A description of hydrometeor layer occurrence statistics derived from the first year of merged Cloudsat and CALIPSO data

Mace, Gerald G.; Zhang, Qiuqing; Vaughan, Mark; Marchand, Roger; Stephens, Graeme L.; Trepte, C. R.; Winker, D. M.

doi:10.1029/2007jd009755

Cited by 408 publications

(467 citation statements)

References 50 publications

Supporting

Mentioning

444

Contrasting

Unclassified

Order By: Relevance

“…When considering all vertical cloud layers, the fraction of low clouds increases slightly, and slightly more with Radar -Lidar GEOPROF data. As indicated in (Mace et al, 2009), dense aerosol layers may be misidentified as low-level clouds by CALIPSO and there may be a surface contamination in the radar data (Mace et al, 2007), leading to an overestimation of low clouds. Nevertheless, features from the different data sets look quite similar, which indicates that low-level clouds also appear as single layer clouds.…”

Section: Average Cloud Properties From 200to 2008mentioning

confidence: 99%

“…Combined with the information on optically thin cloud layers from CALIOP, these two instruments provide a complete vertical profiling of all clouds. The CPR footprint is about 2.5 km×1.4 km, and it provides measurements at a vertical resolution of about 250 m. The method to merge the geometrical profiling of CALIOP and CPR (Mace et al, 2009) was designed to extract maximum information on cloud layering from the combined radar and lidar sensors. The data (version 3) have been acquired from the CloudSat data processing center (http://www.cloudsat.cira.…”

Section: Airs Calipso and L2 Radar-lidar Geoprof Data And Their Collmentioning

confidence: 99%

“…In the latter case, the cloud profiling radar (CPR) of the CloudSat mission (Stephens et al, 2002;Mace et al, 2007) helps to complete the information on vertical cloud layer structure. For this purpose, the Cloudsat Geometrical Profiling Product (GEOPROF; Mace et al, 2007;Marchand et al, 2008) and the CALIPSO Vertical Feature Mask (VFM, Vaughan et al, 2004) have been merged into a combined Radar-Lidar Geometrical Profile Product (Radar -Lidar GEOPROF; Mace et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A 6-year global cloud climatology from the Atmospheric InfraRed Sounder AIRS and a statistical analysis in synergy with CALIPSO and CloudSat

et al. 2010

View full text Add to dashboard Cite

Abstract. We present a six-year global climatology of cloud properties, obtained from observations of the Atmospheric Infrared Sounder (AIRS) onboard the NASA Aqua satellite. Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) combined with CloudSat observations, both missions launched as part of the A-Train in 2006, provide a unique opportunity to evaluate the retrieved AIRS cloud properties such as cloud amount and height. In addition, they permit to explore the vertical structure of different cloud types. AIRS-LMD cloud detection agrees with CALIPSO about 85% over ocean and about 75% over land. Global cloud amount has been estimated from 66% to 74%, depending on the weighting of not cloudy AIRS footprints by partial cloud cover from 0 to 0.3. 42% of all clouds are high clouds, and about 42% of all clouds are single layer low-level clouds. The "radiative" cloud height determined by the AIRS-LMD retrieval corresponds well to the height of the maximum backscatter signal and of the "apparent middle" of the cloud. Whereas the real cloud thickness of high opaque clouds often fills the whole troposphere, their "apparent" cloud thickness (at which optical depth reaches about 5) is on average only 2.5 km. The real geometrical thickness of optically thin cirrus as identified by AIRS-LMD is identical to the "apparent" cloud thickness with an average of about 2.5 km in the tropics and midlatitudes. High clouds in the tropics have slightly more diffusive cloud tops than at higher latitudes. In general, the depth of the maximum backscatter signal increases nearly linearly with increasing "apparent" cloud thickness. For the same "apparent" cloud thicknessCorrespondence to: C. J. Stubenrauch (stubenrauch@lmd.polytechnique.fr) optically thin cirrus show a maximum backscatter about 10% deeper inside the cloud than optically thicker clouds. We also show that only the geometrically thickest opaque clouds and (the probably surrounding anvil) cirrus penetrate the stratosphere in the tropics.

show abstract

Section: Average Cloud Properties From 200to 2008mentioning

confidence: 99%

Section: Airs Calipso and L2 Radar-lidar Geoprof Data And Their Collmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A 6-year global cloud climatology from the Atmospheric InfraRed Sounder AIRS and a statistical analysis in synergy with CALIPSO and CloudSat

et al. 2010

View full text Add to dashboard Cite

show abstract

“…Unfortunately, this quantity is problematic to define from observations: satellite estimates of total cloud amount are extremely sensitive to many observational factors including the scale and sensitivity of the fundamental observations, as well as decisions made during the aggregation to larger scales [Stubenrauch et al, 2009;Mace et al, 2009;Marchand et al, 2010;Pincus et al, 2012]. We make the comparison more robust by restricting the analysis to clouds with t exceeding some minimum threshold t min , which we set to minimize hard-to-detect and partly cloudy observations.…”

Section: Common Improvements and Failures In The Simulation Of Total mentioning

confidence: 99%

Are climate model simulations of clouds improving? An evaluation using the ISCCP simulator

et al. 2013

View full text Add to dashboard Cite

[1] The annual cycle climatology of cloud amount, cloud-top pressure, and optical thickness in two generations of climate models is compared to satellite observations to identify changes over time in the fidelity of simulated clouds. In more recent models, there is widespread reduction of a bias associated with too many highly reflective clouds, with the best models having eliminated this bias. With increased amounts of clouds with lesser reflectivity, the compensating errors that permit models to simulate the time-mean radiation balance have been reduced. Errors in cloud amount as a function of height or climate regime on average show little or no improvement, although greater improvement can be found in individual models. Measuring Changes in the Simulations of Global Cloudiness Over Time[2] The simulation of clouds by climate models is a key ongoing challenge in the numerical representation of Earth's climate. Due to their large impact on Earth's radiation budget, clouds are important for determining aspects of current climate, such as surface air temperatures in many regions [Ma et al., 1996;Curry et al., 1996], the strength and variability of atmospheric circulations [Slingo and Slingo, 1988], and the magnitude of climate changes that result from perturbations in the chemical composition of the atmosphere [IPCC, 2007]. While important, the modeling of clouds is very difficult because most cloud processes happen at scales far smaller than can be resolved by climate models, and thus, their bulk effects must be represented with imperfect parameterizations.[3] Given the efforts of many scientists over several decades to understand cloud processes and improve their representation in models, it is important to ask are climate model simulations of clouds improving and, if so, by how much? Here, we analyze the ability of two generations of climate models to simulate the climatological distribution of clouds and judge fidelity by comparison to several decades of satellite observations. Because of the significant differences between the ways clouds are observed and the ways they are represented in models, we use a "satellite simulator" to increase the chances that differences between the models and observations represent actual model deficiencies. We find that significant progress in the ability of models to simulate clouds has occurred over the last decade, particularly in reducing the over-prediction of highly reflective clouds [Zhang et al., 2005].

show abstract

“…Moreover, it influences the water vapor concentration in the stratosphere (Heymsfield 1986;Sassen et al 1989;McFarquhar et al 2000;Corti et al 2006;Wang and Dessler 2006;Stubenrauch et al 2007;Jensen et al 2008;Mace et al 2009;Yang et al 2010;Schwartz and Mace 2010;Taylor et al 2011;Zhou et al 2014;Hong and Liu 2015;Hardiman et al 2015, and references therein).…”

Section: Introductionmentioning

confidence: 99%

The Impact of Two Coupled Cirrus Microphysics–Radiation Parameterizations on the Temperature and Specific Humidity Biases in the Tropical Tropopause Layer in a Climate Model

et al. 2016

View full text Add to dashboard Cite

The impact of two different coupled cirrus microphysics-radiation parameterizations on the zonally averaged temperature and humidity biases in the tropical tropopause layer (TTL) of a Met Office climate model configuration is assessed. One parameterization is based on a linear coupling between a model prognostic variable, the ice mass mixing ratio q i , and the integral optical properties. The second is based on the integral optical properties being parameterized as functions of q i and temperature, T c , where the mass coefficients (i.e., scattering and extinction) are parameterized as nonlinear functions of the ratio between q i and T c . The cirrus microphysics parameterization is based on a moment estimation parameterization of the particle size distribution (PSD), which relates the mass moment (i.e., second moment if mass is proportional to size raised to the power of 2) of the PSD to all other PSD moments through the magnitude of the second moment and T c . This same microphysics PSD parameterization is applied to calculate the integral optical properties used in both radiation parameterizations and, thus, ensures PSD and mass consistency between the cirrus microphysics and radiation schemes. In this paper, the temperature-nondependent and temperature-dependent parameterizations are shown to increase and decrease the zonally averaged temperature biases in the TTL by about 1 K, respectively. The temperature-dependent radiation parameterization is further demonstrated to have a positive impact on the specific humidity biases in the TTL, as well as decreasing the shortwave and longwave biases in the cloudy radiative effect. The temperature-dependent radiation parameterization is shown to be more consistent with TTL and global radiation observations.

show abstract

A description of hydrometeor layer occurrence statistics derived from the first year of merged Cloudsat and CALIPSO data

Cited by 408 publications

References 50 publications

A 6-year global cloud climatology from the Atmospheric InfraRed Sounder AIRS and a statistical analysis in synergy with CALIPSO and CloudSat

A 6-year global cloud climatology from the Atmospheric InfraRed Sounder AIRS and a statistical analysis in synergy with CALIPSO and CloudSat

Are climate model simulations of clouds improving? An evaluation using the ISCCP simulator

The Impact of Two Coupled Cirrus Microphysics–Radiation Parameterizations on the Temperature and Specific Humidity Biases in the Tropical Tropopause Layer in a Climate Model

Contact Info

Product

Resources

About