flight decision support: The SPartICus campaign

AbstractField campaigns in atmospheric science typically require making challenging decisions about how best to deploy limited resources, especially aircraft flight hours. Algorithmic decision tools have shown the potential to outperform traditional heuristic approaches to allocating limited flight hours in field campaigns. The present study examines the utility of algorithmic decision tools in an application to the Atmospheric Radiation Measurement (ARM) Small Particles in Cirrus (SPartICus) campaign, which sampled cirrus clouds over the ARM Southern Great Plains (SGP) site between January and June 2010. Probabilistic forecasts of suitable data collection conditions were generated using relative humidity forecasts from the Global Forecast System (GFS) and self‐organizing maps. An optimization procedure based on dynamic programming was then used to generate day‐ahead fly/no‐fly decisions for research flights over the SGP site. The quality of flight decisions thus generated were compared with those made by the SPartICus science team. Results showed that the algorithmic decision tool would have delivered 11% more optimal data while shortening the length of the campaign season by 29 days and reducing the per‐day expenditure of investigator time on activities of forecasting and decision‐making.

Cloud fraction at the ARM SGP site

The Atmospheric Radiation Measurement (ARM) Program Southern Great Plains (SGP) site has a rich history of actively sensed cloud observations. Fourteen years (1997–2010) of observations from the Millimeter Cloud Radar (MMCR), Micropulse Lidar (MPL), and Belfort/Vaisala Ceilometers are used to understand how instrument selection and sampling impacts estimates of Cloud Fraction (CF) at this location. Although all instruments should be used in combination for the best estimates of CF, instrument downtime limits available samples and increases observational errors, demanding that users make sacrifices when calculating CF at longer intervals relevant to climate studies. Selection of MMCR or MMCR + MPL cloud masks changes very little in the overall understanding of total CF. Addition of the MPL increases the 14-year average CF by 9 %, mainly through an increase in optically thin high clouds year-round, and mid-level clouds during the summer months. Splitting the period into two equal 7-year periods reveals negligible change in MMCR + MPL CF. For the MMCR, however, CF deceases by 6.1 %. This sudden change in CF occurs around the time the radar was upgraded, suggesting that this decrease is tied to hardware sensitivity or scanning strategy changes. Users must be cognizant of this and other issues when calculating CF from the variety of observations available at the ARM SGP site.