Cloud Radar Research Articles

The Impact of Ground-Based Glaciogenic Seeding on Orographic Clouds and Precipitation: A Multisensor Case Study

AbstractA case study is presented from the 2012 AgI Seeding Cloud Impact Investigation, an experiment conducted over the Sierra Madre in southern Wyoming to study the impact of ground-based glaciogenic seeding on precipitation. In this case, on 21 February, the temperature in the turbulent boundary layer above cloud base in the target region was just below −8°C, the target orographic clouds contained liquid water, and the storm was rather steady during the measurement period, consisting of an untreated period, followed by a treated period. Eight silver iodide (AgI) generators were used, located on the windward mountain slope. This study is unprecedented in its diversity of radar systems, which included the W-band (3 mm) profiling Wyoming Cloud Radar (WCR), a pair of Ka-band (1 cm) profiling Micro Rain Radars (MRRs), and an X-band (3 cm) scanning Doppler-on-Wheels (DOW) radar. The WCR was on board a research aircraft flying geographically fixed tracks, the DOW was located on the main mountain pass in the target region, one MRR was at this pass, and the other was upstream of the generators. Composite data from the three radars indicate that near-surface reflectivity was higher during seeding, a change that could not be accounted for by storm intensification upstream of the generators. Data from a Parsivel disdrometer at the pass indicate that the concentration of snow crystals of all sizes was larger during seeding, although this change was somewhat delayed. This study highlights the challenge of an observational study to unambiguously identify a seeding signal, as well as the value of cumulative corroborative evidence from independent sources.

Ka-band 구름레이더와 천리안위성으로 관측된 운정고도 비교

Ka-band 구름레이더와 천리안위성으로 관측된 운정고도 비교

Scanning ARM Cloud Radars. Part I: Operational Sampling Strategies

Abstract The acquisition of scanning cloud radars by the Atmospheric Radiation Measurement (ARM) program and research institutions around the world generates the need for developing operational scan strategies for cloud radars. Here, the first generation of sampling strategies for the scanning ARM cloud radars (SACRs) is presented. These scan strategies are designed to address the scientific objectives of ARM; however, they introduce an initial framework for operational scanning cloud radars. While the weather community uses scan strategies that are based on a sequence of scans at constant elevations, the SACR scan strategies are based on a sequence of scans at constant azimuth. This is attributed to the cloud geometrical properties, which are vastly different from the rain and snow shafts that are the primary targets of precipitation radars; the need to cover the cone of silence; and the scanning limitations of the SACRs. A “cloud surveillance” scan strategy is introduced that is based on a sequence of horizon-to-horizon range–height indicator (RHI) scans that sample the hemispherical sky (HS) every 30° azimuth (HSRHI). The HSRHI scan strategy is complimented with a low-elevation plan position indicator (PPI) scan. The HSRHI and PPI are repeated every 30 min to provide a static view of the cloud conditions around the SACR location. Between the HSRHI and PPI scan strategies, other scan strategies are introduced depending on the cloud conditions. In the future, information about the atmospheric cloud state will be used in a closed-loop process to optimize the selection of the SACR scan strategy.

Scanning ARM Cloud Radars. Part II: Data Quality Control and Processing

Abstract The scanning Atmospheric Radiation Measurement (ARM) Program cloud radars (SACRs) are the primary instruments for documenting the four-dimensional structure and evolution of clouds within a 20–30-km radius of the ARM fixed and mobile sites. Here, the postprocessing of the calibrated SACR measurements is discussed. First, a feature mask algorithm that objectively determines the presence of significant radar returns is described. The feature mask algorithm is based on the statistical properties of radar receiver noise. It accounts for atmospheric emission and is applicable even for SACR profiles with few or no signal-free range gates. Using the nearest-in-time atmospheric sounding, the SACR radar reflectivities are corrected for gaseous attenuation (water vapor and oxygen) using a line-by-line absorption model. Despite having a high pulse repetition frequency, the SACR has a narrow Nyquist velocity limit and thus Doppler velocity folding is commonly observed. An unfolding algorithm that makes use of a first guess for the true Doppler velocity using horizontal wind measurements from the nearest sounding is described. The retrieval of the horizontal wind profile from the hemispherical sky range–height indicator SACR scan observations and/or nearest sounding is described. The retrieved horizontal wind profile can be used to adaptively configure SACR scan strategies that depend on wind direction. Several remaining challenges are discussed, including the removal of insect and second-trip echoes. The described algorithms significantly enhance SACR data quality and constitute an important step toward the utilization of SACR measurements for cloud research.

Turbulence and Radiation in Stratocumulus-Topped Marine Boundary Layers: A Case Study from VOCALS-REx

AbstractObservations made during a 24-h period as part of the Variability of the American Monsoon Systems (VAMOS) Ocean–Cloud–Atmosphere–Land Study Regional Experiment (VOCALS-REx) are analyzed to study the radiation and turbulence associated with the stratocumulus-topped marine boundary layer (BL). The first 14 h exhibited a well-mixed (coupled) BL with an average cloud-top radiative flux divergence of ~130 W m−2; the BL was decoupled during the last 10 h with negligible radiative flux divergence. The averaged radiative cooling very close to the cloud top was −9.04 K h−1 in coupled conditions and −3.85 K h−1 in decoupled conditions. This is the first study that combined data from a vertically pointing Doppler cloud radar and a Doppler lidar to yield the vertical velocity structure of the entire BL. The averaged vertical velocity variance and updraft mass flux during coupled conditions were higher than those during decoupled conditions at all levels by a factor of 2 or more. The vertical velocity skewness was negative in the entire BL during coupled conditions, whereas it was weakly positive in the lower third of the BL and negative above during decoupled conditions. A formulation of velocity scale is proposed that includes the effect of cloud-top radiative cooling in addition to the surface buoyancy flux. When scaled by the velocity scale, the vertical velocity variance and coherent downdrafts had similar magnitude during the coupled and decoupled conditions. The coherent updrafts that exhibited a constant profile in the entire BL during both the coupled and decoupled conditions scaled well with the convective velocity scale to a value of ~0.5.

基于激光雷达和毫米波云雷达的卷云特性分析

基于激光雷达和毫米波云雷达的卷云特性分析

Weather Research and Forecasting Model simulations of extended warm-season heavy precipitation episode over the US Southern Great Plains: data assimilation and microphysics sensitivity experiments

This study examines eight microphysics schemes (Lin, WSM5, Eta, WSM6, Goddard, Thompson, WDM5, WDM6) in the Advanced Research Weather Research and Forecasting Model (WRF-ARW) for their reproduction of observed strong convection over the US Southern Great Plains (SGP) for three heavy precipitation events of 27–31 May 2001. It also assesses how observational analysis nudging (OBNUD), three-dimensional (3DVAR) and four-dimensional variational (4DVAR) data assimilation (DA) affect simulated cloud properties relative to simulations with no DA (CNTRL). Primary evaluation data were cloud radar reflectivity measurements by the millimetre cloud radar (MMCR) at the Central Facility (CF) of the SGP site of the ARM Climate Research Facility (ACRF). All WRF-ARW microphysics simulations reproduce the intensity and vertical structure of the first two major MMCR-observed storms, although the first simulated storm initiates a few hours earlier than observed. Of three organised convective events, the model best identifies the timing and vertical structure of the second storm more than 50 hours into the simulation. For this well-simulated cloud structure, simulated reflectivities are close to the observed counterparts in the mid- and upper troposphere, and only overestimate observed cloud radar reflectivity in the lower troposphere by less than 10 dBZ. Based on relative measures of skill, no single microphysics scheme excels in all aspects, although the WDM schemes show much-improved frequency bias scores (FBSs) in the lower troposphere for a range of reflectivity thresholds. The WDM6 scheme has improved FBSs and high simulated-observed reflectivity correlations in the lower troposphere, likely due to its large production of liquid water immediately below the melting level. Of all the DA experiments, 3DVAR has the lowest mean errors (MEs) and root mean-squared errors (RMSEs), although both the 3DVAR and 4DVAR simulations reduced noticeably the MEs for seven of eight microphysics schemes relative to CNTRL. Lower-tropospheric θe and convective available potential energy (CAPE) also are closer to the observations for the 4DVAR than CNTRL simulations.

KITcube – a mobile observation platform for convection studies deployed during HyMeX

With the increase of spatial resolution of weather forecast models to order O(1 km), the need for adequate observations for model validation becomes evident. Therefore, we designed and constructed the ‘‘KITcube’’, a mobile observation platform for convection studies of processes on the meso-c scale. The KITcube consists of in-situ and remote sensing systems which allow measuring the energy balance components of the Earth’s surface at different sites; the mean atmospheric conditions by radiosondes, GPS station, and a microwave radiometer; the turbulent characteristics by a sodar and wind lidars; and cloud and precipitation properties by use of a cloud radar, a micro rain radar, disdrometers, rain gauges, and an X-band rain radar. The KITcube was deployed fully for the first time on the French island of Corsica during the HyMeX (Hydrological cycle in the Mediterranean eXperiment) field campaign in 2012. In this article, the components of KITcube and its implementation on the island are described. Moreover, results from one of the HyMeX intensive observation periods are presented to show the capabilities of KITcube.

Fixed-Antenna Pointing-Angle Calibration of Airborne Doppler Cloud Radar

Abstract Doppler velocity measurements from airborne meteorological Doppler radars require removal of the aircraft motion contribution in order to provide radial velocity of hydrometeor targets. This is a critical step for hydrometeor motion and wind retrievals. The aircraft motion contribution is defined as the scalar product between the radar antenna beam-pointing vector and the aircraft velocity vector at the antenna phase center. The accuracy in the removal of the aircraft velocity contribution is determined by the accuracy of the beam-pointing vector, the rigidity of the antenna mount, and the accuracy of the aircraft attitude and velocity measurements. In this paper an optimization technique is proposed to determine the antenna beam-pointing vector and to analyze its uncertainties using aircraft attitude and velocity data from a GPS-aided inertial measurement unit and radar observations of the earth surface. The technique is applied to Wyoming Cloud Radar (WCR) on the University of Wyoming King Air (UWKA) aircraft. The beam-pointing vectors of the two fixed downward-pointing WCR antennas are calibrated using data selected from several calibration flights. The maximum root-mean-square error in the calibrated beam-pointing angles is smaller than 0.03°, resulting in less than 0.1 m s−1 aircraft motion residual error in the Doppler velocities after removing the aircraft motion contribution. Some applicability and limitations to other airborne Doppler radars with fixed antennas are discussed.

Geographically versus dynamically defined boundary layer cloud regimes and their use to evaluate general circulation model cloud parameterizations

Regimes of tropical low‐level clouds are commonly identified according to large‐scale subsidence and lower tropospheric stability (LTS). This definition alone is insufficient for the distinction between regimes and limits the comparison of low‐level clouds from CloudSat radar observations and the ECHAM5 GCM run with the COSP radar simulator. Comparisons of CloudSat radar cloud altitude‐reflectivity histograms for stratocumulus and shallow cumulus regimes, as defined above, show nearly identical reflectivity profiles, because the distinction between the two regimes is dependent upon atmospheric stability below 700 hPa and observations above 1.5 km. Regional subsets, near California and Hawaii, for example, have large differences in reflectivity profiles than the dynamically defined domain; indicating different reflectivity profiles exist under a given large‐scale environment. Regional subsets are better for the evaluation of low‐level clouds in CloudSat and ECHAM5 as there is less contamination between 2.5 km and 7.5 km from precipitating hydrometeors which obscured cloud reflectivities.

Reconciling Ground-Based and Space-Based Estimates of the Frequency of Occurrence and Radiative Effect of Clouds around Darwin, Australia

AbstractThe objective of this paper is to investigate whether estimates of the cloud frequency of occurrence and associated cloud radiative forcing as derived from ground-based and satellite active remote sensing and radiative transfer calculations can be reconciled over a well-instrumented active remote sensing site located in Darwin, Australia, despite the very different viewing geometry and instrument characteristics. It is found that the ground-based radar–lidar combination at Darwin does not detect most of the cirrus clouds above 10 km (because of limited lidar detection capability and signal obscuration by low-level clouds) and that the CloudSat radar–Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) combination underreports the hydrometeor frequency of occurrence below 2-km height because of instrument limitations at these heights. The radiative impact associated with these differences in cloud frequency of occurrence is large on the surface downwelling shortwave fluxes (ground and satellite) and the top-of-atmosphere upwelling shortwave and longwave fluxes (ground). Good agreement is found for other radiative fluxes. Large differences in radiative heating rate as derived from ground and satellite radar–lidar instruments and radiative transfer calculations are also found above 10 km (up to 0.35 K day−1 for the shortwave and 0.8 K day−1 for the longwave). Given that the ground-based and satellite estimates of cloud frequency of occurrence and radiative impact cannot be fully reconciled over Darwin, caution should be exercised when evaluating the representation of clouds and cloud–radiation interactions in large-scale models, and limitations of each set of instrumentation should be considered when interpreting model–observation differences.

3D cloud reconstructions: Evaluation of scanning radar scan strategy with a view to surface shortwave radiation closure

AbstractThe ability of six scanning cloud radar scan strategies to reconstruct cumulus cloud fields for radiation study is assessed. Utilizing snapshots of clean and polluted cloud fields from large eddy simulations, an analysis is undertaken of error in both the liquid water path and monochromatic downwelling surface irradiance at 870 nm of the reconstructed cloud fields. Error introduced by radar sensitivity, choice of radar scan strategy, retrieval of liquid water content (LWC), and reconstruction scheme is explored. Given an infinitely sensitive radar and perfect LWC retrieval, domain average surface irradiance biases are typically less than 3 W m−2 µm−1, corresponding to 5–10% of the cloud radiative effect (CRE). However, when using a realistic radar sensitivity of −37.5 dBZ at 1 km, optically thin areas and edges of clouds are difficult to detect due to their low radar reflectivity; in clean conditions, overestimates are of order 10 W m−2 µm−1 (~20% of the CRE), but in polluted conditions, where the droplets are smaller, this increases to 10–26 W m−2 µm−1 (~40–100% of the CRE). Drizzle drops are also problematic; if treated as cloud droplets, reconstructions are poor, leading to large underestimates of 20–46 W m−2 µm−1 in domain average surface irradiance (~40–80% of the CRE). Nevertheless, a synergistic retrieval approach combining the detailed cloud structure obtained from scanning radar with the droplet‐size information and location of cloud base gained from other instruments would potentially make accurate solar radiative transfer calculations in broken cloud possible for the first time.

Climatology of High Cloud Dynamics Using Profiling ARM Doppler Radar Observations

AbstractIce cloud properties are influenced by cloud-scale vertical air motion. Dynamical properties of ice clouds can be determined via Doppler measurements from ground-based, profiling cloud radars. Here, the decomposition of the Doppler velocities into reflectivity-weighted particle velocity Vt and vertical air motion w is described. The methodology is applied to high clouds observations from 35-GHz profiling millimeter wavelength radars at the Atmospheric Radiation Measurement Program (ARM) Southern Great Plains (SGP) climate research facility in Oklahoma (January 1997–December 2010) and the ARM Tropical Western Pacific (TWP) site in Manus (July 1999–December 2010). The Doppler velocity measurements are used to detect gravity waves (GW), whose correlation with high cloud macrophysical properties is investigated. Cloud turbulence is studied in the absence and presence of GW. High clouds are less turbulent when GW are observed. Probability density functions of Vt, w, and high cloud macrophysical properties for the two cloud subsets (with and without GW) are presented. Air-density-corrected Vt for high clouds for which GW (no GW) were detected amounted to hourly means and standard deviations of 0.89 ± 0.52 m s−1 (0.8 ± 0.48 m s−1) and 1.03 ± 0.41 m s−1 (0.86 ± 0.49 m s−1) at SGP and Manus, respectively. The error of w at one standard deviation was estimated as 0.15 m s−1. Hourly means of w averaged around 0 m s−1 with standard deviations of ±0.27 (SGP) and ±0.29 m s−1 (Manus) for high clouds without GW and ±0.22 m s−1 (both sites) for high clouds with GW. The midlatitude site showed stronger seasonality in detected high cloud properties.

Toward a quantitative characterization of heterogeneous ice formation with lidar/radar: Comparison of CALIPSO/CloudSat with ground‐based observations

We analyze and compare the different sensitivities of aerosol/cloud lidar and 35‐GHz cloud radar to detect ice formation in midlevel clouds in order to harmonize mixed phase cloud observations performed with lidar and radar. We found good agreement between spaceborne Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO)/CloudSat and ground‐based lidar/radar observations at Leipzig, Germany. However, large differences were found to a previous study with an 11‐year cloud statistics solely based on lidar observations which is caused by significantly higher sensitivity of the cloud radar to detect ice crystals. By introducing a lidar detection threshold for the ice water content of 10−6kgm−3, we find that lidar and radar cloud statistics become increasingly similar.

Relationship between cloud characteristics and radar reflectivity based on aircraft and cloud radar co-observations

Cloud properties were investigated based on aircraft and cloud radar co-observation conducted at Yitong, Jilin, Northeast China. The aircraft provided in situ measurements of cloud droplet size distribution, while the millimeter-wavelength cloud radar vertically scanned the same cloud that the aircraft penetrated. The reflectivity factor calculated from aircraft measurements was compared in detail with simultaneous radar observations. The results showed that the two reflectivities were comparable in warm clouds, but in ice cloud there were more differences, which were probably associated with the occurrence of liquid water. The acceptable agreement between reflectivities obtained in water cloud confirmed that it is feasible to derive cloud properties by using aircraft data, and hence for cloud radar to remotely sense cloud properties. Based on the dataset collected in warm clouds, the threshold of reflectivity to diagnose drizzle and cloud particles was studied by analyses of the probability distribution function of reflectivity from cloud particles and drizzle drops. The relationship between reflectivity factor (Z) and cloud liquid water content (LWC) was also derived from data on both cloud particles and drizzle. In comparison with cloud droplets, the relationship for drizzle was blurred by many scatter points and thus was less evident. However, these scatters could be partly removed by filtering out the drop size distribution with a large ratio of reflectivity and large extinction coefficient but small effective radius. Empirical relationships of Z-LWC for both cloud particles and drizzle could then be derived.

Dry and moist convection in the boundary layer over the Black Forest - a combined analysis of in situ and remote sensing data

During the COPS experiment performed in south-western Germany and eastern France in 2007, several insitu and remote sensing systems were operated at Hornisgrinde - the highest summit of the northern Black Forest mountains. For this case study, data from a surface flux station, radiosondes, cloud camera, cloud radar, wind lidar, water vapour differential absorption lidar, and microwave profiler were used to investigate turbulence characteristics in cloud-free and cloud-topped convective boundary layers (CBLs). Short time intervals were analysed, during which dry and moist convective cells occurred, in order to obtain insight of the processes that determine the turbulent characteristic in the CBL. The frequently-used aerosol concentration was used to calculate the CBL height, zi. It was found that active CBL clouds penetrated deeper into the free troposphere than dry convective cells. In the cloud-free CBL the normalised variance of the vertical velocity, rw 2 , decreased to zero approximately at zi, while rw 2 was nearly constant between 0.5 and 1 z/zi in the cloud-topped CBL. The higher normalised rw 2 values in the cloud layer could be attributed to the additional elevated heat source due to condensation. In the cloud-free CBL the latent heat flux showed a strong decrease between 0.7 and 1.1 z/zi, i.e., it considerably moistened the upper part of the CBL and entrainment zone. In the cloud-topped CBL the latent heat flux decreased significantly above the CBL top only and became zero at about 1.4 z/zi. CBL height calculations, which took measures of the turbulence into account, resulted in normalised rw 2 and E profiles, which became zero at the CBL top and appeared more appropriate for CBL height scaling over complex terrain. The case studies demonstrated that only the combined use of different monitoring systems allowed for the recording of the entire structure of the convective cells and that synergetic measurements in cloud-topped CBLs were indispensable to capture the latters’ turbulent characteristics. Also, significant differences between turbulent characteristics in cloud-free and cloud-topped CBLs became evident.

Separating Cloud and Drizzle Radar Moments during Precipitation Onset Using Doppler Spectra

Abstract The retrieval of cloud, drizzle, and turbulence parameters using radar Doppler spectra is challenged by the convolution of microphysical and dynamical influences and the overall uncertainty introduced by turbulence. A new technique that utilizes recorded radar Doppler spectra from profiling cloud radars is presented here. The technique applies to areas in clouds where drizzle is initially produced by the autoconversion process and is detected by a positive skewness in the radar Doppler spectrum. Using the Gaussian-shape property of cloud Doppler spectra, the cloud-only radar Doppler spectrum is estimated and used to separate the cloud and drizzle contributions. Once separated, the cloud spectral peak can be used to retrieve vertical air motion and eddy dissipation rates, while the drizzle peak can be used to estimate the three radar moments of the drizzle particle size distribution. The technique works for nearly 50% of spectra found near cloud top, with efficacy diminishing to roughly 15% of spectra near cloud base. The approach has been tested on a large dataset collected in the Azores during the Atmospheric Radiation Measurement Program (ARM) Mobile Facility deployment on Graciosa Island from May 2009 through December 2010. Validation of the proposed technique is achieved using the cloud base as a natural boundary between radar Doppler spectra with and without cloud droplets. The retrieval algorithm has the potential to characterize the dynamical and microphysical conditions at cloud scale during the transition from cloud to precipitation. This has significant implications for improving the understanding of drizzle onset in liquid clouds and for improving model parameterization schemes of autoconversion of cloud water into drizzle.

From CloudSat‐CALIPSO to EarthCare: Evolution of the DARDAR cloud classification and its comparison to airborne radar‐lidar observations

This paper presents the implementation of a new version of the DARDAR (radar lidar) classification derived from CloudSat and CALIPSO data. The resulting target classification called DARDAR v2 is compared to the first version called DARDAR v1. Overall DARDAR v1 reports more cloud or rain pixels than DARDAR v2. In the low troposphere this is because v1 detects too many liquid cloud pixels, and in the higher troposphere this is because v2 is more restrictive in lidar detection than v1. Nevertheless, the spatial distribution of different types of hydrometeors show similar patterns in both classifications. The French airborne Radar‐Lidar (RALI) platform carries a CloudSat/CALIPSO instrument configuration (lidar at a wavelength of 532nm and a 95GHz cloud radar) as well as an EarthCare instrument configuration (high spectral resolution lidar at 355nm and a 95GHz Doppler cloud radar). It therefore represents an ideal go‐between for A‐Train and EarthCare. The DARDAR v2 classification algorithm is adapted to RALI data for A‐Train overpasses during dedicated airborne field experiments using the lidar at 532nm and the radar Doppler measurements. The results from the RALI classification are compared with the DARDAR v2 classification to identify where the classification should still be interpreted with caution. Finally, the RALI classification algorithm with lidar at 532nm is adapted to RALI with high spectral resolution lidar data at 355nm in preparation for EarthCare.

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 effects on radiative heating rate profiles over Darwin using ARM and A‐train radar/lidar observations

Observations of clouds from the ground‐based U.S. Department of Energy Atmospheric Radiation Measurement (ARM) program and satellite‐based A‐train are used to compute cloud radiative forcing profiles over the ARM Darwin, Australia site. Cloud properties are obtained from both radar (the ARM Millimeter Cloud Radar (MMCR) and the CloudSat satellite in the A‐train) and lidar (the ARM Micropulse lidar (MPL) and the Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellite in the A‐train) observations. Cloud microphysical properties are taken from combined radar and lidar retrievals for ice clouds and radar‐only or lidar‐only retrievals for liquid clouds. Large, statistically significant differences of up to 1.43 K/d exist between the mean ARM and A‐train net cloud radiative forcing profiles. The majority of the difference in cloud radiative forcing profiles is shown to be due to a large difference in the cloud fraction above 12 km. Above this altitude, the A‐train cloud fraction is significantly larger because many more clouds are detected by CALIPSO than by the ground‐based MPL. It is shown that the MPL is unable to observe as many high clouds as CALIPSO due to being more frequently attenuated and a poorer sensitivity. We also isolate the difference in cloud radiative forcing due to sampling and retrieval differences which are of comparable importance but are of smaller impact than cloud fraction differences. This study demonstrates that A‐train observations are better suited for the calculation of cloud radiative forcing profiles at Darwin. In addition, we find that it is necessary to supplement CloudSat with CALIPSO observations to obtain accurate cloud radiative forcing profiles.