Program Southern Great Plains Site Research Articles

Regime‐Specific Cloud Vertical Overlap Characteristics From Radar and Lidar Observations at the ARM Sites

AbstractClimate and numerical weather prediction models require assumptions to represent the vertical distribution of subgrid‐scale clouds, which have radiative transfer implications. In this study, nearly 25 years of ground‐based radar and lidar observations of vertical cloud profiles at the Atmospheric Radiation Measurement Program (ARM) Southern Great Plains (SGP) site are utilized to derive cloud vertical overlap characteristics from the Cloud Type (CLDTYPE) data product. The cloud vertical overlap characteristics are further separated by cloud regime by considering seven cloud types (i.e., low cloud, congestus, deep convection, altocumulus, altostratus, cirrostratus, and cirrus) as well as periods of shallow cumulus. The decorrelation length scale (i.e., exponential transition from maximum to random overlap with layer separation) is found to vary by cloud regime, ranging between 0.04 km for cirrostratus paired with cirrus to 4.58 km for low cloud paired with cirrus at SGP. Cloud vertical overlap characteristics are also considered for other ARM sites including the Tropical Western Pacific (TWP), North Slope of Alaska (NSA), and Eastern North Atlantic (ENA) sites among other shorter term ARM deployments globally. The decorrelation length scale ranged globally from 1.03 km in the Arctic Ocean to 3.06 km in Manacapuru, Brazil. Globally, the decorrelation length scale by cloud regime exhibited similarities (e.g., for cirrus paired with cirrus) and differences (e.g., congestus paired with cirrus). The results could help inform development of cloud vertical overlap assumptions within operational numerical weather prediction models and potentially improve prediction of radiative fluxes for weather, climate, and renewable energy forecasting.

An Investigation of the Ice Cloud Detection Sensitivity of Cloud Radars Using the Raman Lidar at the ARM SGP Site

The ice cloud detection sensitivity of the millimeter cloud radar (MMCR) and the Ka-band Zenith radar (KAZR) is investigated using a collocated Raman lidar (RL) at the Atmospheric Radiation Measurement Program Southern Great Plains site. Only profiles that are transparent to the RL with ice clouds only are considered in this study. The MMCR underestimates the RL ice cloud optical depth (COD) by 20%. The MMCR detects no ice clouds in 37% of the profiles. These profiles where ice cloud goes undetected by the MMCR typically contain very optically thin clouds, with a mean RL ice COD of 0.03. Higher ice cloud detection sensitivity is found for the KAZR, which underestimates the RL ice COD by 15%. The decrease in the ice COD bias for the KAZR compared to the MMCR is largely due to a decrease in the ice COD bias for the situation where the transparent profiles with ice clouds are detected by both the RL and cloud radar. The climatic net ice cloud radiative effects (CREs) from the RL at the top of the atmosphere (TOA) and the surface are 3.2 W m−2 and −0.6 W m−2, respectively. The ice CREs at the TOA and surface are underestimated for the MMCR by 0.7 W m−2 and 0.16 W m−2 (21% and 29%) and underestimated for the KAZR by 0.6 W m−2 and 0.14 W m−2 (17% and 24%). The ice clouds undetected by the cloud radars led to underestimating the climatic net cloud heating rates below 150 hPa by about 0–0.04 K day−1.

Trends in Downwelling Longwave Radiance Over the Southern Great Plains

AbstractDownwelling longwave radiation is an important part of the surface energy budget. Spectral trends in the downwelling longwave radiance (DLR) provide insight into the radiative drivers of climate change. In this research, we process and analyze a 23‐year DLR record measured by the Atmospheric Emitted Radiance Interferometer (AERI) at the U.S. Department of Energy Atmospheric Radiation Program Southern Great Plains (SGP) site. Two AERIs were deployed at SGP with an overlapping observation period of about 10 years, which allows us to examine the consistency and accuracy of the measurements and to account for discrepancies between them due to errors associated with the instruments themselves. We then analyzed the all‐sky radiance trends in DLR, which are associated with the surface warming trend at SGP during this same period and also the complex changes in meteorological conditions. For instance, the observed radiance in the CO2 absorption band follows closely the near‐surface air temperature variations. The significant positive radiance trends in weak absorption channels, such as in the wings of the CO2 band and in the weak absorption channels in the H2O vibration‐rotational band, show earlier detectability of climate change. The magnitude of the radiance trend uncertainty in the DLR record mainly results from internal climate variability rather than from measurement error, which highlights the importance of continuing the DLR spectral measurements to unambiguously detect and attribute climate change.

Aerosol Activation in Radiation Fog at the Atmospheric Radiation Program Southern Great Plains Site

AbstractEnvironmental supersaturation is a key parameter in the formation of fog and clouds, yet it cannot be measured directly and must be inferred. Calculating the ambient supersaturation in fog requires knowledge of the aerosol hygroscopicity as well as particle size distribution, and relatively few values have been reported in the literature. Here we use κ‐Köhler theory to derive aerosol activation properties based on particle hygroscopicity and dry particle size distributions, and then estimate the effective peak supersaturation during eight cases of radiative fog at the Southern Great Plains site in rural north‐central Oklahoma, USA. The mean hygroscopicity parameter κ of particles likely to act as cloud condensation nuclei varied from 0.14 to 0.43. Ambient effective peak supersaturation during the fog episodes was between 0.01%–0.07%, with most values below 0.04%. The minimum 50% dry activation diameter generally ranged between 300–400 nm with little activation of particles with diameters below 300 nm.

The Diurnal Variation of the Aerosol Optical Depth at the ARM SGP Site

AbstractThis study examines the diurnal variation of the aerosol optical depth (AOD) at 355 nm observed by Raman lidar (RL) at the Atmospheric Radiation Measurement Program Southern Great Plains (SGP) site under both clear and cloudy‐sky conditions. Here only cloudy‐skies when the lidar signal is not fully attenuated are considered. The daytime AOD and its variation from the RL showed an excellent agreement with the Aerosol Robotic Network, demonstrating that the RL‐retrieved AOD is not affected by solar background contamination. The climatological annual‐mean daytime‐mean AOD is only slightly larger than the nighttime‐mean AOD (by 1%–3%). However, day‐to‐day variations are observed such that the daytime‐ and nighttime‐mean AOD difference for a given day can be large (about 95% of days have differences within 0.2). The seasonal AOD diurnal range (i.e., the difference between the maximum and minimum values) relative to the mean was10%–15% except in the winter when it was44%. The seasonal‐mean cloudy‐sky AOD diurnal variation is similar to that for clear‐sky, except that the AODs are larger (the annual‐mean cloudy‐sky AOD is larger than the clear‐sky by24%). The aerosol lidar ratio diurnal variations are also examined, which are10%–20% for all seasons with a minimum near 9 a.m. to 15 p.m. for all seasons except winter. Also presented is the annual‐mean AOD from the Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite at SGP site: its daytime AOD is about 0.1 smaller than nighttime AOD because of daytime solar background contamination.

CAUSES: Diagnosis of the Summertime Warm Bias in CMIP5 Climate Models at the ARM Southern Great Plains Site

AbstractAll the weather and climate models participating in the Clouds Above the United States and Errors at the Surface project show a summertime surface air temperature (T2 m) warm bias in the region of the central United States. To understand the warm bias in long‐term climate simulations, we assess the Atmospheric Model Intercomparison Project simulations from the Coupled Model Intercomparison Project Phase 5, with long‐term observations mainly from the Atmospheric Radiation Measurement program Southern Great Plains site. Quantities related to the surface energy and water budget, and large‐scale circulation are analyzed to identify possible factors and plausible links involved in the warm bias. The systematic warm season bias is characterized by an overestimation of T2 m and underestimation of surface humidity, precipitation, and precipitable water. Accompanying the warm bias is an overestimation of absorbed solar radiation at the surface, which is due to a combination of insufficient cloud reflection and clear‐sky shortwave absorption by water vapor and an underestimation in surface albedo. The bias in cloud is shown to contribute most to the radiation bias. The surface layer soil moisture impacts T2 m through its control on evaporative fraction. The error in evaporative fraction is another important contributor to T2 m. Similar sources of error are found in hindcast from other Clouds Above the United States and Errors at the Surface studies. In Atmospheric Model Intercomparison Project simulations, biases in meridional wind velocity associated with the low‐level jet and the 500 hPa vertical velocity may also relate to T2 m bias through their control on the surface energy and water budget.

A New Research Approach for Observing and Characterizing Land–Atmosphere Feedback

AbstractForecast errors with respect to wind, temperature, moisture, clouds, and precipitation largely correspond to the limited capability of current Earth system models to capture and simulate land–atmosphere feedback. To facilitate its realistic simulation in next-generation models, an improved process understanding of the related complex interactions is essential. To this end, accurate 3D observations of key variables in the land–atmosphere (L–A) system with high vertical and temporal resolution from the surface to the free troposphere are indispensable.Recently, we developed a synergy of innovative ground-based, scanning active remote sensing systems for 2D to 3D measurements of wind, temperature, and water vapor from the surface to the lower troposphere that is able to provide comprehensive datasets for characterizing L–A feedback independently of any model input. Several new applications are introduced, such as the mapping of surface momentum, sensible heat, and latent heat fluxes in heterogeneous terrain; the testing of Monin–Obukhov similarity theory and turbulence parameterizations; the direct measurement of entrainment fluxes; and the development of new flux-gradient relationships. An experimental design taking advantage of the sensors’ synergy and advanced capabilities was realized for the first time during the Land Atmosphere Feedback Experiment (LAFE), conducted at the Atmospheric Radiation Measurement Program Southern Great Plains site in August 2017. The scientific goals and the strategy of achieving them with the LAFE dataset are introduced. We envision the initiation of innovative L–A feedback studies in different climate regions to improve weather forecast, climate, and Earth system models worldwide.

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 < −10 dBZe), drizzle and light precipitation (−10 dBZe ≤ reflectivity < 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.

LABLE: A Multi-Institutional, Student-Led, Atmospheric Boundary Layer Experiment

Abstract This paper presents an overview of the Lower Atmospheric Boundary Layer Experiment (LABLE), which included two measurement campaigns conducted at the Atmospheric Radiation Measurement (ARM) Program Southern Great Plains site in Oklahoma during 2012 and 2013. LABLE was conducted as a collaborative effort between the University of Oklahoma (OU), the National Severe Storms Laboratory, Lawrence Livermore National Laboratory (LLNL), and the ARM program. LABLE can be considered unique in that it was designed as a multiphase, low-cost, multiagency collaboration. Graduate students served as principal investigators and took the lead in designing and conducting experiments aimed at examining boundary layer processes. The main objective of LABLE was to study turbulent phenomena in the lowest 2 km of the atmosphere over heterogeneous terrain using a variety of novel atmospheric profiling techniques. Several instruments from OU and LLNL were deployed to augment the suite of in situ and remote sensing instruments at the ARM site. The complementary nature of the deployed instruments with respect to resolution and height coverage provides a near-complete picture of the dynamic and thermodynamic structure of the atmospheric boundary layer. This paper provides an overview of the experiment including 1) instruments deployed, 2) sampling strategies, 3) parameters observed, and 4) student involvement. To illustrate these components, the presented results focus on one particular aspect of LABLE: namely, the study of the nocturnal boundary layer and the formation and structure of nocturnal low-level jets. During LABLE, low-level jets were frequently observed and they often interacted with mesoscale atmospheric disturbances such as frontal passages.

Evaluation of Cloud Fraction Simulated by Seven SCMs against the ARM Observations at the SGP Site*

Abstract This study evaluates the performances of seven single-column models (SCMs) by comparing simulated cloud fraction with observations at the Atmospheric Radiation Measurement Program (ARM) Southern Great Plains (SGP) site from January 1999 to December 2001. Compared with the 3-yr mean observational cloud fraction, the ECMWF SCM underestimates cloud fraction at all levels and the GISS SCM underestimates cloud fraction at levels below 200 hPa. The two GFDL SCMs underestimate lower-to-middle level cloud fraction but overestimate upper-level cloud fraction. The three Community Atmosphere Model (CAM) SCMs overestimate upper-level cloud fraction and produce lower-level cloud fraction similar to the observations but as a result of compensating overproduction of convective cloud fraction and underproduction of stratiform cloud fraction. Besides, the CAM3 and CAM5 SCMs both overestimate midlevel cloud fraction, whereas the CAM4 SCM underestimates. The frequency and partitioning analyses show a large discrepancy among the seven SCMs: Contributions of nonstratiform processes to cloud fraction production are mainly in upper-level cloudy events over the cloud cover range 10%–80% in SCMs with prognostic cloud fraction schemes and in lower-level cloudy events over the cloud cover range 15%–50% in SCMs with diagnostic cloud fraction schemes. Further analysis reveals different relationships between cloud fraction and relative humidity (RH) in the models and observations. The underestimation of lower-level cloud fraction in most SCMs is mainly due to the larger threshold RH used in models. The overestimation of upper-level cloud fraction in the three CAM SCMs and two GFDL SCMs is primarily due to the overestimation of RH and larger mean cloud fraction of cloudy events plus more occurrences of RH around 40%–80%, respectively.

Multiyear Summertime Observations of Daytime Fair-Weather Cumuli at the ARM Southern Great Plains Facility

Abstract A long data record (14 yr) of ground-based observations at the Atmospheric Radiation Measurement Program (ARM) Southern Great Plains (SGP) site is analyzed to document the macroscopic and dynamical properties of daytime fair-weather cumulus clouds during summer months. First, a fuzzy logic–based algorithm is developed to eliminate insect radar echoes in the boundary layer that hinder the ability to develop representative cloud statistics. The refined dataset is used to document the daytime composites of fair-weather cumulus clouds properties. Doppler velocities are processed for lower reflectivity thresholds that contain small cloud droplets having insignificant terminal velocities; thus, Doppler velocities are used as tracers of air motion. The algorithm is implemented to process the entire 14-yr dataset of cloud radar vertical velocity data. Composite diurnal variations of the cloud vertical velocity statistics, surface parameters, and profiles of updraft and downdraft fractions, bulk velocity of updrafts and downdrafts, and updraft and downdraft mass flux are calculated. Statistics on the cloud geometrical properties such as cloud thickness, cloud chord length, cloud spacing, and aspect ratios are calculated on the cloud scale. The present dataset provides a unique insight into the daytime evolution and statistical description of the turbulent structure inside fair-weather cumuli over land.

Turbulence in Continental Stratocumulus, Part I: External Forcings and Turbulence Structures

Comprehensive, ground-based observations from the US Department of Energy Atmospheric Radiation Measurements program Southern Great Plains site are used to study the variability of turbulence forcings and cloud-scale turbulence structures in a continental stratocumulus cloud. The turbulence observations are made from an upward facing cloud (35 GHz) Doppler radar. Cloud base and liquid water path are characterized using a lidar at the surface and a microwave radiometer. The turbulence characterizations are compared and contrasted with those observed in marine stratocumulus clouds. During the 16-h observation period used in this study the cloud-base and cloud-top heights evolve with time and changes in liquid water path observed by the radiometer are consistent with variations in cloud depth. Unlike marine stratocumulus clouds, a diurnal cycle of cloud thickness and liquid water path is not observed. The observed surface latent, sensible, and virtual sensible heat fluxes and the radiative fluxes exhibit a diurnal cycle with values increasing from sunrise to afternoon and decreasing afterwards. During the night, the sensible heat, virtual sensible heat and the net radiative fluxes at the surface are slightly negative. Solar radiative heating prevails in the cloud layer during the day and strong radiative cooling exists at cloud top even during the day. Unlike marine stratocumulus, surface heating described by the convective velocity scale \(W_\mathrm{s}^{*}\) and cloud-top cooling described by \(W_\mathrm{r}^{*}\) are both important in driving the in-cloud turbulence during the day, whereas cloud-top cooling is the exclusive contributor during the night. The combined \(W_\mathrm{s}^{*}\) and \(W_\mathrm{r}^{*}\) (the total velocity scale \(W_\mathrm{t}^{*})\) provides a useful way to track the evolution of the turbulence structure in the cloud. The variance of the radar-measured radial velocity, which is related to resolved turbulence, follows the diurnal cycle and is consistent with the total velocity scale \(W_\mathrm{t}^{*}\) variations. It is higher during the day and lower during the night, which is contrary to that in marine stratocumulus. The \(W_\mathrm{t}^{*}\) values are lowest around sunset when the radiative cooling is also small due to upper-level clouds observed above the low-level stratus. The vertical distribution of the variance results from the surface heating during the day and cloud-top cooling during the night. The squared spectrum width, which is related to turbulence structures within the radar sampling volume (unresolved turbulence) also follows the diurnal cycle. Its vertical distribution indicates that the unresolved turbulence more closely relates to the processes near cloud top. Turbulence in the cloud requires about an hour to respond to the external forcings of surface heating and cloud-top radiative cooling. Positive skewness prevails during the day and negative skewness prevails at night with a sharp transition around sunset. Resolved turbulence dominates near cloud base whereas unresolved turbulence dominates near cloud top. The turbulence characteristics and variability defined in this study can be used to evaluate the time evolution of turbulence structures in large eddy simulation forced by surface and cloud-top radiative forcings.

Evaluation of Precipitation Simulated by Seven SCMs against the ARM Observations at the SGP Site*

Abstract This study evaluates the performances of seven single-column models (SCMs) by comparing simulated surface precipitation with observations at the Atmospheric Radiation Measurement Program Southern Great Plains (SGP) site from January 1999 to December 2001. Results show that although most SCMs can reproduce the observed precipitation reasonably well, there are significant and interesting differences in their details. In the cold season, the model–observation differences in the frequency and mean intensity of rain events tend to compensate each other for most SCMs. In the warm season, most SCMs produce more rain events in daytime than in nighttime, whereas the observations have more rain events in nighttime. The mean intensities of rain events in these SCMs are much stronger in daytime, but weaker in nighttime, than the observations. The higher frequency of rain events during warm-season daytime in most SCMs is related to the fact that most SCMs produce a spurious precipitation peak around the regime of weak vertical motions but rich in moisture content. The models also show distinct biases between nighttime and daytime in simulating significant rain events. In nighttime, all the SCMs have a lower frequency of moderate-to-strong rain events than the observations for both seasons. In daytime, most SCMs have a higher frequency of moderate-to-strong rain events than the observations, especially in the warm season. Further analysis reveals distinct meteorological backgrounds for large underestimation and overestimation events. The former occur in the strong ascending regimes with negative low-level horizontal heat and moisture advection, whereas the latter occur in the weak or moderate ascending regimes with positive low-level horizontal heat and moisture advection.

Liquid Water Cloud Measurements Using the Raman Lidar Technique: Current Understanding and Future Research Needs

AbstractThis paper describes recent work in the Raman lidar liquid water cloud measurement technique. The range-resolved spectral measurements at the National Aeronautics and Space Administration Goddard Space Flight Center indicate that the Raman backscattering spectra measured in and below low clouds agree well with theoretical spectra for vapor and liquid water. The calibration coefficients of the liquid water measurement for the Raman lidar at the Atmospheric Radiation Measurement Program Southern Great Plains site of the U.S. Department of Energy were determined by comparison with the liquid water path (LWP) obtained with Atmospheric Emitted Radiance Interferometer (AERI) and the liquid water content (LWC) obtained with the millimeter wavelength cloud radar and water vapor radiometer (MMCR–WVR) together. These comparisons were used to estimate the Raman liquid water cross-sectional value. The results indicate a bias consistent with an effective liquid water Raman cross-sectional value that is 28%–46% lower than published, which may be explained by the fact that the difference in the detectors' sensitivity has not been accounted for. The LWP of a thin altostratus cloud showed good qualitative agreement between lidar retrievals and AERI. However, the overall ensemble of comparisons of LWP showed considerable scatter, possibly because of the different fields of view of the instruments, the 350-m distance between the instruments, and the horizontal inhomogeneity of the clouds. The LWC profiles for a thick stratus cloud showed agreement between lidar retrievals and MMCR–WVR between the cloud base and 150 m above that where the optical depth was less than 3. Areas requiring further research in this technique are discussed.

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.

Atmosphere–Land Surface Interactions over the Southern Great Plains: Characterization from Pentad Analysis of DOE ARM Field Observations and NARR

Abstract The Department of Energy Atmospheric Radiation Measurement Program (ARM) Southern Great Plains (SGP) site data are analyzed to provide insight into atmosphere–land surface interactions generating summertime precipitation variability. Pentad-averaged (5 days) data are analyzed; the average is long enough to suppress synoptic variability but sufficiently short to resolve atmosphere–land surface interactions. Intercomparison with the precipitation-assimilating North American Regional Reanalysis (NARR) helps with in-depth investigation of the processes. The analysis seeks to ascertain the process sequence, especially the role of evapotranspiration and soil-moisture–radiation feedbacks in the generation of regional precipitation variability at this temporal scale. Transported moisture dominates over evapotranspiration in precipitation variability over the region, from both magnitude of the contribution to regional water balance and its apparent temporal lead at pentad resolution. Antecedent and contemporaneous evapotranspiration are found to be negatively correlated with precipitation, albeit statistically insignificant; only lagging correlations are positive, peaking at 2-pentad lag following precipitation, substantiating the authors’ characterization of the water balance over SGP, and extending the authors’ previous findings on the dominance of moisture flux convergence in generating precipitation variability at monthly scales. Precipitation episodes are linked with net negative surface radiation anomalies (i.e., with an energy-deprived land surface state that cannot fuel evapotranspiration), ruling out radiatively driven positive feedback on precipitation. Although the net longwave signal is positive because of a colder land surface (less upward terrestrial radiation), it is more than offset by the cloudiness-related reduction in downward shortwave radiation. Thus, ARM (NARR) data do not support the soil-moisture–precipitation feedback hypothesis over the SGP at pentad time scales; however, it may work at subpentad resolution and over other regions.

A Comparison of MERRA and NARR Reanalyses with the DOE ARM SGP Data

Atmospheric states from the Modern-Era Retrospective analysis for Research and Applications (MERRA) and the North American Regional Reanalysis (NARR) are compared with data from the Atmospheric Radiation Measurement Program (ARM) Southern Great Plains (SGP) site, including the ARM continuous forcing product and Cloud Modeling Best Estimate (CMBE) soundings, during the period 1999–2001 to understand their validity for single-column model (SCM) and cloud-resolving model (CRM) forcing datasets. Cloud fraction, precipitation, and radiation information are also compared to determine what errors exist within these reanalyses. For the atmospheric state, ARM continuous forcing and the reanalyses have good agreement with the CMBE sounding information, with biases generally within 0.5 K for temperature, 0.5 m s−1 for wind, and 5% for relative humidity. Larger disagreements occur in the upper troposphere (p < 300 hPa) for temperature, humidity, and zonal wind, and in the boundary layer (p > 800 hPa) for meridional wind and humidity. In these regions, larger errors may exist in derived forcing products. Significant differences exist for vertical pressure velocity, with the largest biases occurring during the spring upwelling and summer downwelling periods. Although NARR and MERRA share many resemblances to each other, ARM outperforms these reanalyses in terms of correlation with cloud fraction. Because the ARM forcing is constrained by observed precipitation that gives the adequate mass, heat, and moisture budgets, much of the precipitation (specifically during the late spring/early summer) is caused by smaller-scale forcing that is not captured by the reanalyses. While reanalysis-based forcing appears to be feasible for the majority of the year at this location, it may have limited usage during the late spring and early summer, when convection is common at the ARM SGP site. Both NARR and MERRA capture the seasonal variation of cloud fractions (CFs) observed by ARM radar–lidar and Geostationary Operational Environmental Satellite (GOES) with high correlations (0.92–0.78) but with negative biases of 14% and 3%, respectively. Compared to the ARM observations, MERRA shows better agreement for both shortwave (SW) and longwave (LW) fluxes except for LW-down (due to a negative bias in water vapor): NARR has significant positive bias for SW-down and negative bias for LW-down under clear-sky and all-sky conditions. The NARR biases result from a combination of too few clouds and a lack of sufficient extinction by aerosols and water vapor in the atmospheric column. The results presented here represent only one location for a limited period, and more comparisons at different locations and longer periods are needed.

A 10 year climatology of cloud fraction and vertical distribution derived from both surface and GOES observations over the DOE ARM SPG site

Analysis of one decade of radar‐lidar and Geostationary Operational Environmental Satellite (GOES) observations at the Department of Energy (DOE) Atmospheric Radiation Measurement Program (ARM) Southern Great Plains (SGP) site reveals that there is excellent agreement in the long‐term mean cloud fractions (CFs) derived from the surface and GOES data, and the CF is independent of temporal resolution and spatial scales for grid boxes of size 0.5° to 2.5°. When computed over a a 0.5 h (4 h) period, cloud frequency of occurrence (FREQ) and amount when present (AWP) derived from the point surface data agree very well with the same quantities determined from GOES for a 0.5° (2.5°) region centered on the DOE ARM SGP site. The values of FREQ (AWP) derived from the radar‐lidar observations at a given altitude increase (decrease) as the averaging period increases from 5 min to 6 h. Similarly, CF at a given altitude increases as the vertical resolution increases from 90 to 1000 m. The profiles of CF have distinct bimodal vertical distributions, with a lower peak between 1 and 2 km and a higher one between 8 and 11 km. The 10 year mean total CF, 46.9%, varies seasonally from a summer minimum of 39.8% to a maximum of 54.6% during the winter. The annual mean CF is 1%–2% less than that from previous studies, ∼48%–49%, because fewer clouds occurred during 2005 and 2006, especially during winter. The differences in single‐ and multilayered CFs between this study and an earlier analysis can be explained by the different temporal resolutions used in the two studies, where single‐layered CFs decrease but multilayered CFs increase from a 5 min resolution to a 1 h resolution. The vertical distribution of nighttime GOES high cloud tops agrees well with surface observations, but during the daytime, fewer high clouds are retrieved by the GOES analysis than seen from the surface observations. The FREQs for both daytime and nighttime GOES low cloud tops are significantly higher than surface observations, but the CFs are in good agreement.

Thermodynamic phase profiles of optically thin midlatitude clouds and their relation to temperature

The relationship between cloud thermodynamic phase and temperature in some aircraft measurements conducted in midlatitude frontal clouds suggests that significant liquid does not exist at temperatures colder than 258 K. This data set is often used to verify parameterizations of cloud phase in general circulation models. However, other aircraft campaigns and different instruments suggest a different relationship. Here we examine the temperature‐phase relationship for midlatitude optically thin winter clouds. Cloud phase and temperature profiles derived from 5 years of ground‐based lidar depolarization and radiosonde measurements are analyzed for two midlatitude locations: the U. S. Atmospheric Radiation Measurement Program Southern Great Plains site and the Site Instrumental de Recherche par Télédétection Atmosphérique in France. Because lidars are attenuated in optically thick clouds, the data set only includes clouds with optical thickness of <3. Cloud phase is obtained by using the classical method based on a depolarization ratio threshold of 11% for differentiating liquid from ice. The frequency of occurrence of clouds either completely liquid or completely glaciated in the temperature range from 233 to 273 K is similar to previous observations in the midlatitudes but somewhat greater than in the Arctic. The relationship between ice phase occurrence and temperature only slightly changes between cloud base and top. At both sites, liquid is more prevalent at colder temperatures than has been found previously in some thicker frontal clouds, suggesting different processes for glaciation in nonfrontal optically thin clouds.

A modified method for inferring upper troposphere cloud top height using the GOES 12 imager 10.7 and 13.3 μm data

Passive satellite retrievals using conventional CO2 absorption techniques tend to systematically underestimate the upper transmissive cloud top heights (CTHs). These techniques are based on single‐layer assumptions that the upper cloud occupies a geometrically thin layer above a cloud‐free surface. This study presents a new modified CO2 absorption technique (MCO2AT) to improve the inference of transmissive CTHs in the upper troposphere above 600 hPa. The MCO2AT employs an iterative algorithm that starts with a single‐layer CO2 absorption technique (SCO2AT) followed by an iterative procedure to retrieve an enhanced upper CTH based on inferred effective background radiances. Both techniques are applied to the 10.7 and 13.3 μm channel data of the Twelfth Geostationary Operational Environmental Satellite (GOES 12) imager and their retrievals of upper tropospheric CTHs are compared with two active sensing products: the ground‐based Active Remotely Sensed Cloud Location (ARSCL) products from the Atmospheric Radiation Measurement Program (ARM) Southern Great Plains (SGP) site and the satellite‐based Cloud Aerosol Lidar With Orthogonal Polarization (CALIOP) products. On average, the CTHs from MCO2AT and SCO2AT are lower than those from both of the active sensors by ∼1 and 2.4 km, respectively, possibly due to the different sensitivities and spatial resolutions between passive and active sensors. Preliminary validation of the new modified method is encouraging, especially the improvements for upper transmissive clouds in geometrically thick and/or multilayered cloud situations. The development of the modified method is particularly useful for sensors like the GOES 12, Meteosat‐9, and others, which carry only one CO2 absorption channel at ∼13.3 μm.