Program Southern Great Plains Research Articles

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.

Variability in surface BRDF at different spatial scales (30 m–500 m) over a mixed agricultural landscape as retrieved from airborne and satellite spectral measurements

Over the past decade, the role of multiangle remote sensing has been central to the development of algorithms for the retrieval of global land surface properties including models of the bidirectional reflectance distribution function (BRDF), albedo, land cover/dynamics, burned area extent, as well as other key surface biophysical quantities impacted by the anisotropic reflectance characteristics of vegetation. In this study, a new retrieval strategy for fine-to-moderate resolution multiangle observations was developed, based on the operational sequence used to retrieve the Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 5 reflectance and BRDF/albedo products. The algorithm makes use of a semiempirical kernel-driven bidirectional reflectance model to provide estimates of intrinsic albedo (i.e., directional-hemispherical reflectance and bihemispherical reflectance), model parameters describing the BRDF, and extensive quality assurance information. The new retrieval strategy was applied to NASA's Cloud Absorption Radiometer (CAR) data acquired during the 2007 Cloud and Land Surface Interaction Campaign (CLASIC) over the well-instrumented Atmospheric Radiation Measurement Program (ARM) Southern Great Plains (SGP) Cloud and Radiation Testbed (CART) site in Oklahoma, USA. For the case analyzed, we obtained ~ 1.6 million individual surface bidirectional reflectance factor (BRF) retrievals, from nadir to 75° off-nadir, and at spatial resolutions ranging from 3 m to 500 m. This unique dataset was used to examine the interaction of the spatial and angular characteristics of a mixed agricultural landscape; and provided the basis for detailed assessments of: (1) the use of a land cover type-specific a priori knowledge in kernel-driven BRDF model inversions; (2) the interaction between surface reflectance anisotropy and instrument spatial resolution; and (3) the uncertainties that arise when sub-pixel differences in the BRDF are aggregated to a moderate resolution satellite pixel. Results offer empirical evidence concerning the influence of scale and spatial heterogeneity in kernel-driven BRDF models; providing potential new insights into the behavior and characteristics of different surface radiative properties related to land/use cover change and vegetation structure.

Critical Evaluation of the ISCCP Simulator Using Ground-Based Remote Sensing Data

Abstract Given the known shortcomings in representing clouds in global climate models (GCMs), comparisons with observations are critical. The International Satellite Cloud Climatology Project (ISCCP) diagnostic products provide global descriptions of cloud-top pressure and column optical depth that extend over multiple decades. Given the characteristics of the ISCCP product, the model output must be converted into what the ISCCP algorithm would diagnose from an atmospheric column with similar physical characteristics. This study evaluates one component of this so-called ISCCP simulator by comparing ISCCP results with simulated ISCCP diagnostics that are derived from data collected at the Atmospheric Radiation Measurement Program (ARM) Southern Great Plains (SGP) Climate Research Facility. It is shown that if a model were to simulate the cloud radiative profile with the same accuracy as can be derived from the ARM data, the likelihood of that occurrence being classified with similar cloud-top pressure and optical depth as ISCCP would range from 30% to 70% depending on optical depth. The ISCCP simulator improved the agreement of cloud-top pressure between ground-based remote sensors and satellite observations, and we find only minor discrepancies because of the parameterization of cloud-top pressure in the ISCCP simulator. The differences seem to be primarily due to discrepancies between satellite and ground-based sensors in the visible optical depth. The source of the optical depth bias appears to be due to subpixel cloud field variability in the retrieval of optical depths from satellite sensors. These comparisons suggest that caution should be applied to comparisons between models and ISCCP observations until the differences in visible optical depths are fully understood. The simultaneous use of ground-based and satellite retrievals in the evaluation of model clouds is encouraged.

Long-Term Observations of the Convective Boundary Layer Using Insect Radar Returns at the SGP ARM Climate Research Facility

Abstract A long-term study of the turbulent structure of the convective boundary layer (CBL) at the U.S. Department of Energy Atmospheric Radiation Measurement Program (ARM) Southern Great Plains (SGP) Climate Research Facility is presented. Doppler velocity measurements from insects occupying the lowest 2 km of the boundary layer during summer months are used to map the vertical velocity component in the CBL. The observations cover four summer periods (2004–08) and are classified into cloudy and clear boundary layer conditions. Profiles of vertical velocity variance, skewness, and mass flux are estimated to study the daytime evolution of the convective boundary layer during these conditions. A conditional sampling method is applied to the original Doppler velocity dataset to extract coherent vertical velocity structures and to examine plume dimension and contribution to the turbulent transport. Overall, the derived turbulent statistics are consistent with previous aircraft and lidar observations. The observations provide unique insight into the daytime evolution of the convective boundary layer and the role of increased cloudiness in the turbulent budget of the subcloud layer. Coherent structures (plumes–thermals) are found to be responsible for more than 80% of the total turbulent transport resolved by the cloud radar system. The extended dataset is suitable for evaluating boundary layer parameterizations and testing large-eddy simulations (LESs) for a variety of surface and cloud conditions.

Vertical velocity structure of nonprecipitating continental boundary layer stratocumulus clouds

Continental boundary layer (BL) stratocumulus clouds affect the local weather by modulating the surface energy and moisture budgets and are also intimately tied to the diurnal cycle of the turbulence in the BL. Vertical velocity structure of these clouds is studied using data from the Atmospheric Radiation Measurement Program's Southern Great Plains observing facility located near Lamont, Oklahoma. Data from vertically pointing cloud radar from eleven cases of nonprecipitating BL stratocumulus clouds are used to obtain half‐hourly values of in‐cloud vertical velocity variance, skewness, updraft fraction, downdraft fraction, and mass flux. The variance showed a general decrease with height, while the skewness was weakly positive in the cloud layer and negative near cloud top on half‐hourly time scales. Vertically coherent structures spanning through the entire cloud layer were responsible for ∼40% of the observed vertical velocity variance, while horizontally (temporally) coherent structures lasting at least 20 s were responsible for ∼77% of the vertical velocity variance. The half‐hour periods were then classified on the basis of the surface buoyancy flux (B) as small B periods (B < 10 Wm−2) and large B periods (B > 60Wm−2). Small B periods exhibited lower variance near cloud base compared to large B periods with the differences being reversed near cloud top. The skewness was positive in the cloud layer during large B periods and negative during small B periods, while the skewness was negative near cloud top in both scenarios. The small B periods had about ten times more updrafts greater than 1 m s−1 near cloud top compared to large B periods.

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.

Intercomparison of Ground-Based Radar and Satellite Cloud-Top Height Retrievals for Overcast Single-Layered Cloud Fields

The objective of this paper is to assess the accuracy of the Semi-Analytical CloUd Retrieval Algorithm (SACURA) that retrieves cloud-top heights (CTHs) using hyperspectral SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) onboard Environmental Satellite measurements for overcast single-layer cloud fields. Intercomparisons with ground-based 35-GHz millimeter wave cloud radar CTHs were performed for 14 dates during 2003-2007 at the U.S. Atmospheric Radiation Measurement (ARM) program Southern Great Plains site (36.6deg N, 97.5deg W). In addition, for some of these dates, European Space Agency MEdium Resolution Imaging Spectrometer (MERIS) and the NASA-TERRA Moderate Resolution Imaging Spectroradiometer (MODIS) cloud-top pressure retrievals were also collected, transformed into CTHs using nearby ARM radiosonde profiles, and compared with the SACURA SCIAMACHY and radar retrievals. The accuracy of the SACURA-SCIAMACHY CTH retrievals is better than 0.34 km for low-level clouds and 2.22 km for high-level clouds with an underestimate in CTH on average for all clouds. The average bias in SCIAMACHY CTHs was about 0.07 km for low clouds and about 0.5 km for high-level clouds. Both MODIS and MERIS slightly overestimated the CTHs of low-level clouds by 300 m, with an uncertainty better than 1 km. However, although MODIS accuracy for high-level clouds is close to SCIAMACHY, MERIS CTHs were significantly underestimated for these fairly optically thick cases.

Cirrus Microphysical Properties and Air Motion Statistics Using Cloud Radar Doppler Moments. Part II: Climatology

AbstractThe algorithm described in Part I has been applied to the millimeter cloud radar observations from January 1999 to December 2005 at the Atmospheric Radiation Measurement Program (ARM) Southern Great Plains (SGP) and Tropical Western Pacific (including Manus and Nauru) sites. Approximately 10 000 cirrus hours from each of these sites were analyzed. Retrieved cloud properties including condensed mass, particle size, optical depth, and in-cloud vertical air motions were analyzed in terms of their geographical, seasonal, and diurnal variations. The analysis shows that tropical ice clouds observed by millimeter radar are very different from ice clouds at SGP, with the tropical clouds having slightly larger particle sizes and greater ice masses and being more likely to be associated with ascending air motions, in addition to being colder and higher in altitude. A positive residual of derived in-cloud air motion found in the tropical data likely provides evidence for lofting of air into the tropopause transition layer as a result of radiative heating. The midlatitude cirrus demonstrate strong seasonal variations with more frequent, thicker clouds occurring during the summer than during the winter. Very subtle seasonal variations are found for tropical ice clouds, and evidence is presented that cirrus properties vary interannually and are correlated with El Niño oscillations. In addition, it is found that tropical cirrus demonstrate a stronger diurnal cycle than cirrus of the midlatitudes, with the in-cloud updrafts peaking in the early afternoon.

Cloud Detection with MODIS. Part II: Validation

Abstract An assessment of the performance of the Moderate Resolution Imaging Spectroradiometer (MODIS) cloud mask algorithm for Terra and Aqua satellites is presented. The MODIS cloud mask algorithm output is compared with lidar observations from ground [Arctic High-Spectral Resolution Lidar (AHSRL)], aircraft [Cloud Physics Lidar (CPL)], and satellite-borne [Geoscience Laser Altimeter System (GLAS)] platforms. The comparison with 3 yr of coincident observations of MODIS and combined radar and lidar cloud product from the Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Program Southern Great Plains (SGP) site in Lamont, Oklahoma, indicates that the MODIS algorithm agrees with the lidar about 85% of the time. A comparison with the CPL and AHSRL indicates that the optical depth limitation of the MODIS cloud mask is approximately 0.4. While MODIS algorithm flags scenes with a cloud optical depth of 0.4 as cloudy, approximately 90% of the mislabeled scenes have optical depths less than 0.4. A comparison with the GLAS cloud dataset indicates that cloud detection in polar regions at night remains challenging with the passive infrared imager approach. In anticipation of comparisons with other satellite instruments, the sensitivity of the cloud mask algorithm to instrument characteristics (e.g., instantaneous field of view and viewing geometry) and thresholds is demonstrated. As expected, cloud amount generally increases with scan angle and instantaneous field of view (IFOV). Nadir sampling represents zonal monthly mean cloud amounts but can have large differences for regional studies when compared to full-swath-width analysis.

Analysis of ice crystal habits derived from MISR and MODIS observations over the ARM Southern Great Plains site

We have developed a look‐up table approach for retrieving cloud phase (water or ice) and best‐fit ice crystal scattering model from coincident Multiangle Imaging Spectroradiometer (MISR) and Moderate Resolution Imaging Spectroradiometer (MODIS) data. We present statistics of cloud phase and radiatively effective ice crystal habit for 5 years of Terra overpasses of the Atmospheric Radiation Measurement (ARM) Program's Southern Great Plains (SGP) site. This analysis focuses on clouds which have optical depth >2 and are single‐layer (or have an optically thick upper layer). For this subset of clouds, 48% were best fit by a liquid droplet scattering model. The rough aggregate scattering model was the most commonly retrieved ice crystal habit (27% of all retrieved clouds; 51% of retrieved ice clouds). The results indicate that the distinction between water and ice clouds is more robust than the distinction between individual ice crystal habits. For ice clouds, the strongest distinctions were found between aggregate‐like and plate‐like habits. Comparison with MODIS operational cloud property retrievals (MOD06) illustrates the strong dependence of retrieved optical depth and effective radius on habit assumptions.

How quickly do cloud droplets form on atmospheric particles?

Abstract. The influence of aerosols on cloud properties is an important modulator of the climate system. Traditional Köhler theory predicts the equilibrium concentration of cloud condensation nuclei (CCN); however, it is not known to what extent particles exist in the atmosphere that may be prevented from acting as CCN by kinetic limitations. We measured the rate of cloud droplet formation on atmospheric particles sampled at four sites across the United States during the summer of 2006: Great Smoky Mountain National Park, TN; Bondville, IL; Houston, TX; and the Atmospheric Radiation Measurement Program Southern Great Plains site near Lamont, OK. We express droplet growth rates with the mass accommodation coefficient (α), and report values of α measured in the field normalized to the mean α measured for lab-generated ammonium sulfate (AS) particles (i.e., α'=α/αAS). Overall, 59% of ambient CCN grew at a rate similar to AS. We report the fraction of CCN that were "low-α' " (α'<10−1, corresponding to α<1.5×10−2). Of the 16 days during which these measurements were made, 8 had relatively few low-α' CCN (<16%), 6 had moderate low-α' fractions (27% to 59%), and 2 had large low-α' fractions (>82% during at least one ~30 min period). Day to day variability was greatest in Tennessee and Illinois, and low-α' particles were most prevalent on days when back trajectories suggested that air was arriving from aloft. The highest fractions of low-α' CCN in Houston and Illinois occurred around local noon, and decreased later in the day. These results suggest that for some air masses, accurate quantification of CCN concentrations may need to account for kinetic limitations.

A Comparison of Forecast Errors in CAM2 and CAM3 at the ARM Southern Great Plains Site

Abstract The authors compare short forecast errors and the balance of terms in the moisture and temperature prediction equations that lead to those errors for the Community Atmosphere Model versions 2 and 3 (CAM2 and CAM3, respectively) at T42 truncation. The comparisons are made for an individual model column from global model forecasts at the Atmospheric Radiation Measurement Program (ARM) Southern Great Plains site for the April 1997 and June–July 1997 intensive observing periods. The goal is to provide insight into parameterization errors in the CAM, which ultimately should lead to improvements in the way processes are modeled. The atmospheric initial conditions are obtained from the 40-yr ECMWF Re-Analysis (ERA-40). The land initial conditions are spun up to be consistent with those analyses. The differences between the model formulations that are responsible for the major differences in the forecast errors and/or parameterization behaviors are identified. A sequence of experiments is performed, accumulating the changes from CAM3 back toward CAM2 to demonstrate the effect of the differences in formulations. In June–July 1997 the CAM3 temperature and moisture forecast errors were larger than those of CAM2. The terms identified as being responsible for the differences are 1) the convective time scale assumed for the Zhang–McFarlane deep convection, 2) the energy associated with the conversion between water and ice of the rain associated with the Zhang–McFarlane convection parameterization, and 3) the dependence of the rainfall evaporation on cloud fraction. In April 1997 the CAM2 and CAM3 temperature and moisture forecast errors are very similar, but different tendencies arising from modifications to one parameterization component are compensated by responding changes in another component to yield the same total moisture tendency. The addition of detrainment of water in CAM3 by the Hack shallow convection to the prognostic cloud water scheme is balanced by a responding difference in the advective tendency. A halving of the time scale assumed for the Hack shallow convection was compensated by a responding change in the prognostic cloud water. Changes to the cloud fraction parameterization affect the radiative heating, which in turn modifies the stability of the atmospheric column and affects the convection. The resulting changes in convection tendency are balanced by responding changes in the prognostic cloud water parameterization tendency.

Investigation of the first and second aerosol indirect effects using data from the May 2003 Intensive Operational Period at the Southern Great Plains

The Active Tracer High‐Resolution Atmospheric Model is used to examine the aerosol indirect effect (AIE) for a spring continental stratus cloud on the basis of data collected during the 17 May 2003 Aerosol Intensive Operation Period (AIOP) at the Atmospheric Radiation Measurement (ARM) Program Southern Great Plains site. Model results for our base case, which uses observed aerosol concentrations, agree reasonably well with the available observations, giving confidence that the basic model is reasonable. Sensitivity tests are performed to explore the response of the clouds to changes in the aerosol number concentration and surface fluxes. During the major part of the simulation, from 0630 through 1400 local standard time (LST), an increase in the aerosol number concentration (Na) results in a decrease of the mean cloud droplet size and an increase of the cloud liquid water path (LWP) until aerosol number concentration levels reach 1200 cm−3. Further increases in aerosol concentration do not increase the liquid water path because the depletion of cloud water by precipitation is negligible above this number concentration. After 1400 LST, the liquid water path decreases when aerosols increase as long as Na < 600 cm−3 and remains unchanged for higher aerosol concentrations. The decrease of LWP is associated with the evaporative cooling below cloud base which leads to more condensation of water vapor, a result that is consistent with afternoon satellite observations of the response of continental clouds to increases in droplet concentrations. A sensitivity test with a stronger surface latent flux increases both the cloud geometrical thickness and cloud water content. On the other hand, a sensitivity test with a stronger surface sensible heat flux leads to a higher cloud base and a shallower and drier cloud. The response of the cloud geometrical thickness to changes in surface sensible heat flux dominates that of the cloud water content. The cloud fraction is also reduced at the end of the simulation time period. Because the surface heat fluxes will likely change when aerosol and droplet number concentrations change, these sensitivity tests show that a fully coupled simulation with a land surface model will be needed to fully assess the response of the cloud to changing aerosol concentrations. Nevertheless, since the thermodynamic boundary layer profiles do not change significantly when aerosol concentrations are changed, our results for changing aerosol concentrations are qualitatively correct.

Cloud climatology at the Southern Great Plains and the layer structure, drizzle, and atmospheric modes of continental stratus

Long‐term (6.5 years) cloud observations from the Atmospheric Radiation Measurements (ARM) program Southern Great Plains (SGP) climate research facility in Oklahoma are used to develop detailed cloud climatology. Clouds are classified with respect to their altitude (low, middle, and high), vertical development, and the presence of multilayer clouds. Single‐layered cirrus, middle or low clouds were observed a total of 23% of the time the MilliMeter Cloud Radar (MMCR) was operating, and multilayer clouds were observed 20.5% of the time. Boundary layer clouds exhibit the strongest seasonal variability because of continental stratus associated with midlatitude frontal systems. Cirrus clouds are the most frequently observed cloud type and exhibit strong seasonal variability in cloud base height (higher cloud base during the summer months) and relatively constant cloud fraction. The majority of middle‐level clouds are shallow with vertical extent less than 1 km. No strong seasonal cycle in the fractional coverage of multilayer clouds is observed. Continental stratus clouds exhibit strong seasonal variability with maximum occurrence during the cold seasons. Nondrizzling stratus clouds exhibit a bimodal seasonal variability with maximum occurrences in the fall and spring, while drizzling stratus occur most frequently in the winter. Thermodynamic and dynamic variables from soundings and the European Centre for Medium‐Range Weather Forecasts Model (ECMWF) analyses at the SGP site illustrate an interesting coupling between strong large‐scale forcing and the formation of single‐layered (no other cloud layer is present) continental stratus clouds. Single‐layered stratus clouds (drizzling and nondrizzling) exhibit a strong correlation with positive ω at 500 mbar and strong northerly flow.

A Bootstrap Technique for Testing the Relationship between Local-Scale Radar Observations of Cloud Occurrence and Large-Scale Atmospheric Fields

Abstract A classification scheme is created to map the synoptic-scale (large scale) atmospheric state to distributions of local-scale cloud properties. This mapping is accomplished by a neural network that classifies 17 months of synoptic-scale initial conditions from the rapid update cycle forecast model into 25 different states. The corresponding data from a vertically pointing millimeter-wavelength cloud radar (from the Atmospheric Radiation Measurement Program Southern Great Plains site at Lamont, Oklahoma) are sorted into these 25 states, producing vertical profiles of cloud occurrence. The temporal stability and distinctiveness of these 25 profiles are analyzed using a bootstrap resampling technique. A stable-state-based mapping from synoptic-scale model fields to local-scale cloud properties could be useful in three ways. First, such a mapping may improve the understanding of differences in cloud properties between output from global climate models and observations by providing a physical context. Second, this mapping could be used to identify the cause of errors in the modeled distribution of clouds—whether the cause is a difference in state occurrence (the type of synoptic activity) or the misrepresentation of clouds for a particular state. Third, robust mappings could form the basis of a new statistical cloud parameterization.

Determination of 3D Cloud Ice Water Contents by Combining Multiple Data Sources from Satellite, Ground Radar, and a Numerical Model

Abstract This study aims at determining the three-dimensional distribution of ice water content over a broad area near the Atmospheric Radiation Measurement Program Southern Great Plains site, where cloud radar and meteorological observations have been routinely conducted. Together with wind fields from other measurements, the ice water content retrievals can be used to derive the cloud ice water advective tendency terms needed for single-column model simulations. In this study, a Bayesian retrieval algorithm has been developed that combines multiple data sources from satellite high-frequency microwave radiometry, ground cloud radar observations, and mesoscale numerical model analysis. The cloud radar observations allow the characteristics of vertical ice water content structures to be inferred. The numerical model data are used to locate the cloud height. The satellite data provide information on the integrated ice water path, its horizontal distribution over a broad area, and, to a lesser extent, the vertical structure of ice water content. The approach herein is to retrieve the three-dimensional cloud ice water content in a 10° × 10° area surrounding the cloud radar site by combining all the information contained in the above datasets through a Bayesian framework. Validation of the algorithm has been done by comparing the retrievals with measurements from two ground radars. The comparison shows that the mean ice water content profiles and the two-dimensional (height–ice water content) probability density functions retrieved for 19 coincident cases agree fairly well with the validation data. However, the retrieved ice water contents generally lack detailed vertical structures because of the low sensitivity of satellite data to the vertical variation of cloud ice.

Cloud base heights retrieved during night‐time conditions with MODIS data

The capability to retrieve cloud base heights was developed under the US National Polar‐orbiting Operational Environmental Satellite System (NPOESS) programme as one of 27 data products to be created from data collected by the Visible Infrared Imager Radiometer Suite (VIIRS). First launch of the VIIRS sensor, which is the high‐resolution Earth imager of the NPOESS programme, comes on National Aeronautics & Space Administration's (NASA) NPOESS Preparatory Project (NPP). In preparation for this launch, extensive testing of the VIIRS cloud algorithms was completed to verify that product performance will satisfy system requirements before the cloud algorithms were hosted in the NPOESS ground processing centre. The approach taken to retrieve cloud base height converts cloud optical properties into a geometric thickness which is then subtracted from the cloud top height. Performance of the cloud base height algorithms has been verified recently using MODIS data, together with temporarily and spatial coincident observations of cloud thickness values made with the millimetre cloud radar operated at the US Department of Energy Atmospheric Radiation Measurement (ARM) Program Southern Great Plains site in Oklahoma. Of particular significance is the clear demonstration that both cloud optical properties and cloud base heights are retrieved accurately during night‐time conditions with the VIIRS algorithms, since neither of these products is currently produced by the NASA EOS programme. Based upon these analyses, the VIIRS cloud algorithms are expected to satisfy NPOESS requirements, making VIIRS the first operational satellite sensor capable of retrieving three‐dimensional cloud fields.

Assessment of multispectral ATSR2 stereo cloud-top height retrievals

The Along-Track Scanning Radiometer 2 (ATSR2) instrument has a dual view capability that allows for stereo height retrievals. Stereo heights were retrieved for selected scenes over the United Kingdom Chilbolton Facility for Atmospheric and Radio Research and the United States Atmospheric Radiation Measurement program Southern Great Plains site from 1997 to 2000. Stereo cloud-top heights obtained with the 11 μm and 1.6 μm channels of ATSR2 were compared with ground-based millimeter-wave cloud radar measurements at both sites. On average ATSR2 11 μm channel cloud-top height retrievals were 350 m higher than those from radar with a standard deviation of 1 km. This difference increased with decreasing cloud-top height. One major problem found in the 11 μm channel cloud-top height retrievals was poor delineation between surface (i.e., clear) and low-altitude cloud pixels, though this tends to lower cloud-top heights rather than raise them. The ATSR2 1.6 μm channel stereo cloud-top heights had large discrepancies compared to radar because of the 1.6 μm channel sensitivity to a lower layer in the case of multilayer clouds or the terrain in the case of optically thin single-layer clouds. For single-layer clouds, the agreement between the ATSR2 1.6 μm channel and the radar cloud-top heights was similar to that between ATSR2 11 μm channel and radar cloud-top heights.